Economic Effectiveness of Implementing a Statewide

Building Code The Case of Florida

Kevin Simmons

Austin College

Jeffrey Czajkowski Wharton School Risk Center

University of Pennsylvania

James M Done

National Center for Atmospheric

Research Boulder CO

May 2016

Working Paper 2016-01

_____________________________________________________________________

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3730 Walnut Street Jon Huntsman Hall Suite 500

Philadelphia PA 19104 USA

Phone 215-898-5688 Fax 215-573-2130

httpsriskcenterwhartonupennedu ___________________________________________________________________________

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The Risk Centerrsquos neutrality allows it to undertake large-scale projects in conjunction with other researchers and organizations in the public and private sectors Building on the disciplines of economics decision sciences finance insurance marketing and psychology the Center supports and undertakes field and experimental studies of risk and uncertainty to better understand how individuals and organizations make choices under conditions of risk and uncertainty Risk Center research also investigates the effectiveness of strategies such as risk communication information sharing incentive systems insurance regulation and public-private collaborations at a national and international scale From these findings the Wharton Risk Centerrsquos research team ndash over 50 faculty fellows and doctoral students ndash is able to design new approaches to enable individuals and organizations to make better decisions regarding risk under various regulatory and market conditions

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1

Economic Effectiveness of Implementing a Statewide Building Code The Case of Florida

Kevin M Simmons PhD

Austin College

ksimmonsaustincollegeedu

Jeffrey Czajkowski PhD

Wharton Risk Management and Decision Processes Center

University of Pennsylvania

jczajwhartonupennedu

James M Done PhD

National Center for Atmospheric Research Boulder CO

doneucaredu

May 1 2017

Abstract

Hurricane Andrew revealed inadequate construction practices were

utilized in Florida for decades In response Florida adopted a new

statewide code ndash the 2001 Florida Building Code (FBC) which became

one of the strictest in the nation We use ten years of insured loss data

to show that the FBC reduced windstorm losses by up to 72 then use

our results to conduct a benefit-cost analysis (BCA) The FBC passes

the BCA by a margin of 5 dollars in reduced loss to 1 dollar of added

cost with a payback period of approximately 10 years

The authors would like to acknowledge the assistance of the Insurance Services Office the Florida

Department of Emergency Management and Florida International University for data and research support

2

I Introduction

Despite the recognition that strong building codes are a key risk reduction strategy in reducing

total property damage due to natural disaster occurrence as well as making communities more

resilient (Mills et al 2005 Kunreuther and Useem 2010 McHale and Leurig 2012 Vaughn and

Turner 2014 NIBS 2015 Rochman 2015 Jain 2009) in the United States there is not a single

national building code for all states to follow Rather building code adoption and enforcement is

left to individual state discretion Consequently across the country there is a spectrum of building

code implementation (both commercial and residential) where on one end there are states

implementing a mandatory statewide code on the other end building codes are left up to local

jurisdictions and a mix in-betweeni

Moreover even for those states that do have a statewide code in place there is much

variation in the overall effectiveness of its implementation The Insurance Institute for Business

and Home Safety (IBHS) ranks the residential building codes adopted in 18 states along the

Atlantic and Gulf Coasts most vulnerable to hurricane damages on a scale of 0 (worst) to 100 (best)

with the ranking accounting for each statersquos code strength and enforcement building official

certification and training and contractor licensing For the 14 states having some notion of a

mandatory residential statewide code in place scores ranged from 28 (Mississippi) to 95

(Virginia) with 43 percent of the 14 mandatory states scoring below 80 (IBHS 2015) Given the

increasing attention natural disasters receive this is surprising as public sector involvement can be

an important element toward reducing disaster losses in a cost effective manner (Kunreuther

2006)

Florida is highly vulnerable to hurricane damages ndash approximately $18 trillion of

residential property exposure (Hamid et al 2011) ndash as well as the oft-referenced gold standard of

3

a strong statewide building code ndash IBHS score of 94 in 2015 (2nd) and 95 in 2012 (1st) (IBHS

2015) Although the extensive property exposure at risk to hurricanes relative to other states has

been continual for Florida since the early part of the 20th century a strong and uniform building

code standard has not Hurricane Andrew which made landfall in South Florida as a category 5

hurricane in 1992 destroyed more than 25000 homes and damaged 100000 others causing $26

billion in total damage (inflation adjusted) making it the costliest catastrophic event in history at

that time (Fronstin and Holtmann 1994) Eleven insurance companies became insolvent as a

result

After Hurricane Andrew it became clear that construction practices in place during the

1980s had not been sufficient to withstand such a powerful wind storm (Sparks et al 1994) Post-

storm inspections detected inferior construction practices which had thus unnecessarily magnified

the extensive damage (Fronstin and Holtmann 1994 Keith and Rose 1994) In the aftermath of

Hurricane Andrew Florida began enacting statewide building code change that wrested away

building code adoption control from individual localities The first communities to strengthen

their building code were the counties of Broward Dade and Monroe all of which already adhered

to the stronger South Florida Building Code (SFBC) Standards for the SFBC were upgraded in

1994 with an emphasis on improving the integrity of the building envelope including impact

protection for exterior windows and doors Beyond the counties in the SFBC some communities

began adopting stronger local codes as well In 1996 the Florida Building Code Commission

began a study to make recommendations on a statewide basis in consultation with wind engineers

The Florida Legislature in 1998 authorized the recommended changes statewide creating the

2001 Florida Building Code The FBC is based on the national model codes developed by the

International Code Council (ICC) and is among the strictest in the nation heavily emphasizing

4

wind engineering standards and other additions for Floridarsquos specific needs including for hurricane

protection (Dixon 2009)

In this study we first quantify the reduction of residential property wind damages due to

the implementation of the FBC utilizing realized insurance policy claim and loss data across the

entire state of Florida spanning the years 2001 to 2010 We utilize a Regression Discontinuity

(RD) model using a treatment of Post FBC construction and a rating variable of structure age

Following from our claim-based empirical loss estimations we then further assess the economic

effectiveness of the FBC through a benefit-cost analysis (BCA) a relatively underserved yet

important research component in wholly assessing building code augmentations Especially as

enhanced building codes increase new construction costs moving forward both pieces of

information are critical in not only highlighting the value of a statewide building code but also in

generating political and consumer support for its implementation (Kunreuther 2006 Vaughan and

Turner 2014 NIBS 2015)

The article proceeds as follows Section 2 is a discussion of existing assessments of

windstorm building code effectiveness Section 3 is an overview of the data and Section 4 provides

a discussion of the econometric methodology Section 5 discusses regression results and provides

an evaluation of the regression model Section 6 is a BenefitCost Analysis of the FBC and Section

7 concludes the article

II Review of Existing Assessments of Windstorm Building Code Effectiveness

Several studies have identified the reduction in windstorm losses due to stronger building

codes utilizing event-based realized loss or insurance claim dataii Fronstin and Holtmann (1994)

in their analysis of 1992 Hurricane Andrew damages in southeast Florida find that older homes

built prior to the 1960rsquos suffered less damage on average than those built after 1960 due to an

5

eroding building code over time Post-Andrew the catastrophic hurricane seasons of 2004 and

2005 in Florida provided a natural opportunity to test how well the implemented FBC performed

A study by IBHS following Hurricane Charley in 2004 (IBHS 2004) found that homes built after

1996 had lower claim frequency (60 percent less) and severity (42 percent less) as compared to

homes built before 1996 This suggests the trend of an eroding building code reversed after

Hurricane Andrew Applied Research Associates (2008) investigated policy level claim data from

eight different insurance companies following the 2004 and 2005 hurricane seasons and found

similar results with post-2002 homes showing significant loss reduction results compared to pre-

2002 homes They further found that overall losses were reduced in year built from the mid-1990s

onward Although only indirectly associated with actual damages incurred stronger building

codes reduced post-storm federal disaster spending in 795 unique Florida ZIP codes impacted at

least once by the 2004 hurricanes of Charley Frances Ivan and Jeanne as well as Tropical Storm

Bonnie (Deryugina 2013) However contrary to these results Dehring and Halek (2013) find that

for the 264 residential properties in a coastal building zone in Lee County following Hurricane

Charley there is no evidence of less damage for homes built after the revised 1992 Florida building

code

Our study advances this building code literature in several important ways First we

collect annualized private market insured policy and loss data (number of claims and total damages

for all represented earned house years in the insured portfolio) from the Insurance Services Office

(ISO) aggregated at the ZIP code level for all Florida ZIP codes spanning the years 2001 to 2010

inclusive We are therefore able to analyze a decade of data post-FBC implementation ISO

industry data represents a significant percent of total private propertycasualty insurance annual

market share in FLiii and we utilize aggregated policy data in any one year ranging from 669000

6

to just over 1 million insured policyholders Thus we utilize more comprehensive ndash in number

space and time ndash insured loss and premium data for this analysis than previous studies Lastly

Florida was affected by 18 tropical cyclones over the period 2001-2010 not just those in 2004 and

2005 and our study utilizes a more comprehensive set of extreme wind events extending beyond

2004 and 2005

Finally following from our claim and loss analysis we perform a BCA on the

implementation of the FBC Our BCA is unique in that we use actual loss data rather than

probabilistic estimates of future loss as previous studies have and our loss data spans a longer time

period of 10 years in order to control for the effect of post FBC construction

III Florida Windstorm Losses and Associated Data

We quantify historical Florida wind event loss reductions due to the implemented FBC

through an econometric driven loss methodology that systematically accounts for relevant wind

hazard exposure and vulnerability characteristics evolving over time from the adoption of the

new uniform codes ISO provided annual insured loss data aggregated at the ZIP code by decade

of construction In addition to insured loss data we have several variables from ISO collected by

insurers EHY Premiums and BrickMasonry EHY is an acronym for earned house years and

represents the number of policyholders in each ZIP code Premiums is the total annual premiums

collected and BrickMasonry is the percent of homes that have exterior cladding made from brick

or other masonry products

Florida Insured Loss Data

For the years 2001 to 2010 we obtained Florida propertycasualty insurance industry data

from ISO aggregated at the ZIP code Again the ISO industry data has aggregated policy data in

any one year ranging from 669000 to just over 1 million insured policyholders representing

7

125 of all residential structures in Floridaiv A total of $8023 billion (2010 inflation adjusted)

of property losses was incurred over this time (net of deductibles) from 593663 total property loss

claims incurred From 2001 to 2010 windstorm hazards are the largest cause of loss in Florida

totaling $5178 billion in losses (65 percent of total hazard damage) as well as being the most

frequent source of a loss claim with 317005 claims incurred (53 percent of total hazard claims

incurred) Clearly windstorm is a significant source of losses for Florida property insurers and

owners

Of course Florida windstorm losses vary over time and as expected are significantly

linked to the occurrence of hurricanes Table 1 provides a further detailed view of the ISO Florida

windstorm incurred losses and claims over time Across all years an average of $517 million in

losses and 31701 claims are incurred each year with an average windstorm claim being $10089

incurred at the rate of 324 claims per 1000 insured exposures (earned house years) However

excluding the significant hurricane years of 2004 and 2005 an average of $25 million in losses

and 3900 claims are incurred each year with an average windstorm claim of $8353 per claim

incurred at the rate of 48 claims per 1000 insured exposures (earned house years) Although

windstorm losses and claims are considerably higher in significant hurricane years they are still a

substantial annual property risk For example 2007 had average windstorm claims of $25399 per

claim and 2001 had 131 windstorm claims per 1000 insured ndash both outside the significant

hurricane years of 2004 and 2005 Lastly average annual premiums collected over this timeframe

(data not shown) are just over $1 billion per year Although these premiums are sufficient to cover

incurred loss amounts in non-hurricane years major windstorm year loss amounts (for example

2004 windstorm losses are nearly 4 times higher than annual average premiums collected) indicate

the critical role of further windstorm risk reduction measures in Florida

8

Insert Table 1 Here

One further split of the ISO loss data obtained is by decade of construction That is for

each year of ISO data from 2001 to 2010 each Florida ZIP code in that year contains a split of the

losses claims premiums and earned house years by the year of construction decade beginning in

1900 up to 2010 Given the loss timeframe of the ISO data from 2001 to 2010 in any one year

the majority of the overall ISO portfolio (ie proportion of earned house years EHY) is

represented by properties built prior to the year 2000 However given the growth of new

construction in Florida during this decade over time newer construction practices make up a more

significant portion of the ISO portfolio (Figure 1)v For example in 2001 post-2000 year of

construction (YOC) properties are less than 10 percent of the total ISO portfolio of 869645 total

EHYs but by 2010 they represent over 30 percent of the total ISO portfolio of 669770 total EHYs

And it is these newer housing units (ie primarily the post-2000 YOC properties) to which the

statewide FBC would have the most effect given its full implementation in 2002

Insert Figure 1 Here

Therefore as would be expected given the significant absolute portion of the EHY being

from pre-2000 YOC properties the majority of the 317005 total wind related claims and

associated $5178 billion in total wind-related losses (approximately 86 percent each) in identified

ZIP codes are incurred by properties that were built prior to the year 2000 But more importantly

the raw loss data on the numbers of claims and losses when normalized for the EHYs per YOC are

also higher on average for properties built prior to the year 2000 (Table 2) That is normalizing

for the number of policyholders in each YOC category (which again are significantly higher in

pre-2000 YOC as per Table 2) pre-2000 YOC buildings have a higher rate of claims incurred as

well as higher average incurred losses per each claim For example in 2004 206 percent of pre-

2000 YOC insured policyholders incurred a claim with an average loss of $3605 across all pre-

9

2000 YOC policyholdersvi This compares to 104 percent of post-2000 YOC insured

policyholders incurring a claim with an average loss of $1211 across all post-2000 YOC

policyholders Although this is true for the normalized raw loss data a number of other hazard

exposure and vulnerability factors need to be controlled for to ascertain that post-2000 YOC losses

are indeed lower than pre-2000 construction

Insert Table 2 Here

Outcome Variable

Our dependent variable is aggregate loss for each ZIP code by year (2001-2010) and by

decade of construction In total we have 69442 observations We transform this variable by

taking the natural log While we do not have individual customer data we do have the number of

insured customers (EHY) for each ZIPyeardecade of construction that we include as an

explanatory variable to control for the differences between ZIPyeardecade of construction

observations with high numbers of insured customers versus those with lower numbers

Treatment Variable

To test for the effect of homes built after the introduction of the statewide building code

we construct a dummy variablecedil Post FBC for observations that are after 2000 By using this

dummy variable we can test the effect on losses for homes built after the statewide code was

implemented The dummy variable for Post FBC construction is related to structure age but does

not capture the separate effect age may have on loss So we add structure age into the regression

We only have data on structure age by decade which goes back to 1900 To introduce some

variability to this variable we calculate age by taking the difference between the year of loss and

the first year in the decade for the observation So for an observation that is for year 2004 where

the decade of construction was 1950-1959 age would equal 54 2004-1950 We turn now to the

other data

10

Wind Hazard Data

Florida was affected by 18 tropical cyclones over the period 2001-2010 Spatial wind

hazard data over Florida are sourced from the National Center for Environmental Predictionrsquos

(NCEP) North American Regional Reanalysis (NARR 2015 Mesinger et al 2006) NARR is a

dynamically consistent historical climate dataset based on historical climate observations Data are

available 3-hourly on a 32km grid Of importance to this study Mesinger et al (2006) showed that

the winds just above the surface compare well with surface station observations The 32-km grid

is too coarse to resolve high-impact small-scale features in the wind field such as thunderstorm

winds or tornadoes It is also too coarse to capture the intensity of the strongest hurricanes (as

discussed in Done et al 2015) Rather than downscaling the NARR data to obtain these small-

scale details using dynamical (eg Laprise et al 2008) or statistical (eg Tye et al 2014)

methods (that could introduce further uncertainties) we choose to sacrifice the small-scale details

of the wind field and peak hurricane intensity for location accuracy of the NARR data To account

for these missing wind extremes all wind speed values are normalized by the maximum value of

wind speed in the dataset

Specifically the 3-hourly wind data are interpolated from the 32-km grid to the ZIP-code

level and two wind field parameters are derived for use in the loss regressions the normalized

annual maximum wind speed and the annual number of times the wind speed exceeds the Florida

mean wind speed plus one standard deviation for at least 12 hours The choice of hazard variables

is based on recent work that highlighted the potential for wind parameters other than the maximum

wind to drive losses (Czajkowski and Done 2014 Zhai and Jiang 2014 Jain 2010)

11

Additional Data

We have 2000 and 2010 demographic data from the decennial census at the ZIP code level

for population area (in square miles) of the ZIP median household income and housing counts

Population growth across the decade is not even so we use building permits to help estimate

intervening years Each year is interpolated from decennial data for population and total housing

counts with an allocation factor based on the number of building permits for each ZIP and each

year Building permits are collected from census by place codes so we must re-allocate to ZIP

codes To convert from place to ZIP code we use allocation factors based on 2010 housing counts

provided by MABLE a service of the Missouri Census Data Center (MABLE 2015) For

example if a municipality has two ZIP codes with 60 of the homes in one and the remaining

40 in the other MABLE would use those percentages as the allocation factors from the

municipality to its corresponding ZIP codes In unincorporated areas we use allocation factors

from county to ZIP from the same service For median household income a straight-line

interpolation method is used adjusted for changes in the consumer price index (CPI-U) to 2010

CPI data are from the Bureau of Labor Statistics

Several factors were utilized to represent the overall geographic hazard risk of a ZIP code

The distance of the centroid of the ZIP to the coast was calculated to account for the overall

distance to the coast of each ZIP code Following Dehring and Halek (2013) dummy variables

that signifies whether a ZIP code contains a coastal construction control line (CCCL) were created

(1 equals CCCL in place) to account for stricter building codes in these areas Finally following

the 2005 hurricane season there was a significant increase in the number of policies underwritten

by Citizens the state-run wind-pool and insurer of last resort (Florida Catastrophic Storm Risk

Management Center 2013) Areas with large percentages of insured policies underwritten by

12

Citizens could represent inherently higher windstorm risk We spatially matched our Florida ZIP

codes to the Florida house districts and took the percentage of Citizens policies of the number of

occupied housing units as of December 31 2011 (Florida Catastrophic Storm Risk Management

Center 2013) Given the potential for adverse selection or offloading of high risk policies by the

private market in these areas it is unclear whether higher Citizensrsquo market penetration would lead

to a positive relationship with losses due to the higher risk or a negative relationship with private

losses as many of the bad risks have been transferred to the residual wind pool

IV Econometric Methodology

Better construction limits loss from windstorms through two channels first the direct effect

of decreasing loss on homes that experience damage and second through fewer claims on better

built homes Our data from ISO is aggregated at the ZIP codedecade of construction level So a

ZIP code where all homes experienced damage would have varying levels of damage between

homes built before and after implementation of the FBC Other ZIP codes may have damage for

older homes but little to no damage for homes built post FBC Our first challenge was to use

models that would provide an estimate of the full effect of the FBC lower levels of damage plus

the effect of fewer claims then an estimate for the direct effect alone To accomplish this we

employ two models The first includes all observations even if no claims have been filed and

second a hurdle model where the first stage models the probability of experiencing a loss and the

second stage isolates only the observations where a loss has been experienced

Base Model

The regression model is a semi-log ordinary least squares (OLS) fixed effects (time and

space) model with the natural log of loss as the dependent variable The base level of observation

is ZIP codeyeardecade of construction Explanatory variables include insurance information

13

(exposures and premiums) construction type demographic data based on the ZIP code measures

of the ZIP code hazard risk (how close to the coast the ZIP code is etc) and hazard data

concerning the wind speed and duration

Our test of the FBC creates a discontinuity that must be accounted for in the model All

observations with decade of construction post 2000 are considered under the new building code

regime But that dummy variable is a function of structure age so we employ a regression

discontinuity (RD) analysis to determine the best specification to estimate the effect of the FBC

allowing for the effect that structure age has on damage Intuitively structure age should increase

loss as older homes depreciate across their life making them more vulnerable to wind storms But

the effect of structure age is more than depreciation Over time construction practices and

materials used have changed which also affect how a structure responds to the stress of a violent

wind storm Indeed after Hurricane Andrew in 1992 it was noted that inferior construction

practices of the 1970rsquos and 1980rsquos had exacerbated the losses (Fronstin and Holtmann 1994 Keith

and Rose 1994)

This suggests that the effect of age is non-linear so a model that includes age as a

polynomial would be reasonable Determining the best specification requires testing a series of

models that include age as a polynomial andor interacted with our treatment variable Post FBC

(Lee and Lemieux 2010) (Jacob and Zhu 2012) The full analysis to choose our specification is

included in the Appendix The model that provided the best tradeoff between bias and precision

based on the AIC adds age and its square with the functional form

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

where the variable definitions are given in Table 3

14

Insert Table 3 Here

A positive sign is expected for both wind variables indicating that as wind speeds increase

andor the ZIP code is exposed to high winds for an extended period of time losses will increase

Post FBC construction should decrease loss so a negative sign is expected

Hurdle Model

One problem potentially encountered in attempting to model losses is there may be a

separate process occurring in the data that determines whether a loss is realized at all which could

affect the estimate of overall losses To address this issue hurdle models are used as they divide

the analysis into two stages We use a hurdle model to find the direct effect of the FBC The first

stage models the probability that a loss occurs and the second stage models the loss using only

observations that sustained a loss The dependent variable in the first stage equals one if there was

a loss and zero otherwise This binary dependent variable is then regressed against variables that

would affect the probability that a loss occurred Its form is

(2a)

Loss or No Loss = β0 + β1 Max Wind + β2 Wind Duration + β3 Population Density

+ β4 Post FBC

We expect that both wind variables max wind speed and duration as well as population

density will increase the probability of a loss Post FBC construction however should decrease

the probability of a loss

The second stage in the hurdle model is the same as Equation 1 with the exception that

only observations with a loss are included There are 19107 observations for the second stage and

its form is

15

(2b)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Model Validity

Regression models are limited by available data to understand how the dependent variable

varies as explanatory variables change If important variables are left out of the model some bias

can be expected This omitted variable bias is a common problem encountered with econometric

models Kuminoff et al 2010 found that one of the best approaches to reducing omitted variable

bias is to employ a spatial fixed effects model To accomplish this we use individual ZIP dummy

variables as a spatial fixed effect and dummy variables for each year in our data to control for

changes that may be related to time not otherwise controlled for within our co-variates These

dummy variables will contain all across-group variation leaving the remainder of the model to

contain the within-group variation (Greene 2003)

A second challenge to the validity of our model is another common problem

heteroscedasticity For Equation 1 we use clustered standard errors at the ZIP code through Proc

GLM in SAS Our hurdle model (Eq 2a and 2b) utilizes Proc Qlim which has a separate statement

(Hetero) that we invoked to model the error variance

V Regression Results

Our first regression (Equation 1) serves as a base from which we examine the effect of

basic explanatory variables on loss The results from this regression can be found in Regression

Table 4

Insert Table 4 Here

16

The performance of our regression model is satisfactory in terms of the performance of the

explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and

the coefficient on our treatment variable Post FBC is -126 and highly significant

Overall our results show the strong effect the statewide FBC had on losses from wind

storms during this timeframe Using the results from the regression in Table 4 the coefficient on

the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses

than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the

results from a study conducted by the Insurance Institute for Business and Home Safety after

Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60

percent less likely to suffer damage at all and those that were damaged sustained 42 percent less

damage than older homes Our result of 72 percent lower damage reflects both those attributes as

our data included ZIP codeyearYOC observations that suffered damage as well as those that did

not

Our variables to measure the effect of wind hazard are wind speed and duration For both

variables we have a positive sign and each is highly significant Higher wind speed and higher

duration of high wind speeds increases damage and thus loss The remaining variables perform as

expected

Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In

the first stage variables such as Max Wind and Wind Duration significantly increase the

probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post

FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower

for homes built after new building codes were adopted In the second stage we see that our wind

variables continue to significantly increase the size of the loss and our treatment variable Post

17

FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now

lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy

we see that losses are reduced by about 47 as opposed to 72 when all observations are

includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better

construction reduces loss in two ways First it lowers claims and reduces the amount of a loss

when a claim is filedviii

Model Evaluation

To evaluate our model we used the second stage of the hurdle models and broke our data

into two groups The first group represents 90 of the data randomly selected and was used to

run the model and collect parameter estimates The second group is an out of sample control group

to test the validity of the model Parameter estimates from the first group are applied to the control

group which gave us a predicted loss for each observation in the control group that can be

compared to the actual loss for each observation in the control group We then regressed the

predicted loss from the control group against the actual loss

Insert Figure 2 Here

Figure 2 plots the predicted loss against the actual loss and provides the fitted line with

95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears

to do a good job of predicting most losses

Robustness of Table 4 Base Model Results

To test the robustness of our results we run three separate analyses 1) We first run a

regression with few co-variates 2) As wind design speeds have been used as a proxy for building

code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss

18

analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking

Florida in 2004 and 2005

Regressions using Few Co-Variates

Additional relevant co-variates add precision to a model But the value of the focus

variable should be apparent with a smaller set So we ran a model with only insured customer

based variables EHY and paid premiums leaving out all other demographic and hazard related

variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and

significance

Regressions Using Design Speed

The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads

for Buildings and Other Structures published by the American Society of Civil Engineers and the

Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds

for most regions of the United States and their territories These reference wind speeds are used in

calculations to determine design wind pressures for the primary structure of a building and the

cladding and components attached to a building These calculations take into account the building

geometry the importance of a building the exposuresurrounding terrain and other parameters that

influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design

speeds as a proxy for building code strength and we similarly add this as an additional control in

our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps

were provided by the Applied Technology Council (ATC) Although this version of the wind

speed map was not utilized during the period under consideration the relative values in general

between two locations would be the sameix

19

We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded

form from the ATC and spatially joined the values to our Florida ZIP codes We then further

categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat

4 and Cat 5 hurricane levels

Insert Table 5 Here

The regression adds two dummy variables first for ZIP codes whose design speed exceeds

the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed

reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables

for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that

communities in higher wind zones may take further measures in local codes However the effect

is not significant Notably our variable for Post FBC construction maintains its negative sign

magnitude and significance

Regressions Limited to 2004 and 2005

Our next regression also shown in Table 5 is limited to observations that occurred during

the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and

2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not

surprisingly the magnitude on wind speed increases while maintaining its significance and the

magnitude on age does the same But the effect of the FBC remains the same a 72 reduction

Summary of Results on the FBC

We have collected a comprehensive set of data on insured paid losses from 2001 to 2010

windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity

model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of

the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction

20

in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to

determine if the policy is one that is cost effective

VI Benefit and Costs of the FBC

Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a

number of cases the economic effectiveness of the improved building codes has not been as well

documented especially from a statewide implementation perspective The multi-hazard

mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose

et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar

spent) was appropriate for process activity grant spending related to improved building codes

However this information was gathered from a limited number of studies (mainly earthquake

oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in

generating it and the ratio provided due to improvement would not be the same as those for

adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted

an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for

ten related counties and determined that the net present value of the revisions was $7 billion or

benefit-cost ratio greater than 1 Importantly though this study did not have access to actual

building code damage reduction data to utilize in the analysis In 2002 Applied Research

Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC

for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how

the three types of FBC built houses would perform in probabilistic hurricane scenarios compared

to the same houses built under the previous code Given the probabilistic nature of the analysis

average annual losses were generated that demonstrated post-FBC housing having loss reductions

54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were

21

then compared to their estimated cost impacts of the FBC for these housing types with at least

break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and

above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al

(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss

reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results

ranged from 041 to 183 for the retrofit mitigation activities to existing housing

We propose a BCA that differs from earlier work in several important ways First we use

realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of

how much loss can be reduced by the FBC Second our loss data spans 10 years which include a

combination of major hurricanes and smaller wind storms

BenefitCost Methodology

The elements of a BCA requires three inputs 1) an estimate of the added cost to implement

the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related

storms from our realized ISO loss data and then from a statewide catastrophe model estimate and

3) the percentage of expected loss that will be mitigated due to implementation of the FBC We

first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis

but use the full reduction in loss which includes the value of reduced claims Finally our ISO data

is paid losses and does not include deductibles so we add an estimate for deductibles

Additional Cost

In their 2002 benefit-cost comparison study of the enactment of the FBC for three related

housing types three actual sample homes were built to the FBC to evaluate the change in

construction costs (ARA 2002) For the purposes of code implementation the state was divided

into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region

22

(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account

for cost differences between the two regions

In the WBDR an added requirement is impact protection to windows and doors to reduce

damage from flying debris Along the coast and much of South Florida is classified as the WBDR

The N-WBDR is mainly classified in the interior of the state where impact protection is not

required Importantly the study provided a range of added costs for the N-WBDR and the WBDR

Three counties in South Florida Dade Broward and Monroe were under the South Florida

Building Code (SFBC) prior to the implementation of the FBC According to the ARA study

(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges

of incremental cost per square foot for the N-WBDR and WBDR along with the percent of

residential units that reside in each area This allows a calculation of a weighted average added

cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost

per square foot of $166 The cost compares favorably with a similar building code enhancement

adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in

2013 Consulting engineers and the Moore Association of Homebuilders estimated the code

enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is

1960 square feet which means that on average the FBC increases construction cost by $3254 per

structurexii

Insert Table 6 Here

Benefit of the FBC

Benefits stemming from the FBC are the expected reduction in losses from windstorms during

the life of the home We first find an average annual loss (AAL) use that number to estimate

losses for the next 50 years and then find the present value of those losses in 2010 Here we are

23

assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the

next 50 years A wealth of literature suggests the potential for changes to hurricane activity over

the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty

on future changes in wind hazard on the scale of a single state we choose to assume a stationary

climate

Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes

built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our

data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of

$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year

Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses

per structure of $21474

Finally we use parameter estimates from our regression for the Post FBC dummy variable

(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC

The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post

FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what

would be a reduction of $10093 from the projected $21474 in future losses

Insert Table 7 Here

BenefitCost Analysis

Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio

of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the

statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an

economically effective public policy following from our ISO loss data and results

Insert Table 8 Here

24

Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years

This relatively short timeframe makes it difficult to truly approximate an AAL as would be

provided from a probabilistically based catastrophe model that generates an AAL from thousands

of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state

of Florida to estimate an average annual wind loss for all residential properties in Florida of

approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL

estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion

with deductibles) Using this aggregate AAL and the number of residential units in Florida based

on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses

net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and

an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as

before derived from our regression results 47 we find $7710 of reduced loss from the projected

$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added

$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective

building code public policy

We run two additional analyses on our BCA results Our estimate of expected loss

reduction comes from the second stage of the hurdle model This is an estimate of the direct loss

reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the

number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests

as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which

includes the reduced magnitude of loss from affected homes and the reduction in claims for Post

FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table

8) However a 72 loss reduction seems too dramatic an expectation when planning so far in

25

advance For that reason we offer a third level of expected loss reduction of 60 which is the

midpoint between our two loss reduction estimates This estimate captures the expected direct loss

reduction suggested by the second stage of our hurdle model but still recognizes that in some areas

the number of claims is reduced by the FBC This appears to be a reasonable assumption and

provides a BCA ratio of 396 for the ISO sample and 302 for all residential

The ISO data are net of deductibles so our BCA thus far only includes losses compensated by

the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion

also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the

ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all

loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA

values from a low of 237 to a high of 793

Payback of the FBC

Finally we use our BCA results to calculate a payback period for the investment of stronger

codes To convert our BCA ratio to a payback period we simply divide our 50-year planning

horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss

and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years

This is important for gauging potential political support or non-support for enactment of the new

codes Payback periods that approach the typical mortgage term 30 years would in theory be

difficult to achieve and that is not what our analysis indicates for the FBC

VI - Concluding Comments

In the aftermath of Hurricane Andrew which had exposed not only poor building

construction but also poor building code enforcement the state of Florida enacted statewide

building code changes that wrested away building code adoption control from individual localities

26

With full implementation of the statewide building code associated expectations are that

windstorm losses from extreme events such as hurricanes should be reduced moving forward

There have been a few studies confirming these expectations following the 2004 and 2005

hurricane season In this article we further verify and quantify these findings and expand the

existing building code risk reduction research in several important ways

Overall we empirically test the statewide implementation of a building code in reducing

wind related damages in Florida controlling for other relevant wind hazard exposure and

vulnerability characteristics from a traditional risk assessment perspective Our results show the

strong effect the statewide FBC had on losses from wind storms during this timeframe From the

treatment variable that measures implementation of the statewide codes the post 2000 year of

construction losses are shown to be reduced by as much as 72 percent consistent with other

previous findings

Finally we have conducted a BCA of the FBC to determine if expected benefits exceed

the cost of implementation Using a direct estimate for mitigated losses and an estimate that

includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public

policy from an economic perspective This result is close to that recommended by the multi-hazard

mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a

statewide BCA Importantly this information is essential in generating political and consumer

support for such building code public policy implementation

For example the economic effectiveness results shown here have implications for ongoing

policy discussions about reforming building codes from a national US perspective Moore OK

independently adopted enhanced building codes after its third violent tornado in 14 years killed 24

including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)

27

Construction practices in North Texas were brought under scrutiny after the December 2015

tornado revealed inadequate construction including an elementary school whose exterior walls

failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House

announced initiatives to increase community resilience with building codes as a major component

of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748

and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better

construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397

would provide tax credits for owners andor contractors who use techniques designed for resiliency

in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one

recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to

encourage states to use current building codes (Vaughn and Turner 2014)

Future research in the BCA of the FBC will further inform the public policy debate on

enhanced building codes The issue has national implications as other states find that wind hazards

impact them as well We have sufficient wind data to examine how the BCA performs under

different wind hazards Additionally it will be important to consider how future economic

development affects the BCA as well as varying climate change scenarios As the FBC is

mandatory for all new construction a statewide analysis was appropriate But individual

homeowners in older homes can invest in the retrofit of their home and qualify for discounts on

their homeowners insurance This topic is deserving of a robust analysis Although our BCA is

statewide regions within the state will likely have a spectrum of results For instance the ARA

2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their

analysis did not use realized loss data so confirmation of how the BCA varies between those

regions would be an important contribution Finally our sensitivity analysis was limited to two

28

variables reduction in future loss and the inclusion of deductibles Additional work will highlight

other variables that could modify the results

29

Appendix

We use this appendix to conduct more detailed analysis on several topics First selection

of the model specification using a regression discontinuity approach Second we provide an in

depth examination of the relationship between structure age and losses Third we perform a

Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an

alternative specification to see if our RD results are similar followed by regressions to examine

the year to year consistency of our Post FBC result Next we run a regression on claims to verify

the difference between our direct reduction result and our full reduction result Finally we perform

a regression on homes built to the SFBC which had adopted enhanced building codes in advance

of the FBC to assess the effect of earlier adoption of enhanced construction

Regression Discontinuity

Regression Discontinuity (RD) applies when an observation receives a treatment in our case

homes built under the FBC based on a rating variable in our case age of the structure at the year

of observation So for observations in 2005 homes built post 2000 received the treatment

adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify

how observations on either side of the implementation of the FBC (2000) perform in suffering loss

from windstorms The treatment variable is a function of the age of the home and age affects loss

in ways not related to the FBC such as depreciation and differences in materials and construction

practices across time To account for both the effect of age on loss as well as the implementation

of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment

The data we have from ISO is aggregated loss data by zip code and decade of construction So

we cannot get an annualized age To approach a true age we set the year built for each decade of

construction at the beginning of the decade then subtract that from the year of each observation to

get an approximate agexiv

30

To find the best specification we began with a simpler model which used a series of

categorical variables for each decade of construction to examine the effect of the code compared

to the omitted decade This method would approximate the changes in materials and construction

practices but was less effective in controlling for depreciation But it would give us a first

approximation of the code effect that we used as a benchmark when testing the best RD

specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970

with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When

the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When

either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81

A rough approximation of the codersquos effect from this approach would suggest a reduction in the

mid 70 percent range

Insert Table 1 ndash Appendix Here

Next we used a standard procedure with RD to search for the best way to include the rating

variable This process creates specifications that include age in increasing polynomials and

interacted with the treatment variable The goal is to find the specification with the lowest AIC

that comes close to the benchmark value of the treatment variable

Insert Tables 2 and 3 ndash Appendix Here

We did this first with regressions that limited the co-variates then with our full model In both

sets AIC reaches a minimum on the specification with age and age squared The interaction model

after that increases the AIC then the AIC goes down again with a cubed model and its interaction

model with the overall lowest AIC found on the cubed interaction model But we chose not to

use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order

models the magnitude of the treatment variable in the models with just polynomials compared to

31

the corresponding interaction models were close with the interaction models providing a larger

magnitude When the cubed models were added the magnitude jumped where the polynomial

cubed model went down well below our benchmark and the interaction model went up above our

benchmark We felt this made use of the cubed model inappropriate So we now need to choose

between the squared model and the one with the interaction terms The squared model (Model 4)

had a lower AIC and the interaction variables on the interaction model (Model 5) were not

significant so we chose to use the squared model without the interaction term This model gave a

magnitude for the treatment variable of a 72 reduction somewhat lower than the expected

magnitude in the mid 70rsquos percent The general form of the model is

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1

of the observations on either end of our data sorted by loss Our treatment variable Post FBC

remains highly significant with a coefficient value of -117 which compares favorably to our

coefficient value of -126 when the entire sample is used

Structure Age and Wind Losses

Our study is similar to recent studies on the effect of energy efficiency building codes

adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that

better insulation caulking and more efficient HVAC systems would result in lower energy

consumption But the change in energy consumption is less than engineering estimates projected

Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for

homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study

32

may be confounding age with vintage and found a decrease in energy use related to the home

simply being new rather than the change in building code Indeed Kotchen (2015) revisited the

question with data 10 years older and found the effect on electricity had disappeared while the

reduction in natural gas use increased Something is occurring in energy use unrelated to the code

and could be explained by residents changing their use of energy as they adapt to their new home

Residents of an energy efficient home can undermine the intent of lower energy use by using the

efficient design to heat and cool their homes with a motivation toward increased comfort at the

same energy cost rather than energy savings Our study does not have the behavioral component

found in the case of energy efficiency In our application the construction elements that make the

structure able to withstand high winds are installed when the home is built and lie ldquobehind the

wallsrdquo making it unlikely for individual preferences to alter the homes performance against the

threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC

homes due to the code or simply an artifact of new versus old construction when confronted with

a windstorm

To first address our analysis of age versus the FBC we rerun our base regression but limit

our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more

than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14

years during the highest loss year of 2004 Since this is a comparison between two adjacent

decades on either side of our cut point of year 2000 we remove age and age squared Results are

shown in Table 4-Appendix

Insert Table 4-Appendix Here

The coefficient on Post FBC is still negative highly significant with a magnitude very close to

what we saw with the entire database and the age variables This result suggests that the code

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

2

I Introduction

Despite the recognition that strong building codes are a key risk reduction strategy in reducing

total property damage due to natural disaster occurrence as well as making communities more

resilient (Mills et al 2005 Kunreuther and Useem 2010 McHale and Leurig 2012 Vaughn and

Turner 2014 NIBS 2015 Rochman 2015 Jain 2009) in the United States there is not a single

national building code for all states to follow Rather building code adoption and enforcement is

left to individual state discretion Consequently across the country there is a spectrum of building

code implementation (both commercial and residential) where on one end there are states

implementing a mandatory statewide code on the other end building codes are left up to local

jurisdictions and a mix in-betweeni

Moreover even for those states that do have a statewide code in place there is much

variation in the overall effectiveness of its implementation The Insurance Institute for Business

and Home Safety (IBHS) ranks the residential building codes adopted in 18 states along the

Atlantic and Gulf Coasts most vulnerable to hurricane damages on a scale of 0 (worst) to 100 (best)

with the ranking accounting for each statersquos code strength and enforcement building official

certification and training and contractor licensing For the 14 states having some notion of a

mandatory residential statewide code in place scores ranged from 28 (Mississippi) to 95

(Virginia) with 43 percent of the 14 mandatory states scoring below 80 (IBHS 2015) Given the

increasing attention natural disasters receive this is surprising as public sector involvement can be

an important element toward reducing disaster losses in a cost effective manner (Kunreuther

2006)

Florida is highly vulnerable to hurricane damages ndash approximately $18 trillion of

residential property exposure (Hamid et al 2011) ndash as well as the oft-referenced gold standard of

3

a strong statewide building code ndash IBHS score of 94 in 2015 (2nd) and 95 in 2012 (1st) (IBHS

2015) Although the extensive property exposure at risk to hurricanes relative to other states has

been continual for Florida since the early part of the 20th century a strong and uniform building

code standard has not Hurricane Andrew which made landfall in South Florida as a category 5

hurricane in 1992 destroyed more than 25000 homes and damaged 100000 others causing $26

billion in total damage (inflation adjusted) making it the costliest catastrophic event in history at

that time (Fronstin and Holtmann 1994) Eleven insurance companies became insolvent as a

result

After Hurricane Andrew it became clear that construction practices in place during the

1980s had not been sufficient to withstand such a powerful wind storm (Sparks et al 1994) Post-

storm inspections detected inferior construction practices which had thus unnecessarily magnified

the extensive damage (Fronstin and Holtmann 1994 Keith and Rose 1994) In the aftermath of

Hurricane Andrew Florida began enacting statewide building code change that wrested away

building code adoption control from individual localities The first communities to strengthen

their building code were the counties of Broward Dade and Monroe all of which already adhered

to the stronger South Florida Building Code (SFBC) Standards for the SFBC were upgraded in

1994 with an emphasis on improving the integrity of the building envelope including impact

protection for exterior windows and doors Beyond the counties in the SFBC some communities

began adopting stronger local codes as well In 1996 the Florida Building Code Commission

began a study to make recommendations on a statewide basis in consultation with wind engineers

The Florida Legislature in 1998 authorized the recommended changes statewide creating the

2001 Florida Building Code The FBC is based on the national model codes developed by the

International Code Council (ICC) and is among the strictest in the nation heavily emphasizing

4

wind engineering standards and other additions for Floridarsquos specific needs including for hurricane

protection (Dixon 2009)

In this study we first quantify the reduction of residential property wind damages due to

the implementation of the FBC utilizing realized insurance policy claim and loss data across the

entire state of Florida spanning the years 2001 to 2010 We utilize a Regression Discontinuity

(RD) model using a treatment of Post FBC construction and a rating variable of structure age

Following from our claim-based empirical loss estimations we then further assess the economic

effectiveness of the FBC through a benefit-cost analysis (BCA) a relatively underserved yet

important research component in wholly assessing building code augmentations Especially as

enhanced building codes increase new construction costs moving forward both pieces of

information are critical in not only highlighting the value of a statewide building code but also in

generating political and consumer support for its implementation (Kunreuther 2006 Vaughan and

Turner 2014 NIBS 2015)

The article proceeds as follows Section 2 is a discussion of existing assessments of

windstorm building code effectiveness Section 3 is an overview of the data and Section 4 provides

a discussion of the econometric methodology Section 5 discusses regression results and provides

an evaluation of the regression model Section 6 is a BenefitCost Analysis of the FBC and Section

7 concludes the article

II Review of Existing Assessments of Windstorm Building Code Effectiveness

Several studies have identified the reduction in windstorm losses due to stronger building

codes utilizing event-based realized loss or insurance claim dataii Fronstin and Holtmann (1994)

in their analysis of 1992 Hurricane Andrew damages in southeast Florida find that older homes

built prior to the 1960rsquos suffered less damage on average than those built after 1960 due to an

5

eroding building code over time Post-Andrew the catastrophic hurricane seasons of 2004 and

2005 in Florida provided a natural opportunity to test how well the implemented FBC performed

A study by IBHS following Hurricane Charley in 2004 (IBHS 2004) found that homes built after

1996 had lower claim frequency (60 percent less) and severity (42 percent less) as compared to

homes built before 1996 This suggests the trend of an eroding building code reversed after

Hurricane Andrew Applied Research Associates (2008) investigated policy level claim data from

eight different insurance companies following the 2004 and 2005 hurricane seasons and found

similar results with post-2002 homes showing significant loss reduction results compared to pre-

2002 homes They further found that overall losses were reduced in year built from the mid-1990s

onward Although only indirectly associated with actual damages incurred stronger building

codes reduced post-storm federal disaster spending in 795 unique Florida ZIP codes impacted at

least once by the 2004 hurricanes of Charley Frances Ivan and Jeanne as well as Tropical Storm

Bonnie (Deryugina 2013) However contrary to these results Dehring and Halek (2013) find that

for the 264 residential properties in a coastal building zone in Lee County following Hurricane

Charley there is no evidence of less damage for homes built after the revised 1992 Florida building

code

Our study advances this building code literature in several important ways First we

collect annualized private market insured policy and loss data (number of claims and total damages

for all represented earned house years in the insured portfolio) from the Insurance Services Office

(ISO) aggregated at the ZIP code level for all Florida ZIP codes spanning the years 2001 to 2010

inclusive We are therefore able to analyze a decade of data post-FBC implementation ISO

industry data represents a significant percent of total private propertycasualty insurance annual

market share in FLiii and we utilize aggregated policy data in any one year ranging from 669000

6

to just over 1 million insured policyholders Thus we utilize more comprehensive ndash in number

space and time ndash insured loss and premium data for this analysis than previous studies Lastly

Florida was affected by 18 tropical cyclones over the period 2001-2010 not just those in 2004 and

2005 and our study utilizes a more comprehensive set of extreme wind events extending beyond

2004 and 2005

Finally following from our claim and loss analysis we perform a BCA on the

implementation of the FBC Our BCA is unique in that we use actual loss data rather than

probabilistic estimates of future loss as previous studies have and our loss data spans a longer time

period of 10 years in order to control for the effect of post FBC construction

III Florida Windstorm Losses and Associated Data

We quantify historical Florida wind event loss reductions due to the implemented FBC

through an econometric driven loss methodology that systematically accounts for relevant wind

hazard exposure and vulnerability characteristics evolving over time from the adoption of the

new uniform codes ISO provided annual insured loss data aggregated at the ZIP code by decade

of construction In addition to insured loss data we have several variables from ISO collected by

insurers EHY Premiums and BrickMasonry EHY is an acronym for earned house years and

represents the number of policyholders in each ZIP code Premiums is the total annual premiums

collected and BrickMasonry is the percent of homes that have exterior cladding made from brick

or other masonry products

Florida Insured Loss Data

For the years 2001 to 2010 we obtained Florida propertycasualty insurance industry data

from ISO aggregated at the ZIP code Again the ISO industry data has aggregated policy data in

any one year ranging from 669000 to just over 1 million insured policyholders representing

7

125 of all residential structures in Floridaiv A total of $8023 billion (2010 inflation adjusted)

of property losses was incurred over this time (net of deductibles) from 593663 total property loss

claims incurred From 2001 to 2010 windstorm hazards are the largest cause of loss in Florida

totaling $5178 billion in losses (65 percent of total hazard damage) as well as being the most

frequent source of a loss claim with 317005 claims incurred (53 percent of total hazard claims

incurred) Clearly windstorm is a significant source of losses for Florida property insurers and

owners

Of course Florida windstorm losses vary over time and as expected are significantly

linked to the occurrence of hurricanes Table 1 provides a further detailed view of the ISO Florida

windstorm incurred losses and claims over time Across all years an average of $517 million in

losses and 31701 claims are incurred each year with an average windstorm claim being $10089

incurred at the rate of 324 claims per 1000 insured exposures (earned house years) However

excluding the significant hurricane years of 2004 and 2005 an average of $25 million in losses

and 3900 claims are incurred each year with an average windstorm claim of $8353 per claim

incurred at the rate of 48 claims per 1000 insured exposures (earned house years) Although

windstorm losses and claims are considerably higher in significant hurricane years they are still a

substantial annual property risk For example 2007 had average windstorm claims of $25399 per

claim and 2001 had 131 windstorm claims per 1000 insured ndash both outside the significant

hurricane years of 2004 and 2005 Lastly average annual premiums collected over this timeframe

(data not shown) are just over $1 billion per year Although these premiums are sufficient to cover

incurred loss amounts in non-hurricane years major windstorm year loss amounts (for example

2004 windstorm losses are nearly 4 times higher than annual average premiums collected) indicate

the critical role of further windstorm risk reduction measures in Florida

8

Insert Table 1 Here

One further split of the ISO loss data obtained is by decade of construction That is for

each year of ISO data from 2001 to 2010 each Florida ZIP code in that year contains a split of the

losses claims premiums and earned house years by the year of construction decade beginning in

1900 up to 2010 Given the loss timeframe of the ISO data from 2001 to 2010 in any one year

the majority of the overall ISO portfolio (ie proportion of earned house years EHY) is

represented by properties built prior to the year 2000 However given the growth of new

construction in Florida during this decade over time newer construction practices make up a more

significant portion of the ISO portfolio (Figure 1)v For example in 2001 post-2000 year of

construction (YOC) properties are less than 10 percent of the total ISO portfolio of 869645 total

EHYs but by 2010 they represent over 30 percent of the total ISO portfolio of 669770 total EHYs

And it is these newer housing units (ie primarily the post-2000 YOC properties) to which the

statewide FBC would have the most effect given its full implementation in 2002

Insert Figure 1 Here

Therefore as would be expected given the significant absolute portion of the EHY being

from pre-2000 YOC properties the majority of the 317005 total wind related claims and

associated $5178 billion in total wind-related losses (approximately 86 percent each) in identified

ZIP codes are incurred by properties that were built prior to the year 2000 But more importantly

the raw loss data on the numbers of claims and losses when normalized for the EHYs per YOC are

also higher on average for properties built prior to the year 2000 (Table 2) That is normalizing

for the number of policyholders in each YOC category (which again are significantly higher in

pre-2000 YOC as per Table 2) pre-2000 YOC buildings have a higher rate of claims incurred as

well as higher average incurred losses per each claim For example in 2004 206 percent of pre-

2000 YOC insured policyholders incurred a claim with an average loss of $3605 across all pre-

9

2000 YOC policyholdersvi This compares to 104 percent of post-2000 YOC insured

policyholders incurring a claim with an average loss of $1211 across all post-2000 YOC

policyholders Although this is true for the normalized raw loss data a number of other hazard

exposure and vulnerability factors need to be controlled for to ascertain that post-2000 YOC losses

are indeed lower than pre-2000 construction

Insert Table 2 Here

Outcome Variable

Our dependent variable is aggregate loss for each ZIP code by year (2001-2010) and by

decade of construction In total we have 69442 observations We transform this variable by

taking the natural log While we do not have individual customer data we do have the number of

insured customers (EHY) for each ZIPyeardecade of construction that we include as an

explanatory variable to control for the differences between ZIPyeardecade of construction

observations with high numbers of insured customers versus those with lower numbers

Treatment Variable

To test for the effect of homes built after the introduction of the statewide building code

we construct a dummy variablecedil Post FBC for observations that are after 2000 By using this

dummy variable we can test the effect on losses for homes built after the statewide code was

implemented The dummy variable for Post FBC construction is related to structure age but does

not capture the separate effect age may have on loss So we add structure age into the regression

We only have data on structure age by decade which goes back to 1900 To introduce some

variability to this variable we calculate age by taking the difference between the year of loss and

the first year in the decade for the observation So for an observation that is for year 2004 where

the decade of construction was 1950-1959 age would equal 54 2004-1950 We turn now to the

other data

10

Wind Hazard Data

Florida was affected by 18 tropical cyclones over the period 2001-2010 Spatial wind

hazard data over Florida are sourced from the National Center for Environmental Predictionrsquos

(NCEP) North American Regional Reanalysis (NARR 2015 Mesinger et al 2006) NARR is a

dynamically consistent historical climate dataset based on historical climate observations Data are

available 3-hourly on a 32km grid Of importance to this study Mesinger et al (2006) showed that

the winds just above the surface compare well with surface station observations The 32-km grid

is too coarse to resolve high-impact small-scale features in the wind field such as thunderstorm

winds or tornadoes It is also too coarse to capture the intensity of the strongest hurricanes (as

discussed in Done et al 2015) Rather than downscaling the NARR data to obtain these small-

scale details using dynamical (eg Laprise et al 2008) or statistical (eg Tye et al 2014)

methods (that could introduce further uncertainties) we choose to sacrifice the small-scale details

of the wind field and peak hurricane intensity for location accuracy of the NARR data To account

for these missing wind extremes all wind speed values are normalized by the maximum value of

wind speed in the dataset

Specifically the 3-hourly wind data are interpolated from the 32-km grid to the ZIP-code

level and two wind field parameters are derived for use in the loss regressions the normalized

annual maximum wind speed and the annual number of times the wind speed exceeds the Florida

mean wind speed plus one standard deviation for at least 12 hours The choice of hazard variables

is based on recent work that highlighted the potential for wind parameters other than the maximum

wind to drive losses (Czajkowski and Done 2014 Zhai and Jiang 2014 Jain 2010)

11

Additional Data

We have 2000 and 2010 demographic data from the decennial census at the ZIP code level

for population area (in square miles) of the ZIP median household income and housing counts

Population growth across the decade is not even so we use building permits to help estimate

intervening years Each year is interpolated from decennial data for population and total housing

counts with an allocation factor based on the number of building permits for each ZIP and each

year Building permits are collected from census by place codes so we must re-allocate to ZIP

codes To convert from place to ZIP code we use allocation factors based on 2010 housing counts

provided by MABLE a service of the Missouri Census Data Center (MABLE 2015) For

example if a municipality has two ZIP codes with 60 of the homes in one and the remaining

40 in the other MABLE would use those percentages as the allocation factors from the

municipality to its corresponding ZIP codes In unincorporated areas we use allocation factors

from county to ZIP from the same service For median household income a straight-line

interpolation method is used adjusted for changes in the consumer price index (CPI-U) to 2010

CPI data are from the Bureau of Labor Statistics

Several factors were utilized to represent the overall geographic hazard risk of a ZIP code

The distance of the centroid of the ZIP to the coast was calculated to account for the overall

distance to the coast of each ZIP code Following Dehring and Halek (2013) dummy variables

that signifies whether a ZIP code contains a coastal construction control line (CCCL) were created

(1 equals CCCL in place) to account for stricter building codes in these areas Finally following

the 2005 hurricane season there was a significant increase in the number of policies underwritten

by Citizens the state-run wind-pool and insurer of last resort (Florida Catastrophic Storm Risk

Management Center 2013) Areas with large percentages of insured policies underwritten by

12

Citizens could represent inherently higher windstorm risk We spatially matched our Florida ZIP

codes to the Florida house districts and took the percentage of Citizens policies of the number of

occupied housing units as of December 31 2011 (Florida Catastrophic Storm Risk Management

Center 2013) Given the potential for adverse selection or offloading of high risk policies by the

private market in these areas it is unclear whether higher Citizensrsquo market penetration would lead

to a positive relationship with losses due to the higher risk or a negative relationship with private

losses as many of the bad risks have been transferred to the residual wind pool

IV Econometric Methodology

Better construction limits loss from windstorms through two channels first the direct effect

of decreasing loss on homes that experience damage and second through fewer claims on better

built homes Our data from ISO is aggregated at the ZIP codedecade of construction level So a

ZIP code where all homes experienced damage would have varying levels of damage between

homes built before and after implementation of the FBC Other ZIP codes may have damage for

older homes but little to no damage for homes built post FBC Our first challenge was to use

models that would provide an estimate of the full effect of the FBC lower levels of damage plus

the effect of fewer claims then an estimate for the direct effect alone To accomplish this we

employ two models The first includes all observations even if no claims have been filed and

second a hurdle model where the first stage models the probability of experiencing a loss and the

second stage isolates only the observations where a loss has been experienced

Base Model

The regression model is a semi-log ordinary least squares (OLS) fixed effects (time and

space) model with the natural log of loss as the dependent variable The base level of observation

is ZIP codeyeardecade of construction Explanatory variables include insurance information

13

(exposures and premiums) construction type demographic data based on the ZIP code measures

of the ZIP code hazard risk (how close to the coast the ZIP code is etc) and hazard data

concerning the wind speed and duration

Our test of the FBC creates a discontinuity that must be accounted for in the model All

observations with decade of construction post 2000 are considered under the new building code

regime But that dummy variable is a function of structure age so we employ a regression

discontinuity (RD) analysis to determine the best specification to estimate the effect of the FBC

allowing for the effect that structure age has on damage Intuitively structure age should increase

loss as older homes depreciate across their life making them more vulnerable to wind storms But

the effect of structure age is more than depreciation Over time construction practices and

materials used have changed which also affect how a structure responds to the stress of a violent

wind storm Indeed after Hurricane Andrew in 1992 it was noted that inferior construction

practices of the 1970rsquos and 1980rsquos had exacerbated the losses (Fronstin and Holtmann 1994 Keith

and Rose 1994)

This suggests that the effect of age is non-linear so a model that includes age as a

polynomial would be reasonable Determining the best specification requires testing a series of

models that include age as a polynomial andor interacted with our treatment variable Post FBC

(Lee and Lemieux 2010) (Jacob and Zhu 2012) The full analysis to choose our specification is

included in the Appendix The model that provided the best tradeoff between bias and precision

based on the AIC adds age and its square with the functional form

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

where the variable definitions are given in Table 3

14

Insert Table 3 Here

A positive sign is expected for both wind variables indicating that as wind speeds increase

andor the ZIP code is exposed to high winds for an extended period of time losses will increase

Post FBC construction should decrease loss so a negative sign is expected

Hurdle Model

One problem potentially encountered in attempting to model losses is there may be a

separate process occurring in the data that determines whether a loss is realized at all which could

affect the estimate of overall losses To address this issue hurdle models are used as they divide

the analysis into two stages We use a hurdle model to find the direct effect of the FBC The first

stage models the probability that a loss occurs and the second stage models the loss using only

observations that sustained a loss The dependent variable in the first stage equals one if there was

a loss and zero otherwise This binary dependent variable is then regressed against variables that

would affect the probability that a loss occurred Its form is

(2a)

Loss or No Loss = β0 + β1 Max Wind + β2 Wind Duration + β3 Population Density

+ β4 Post FBC

We expect that both wind variables max wind speed and duration as well as population

density will increase the probability of a loss Post FBC construction however should decrease

the probability of a loss

The second stage in the hurdle model is the same as Equation 1 with the exception that

only observations with a loss are included There are 19107 observations for the second stage and

its form is

15

(2b)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Model Validity

Regression models are limited by available data to understand how the dependent variable

varies as explanatory variables change If important variables are left out of the model some bias

can be expected This omitted variable bias is a common problem encountered with econometric

models Kuminoff et al 2010 found that one of the best approaches to reducing omitted variable

bias is to employ a spatial fixed effects model To accomplish this we use individual ZIP dummy

variables as a spatial fixed effect and dummy variables for each year in our data to control for

changes that may be related to time not otherwise controlled for within our co-variates These

dummy variables will contain all across-group variation leaving the remainder of the model to

contain the within-group variation (Greene 2003)

A second challenge to the validity of our model is another common problem

heteroscedasticity For Equation 1 we use clustered standard errors at the ZIP code through Proc

GLM in SAS Our hurdle model (Eq 2a and 2b) utilizes Proc Qlim which has a separate statement

(Hetero) that we invoked to model the error variance

V Regression Results

Our first regression (Equation 1) serves as a base from which we examine the effect of

basic explanatory variables on loss The results from this regression can be found in Regression

Table 4

Insert Table 4 Here

16

The performance of our regression model is satisfactory in terms of the performance of the

explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and

the coefficient on our treatment variable Post FBC is -126 and highly significant

Overall our results show the strong effect the statewide FBC had on losses from wind

storms during this timeframe Using the results from the regression in Table 4 the coefficient on

the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses

than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the

results from a study conducted by the Insurance Institute for Business and Home Safety after

Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60

percent less likely to suffer damage at all and those that were damaged sustained 42 percent less

damage than older homes Our result of 72 percent lower damage reflects both those attributes as

our data included ZIP codeyearYOC observations that suffered damage as well as those that did

not

Our variables to measure the effect of wind hazard are wind speed and duration For both

variables we have a positive sign and each is highly significant Higher wind speed and higher

duration of high wind speeds increases damage and thus loss The remaining variables perform as

expected

Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In

the first stage variables such as Max Wind and Wind Duration significantly increase the

probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post

FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower

for homes built after new building codes were adopted In the second stage we see that our wind

variables continue to significantly increase the size of the loss and our treatment variable Post

17

FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now

lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy

we see that losses are reduced by about 47 as opposed to 72 when all observations are

includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better

construction reduces loss in two ways First it lowers claims and reduces the amount of a loss

when a claim is filedviii

Model Evaluation

To evaluate our model we used the second stage of the hurdle models and broke our data

into two groups The first group represents 90 of the data randomly selected and was used to

run the model and collect parameter estimates The second group is an out of sample control group

to test the validity of the model Parameter estimates from the first group are applied to the control

group which gave us a predicted loss for each observation in the control group that can be

compared to the actual loss for each observation in the control group We then regressed the

predicted loss from the control group against the actual loss

Insert Figure 2 Here

Figure 2 plots the predicted loss against the actual loss and provides the fitted line with

95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears

to do a good job of predicting most losses

Robustness of Table 4 Base Model Results

To test the robustness of our results we run three separate analyses 1) We first run a

regression with few co-variates 2) As wind design speeds have been used as a proxy for building

code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss

18

analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking

Florida in 2004 and 2005

Regressions using Few Co-Variates

Additional relevant co-variates add precision to a model But the value of the focus

variable should be apparent with a smaller set So we ran a model with only insured customer

based variables EHY and paid premiums leaving out all other demographic and hazard related

variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and

significance

Regressions Using Design Speed

The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads

for Buildings and Other Structures published by the American Society of Civil Engineers and the

Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds

for most regions of the United States and their territories These reference wind speeds are used in

calculations to determine design wind pressures for the primary structure of a building and the

cladding and components attached to a building These calculations take into account the building

geometry the importance of a building the exposuresurrounding terrain and other parameters that

influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design

speeds as a proxy for building code strength and we similarly add this as an additional control in

our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps

were provided by the Applied Technology Council (ATC) Although this version of the wind

speed map was not utilized during the period under consideration the relative values in general

between two locations would be the sameix

19

We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded

form from the ATC and spatially joined the values to our Florida ZIP codes We then further

categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat

4 and Cat 5 hurricane levels

Insert Table 5 Here

The regression adds two dummy variables first for ZIP codes whose design speed exceeds

the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed

reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables

for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that

communities in higher wind zones may take further measures in local codes However the effect

is not significant Notably our variable for Post FBC construction maintains its negative sign

magnitude and significance

Regressions Limited to 2004 and 2005

Our next regression also shown in Table 5 is limited to observations that occurred during

the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and

2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not

surprisingly the magnitude on wind speed increases while maintaining its significance and the

magnitude on age does the same But the effect of the FBC remains the same a 72 reduction

Summary of Results on the FBC

We have collected a comprehensive set of data on insured paid losses from 2001 to 2010

windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity

model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of

the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction

20

in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to

determine if the policy is one that is cost effective

VI Benefit and Costs of the FBC

Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a

number of cases the economic effectiveness of the improved building codes has not been as well

documented especially from a statewide implementation perspective The multi-hazard

mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose

et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar

spent) was appropriate for process activity grant spending related to improved building codes

However this information was gathered from a limited number of studies (mainly earthquake

oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in

generating it and the ratio provided due to improvement would not be the same as those for

adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted

an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for

ten related counties and determined that the net present value of the revisions was $7 billion or

benefit-cost ratio greater than 1 Importantly though this study did not have access to actual

building code damage reduction data to utilize in the analysis In 2002 Applied Research

Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC

for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how

the three types of FBC built houses would perform in probabilistic hurricane scenarios compared

to the same houses built under the previous code Given the probabilistic nature of the analysis

average annual losses were generated that demonstrated post-FBC housing having loss reductions

54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were

21

then compared to their estimated cost impacts of the FBC for these housing types with at least

break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and

above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al

(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss

reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results

ranged from 041 to 183 for the retrofit mitigation activities to existing housing

We propose a BCA that differs from earlier work in several important ways First we use

realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of

how much loss can be reduced by the FBC Second our loss data spans 10 years which include a

combination of major hurricanes and smaller wind storms

BenefitCost Methodology

The elements of a BCA requires three inputs 1) an estimate of the added cost to implement

the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related

storms from our realized ISO loss data and then from a statewide catastrophe model estimate and

3) the percentage of expected loss that will be mitigated due to implementation of the FBC We

first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis

but use the full reduction in loss which includes the value of reduced claims Finally our ISO data

is paid losses and does not include deductibles so we add an estimate for deductibles

Additional Cost

In their 2002 benefit-cost comparison study of the enactment of the FBC for three related

housing types three actual sample homes were built to the FBC to evaluate the change in

construction costs (ARA 2002) For the purposes of code implementation the state was divided

into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region

22

(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account

for cost differences between the two regions

In the WBDR an added requirement is impact protection to windows and doors to reduce

damage from flying debris Along the coast and much of South Florida is classified as the WBDR

The N-WBDR is mainly classified in the interior of the state where impact protection is not

required Importantly the study provided a range of added costs for the N-WBDR and the WBDR

Three counties in South Florida Dade Broward and Monroe were under the South Florida

Building Code (SFBC) prior to the implementation of the FBC According to the ARA study

(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges

of incremental cost per square foot for the N-WBDR and WBDR along with the percent of

residential units that reside in each area This allows a calculation of a weighted average added

cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost

per square foot of $166 The cost compares favorably with a similar building code enhancement

adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in

2013 Consulting engineers and the Moore Association of Homebuilders estimated the code

enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is

1960 square feet which means that on average the FBC increases construction cost by $3254 per

structurexii

Insert Table 6 Here

Benefit of the FBC

Benefits stemming from the FBC are the expected reduction in losses from windstorms during

the life of the home We first find an average annual loss (AAL) use that number to estimate

losses for the next 50 years and then find the present value of those losses in 2010 Here we are

23

assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the

next 50 years A wealth of literature suggests the potential for changes to hurricane activity over

the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty

on future changes in wind hazard on the scale of a single state we choose to assume a stationary

climate

Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes

built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our

data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of

$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year

Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses

per structure of $21474

Finally we use parameter estimates from our regression for the Post FBC dummy variable

(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC

The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post

FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what

would be a reduction of $10093 from the projected $21474 in future losses

Insert Table 7 Here

BenefitCost Analysis

Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio

of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the

statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an

economically effective public policy following from our ISO loss data and results

Insert Table 8 Here

24

Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years

This relatively short timeframe makes it difficult to truly approximate an AAL as would be

provided from a probabilistically based catastrophe model that generates an AAL from thousands

of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state

of Florida to estimate an average annual wind loss for all residential properties in Florida of

approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL

estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion

with deductibles) Using this aggregate AAL and the number of residential units in Florida based

on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses

net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and

an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as

before derived from our regression results 47 we find $7710 of reduced loss from the projected

$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added

$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective

building code public policy

We run two additional analyses on our BCA results Our estimate of expected loss

reduction comes from the second stage of the hurdle model This is an estimate of the direct loss

reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the

number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests

as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which

includes the reduced magnitude of loss from affected homes and the reduction in claims for Post

FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table

8) However a 72 loss reduction seems too dramatic an expectation when planning so far in

25

advance For that reason we offer a third level of expected loss reduction of 60 which is the

midpoint between our two loss reduction estimates This estimate captures the expected direct loss

reduction suggested by the second stage of our hurdle model but still recognizes that in some areas

the number of claims is reduced by the FBC This appears to be a reasonable assumption and

provides a BCA ratio of 396 for the ISO sample and 302 for all residential

The ISO data are net of deductibles so our BCA thus far only includes losses compensated by

the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion

also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the

ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all

loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA

values from a low of 237 to a high of 793

Payback of the FBC

Finally we use our BCA results to calculate a payback period for the investment of stronger

codes To convert our BCA ratio to a payback period we simply divide our 50-year planning

horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss

and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years

This is important for gauging potential political support or non-support for enactment of the new

codes Payback periods that approach the typical mortgage term 30 years would in theory be

difficult to achieve and that is not what our analysis indicates for the FBC

VI - Concluding Comments

In the aftermath of Hurricane Andrew which had exposed not only poor building

construction but also poor building code enforcement the state of Florida enacted statewide

building code changes that wrested away building code adoption control from individual localities

26

With full implementation of the statewide building code associated expectations are that

windstorm losses from extreme events such as hurricanes should be reduced moving forward

There have been a few studies confirming these expectations following the 2004 and 2005

hurricane season In this article we further verify and quantify these findings and expand the

existing building code risk reduction research in several important ways

Overall we empirically test the statewide implementation of a building code in reducing

wind related damages in Florida controlling for other relevant wind hazard exposure and

vulnerability characteristics from a traditional risk assessment perspective Our results show the

strong effect the statewide FBC had on losses from wind storms during this timeframe From the

treatment variable that measures implementation of the statewide codes the post 2000 year of

construction losses are shown to be reduced by as much as 72 percent consistent with other

previous findings

Finally we have conducted a BCA of the FBC to determine if expected benefits exceed

the cost of implementation Using a direct estimate for mitigated losses and an estimate that

includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public

policy from an economic perspective This result is close to that recommended by the multi-hazard

mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a

statewide BCA Importantly this information is essential in generating political and consumer

support for such building code public policy implementation

For example the economic effectiveness results shown here have implications for ongoing

policy discussions about reforming building codes from a national US perspective Moore OK

independently adopted enhanced building codes after its third violent tornado in 14 years killed 24

including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)

27

Construction practices in North Texas were brought under scrutiny after the December 2015

tornado revealed inadequate construction including an elementary school whose exterior walls

failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House

announced initiatives to increase community resilience with building codes as a major component

of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748

and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better

construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397

would provide tax credits for owners andor contractors who use techniques designed for resiliency

in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one

recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to

encourage states to use current building codes (Vaughn and Turner 2014)

Future research in the BCA of the FBC will further inform the public policy debate on

enhanced building codes The issue has national implications as other states find that wind hazards

impact them as well We have sufficient wind data to examine how the BCA performs under

different wind hazards Additionally it will be important to consider how future economic

development affects the BCA as well as varying climate change scenarios As the FBC is

mandatory for all new construction a statewide analysis was appropriate But individual

homeowners in older homes can invest in the retrofit of their home and qualify for discounts on

their homeowners insurance This topic is deserving of a robust analysis Although our BCA is

statewide regions within the state will likely have a spectrum of results For instance the ARA

2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their

analysis did not use realized loss data so confirmation of how the BCA varies between those

regions would be an important contribution Finally our sensitivity analysis was limited to two

28

variables reduction in future loss and the inclusion of deductibles Additional work will highlight

other variables that could modify the results

29

Appendix

We use this appendix to conduct more detailed analysis on several topics First selection

of the model specification using a regression discontinuity approach Second we provide an in

depth examination of the relationship between structure age and losses Third we perform a

Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an

alternative specification to see if our RD results are similar followed by regressions to examine

the year to year consistency of our Post FBC result Next we run a regression on claims to verify

the difference between our direct reduction result and our full reduction result Finally we perform

a regression on homes built to the SFBC which had adopted enhanced building codes in advance

of the FBC to assess the effect of earlier adoption of enhanced construction

Regression Discontinuity

Regression Discontinuity (RD) applies when an observation receives a treatment in our case

homes built under the FBC based on a rating variable in our case age of the structure at the year

of observation So for observations in 2005 homes built post 2000 received the treatment

adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify

how observations on either side of the implementation of the FBC (2000) perform in suffering loss

from windstorms The treatment variable is a function of the age of the home and age affects loss

in ways not related to the FBC such as depreciation and differences in materials and construction

practices across time To account for both the effect of age on loss as well as the implementation

of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment

The data we have from ISO is aggregated loss data by zip code and decade of construction So

we cannot get an annualized age To approach a true age we set the year built for each decade of

construction at the beginning of the decade then subtract that from the year of each observation to

get an approximate agexiv

30

To find the best specification we began with a simpler model which used a series of

categorical variables for each decade of construction to examine the effect of the code compared

to the omitted decade This method would approximate the changes in materials and construction

practices but was less effective in controlling for depreciation But it would give us a first

approximation of the code effect that we used as a benchmark when testing the best RD

specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970

with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When

the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When

either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81

A rough approximation of the codersquos effect from this approach would suggest a reduction in the

mid 70 percent range

Insert Table 1 ndash Appendix Here

Next we used a standard procedure with RD to search for the best way to include the rating

variable This process creates specifications that include age in increasing polynomials and

interacted with the treatment variable The goal is to find the specification with the lowest AIC

that comes close to the benchmark value of the treatment variable

Insert Tables 2 and 3 ndash Appendix Here

We did this first with regressions that limited the co-variates then with our full model In both

sets AIC reaches a minimum on the specification with age and age squared The interaction model

after that increases the AIC then the AIC goes down again with a cubed model and its interaction

model with the overall lowest AIC found on the cubed interaction model But we chose not to

use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order

models the magnitude of the treatment variable in the models with just polynomials compared to

31

the corresponding interaction models were close with the interaction models providing a larger

magnitude When the cubed models were added the magnitude jumped where the polynomial

cubed model went down well below our benchmark and the interaction model went up above our

benchmark We felt this made use of the cubed model inappropriate So we now need to choose

between the squared model and the one with the interaction terms The squared model (Model 4)

had a lower AIC and the interaction variables on the interaction model (Model 5) were not

significant so we chose to use the squared model without the interaction term This model gave a

magnitude for the treatment variable of a 72 reduction somewhat lower than the expected

magnitude in the mid 70rsquos percent The general form of the model is

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1

of the observations on either end of our data sorted by loss Our treatment variable Post FBC

remains highly significant with a coefficient value of -117 which compares favorably to our

coefficient value of -126 when the entire sample is used

Structure Age and Wind Losses

Our study is similar to recent studies on the effect of energy efficiency building codes

adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that

better insulation caulking and more efficient HVAC systems would result in lower energy

consumption But the change in energy consumption is less than engineering estimates projected

Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for

homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study

32

may be confounding age with vintage and found a decrease in energy use related to the home

simply being new rather than the change in building code Indeed Kotchen (2015) revisited the

question with data 10 years older and found the effect on electricity had disappeared while the

reduction in natural gas use increased Something is occurring in energy use unrelated to the code

and could be explained by residents changing their use of energy as they adapt to their new home

Residents of an energy efficient home can undermine the intent of lower energy use by using the

efficient design to heat and cool their homes with a motivation toward increased comfort at the

same energy cost rather than energy savings Our study does not have the behavioral component

found in the case of energy efficiency In our application the construction elements that make the

structure able to withstand high winds are installed when the home is built and lie ldquobehind the

wallsrdquo making it unlikely for individual preferences to alter the homes performance against the

threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC

homes due to the code or simply an artifact of new versus old construction when confronted with

a windstorm

To first address our analysis of age versus the FBC we rerun our base regression but limit

our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more

than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14

years during the highest loss year of 2004 Since this is a comparison between two adjacent

decades on either side of our cut point of year 2000 we remove age and age squared Results are

shown in Table 4-Appendix

Insert Table 4-Appendix Here

The coefficient on Post FBC is still negative highly significant with a magnitude very close to

what we saw with the entire database and the age variables This result suggests that the code

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

3

a strong statewide building code ndash IBHS score of 94 in 2015 (2nd) and 95 in 2012 (1st) (IBHS

2015) Although the extensive property exposure at risk to hurricanes relative to other states has

been continual for Florida since the early part of the 20th century a strong and uniform building

code standard has not Hurricane Andrew which made landfall in South Florida as a category 5

hurricane in 1992 destroyed more than 25000 homes and damaged 100000 others causing $26

billion in total damage (inflation adjusted) making it the costliest catastrophic event in history at

that time (Fronstin and Holtmann 1994) Eleven insurance companies became insolvent as a

result

After Hurricane Andrew it became clear that construction practices in place during the

1980s had not been sufficient to withstand such a powerful wind storm (Sparks et al 1994) Post-

storm inspections detected inferior construction practices which had thus unnecessarily magnified

the extensive damage (Fronstin and Holtmann 1994 Keith and Rose 1994) In the aftermath of

Hurricane Andrew Florida began enacting statewide building code change that wrested away

building code adoption control from individual localities The first communities to strengthen

their building code were the counties of Broward Dade and Monroe all of which already adhered

to the stronger South Florida Building Code (SFBC) Standards for the SFBC were upgraded in

1994 with an emphasis on improving the integrity of the building envelope including impact

protection for exterior windows and doors Beyond the counties in the SFBC some communities

began adopting stronger local codes as well In 1996 the Florida Building Code Commission

began a study to make recommendations on a statewide basis in consultation with wind engineers

The Florida Legislature in 1998 authorized the recommended changes statewide creating the

2001 Florida Building Code The FBC is based on the national model codes developed by the

International Code Council (ICC) and is among the strictest in the nation heavily emphasizing

4

wind engineering standards and other additions for Floridarsquos specific needs including for hurricane

protection (Dixon 2009)

In this study we first quantify the reduction of residential property wind damages due to

the implementation of the FBC utilizing realized insurance policy claim and loss data across the

entire state of Florida spanning the years 2001 to 2010 We utilize a Regression Discontinuity

(RD) model using a treatment of Post FBC construction and a rating variable of structure age

Following from our claim-based empirical loss estimations we then further assess the economic

effectiveness of the FBC through a benefit-cost analysis (BCA) a relatively underserved yet

important research component in wholly assessing building code augmentations Especially as

enhanced building codes increase new construction costs moving forward both pieces of

information are critical in not only highlighting the value of a statewide building code but also in

generating political and consumer support for its implementation (Kunreuther 2006 Vaughan and

Turner 2014 NIBS 2015)

The article proceeds as follows Section 2 is a discussion of existing assessments of

windstorm building code effectiveness Section 3 is an overview of the data and Section 4 provides

a discussion of the econometric methodology Section 5 discusses regression results and provides

an evaluation of the regression model Section 6 is a BenefitCost Analysis of the FBC and Section

7 concludes the article

II Review of Existing Assessments of Windstorm Building Code Effectiveness

Several studies have identified the reduction in windstorm losses due to stronger building

codes utilizing event-based realized loss or insurance claim dataii Fronstin and Holtmann (1994)

in their analysis of 1992 Hurricane Andrew damages in southeast Florida find that older homes

built prior to the 1960rsquos suffered less damage on average than those built after 1960 due to an

5

eroding building code over time Post-Andrew the catastrophic hurricane seasons of 2004 and

2005 in Florida provided a natural opportunity to test how well the implemented FBC performed

A study by IBHS following Hurricane Charley in 2004 (IBHS 2004) found that homes built after

1996 had lower claim frequency (60 percent less) and severity (42 percent less) as compared to

homes built before 1996 This suggests the trend of an eroding building code reversed after

Hurricane Andrew Applied Research Associates (2008) investigated policy level claim data from

eight different insurance companies following the 2004 and 2005 hurricane seasons and found

similar results with post-2002 homes showing significant loss reduction results compared to pre-

2002 homes They further found that overall losses were reduced in year built from the mid-1990s

onward Although only indirectly associated with actual damages incurred stronger building

codes reduced post-storm federal disaster spending in 795 unique Florida ZIP codes impacted at

least once by the 2004 hurricanes of Charley Frances Ivan and Jeanne as well as Tropical Storm

Bonnie (Deryugina 2013) However contrary to these results Dehring and Halek (2013) find that

for the 264 residential properties in a coastal building zone in Lee County following Hurricane

Charley there is no evidence of less damage for homes built after the revised 1992 Florida building

code

Our study advances this building code literature in several important ways First we

collect annualized private market insured policy and loss data (number of claims and total damages

for all represented earned house years in the insured portfolio) from the Insurance Services Office

(ISO) aggregated at the ZIP code level for all Florida ZIP codes spanning the years 2001 to 2010

inclusive We are therefore able to analyze a decade of data post-FBC implementation ISO

industry data represents a significant percent of total private propertycasualty insurance annual

market share in FLiii and we utilize aggregated policy data in any one year ranging from 669000

6

to just over 1 million insured policyholders Thus we utilize more comprehensive ndash in number

space and time ndash insured loss and premium data for this analysis than previous studies Lastly

Florida was affected by 18 tropical cyclones over the period 2001-2010 not just those in 2004 and

2005 and our study utilizes a more comprehensive set of extreme wind events extending beyond

2004 and 2005

Finally following from our claim and loss analysis we perform a BCA on the

implementation of the FBC Our BCA is unique in that we use actual loss data rather than

probabilistic estimates of future loss as previous studies have and our loss data spans a longer time

period of 10 years in order to control for the effect of post FBC construction

III Florida Windstorm Losses and Associated Data

We quantify historical Florida wind event loss reductions due to the implemented FBC

through an econometric driven loss methodology that systematically accounts for relevant wind

hazard exposure and vulnerability characteristics evolving over time from the adoption of the

new uniform codes ISO provided annual insured loss data aggregated at the ZIP code by decade

of construction In addition to insured loss data we have several variables from ISO collected by

insurers EHY Premiums and BrickMasonry EHY is an acronym for earned house years and

represents the number of policyholders in each ZIP code Premiums is the total annual premiums

collected and BrickMasonry is the percent of homes that have exterior cladding made from brick

or other masonry products

Florida Insured Loss Data

For the years 2001 to 2010 we obtained Florida propertycasualty insurance industry data

from ISO aggregated at the ZIP code Again the ISO industry data has aggregated policy data in

any one year ranging from 669000 to just over 1 million insured policyholders representing

7

125 of all residential structures in Floridaiv A total of $8023 billion (2010 inflation adjusted)

of property losses was incurred over this time (net of deductibles) from 593663 total property loss

claims incurred From 2001 to 2010 windstorm hazards are the largest cause of loss in Florida

totaling $5178 billion in losses (65 percent of total hazard damage) as well as being the most

frequent source of a loss claim with 317005 claims incurred (53 percent of total hazard claims

incurred) Clearly windstorm is a significant source of losses for Florida property insurers and

owners

Of course Florida windstorm losses vary over time and as expected are significantly

linked to the occurrence of hurricanes Table 1 provides a further detailed view of the ISO Florida

windstorm incurred losses and claims over time Across all years an average of $517 million in

losses and 31701 claims are incurred each year with an average windstorm claim being $10089

incurred at the rate of 324 claims per 1000 insured exposures (earned house years) However

excluding the significant hurricane years of 2004 and 2005 an average of $25 million in losses

and 3900 claims are incurred each year with an average windstorm claim of $8353 per claim

incurred at the rate of 48 claims per 1000 insured exposures (earned house years) Although

windstorm losses and claims are considerably higher in significant hurricane years they are still a

substantial annual property risk For example 2007 had average windstorm claims of $25399 per

claim and 2001 had 131 windstorm claims per 1000 insured ndash both outside the significant

hurricane years of 2004 and 2005 Lastly average annual premiums collected over this timeframe

(data not shown) are just over $1 billion per year Although these premiums are sufficient to cover

incurred loss amounts in non-hurricane years major windstorm year loss amounts (for example

2004 windstorm losses are nearly 4 times higher than annual average premiums collected) indicate

the critical role of further windstorm risk reduction measures in Florida

8

Insert Table 1 Here

One further split of the ISO loss data obtained is by decade of construction That is for

each year of ISO data from 2001 to 2010 each Florida ZIP code in that year contains a split of the

losses claims premiums and earned house years by the year of construction decade beginning in

1900 up to 2010 Given the loss timeframe of the ISO data from 2001 to 2010 in any one year

the majority of the overall ISO portfolio (ie proportion of earned house years EHY) is

represented by properties built prior to the year 2000 However given the growth of new

construction in Florida during this decade over time newer construction practices make up a more

significant portion of the ISO portfolio (Figure 1)v For example in 2001 post-2000 year of

construction (YOC) properties are less than 10 percent of the total ISO portfolio of 869645 total

EHYs but by 2010 they represent over 30 percent of the total ISO portfolio of 669770 total EHYs

And it is these newer housing units (ie primarily the post-2000 YOC properties) to which the

statewide FBC would have the most effect given its full implementation in 2002

Insert Figure 1 Here

Therefore as would be expected given the significant absolute portion of the EHY being

from pre-2000 YOC properties the majority of the 317005 total wind related claims and

associated $5178 billion in total wind-related losses (approximately 86 percent each) in identified

ZIP codes are incurred by properties that were built prior to the year 2000 But more importantly

the raw loss data on the numbers of claims and losses when normalized for the EHYs per YOC are

also higher on average for properties built prior to the year 2000 (Table 2) That is normalizing

for the number of policyholders in each YOC category (which again are significantly higher in

pre-2000 YOC as per Table 2) pre-2000 YOC buildings have a higher rate of claims incurred as

well as higher average incurred losses per each claim For example in 2004 206 percent of pre-

2000 YOC insured policyholders incurred a claim with an average loss of $3605 across all pre-

9

2000 YOC policyholdersvi This compares to 104 percent of post-2000 YOC insured

policyholders incurring a claim with an average loss of $1211 across all post-2000 YOC

policyholders Although this is true for the normalized raw loss data a number of other hazard

exposure and vulnerability factors need to be controlled for to ascertain that post-2000 YOC losses

are indeed lower than pre-2000 construction

Insert Table 2 Here

Outcome Variable

Our dependent variable is aggregate loss for each ZIP code by year (2001-2010) and by

decade of construction In total we have 69442 observations We transform this variable by

taking the natural log While we do not have individual customer data we do have the number of

insured customers (EHY) for each ZIPyeardecade of construction that we include as an

explanatory variable to control for the differences between ZIPyeardecade of construction

observations with high numbers of insured customers versus those with lower numbers

Treatment Variable

To test for the effect of homes built after the introduction of the statewide building code

we construct a dummy variablecedil Post FBC for observations that are after 2000 By using this

dummy variable we can test the effect on losses for homes built after the statewide code was

implemented The dummy variable for Post FBC construction is related to structure age but does

not capture the separate effect age may have on loss So we add structure age into the regression

We only have data on structure age by decade which goes back to 1900 To introduce some

variability to this variable we calculate age by taking the difference between the year of loss and

the first year in the decade for the observation So for an observation that is for year 2004 where

the decade of construction was 1950-1959 age would equal 54 2004-1950 We turn now to the

other data

10

Wind Hazard Data

Florida was affected by 18 tropical cyclones over the period 2001-2010 Spatial wind

hazard data over Florida are sourced from the National Center for Environmental Predictionrsquos

(NCEP) North American Regional Reanalysis (NARR 2015 Mesinger et al 2006) NARR is a

dynamically consistent historical climate dataset based on historical climate observations Data are

available 3-hourly on a 32km grid Of importance to this study Mesinger et al (2006) showed that

the winds just above the surface compare well with surface station observations The 32-km grid

is too coarse to resolve high-impact small-scale features in the wind field such as thunderstorm

winds or tornadoes It is also too coarse to capture the intensity of the strongest hurricanes (as

discussed in Done et al 2015) Rather than downscaling the NARR data to obtain these small-

scale details using dynamical (eg Laprise et al 2008) or statistical (eg Tye et al 2014)

methods (that could introduce further uncertainties) we choose to sacrifice the small-scale details

of the wind field and peak hurricane intensity for location accuracy of the NARR data To account

for these missing wind extremes all wind speed values are normalized by the maximum value of

wind speed in the dataset

Specifically the 3-hourly wind data are interpolated from the 32-km grid to the ZIP-code

level and two wind field parameters are derived for use in the loss regressions the normalized

annual maximum wind speed and the annual number of times the wind speed exceeds the Florida

mean wind speed plus one standard deviation for at least 12 hours The choice of hazard variables

is based on recent work that highlighted the potential for wind parameters other than the maximum

wind to drive losses (Czajkowski and Done 2014 Zhai and Jiang 2014 Jain 2010)

11

Additional Data

We have 2000 and 2010 demographic data from the decennial census at the ZIP code level

for population area (in square miles) of the ZIP median household income and housing counts

Population growth across the decade is not even so we use building permits to help estimate

intervening years Each year is interpolated from decennial data for population and total housing

counts with an allocation factor based on the number of building permits for each ZIP and each

year Building permits are collected from census by place codes so we must re-allocate to ZIP

codes To convert from place to ZIP code we use allocation factors based on 2010 housing counts

provided by MABLE a service of the Missouri Census Data Center (MABLE 2015) For

example if a municipality has two ZIP codes with 60 of the homes in one and the remaining

40 in the other MABLE would use those percentages as the allocation factors from the

municipality to its corresponding ZIP codes In unincorporated areas we use allocation factors

from county to ZIP from the same service For median household income a straight-line

interpolation method is used adjusted for changes in the consumer price index (CPI-U) to 2010

CPI data are from the Bureau of Labor Statistics

Several factors were utilized to represent the overall geographic hazard risk of a ZIP code

The distance of the centroid of the ZIP to the coast was calculated to account for the overall

distance to the coast of each ZIP code Following Dehring and Halek (2013) dummy variables

that signifies whether a ZIP code contains a coastal construction control line (CCCL) were created

(1 equals CCCL in place) to account for stricter building codes in these areas Finally following

the 2005 hurricane season there was a significant increase in the number of policies underwritten

by Citizens the state-run wind-pool and insurer of last resort (Florida Catastrophic Storm Risk

Management Center 2013) Areas with large percentages of insured policies underwritten by

12

Citizens could represent inherently higher windstorm risk We spatially matched our Florida ZIP

codes to the Florida house districts and took the percentage of Citizens policies of the number of

occupied housing units as of December 31 2011 (Florida Catastrophic Storm Risk Management

Center 2013) Given the potential for adverse selection or offloading of high risk policies by the

private market in these areas it is unclear whether higher Citizensrsquo market penetration would lead

to a positive relationship with losses due to the higher risk or a negative relationship with private

losses as many of the bad risks have been transferred to the residual wind pool

IV Econometric Methodology

Better construction limits loss from windstorms through two channels first the direct effect

of decreasing loss on homes that experience damage and second through fewer claims on better

built homes Our data from ISO is aggregated at the ZIP codedecade of construction level So a

ZIP code where all homes experienced damage would have varying levels of damage between

homes built before and after implementation of the FBC Other ZIP codes may have damage for

older homes but little to no damage for homes built post FBC Our first challenge was to use

models that would provide an estimate of the full effect of the FBC lower levels of damage plus

the effect of fewer claims then an estimate for the direct effect alone To accomplish this we

employ two models The first includes all observations even if no claims have been filed and

second a hurdle model where the first stage models the probability of experiencing a loss and the

second stage isolates only the observations where a loss has been experienced

Base Model

The regression model is a semi-log ordinary least squares (OLS) fixed effects (time and

space) model with the natural log of loss as the dependent variable The base level of observation

is ZIP codeyeardecade of construction Explanatory variables include insurance information

13

(exposures and premiums) construction type demographic data based on the ZIP code measures

of the ZIP code hazard risk (how close to the coast the ZIP code is etc) and hazard data

concerning the wind speed and duration

Our test of the FBC creates a discontinuity that must be accounted for in the model All

observations with decade of construction post 2000 are considered under the new building code

regime But that dummy variable is a function of structure age so we employ a regression

discontinuity (RD) analysis to determine the best specification to estimate the effect of the FBC

allowing for the effect that structure age has on damage Intuitively structure age should increase

loss as older homes depreciate across their life making them more vulnerable to wind storms But

the effect of structure age is more than depreciation Over time construction practices and

materials used have changed which also affect how a structure responds to the stress of a violent

wind storm Indeed after Hurricane Andrew in 1992 it was noted that inferior construction

practices of the 1970rsquos and 1980rsquos had exacerbated the losses (Fronstin and Holtmann 1994 Keith

and Rose 1994)

This suggests that the effect of age is non-linear so a model that includes age as a

polynomial would be reasonable Determining the best specification requires testing a series of

models that include age as a polynomial andor interacted with our treatment variable Post FBC

(Lee and Lemieux 2010) (Jacob and Zhu 2012) The full analysis to choose our specification is

included in the Appendix The model that provided the best tradeoff between bias and precision

based on the AIC adds age and its square with the functional form

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

where the variable definitions are given in Table 3

14

Insert Table 3 Here

A positive sign is expected for both wind variables indicating that as wind speeds increase

andor the ZIP code is exposed to high winds for an extended period of time losses will increase

Post FBC construction should decrease loss so a negative sign is expected

Hurdle Model

One problem potentially encountered in attempting to model losses is there may be a

separate process occurring in the data that determines whether a loss is realized at all which could

affect the estimate of overall losses To address this issue hurdle models are used as they divide

the analysis into two stages We use a hurdle model to find the direct effect of the FBC The first

stage models the probability that a loss occurs and the second stage models the loss using only

observations that sustained a loss The dependent variable in the first stage equals one if there was

a loss and zero otherwise This binary dependent variable is then regressed against variables that

would affect the probability that a loss occurred Its form is

(2a)

Loss or No Loss = β0 + β1 Max Wind + β2 Wind Duration + β3 Population Density

+ β4 Post FBC

We expect that both wind variables max wind speed and duration as well as population

density will increase the probability of a loss Post FBC construction however should decrease

the probability of a loss

The second stage in the hurdle model is the same as Equation 1 with the exception that

only observations with a loss are included There are 19107 observations for the second stage and

its form is

15

(2b)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Model Validity

Regression models are limited by available data to understand how the dependent variable

varies as explanatory variables change If important variables are left out of the model some bias

can be expected This omitted variable bias is a common problem encountered with econometric

models Kuminoff et al 2010 found that one of the best approaches to reducing omitted variable

bias is to employ a spatial fixed effects model To accomplish this we use individual ZIP dummy

variables as a spatial fixed effect and dummy variables for each year in our data to control for

changes that may be related to time not otherwise controlled for within our co-variates These

dummy variables will contain all across-group variation leaving the remainder of the model to

contain the within-group variation (Greene 2003)

A second challenge to the validity of our model is another common problem

heteroscedasticity For Equation 1 we use clustered standard errors at the ZIP code through Proc

GLM in SAS Our hurdle model (Eq 2a and 2b) utilizes Proc Qlim which has a separate statement

(Hetero) that we invoked to model the error variance

V Regression Results

Our first regression (Equation 1) serves as a base from which we examine the effect of

basic explanatory variables on loss The results from this regression can be found in Regression

Table 4

Insert Table 4 Here

16

The performance of our regression model is satisfactory in terms of the performance of the

explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and

the coefficient on our treatment variable Post FBC is -126 and highly significant

Overall our results show the strong effect the statewide FBC had on losses from wind

storms during this timeframe Using the results from the regression in Table 4 the coefficient on

the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses

than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the

results from a study conducted by the Insurance Institute for Business and Home Safety after

Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60

percent less likely to suffer damage at all and those that were damaged sustained 42 percent less

damage than older homes Our result of 72 percent lower damage reflects both those attributes as

our data included ZIP codeyearYOC observations that suffered damage as well as those that did

not

Our variables to measure the effect of wind hazard are wind speed and duration For both

variables we have a positive sign and each is highly significant Higher wind speed and higher

duration of high wind speeds increases damage and thus loss The remaining variables perform as

expected

Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In

the first stage variables such as Max Wind and Wind Duration significantly increase the

probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post

FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower

for homes built after new building codes were adopted In the second stage we see that our wind

variables continue to significantly increase the size of the loss and our treatment variable Post

17

FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now

lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy

we see that losses are reduced by about 47 as opposed to 72 when all observations are

includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better

construction reduces loss in two ways First it lowers claims and reduces the amount of a loss

when a claim is filedviii

Model Evaluation

To evaluate our model we used the second stage of the hurdle models and broke our data

into two groups The first group represents 90 of the data randomly selected and was used to

run the model and collect parameter estimates The second group is an out of sample control group

to test the validity of the model Parameter estimates from the first group are applied to the control

group which gave us a predicted loss for each observation in the control group that can be

compared to the actual loss for each observation in the control group We then regressed the

predicted loss from the control group against the actual loss

Insert Figure 2 Here

Figure 2 plots the predicted loss against the actual loss and provides the fitted line with

95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears

to do a good job of predicting most losses

Robustness of Table 4 Base Model Results

To test the robustness of our results we run three separate analyses 1) We first run a

regression with few co-variates 2) As wind design speeds have been used as a proxy for building

code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss

18

analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking

Florida in 2004 and 2005

Regressions using Few Co-Variates

Additional relevant co-variates add precision to a model But the value of the focus

variable should be apparent with a smaller set So we ran a model with only insured customer

based variables EHY and paid premiums leaving out all other demographic and hazard related

variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and

significance

Regressions Using Design Speed

The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads

for Buildings and Other Structures published by the American Society of Civil Engineers and the

Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds

for most regions of the United States and their territories These reference wind speeds are used in

calculations to determine design wind pressures for the primary structure of a building and the

cladding and components attached to a building These calculations take into account the building

geometry the importance of a building the exposuresurrounding terrain and other parameters that

influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design

speeds as a proxy for building code strength and we similarly add this as an additional control in

our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps

were provided by the Applied Technology Council (ATC) Although this version of the wind

speed map was not utilized during the period under consideration the relative values in general

between two locations would be the sameix

19

We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded

form from the ATC and spatially joined the values to our Florida ZIP codes We then further

categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat

4 and Cat 5 hurricane levels

Insert Table 5 Here

The regression adds two dummy variables first for ZIP codes whose design speed exceeds

the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed

reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables

for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that

communities in higher wind zones may take further measures in local codes However the effect

is not significant Notably our variable for Post FBC construction maintains its negative sign

magnitude and significance

Regressions Limited to 2004 and 2005

Our next regression also shown in Table 5 is limited to observations that occurred during

the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and

2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not

surprisingly the magnitude on wind speed increases while maintaining its significance and the

magnitude on age does the same But the effect of the FBC remains the same a 72 reduction

Summary of Results on the FBC

We have collected a comprehensive set of data on insured paid losses from 2001 to 2010

windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity

model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of

the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction

20

in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to

determine if the policy is one that is cost effective

VI Benefit and Costs of the FBC

Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a

number of cases the economic effectiveness of the improved building codes has not been as well

documented especially from a statewide implementation perspective The multi-hazard

mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose

et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar

spent) was appropriate for process activity grant spending related to improved building codes

However this information was gathered from a limited number of studies (mainly earthquake

oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in

generating it and the ratio provided due to improvement would not be the same as those for

adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted

an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for

ten related counties and determined that the net present value of the revisions was $7 billion or

benefit-cost ratio greater than 1 Importantly though this study did not have access to actual

building code damage reduction data to utilize in the analysis In 2002 Applied Research

Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC

for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how

the three types of FBC built houses would perform in probabilistic hurricane scenarios compared

to the same houses built under the previous code Given the probabilistic nature of the analysis

average annual losses were generated that demonstrated post-FBC housing having loss reductions

54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were

21

then compared to their estimated cost impacts of the FBC for these housing types with at least

break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and

above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al

(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss

reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results

ranged from 041 to 183 for the retrofit mitigation activities to existing housing

We propose a BCA that differs from earlier work in several important ways First we use

realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of

how much loss can be reduced by the FBC Second our loss data spans 10 years which include a

combination of major hurricanes and smaller wind storms

BenefitCost Methodology

The elements of a BCA requires three inputs 1) an estimate of the added cost to implement

the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related

storms from our realized ISO loss data and then from a statewide catastrophe model estimate and

3) the percentage of expected loss that will be mitigated due to implementation of the FBC We

first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis

but use the full reduction in loss which includes the value of reduced claims Finally our ISO data

is paid losses and does not include deductibles so we add an estimate for deductibles

Additional Cost

In their 2002 benefit-cost comparison study of the enactment of the FBC for three related

housing types three actual sample homes were built to the FBC to evaluate the change in

construction costs (ARA 2002) For the purposes of code implementation the state was divided

into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region

22

(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account

for cost differences between the two regions

In the WBDR an added requirement is impact protection to windows and doors to reduce

damage from flying debris Along the coast and much of South Florida is classified as the WBDR

The N-WBDR is mainly classified in the interior of the state where impact protection is not

required Importantly the study provided a range of added costs for the N-WBDR and the WBDR

Three counties in South Florida Dade Broward and Monroe were under the South Florida

Building Code (SFBC) prior to the implementation of the FBC According to the ARA study

(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges

of incremental cost per square foot for the N-WBDR and WBDR along with the percent of

residential units that reside in each area This allows a calculation of a weighted average added

cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost

per square foot of $166 The cost compares favorably with a similar building code enhancement

adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in

2013 Consulting engineers and the Moore Association of Homebuilders estimated the code

enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is

1960 square feet which means that on average the FBC increases construction cost by $3254 per

structurexii

Insert Table 6 Here

Benefit of the FBC

Benefits stemming from the FBC are the expected reduction in losses from windstorms during

the life of the home We first find an average annual loss (AAL) use that number to estimate

losses for the next 50 years and then find the present value of those losses in 2010 Here we are

23

assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the

next 50 years A wealth of literature suggests the potential for changes to hurricane activity over

the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty

on future changes in wind hazard on the scale of a single state we choose to assume a stationary

climate

Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes

built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our

data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of

$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year

Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses

per structure of $21474

Finally we use parameter estimates from our regression for the Post FBC dummy variable

(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC

The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post

FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what

would be a reduction of $10093 from the projected $21474 in future losses

Insert Table 7 Here

BenefitCost Analysis

Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio

of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the

statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an

economically effective public policy following from our ISO loss data and results

Insert Table 8 Here

24

Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years

This relatively short timeframe makes it difficult to truly approximate an AAL as would be

provided from a probabilistically based catastrophe model that generates an AAL from thousands

of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state

of Florida to estimate an average annual wind loss for all residential properties in Florida of

approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL

estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion

with deductibles) Using this aggregate AAL and the number of residential units in Florida based

on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses

net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and

an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as

before derived from our regression results 47 we find $7710 of reduced loss from the projected

$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added

$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective

building code public policy

We run two additional analyses on our BCA results Our estimate of expected loss

reduction comes from the second stage of the hurdle model This is an estimate of the direct loss

reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the

number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests

as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which

includes the reduced magnitude of loss from affected homes and the reduction in claims for Post

FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table

8) However a 72 loss reduction seems too dramatic an expectation when planning so far in

25

advance For that reason we offer a third level of expected loss reduction of 60 which is the

midpoint between our two loss reduction estimates This estimate captures the expected direct loss

reduction suggested by the second stage of our hurdle model but still recognizes that in some areas

the number of claims is reduced by the FBC This appears to be a reasonable assumption and

provides a BCA ratio of 396 for the ISO sample and 302 for all residential

The ISO data are net of deductibles so our BCA thus far only includes losses compensated by

the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion

also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the

ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all

loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA

values from a low of 237 to a high of 793

Payback of the FBC

Finally we use our BCA results to calculate a payback period for the investment of stronger

codes To convert our BCA ratio to a payback period we simply divide our 50-year planning

horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss

and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years

This is important for gauging potential political support or non-support for enactment of the new

codes Payback periods that approach the typical mortgage term 30 years would in theory be

difficult to achieve and that is not what our analysis indicates for the FBC

VI - Concluding Comments

In the aftermath of Hurricane Andrew which had exposed not only poor building

construction but also poor building code enforcement the state of Florida enacted statewide

building code changes that wrested away building code adoption control from individual localities

26

With full implementation of the statewide building code associated expectations are that

windstorm losses from extreme events such as hurricanes should be reduced moving forward

There have been a few studies confirming these expectations following the 2004 and 2005

hurricane season In this article we further verify and quantify these findings and expand the

existing building code risk reduction research in several important ways

Overall we empirically test the statewide implementation of a building code in reducing

wind related damages in Florida controlling for other relevant wind hazard exposure and

vulnerability characteristics from a traditional risk assessment perspective Our results show the

strong effect the statewide FBC had on losses from wind storms during this timeframe From the

treatment variable that measures implementation of the statewide codes the post 2000 year of

construction losses are shown to be reduced by as much as 72 percent consistent with other

previous findings

Finally we have conducted a BCA of the FBC to determine if expected benefits exceed

the cost of implementation Using a direct estimate for mitigated losses and an estimate that

includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public

policy from an economic perspective This result is close to that recommended by the multi-hazard

mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a

statewide BCA Importantly this information is essential in generating political and consumer

support for such building code public policy implementation

For example the economic effectiveness results shown here have implications for ongoing

policy discussions about reforming building codes from a national US perspective Moore OK

independently adopted enhanced building codes after its third violent tornado in 14 years killed 24

including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)

27

Construction practices in North Texas were brought under scrutiny after the December 2015

tornado revealed inadequate construction including an elementary school whose exterior walls

failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House

announced initiatives to increase community resilience with building codes as a major component

of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748

and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better

construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397

would provide tax credits for owners andor contractors who use techniques designed for resiliency

in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one

recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to

encourage states to use current building codes (Vaughn and Turner 2014)

Future research in the BCA of the FBC will further inform the public policy debate on

enhanced building codes The issue has national implications as other states find that wind hazards

impact them as well We have sufficient wind data to examine how the BCA performs under

different wind hazards Additionally it will be important to consider how future economic

development affects the BCA as well as varying climate change scenarios As the FBC is

mandatory for all new construction a statewide analysis was appropriate But individual

homeowners in older homes can invest in the retrofit of their home and qualify for discounts on

their homeowners insurance This topic is deserving of a robust analysis Although our BCA is

statewide regions within the state will likely have a spectrum of results For instance the ARA

2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their

analysis did not use realized loss data so confirmation of how the BCA varies between those

regions would be an important contribution Finally our sensitivity analysis was limited to two

28

variables reduction in future loss and the inclusion of deductibles Additional work will highlight

other variables that could modify the results

29

Appendix

We use this appendix to conduct more detailed analysis on several topics First selection

of the model specification using a regression discontinuity approach Second we provide an in

depth examination of the relationship between structure age and losses Third we perform a

Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an

alternative specification to see if our RD results are similar followed by regressions to examine

the year to year consistency of our Post FBC result Next we run a regression on claims to verify

the difference between our direct reduction result and our full reduction result Finally we perform

a regression on homes built to the SFBC which had adopted enhanced building codes in advance

of the FBC to assess the effect of earlier adoption of enhanced construction

Regression Discontinuity

Regression Discontinuity (RD) applies when an observation receives a treatment in our case

homes built under the FBC based on a rating variable in our case age of the structure at the year

of observation So for observations in 2005 homes built post 2000 received the treatment

adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify

how observations on either side of the implementation of the FBC (2000) perform in suffering loss

from windstorms The treatment variable is a function of the age of the home and age affects loss

in ways not related to the FBC such as depreciation and differences in materials and construction

practices across time To account for both the effect of age on loss as well as the implementation

of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment

The data we have from ISO is aggregated loss data by zip code and decade of construction So

we cannot get an annualized age To approach a true age we set the year built for each decade of

construction at the beginning of the decade then subtract that from the year of each observation to

get an approximate agexiv

30

To find the best specification we began with a simpler model which used a series of

categorical variables for each decade of construction to examine the effect of the code compared

to the omitted decade This method would approximate the changes in materials and construction

practices but was less effective in controlling for depreciation But it would give us a first

approximation of the code effect that we used as a benchmark when testing the best RD

specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970

with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When

the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When

either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81

A rough approximation of the codersquos effect from this approach would suggest a reduction in the

mid 70 percent range

Insert Table 1 ndash Appendix Here

Next we used a standard procedure with RD to search for the best way to include the rating

variable This process creates specifications that include age in increasing polynomials and

interacted with the treatment variable The goal is to find the specification with the lowest AIC

that comes close to the benchmark value of the treatment variable

Insert Tables 2 and 3 ndash Appendix Here

We did this first with regressions that limited the co-variates then with our full model In both

sets AIC reaches a minimum on the specification with age and age squared The interaction model

after that increases the AIC then the AIC goes down again with a cubed model and its interaction

model with the overall lowest AIC found on the cubed interaction model But we chose not to

use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order

models the magnitude of the treatment variable in the models with just polynomials compared to

31

the corresponding interaction models were close with the interaction models providing a larger

magnitude When the cubed models were added the magnitude jumped where the polynomial

cubed model went down well below our benchmark and the interaction model went up above our

benchmark We felt this made use of the cubed model inappropriate So we now need to choose

between the squared model and the one with the interaction terms The squared model (Model 4)

had a lower AIC and the interaction variables on the interaction model (Model 5) were not

significant so we chose to use the squared model without the interaction term This model gave a

magnitude for the treatment variable of a 72 reduction somewhat lower than the expected

magnitude in the mid 70rsquos percent The general form of the model is

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1

of the observations on either end of our data sorted by loss Our treatment variable Post FBC

remains highly significant with a coefficient value of -117 which compares favorably to our

coefficient value of -126 when the entire sample is used

Structure Age and Wind Losses

Our study is similar to recent studies on the effect of energy efficiency building codes

adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that

better insulation caulking and more efficient HVAC systems would result in lower energy

consumption But the change in energy consumption is less than engineering estimates projected

Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for

homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study

32

may be confounding age with vintage and found a decrease in energy use related to the home

simply being new rather than the change in building code Indeed Kotchen (2015) revisited the

question with data 10 years older and found the effect on electricity had disappeared while the

reduction in natural gas use increased Something is occurring in energy use unrelated to the code

and could be explained by residents changing their use of energy as they adapt to their new home

Residents of an energy efficient home can undermine the intent of lower energy use by using the

efficient design to heat and cool their homes with a motivation toward increased comfort at the

same energy cost rather than energy savings Our study does not have the behavioral component

found in the case of energy efficiency In our application the construction elements that make the

structure able to withstand high winds are installed when the home is built and lie ldquobehind the

wallsrdquo making it unlikely for individual preferences to alter the homes performance against the

threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC

homes due to the code or simply an artifact of new versus old construction when confronted with

a windstorm

To first address our analysis of age versus the FBC we rerun our base regression but limit

our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more

than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14

years during the highest loss year of 2004 Since this is a comparison between two adjacent

decades on either side of our cut point of year 2000 we remove age and age squared Results are

shown in Table 4-Appendix

Insert Table 4-Appendix Here

The coefficient on Post FBC is still negative highly significant with a magnitude very close to

what we saw with the entire database and the age variables This result suggests that the code

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

4

wind engineering standards and other additions for Floridarsquos specific needs including for hurricane

protection (Dixon 2009)

In this study we first quantify the reduction of residential property wind damages due to

the implementation of the FBC utilizing realized insurance policy claim and loss data across the

entire state of Florida spanning the years 2001 to 2010 We utilize a Regression Discontinuity

(RD) model using a treatment of Post FBC construction and a rating variable of structure age

Following from our claim-based empirical loss estimations we then further assess the economic

effectiveness of the FBC through a benefit-cost analysis (BCA) a relatively underserved yet

important research component in wholly assessing building code augmentations Especially as

enhanced building codes increase new construction costs moving forward both pieces of

information are critical in not only highlighting the value of a statewide building code but also in

generating political and consumer support for its implementation (Kunreuther 2006 Vaughan and

Turner 2014 NIBS 2015)

The article proceeds as follows Section 2 is a discussion of existing assessments of

windstorm building code effectiveness Section 3 is an overview of the data and Section 4 provides

a discussion of the econometric methodology Section 5 discusses regression results and provides

an evaluation of the regression model Section 6 is a BenefitCost Analysis of the FBC and Section

7 concludes the article

II Review of Existing Assessments of Windstorm Building Code Effectiveness

Several studies have identified the reduction in windstorm losses due to stronger building

codes utilizing event-based realized loss or insurance claim dataii Fronstin and Holtmann (1994)

in their analysis of 1992 Hurricane Andrew damages in southeast Florida find that older homes

built prior to the 1960rsquos suffered less damage on average than those built after 1960 due to an

5

eroding building code over time Post-Andrew the catastrophic hurricane seasons of 2004 and

2005 in Florida provided a natural opportunity to test how well the implemented FBC performed

A study by IBHS following Hurricane Charley in 2004 (IBHS 2004) found that homes built after

1996 had lower claim frequency (60 percent less) and severity (42 percent less) as compared to

homes built before 1996 This suggests the trend of an eroding building code reversed after

Hurricane Andrew Applied Research Associates (2008) investigated policy level claim data from

eight different insurance companies following the 2004 and 2005 hurricane seasons and found

similar results with post-2002 homes showing significant loss reduction results compared to pre-

2002 homes They further found that overall losses were reduced in year built from the mid-1990s

onward Although only indirectly associated with actual damages incurred stronger building

codes reduced post-storm federal disaster spending in 795 unique Florida ZIP codes impacted at

least once by the 2004 hurricanes of Charley Frances Ivan and Jeanne as well as Tropical Storm

Bonnie (Deryugina 2013) However contrary to these results Dehring and Halek (2013) find that

for the 264 residential properties in a coastal building zone in Lee County following Hurricane

Charley there is no evidence of less damage for homes built after the revised 1992 Florida building

code

Our study advances this building code literature in several important ways First we

collect annualized private market insured policy and loss data (number of claims and total damages

for all represented earned house years in the insured portfolio) from the Insurance Services Office

(ISO) aggregated at the ZIP code level for all Florida ZIP codes spanning the years 2001 to 2010

inclusive We are therefore able to analyze a decade of data post-FBC implementation ISO

industry data represents a significant percent of total private propertycasualty insurance annual

market share in FLiii and we utilize aggregated policy data in any one year ranging from 669000

6

to just over 1 million insured policyholders Thus we utilize more comprehensive ndash in number

space and time ndash insured loss and premium data for this analysis than previous studies Lastly

Florida was affected by 18 tropical cyclones over the period 2001-2010 not just those in 2004 and

2005 and our study utilizes a more comprehensive set of extreme wind events extending beyond

2004 and 2005

Finally following from our claim and loss analysis we perform a BCA on the

implementation of the FBC Our BCA is unique in that we use actual loss data rather than

probabilistic estimates of future loss as previous studies have and our loss data spans a longer time

period of 10 years in order to control for the effect of post FBC construction

III Florida Windstorm Losses and Associated Data

We quantify historical Florida wind event loss reductions due to the implemented FBC

through an econometric driven loss methodology that systematically accounts for relevant wind

hazard exposure and vulnerability characteristics evolving over time from the adoption of the

new uniform codes ISO provided annual insured loss data aggregated at the ZIP code by decade

of construction In addition to insured loss data we have several variables from ISO collected by

insurers EHY Premiums and BrickMasonry EHY is an acronym for earned house years and

represents the number of policyholders in each ZIP code Premiums is the total annual premiums

collected and BrickMasonry is the percent of homes that have exterior cladding made from brick

or other masonry products

Florida Insured Loss Data

For the years 2001 to 2010 we obtained Florida propertycasualty insurance industry data

from ISO aggregated at the ZIP code Again the ISO industry data has aggregated policy data in

any one year ranging from 669000 to just over 1 million insured policyholders representing

7

125 of all residential structures in Floridaiv A total of $8023 billion (2010 inflation adjusted)

of property losses was incurred over this time (net of deductibles) from 593663 total property loss

claims incurred From 2001 to 2010 windstorm hazards are the largest cause of loss in Florida

totaling $5178 billion in losses (65 percent of total hazard damage) as well as being the most

frequent source of a loss claim with 317005 claims incurred (53 percent of total hazard claims

incurred) Clearly windstorm is a significant source of losses for Florida property insurers and

owners

Of course Florida windstorm losses vary over time and as expected are significantly

linked to the occurrence of hurricanes Table 1 provides a further detailed view of the ISO Florida

windstorm incurred losses and claims over time Across all years an average of $517 million in

losses and 31701 claims are incurred each year with an average windstorm claim being $10089

incurred at the rate of 324 claims per 1000 insured exposures (earned house years) However

excluding the significant hurricane years of 2004 and 2005 an average of $25 million in losses

and 3900 claims are incurred each year with an average windstorm claim of $8353 per claim

incurred at the rate of 48 claims per 1000 insured exposures (earned house years) Although

windstorm losses and claims are considerably higher in significant hurricane years they are still a

substantial annual property risk For example 2007 had average windstorm claims of $25399 per

claim and 2001 had 131 windstorm claims per 1000 insured ndash both outside the significant

hurricane years of 2004 and 2005 Lastly average annual premiums collected over this timeframe

(data not shown) are just over $1 billion per year Although these premiums are sufficient to cover

incurred loss amounts in non-hurricane years major windstorm year loss amounts (for example

2004 windstorm losses are nearly 4 times higher than annual average premiums collected) indicate

the critical role of further windstorm risk reduction measures in Florida

8

Insert Table 1 Here

One further split of the ISO loss data obtained is by decade of construction That is for

each year of ISO data from 2001 to 2010 each Florida ZIP code in that year contains a split of the

losses claims premiums and earned house years by the year of construction decade beginning in

1900 up to 2010 Given the loss timeframe of the ISO data from 2001 to 2010 in any one year

the majority of the overall ISO portfolio (ie proportion of earned house years EHY) is

represented by properties built prior to the year 2000 However given the growth of new

construction in Florida during this decade over time newer construction practices make up a more

significant portion of the ISO portfolio (Figure 1)v For example in 2001 post-2000 year of

construction (YOC) properties are less than 10 percent of the total ISO portfolio of 869645 total

EHYs but by 2010 they represent over 30 percent of the total ISO portfolio of 669770 total EHYs

And it is these newer housing units (ie primarily the post-2000 YOC properties) to which the

statewide FBC would have the most effect given its full implementation in 2002

Insert Figure 1 Here

Therefore as would be expected given the significant absolute portion of the EHY being

from pre-2000 YOC properties the majority of the 317005 total wind related claims and

associated $5178 billion in total wind-related losses (approximately 86 percent each) in identified

ZIP codes are incurred by properties that were built prior to the year 2000 But more importantly

the raw loss data on the numbers of claims and losses when normalized for the EHYs per YOC are

also higher on average for properties built prior to the year 2000 (Table 2) That is normalizing

for the number of policyholders in each YOC category (which again are significantly higher in

pre-2000 YOC as per Table 2) pre-2000 YOC buildings have a higher rate of claims incurred as

well as higher average incurred losses per each claim For example in 2004 206 percent of pre-

2000 YOC insured policyholders incurred a claim with an average loss of $3605 across all pre-

9

2000 YOC policyholdersvi This compares to 104 percent of post-2000 YOC insured

policyholders incurring a claim with an average loss of $1211 across all post-2000 YOC

policyholders Although this is true for the normalized raw loss data a number of other hazard

exposure and vulnerability factors need to be controlled for to ascertain that post-2000 YOC losses

are indeed lower than pre-2000 construction

Insert Table 2 Here

Outcome Variable

Our dependent variable is aggregate loss for each ZIP code by year (2001-2010) and by

decade of construction In total we have 69442 observations We transform this variable by

taking the natural log While we do not have individual customer data we do have the number of

insured customers (EHY) for each ZIPyeardecade of construction that we include as an

explanatory variable to control for the differences between ZIPyeardecade of construction

observations with high numbers of insured customers versus those with lower numbers

Treatment Variable

To test for the effect of homes built after the introduction of the statewide building code

we construct a dummy variablecedil Post FBC for observations that are after 2000 By using this

dummy variable we can test the effect on losses for homes built after the statewide code was

implemented The dummy variable for Post FBC construction is related to structure age but does

not capture the separate effect age may have on loss So we add structure age into the regression

We only have data on structure age by decade which goes back to 1900 To introduce some

variability to this variable we calculate age by taking the difference between the year of loss and

the first year in the decade for the observation So for an observation that is for year 2004 where

the decade of construction was 1950-1959 age would equal 54 2004-1950 We turn now to the

other data

10

Wind Hazard Data

Florida was affected by 18 tropical cyclones over the period 2001-2010 Spatial wind

hazard data over Florida are sourced from the National Center for Environmental Predictionrsquos

(NCEP) North American Regional Reanalysis (NARR 2015 Mesinger et al 2006) NARR is a

dynamically consistent historical climate dataset based on historical climate observations Data are

available 3-hourly on a 32km grid Of importance to this study Mesinger et al (2006) showed that

the winds just above the surface compare well with surface station observations The 32-km grid

is too coarse to resolve high-impact small-scale features in the wind field such as thunderstorm

winds or tornadoes It is also too coarse to capture the intensity of the strongest hurricanes (as

discussed in Done et al 2015) Rather than downscaling the NARR data to obtain these small-

scale details using dynamical (eg Laprise et al 2008) or statistical (eg Tye et al 2014)

methods (that could introduce further uncertainties) we choose to sacrifice the small-scale details

of the wind field and peak hurricane intensity for location accuracy of the NARR data To account

for these missing wind extremes all wind speed values are normalized by the maximum value of

wind speed in the dataset

Specifically the 3-hourly wind data are interpolated from the 32-km grid to the ZIP-code

level and two wind field parameters are derived for use in the loss regressions the normalized

annual maximum wind speed and the annual number of times the wind speed exceeds the Florida

mean wind speed plus one standard deviation for at least 12 hours The choice of hazard variables

is based on recent work that highlighted the potential for wind parameters other than the maximum

wind to drive losses (Czajkowski and Done 2014 Zhai and Jiang 2014 Jain 2010)

11

Additional Data

We have 2000 and 2010 demographic data from the decennial census at the ZIP code level

for population area (in square miles) of the ZIP median household income and housing counts

Population growth across the decade is not even so we use building permits to help estimate

intervening years Each year is interpolated from decennial data for population and total housing

counts with an allocation factor based on the number of building permits for each ZIP and each

year Building permits are collected from census by place codes so we must re-allocate to ZIP

codes To convert from place to ZIP code we use allocation factors based on 2010 housing counts

provided by MABLE a service of the Missouri Census Data Center (MABLE 2015) For

example if a municipality has two ZIP codes with 60 of the homes in one and the remaining

40 in the other MABLE would use those percentages as the allocation factors from the

municipality to its corresponding ZIP codes In unincorporated areas we use allocation factors

from county to ZIP from the same service For median household income a straight-line

interpolation method is used adjusted for changes in the consumer price index (CPI-U) to 2010

CPI data are from the Bureau of Labor Statistics

Several factors were utilized to represent the overall geographic hazard risk of a ZIP code

The distance of the centroid of the ZIP to the coast was calculated to account for the overall

distance to the coast of each ZIP code Following Dehring and Halek (2013) dummy variables

that signifies whether a ZIP code contains a coastal construction control line (CCCL) were created

(1 equals CCCL in place) to account for stricter building codes in these areas Finally following

the 2005 hurricane season there was a significant increase in the number of policies underwritten

by Citizens the state-run wind-pool and insurer of last resort (Florida Catastrophic Storm Risk

Management Center 2013) Areas with large percentages of insured policies underwritten by

12

Citizens could represent inherently higher windstorm risk We spatially matched our Florida ZIP

codes to the Florida house districts and took the percentage of Citizens policies of the number of

occupied housing units as of December 31 2011 (Florida Catastrophic Storm Risk Management

Center 2013) Given the potential for adverse selection or offloading of high risk policies by the

private market in these areas it is unclear whether higher Citizensrsquo market penetration would lead

to a positive relationship with losses due to the higher risk or a negative relationship with private

losses as many of the bad risks have been transferred to the residual wind pool

IV Econometric Methodology

Better construction limits loss from windstorms through two channels first the direct effect

of decreasing loss on homes that experience damage and second through fewer claims on better

built homes Our data from ISO is aggregated at the ZIP codedecade of construction level So a

ZIP code where all homes experienced damage would have varying levels of damage between

homes built before and after implementation of the FBC Other ZIP codes may have damage for

older homes but little to no damage for homes built post FBC Our first challenge was to use

models that would provide an estimate of the full effect of the FBC lower levels of damage plus

the effect of fewer claims then an estimate for the direct effect alone To accomplish this we

employ two models The first includes all observations even if no claims have been filed and

second a hurdle model where the first stage models the probability of experiencing a loss and the

second stage isolates only the observations where a loss has been experienced

Base Model

The regression model is a semi-log ordinary least squares (OLS) fixed effects (time and

space) model with the natural log of loss as the dependent variable The base level of observation

is ZIP codeyeardecade of construction Explanatory variables include insurance information

13

(exposures and premiums) construction type demographic data based on the ZIP code measures

of the ZIP code hazard risk (how close to the coast the ZIP code is etc) and hazard data

concerning the wind speed and duration

Our test of the FBC creates a discontinuity that must be accounted for in the model All

observations with decade of construction post 2000 are considered under the new building code

regime But that dummy variable is a function of structure age so we employ a regression

discontinuity (RD) analysis to determine the best specification to estimate the effect of the FBC

allowing for the effect that structure age has on damage Intuitively structure age should increase

loss as older homes depreciate across their life making them more vulnerable to wind storms But

the effect of structure age is more than depreciation Over time construction practices and

materials used have changed which also affect how a structure responds to the stress of a violent

wind storm Indeed after Hurricane Andrew in 1992 it was noted that inferior construction

practices of the 1970rsquos and 1980rsquos had exacerbated the losses (Fronstin and Holtmann 1994 Keith

and Rose 1994)

This suggests that the effect of age is non-linear so a model that includes age as a

polynomial would be reasonable Determining the best specification requires testing a series of

models that include age as a polynomial andor interacted with our treatment variable Post FBC

(Lee and Lemieux 2010) (Jacob and Zhu 2012) The full analysis to choose our specification is

included in the Appendix The model that provided the best tradeoff between bias and precision

based on the AIC adds age and its square with the functional form

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

where the variable definitions are given in Table 3

14

Insert Table 3 Here

A positive sign is expected for both wind variables indicating that as wind speeds increase

andor the ZIP code is exposed to high winds for an extended period of time losses will increase

Post FBC construction should decrease loss so a negative sign is expected

Hurdle Model

One problem potentially encountered in attempting to model losses is there may be a

separate process occurring in the data that determines whether a loss is realized at all which could

affect the estimate of overall losses To address this issue hurdle models are used as they divide

the analysis into two stages We use a hurdle model to find the direct effect of the FBC The first

stage models the probability that a loss occurs and the second stage models the loss using only

observations that sustained a loss The dependent variable in the first stage equals one if there was

a loss and zero otherwise This binary dependent variable is then regressed against variables that

would affect the probability that a loss occurred Its form is

(2a)

Loss or No Loss = β0 + β1 Max Wind + β2 Wind Duration + β3 Population Density

+ β4 Post FBC

We expect that both wind variables max wind speed and duration as well as population

density will increase the probability of a loss Post FBC construction however should decrease

the probability of a loss

The second stage in the hurdle model is the same as Equation 1 with the exception that

only observations with a loss are included There are 19107 observations for the second stage and

its form is

15

(2b)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Model Validity

Regression models are limited by available data to understand how the dependent variable

varies as explanatory variables change If important variables are left out of the model some bias

can be expected This omitted variable bias is a common problem encountered with econometric

models Kuminoff et al 2010 found that one of the best approaches to reducing omitted variable

bias is to employ a spatial fixed effects model To accomplish this we use individual ZIP dummy

variables as a spatial fixed effect and dummy variables for each year in our data to control for

changes that may be related to time not otherwise controlled for within our co-variates These

dummy variables will contain all across-group variation leaving the remainder of the model to

contain the within-group variation (Greene 2003)

A second challenge to the validity of our model is another common problem

heteroscedasticity For Equation 1 we use clustered standard errors at the ZIP code through Proc

GLM in SAS Our hurdle model (Eq 2a and 2b) utilizes Proc Qlim which has a separate statement

(Hetero) that we invoked to model the error variance

V Regression Results

Our first regression (Equation 1) serves as a base from which we examine the effect of

basic explanatory variables on loss The results from this regression can be found in Regression

Table 4

Insert Table 4 Here

16

The performance of our regression model is satisfactory in terms of the performance of the

explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and

the coefficient on our treatment variable Post FBC is -126 and highly significant

Overall our results show the strong effect the statewide FBC had on losses from wind

storms during this timeframe Using the results from the regression in Table 4 the coefficient on

the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses

than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the

results from a study conducted by the Insurance Institute for Business and Home Safety after

Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60

percent less likely to suffer damage at all and those that were damaged sustained 42 percent less

damage than older homes Our result of 72 percent lower damage reflects both those attributes as

our data included ZIP codeyearYOC observations that suffered damage as well as those that did

not

Our variables to measure the effect of wind hazard are wind speed and duration For both

variables we have a positive sign and each is highly significant Higher wind speed and higher

duration of high wind speeds increases damage and thus loss The remaining variables perform as

expected

Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In

the first stage variables such as Max Wind and Wind Duration significantly increase the

probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post

FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower

for homes built after new building codes were adopted In the second stage we see that our wind

variables continue to significantly increase the size of the loss and our treatment variable Post

17

FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now

lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy

we see that losses are reduced by about 47 as opposed to 72 when all observations are

includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better

construction reduces loss in two ways First it lowers claims and reduces the amount of a loss

when a claim is filedviii

Model Evaluation

To evaluate our model we used the second stage of the hurdle models and broke our data

into two groups The first group represents 90 of the data randomly selected and was used to

run the model and collect parameter estimates The second group is an out of sample control group

to test the validity of the model Parameter estimates from the first group are applied to the control

group which gave us a predicted loss for each observation in the control group that can be

compared to the actual loss for each observation in the control group We then regressed the

predicted loss from the control group against the actual loss

Insert Figure 2 Here

Figure 2 plots the predicted loss against the actual loss and provides the fitted line with

95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears

to do a good job of predicting most losses

Robustness of Table 4 Base Model Results

To test the robustness of our results we run three separate analyses 1) We first run a

regression with few co-variates 2) As wind design speeds have been used as a proxy for building

code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss

18

analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking

Florida in 2004 and 2005

Regressions using Few Co-Variates

Additional relevant co-variates add precision to a model But the value of the focus

variable should be apparent with a smaller set So we ran a model with only insured customer

based variables EHY and paid premiums leaving out all other demographic and hazard related

variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and

significance

Regressions Using Design Speed

The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads

for Buildings and Other Structures published by the American Society of Civil Engineers and the

Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds

for most regions of the United States and their territories These reference wind speeds are used in

calculations to determine design wind pressures for the primary structure of a building and the

cladding and components attached to a building These calculations take into account the building

geometry the importance of a building the exposuresurrounding terrain and other parameters that

influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design

speeds as a proxy for building code strength and we similarly add this as an additional control in

our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps

were provided by the Applied Technology Council (ATC) Although this version of the wind

speed map was not utilized during the period under consideration the relative values in general

between two locations would be the sameix

19

We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded

form from the ATC and spatially joined the values to our Florida ZIP codes We then further

categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat

4 and Cat 5 hurricane levels

Insert Table 5 Here

The regression adds two dummy variables first for ZIP codes whose design speed exceeds

the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed

reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables

for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that

communities in higher wind zones may take further measures in local codes However the effect

is not significant Notably our variable for Post FBC construction maintains its negative sign

magnitude and significance

Regressions Limited to 2004 and 2005

Our next regression also shown in Table 5 is limited to observations that occurred during

the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and

2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not

surprisingly the magnitude on wind speed increases while maintaining its significance and the

magnitude on age does the same But the effect of the FBC remains the same a 72 reduction

Summary of Results on the FBC

We have collected a comprehensive set of data on insured paid losses from 2001 to 2010

windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity

model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of

the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction

20

in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to

determine if the policy is one that is cost effective

VI Benefit and Costs of the FBC

Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a

number of cases the economic effectiveness of the improved building codes has not been as well

documented especially from a statewide implementation perspective The multi-hazard

mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose

et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar

spent) was appropriate for process activity grant spending related to improved building codes

However this information was gathered from a limited number of studies (mainly earthquake

oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in

generating it and the ratio provided due to improvement would not be the same as those for

adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted

an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for

ten related counties and determined that the net present value of the revisions was $7 billion or

benefit-cost ratio greater than 1 Importantly though this study did not have access to actual

building code damage reduction data to utilize in the analysis In 2002 Applied Research

Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC

for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how

the three types of FBC built houses would perform in probabilistic hurricane scenarios compared

to the same houses built under the previous code Given the probabilistic nature of the analysis

average annual losses were generated that demonstrated post-FBC housing having loss reductions

54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were

21

then compared to their estimated cost impacts of the FBC for these housing types with at least

break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and

above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al

(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss

reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results

ranged from 041 to 183 for the retrofit mitigation activities to existing housing

We propose a BCA that differs from earlier work in several important ways First we use

realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of

how much loss can be reduced by the FBC Second our loss data spans 10 years which include a

combination of major hurricanes and smaller wind storms

BenefitCost Methodology

The elements of a BCA requires three inputs 1) an estimate of the added cost to implement

the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related

storms from our realized ISO loss data and then from a statewide catastrophe model estimate and

3) the percentage of expected loss that will be mitigated due to implementation of the FBC We

first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis

but use the full reduction in loss which includes the value of reduced claims Finally our ISO data

is paid losses and does not include deductibles so we add an estimate for deductibles

Additional Cost

In their 2002 benefit-cost comparison study of the enactment of the FBC for three related

housing types three actual sample homes were built to the FBC to evaluate the change in

construction costs (ARA 2002) For the purposes of code implementation the state was divided

into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region

22

(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account

for cost differences between the two regions

In the WBDR an added requirement is impact protection to windows and doors to reduce

damage from flying debris Along the coast and much of South Florida is classified as the WBDR

The N-WBDR is mainly classified in the interior of the state where impact protection is not

required Importantly the study provided a range of added costs for the N-WBDR and the WBDR

Three counties in South Florida Dade Broward and Monroe were under the South Florida

Building Code (SFBC) prior to the implementation of the FBC According to the ARA study

(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges

of incremental cost per square foot for the N-WBDR and WBDR along with the percent of

residential units that reside in each area This allows a calculation of a weighted average added

cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost

per square foot of $166 The cost compares favorably with a similar building code enhancement

adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in

2013 Consulting engineers and the Moore Association of Homebuilders estimated the code

enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is

1960 square feet which means that on average the FBC increases construction cost by $3254 per

structurexii

Insert Table 6 Here

Benefit of the FBC

Benefits stemming from the FBC are the expected reduction in losses from windstorms during

the life of the home We first find an average annual loss (AAL) use that number to estimate

losses for the next 50 years and then find the present value of those losses in 2010 Here we are

23

assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the

next 50 years A wealth of literature suggests the potential for changes to hurricane activity over

the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty

on future changes in wind hazard on the scale of a single state we choose to assume a stationary

climate

Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes

built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our

data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of

$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year

Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses

per structure of $21474

Finally we use parameter estimates from our regression for the Post FBC dummy variable

(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC

The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post

FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what

would be a reduction of $10093 from the projected $21474 in future losses

Insert Table 7 Here

BenefitCost Analysis

Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio

of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the

statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an

economically effective public policy following from our ISO loss data and results

Insert Table 8 Here

24

Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years

This relatively short timeframe makes it difficult to truly approximate an AAL as would be

provided from a probabilistically based catastrophe model that generates an AAL from thousands

of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state

of Florida to estimate an average annual wind loss for all residential properties in Florida of

approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL

estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion

with deductibles) Using this aggregate AAL and the number of residential units in Florida based

on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses

net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and

an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as

before derived from our regression results 47 we find $7710 of reduced loss from the projected

$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added

$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective

building code public policy

We run two additional analyses on our BCA results Our estimate of expected loss

reduction comes from the second stage of the hurdle model This is an estimate of the direct loss

reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the

number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests

as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which

includes the reduced magnitude of loss from affected homes and the reduction in claims for Post

FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table

8) However a 72 loss reduction seems too dramatic an expectation when planning so far in

25

advance For that reason we offer a third level of expected loss reduction of 60 which is the

midpoint between our two loss reduction estimates This estimate captures the expected direct loss

reduction suggested by the second stage of our hurdle model but still recognizes that in some areas

the number of claims is reduced by the FBC This appears to be a reasonable assumption and

provides a BCA ratio of 396 for the ISO sample and 302 for all residential

The ISO data are net of deductibles so our BCA thus far only includes losses compensated by

the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion

also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the

ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all

loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA

values from a low of 237 to a high of 793

Payback of the FBC

Finally we use our BCA results to calculate a payback period for the investment of stronger

codes To convert our BCA ratio to a payback period we simply divide our 50-year planning

horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss

and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years

This is important for gauging potential political support or non-support for enactment of the new

codes Payback periods that approach the typical mortgage term 30 years would in theory be

difficult to achieve and that is not what our analysis indicates for the FBC

VI - Concluding Comments

In the aftermath of Hurricane Andrew which had exposed not only poor building

construction but also poor building code enforcement the state of Florida enacted statewide

building code changes that wrested away building code adoption control from individual localities

26

With full implementation of the statewide building code associated expectations are that

windstorm losses from extreme events such as hurricanes should be reduced moving forward

There have been a few studies confirming these expectations following the 2004 and 2005

hurricane season In this article we further verify and quantify these findings and expand the

existing building code risk reduction research in several important ways

Overall we empirically test the statewide implementation of a building code in reducing

wind related damages in Florida controlling for other relevant wind hazard exposure and

vulnerability characteristics from a traditional risk assessment perspective Our results show the

strong effect the statewide FBC had on losses from wind storms during this timeframe From the

treatment variable that measures implementation of the statewide codes the post 2000 year of

construction losses are shown to be reduced by as much as 72 percent consistent with other

previous findings

Finally we have conducted a BCA of the FBC to determine if expected benefits exceed

the cost of implementation Using a direct estimate for mitigated losses and an estimate that

includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public

policy from an economic perspective This result is close to that recommended by the multi-hazard

mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a

statewide BCA Importantly this information is essential in generating political and consumer

support for such building code public policy implementation

For example the economic effectiveness results shown here have implications for ongoing

policy discussions about reforming building codes from a national US perspective Moore OK

independently adopted enhanced building codes after its third violent tornado in 14 years killed 24

including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)

27

Construction practices in North Texas were brought under scrutiny after the December 2015

tornado revealed inadequate construction including an elementary school whose exterior walls

failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House

announced initiatives to increase community resilience with building codes as a major component

of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748

and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better

construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397

would provide tax credits for owners andor contractors who use techniques designed for resiliency

in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one

recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to

encourage states to use current building codes (Vaughn and Turner 2014)

Future research in the BCA of the FBC will further inform the public policy debate on

enhanced building codes The issue has national implications as other states find that wind hazards

impact them as well We have sufficient wind data to examine how the BCA performs under

different wind hazards Additionally it will be important to consider how future economic

development affects the BCA as well as varying climate change scenarios As the FBC is

mandatory for all new construction a statewide analysis was appropriate But individual

homeowners in older homes can invest in the retrofit of their home and qualify for discounts on

their homeowners insurance This topic is deserving of a robust analysis Although our BCA is

statewide regions within the state will likely have a spectrum of results For instance the ARA

2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their

analysis did not use realized loss data so confirmation of how the BCA varies between those

regions would be an important contribution Finally our sensitivity analysis was limited to two

28

variables reduction in future loss and the inclusion of deductibles Additional work will highlight

other variables that could modify the results

29

Appendix

We use this appendix to conduct more detailed analysis on several topics First selection

of the model specification using a regression discontinuity approach Second we provide an in

depth examination of the relationship between structure age and losses Third we perform a

Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an

alternative specification to see if our RD results are similar followed by regressions to examine

the year to year consistency of our Post FBC result Next we run a regression on claims to verify

the difference between our direct reduction result and our full reduction result Finally we perform

a regression on homes built to the SFBC which had adopted enhanced building codes in advance

of the FBC to assess the effect of earlier adoption of enhanced construction

Regression Discontinuity

Regression Discontinuity (RD) applies when an observation receives a treatment in our case

homes built under the FBC based on a rating variable in our case age of the structure at the year

of observation So for observations in 2005 homes built post 2000 received the treatment

adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify

how observations on either side of the implementation of the FBC (2000) perform in suffering loss

from windstorms The treatment variable is a function of the age of the home and age affects loss

in ways not related to the FBC such as depreciation and differences in materials and construction

practices across time To account for both the effect of age on loss as well as the implementation

of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment

The data we have from ISO is aggregated loss data by zip code and decade of construction So

we cannot get an annualized age To approach a true age we set the year built for each decade of

construction at the beginning of the decade then subtract that from the year of each observation to

get an approximate agexiv

30

To find the best specification we began with a simpler model which used a series of

categorical variables for each decade of construction to examine the effect of the code compared

to the omitted decade This method would approximate the changes in materials and construction

practices but was less effective in controlling for depreciation But it would give us a first

approximation of the code effect that we used as a benchmark when testing the best RD

specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970

with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When

the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When

either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81

A rough approximation of the codersquos effect from this approach would suggest a reduction in the

mid 70 percent range

Insert Table 1 ndash Appendix Here

Next we used a standard procedure with RD to search for the best way to include the rating

variable This process creates specifications that include age in increasing polynomials and

interacted with the treatment variable The goal is to find the specification with the lowest AIC

that comes close to the benchmark value of the treatment variable

Insert Tables 2 and 3 ndash Appendix Here

We did this first with regressions that limited the co-variates then with our full model In both

sets AIC reaches a minimum on the specification with age and age squared The interaction model

after that increases the AIC then the AIC goes down again with a cubed model and its interaction

model with the overall lowest AIC found on the cubed interaction model But we chose not to

use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order

models the magnitude of the treatment variable in the models with just polynomials compared to

31

the corresponding interaction models were close with the interaction models providing a larger

magnitude When the cubed models were added the magnitude jumped where the polynomial

cubed model went down well below our benchmark and the interaction model went up above our

benchmark We felt this made use of the cubed model inappropriate So we now need to choose

between the squared model and the one with the interaction terms The squared model (Model 4)

had a lower AIC and the interaction variables on the interaction model (Model 5) were not

significant so we chose to use the squared model without the interaction term This model gave a

magnitude for the treatment variable of a 72 reduction somewhat lower than the expected

magnitude in the mid 70rsquos percent The general form of the model is

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1

of the observations on either end of our data sorted by loss Our treatment variable Post FBC

remains highly significant with a coefficient value of -117 which compares favorably to our

coefficient value of -126 when the entire sample is used

Structure Age and Wind Losses

Our study is similar to recent studies on the effect of energy efficiency building codes

adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that

better insulation caulking and more efficient HVAC systems would result in lower energy

consumption But the change in energy consumption is less than engineering estimates projected

Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for

homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study

32

may be confounding age with vintage and found a decrease in energy use related to the home

simply being new rather than the change in building code Indeed Kotchen (2015) revisited the

question with data 10 years older and found the effect on electricity had disappeared while the

reduction in natural gas use increased Something is occurring in energy use unrelated to the code

and could be explained by residents changing their use of energy as they adapt to their new home

Residents of an energy efficient home can undermine the intent of lower energy use by using the

efficient design to heat and cool their homes with a motivation toward increased comfort at the

same energy cost rather than energy savings Our study does not have the behavioral component

found in the case of energy efficiency In our application the construction elements that make the

structure able to withstand high winds are installed when the home is built and lie ldquobehind the

wallsrdquo making it unlikely for individual preferences to alter the homes performance against the

threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC

homes due to the code or simply an artifact of new versus old construction when confronted with

a windstorm

To first address our analysis of age versus the FBC we rerun our base regression but limit

our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more

than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14

years during the highest loss year of 2004 Since this is a comparison between two adjacent

decades on either side of our cut point of year 2000 we remove age and age squared Results are

shown in Table 4-Appendix

Insert Table 4-Appendix Here

The coefficient on Post FBC is still negative highly significant with a magnitude very close to

what we saw with the entire database and the age variables This result suggests that the code

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

5

eroding building code over time Post-Andrew the catastrophic hurricane seasons of 2004 and

2005 in Florida provided a natural opportunity to test how well the implemented FBC performed

A study by IBHS following Hurricane Charley in 2004 (IBHS 2004) found that homes built after

1996 had lower claim frequency (60 percent less) and severity (42 percent less) as compared to

homes built before 1996 This suggests the trend of an eroding building code reversed after

Hurricane Andrew Applied Research Associates (2008) investigated policy level claim data from

eight different insurance companies following the 2004 and 2005 hurricane seasons and found

similar results with post-2002 homes showing significant loss reduction results compared to pre-

2002 homes They further found that overall losses were reduced in year built from the mid-1990s

onward Although only indirectly associated with actual damages incurred stronger building

codes reduced post-storm federal disaster spending in 795 unique Florida ZIP codes impacted at

least once by the 2004 hurricanes of Charley Frances Ivan and Jeanne as well as Tropical Storm

Bonnie (Deryugina 2013) However contrary to these results Dehring and Halek (2013) find that

for the 264 residential properties in a coastal building zone in Lee County following Hurricane

Charley there is no evidence of less damage for homes built after the revised 1992 Florida building

code

Our study advances this building code literature in several important ways First we

collect annualized private market insured policy and loss data (number of claims and total damages

for all represented earned house years in the insured portfolio) from the Insurance Services Office

(ISO) aggregated at the ZIP code level for all Florida ZIP codes spanning the years 2001 to 2010

inclusive We are therefore able to analyze a decade of data post-FBC implementation ISO

industry data represents a significant percent of total private propertycasualty insurance annual

market share in FLiii and we utilize aggregated policy data in any one year ranging from 669000

6

to just over 1 million insured policyholders Thus we utilize more comprehensive ndash in number

space and time ndash insured loss and premium data for this analysis than previous studies Lastly

Florida was affected by 18 tropical cyclones over the period 2001-2010 not just those in 2004 and

2005 and our study utilizes a more comprehensive set of extreme wind events extending beyond

2004 and 2005

Finally following from our claim and loss analysis we perform a BCA on the

implementation of the FBC Our BCA is unique in that we use actual loss data rather than

probabilistic estimates of future loss as previous studies have and our loss data spans a longer time

period of 10 years in order to control for the effect of post FBC construction

III Florida Windstorm Losses and Associated Data

We quantify historical Florida wind event loss reductions due to the implemented FBC

through an econometric driven loss methodology that systematically accounts for relevant wind

hazard exposure and vulnerability characteristics evolving over time from the adoption of the

new uniform codes ISO provided annual insured loss data aggregated at the ZIP code by decade

of construction In addition to insured loss data we have several variables from ISO collected by

insurers EHY Premiums and BrickMasonry EHY is an acronym for earned house years and

represents the number of policyholders in each ZIP code Premiums is the total annual premiums

collected and BrickMasonry is the percent of homes that have exterior cladding made from brick

or other masonry products

Florida Insured Loss Data

For the years 2001 to 2010 we obtained Florida propertycasualty insurance industry data

from ISO aggregated at the ZIP code Again the ISO industry data has aggregated policy data in

any one year ranging from 669000 to just over 1 million insured policyholders representing

7

125 of all residential structures in Floridaiv A total of $8023 billion (2010 inflation adjusted)

of property losses was incurred over this time (net of deductibles) from 593663 total property loss

claims incurred From 2001 to 2010 windstorm hazards are the largest cause of loss in Florida

totaling $5178 billion in losses (65 percent of total hazard damage) as well as being the most

frequent source of a loss claim with 317005 claims incurred (53 percent of total hazard claims

incurred) Clearly windstorm is a significant source of losses for Florida property insurers and

owners

Of course Florida windstorm losses vary over time and as expected are significantly

linked to the occurrence of hurricanes Table 1 provides a further detailed view of the ISO Florida

windstorm incurred losses and claims over time Across all years an average of $517 million in

losses and 31701 claims are incurred each year with an average windstorm claim being $10089

incurred at the rate of 324 claims per 1000 insured exposures (earned house years) However

excluding the significant hurricane years of 2004 and 2005 an average of $25 million in losses

and 3900 claims are incurred each year with an average windstorm claim of $8353 per claim

incurred at the rate of 48 claims per 1000 insured exposures (earned house years) Although

windstorm losses and claims are considerably higher in significant hurricane years they are still a

substantial annual property risk For example 2007 had average windstorm claims of $25399 per

claim and 2001 had 131 windstorm claims per 1000 insured ndash both outside the significant

hurricane years of 2004 and 2005 Lastly average annual premiums collected over this timeframe

(data not shown) are just over $1 billion per year Although these premiums are sufficient to cover

incurred loss amounts in non-hurricane years major windstorm year loss amounts (for example

2004 windstorm losses are nearly 4 times higher than annual average premiums collected) indicate

the critical role of further windstorm risk reduction measures in Florida

8

Insert Table 1 Here

One further split of the ISO loss data obtained is by decade of construction That is for

each year of ISO data from 2001 to 2010 each Florida ZIP code in that year contains a split of the

losses claims premiums and earned house years by the year of construction decade beginning in

1900 up to 2010 Given the loss timeframe of the ISO data from 2001 to 2010 in any one year

the majority of the overall ISO portfolio (ie proportion of earned house years EHY) is

represented by properties built prior to the year 2000 However given the growth of new

construction in Florida during this decade over time newer construction practices make up a more

significant portion of the ISO portfolio (Figure 1)v For example in 2001 post-2000 year of

construction (YOC) properties are less than 10 percent of the total ISO portfolio of 869645 total

EHYs but by 2010 they represent over 30 percent of the total ISO portfolio of 669770 total EHYs

And it is these newer housing units (ie primarily the post-2000 YOC properties) to which the

statewide FBC would have the most effect given its full implementation in 2002

Insert Figure 1 Here

Therefore as would be expected given the significant absolute portion of the EHY being

from pre-2000 YOC properties the majority of the 317005 total wind related claims and

associated $5178 billion in total wind-related losses (approximately 86 percent each) in identified

ZIP codes are incurred by properties that were built prior to the year 2000 But more importantly

the raw loss data on the numbers of claims and losses when normalized for the EHYs per YOC are

also higher on average for properties built prior to the year 2000 (Table 2) That is normalizing

for the number of policyholders in each YOC category (which again are significantly higher in

pre-2000 YOC as per Table 2) pre-2000 YOC buildings have a higher rate of claims incurred as

well as higher average incurred losses per each claim For example in 2004 206 percent of pre-

2000 YOC insured policyholders incurred a claim with an average loss of $3605 across all pre-

9

2000 YOC policyholdersvi This compares to 104 percent of post-2000 YOC insured

policyholders incurring a claim with an average loss of $1211 across all post-2000 YOC

policyholders Although this is true for the normalized raw loss data a number of other hazard

exposure and vulnerability factors need to be controlled for to ascertain that post-2000 YOC losses

are indeed lower than pre-2000 construction

Insert Table 2 Here

Outcome Variable

Our dependent variable is aggregate loss for each ZIP code by year (2001-2010) and by

decade of construction In total we have 69442 observations We transform this variable by

taking the natural log While we do not have individual customer data we do have the number of

insured customers (EHY) for each ZIPyeardecade of construction that we include as an

explanatory variable to control for the differences between ZIPyeardecade of construction

observations with high numbers of insured customers versus those with lower numbers

Treatment Variable

To test for the effect of homes built after the introduction of the statewide building code

we construct a dummy variablecedil Post FBC for observations that are after 2000 By using this

dummy variable we can test the effect on losses for homes built after the statewide code was

implemented The dummy variable for Post FBC construction is related to structure age but does

not capture the separate effect age may have on loss So we add structure age into the regression

We only have data on structure age by decade which goes back to 1900 To introduce some

variability to this variable we calculate age by taking the difference between the year of loss and

the first year in the decade for the observation So for an observation that is for year 2004 where

the decade of construction was 1950-1959 age would equal 54 2004-1950 We turn now to the

other data

10

Wind Hazard Data

Florida was affected by 18 tropical cyclones over the period 2001-2010 Spatial wind

hazard data over Florida are sourced from the National Center for Environmental Predictionrsquos

(NCEP) North American Regional Reanalysis (NARR 2015 Mesinger et al 2006) NARR is a

dynamically consistent historical climate dataset based on historical climate observations Data are

available 3-hourly on a 32km grid Of importance to this study Mesinger et al (2006) showed that

the winds just above the surface compare well with surface station observations The 32-km grid

is too coarse to resolve high-impact small-scale features in the wind field such as thunderstorm

winds or tornadoes It is also too coarse to capture the intensity of the strongest hurricanes (as

discussed in Done et al 2015) Rather than downscaling the NARR data to obtain these small-

scale details using dynamical (eg Laprise et al 2008) or statistical (eg Tye et al 2014)

methods (that could introduce further uncertainties) we choose to sacrifice the small-scale details

of the wind field and peak hurricane intensity for location accuracy of the NARR data To account

for these missing wind extremes all wind speed values are normalized by the maximum value of

wind speed in the dataset

Specifically the 3-hourly wind data are interpolated from the 32-km grid to the ZIP-code

level and two wind field parameters are derived for use in the loss regressions the normalized

annual maximum wind speed and the annual number of times the wind speed exceeds the Florida

mean wind speed plus one standard deviation for at least 12 hours The choice of hazard variables

is based on recent work that highlighted the potential for wind parameters other than the maximum

wind to drive losses (Czajkowski and Done 2014 Zhai and Jiang 2014 Jain 2010)

11

Additional Data

We have 2000 and 2010 demographic data from the decennial census at the ZIP code level

for population area (in square miles) of the ZIP median household income and housing counts

Population growth across the decade is not even so we use building permits to help estimate

intervening years Each year is interpolated from decennial data for population and total housing

counts with an allocation factor based on the number of building permits for each ZIP and each

year Building permits are collected from census by place codes so we must re-allocate to ZIP

codes To convert from place to ZIP code we use allocation factors based on 2010 housing counts

provided by MABLE a service of the Missouri Census Data Center (MABLE 2015) For

example if a municipality has two ZIP codes with 60 of the homes in one and the remaining

40 in the other MABLE would use those percentages as the allocation factors from the

municipality to its corresponding ZIP codes In unincorporated areas we use allocation factors

from county to ZIP from the same service For median household income a straight-line

interpolation method is used adjusted for changes in the consumer price index (CPI-U) to 2010

CPI data are from the Bureau of Labor Statistics

Several factors were utilized to represent the overall geographic hazard risk of a ZIP code

The distance of the centroid of the ZIP to the coast was calculated to account for the overall

distance to the coast of each ZIP code Following Dehring and Halek (2013) dummy variables

that signifies whether a ZIP code contains a coastal construction control line (CCCL) were created

(1 equals CCCL in place) to account for stricter building codes in these areas Finally following

the 2005 hurricane season there was a significant increase in the number of policies underwritten

by Citizens the state-run wind-pool and insurer of last resort (Florida Catastrophic Storm Risk

Management Center 2013) Areas with large percentages of insured policies underwritten by

12

Citizens could represent inherently higher windstorm risk We spatially matched our Florida ZIP

codes to the Florida house districts and took the percentage of Citizens policies of the number of

occupied housing units as of December 31 2011 (Florida Catastrophic Storm Risk Management

Center 2013) Given the potential for adverse selection or offloading of high risk policies by the

private market in these areas it is unclear whether higher Citizensrsquo market penetration would lead

to a positive relationship with losses due to the higher risk or a negative relationship with private

losses as many of the bad risks have been transferred to the residual wind pool

IV Econometric Methodology

Better construction limits loss from windstorms through two channels first the direct effect

of decreasing loss on homes that experience damage and second through fewer claims on better

built homes Our data from ISO is aggregated at the ZIP codedecade of construction level So a

ZIP code where all homes experienced damage would have varying levels of damage between

homes built before and after implementation of the FBC Other ZIP codes may have damage for

older homes but little to no damage for homes built post FBC Our first challenge was to use

models that would provide an estimate of the full effect of the FBC lower levels of damage plus

the effect of fewer claims then an estimate for the direct effect alone To accomplish this we

employ two models The first includes all observations even if no claims have been filed and

second a hurdle model where the first stage models the probability of experiencing a loss and the

second stage isolates only the observations where a loss has been experienced

Base Model

The regression model is a semi-log ordinary least squares (OLS) fixed effects (time and

space) model with the natural log of loss as the dependent variable The base level of observation

is ZIP codeyeardecade of construction Explanatory variables include insurance information

13

(exposures and premiums) construction type demographic data based on the ZIP code measures

of the ZIP code hazard risk (how close to the coast the ZIP code is etc) and hazard data

concerning the wind speed and duration

Our test of the FBC creates a discontinuity that must be accounted for in the model All

observations with decade of construction post 2000 are considered under the new building code

regime But that dummy variable is a function of structure age so we employ a regression

discontinuity (RD) analysis to determine the best specification to estimate the effect of the FBC

allowing for the effect that structure age has on damage Intuitively structure age should increase

loss as older homes depreciate across their life making them more vulnerable to wind storms But

the effect of structure age is more than depreciation Over time construction practices and

materials used have changed which also affect how a structure responds to the stress of a violent

wind storm Indeed after Hurricane Andrew in 1992 it was noted that inferior construction

practices of the 1970rsquos and 1980rsquos had exacerbated the losses (Fronstin and Holtmann 1994 Keith

and Rose 1994)

This suggests that the effect of age is non-linear so a model that includes age as a

polynomial would be reasonable Determining the best specification requires testing a series of

models that include age as a polynomial andor interacted with our treatment variable Post FBC

(Lee and Lemieux 2010) (Jacob and Zhu 2012) The full analysis to choose our specification is

included in the Appendix The model that provided the best tradeoff between bias and precision

based on the AIC adds age and its square with the functional form

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

where the variable definitions are given in Table 3

14

Insert Table 3 Here

A positive sign is expected for both wind variables indicating that as wind speeds increase

andor the ZIP code is exposed to high winds for an extended period of time losses will increase

Post FBC construction should decrease loss so a negative sign is expected

Hurdle Model

One problem potentially encountered in attempting to model losses is there may be a

separate process occurring in the data that determines whether a loss is realized at all which could

affect the estimate of overall losses To address this issue hurdle models are used as they divide

the analysis into two stages We use a hurdle model to find the direct effect of the FBC The first

stage models the probability that a loss occurs and the second stage models the loss using only

observations that sustained a loss The dependent variable in the first stage equals one if there was

a loss and zero otherwise This binary dependent variable is then regressed against variables that

would affect the probability that a loss occurred Its form is

(2a)

Loss or No Loss = β0 + β1 Max Wind + β2 Wind Duration + β3 Population Density

+ β4 Post FBC

We expect that both wind variables max wind speed and duration as well as population

density will increase the probability of a loss Post FBC construction however should decrease

the probability of a loss

The second stage in the hurdle model is the same as Equation 1 with the exception that

only observations with a loss are included There are 19107 observations for the second stage and

its form is

15

(2b)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Model Validity

Regression models are limited by available data to understand how the dependent variable

varies as explanatory variables change If important variables are left out of the model some bias

can be expected This omitted variable bias is a common problem encountered with econometric

models Kuminoff et al 2010 found that one of the best approaches to reducing omitted variable

bias is to employ a spatial fixed effects model To accomplish this we use individual ZIP dummy

variables as a spatial fixed effect and dummy variables for each year in our data to control for

changes that may be related to time not otherwise controlled for within our co-variates These

dummy variables will contain all across-group variation leaving the remainder of the model to

contain the within-group variation (Greene 2003)

A second challenge to the validity of our model is another common problem

heteroscedasticity For Equation 1 we use clustered standard errors at the ZIP code through Proc

GLM in SAS Our hurdle model (Eq 2a and 2b) utilizes Proc Qlim which has a separate statement

(Hetero) that we invoked to model the error variance

V Regression Results

Our first regression (Equation 1) serves as a base from which we examine the effect of

basic explanatory variables on loss The results from this regression can be found in Regression

Table 4

Insert Table 4 Here

16

The performance of our regression model is satisfactory in terms of the performance of the

explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and

the coefficient on our treatment variable Post FBC is -126 and highly significant

Overall our results show the strong effect the statewide FBC had on losses from wind

storms during this timeframe Using the results from the regression in Table 4 the coefficient on

the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses

than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the

results from a study conducted by the Insurance Institute for Business and Home Safety after

Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60

percent less likely to suffer damage at all and those that were damaged sustained 42 percent less

damage than older homes Our result of 72 percent lower damage reflects both those attributes as

our data included ZIP codeyearYOC observations that suffered damage as well as those that did

not

Our variables to measure the effect of wind hazard are wind speed and duration For both

variables we have a positive sign and each is highly significant Higher wind speed and higher

duration of high wind speeds increases damage and thus loss The remaining variables perform as

expected

Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In

the first stage variables such as Max Wind and Wind Duration significantly increase the

probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post

FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower

for homes built after new building codes were adopted In the second stage we see that our wind

variables continue to significantly increase the size of the loss and our treatment variable Post

17

FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now

lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy

we see that losses are reduced by about 47 as opposed to 72 when all observations are

includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better

construction reduces loss in two ways First it lowers claims and reduces the amount of a loss

when a claim is filedviii

Model Evaluation

To evaluate our model we used the second stage of the hurdle models and broke our data

into two groups The first group represents 90 of the data randomly selected and was used to

run the model and collect parameter estimates The second group is an out of sample control group

to test the validity of the model Parameter estimates from the first group are applied to the control

group which gave us a predicted loss for each observation in the control group that can be

compared to the actual loss for each observation in the control group We then regressed the

predicted loss from the control group against the actual loss

Insert Figure 2 Here

Figure 2 plots the predicted loss against the actual loss and provides the fitted line with

95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears

to do a good job of predicting most losses

Robustness of Table 4 Base Model Results

To test the robustness of our results we run three separate analyses 1) We first run a

regression with few co-variates 2) As wind design speeds have been used as a proxy for building

code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss

18

analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking

Florida in 2004 and 2005

Regressions using Few Co-Variates

Additional relevant co-variates add precision to a model But the value of the focus

variable should be apparent with a smaller set So we ran a model with only insured customer

based variables EHY and paid premiums leaving out all other demographic and hazard related

variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and

significance

Regressions Using Design Speed

The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads

for Buildings and Other Structures published by the American Society of Civil Engineers and the

Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds

for most regions of the United States and their territories These reference wind speeds are used in

calculations to determine design wind pressures for the primary structure of a building and the

cladding and components attached to a building These calculations take into account the building

geometry the importance of a building the exposuresurrounding terrain and other parameters that

influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design

speeds as a proxy for building code strength and we similarly add this as an additional control in

our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps

were provided by the Applied Technology Council (ATC) Although this version of the wind

speed map was not utilized during the period under consideration the relative values in general

between two locations would be the sameix

19

We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded

form from the ATC and spatially joined the values to our Florida ZIP codes We then further

categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat

4 and Cat 5 hurricane levels

Insert Table 5 Here

The regression adds two dummy variables first for ZIP codes whose design speed exceeds

the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed

reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables

for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that

communities in higher wind zones may take further measures in local codes However the effect

is not significant Notably our variable for Post FBC construction maintains its negative sign

magnitude and significance

Regressions Limited to 2004 and 2005

Our next regression also shown in Table 5 is limited to observations that occurred during

the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and

2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not

surprisingly the magnitude on wind speed increases while maintaining its significance and the

magnitude on age does the same But the effect of the FBC remains the same a 72 reduction

Summary of Results on the FBC

We have collected a comprehensive set of data on insured paid losses from 2001 to 2010

windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity

model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of

the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction

20

in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to

determine if the policy is one that is cost effective

VI Benefit and Costs of the FBC

Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a

number of cases the economic effectiveness of the improved building codes has not been as well

documented especially from a statewide implementation perspective The multi-hazard

mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose

et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar

spent) was appropriate for process activity grant spending related to improved building codes

However this information was gathered from a limited number of studies (mainly earthquake

oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in

generating it and the ratio provided due to improvement would not be the same as those for

adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted

an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for

ten related counties and determined that the net present value of the revisions was $7 billion or

benefit-cost ratio greater than 1 Importantly though this study did not have access to actual

building code damage reduction data to utilize in the analysis In 2002 Applied Research

Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC

for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how

the three types of FBC built houses would perform in probabilistic hurricane scenarios compared

to the same houses built under the previous code Given the probabilistic nature of the analysis

average annual losses were generated that demonstrated post-FBC housing having loss reductions

54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were

21

then compared to their estimated cost impacts of the FBC for these housing types with at least

break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and

above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al

(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss

reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results

ranged from 041 to 183 for the retrofit mitigation activities to existing housing

We propose a BCA that differs from earlier work in several important ways First we use

realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of

how much loss can be reduced by the FBC Second our loss data spans 10 years which include a

combination of major hurricanes and smaller wind storms

BenefitCost Methodology

The elements of a BCA requires three inputs 1) an estimate of the added cost to implement

the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related

storms from our realized ISO loss data and then from a statewide catastrophe model estimate and

3) the percentage of expected loss that will be mitigated due to implementation of the FBC We

first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis

but use the full reduction in loss which includes the value of reduced claims Finally our ISO data

is paid losses and does not include deductibles so we add an estimate for deductibles

Additional Cost

In their 2002 benefit-cost comparison study of the enactment of the FBC for three related

housing types three actual sample homes were built to the FBC to evaluate the change in

construction costs (ARA 2002) For the purposes of code implementation the state was divided

into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region

22

(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account

for cost differences between the two regions

In the WBDR an added requirement is impact protection to windows and doors to reduce

damage from flying debris Along the coast and much of South Florida is classified as the WBDR

The N-WBDR is mainly classified in the interior of the state where impact protection is not

required Importantly the study provided a range of added costs for the N-WBDR and the WBDR

Three counties in South Florida Dade Broward and Monroe were under the South Florida

Building Code (SFBC) prior to the implementation of the FBC According to the ARA study

(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges

of incremental cost per square foot for the N-WBDR and WBDR along with the percent of

residential units that reside in each area This allows a calculation of a weighted average added

cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost

per square foot of $166 The cost compares favorably with a similar building code enhancement

adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in

2013 Consulting engineers and the Moore Association of Homebuilders estimated the code

enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is

1960 square feet which means that on average the FBC increases construction cost by $3254 per

structurexii

Insert Table 6 Here

Benefit of the FBC

Benefits stemming from the FBC are the expected reduction in losses from windstorms during

the life of the home We first find an average annual loss (AAL) use that number to estimate

losses for the next 50 years and then find the present value of those losses in 2010 Here we are

23

assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the

next 50 years A wealth of literature suggests the potential for changes to hurricane activity over

the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty

on future changes in wind hazard on the scale of a single state we choose to assume a stationary

climate

Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes

built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our

data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of

$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year

Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses

per structure of $21474

Finally we use parameter estimates from our regression for the Post FBC dummy variable

(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC

The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post

FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what

would be a reduction of $10093 from the projected $21474 in future losses

Insert Table 7 Here

BenefitCost Analysis

Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio

of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the

statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an

economically effective public policy following from our ISO loss data and results

Insert Table 8 Here

24

Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years

This relatively short timeframe makes it difficult to truly approximate an AAL as would be

provided from a probabilistically based catastrophe model that generates an AAL from thousands

of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state

of Florida to estimate an average annual wind loss for all residential properties in Florida of

approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL

estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion

with deductibles) Using this aggregate AAL and the number of residential units in Florida based

on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses

net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and

an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as

before derived from our regression results 47 we find $7710 of reduced loss from the projected

$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added

$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective

building code public policy

We run two additional analyses on our BCA results Our estimate of expected loss

reduction comes from the second stage of the hurdle model This is an estimate of the direct loss

reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the

number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests

as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which

includes the reduced magnitude of loss from affected homes and the reduction in claims for Post

FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table

8) However a 72 loss reduction seems too dramatic an expectation when planning so far in

25

advance For that reason we offer a third level of expected loss reduction of 60 which is the

midpoint between our two loss reduction estimates This estimate captures the expected direct loss

reduction suggested by the second stage of our hurdle model but still recognizes that in some areas

the number of claims is reduced by the FBC This appears to be a reasonable assumption and

provides a BCA ratio of 396 for the ISO sample and 302 for all residential

The ISO data are net of deductibles so our BCA thus far only includes losses compensated by

the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion

also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the

ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all

loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA

values from a low of 237 to a high of 793

Payback of the FBC

Finally we use our BCA results to calculate a payback period for the investment of stronger

codes To convert our BCA ratio to a payback period we simply divide our 50-year planning

horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss

and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years

This is important for gauging potential political support or non-support for enactment of the new

codes Payback periods that approach the typical mortgage term 30 years would in theory be

difficult to achieve and that is not what our analysis indicates for the FBC

VI - Concluding Comments

In the aftermath of Hurricane Andrew which had exposed not only poor building

construction but also poor building code enforcement the state of Florida enacted statewide

building code changes that wrested away building code adoption control from individual localities

26

With full implementation of the statewide building code associated expectations are that

windstorm losses from extreme events such as hurricanes should be reduced moving forward

There have been a few studies confirming these expectations following the 2004 and 2005

hurricane season In this article we further verify and quantify these findings and expand the

existing building code risk reduction research in several important ways

Overall we empirically test the statewide implementation of a building code in reducing

wind related damages in Florida controlling for other relevant wind hazard exposure and

vulnerability characteristics from a traditional risk assessment perspective Our results show the

strong effect the statewide FBC had on losses from wind storms during this timeframe From the

treatment variable that measures implementation of the statewide codes the post 2000 year of

construction losses are shown to be reduced by as much as 72 percent consistent with other

previous findings

Finally we have conducted a BCA of the FBC to determine if expected benefits exceed

the cost of implementation Using a direct estimate for mitigated losses and an estimate that

includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public

policy from an economic perspective This result is close to that recommended by the multi-hazard

mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a

statewide BCA Importantly this information is essential in generating political and consumer

support for such building code public policy implementation

For example the economic effectiveness results shown here have implications for ongoing

policy discussions about reforming building codes from a national US perspective Moore OK

independently adopted enhanced building codes after its third violent tornado in 14 years killed 24

including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)

27

Construction practices in North Texas were brought under scrutiny after the December 2015

tornado revealed inadequate construction including an elementary school whose exterior walls

failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House

announced initiatives to increase community resilience with building codes as a major component

of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748

and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better

construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397

would provide tax credits for owners andor contractors who use techniques designed for resiliency

in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one

recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to

encourage states to use current building codes (Vaughn and Turner 2014)

Future research in the BCA of the FBC will further inform the public policy debate on

enhanced building codes The issue has national implications as other states find that wind hazards

impact them as well We have sufficient wind data to examine how the BCA performs under

different wind hazards Additionally it will be important to consider how future economic

development affects the BCA as well as varying climate change scenarios As the FBC is

mandatory for all new construction a statewide analysis was appropriate But individual

homeowners in older homes can invest in the retrofit of their home and qualify for discounts on

their homeowners insurance This topic is deserving of a robust analysis Although our BCA is

statewide regions within the state will likely have a spectrum of results For instance the ARA

2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their

analysis did not use realized loss data so confirmation of how the BCA varies between those

regions would be an important contribution Finally our sensitivity analysis was limited to two

28

variables reduction in future loss and the inclusion of deductibles Additional work will highlight

other variables that could modify the results

29

Appendix

We use this appendix to conduct more detailed analysis on several topics First selection

of the model specification using a regression discontinuity approach Second we provide an in

depth examination of the relationship between structure age and losses Third we perform a

Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an

alternative specification to see if our RD results are similar followed by regressions to examine

the year to year consistency of our Post FBC result Next we run a regression on claims to verify

the difference between our direct reduction result and our full reduction result Finally we perform

a regression on homes built to the SFBC which had adopted enhanced building codes in advance

of the FBC to assess the effect of earlier adoption of enhanced construction

Regression Discontinuity

Regression Discontinuity (RD) applies when an observation receives a treatment in our case

homes built under the FBC based on a rating variable in our case age of the structure at the year

of observation So for observations in 2005 homes built post 2000 received the treatment

adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify

how observations on either side of the implementation of the FBC (2000) perform in suffering loss

from windstorms The treatment variable is a function of the age of the home and age affects loss

in ways not related to the FBC such as depreciation and differences in materials and construction

practices across time To account for both the effect of age on loss as well as the implementation

of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment

The data we have from ISO is aggregated loss data by zip code and decade of construction So

we cannot get an annualized age To approach a true age we set the year built for each decade of

construction at the beginning of the decade then subtract that from the year of each observation to

get an approximate agexiv

30

To find the best specification we began with a simpler model which used a series of

categorical variables for each decade of construction to examine the effect of the code compared

to the omitted decade This method would approximate the changes in materials and construction

practices but was less effective in controlling for depreciation But it would give us a first

approximation of the code effect that we used as a benchmark when testing the best RD

specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970

with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When

the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When

either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81

A rough approximation of the codersquos effect from this approach would suggest a reduction in the

mid 70 percent range

Insert Table 1 ndash Appendix Here

Next we used a standard procedure with RD to search for the best way to include the rating

variable This process creates specifications that include age in increasing polynomials and

interacted with the treatment variable The goal is to find the specification with the lowest AIC

that comes close to the benchmark value of the treatment variable

Insert Tables 2 and 3 ndash Appendix Here

We did this first with regressions that limited the co-variates then with our full model In both

sets AIC reaches a minimum on the specification with age and age squared The interaction model

after that increases the AIC then the AIC goes down again with a cubed model and its interaction

model with the overall lowest AIC found on the cubed interaction model But we chose not to

use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order

models the magnitude of the treatment variable in the models with just polynomials compared to

31

the corresponding interaction models were close with the interaction models providing a larger

magnitude When the cubed models were added the magnitude jumped where the polynomial

cubed model went down well below our benchmark and the interaction model went up above our

benchmark We felt this made use of the cubed model inappropriate So we now need to choose

between the squared model and the one with the interaction terms The squared model (Model 4)

had a lower AIC and the interaction variables on the interaction model (Model 5) were not

significant so we chose to use the squared model without the interaction term This model gave a

magnitude for the treatment variable of a 72 reduction somewhat lower than the expected

magnitude in the mid 70rsquos percent The general form of the model is

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1

of the observations on either end of our data sorted by loss Our treatment variable Post FBC

remains highly significant with a coefficient value of -117 which compares favorably to our

coefficient value of -126 when the entire sample is used

Structure Age and Wind Losses

Our study is similar to recent studies on the effect of energy efficiency building codes

adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that

better insulation caulking and more efficient HVAC systems would result in lower energy

consumption But the change in energy consumption is less than engineering estimates projected

Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for

homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study

32

may be confounding age with vintage and found a decrease in energy use related to the home

simply being new rather than the change in building code Indeed Kotchen (2015) revisited the

question with data 10 years older and found the effect on electricity had disappeared while the

reduction in natural gas use increased Something is occurring in energy use unrelated to the code

and could be explained by residents changing their use of energy as they adapt to their new home

Residents of an energy efficient home can undermine the intent of lower energy use by using the

efficient design to heat and cool their homes with a motivation toward increased comfort at the

same energy cost rather than energy savings Our study does not have the behavioral component

found in the case of energy efficiency In our application the construction elements that make the

structure able to withstand high winds are installed when the home is built and lie ldquobehind the

wallsrdquo making it unlikely for individual preferences to alter the homes performance against the

threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC

homes due to the code or simply an artifact of new versus old construction when confronted with

a windstorm

To first address our analysis of age versus the FBC we rerun our base regression but limit

our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more

than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14

years during the highest loss year of 2004 Since this is a comparison between two adjacent

decades on either side of our cut point of year 2000 we remove age and age squared Results are

shown in Table 4-Appendix

Insert Table 4-Appendix Here

The coefficient on Post FBC is still negative highly significant with a magnitude very close to

what we saw with the entire database and the age variables This result suggests that the code

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

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Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

6

to just over 1 million insured policyholders Thus we utilize more comprehensive ndash in number

space and time ndash insured loss and premium data for this analysis than previous studies Lastly

Florida was affected by 18 tropical cyclones over the period 2001-2010 not just those in 2004 and

2005 and our study utilizes a more comprehensive set of extreme wind events extending beyond

2004 and 2005

Finally following from our claim and loss analysis we perform a BCA on the

implementation of the FBC Our BCA is unique in that we use actual loss data rather than

probabilistic estimates of future loss as previous studies have and our loss data spans a longer time

period of 10 years in order to control for the effect of post FBC construction

III Florida Windstorm Losses and Associated Data

We quantify historical Florida wind event loss reductions due to the implemented FBC

through an econometric driven loss methodology that systematically accounts for relevant wind

hazard exposure and vulnerability characteristics evolving over time from the adoption of the

new uniform codes ISO provided annual insured loss data aggregated at the ZIP code by decade

of construction In addition to insured loss data we have several variables from ISO collected by

insurers EHY Premiums and BrickMasonry EHY is an acronym for earned house years and

represents the number of policyholders in each ZIP code Premiums is the total annual premiums

collected and BrickMasonry is the percent of homes that have exterior cladding made from brick

or other masonry products

Florida Insured Loss Data

For the years 2001 to 2010 we obtained Florida propertycasualty insurance industry data

from ISO aggregated at the ZIP code Again the ISO industry data has aggregated policy data in

any one year ranging from 669000 to just over 1 million insured policyholders representing

7

125 of all residential structures in Floridaiv A total of $8023 billion (2010 inflation adjusted)

of property losses was incurred over this time (net of deductibles) from 593663 total property loss

claims incurred From 2001 to 2010 windstorm hazards are the largest cause of loss in Florida

totaling $5178 billion in losses (65 percent of total hazard damage) as well as being the most

frequent source of a loss claim with 317005 claims incurred (53 percent of total hazard claims

incurred) Clearly windstorm is a significant source of losses for Florida property insurers and

owners

Of course Florida windstorm losses vary over time and as expected are significantly

linked to the occurrence of hurricanes Table 1 provides a further detailed view of the ISO Florida

windstorm incurred losses and claims over time Across all years an average of $517 million in

losses and 31701 claims are incurred each year with an average windstorm claim being $10089

incurred at the rate of 324 claims per 1000 insured exposures (earned house years) However

excluding the significant hurricane years of 2004 and 2005 an average of $25 million in losses

and 3900 claims are incurred each year with an average windstorm claim of $8353 per claim

incurred at the rate of 48 claims per 1000 insured exposures (earned house years) Although

windstorm losses and claims are considerably higher in significant hurricane years they are still a

substantial annual property risk For example 2007 had average windstorm claims of $25399 per

claim and 2001 had 131 windstorm claims per 1000 insured ndash both outside the significant

hurricane years of 2004 and 2005 Lastly average annual premiums collected over this timeframe

(data not shown) are just over $1 billion per year Although these premiums are sufficient to cover

incurred loss amounts in non-hurricane years major windstorm year loss amounts (for example

2004 windstorm losses are nearly 4 times higher than annual average premiums collected) indicate

the critical role of further windstorm risk reduction measures in Florida

8

Insert Table 1 Here

One further split of the ISO loss data obtained is by decade of construction That is for

each year of ISO data from 2001 to 2010 each Florida ZIP code in that year contains a split of the

losses claims premiums and earned house years by the year of construction decade beginning in

1900 up to 2010 Given the loss timeframe of the ISO data from 2001 to 2010 in any one year

the majority of the overall ISO portfolio (ie proportion of earned house years EHY) is

represented by properties built prior to the year 2000 However given the growth of new

construction in Florida during this decade over time newer construction practices make up a more

significant portion of the ISO portfolio (Figure 1)v For example in 2001 post-2000 year of

construction (YOC) properties are less than 10 percent of the total ISO portfolio of 869645 total

EHYs but by 2010 they represent over 30 percent of the total ISO portfolio of 669770 total EHYs

And it is these newer housing units (ie primarily the post-2000 YOC properties) to which the

statewide FBC would have the most effect given its full implementation in 2002

Insert Figure 1 Here

Therefore as would be expected given the significant absolute portion of the EHY being

from pre-2000 YOC properties the majority of the 317005 total wind related claims and

associated $5178 billion in total wind-related losses (approximately 86 percent each) in identified

ZIP codes are incurred by properties that were built prior to the year 2000 But more importantly

the raw loss data on the numbers of claims and losses when normalized for the EHYs per YOC are

also higher on average for properties built prior to the year 2000 (Table 2) That is normalizing

for the number of policyholders in each YOC category (which again are significantly higher in

pre-2000 YOC as per Table 2) pre-2000 YOC buildings have a higher rate of claims incurred as

well as higher average incurred losses per each claim For example in 2004 206 percent of pre-

2000 YOC insured policyholders incurred a claim with an average loss of $3605 across all pre-

9

2000 YOC policyholdersvi This compares to 104 percent of post-2000 YOC insured

policyholders incurring a claim with an average loss of $1211 across all post-2000 YOC

policyholders Although this is true for the normalized raw loss data a number of other hazard

exposure and vulnerability factors need to be controlled for to ascertain that post-2000 YOC losses

are indeed lower than pre-2000 construction

Insert Table 2 Here

Outcome Variable

Our dependent variable is aggregate loss for each ZIP code by year (2001-2010) and by

decade of construction In total we have 69442 observations We transform this variable by

taking the natural log While we do not have individual customer data we do have the number of

insured customers (EHY) for each ZIPyeardecade of construction that we include as an

explanatory variable to control for the differences between ZIPyeardecade of construction

observations with high numbers of insured customers versus those with lower numbers

Treatment Variable

To test for the effect of homes built after the introduction of the statewide building code

we construct a dummy variablecedil Post FBC for observations that are after 2000 By using this

dummy variable we can test the effect on losses for homes built after the statewide code was

implemented The dummy variable for Post FBC construction is related to structure age but does

not capture the separate effect age may have on loss So we add structure age into the regression

We only have data on structure age by decade which goes back to 1900 To introduce some

variability to this variable we calculate age by taking the difference between the year of loss and

the first year in the decade for the observation So for an observation that is for year 2004 where

the decade of construction was 1950-1959 age would equal 54 2004-1950 We turn now to the

other data

10

Wind Hazard Data

Florida was affected by 18 tropical cyclones over the period 2001-2010 Spatial wind

hazard data over Florida are sourced from the National Center for Environmental Predictionrsquos

(NCEP) North American Regional Reanalysis (NARR 2015 Mesinger et al 2006) NARR is a

dynamically consistent historical climate dataset based on historical climate observations Data are

available 3-hourly on a 32km grid Of importance to this study Mesinger et al (2006) showed that

the winds just above the surface compare well with surface station observations The 32-km grid

is too coarse to resolve high-impact small-scale features in the wind field such as thunderstorm

winds or tornadoes It is also too coarse to capture the intensity of the strongest hurricanes (as

discussed in Done et al 2015) Rather than downscaling the NARR data to obtain these small-

scale details using dynamical (eg Laprise et al 2008) or statistical (eg Tye et al 2014)

methods (that could introduce further uncertainties) we choose to sacrifice the small-scale details

of the wind field and peak hurricane intensity for location accuracy of the NARR data To account

for these missing wind extremes all wind speed values are normalized by the maximum value of

wind speed in the dataset

Specifically the 3-hourly wind data are interpolated from the 32-km grid to the ZIP-code

level and two wind field parameters are derived for use in the loss regressions the normalized

annual maximum wind speed and the annual number of times the wind speed exceeds the Florida

mean wind speed plus one standard deviation for at least 12 hours The choice of hazard variables

is based on recent work that highlighted the potential for wind parameters other than the maximum

wind to drive losses (Czajkowski and Done 2014 Zhai and Jiang 2014 Jain 2010)

11

Additional Data

We have 2000 and 2010 demographic data from the decennial census at the ZIP code level

for population area (in square miles) of the ZIP median household income and housing counts

Population growth across the decade is not even so we use building permits to help estimate

intervening years Each year is interpolated from decennial data for population and total housing

counts with an allocation factor based on the number of building permits for each ZIP and each

year Building permits are collected from census by place codes so we must re-allocate to ZIP

codes To convert from place to ZIP code we use allocation factors based on 2010 housing counts

provided by MABLE a service of the Missouri Census Data Center (MABLE 2015) For

example if a municipality has two ZIP codes with 60 of the homes in one and the remaining

40 in the other MABLE would use those percentages as the allocation factors from the

municipality to its corresponding ZIP codes In unincorporated areas we use allocation factors

from county to ZIP from the same service For median household income a straight-line

interpolation method is used adjusted for changes in the consumer price index (CPI-U) to 2010

CPI data are from the Bureau of Labor Statistics

Several factors were utilized to represent the overall geographic hazard risk of a ZIP code

The distance of the centroid of the ZIP to the coast was calculated to account for the overall

distance to the coast of each ZIP code Following Dehring and Halek (2013) dummy variables

that signifies whether a ZIP code contains a coastal construction control line (CCCL) were created

(1 equals CCCL in place) to account for stricter building codes in these areas Finally following

the 2005 hurricane season there was a significant increase in the number of policies underwritten

by Citizens the state-run wind-pool and insurer of last resort (Florida Catastrophic Storm Risk

Management Center 2013) Areas with large percentages of insured policies underwritten by

12

Citizens could represent inherently higher windstorm risk We spatially matched our Florida ZIP

codes to the Florida house districts and took the percentage of Citizens policies of the number of

occupied housing units as of December 31 2011 (Florida Catastrophic Storm Risk Management

Center 2013) Given the potential for adverse selection or offloading of high risk policies by the

private market in these areas it is unclear whether higher Citizensrsquo market penetration would lead

to a positive relationship with losses due to the higher risk or a negative relationship with private

losses as many of the bad risks have been transferred to the residual wind pool

IV Econometric Methodology

Better construction limits loss from windstorms through two channels first the direct effect

of decreasing loss on homes that experience damage and second through fewer claims on better

built homes Our data from ISO is aggregated at the ZIP codedecade of construction level So a

ZIP code where all homes experienced damage would have varying levels of damage between

homes built before and after implementation of the FBC Other ZIP codes may have damage for

older homes but little to no damage for homes built post FBC Our first challenge was to use

models that would provide an estimate of the full effect of the FBC lower levels of damage plus

the effect of fewer claims then an estimate for the direct effect alone To accomplish this we

employ two models The first includes all observations even if no claims have been filed and

second a hurdle model where the first stage models the probability of experiencing a loss and the

second stage isolates only the observations where a loss has been experienced

Base Model

The regression model is a semi-log ordinary least squares (OLS) fixed effects (time and

space) model with the natural log of loss as the dependent variable The base level of observation

is ZIP codeyeardecade of construction Explanatory variables include insurance information

13

(exposures and premiums) construction type demographic data based on the ZIP code measures

of the ZIP code hazard risk (how close to the coast the ZIP code is etc) and hazard data

concerning the wind speed and duration

Our test of the FBC creates a discontinuity that must be accounted for in the model All

observations with decade of construction post 2000 are considered under the new building code

regime But that dummy variable is a function of structure age so we employ a regression

discontinuity (RD) analysis to determine the best specification to estimate the effect of the FBC

allowing for the effect that structure age has on damage Intuitively structure age should increase

loss as older homes depreciate across their life making them more vulnerable to wind storms But

the effect of structure age is more than depreciation Over time construction practices and

materials used have changed which also affect how a structure responds to the stress of a violent

wind storm Indeed after Hurricane Andrew in 1992 it was noted that inferior construction

practices of the 1970rsquos and 1980rsquos had exacerbated the losses (Fronstin and Holtmann 1994 Keith

and Rose 1994)

This suggests that the effect of age is non-linear so a model that includes age as a

polynomial would be reasonable Determining the best specification requires testing a series of

models that include age as a polynomial andor interacted with our treatment variable Post FBC

(Lee and Lemieux 2010) (Jacob and Zhu 2012) The full analysis to choose our specification is

included in the Appendix The model that provided the best tradeoff between bias and precision

based on the AIC adds age and its square with the functional form

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

where the variable definitions are given in Table 3

14

Insert Table 3 Here

A positive sign is expected for both wind variables indicating that as wind speeds increase

andor the ZIP code is exposed to high winds for an extended period of time losses will increase

Post FBC construction should decrease loss so a negative sign is expected

Hurdle Model

One problem potentially encountered in attempting to model losses is there may be a

separate process occurring in the data that determines whether a loss is realized at all which could

affect the estimate of overall losses To address this issue hurdle models are used as they divide

the analysis into two stages We use a hurdle model to find the direct effect of the FBC The first

stage models the probability that a loss occurs and the second stage models the loss using only

observations that sustained a loss The dependent variable in the first stage equals one if there was

a loss and zero otherwise This binary dependent variable is then regressed against variables that

would affect the probability that a loss occurred Its form is

(2a)

Loss or No Loss = β0 + β1 Max Wind + β2 Wind Duration + β3 Population Density

+ β4 Post FBC

We expect that both wind variables max wind speed and duration as well as population

density will increase the probability of a loss Post FBC construction however should decrease

the probability of a loss

The second stage in the hurdle model is the same as Equation 1 with the exception that

only observations with a loss are included There are 19107 observations for the second stage and

its form is

15

(2b)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Model Validity

Regression models are limited by available data to understand how the dependent variable

varies as explanatory variables change If important variables are left out of the model some bias

can be expected This omitted variable bias is a common problem encountered with econometric

models Kuminoff et al 2010 found that one of the best approaches to reducing omitted variable

bias is to employ a spatial fixed effects model To accomplish this we use individual ZIP dummy

variables as a spatial fixed effect and dummy variables for each year in our data to control for

changes that may be related to time not otherwise controlled for within our co-variates These

dummy variables will contain all across-group variation leaving the remainder of the model to

contain the within-group variation (Greene 2003)

A second challenge to the validity of our model is another common problem

heteroscedasticity For Equation 1 we use clustered standard errors at the ZIP code through Proc

GLM in SAS Our hurdle model (Eq 2a and 2b) utilizes Proc Qlim which has a separate statement

(Hetero) that we invoked to model the error variance

V Regression Results

Our first regression (Equation 1) serves as a base from which we examine the effect of

basic explanatory variables on loss The results from this regression can be found in Regression

Table 4

Insert Table 4 Here

16

The performance of our regression model is satisfactory in terms of the performance of the

explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and

the coefficient on our treatment variable Post FBC is -126 and highly significant

Overall our results show the strong effect the statewide FBC had on losses from wind

storms during this timeframe Using the results from the regression in Table 4 the coefficient on

the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses

than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the

results from a study conducted by the Insurance Institute for Business and Home Safety after

Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60

percent less likely to suffer damage at all and those that were damaged sustained 42 percent less

damage than older homes Our result of 72 percent lower damage reflects both those attributes as

our data included ZIP codeyearYOC observations that suffered damage as well as those that did

not

Our variables to measure the effect of wind hazard are wind speed and duration For both

variables we have a positive sign and each is highly significant Higher wind speed and higher

duration of high wind speeds increases damage and thus loss The remaining variables perform as

expected

Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In

the first stage variables such as Max Wind and Wind Duration significantly increase the

probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post

FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower

for homes built after new building codes were adopted In the second stage we see that our wind

variables continue to significantly increase the size of the loss and our treatment variable Post

17

FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now

lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy

we see that losses are reduced by about 47 as opposed to 72 when all observations are

includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better

construction reduces loss in two ways First it lowers claims and reduces the amount of a loss

when a claim is filedviii

Model Evaluation

To evaluate our model we used the second stage of the hurdle models and broke our data

into two groups The first group represents 90 of the data randomly selected and was used to

run the model and collect parameter estimates The second group is an out of sample control group

to test the validity of the model Parameter estimates from the first group are applied to the control

group which gave us a predicted loss for each observation in the control group that can be

compared to the actual loss for each observation in the control group We then regressed the

predicted loss from the control group against the actual loss

Insert Figure 2 Here

Figure 2 plots the predicted loss against the actual loss and provides the fitted line with

95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears

to do a good job of predicting most losses

Robustness of Table 4 Base Model Results

To test the robustness of our results we run three separate analyses 1) We first run a

regression with few co-variates 2) As wind design speeds have been used as a proxy for building

code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss

18

analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking

Florida in 2004 and 2005

Regressions using Few Co-Variates

Additional relevant co-variates add precision to a model But the value of the focus

variable should be apparent with a smaller set So we ran a model with only insured customer

based variables EHY and paid premiums leaving out all other demographic and hazard related

variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and

significance

Regressions Using Design Speed

The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads

for Buildings and Other Structures published by the American Society of Civil Engineers and the

Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds

for most regions of the United States and their territories These reference wind speeds are used in

calculations to determine design wind pressures for the primary structure of a building and the

cladding and components attached to a building These calculations take into account the building

geometry the importance of a building the exposuresurrounding terrain and other parameters that

influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design

speeds as a proxy for building code strength and we similarly add this as an additional control in

our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps

were provided by the Applied Technology Council (ATC) Although this version of the wind

speed map was not utilized during the period under consideration the relative values in general

between two locations would be the sameix

19

We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded

form from the ATC and spatially joined the values to our Florida ZIP codes We then further

categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat

4 and Cat 5 hurricane levels

Insert Table 5 Here

The regression adds two dummy variables first for ZIP codes whose design speed exceeds

the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed

reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables

for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that

communities in higher wind zones may take further measures in local codes However the effect

is not significant Notably our variable for Post FBC construction maintains its negative sign

magnitude and significance

Regressions Limited to 2004 and 2005

Our next regression also shown in Table 5 is limited to observations that occurred during

the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and

2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not

surprisingly the magnitude on wind speed increases while maintaining its significance and the

magnitude on age does the same But the effect of the FBC remains the same a 72 reduction

Summary of Results on the FBC

We have collected a comprehensive set of data on insured paid losses from 2001 to 2010

windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity

model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of

the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction

20

in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to

determine if the policy is one that is cost effective

VI Benefit and Costs of the FBC

Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a

number of cases the economic effectiveness of the improved building codes has not been as well

documented especially from a statewide implementation perspective The multi-hazard

mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose

et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar

spent) was appropriate for process activity grant spending related to improved building codes

However this information was gathered from a limited number of studies (mainly earthquake

oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in

generating it and the ratio provided due to improvement would not be the same as those for

adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted

an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for

ten related counties and determined that the net present value of the revisions was $7 billion or

benefit-cost ratio greater than 1 Importantly though this study did not have access to actual

building code damage reduction data to utilize in the analysis In 2002 Applied Research

Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC

for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how

the three types of FBC built houses would perform in probabilistic hurricane scenarios compared

to the same houses built under the previous code Given the probabilistic nature of the analysis

average annual losses were generated that demonstrated post-FBC housing having loss reductions

54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were

21

then compared to their estimated cost impacts of the FBC for these housing types with at least

break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and

above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al

(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss

reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results

ranged from 041 to 183 for the retrofit mitigation activities to existing housing

We propose a BCA that differs from earlier work in several important ways First we use

realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of

how much loss can be reduced by the FBC Second our loss data spans 10 years which include a

combination of major hurricanes and smaller wind storms

BenefitCost Methodology

The elements of a BCA requires three inputs 1) an estimate of the added cost to implement

the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related

storms from our realized ISO loss data and then from a statewide catastrophe model estimate and

3) the percentage of expected loss that will be mitigated due to implementation of the FBC We

first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis

but use the full reduction in loss which includes the value of reduced claims Finally our ISO data

is paid losses and does not include deductibles so we add an estimate for deductibles

Additional Cost

In their 2002 benefit-cost comparison study of the enactment of the FBC for three related

housing types three actual sample homes were built to the FBC to evaluate the change in

construction costs (ARA 2002) For the purposes of code implementation the state was divided

into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region

22

(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account

for cost differences between the two regions

In the WBDR an added requirement is impact protection to windows and doors to reduce

damage from flying debris Along the coast and much of South Florida is classified as the WBDR

The N-WBDR is mainly classified in the interior of the state where impact protection is not

required Importantly the study provided a range of added costs for the N-WBDR and the WBDR

Three counties in South Florida Dade Broward and Monroe were under the South Florida

Building Code (SFBC) prior to the implementation of the FBC According to the ARA study

(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges

of incremental cost per square foot for the N-WBDR and WBDR along with the percent of

residential units that reside in each area This allows a calculation of a weighted average added

cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost

per square foot of $166 The cost compares favorably with a similar building code enhancement

adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in

2013 Consulting engineers and the Moore Association of Homebuilders estimated the code

enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is

1960 square feet which means that on average the FBC increases construction cost by $3254 per

structurexii

Insert Table 6 Here

Benefit of the FBC

Benefits stemming from the FBC are the expected reduction in losses from windstorms during

the life of the home We first find an average annual loss (AAL) use that number to estimate

losses for the next 50 years and then find the present value of those losses in 2010 Here we are

23

assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the

next 50 years A wealth of literature suggests the potential for changes to hurricane activity over

the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty

on future changes in wind hazard on the scale of a single state we choose to assume a stationary

climate

Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes

built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our

data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of

$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year

Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses

per structure of $21474

Finally we use parameter estimates from our regression for the Post FBC dummy variable

(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC

The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post

FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what

would be a reduction of $10093 from the projected $21474 in future losses

Insert Table 7 Here

BenefitCost Analysis

Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio

of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the

statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an

economically effective public policy following from our ISO loss data and results

Insert Table 8 Here

24

Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years

This relatively short timeframe makes it difficult to truly approximate an AAL as would be

provided from a probabilistically based catastrophe model that generates an AAL from thousands

of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state

of Florida to estimate an average annual wind loss for all residential properties in Florida of

approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL

estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion

with deductibles) Using this aggregate AAL and the number of residential units in Florida based

on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses

net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and

an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as

before derived from our regression results 47 we find $7710 of reduced loss from the projected

$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added

$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective

building code public policy

We run two additional analyses on our BCA results Our estimate of expected loss

reduction comes from the second stage of the hurdle model This is an estimate of the direct loss

reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the

number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests

as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which

includes the reduced magnitude of loss from affected homes and the reduction in claims for Post

FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table

8) However a 72 loss reduction seems too dramatic an expectation when planning so far in

25

advance For that reason we offer a third level of expected loss reduction of 60 which is the

midpoint between our two loss reduction estimates This estimate captures the expected direct loss

reduction suggested by the second stage of our hurdle model but still recognizes that in some areas

the number of claims is reduced by the FBC This appears to be a reasonable assumption and

provides a BCA ratio of 396 for the ISO sample and 302 for all residential

The ISO data are net of deductibles so our BCA thus far only includes losses compensated by

the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion

also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the

ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all

loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA

values from a low of 237 to a high of 793

Payback of the FBC

Finally we use our BCA results to calculate a payback period for the investment of stronger

codes To convert our BCA ratio to a payback period we simply divide our 50-year planning

horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss

and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years

This is important for gauging potential political support or non-support for enactment of the new

codes Payback periods that approach the typical mortgage term 30 years would in theory be

difficult to achieve and that is not what our analysis indicates for the FBC

VI - Concluding Comments

In the aftermath of Hurricane Andrew which had exposed not only poor building

construction but also poor building code enforcement the state of Florida enacted statewide

building code changes that wrested away building code adoption control from individual localities

26

With full implementation of the statewide building code associated expectations are that

windstorm losses from extreme events such as hurricanes should be reduced moving forward

There have been a few studies confirming these expectations following the 2004 and 2005

hurricane season In this article we further verify and quantify these findings and expand the

existing building code risk reduction research in several important ways

Overall we empirically test the statewide implementation of a building code in reducing

wind related damages in Florida controlling for other relevant wind hazard exposure and

vulnerability characteristics from a traditional risk assessment perspective Our results show the

strong effect the statewide FBC had on losses from wind storms during this timeframe From the

treatment variable that measures implementation of the statewide codes the post 2000 year of

construction losses are shown to be reduced by as much as 72 percent consistent with other

previous findings

Finally we have conducted a BCA of the FBC to determine if expected benefits exceed

the cost of implementation Using a direct estimate for mitigated losses and an estimate that

includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public

policy from an economic perspective This result is close to that recommended by the multi-hazard

mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a

statewide BCA Importantly this information is essential in generating political and consumer

support for such building code public policy implementation

For example the economic effectiveness results shown here have implications for ongoing

policy discussions about reforming building codes from a national US perspective Moore OK

independently adopted enhanced building codes after its third violent tornado in 14 years killed 24

including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)

27

Construction practices in North Texas were brought under scrutiny after the December 2015

tornado revealed inadequate construction including an elementary school whose exterior walls

failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House

announced initiatives to increase community resilience with building codes as a major component

of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748

and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better

construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397

would provide tax credits for owners andor contractors who use techniques designed for resiliency

in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one

recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to

encourage states to use current building codes (Vaughn and Turner 2014)

Future research in the BCA of the FBC will further inform the public policy debate on

enhanced building codes The issue has national implications as other states find that wind hazards

impact them as well We have sufficient wind data to examine how the BCA performs under

different wind hazards Additionally it will be important to consider how future economic

development affects the BCA as well as varying climate change scenarios As the FBC is

mandatory for all new construction a statewide analysis was appropriate But individual

homeowners in older homes can invest in the retrofit of their home and qualify for discounts on

their homeowners insurance This topic is deserving of a robust analysis Although our BCA is

statewide regions within the state will likely have a spectrum of results For instance the ARA

2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their

analysis did not use realized loss data so confirmation of how the BCA varies between those

regions would be an important contribution Finally our sensitivity analysis was limited to two

28

variables reduction in future loss and the inclusion of deductibles Additional work will highlight

other variables that could modify the results

29

Appendix

We use this appendix to conduct more detailed analysis on several topics First selection

of the model specification using a regression discontinuity approach Second we provide an in

depth examination of the relationship between structure age and losses Third we perform a

Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an

alternative specification to see if our RD results are similar followed by regressions to examine

the year to year consistency of our Post FBC result Next we run a regression on claims to verify

the difference between our direct reduction result and our full reduction result Finally we perform

a regression on homes built to the SFBC which had adopted enhanced building codes in advance

of the FBC to assess the effect of earlier adoption of enhanced construction

Regression Discontinuity

Regression Discontinuity (RD) applies when an observation receives a treatment in our case

homes built under the FBC based on a rating variable in our case age of the structure at the year

of observation So for observations in 2005 homes built post 2000 received the treatment

adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify

how observations on either side of the implementation of the FBC (2000) perform in suffering loss

from windstorms The treatment variable is a function of the age of the home and age affects loss

in ways not related to the FBC such as depreciation and differences in materials and construction

practices across time To account for both the effect of age on loss as well as the implementation

of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment

The data we have from ISO is aggregated loss data by zip code and decade of construction So

we cannot get an annualized age To approach a true age we set the year built for each decade of

construction at the beginning of the decade then subtract that from the year of each observation to

get an approximate agexiv

30

To find the best specification we began with a simpler model which used a series of

categorical variables for each decade of construction to examine the effect of the code compared

to the omitted decade This method would approximate the changes in materials and construction

practices but was less effective in controlling for depreciation But it would give us a first

approximation of the code effect that we used as a benchmark when testing the best RD

specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970

with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When

the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When

either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81

A rough approximation of the codersquos effect from this approach would suggest a reduction in the

mid 70 percent range

Insert Table 1 ndash Appendix Here

Next we used a standard procedure with RD to search for the best way to include the rating

variable This process creates specifications that include age in increasing polynomials and

interacted with the treatment variable The goal is to find the specification with the lowest AIC

that comes close to the benchmark value of the treatment variable

Insert Tables 2 and 3 ndash Appendix Here

We did this first with regressions that limited the co-variates then with our full model In both

sets AIC reaches a minimum on the specification with age and age squared The interaction model

after that increases the AIC then the AIC goes down again with a cubed model and its interaction

model with the overall lowest AIC found on the cubed interaction model But we chose not to

use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order

models the magnitude of the treatment variable in the models with just polynomials compared to

31

the corresponding interaction models were close with the interaction models providing a larger

magnitude When the cubed models were added the magnitude jumped where the polynomial

cubed model went down well below our benchmark and the interaction model went up above our

benchmark We felt this made use of the cubed model inappropriate So we now need to choose

between the squared model and the one with the interaction terms The squared model (Model 4)

had a lower AIC and the interaction variables on the interaction model (Model 5) were not

significant so we chose to use the squared model without the interaction term This model gave a

magnitude for the treatment variable of a 72 reduction somewhat lower than the expected

magnitude in the mid 70rsquos percent The general form of the model is

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1

of the observations on either end of our data sorted by loss Our treatment variable Post FBC

remains highly significant with a coefficient value of -117 which compares favorably to our

coefficient value of -126 when the entire sample is used

Structure Age and Wind Losses

Our study is similar to recent studies on the effect of energy efficiency building codes

adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that

better insulation caulking and more efficient HVAC systems would result in lower energy

consumption But the change in energy consumption is less than engineering estimates projected

Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for

homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study

32

may be confounding age with vintage and found a decrease in energy use related to the home

simply being new rather than the change in building code Indeed Kotchen (2015) revisited the

question with data 10 years older and found the effect on electricity had disappeared while the

reduction in natural gas use increased Something is occurring in energy use unrelated to the code

and could be explained by residents changing their use of energy as they adapt to their new home

Residents of an energy efficient home can undermine the intent of lower energy use by using the

efficient design to heat and cool their homes with a motivation toward increased comfort at the

same energy cost rather than energy savings Our study does not have the behavioral component

found in the case of energy efficiency In our application the construction elements that make the

structure able to withstand high winds are installed when the home is built and lie ldquobehind the

wallsrdquo making it unlikely for individual preferences to alter the homes performance against the

threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC

homes due to the code or simply an artifact of new versus old construction when confronted with

a windstorm

To first address our analysis of age versus the FBC we rerun our base regression but limit

our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more

than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14

years during the highest loss year of 2004 Since this is a comparison between two adjacent

decades on either side of our cut point of year 2000 we remove age and age squared Results are

shown in Table 4-Appendix

Insert Table 4-Appendix Here

The coefficient on Post FBC is still negative highly significant with a magnitude very close to

what we saw with the entire database and the age variables This result suggests that the code

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

7

125 of all residential structures in Floridaiv A total of $8023 billion (2010 inflation adjusted)

of property losses was incurred over this time (net of deductibles) from 593663 total property loss

claims incurred From 2001 to 2010 windstorm hazards are the largest cause of loss in Florida

totaling $5178 billion in losses (65 percent of total hazard damage) as well as being the most

frequent source of a loss claim with 317005 claims incurred (53 percent of total hazard claims

incurred) Clearly windstorm is a significant source of losses for Florida property insurers and

owners

Of course Florida windstorm losses vary over time and as expected are significantly

linked to the occurrence of hurricanes Table 1 provides a further detailed view of the ISO Florida

windstorm incurred losses and claims over time Across all years an average of $517 million in

losses and 31701 claims are incurred each year with an average windstorm claim being $10089

incurred at the rate of 324 claims per 1000 insured exposures (earned house years) However

excluding the significant hurricane years of 2004 and 2005 an average of $25 million in losses

and 3900 claims are incurred each year with an average windstorm claim of $8353 per claim

incurred at the rate of 48 claims per 1000 insured exposures (earned house years) Although

windstorm losses and claims are considerably higher in significant hurricane years they are still a

substantial annual property risk For example 2007 had average windstorm claims of $25399 per

claim and 2001 had 131 windstorm claims per 1000 insured ndash both outside the significant

hurricane years of 2004 and 2005 Lastly average annual premiums collected over this timeframe

(data not shown) are just over $1 billion per year Although these premiums are sufficient to cover

incurred loss amounts in non-hurricane years major windstorm year loss amounts (for example

2004 windstorm losses are nearly 4 times higher than annual average premiums collected) indicate

the critical role of further windstorm risk reduction measures in Florida

8

Insert Table 1 Here

One further split of the ISO loss data obtained is by decade of construction That is for

each year of ISO data from 2001 to 2010 each Florida ZIP code in that year contains a split of the

losses claims premiums and earned house years by the year of construction decade beginning in

1900 up to 2010 Given the loss timeframe of the ISO data from 2001 to 2010 in any one year

the majority of the overall ISO portfolio (ie proportion of earned house years EHY) is

represented by properties built prior to the year 2000 However given the growth of new

construction in Florida during this decade over time newer construction practices make up a more

significant portion of the ISO portfolio (Figure 1)v For example in 2001 post-2000 year of

construction (YOC) properties are less than 10 percent of the total ISO portfolio of 869645 total

EHYs but by 2010 they represent over 30 percent of the total ISO portfolio of 669770 total EHYs

And it is these newer housing units (ie primarily the post-2000 YOC properties) to which the

statewide FBC would have the most effect given its full implementation in 2002

Insert Figure 1 Here

Therefore as would be expected given the significant absolute portion of the EHY being

from pre-2000 YOC properties the majority of the 317005 total wind related claims and

associated $5178 billion in total wind-related losses (approximately 86 percent each) in identified

ZIP codes are incurred by properties that were built prior to the year 2000 But more importantly

the raw loss data on the numbers of claims and losses when normalized for the EHYs per YOC are

also higher on average for properties built prior to the year 2000 (Table 2) That is normalizing

for the number of policyholders in each YOC category (which again are significantly higher in

pre-2000 YOC as per Table 2) pre-2000 YOC buildings have a higher rate of claims incurred as

well as higher average incurred losses per each claim For example in 2004 206 percent of pre-

2000 YOC insured policyholders incurred a claim with an average loss of $3605 across all pre-

9

2000 YOC policyholdersvi This compares to 104 percent of post-2000 YOC insured

policyholders incurring a claim with an average loss of $1211 across all post-2000 YOC

policyholders Although this is true for the normalized raw loss data a number of other hazard

exposure and vulnerability factors need to be controlled for to ascertain that post-2000 YOC losses

are indeed lower than pre-2000 construction

Insert Table 2 Here

Outcome Variable

Our dependent variable is aggregate loss for each ZIP code by year (2001-2010) and by

decade of construction In total we have 69442 observations We transform this variable by

taking the natural log While we do not have individual customer data we do have the number of

insured customers (EHY) for each ZIPyeardecade of construction that we include as an

explanatory variable to control for the differences between ZIPyeardecade of construction

observations with high numbers of insured customers versus those with lower numbers

Treatment Variable

To test for the effect of homes built after the introduction of the statewide building code

we construct a dummy variablecedil Post FBC for observations that are after 2000 By using this

dummy variable we can test the effect on losses for homes built after the statewide code was

implemented The dummy variable for Post FBC construction is related to structure age but does

not capture the separate effect age may have on loss So we add structure age into the regression

We only have data on structure age by decade which goes back to 1900 To introduce some

variability to this variable we calculate age by taking the difference between the year of loss and

the first year in the decade for the observation So for an observation that is for year 2004 where

the decade of construction was 1950-1959 age would equal 54 2004-1950 We turn now to the

other data

10

Wind Hazard Data

Florida was affected by 18 tropical cyclones over the period 2001-2010 Spatial wind

hazard data over Florida are sourced from the National Center for Environmental Predictionrsquos

(NCEP) North American Regional Reanalysis (NARR 2015 Mesinger et al 2006) NARR is a

dynamically consistent historical climate dataset based on historical climate observations Data are

available 3-hourly on a 32km grid Of importance to this study Mesinger et al (2006) showed that

the winds just above the surface compare well with surface station observations The 32-km grid

is too coarse to resolve high-impact small-scale features in the wind field such as thunderstorm

winds or tornadoes It is also too coarse to capture the intensity of the strongest hurricanes (as

discussed in Done et al 2015) Rather than downscaling the NARR data to obtain these small-

scale details using dynamical (eg Laprise et al 2008) or statistical (eg Tye et al 2014)

methods (that could introduce further uncertainties) we choose to sacrifice the small-scale details

of the wind field and peak hurricane intensity for location accuracy of the NARR data To account

for these missing wind extremes all wind speed values are normalized by the maximum value of

wind speed in the dataset

Specifically the 3-hourly wind data are interpolated from the 32-km grid to the ZIP-code

level and two wind field parameters are derived for use in the loss regressions the normalized

annual maximum wind speed and the annual number of times the wind speed exceeds the Florida

mean wind speed plus one standard deviation for at least 12 hours The choice of hazard variables

is based on recent work that highlighted the potential for wind parameters other than the maximum

wind to drive losses (Czajkowski and Done 2014 Zhai and Jiang 2014 Jain 2010)

11

Additional Data

We have 2000 and 2010 demographic data from the decennial census at the ZIP code level

for population area (in square miles) of the ZIP median household income and housing counts

Population growth across the decade is not even so we use building permits to help estimate

intervening years Each year is interpolated from decennial data for population and total housing

counts with an allocation factor based on the number of building permits for each ZIP and each

year Building permits are collected from census by place codes so we must re-allocate to ZIP

codes To convert from place to ZIP code we use allocation factors based on 2010 housing counts

provided by MABLE a service of the Missouri Census Data Center (MABLE 2015) For

example if a municipality has two ZIP codes with 60 of the homes in one and the remaining

40 in the other MABLE would use those percentages as the allocation factors from the

municipality to its corresponding ZIP codes In unincorporated areas we use allocation factors

from county to ZIP from the same service For median household income a straight-line

interpolation method is used adjusted for changes in the consumer price index (CPI-U) to 2010

CPI data are from the Bureau of Labor Statistics

Several factors were utilized to represent the overall geographic hazard risk of a ZIP code

The distance of the centroid of the ZIP to the coast was calculated to account for the overall

distance to the coast of each ZIP code Following Dehring and Halek (2013) dummy variables

that signifies whether a ZIP code contains a coastal construction control line (CCCL) were created

(1 equals CCCL in place) to account for stricter building codes in these areas Finally following

the 2005 hurricane season there was a significant increase in the number of policies underwritten

by Citizens the state-run wind-pool and insurer of last resort (Florida Catastrophic Storm Risk

Management Center 2013) Areas with large percentages of insured policies underwritten by

12

Citizens could represent inherently higher windstorm risk We spatially matched our Florida ZIP

codes to the Florida house districts and took the percentage of Citizens policies of the number of

occupied housing units as of December 31 2011 (Florida Catastrophic Storm Risk Management

Center 2013) Given the potential for adverse selection or offloading of high risk policies by the

private market in these areas it is unclear whether higher Citizensrsquo market penetration would lead

to a positive relationship with losses due to the higher risk or a negative relationship with private

losses as many of the bad risks have been transferred to the residual wind pool

IV Econometric Methodology

Better construction limits loss from windstorms through two channels first the direct effect

of decreasing loss on homes that experience damage and second through fewer claims on better

built homes Our data from ISO is aggregated at the ZIP codedecade of construction level So a

ZIP code where all homes experienced damage would have varying levels of damage between

homes built before and after implementation of the FBC Other ZIP codes may have damage for

older homes but little to no damage for homes built post FBC Our first challenge was to use

models that would provide an estimate of the full effect of the FBC lower levels of damage plus

the effect of fewer claims then an estimate for the direct effect alone To accomplish this we

employ two models The first includes all observations even if no claims have been filed and

second a hurdle model where the first stage models the probability of experiencing a loss and the

second stage isolates only the observations where a loss has been experienced

Base Model

The regression model is a semi-log ordinary least squares (OLS) fixed effects (time and

space) model with the natural log of loss as the dependent variable The base level of observation

is ZIP codeyeardecade of construction Explanatory variables include insurance information

13

(exposures and premiums) construction type demographic data based on the ZIP code measures

of the ZIP code hazard risk (how close to the coast the ZIP code is etc) and hazard data

concerning the wind speed and duration

Our test of the FBC creates a discontinuity that must be accounted for in the model All

observations with decade of construction post 2000 are considered under the new building code

regime But that dummy variable is a function of structure age so we employ a regression

discontinuity (RD) analysis to determine the best specification to estimate the effect of the FBC

allowing for the effect that structure age has on damage Intuitively structure age should increase

loss as older homes depreciate across their life making them more vulnerable to wind storms But

the effect of structure age is more than depreciation Over time construction practices and

materials used have changed which also affect how a structure responds to the stress of a violent

wind storm Indeed after Hurricane Andrew in 1992 it was noted that inferior construction

practices of the 1970rsquos and 1980rsquos had exacerbated the losses (Fronstin and Holtmann 1994 Keith

and Rose 1994)

This suggests that the effect of age is non-linear so a model that includes age as a

polynomial would be reasonable Determining the best specification requires testing a series of

models that include age as a polynomial andor interacted with our treatment variable Post FBC

(Lee and Lemieux 2010) (Jacob and Zhu 2012) The full analysis to choose our specification is

included in the Appendix The model that provided the best tradeoff between bias and precision

based on the AIC adds age and its square with the functional form

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

where the variable definitions are given in Table 3

14

Insert Table 3 Here

A positive sign is expected for both wind variables indicating that as wind speeds increase

andor the ZIP code is exposed to high winds for an extended period of time losses will increase

Post FBC construction should decrease loss so a negative sign is expected

Hurdle Model

One problem potentially encountered in attempting to model losses is there may be a

separate process occurring in the data that determines whether a loss is realized at all which could

affect the estimate of overall losses To address this issue hurdle models are used as they divide

the analysis into two stages We use a hurdle model to find the direct effect of the FBC The first

stage models the probability that a loss occurs and the second stage models the loss using only

observations that sustained a loss The dependent variable in the first stage equals one if there was

a loss and zero otherwise This binary dependent variable is then regressed against variables that

would affect the probability that a loss occurred Its form is

(2a)

Loss or No Loss = β0 + β1 Max Wind + β2 Wind Duration + β3 Population Density

+ β4 Post FBC

We expect that both wind variables max wind speed and duration as well as population

density will increase the probability of a loss Post FBC construction however should decrease

the probability of a loss

The second stage in the hurdle model is the same as Equation 1 with the exception that

only observations with a loss are included There are 19107 observations for the second stage and

its form is

15

(2b)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Model Validity

Regression models are limited by available data to understand how the dependent variable

varies as explanatory variables change If important variables are left out of the model some bias

can be expected This omitted variable bias is a common problem encountered with econometric

models Kuminoff et al 2010 found that one of the best approaches to reducing omitted variable

bias is to employ a spatial fixed effects model To accomplish this we use individual ZIP dummy

variables as a spatial fixed effect and dummy variables for each year in our data to control for

changes that may be related to time not otherwise controlled for within our co-variates These

dummy variables will contain all across-group variation leaving the remainder of the model to

contain the within-group variation (Greene 2003)

A second challenge to the validity of our model is another common problem

heteroscedasticity For Equation 1 we use clustered standard errors at the ZIP code through Proc

GLM in SAS Our hurdle model (Eq 2a and 2b) utilizes Proc Qlim which has a separate statement

(Hetero) that we invoked to model the error variance

V Regression Results

Our first regression (Equation 1) serves as a base from which we examine the effect of

basic explanatory variables on loss The results from this regression can be found in Regression

Table 4

Insert Table 4 Here

16

The performance of our regression model is satisfactory in terms of the performance of the

explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and

the coefficient on our treatment variable Post FBC is -126 and highly significant

Overall our results show the strong effect the statewide FBC had on losses from wind

storms during this timeframe Using the results from the regression in Table 4 the coefficient on

the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses

than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the

results from a study conducted by the Insurance Institute for Business and Home Safety after

Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60

percent less likely to suffer damage at all and those that were damaged sustained 42 percent less

damage than older homes Our result of 72 percent lower damage reflects both those attributes as

our data included ZIP codeyearYOC observations that suffered damage as well as those that did

not

Our variables to measure the effect of wind hazard are wind speed and duration For both

variables we have a positive sign and each is highly significant Higher wind speed and higher

duration of high wind speeds increases damage and thus loss The remaining variables perform as

expected

Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In

the first stage variables such as Max Wind and Wind Duration significantly increase the

probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post

FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower

for homes built after new building codes were adopted In the second stage we see that our wind

variables continue to significantly increase the size of the loss and our treatment variable Post

17

FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now

lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy

we see that losses are reduced by about 47 as opposed to 72 when all observations are

includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better

construction reduces loss in two ways First it lowers claims and reduces the amount of a loss

when a claim is filedviii

Model Evaluation

To evaluate our model we used the second stage of the hurdle models and broke our data

into two groups The first group represents 90 of the data randomly selected and was used to

run the model and collect parameter estimates The second group is an out of sample control group

to test the validity of the model Parameter estimates from the first group are applied to the control

group which gave us a predicted loss for each observation in the control group that can be

compared to the actual loss for each observation in the control group We then regressed the

predicted loss from the control group against the actual loss

Insert Figure 2 Here

Figure 2 plots the predicted loss against the actual loss and provides the fitted line with

95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears

to do a good job of predicting most losses

Robustness of Table 4 Base Model Results

To test the robustness of our results we run three separate analyses 1) We first run a

regression with few co-variates 2) As wind design speeds have been used as a proxy for building

code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss

18

analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking

Florida in 2004 and 2005

Regressions using Few Co-Variates

Additional relevant co-variates add precision to a model But the value of the focus

variable should be apparent with a smaller set So we ran a model with only insured customer

based variables EHY and paid premiums leaving out all other demographic and hazard related

variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and

significance

Regressions Using Design Speed

The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads

for Buildings and Other Structures published by the American Society of Civil Engineers and the

Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds

for most regions of the United States and their territories These reference wind speeds are used in

calculations to determine design wind pressures for the primary structure of a building and the

cladding and components attached to a building These calculations take into account the building

geometry the importance of a building the exposuresurrounding terrain and other parameters that

influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design

speeds as a proxy for building code strength and we similarly add this as an additional control in

our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps

were provided by the Applied Technology Council (ATC) Although this version of the wind

speed map was not utilized during the period under consideration the relative values in general

between two locations would be the sameix

19

We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded

form from the ATC and spatially joined the values to our Florida ZIP codes We then further

categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat

4 and Cat 5 hurricane levels

Insert Table 5 Here

The regression adds two dummy variables first for ZIP codes whose design speed exceeds

the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed

reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables

for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that

communities in higher wind zones may take further measures in local codes However the effect

is not significant Notably our variable for Post FBC construction maintains its negative sign

magnitude and significance

Regressions Limited to 2004 and 2005

Our next regression also shown in Table 5 is limited to observations that occurred during

the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and

2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not

surprisingly the magnitude on wind speed increases while maintaining its significance and the

magnitude on age does the same But the effect of the FBC remains the same a 72 reduction

Summary of Results on the FBC

We have collected a comprehensive set of data on insured paid losses from 2001 to 2010

windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity

model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of

the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction

20

in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to

determine if the policy is one that is cost effective

VI Benefit and Costs of the FBC

Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a

number of cases the economic effectiveness of the improved building codes has not been as well

documented especially from a statewide implementation perspective The multi-hazard

mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose

et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar

spent) was appropriate for process activity grant spending related to improved building codes

However this information was gathered from a limited number of studies (mainly earthquake

oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in

generating it and the ratio provided due to improvement would not be the same as those for

adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted

an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for

ten related counties and determined that the net present value of the revisions was $7 billion or

benefit-cost ratio greater than 1 Importantly though this study did not have access to actual

building code damage reduction data to utilize in the analysis In 2002 Applied Research

Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC

for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how

the three types of FBC built houses would perform in probabilistic hurricane scenarios compared

to the same houses built under the previous code Given the probabilistic nature of the analysis

average annual losses were generated that demonstrated post-FBC housing having loss reductions

54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were

21

then compared to their estimated cost impacts of the FBC for these housing types with at least

break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and

above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al

(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss

reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results

ranged from 041 to 183 for the retrofit mitigation activities to existing housing

We propose a BCA that differs from earlier work in several important ways First we use

realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of

how much loss can be reduced by the FBC Second our loss data spans 10 years which include a

combination of major hurricanes and smaller wind storms

BenefitCost Methodology

The elements of a BCA requires three inputs 1) an estimate of the added cost to implement

the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related

storms from our realized ISO loss data and then from a statewide catastrophe model estimate and

3) the percentage of expected loss that will be mitigated due to implementation of the FBC We

first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis

but use the full reduction in loss which includes the value of reduced claims Finally our ISO data

is paid losses and does not include deductibles so we add an estimate for deductibles

Additional Cost

In their 2002 benefit-cost comparison study of the enactment of the FBC for three related

housing types three actual sample homes were built to the FBC to evaluate the change in

construction costs (ARA 2002) For the purposes of code implementation the state was divided

into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region

22

(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account

for cost differences between the two regions

In the WBDR an added requirement is impact protection to windows and doors to reduce

damage from flying debris Along the coast and much of South Florida is classified as the WBDR

The N-WBDR is mainly classified in the interior of the state where impact protection is not

required Importantly the study provided a range of added costs for the N-WBDR and the WBDR

Three counties in South Florida Dade Broward and Monroe were under the South Florida

Building Code (SFBC) prior to the implementation of the FBC According to the ARA study

(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges

of incremental cost per square foot for the N-WBDR and WBDR along with the percent of

residential units that reside in each area This allows a calculation of a weighted average added

cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost

per square foot of $166 The cost compares favorably with a similar building code enhancement

adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in

2013 Consulting engineers and the Moore Association of Homebuilders estimated the code

enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is

1960 square feet which means that on average the FBC increases construction cost by $3254 per

structurexii

Insert Table 6 Here

Benefit of the FBC

Benefits stemming from the FBC are the expected reduction in losses from windstorms during

the life of the home We first find an average annual loss (AAL) use that number to estimate

losses for the next 50 years and then find the present value of those losses in 2010 Here we are

23

assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the

next 50 years A wealth of literature suggests the potential for changes to hurricane activity over

the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty

on future changes in wind hazard on the scale of a single state we choose to assume a stationary

climate

Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes

built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our

data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of

$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year

Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses

per structure of $21474

Finally we use parameter estimates from our regression for the Post FBC dummy variable

(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC

The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post

FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what

would be a reduction of $10093 from the projected $21474 in future losses

Insert Table 7 Here

BenefitCost Analysis

Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio

of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the

statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an

economically effective public policy following from our ISO loss data and results

Insert Table 8 Here

24

Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years

This relatively short timeframe makes it difficult to truly approximate an AAL as would be

provided from a probabilistically based catastrophe model that generates an AAL from thousands

of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state

of Florida to estimate an average annual wind loss for all residential properties in Florida of

approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL

estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion

with deductibles) Using this aggregate AAL and the number of residential units in Florida based

on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses

net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and

an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as

before derived from our regression results 47 we find $7710 of reduced loss from the projected

$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added

$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective

building code public policy

We run two additional analyses on our BCA results Our estimate of expected loss

reduction comes from the second stage of the hurdle model This is an estimate of the direct loss

reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the

number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests

as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which

includes the reduced magnitude of loss from affected homes and the reduction in claims for Post

FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table

8) However a 72 loss reduction seems too dramatic an expectation when planning so far in

25

advance For that reason we offer a third level of expected loss reduction of 60 which is the

midpoint between our two loss reduction estimates This estimate captures the expected direct loss

reduction suggested by the second stage of our hurdle model but still recognizes that in some areas

the number of claims is reduced by the FBC This appears to be a reasonable assumption and

provides a BCA ratio of 396 for the ISO sample and 302 for all residential

The ISO data are net of deductibles so our BCA thus far only includes losses compensated by

the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion

also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the

ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all

loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA

values from a low of 237 to a high of 793

Payback of the FBC

Finally we use our BCA results to calculate a payback period for the investment of stronger

codes To convert our BCA ratio to a payback period we simply divide our 50-year planning

horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss

and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years

This is important for gauging potential political support or non-support for enactment of the new

codes Payback periods that approach the typical mortgage term 30 years would in theory be

difficult to achieve and that is not what our analysis indicates for the FBC

VI - Concluding Comments

In the aftermath of Hurricane Andrew which had exposed not only poor building

construction but also poor building code enforcement the state of Florida enacted statewide

building code changes that wrested away building code adoption control from individual localities

26

With full implementation of the statewide building code associated expectations are that

windstorm losses from extreme events such as hurricanes should be reduced moving forward

There have been a few studies confirming these expectations following the 2004 and 2005

hurricane season In this article we further verify and quantify these findings and expand the

existing building code risk reduction research in several important ways

Overall we empirically test the statewide implementation of a building code in reducing

wind related damages in Florida controlling for other relevant wind hazard exposure and

vulnerability characteristics from a traditional risk assessment perspective Our results show the

strong effect the statewide FBC had on losses from wind storms during this timeframe From the

treatment variable that measures implementation of the statewide codes the post 2000 year of

construction losses are shown to be reduced by as much as 72 percent consistent with other

previous findings

Finally we have conducted a BCA of the FBC to determine if expected benefits exceed

the cost of implementation Using a direct estimate for mitigated losses and an estimate that

includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public

policy from an economic perspective This result is close to that recommended by the multi-hazard

mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a

statewide BCA Importantly this information is essential in generating political and consumer

support for such building code public policy implementation

For example the economic effectiveness results shown here have implications for ongoing

policy discussions about reforming building codes from a national US perspective Moore OK

independently adopted enhanced building codes after its third violent tornado in 14 years killed 24

including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)

27

Construction practices in North Texas were brought under scrutiny after the December 2015

tornado revealed inadequate construction including an elementary school whose exterior walls

failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House

announced initiatives to increase community resilience with building codes as a major component

of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748

and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better

construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397

would provide tax credits for owners andor contractors who use techniques designed for resiliency

in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one

recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to

encourage states to use current building codes (Vaughn and Turner 2014)

Future research in the BCA of the FBC will further inform the public policy debate on

enhanced building codes The issue has national implications as other states find that wind hazards

impact them as well We have sufficient wind data to examine how the BCA performs under

different wind hazards Additionally it will be important to consider how future economic

development affects the BCA as well as varying climate change scenarios As the FBC is

mandatory for all new construction a statewide analysis was appropriate But individual

homeowners in older homes can invest in the retrofit of their home and qualify for discounts on

their homeowners insurance This topic is deserving of a robust analysis Although our BCA is

statewide regions within the state will likely have a spectrum of results For instance the ARA

2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their

analysis did not use realized loss data so confirmation of how the BCA varies between those

regions would be an important contribution Finally our sensitivity analysis was limited to two

28

variables reduction in future loss and the inclusion of deductibles Additional work will highlight

other variables that could modify the results

29

Appendix

We use this appendix to conduct more detailed analysis on several topics First selection

of the model specification using a regression discontinuity approach Second we provide an in

depth examination of the relationship between structure age and losses Third we perform a

Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an

alternative specification to see if our RD results are similar followed by regressions to examine

the year to year consistency of our Post FBC result Next we run a regression on claims to verify

the difference between our direct reduction result and our full reduction result Finally we perform

a regression on homes built to the SFBC which had adopted enhanced building codes in advance

of the FBC to assess the effect of earlier adoption of enhanced construction

Regression Discontinuity

Regression Discontinuity (RD) applies when an observation receives a treatment in our case

homes built under the FBC based on a rating variable in our case age of the structure at the year

of observation So for observations in 2005 homes built post 2000 received the treatment

adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify

how observations on either side of the implementation of the FBC (2000) perform in suffering loss

from windstorms The treatment variable is a function of the age of the home and age affects loss

in ways not related to the FBC such as depreciation and differences in materials and construction

practices across time To account for both the effect of age on loss as well as the implementation

of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment

The data we have from ISO is aggregated loss data by zip code and decade of construction So

we cannot get an annualized age To approach a true age we set the year built for each decade of

construction at the beginning of the decade then subtract that from the year of each observation to

get an approximate agexiv

30

To find the best specification we began with a simpler model which used a series of

categorical variables for each decade of construction to examine the effect of the code compared

to the omitted decade This method would approximate the changes in materials and construction

practices but was less effective in controlling for depreciation But it would give us a first

approximation of the code effect that we used as a benchmark when testing the best RD

specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970

with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When

the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When

either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81

A rough approximation of the codersquos effect from this approach would suggest a reduction in the

mid 70 percent range

Insert Table 1 ndash Appendix Here

Next we used a standard procedure with RD to search for the best way to include the rating

variable This process creates specifications that include age in increasing polynomials and

interacted with the treatment variable The goal is to find the specification with the lowest AIC

that comes close to the benchmark value of the treatment variable

Insert Tables 2 and 3 ndash Appendix Here

We did this first with regressions that limited the co-variates then with our full model In both

sets AIC reaches a minimum on the specification with age and age squared The interaction model

after that increases the AIC then the AIC goes down again with a cubed model and its interaction

model with the overall lowest AIC found on the cubed interaction model But we chose not to

use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order

models the magnitude of the treatment variable in the models with just polynomials compared to

31

the corresponding interaction models were close with the interaction models providing a larger

magnitude When the cubed models were added the magnitude jumped where the polynomial

cubed model went down well below our benchmark and the interaction model went up above our

benchmark We felt this made use of the cubed model inappropriate So we now need to choose

between the squared model and the one with the interaction terms The squared model (Model 4)

had a lower AIC and the interaction variables on the interaction model (Model 5) were not

significant so we chose to use the squared model without the interaction term This model gave a

magnitude for the treatment variable of a 72 reduction somewhat lower than the expected

magnitude in the mid 70rsquos percent The general form of the model is

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1

of the observations on either end of our data sorted by loss Our treatment variable Post FBC

remains highly significant with a coefficient value of -117 which compares favorably to our

coefficient value of -126 when the entire sample is used

Structure Age and Wind Losses

Our study is similar to recent studies on the effect of energy efficiency building codes

adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that

better insulation caulking and more efficient HVAC systems would result in lower energy

consumption But the change in energy consumption is less than engineering estimates projected

Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for

homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study

32

may be confounding age with vintage and found a decrease in energy use related to the home

simply being new rather than the change in building code Indeed Kotchen (2015) revisited the

question with data 10 years older and found the effect on electricity had disappeared while the

reduction in natural gas use increased Something is occurring in energy use unrelated to the code

and could be explained by residents changing their use of energy as they adapt to their new home

Residents of an energy efficient home can undermine the intent of lower energy use by using the

efficient design to heat and cool their homes with a motivation toward increased comfort at the

same energy cost rather than energy savings Our study does not have the behavioral component

found in the case of energy efficiency In our application the construction elements that make the

structure able to withstand high winds are installed when the home is built and lie ldquobehind the

wallsrdquo making it unlikely for individual preferences to alter the homes performance against the

threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC

homes due to the code or simply an artifact of new versus old construction when confronted with

a windstorm

To first address our analysis of age versus the FBC we rerun our base regression but limit

our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more

than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14

years during the highest loss year of 2004 Since this is a comparison between two adjacent

decades on either side of our cut point of year 2000 we remove age and age squared Results are

shown in Table 4-Appendix

Insert Table 4-Appendix Here

The coefficient on Post FBC is still negative highly significant with a magnitude very close to

what we saw with the entire database and the age variables This result suggests that the code

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

8

Insert Table 1 Here

One further split of the ISO loss data obtained is by decade of construction That is for

each year of ISO data from 2001 to 2010 each Florida ZIP code in that year contains a split of the

losses claims premiums and earned house years by the year of construction decade beginning in

1900 up to 2010 Given the loss timeframe of the ISO data from 2001 to 2010 in any one year

the majority of the overall ISO portfolio (ie proportion of earned house years EHY) is

represented by properties built prior to the year 2000 However given the growth of new

construction in Florida during this decade over time newer construction practices make up a more

significant portion of the ISO portfolio (Figure 1)v For example in 2001 post-2000 year of

construction (YOC) properties are less than 10 percent of the total ISO portfolio of 869645 total

EHYs but by 2010 they represent over 30 percent of the total ISO portfolio of 669770 total EHYs

And it is these newer housing units (ie primarily the post-2000 YOC properties) to which the

statewide FBC would have the most effect given its full implementation in 2002

Insert Figure 1 Here

Therefore as would be expected given the significant absolute portion of the EHY being

from pre-2000 YOC properties the majority of the 317005 total wind related claims and

associated $5178 billion in total wind-related losses (approximately 86 percent each) in identified

ZIP codes are incurred by properties that were built prior to the year 2000 But more importantly

the raw loss data on the numbers of claims and losses when normalized for the EHYs per YOC are

also higher on average for properties built prior to the year 2000 (Table 2) That is normalizing

for the number of policyholders in each YOC category (which again are significantly higher in

pre-2000 YOC as per Table 2) pre-2000 YOC buildings have a higher rate of claims incurred as

well as higher average incurred losses per each claim For example in 2004 206 percent of pre-

2000 YOC insured policyholders incurred a claim with an average loss of $3605 across all pre-

9

2000 YOC policyholdersvi This compares to 104 percent of post-2000 YOC insured

policyholders incurring a claim with an average loss of $1211 across all post-2000 YOC

policyholders Although this is true for the normalized raw loss data a number of other hazard

exposure and vulnerability factors need to be controlled for to ascertain that post-2000 YOC losses

are indeed lower than pre-2000 construction

Insert Table 2 Here

Outcome Variable

Our dependent variable is aggregate loss for each ZIP code by year (2001-2010) and by

decade of construction In total we have 69442 observations We transform this variable by

taking the natural log While we do not have individual customer data we do have the number of

insured customers (EHY) for each ZIPyeardecade of construction that we include as an

explanatory variable to control for the differences between ZIPyeardecade of construction

observations with high numbers of insured customers versus those with lower numbers

Treatment Variable

To test for the effect of homes built after the introduction of the statewide building code

we construct a dummy variablecedil Post FBC for observations that are after 2000 By using this

dummy variable we can test the effect on losses for homes built after the statewide code was

implemented The dummy variable for Post FBC construction is related to structure age but does

not capture the separate effect age may have on loss So we add structure age into the regression

We only have data on structure age by decade which goes back to 1900 To introduce some

variability to this variable we calculate age by taking the difference between the year of loss and

the first year in the decade for the observation So for an observation that is for year 2004 where

the decade of construction was 1950-1959 age would equal 54 2004-1950 We turn now to the

other data

10

Wind Hazard Data

Florida was affected by 18 tropical cyclones over the period 2001-2010 Spatial wind

hazard data over Florida are sourced from the National Center for Environmental Predictionrsquos

(NCEP) North American Regional Reanalysis (NARR 2015 Mesinger et al 2006) NARR is a

dynamically consistent historical climate dataset based on historical climate observations Data are

available 3-hourly on a 32km grid Of importance to this study Mesinger et al (2006) showed that

the winds just above the surface compare well with surface station observations The 32-km grid

is too coarse to resolve high-impact small-scale features in the wind field such as thunderstorm

winds or tornadoes It is also too coarse to capture the intensity of the strongest hurricanes (as

discussed in Done et al 2015) Rather than downscaling the NARR data to obtain these small-

scale details using dynamical (eg Laprise et al 2008) or statistical (eg Tye et al 2014)

methods (that could introduce further uncertainties) we choose to sacrifice the small-scale details

of the wind field and peak hurricane intensity for location accuracy of the NARR data To account

for these missing wind extremes all wind speed values are normalized by the maximum value of

wind speed in the dataset

Specifically the 3-hourly wind data are interpolated from the 32-km grid to the ZIP-code

level and two wind field parameters are derived for use in the loss regressions the normalized

annual maximum wind speed and the annual number of times the wind speed exceeds the Florida

mean wind speed plus one standard deviation for at least 12 hours The choice of hazard variables

is based on recent work that highlighted the potential for wind parameters other than the maximum

wind to drive losses (Czajkowski and Done 2014 Zhai and Jiang 2014 Jain 2010)

11

Additional Data

We have 2000 and 2010 demographic data from the decennial census at the ZIP code level

for population area (in square miles) of the ZIP median household income and housing counts

Population growth across the decade is not even so we use building permits to help estimate

intervening years Each year is interpolated from decennial data for population and total housing

counts with an allocation factor based on the number of building permits for each ZIP and each

year Building permits are collected from census by place codes so we must re-allocate to ZIP

codes To convert from place to ZIP code we use allocation factors based on 2010 housing counts

provided by MABLE a service of the Missouri Census Data Center (MABLE 2015) For

example if a municipality has two ZIP codes with 60 of the homes in one and the remaining

40 in the other MABLE would use those percentages as the allocation factors from the

municipality to its corresponding ZIP codes In unincorporated areas we use allocation factors

from county to ZIP from the same service For median household income a straight-line

interpolation method is used adjusted for changes in the consumer price index (CPI-U) to 2010

CPI data are from the Bureau of Labor Statistics

Several factors were utilized to represent the overall geographic hazard risk of a ZIP code

The distance of the centroid of the ZIP to the coast was calculated to account for the overall

distance to the coast of each ZIP code Following Dehring and Halek (2013) dummy variables

that signifies whether a ZIP code contains a coastal construction control line (CCCL) were created

(1 equals CCCL in place) to account for stricter building codes in these areas Finally following

the 2005 hurricane season there was a significant increase in the number of policies underwritten

by Citizens the state-run wind-pool and insurer of last resort (Florida Catastrophic Storm Risk

Management Center 2013) Areas with large percentages of insured policies underwritten by

12

Citizens could represent inherently higher windstorm risk We spatially matched our Florida ZIP

codes to the Florida house districts and took the percentage of Citizens policies of the number of

occupied housing units as of December 31 2011 (Florida Catastrophic Storm Risk Management

Center 2013) Given the potential for adverse selection or offloading of high risk policies by the

private market in these areas it is unclear whether higher Citizensrsquo market penetration would lead

to a positive relationship with losses due to the higher risk or a negative relationship with private

losses as many of the bad risks have been transferred to the residual wind pool

IV Econometric Methodology

Better construction limits loss from windstorms through two channels first the direct effect

of decreasing loss on homes that experience damage and second through fewer claims on better

built homes Our data from ISO is aggregated at the ZIP codedecade of construction level So a

ZIP code where all homes experienced damage would have varying levels of damage between

homes built before and after implementation of the FBC Other ZIP codes may have damage for

older homes but little to no damage for homes built post FBC Our first challenge was to use

models that would provide an estimate of the full effect of the FBC lower levels of damage plus

the effect of fewer claims then an estimate for the direct effect alone To accomplish this we

employ two models The first includes all observations even if no claims have been filed and

second a hurdle model where the first stage models the probability of experiencing a loss and the

second stage isolates only the observations where a loss has been experienced

Base Model

The regression model is a semi-log ordinary least squares (OLS) fixed effects (time and

space) model with the natural log of loss as the dependent variable The base level of observation

is ZIP codeyeardecade of construction Explanatory variables include insurance information

13

(exposures and premiums) construction type demographic data based on the ZIP code measures

of the ZIP code hazard risk (how close to the coast the ZIP code is etc) and hazard data

concerning the wind speed and duration

Our test of the FBC creates a discontinuity that must be accounted for in the model All

observations with decade of construction post 2000 are considered under the new building code

regime But that dummy variable is a function of structure age so we employ a regression

discontinuity (RD) analysis to determine the best specification to estimate the effect of the FBC

allowing for the effect that structure age has on damage Intuitively structure age should increase

loss as older homes depreciate across their life making them more vulnerable to wind storms But

the effect of structure age is more than depreciation Over time construction practices and

materials used have changed which also affect how a structure responds to the stress of a violent

wind storm Indeed after Hurricane Andrew in 1992 it was noted that inferior construction

practices of the 1970rsquos and 1980rsquos had exacerbated the losses (Fronstin and Holtmann 1994 Keith

and Rose 1994)

This suggests that the effect of age is non-linear so a model that includes age as a

polynomial would be reasonable Determining the best specification requires testing a series of

models that include age as a polynomial andor interacted with our treatment variable Post FBC

(Lee and Lemieux 2010) (Jacob and Zhu 2012) The full analysis to choose our specification is

included in the Appendix The model that provided the best tradeoff between bias and precision

based on the AIC adds age and its square with the functional form

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

where the variable definitions are given in Table 3

14

Insert Table 3 Here

A positive sign is expected for both wind variables indicating that as wind speeds increase

andor the ZIP code is exposed to high winds for an extended period of time losses will increase

Post FBC construction should decrease loss so a negative sign is expected

Hurdle Model

One problem potentially encountered in attempting to model losses is there may be a

separate process occurring in the data that determines whether a loss is realized at all which could

affect the estimate of overall losses To address this issue hurdle models are used as they divide

the analysis into two stages We use a hurdle model to find the direct effect of the FBC The first

stage models the probability that a loss occurs and the second stage models the loss using only

observations that sustained a loss The dependent variable in the first stage equals one if there was

a loss and zero otherwise This binary dependent variable is then regressed against variables that

would affect the probability that a loss occurred Its form is

(2a)

Loss or No Loss = β0 + β1 Max Wind + β2 Wind Duration + β3 Population Density

+ β4 Post FBC

We expect that both wind variables max wind speed and duration as well as population

density will increase the probability of a loss Post FBC construction however should decrease

the probability of a loss

The second stage in the hurdle model is the same as Equation 1 with the exception that

only observations with a loss are included There are 19107 observations for the second stage and

its form is

15

(2b)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Model Validity

Regression models are limited by available data to understand how the dependent variable

varies as explanatory variables change If important variables are left out of the model some bias

can be expected This omitted variable bias is a common problem encountered with econometric

models Kuminoff et al 2010 found that one of the best approaches to reducing omitted variable

bias is to employ a spatial fixed effects model To accomplish this we use individual ZIP dummy

variables as a spatial fixed effect and dummy variables for each year in our data to control for

changes that may be related to time not otherwise controlled for within our co-variates These

dummy variables will contain all across-group variation leaving the remainder of the model to

contain the within-group variation (Greene 2003)

A second challenge to the validity of our model is another common problem

heteroscedasticity For Equation 1 we use clustered standard errors at the ZIP code through Proc

GLM in SAS Our hurdle model (Eq 2a and 2b) utilizes Proc Qlim which has a separate statement

(Hetero) that we invoked to model the error variance

V Regression Results

Our first regression (Equation 1) serves as a base from which we examine the effect of

basic explanatory variables on loss The results from this regression can be found in Regression

Table 4

Insert Table 4 Here

16

The performance of our regression model is satisfactory in terms of the performance of the

explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and

the coefficient on our treatment variable Post FBC is -126 and highly significant

Overall our results show the strong effect the statewide FBC had on losses from wind

storms during this timeframe Using the results from the regression in Table 4 the coefficient on

the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses

than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the

results from a study conducted by the Insurance Institute for Business and Home Safety after

Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60

percent less likely to suffer damage at all and those that were damaged sustained 42 percent less

damage than older homes Our result of 72 percent lower damage reflects both those attributes as

our data included ZIP codeyearYOC observations that suffered damage as well as those that did

not

Our variables to measure the effect of wind hazard are wind speed and duration For both

variables we have a positive sign and each is highly significant Higher wind speed and higher

duration of high wind speeds increases damage and thus loss The remaining variables perform as

expected

Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In

the first stage variables such as Max Wind and Wind Duration significantly increase the

probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post

FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower

for homes built after new building codes were adopted In the second stage we see that our wind

variables continue to significantly increase the size of the loss and our treatment variable Post

17

FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now

lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy

we see that losses are reduced by about 47 as opposed to 72 when all observations are

includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better

construction reduces loss in two ways First it lowers claims and reduces the amount of a loss

when a claim is filedviii

Model Evaluation

To evaluate our model we used the second stage of the hurdle models and broke our data

into two groups The first group represents 90 of the data randomly selected and was used to

run the model and collect parameter estimates The second group is an out of sample control group

to test the validity of the model Parameter estimates from the first group are applied to the control

group which gave us a predicted loss for each observation in the control group that can be

compared to the actual loss for each observation in the control group We then regressed the

predicted loss from the control group against the actual loss

Insert Figure 2 Here

Figure 2 plots the predicted loss against the actual loss and provides the fitted line with

95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears

to do a good job of predicting most losses

Robustness of Table 4 Base Model Results

To test the robustness of our results we run three separate analyses 1) We first run a

regression with few co-variates 2) As wind design speeds have been used as a proxy for building

code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss

18

analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking

Florida in 2004 and 2005

Regressions using Few Co-Variates

Additional relevant co-variates add precision to a model But the value of the focus

variable should be apparent with a smaller set So we ran a model with only insured customer

based variables EHY and paid premiums leaving out all other demographic and hazard related

variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and

significance

Regressions Using Design Speed

The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads

for Buildings and Other Structures published by the American Society of Civil Engineers and the

Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds

for most regions of the United States and their territories These reference wind speeds are used in

calculations to determine design wind pressures for the primary structure of a building and the

cladding and components attached to a building These calculations take into account the building

geometry the importance of a building the exposuresurrounding terrain and other parameters that

influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design

speeds as a proxy for building code strength and we similarly add this as an additional control in

our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps

were provided by the Applied Technology Council (ATC) Although this version of the wind

speed map was not utilized during the period under consideration the relative values in general

between two locations would be the sameix

19

We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded

form from the ATC and spatially joined the values to our Florida ZIP codes We then further

categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat

4 and Cat 5 hurricane levels

Insert Table 5 Here

The regression adds two dummy variables first for ZIP codes whose design speed exceeds

the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed

reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables

for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that

communities in higher wind zones may take further measures in local codes However the effect

is not significant Notably our variable for Post FBC construction maintains its negative sign

magnitude and significance

Regressions Limited to 2004 and 2005

Our next regression also shown in Table 5 is limited to observations that occurred during

the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and

2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not

surprisingly the magnitude on wind speed increases while maintaining its significance and the

magnitude on age does the same But the effect of the FBC remains the same a 72 reduction

Summary of Results on the FBC

We have collected a comprehensive set of data on insured paid losses from 2001 to 2010

windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity

model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of

the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction

20

in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to

determine if the policy is one that is cost effective

VI Benefit and Costs of the FBC

Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a

number of cases the economic effectiveness of the improved building codes has not been as well

documented especially from a statewide implementation perspective The multi-hazard

mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose

et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar

spent) was appropriate for process activity grant spending related to improved building codes

However this information was gathered from a limited number of studies (mainly earthquake

oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in

generating it and the ratio provided due to improvement would not be the same as those for

adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted

an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for

ten related counties and determined that the net present value of the revisions was $7 billion or

benefit-cost ratio greater than 1 Importantly though this study did not have access to actual

building code damage reduction data to utilize in the analysis In 2002 Applied Research

Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC

for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how

the three types of FBC built houses would perform in probabilistic hurricane scenarios compared

to the same houses built under the previous code Given the probabilistic nature of the analysis

average annual losses were generated that demonstrated post-FBC housing having loss reductions

54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were

21

then compared to their estimated cost impacts of the FBC for these housing types with at least

break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and

above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al

(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss

reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results

ranged from 041 to 183 for the retrofit mitigation activities to existing housing

We propose a BCA that differs from earlier work in several important ways First we use

realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of

how much loss can be reduced by the FBC Second our loss data spans 10 years which include a

combination of major hurricanes and smaller wind storms

BenefitCost Methodology

The elements of a BCA requires three inputs 1) an estimate of the added cost to implement

the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related

storms from our realized ISO loss data and then from a statewide catastrophe model estimate and

3) the percentage of expected loss that will be mitigated due to implementation of the FBC We

first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis

but use the full reduction in loss which includes the value of reduced claims Finally our ISO data

is paid losses and does not include deductibles so we add an estimate for deductibles

Additional Cost

In their 2002 benefit-cost comparison study of the enactment of the FBC for three related

housing types three actual sample homes were built to the FBC to evaluate the change in

construction costs (ARA 2002) For the purposes of code implementation the state was divided

into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region

22

(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account

for cost differences between the two regions

In the WBDR an added requirement is impact protection to windows and doors to reduce

damage from flying debris Along the coast and much of South Florida is classified as the WBDR

The N-WBDR is mainly classified in the interior of the state where impact protection is not

required Importantly the study provided a range of added costs for the N-WBDR and the WBDR

Three counties in South Florida Dade Broward and Monroe were under the South Florida

Building Code (SFBC) prior to the implementation of the FBC According to the ARA study

(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges

of incremental cost per square foot for the N-WBDR and WBDR along with the percent of

residential units that reside in each area This allows a calculation of a weighted average added

cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost

per square foot of $166 The cost compares favorably with a similar building code enhancement

adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in

2013 Consulting engineers and the Moore Association of Homebuilders estimated the code

enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is

1960 square feet which means that on average the FBC increases construction cost by $3254 per

structurexii

Insert Table 6 Here

Benefit of the FBC

Benefits stemming from the FBC are the expected reduction in losses from windstorms during

the life of the home We first find an average annual loss (AAL) use that number to estimate

losses for the next 50 years and then find the present value of those losses in 2010 Here we are

23

assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the

next 50 years A wealth of literature suggests the potential for changes to hurricane activity over

the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty

on future changes in wind hazard on the scale of a single state we choose to assume a stationary

climate

Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes

built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our

data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of

$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year

Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses

per structure of $21474

Finally we use parameter estimates from our regression for the Post FBC dummy variable

(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC

The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post

FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what

would be a reduction of $10093 from the projected $21474 in future losses

Insert Table 7 Here

BenefitCost Analysis

Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio

of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the

statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an

economically effective public policy following from our ISO loss data and results

Insert Table 8 Here

24

Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years

This relatively short timeframe makes it difficult to truly approximate an AAL as would be

provided from a probabilistically based catastrophe model that generates an AAL from thousands

of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state

of Florida to estimate an average annual wind loss for all residential properties in Florida of

approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL

estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion

with deductibles) Using this aggregate AAL and the number of residential units in Florida based

on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses

net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and

an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as

before derived from our regression results 47 we find $7710 of reduced loss from the projected

$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added

$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective

building code public policy

We run two additional analyses on our BCA results Our estimate of expected loss

reduction comes from the second stage of the hurdle model This is an estimate of the direct loss

reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the

number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests

as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which

includes the reduced magnitude of loss from affected homes and the reduction in claims for Post

FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table

8) However a 72 loss reduction seems too dramatic an expectation when planning so far in

25

advance For that reason we offer a third level of expected loss reduction of 60 which is the

midpoint between our two loss reduction estimates This estimate captures the expected direct loss

reduction suggested by the second stage of our hurdle model but still recognizes that in some areas

the number of claims is reduced by the FBC This appears to be a reasonable assumption and

provides a BCA ratio of 396 for the ISO sample and 302 for all residential

The ISO data are net of deductibles so our BCA thus far only includes losses compensated by

the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion

also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the

ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all

loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA

values from a low of 237 to a high of 793

Payback of the FBC

Finally we use our BCA results to calculate a payback period for the investment of stronger

codes To convert our BCA ratio to a payback period we simply divide our 50-year planning

horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss

and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years

This is important for gauging potential political support or non-support for enactment of the new

codes Payback periods that approach the typical mortgage term 30 years would in theory be

difficult to achieve and that is not what our analysis indicates for the FBC

VI - Concluding Comments

In the aftermath of Hurricane Andrew which had exposed not only poor building

construction but also poor building code enforcement the state of Florida enacted statewide

building code changes that wrested away building code adoption control from individual localities

26

With full implementation of the statewide building code associated expectations are that

windstorm losses from extreme events such as hurricanes should be reduced moving forward

There have been a few studies confirming these expectations following the 2004 and 2005

hurricane season In this article we further verify and quantify these findings and expand the

existing building code risk reduction research in several important ways

Overall we empirically test the statewide implementation of a building code in reducing

wind related damages in Florida controlling for other relevant wind hazard exposure and

vulnerability characteristics from a traditional risk assessment perspective Our results show the

strong effect the statewide FBC had on losses from wind storms during this timeframe From the

treatment variable that measures implementation of the statewide codes the post 2000 year of

construction losses are shown to be reduced by as much as 72 percent consistent with other

previous findings

Finally we have conducted a BCA of the FBC to determine if expected benefits exceed

the cost of implementation Using a direct estimate for mitigated losses and an estimate that

includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public

policy from an economic perspective This result is close to that recommended by the multi-hazard

mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a

statewide BCA Importantly this information is essential in generating political and consumer

support for such building code public policy implementation

For example the economic effectiveness results shown here have implications for ongoing

policy discussions about reforming building codes from a national US perspective Moore OK

independently adopted enhanced building codes after its third violent tornado in 14 years killed 24

including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)

27

Construction practices in North Texas were brought under scrutiny after the December 2015

tornado revealed inadequate construction including an elementary school whose exterior walls

failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House

announced initiatives to increase community resilience with building codes as a major component

of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748

and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better

construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397

would provide tax credits for owners andor contractors who use techniques designed for resiliency

in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one

recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to

encourage states to use current building codes (Vaughn and Turner 2014)

Future research in the BCA of the FBC will further inform the public policy debate on

enhanced building codes The issue has national implications as other states find that wind hazards

impact them as well We have sufficient wind data to examine how the BCA performs under

different wind hazards Additionally it will be important to consider how future economic

development affects the BCA as well as varying climate change scenarios As the FBC is

mandatory for all new construction a statewide analysis was appropriate But individual

homeowners in older homes can invest in the retrofit of their home and qualify for discounts on

their homeowners insurance This topic is deserving of a robust analysis Although our BCA is

statewide regions within the state will likely have a spectrum of results For instance the ARA

2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their

analysis did not use realized loss data so confirmation of how the BCA varies between those

regions would be an important contribution Finally our sensitivity analysis was limited to two

28

variables reduction in future loss and the inclusion of deductibles Additional work will highlight

other variables that could modify the results

29

Appendix

We use this appendix to conduct more detailed analysis on several topics First selection

of the model specification using a regression discontinuity approach Second we provide an in

depth examination of the relationship between structure age and losses Third we perform a

Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an

alternative specification to see if our RD results are similar followed by regressions to examine

the year to year consistency of our Post FBC result Next we run a regression on claims to verify

the difference between our direct reduction result and our full reduction result Finally we perform

a regression on homes built to the SFBC which had adopted enhanced building codes in advance

of the FBC to assess the effect of earlier adoption of enhanced construction

Regression Discontinuity

Regression Discontinuity (RD) applies when an observation receives a treatment in our case

homes built under the FBC based on a rating variable in our case age of the structure at the year

of observation So for observations in 2005 homes built post 2000 received the treatment

adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify

how observations on either side of the implementation of the FBC (2000) perform in suffering loss

from windstorms The treatment variable is a function of the age of the home and age affects loss

in ways not related to the FBC such as depreciation and differences in materials and construction

practices across time To account for both the effect of age on loss as well as the implementation

of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment

The data we have from ISO is aggregated loss data by zip code and decade of construction So

we cannot get an annualized age To approach a true age we set the year built for each decade of

construction at the beginning of the decade then subtract that from the year of each observation to

get an approximate agexiv

30

To find the best specification we began with a simpler model which used a series of

categorical variables for each decade of construction to examine the effect of the code compared

to the omitted decade This method would approximate the changes in materials and construction

practices but was less effective in controlling for depreciation But it would give us a first

approximation of the code effect that we used as a benchmark when testing the best RD

specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970

with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When

the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When

either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81

A rough approximation of the codersquos effect from this approach would suggest a reduction in the

mid 70 percent range

Insert Table 1 ndash Appendix Here

Next we used a standard procedure with RD to search for the best way to include the rating

variable This process creates specifications that include age in increasing polynomials and

interacted with the treatment variable The goal is to find the specification with the lowest AIC

that comes close to the benchmark value of the treatment variable

Insert Tables 2 and 3 ndash Appendix Here

We did this first with regressions that limited the co-variates then with our full model In both

sets AIC reaches a minimum on the specification with age and age squared The interaction model

after that increases the AIC then the AIC goes down again with a cubed model and its interaction

model with the overall lowest AIC found on the cubed interaction model But we chose not to

use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order

models the magnitude of the treatment variable in the models with just polynomials compared to

31

the corresponding interaction models were close with the interaction models providing a larger

magnitude When the cubed models were added the magnitude jumped where the polynomial

cubed model went down well below our benchmark and the interaction model went up above our

benchmark We felt this made use of the cubed model inappropriate So we now need to choose

between the squared model and the one with the interaction terms The squared model (Model 4)

had a lower AIC and the interaction variables on the interaction model (Model 5) were not

significant so we chose to use the squared model without the interaction term This model gave a

magnitude for the treatment variable of a 72 reduction somewhat lower than the expected

magnitude in the mid 70rsquos percent The general form of the model is

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1

of the observations on either end of our data sorted by loss Our treatment variable Post FBC

remains highly significant with a coefficient value of -117 which compares favorably to our

coefficient value of -126 when the entire sample is used

Structure Age and Wind Losses

Our study is similar to recent studies on the effect of energy efficiency building codes

adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that

better insulation caulking and more efficient HVAC systems would result in lower energy

consumption But the change in energy consumption is less than engineering estimates projected

Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for

homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study

32

may be confounding age with vintage and found a decrease in energy use related to the home

simply being new rather than the change in building code Indeed Kotchen (2015) revisited the

question with data 10 years older and found the effect on electricity had disappeared while the

reduction in natural gas use increased Something is occurring in energy use unrelated to the code

and could be explained by residents changing their use of energy as they adapt to their new home

Residents of an energy efficient home can undermine the intent of lower energy use by using the

efficient design to heat and cool their homes with a motivation toward increased comfort at the

same energy cost rather than energy savings Our study does not have the behavioral component

found in the case of energy efficiency In our application the construction elements that make the

structure able to withstand high winds are installed when the home is built and lie ldquobehind the

wallsrdquo making it unlikely for individual preferences to alter the homes performance against the

threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC

homes due to the code or simply an artifact of new versus old construction when confronted with

a windstorm

To first address our analysis of age versus the FBC we rerun our base regression but limit

our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more

than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14

years during the highest loss year of 2004 Since this is a comparison between two adjacent

decades on either side of our cut point of year 2000 we remove age and age squared Results are

shown in Table 4-Appendix

Insert Table 4-Appendix Here

The coefficient on Post FBC is still negative highly significant with a magnitude very close to

what we saw with the entire database and the age variables This result suggests that the code

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

9

2000 YOC policyholdersvi This compares to 104 percent of post-2000 YOC insured

policyholders incurring a claim with an average loss of $1211 across all post-2000 YOC

policyholders Although this is true for the normalized raw loss data a number of other hazard

exposure and vulnerability factors need to be controlled for to ascertain that post-2000 YOC losses

are indeed lower than pre-2000 construction

Insert Table 2 Here

Outcome Variable

Our dependent variable is aggregate loss for each ZIP code by year (2001-2010) and by

decade of construction In total we have 69442 observations We transform this variable by

taking the natural log While we do not have individual customer data we do have the number of

insured customers (EHY) for each ZIPyeardecade of construction that we include as an

explanatory variable to control for the differences between ZIPyeardecade of construction

observations with high numbers of insured customers versus those with lower numbers

Treatment Variable

To test for the effect of homes built after the introduction of the statewide building code

we construct a dummy variablecedil Post FBC for observations that are after 2000 By using this

dummy variable we can test the effect on losses for homes built after the statewide code was

implemented The dummy variable for Post FBC construction is related to structure age but does

not capture the separate effect age may have on loss So we add structure age into the regression

We only have data on structure age by decade which goes back to 1900 To introduce some

variability to this variable we calculate age by taking the difference between the year of loss and

the first year in the decade for the observation So for an observation that is for year 2004 where

the decade of construction was 1950-1959 age would equal 54 2004-1950 We turn now to the

other data

10

Wind Hazard Data

Florida was affected by 18 tropical cyclones over the period 2001-2010 Spatial wind

hazard data over Florida are sourced from the National Center for Environmental Predictionrsquos

(NCEP) North American Regional Reanalysis (NARR 2015 Mesinger et al 2006) NARR is a

dynamically consistent historical climate dataset based on historical climate observations Data are

available 3-hourly on a 32km grid Of importance to this study Mesinger et al (2006) showed that

the winds just above the surface compare well with surface station observations The 32-km grid

is too coarse to resolve high-impact small-scale features in the wind field such as thunderstorm

winds or tornadoes It is also too coarse to capture the intensity of the strongest hurricanes (as

discussed in Done et al 2015) Rather than downscaling the NARR data to obtain these small-

scale details using dynamical (eg Laprise et al 2008) or statistical (eg Tye et al 2014)

methods (that could introduce further uncertainties) we choose to sacrifice the small-scale details

of the wind field and peak hurricane intensity for location accuracy of the NARR data To account

for these missing wind extremes all wind speed values are normalized by the maximum value of

wind speed in the dataset

Specifically the 3-hourly wind data are interpolated from the 32-km grid to the ZIP-code

level and two wind field parameters are derived for use in the loss regressions the normalized

annual maximum wind speed and the annual number of times the wind speed exceeds the Florida

mean wind speed plus one standard deviation for at least 12 hours The choice of hazard variables

is based on recent work that highlighted the potential for wind parameters other than the maximum

wind to drive losses (Czajkowski and Done 2014 Zhai and Jiang 2014 Jain 2010)

11

Additional Data

We have 2000 and 2010 demographic data from the decennial census at the ZIP code level

for population area (in square miles) of the ZIP median household income and housing counts

Population growth across the decade is not even so we use building permits to help estimate

intervening years Each year is interpolated from decennial data for population and total housing

counts with an allocation factor based on the number of building permits for each ZIP and each

year Building permits are collected from census by place codes so we must re-allocate to ZIP

codes To convert from place to ZIP code we use allocation factors based on 2010 housing counts

provided by MABLE a service of the Missouri Census Data Center (MABLE 2015) For

example if a municipality has two ZIP codes with 60 of the homes in one and the remaining

40 in the other MABLE would use those percentages as the allocation factors from the

municipality to its corresponding ZIP codes In unincorporated areas we use allocation factors

from county to ZIP from the same service For median household income a straight-line

interpolation method is used adjusted for changes in the consumer price index (CPI-U) to 2010

CPI data are from the Bureau of Labor Statistics

Several factors were utilized to represent the overall geographic hazard risk of a ZIP code

The distance of the centroid of the ZIP to the coast was calculated to account for the overall

distance to the coast of each ZIP code Following Dehring and Halek (2013) dummy variables

that signifies whether a ZIP code contains a coastal construction control line (CCCL) were created

(1 equals CCCL in place) to account for stricter building codes in these areas Finally following

the 2005 hurricane season there was a significant increase in the number of policies underwritten

by Citizens the state-run wind-pool and insurer of last resort (Florida Catastrophic Storm Risk

Management Center 2013) Areas with large percentages of insured policies underwritten by

12

Citizens could represent inherently higher windstorm risk We spatially matched our Florida ZIP

codes to the Florida house districts and took the percentage of Citizens policies of the number of

occupied housing units as of December 31 2011 (Florida Catastrophic Storm Risk Management

Center 2013) Given the potential for adverse selection or offloading of high risk policies by the

private market in these areas it is unclear whether higher Citizensrsquo market penetration would lead

to a positive relationship with losses due to the higher risk or a negative relationship with private

losses as many of the bad risks have been transferred to the residual wind pool

IV Econometric Methodology

Better construction limits loss from windstorms through two channels first the direct effect

of decreasing loss on homes that experience damage and second through fewer claims on better

built homes Our data from ISO is aggregated at the ZIP codedecade of construction level So a

ZIP code where all homes experienced damage would have varying levels of damage between

homes built before and after implementation of the FBC Other ZIP codes may have damage for

older homes but little to no damage for homes built post FBC Our first challenge was to use

models that would provide an estimate of the full effect of the FBC lower levels of damage plus

the effect of fewer claims then an estimate for the direct effect alone To accomplish this we

employ two models The first includes all observations even if no claims have been filed and

second a hurdle model where the first stage models the probability of experiencing a loss and the

second stage isolates only the observations where a loss has been experienced

Base Model

The regression model is a semi-log ordinary least squares (OLS) fixed effects (time and

space) model with the natural log of loss as the dependent variable The base level of observation

is ZIP codeyeardecade of construction Explanatory variables include insurance information

13

(exposures and premiums) construction type demographic data based on the ZIP code measures

of the ZIP code hazard risk (how close to the coast the ZIP code is etc) and hazard data

concerning the wind speed and duration

Our test of the FBC creates a discontinuity that must be accounted for in the model All

observations with decade of construction post 2000 are considered under the new building code

regime But that dummy variable is a function of structure age so we employ a regression

discontinuity (RD) analysis to determine the best specification to estimate the effect of the FBC

allowing for the effect that structure age has on damage Intuitively structure age should increase

loss as older homes depreciate across their life making them more vulnerable to wind storms But

the effect of structure age is more than depreciation Over time construction practices and

materials used have changed which also affect how a structure responds to the stress of a violent

wind storm Indeed after Hurricane Andrew in 1992 it was noted that inferior construction

practices of the 1970rsquos and 1980rsquos had exacerbated the losses (Fronstin and Holtmann 1994 Keith

and Rose 1994)

This suggests that the effect of age is non-linear so a model that includes age as a

polynomial would be reasonable Determining the best specification requires testing a series of

models that include age as a polynomial andor interacted with our treatment variable Post FBC

(Lee and Lemieux 2010) (Jacob and Zhu 2012) The full analysis to choose our specification is

included in the Appendix The model that provided the best tradeoff between bias and precision

based on the AIC adds age and its square with the functional form

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

where the variable definitions are given in Table 3

14

Insert Table 3 Here

A positive sign is expected for both wind variables indicating that as wind speeds increase

andor the ZIP code is exposed to high winds for an extended period of time losses will increase

Post FBC construction should decrease loss so a negative sign is expected

Hurdle Model

One problem potentially encountered in attempting to model losses is there may be a

separate process occurring in the data that determines whether a loss is realized at all which could

affect the estimate of overall losses To address this issue hurdle models are used as they divide

the analysis into two stages We use a hurdle model to find the direct effect of the FBC The first

stage models the probability that a loss occurs and the second stage models the loss using only

observations that sustained a loss The dependent variable in the first stage equals one if there was

a loss and zero otherwise This binary dependent variable is then regressed against variables that

would affect the probability that a loss occurred Its form is

(2a)

Loss or No Loss = β0 + β1 Max Wind + β2 Wind Duration + β3 Population Density

+ β4 Post FBC

We expect that both wind variables max wind speed and duration as well as population

density will increase the probability of a loss Post FBC construction however should decrease

the probability of a loss

The second stage in the hurdle model is the same as Equation 1 with the exception that

only observations with a loss are included There are 19107 observations for the second stage and

its form is

15

(2b)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Model Validity

Regression models are limited by available data to understand how the dependent variable

varies as explanatory variables change If important variables are left out of the model some bias

can be expected This omitted variable bias is a common problem encountered with econometric

models Kuminoff et al 2010 found that one of the best approaches to reducing omitted variable

bias is to employ a spatial fixed effects model To accomplish this we use individual ZIP dummy

variables as a spatial fixed effect and dummy variables for each year in our data to control for

changes that may be related to time not otherwise controlled for within our co-variates These

dummy variables will contain all across-group variation leaving the remainder of the model to

contain the within-group variation (Greene 2003)

A second challenge to the validity of our model is another common problem

heteroscedasticity For Equation 1 we use clustered standard errors at the ZIP code through Proc

GLM in SAS Our hurdle model (Eq 2a and 2b) utilizes Proc Qlim which has a separate statement

(Hetero) that we invoked to model the error variance

V Regression Results

Our first regression (Equation 1) serves as a base from which we examine the effect of

basic explanatory variables on loss The results from this regression can be found in Regression

Table 4

Insert Table 4 Here

16

The performance of our regression model is satisfactory in terms of the performance of the

explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and

the coefficient on our treatment variable Post FBC is -126 and highly significant

Overall our results show the strong effect the statewide FBC had on losses from wind

storms during this timeframe Using the results from the regression in Table 4 the coefficient on

the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses

than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the

results from a study conducted by the Insurance Institute for Business and Home Safety after

Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60

percent less likely to suffer damage at all and those that were damaged sustained 42 percent less

damage than older homes Our result of 72 percent lower damage reflects both those attributes as

our data included ZIP codeyearYOC observations that suffered damage as well as those that did

not

Our variables to measure the effect of wind hazard are wind speed and duration For both

variables we have a positive sign and each is highly significant Higher wind speed and higher

duration of high wind speeds increases damage and thus loss The remaining variables perform as

expected

Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In

the first stage variables such as Max Wind and Wind Duration significantly increase the

probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post

FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower

for homes built after new building codes were adopted In the second stage we see that our wind

variables continue to significantly increase the size of the loss and our treatment variable Post

17

FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now

lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy

we see that losses are reduced by about 47 as opposed to 72 when all observations are

includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better

construction reduces loss in two ways First it lowers claims and reduces the amount of a loss

when a claim is filedviii

Model Evaluation

To evaluate our model we used the second stage of the hurdle models and broke our data

into two groups The first group represents 90 of the data randomly selected and was used to

run the model and collect parameter estimates The second group is an out of sample control group

to test the validity of the model Parameter estimates from the first group are applied to the control

group which gave us a predicted loss for each observation in the control group that can be

compared to the actual loss for each observation in the control group We then regressed the

predicted loss from the control group against the actual loss

Insert Figure 2 Here

Figure 2 plots the predicted loss against the actual loss and provides the fitted line with

95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears

to do a good job of predicting most losses

Robustness of Table 4 Base Model Results

To test the robustness of our results we run three separate analyses 1) We first run a

regression with few co-variates 2) As wind design speeds have been used as a proxy for building

code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss

18

analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking

Florida in 2004 and 2005

Regressions using Few Co-Variates

Additional relevant co-variates add precision to a model But the value of the focus

variable should be apparent with a smaller set So we ran a model with only insured customer

based variables EHY and paid premiums leaving out all other demographic and hazard related

variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and

significance

Regressions Using Design Speed

The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads

for Buildings and Other Structures published by the American Society of Civil Engineers and the

Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds

for most regions of the United States and their territories These reference wind speeds are used in

calculations to determine design wind pressures for the primary structure of a building and the

cladding and components attached to a building These calculations take into account the building

geometry the importance of a building the exposuresurrounding terrain and other parameters that

influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design

speeds as a proxy for building code strength and we similarly add this as an additional control in

our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps

were provided by the Applied Technology Council (ATC) Although this version of the wind

speed map was not utilized during the period under consideration the relative values in general

between two locations would be the sameix

19

We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded

form from the ATC and spatially joined the values to our Florida ZIP codes We then further

categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat

4 and Cat 5 hurricane levels

Insert Table 5 Here

The regression adds two dummy variables first for ZIP codes whose design speed exceeds

the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed

reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables

for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that

communities in higher wind zones may take further measures in local codes However the effect

is not significant Notably our variable for Post FBC construction maintains its negative sign

magnitude and significance

Regressions Limited to 2004 and 2005

Our next regression also shown in Table 5 is limited to observations that occurred during

the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and

2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not

surprisingly the magnitude on wind speed increases while maintaining its significance and the

magnitude on age does the same But the effect of the FBC remains the same a 72 reduction

Summary of Results on the FBC

We have collected a comprehensive set of data on insured paid losses from 2001 to 2010

windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity

model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of

the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction

20

in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to

determine if the policy is one that is cost effective

VI Benefit and Costs of the FBC

Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a

number of cases the economic effectiveness of the improved building codes has not been as well

documented especially from a statewide implementation perspective The multi-hazard

mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose

et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar

spent) was appropriate for process activity grant spending related to improved building codes

However this information was gathered from a limited number of studies (mainly earthquake

oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in

generating it and the ratio provided due to improvement would not be the same as those for

adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted

an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for

ten related counties and determined that the net present value of the revisions was $7 billion or

benefit-cost ratio greater than 1 Importantly though this study did not have access to actual

building code damage reduction data to utilize in the analysis In 2002 Applied Research

Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC

for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how

the three types of FBC built houses would perform in probabilistic hurricane scenarios compared

to the same houses built under the previous code Given the probabilistic nature of the analysis

average annual losses were generated that demonstrated post-FBC housing having loss reductions

54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were

21

then compared to their estimated cost impacts of the FBC for these housing types with at least

break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and

above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al

(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss

reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results

ranged from 041 to 183 for the retrofit mitigation activities to existing housing

We propose a BCA that differs from earlier work in several important ways First we use

realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of

how much loss can be reduced by the FBC Second our loss data spans 10 years which include a

combination of major hurricanes and smaller wind storms

BenefitCost Methodology

The elements of a BCA requires three inputs 1) an estimate of the added cost to implement

the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related

storms from our realized ISO loss data and then from a statewide catastrophe model estimate and

3) the percentage of expected loss that will be mitigated due to implementation of the FBC We

first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis

but use the full reduction in loss which includes the value of reduced claims Finally our ISO data

is paid losses and does not include deductibles so we add an estimate for deductibles

Additional Cost

In their 2002 benefit-cost comparison study of the enactment of the FBC for three related

housing types three actual sample homes were built to the FBC to evaluate the change in

construction costs (ARA 2002) For the purposes of code implementation the state was divided

into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region

22

(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account

for cost differences between the two regions

In the WBDR an added requirement is impact protection to windows and doors to reduce

damage from flying debris Along the coast and much of South Florida is classified as the WBDR

The N-WBDR is mainly classified in the interior of the state where impact protection is not

required Importantly the study provided a range of added costs for the N-WBDR and the WBDR

Three counties in South Florida Dade Broward and Monroe were under the South Florida

Building Code (SFBC) prior to the implementation of the FBC According to the ARA study

(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges

of incremental cost per square foot for the N-WBDR and WBDR along with the percent of

residential units that reside in each area This allows a calculation of a weighted average added

cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost

per square foot of $166 The cost compares favorably with a similar building code enhancement

adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in

2013 Consulting engineers and the Moore Association of Homebuilders estimated the code

enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is

1960 square feet which means that on average the FBC increases construction cost by $3254 per

structurexii

Insert Table 6 Here

Benefit of the FBC

Benefits stemming from the FBC are the expected reduction in losses from windstorms during

the life of the home We first find an average annual loss (AAL) use that number to estimate

losses for the next 50 years and then find the present value of those losses in 2010 Here we are

23

assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the

next 50 years A wealth of literature suggests the potential for changes to hurricane activity over

the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty

on future changes in wind hazard on the scale of a single state we choose to assume a stationary

climate

Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes

built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our

data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of

$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year

Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses

per structure of $21474

Finally we use parameter estimates from our regression for the Post FBC dummy variable

(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC

The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post

FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what

would be a reduction of $10093 from the projected $21474 in future losses

Insert Table 7 Here

BenefitCost Analysis

Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio

of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the

statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an

economically effective public policy following from our ISO loss data and results

Insert Table 8 Here

24

Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years

This relatively short timeframe makes it difficult to truly approximate an AAL as would be

provided from a probabilistically based catastrophe model that generates an AAL from thousands

of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state

of Florida to estimate an average annual wind loss for all residential properties in Florida of

approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL

estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion

with deductibles) Using this aggregate AAL and the number of residential units in Florida based

on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses

net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and

an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as

before derived from our regression results 47 we find $7710 of reduced loss from the projected

$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added

$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective

building code public policy

We run two additional analyses on our BCA results Our estimate of expected loss

reduction comes from the second stage of the hurdle model This is an estimate of the direct loss

reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the

number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests

as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which

includes the reduced magnitude of loss from affected homes and the reduction in claims for Post

FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table

8) However a 72 loss reduction seems too dramatic an expectation when planning so far in

25

advance For that reason we offer a third level of expected loss reduction of 60 which is the

midpoint between our two loss reduction estimates This estimate captures the expected direct loss

reduction suggested by the second stage of our hurdle model but still recognizes that in some areas

the number of claims is reduced by the FBC This appears to be a reasonable assumption and

provides a BCA ratio of 396 for the ISO sample and 302 for all residential

The ISO data are net of deductibles so our BCA thus far only includes losses compensated by

the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion

also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the

ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all

loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA

values from a low of 237 to a high of 793

Payback of the FBC

Finally we use our BCA results to calculate a payback period for the investment of stronger

codes To convert our BCA ratio to a payback period we simply divide our 50-year planning

horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss

and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years

This is important for gauging potential political support or non-support for enactment of the new

codes Payback periods that approach the typical mortgage term 30 years would in theory be

difficult to achieve and that is not what our analysis indicates for the FBC

VI - Concluding Comments

In the aftermath of Hurricane Andrew which had exposed not only poor building

construction but also poor building code enforcement the state of Florida enacted statewide

building code changes that wrested away building code adoption control from individual localities

26

With full implementation of the statewide building code associated expectations are that

windstorm losses from extreme events such as hurricanes should be reduced moving forward

There have been a few studies confirming these expectations following the 2004 and 2005

hurricane season In this article we further verify and quantify these findings and expand the

existing building code risk reduction research in several important ways

Overall we empirically test the statewide implementation of a building code in reducing

wind related damages in Florida controlling for other relevant wind hazard exposure and

vulnerability characteristics from a traditional risk assessment perspective Our results show the

strong effect the statewide FBC had on losses from wind storms during this timeframe From the

treatment variable that measures implementation of the statewide codes the post 2000 year of

construction losses are shown to be reduced by as much as 72 percent consistent with other

previous findings

Finally we have conducted a BCA of the FBC to determine if expected benefits exceed

the cost of implementation Using a direct estimate for mitigated losses and an estimate that

includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public

policy from an economic perspective This result is close to that recommended by the multi-hazard

mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a

statewide BCA Importantly this information is essential in generating political and consumer

support for such building code public policy implementation

For example the economic effectiveness results shown here have implications for ongoing

policy discussions about reforming building codes from a national US perspective Moore OK

independently adopted enhanced building codes after its third violent tornado in 14 years killed 24

including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)

27

Construction practices in North Texas were brought under scrutiny after the December 2015

tornado revealed inadequate construction including an elementary school whose exterior walls

failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House

announced initiatives to increase community resilience with building codes as a major component

of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748

and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better

construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397

would provide tax credits for owners andor contractors who use techniques designed for resiliency

in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one

recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to

encourage states to use current building codes (Vaughn and Turner 2014)

Future research in the BCA of the FBC will further inform the public policy debate on

enhanced building codes The issue has national implications as other states find that wind hazards

impact them as well We have sufficient wind data to examine how the BCA performs under

different wind hazards Additionally it will be important to consider how future economic

development affects the BCA as well as varying climate change scenarios As the FBC is

mandatory for all new construction a statewide analysis was appropriate But individual

homeowners in older homes can invest in the retrofit of their home and qualify for discounts on

their homeowners insurance This topic is deserving of a robust analysis Although our BCA is

statewide regions within the state will likely have a spectrum of results For instance the ARA

2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their

analysis did not use realized loss data so confirmation of how the BCA varies between those

regions would be an important contribution Finally our sensitivity analysis was limited to two

28

variables reduction in future loss and the inclusion of deductibles Additional work will highlight

other variables that could modify the results

29

Appendix

We use this appendix to conduct more detailed analysis on several topics First selection

of the model specification using a regression discontinuity approach Second we provide an in

depth examination of the relationship between structure age and losses Third we perform a

Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an

alternative specification to see if our RD results are similar followed by regressions to examine

the year to year consistency of our Post FBC result Next we run a regression on claims to verify

the difference between our direct reduction result and our full reduction result Finally we perform

a regression on homes built to the SFBC which had adopted enhanced building codes in advance

of the FBC to assess the effect of earlier adoption of enhanced construction

Regression Discontinuity

Regression Discontinuity (RD) applies when an observation receives a treatment in our case

homes built under the FBC based on a rating variable in our case age of the structure at the year

of observation So for observations in 2005 homes built post 2000 received the treatment

adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify

how observations on either side of the implementation of the FBC (2000) perform in suffering loss

from windstorms The treatment variable is a function of the age of the home and age affects loss

in ways not related to the FBC such as depreciation and differences in materials and construction

practices across time To account for both the effect of age on loss as well as the implementation

of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment

The data we have from ISO is aggregated loss data by zip code and decade of construction So

we cannot get an annualized age To approach a true age we set the year built for each decade of

construction at the beginning of the decade then subtract that from the year of each observation to

get an approximate agexiv

30

To find the best specification we began with a simpler model which used a series of

categorical variables for each decade of construction to examine the effect of the code compared

to the omitted decade This method would approximate the changes in materials and construction

practices but was less effective in controlling for depreciation But it would give us a first

approximation of the code effect that we used as a benchmark when testing the best RD

specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970

with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When

the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When

either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81

A rough approximation of the codersquos effect from this approach would suggest a reduction in the

mid 70 percent range

Insert Table 1 ndash Appendix Here

Next we used a standard procedure with RD to search for the best way to include the rating

variable This process creates specifications that include age in increasing polynomials and

interacted with the treatment variable The goal is to find the specification with the lowest AIC

that comes close to the benchmark value of the treatment variable

Insert Tables 2 and 3 ndash Appendix Here

We did this first with regressions that limited the co-variates then with our full model In both

sets AIC reaches a minimum on the specification with age and age squared The interaction model

after that increases the AIC then the AIC goes down again with a cubed model and its interaction

model with the overall lowest AIC found on the cubed interaction model But we chose not to

use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order

models the magnitude of the treatment variable in the models with just polynomials compared to

31

the corresponding interaction models were close with the interaction models providing a larger

magnitude When the cubed models were added the magnitude jumped where the polynomial

cubed model went down well below our benchmark and the interaction model went up above our

benchmark We felt this made use of the cubed model inappropriate So we now need to choose

between the squared model and the one with the interaction terms The squared model (Model 4)

had a lower AIC and the interaction variables on the interaction model (Model 5) were not

significant so we chose to use the squared model without the interaction term This model gave a

magnitude for the treatment variable of a 72 reduction somewhat lower than the expected

magnitude in the mid 70rsquos percent The general form of the model is

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1

of the observations on either end of our data sorted by loss Our treatment variable Post FBC

remains highly significant with a coefficient value of -117 which compares favorably to our

coefficient value of -126 when the entire sample is used

Structure Age and Wind Losses

Our study is similar to recent studies on the effect of energy efficiency building codes

adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that

better insulation caulking and more efficient HVAC systems would result in lower energy

consumption But the change in energy consumption is less than engineering estimates projected

Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for

homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study

32

may be confounding age with vintage and found a decrease in energy use related to the home

simply being new rather than the change in building code Indeed Kotchen (2015) revisited the

question with data 10 years older and found the effect on electricity had disappeared while the

reduction in natural gas use increased Something is occurring in energy use unrelated to the code

and could be explained by residents changing their use of energy as they adapt to their new home

Residents of an energy efficient home can undermine the intent of lower energy use by using the

efficient design to heat and cool their homes with a motivation toward increased comfort at the

same energy cost rather than energy savings Our study does not have the behavioral component

found in the case of energy efficiency In our application the construction elements that make the

structure able to withstand high winds are installed when the home is built and lie ldquobehind the

wallsrdquo making it unlikely for individual preferences to alter the homes performance against the

threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC

homes due to the code or simply an artifact of new versus old construction when confronted with

a windstorm

To first address our analysis of age versus the FBC we rerun our base regression but limit

our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more

than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14

years during the highest loss year of 2004 Since this is a comparison between two adjacent

decades on either side of our cut point of year 2000 we remove age and age squared Results are

shown in Table 4-Appendix

Insert Table 4-Appendix Here

The coefficient on Post FBC is still negative highly significant with a magnitude very close to

what we saw with the entire database and the age variables This result suggests that the code

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

10

Wind Hazard Data

Florida was affected by 18 tropical cyclones over the period 2001-2010 Spatial wind

hazard data over Florida are sourced from the National Center for Environmental Predictionrsquos

(NCEP) North American Regional Reanalysis (NARR 2015 Mesinger et al 2006) NARR is a

dynamically consistent historical climate dataset based on historical climate observations Data are

available 3-hourly on a 32km grid Of importance to this study Mesinger et al (2006) showed that

the winds just above the surface compare well with surface station observations The 32-km grid

is too coarse to resolve high-impact small-scale features in the wind field such as thunderstorm

winds or tornadoes It is also too coarse to capture the intensity of the strongest hurricanes (as

discussed in Done et al 2015) Rather than downscaling the NARR data to obtain these small-

scale details using dynamical (eg Laprise et al 2008) or statistical (eg Tye et al 2014)

methods (that could introduce further uncertainties) we choose to sacrifice the small-scale details

of the wind field and peak hurricane intensity for location accuracy of the NARR data To account

for these missing wind extremes all wind speed values are normalized by the maximum value of

wind speed in the dataset

Specifically the 3-hourly wind data are interpolated from the 32-km grid to the ZIP-code

level and two wind field parameters are derived for use in the loss regressions the normalized

annual maximum wind speed and the annual number of times the wind speed exceeds the Florida

mean wind speed plus one standard deviation for at least 12 hours The choice of hazard variables

is based on recent work that highlighted the potential for wind parameters other than the maximum

wind to drive losses (Czajkowski and Done 2014 Zhai and Jiang 2014 Jain 2010)

11

Additional Data

We have 2000 and 2010 demographic data from the decennial census at the ZIP code level

for population area (in square miles) of the ZIP median household income and housing counts

Population growth across the decade is not even so we use building permits to help estimate

intervening years Each year is interpolated from decennial data for population and total housing

counts with an allocation factor based on the number of building permits for each ZIP and each

year Building permits are collected from census by place codes so we must re-allocate to ZIP

codes To convert from place to ZIP code we use allocation factors based on 2010 housing counts

provided by MABLE a service of the Missouri Census Data Center (MABLE 2015) For

example if a municipality has two ZIP codes with 60 of the homes in one and the remaining

40 in the other MABLE would use those percentages as the allocation factors from the

municipality to its corresponding ZIP codes In unincorporated areas we use allocation factors

from county to ZIP from the same service For median household income a straight-line

interpolation method is used adjusted for changes in the consumer price index (CPI-U) to 2010

CPI data are from the Bureau of Labor Statistics

Several factors were utilized to represent the overall geographic hazard risk of a ZIP code

The distance of the centroid of the ZIP to the coast was calculated to account for the overall

distance to the coast of each ZIP code Following Dehring and Halek (2013) dummy variables

that signifies whether a ZIP code contains a coastal construction control line (CCCL) were created

(1 equals CCCL in place) to account for stricter building codes in these areas Finally following

the 2005 hurricane season there was a significant increase in the number of policies underwritten

by Citizens the state-run wind-pool and insurer of last resort (Florida Catastrophic Storm Risk

Management Center 2013) Areas with large percentages of insured policies underwritten by

12

Citizens could represent inherently higher windstorm risk We spatially matched our Florida ZIP

codes to the Florida house districts and took the percentage of Citizens policies of the number of

occupied housing units as of December 31 2011 (Florida Catastrophic Storm Risk Management

Center 2013) Given the potential for adverse selection or offloading of high risk policies by the

private market in these areas it is unclear whether higher Citizensrsquo market penetration would lead

to a positive relationship with losses due to the higher risk or a negative relationship with private

losses as many of the bad risks have been transferred to the residual wind pool

IV Econometric Methodology

Better construction limits loss from windstorms through two channels first the direct effect

of decreasing loss on homes that experience damage and second through fewer claims on better

built homes Our data from ISO is aggregated at the ZIP codedecade of construction level So a

ZIP code where all homes experienced damage would have varying levels of damage between

homes built before and after implementation of the FBC Other ZIP codes may have damage for

older homes but little to no damage for homes built post FBC Our first challenge was to use

models that would provide an estimate of the full effect of the FBC lower levels of damage plus

the effect of fewer claims then an estimate for the direct effect alone To accomplish this we

employ two models The first includes all observations even if no claims have been filed and

second a hurdle model where the first stage models the probability of experiencing a loss and the

second stage isolates only the observations where a loss has been experienced

Base Model

The regression model is a semi-log ordinary least squares (OLS) fixed effects (time and

space) model with the natural log of loss as the dependent variable The base level of observation

is ZIP codeyeardecade of construction Explanatory variables include insurance information

13

(exposures and premiums) construction type demographic data based on the ZIP code measures

of the ZIP code hazard risk (how close to the coast the ZIP code is etc) and hazard data

concerning the wind speed and duration

Our test of the FBC creates a discontinuity that must be accounted for in the model All

observations with decade of construction post 2000 are considered under the new building code

regime But that dummy variable is a function of structure age so we employ a regression

discontinuity (RD) analysis to determine the best specification to estimate the effect of the FBC

allowing for the effect that structure age has on damage Intuitively structure age should increase

loss as older homes depreciate across their life making them more vulnerable to wind storms But

the effect of structure age is more than depreciation Over time construction practices and

materials used have changed which also affect how a structure responds to the stress of a violent

wind storm Indeed after Hurricane Andrew in 1992 it was noted that inferior construction

practices of the 1970rsquos and 1980rsquos had exacerbated the losses (Fronstin and Holtmann 1994 Keith

and Rose 1994)

This suggests that the effect of age is non-linear so a model that includes age as a

polynomial would be reasonable Determining the best specification requires testing a series of

models that include age as a polynomial andor interacted with our treatment variable Post FBC

(Lee and Lemieux 2010) (Jacob and Zhu 2012) The full analysis to choose our specification is

included in the Appendix The model that provided the best tradeoff between bias and precision

based on the AIC adds age and its square with the functional form

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

where the variable definitions are given in Table 3

14

Insert Table 3 Here

A positive sign is expected for both wind variables indicating that as wind speeds increase

andor the ZIP code is exposed to high winds for an extended period of time losses will increase

Post FBC construction should decrease loss so a negative sign is expected

Hurdle Model

One problem potentially encountered in attempting to model losses is there may be a

separate process occurring in the data that determines whether a loss is realized at all which could

affect the estimate of overall losses To address this issue hurdle models are used as they divide

the analysis into two stages We use a hurdle model to find the direct effect of the FBC The first

stage models the probability that a loss occurs and the second stage models the loss using only

observations that sustained a loss The dependent variable in the first stage equals one if there was

a loss and zero otherwise This binary dependent variable is then regressed against variables that

would affect the probability that a loss occurred Its form is

(2a)

Loss or No Loss = β0 + β1 Max Wind + β2 Wind Duration + β3 Population Density

+ β4 Post FBC

We expect that both wind variables max wind speed and duration as well as population

density will increase the probability of a loss Post FBC construction however should decrease

the probability of a loss

The second stage in the hurdle model is the same as Equation 1 with the exception that

only observations with a loss are included There are 19107 observations for the second stage and

its form is

15

(2b)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Model Validity

Regression models are limited by available data to understand how the dependent variable

varies as explanatory variables change If important variables are left out of the model some bias

can be expected This omitted variable bias is a common problem encountered with econometric

models Kuminoff et al 2010 found that one of the best approaches to reducing omitted variable

bias is to employ a spatial fixed effects model To accomplish this we use individual ZIP dummy

variables as a spatial fixed effect and dummy variables for each year in our data to control for

changes that may be related to time not otherwise controlled for within our co-variates These

dummy variables will contain all across-group variation leaving the remainder of the model to

contain the within-group variation (Greene 2003)

A second challenge to the validity of our model is another common problem

heteroscedasticity For Equation 1 we use clustered standard errors at the ZIP code through Proc

GLM in SAS Our hurdle model (Eq 2a and 2b) utilizes Proc Qlim which has a separate statement

(Hetero) that we invoked to model the error variance

V Regression Results

Our first regression (Equation 1) serves as a base from which we examine the effect of

basic explanatory variables on loss The results from this regression can be found in Regression

Table 4

Insert Table 4 Here

16

The performance of our regression model is satisfactory in terms of the performance of the

explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and

the coefficient on our treatment variable Post FBC is -126 and highly significant

Overall our results show the strong effect the statewide FBC had on losses from wind

storms during this timeframe Using the results from the regression in Table 4 the coefficient on

the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses

than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the

results from a study conducted by the Insurance Institute for Business and Home Safety after

Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60

percent less likely to suffer damage at all and those that were damaged sustained 42 percent less

damage than older homes Our result of 72 percent lower damage reflects both those attributes as

our data included ZIP codeyearYOC observations that suffered damage as well as those that did

not

Our variables to measure the effect of wind hazard are wind speed and duration For both

variables we have a positive sign and each is highly significant Higher wind speed and higher

duration of high wind speeds increases damage and thus loss The remaining variables perform as

expected

Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In

the first stage variables such as Max Wind and Wind Duration significantly increase the

probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post

FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower

for homes built after new building codes were adopted In the second stage we see that our wind

variables continue to significantly increase the size of the loss and our treatment variable Post

17

FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now

lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy

we see that losses are reduced by about 47 as opposed to 72 when all observations are

includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better

construction reduces loss in two ways First it lowers claims and reduces the amount of a loss

when a claim is filedviii

Model Evaluation

To evaluate our model we used the second stage of the hurdle models and broke our data

into two groups The first group represents 90 of the data randomly selected and was used to

run the model and collect parameter estimates The second group is an out of sample control group

to test the validity of the model Parameter estimates from the first group are applied to the control

group which gave us a predicted loss for each observation in the control group that can be

compared to the actual loss for each observation in the control group We then regressed the

predicted loss from the control group against the actual loss

Insert Figure 2 Here

Figure 2 plots the predicted loss against the actual loss and provides the fitted line with

95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears

to do a good job of predicting most losses

Robustness of Table 4 Base Model Results

To test the robustness of our results we run three separate analyses 1) We first run a

regression with few co-variates 2) As wind design speeds have been used as a proxy for building

code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss

18

analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking

Florida in 2004 and 2005

Regressions using Few Co-Variates

Additional relevant co-variates add precision to a model But the value of the focus

variable should be apparent with a smaller set So we ran a model with only insured customer

based variables EHY and paid premiums leaving out all other demographic and hazard related

variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and

significance

Regressions Using Design Speed

The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads

for Buildings and Other Structures published by the American Society of Civil Engineers and the

Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds

for most regions of the United States and their territories These reference wind speeds are used in

calculations to determine design wind pressures for the primary structure of a building and the

cladding and components attached to a building These calculations take into account the building

geometry the importance of a building the exposuresurrounding terrain and other parameters that

influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design

speeds as a proxy for building code strength and we similarly add this as an additional control in

our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps

were provided by the Applied Technology Council (ATC) Although this version of the wind

speed map was not utilized during the period under consideration the relative values in general

between two locations would be the sameix

19

We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded

form from the ATC and spatially joined the values to our Florida ZIP codes We then further

categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat

4 and Cat 5 hurricane levels

Insert Table 5 Here

The regression adds two dummy variables first for ZIP codes whose design speed exceeds

the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed

reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables

for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that

communities in higher wind zones may take further measures in local codes However the effect

is not significant Notably our variable for Post FBC construction maintains its negative sign

magnitude and significance

Regressions Limited to 2004 and 2005

Our next regression also shown in Table 5 is limited to observations that occurred during

the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and

2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not

surprisingly the magnitude on wind speed increases while maintaining its significance and the

magnitude on age does the same But the effect of the FBC remains the same a 72 reduction

Summary of Results on the FBC

We have collected a comprehensive set of data on insured paid losses from 2001 to 2010

windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity

model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of

the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction

20

in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to

determine if the policy is one that is cost effective

VI Benefit and Costs of the FBC

Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a

number of cases the economic effectiveness of the improved building codes has not been as well

documented especially from a statewide implementation perspective The multi-hazard

mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose

et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar

spent) was appropriate for process activity grant spending related to improved building codes

However this information was gathered from a limited number of studies (mainly earthquake

oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in

generating it and the ratio provided due to improvement would not be the same as those for

adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted

an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for

ten related counties and determined that the net present value of the revisions was $7 billion or

benefit-cost ratio greater than 1 Importantly though this study did not have access to actual

building code damage reduction data to utilize in the analysis In 2002 Applied Research

Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC

for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how

the three types of FBC built houses would perform in probabilistic hurricane scenarios compared

to the same houses built under the previous code Given the probabilistic nature of the analysis

average annual losses were generated that demonstrated post-FBC housing having loss reductions

54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were

21

then compared to their estimated cost impacts of the FBC for these housing types with at least

break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and

above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al

(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss

reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results

ranged from 041 to 183 for the retrofit mitigation activities to existing housing

We propose a BCA that differs from earlier work in several important ways First we use

realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of

how much loss can be reduced by the FBC Second our loss data spans 10 years which include a

combination of major hurricanes and smaller wind storms

BenefitCost Methodology

The elements of a BCA requires three inputs 1) an estimate of the added cost to implement

the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related

storms from our realized ISO loss data and then from a statewide catastrophe model estimate and

3) the percentage of expected loss that will be mitigated due to implementation of the FBC We

first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis

but use the full reduction in loss which includes the value of reduced claims Finally our ISO data

is paid losses and does not include deductibles so we add an estimate for deductibles

Additional Cost

In their 2002 benefit-cost comparison study of the enactment of the FBC for three related

housing types three actual sample homes were built to the FBC to evaluate the change in

construction costs (ARA 2002) For the purposes of code implementation the state was divided

into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region

22

(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account

for cost differences between the two regions

In the WBDR an added requirement is impact protection to windows and doors to reduce

damage from flying debris Along the coast and much of South Florida is classified as the WBDR

The N-WBDR is mainly classified in the interior of the state where impact protection is not

required Importantly the study provided a range of added costs for the N-WBDR and the WBDR

Three counties in South Florida Dade Broward and Monroe were under the South Florida

Building Code (SFBC) prior to the implementation of the FBC According to the ARA study

(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges

of incremental cost per square foot for the N-WBDR and WBDR along with the percent of

residential units that reside in each area This allows a calculation of a weighted average added

cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost

per square foot of $166 The cost compares favorably with a similar building code enhancement

adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in

2013 Consulting engineers and the Moore Association of Homebuilders estimated the code

enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is

1960 square feet which means that on average the FBC increases construction cost by $3254 per

structurexii

Insert Table 6 Here

Benefit of the FBC

Benefits stemming from the FBC are the expected reduction in losses from windstorms during

the life of the home We first find an average annual loss (AAL) use that number to estimate

losses for the next 50 years and then find the present value of those losses in 2010 Here we are

23

assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the

next 50 years A wealth of literature suggests the potential for changes to hurricane activity over

the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty

on future changes in wind hazard on the scale of a single state we choose to assume a stationary

climate

Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes

built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our

data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of

$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year

Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses

per structure of $21474

Finally we use parameter estimates from our regression for the Post FBC dummy variable

(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC

The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post

FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what

would be a reduction of $10093 from the projected $21474 in future losses

Insert Table 7 Here

BenefitCost Analysis

Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio

of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the

statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an

economically effective public policy following from our ISO loss data and results

Insert Table 8 Here

24

Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years

This relatively short timeframe makes it difficult to truly approximate an AAL as would be

provided from a probabilistically based catastrophe model that generates an AAL from thousands

of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state

of Florida to estimate an average annual wind loss for all residential properties in Florida of

approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL

estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion

with deductibles) Using this aggregate AAL and the number of residential units in Florida based

on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses

net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and

an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as

before derived from our regression results 47 we find $7710 of reduced loss from the projected

$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added

$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective

building code public policy

We run two additional analyses on our BCA results Our estimate of expected loss

reduction comes from the second stage of the hurdle model This is an estimate of the direct loss

reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the

number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests

as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which

includes the reduced magnitude of loss from affected homes and the reduction in claims for Post

FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table

8) However a 72 loss reduction seems too dramatic an expectation when planning so far in

25

advance For that reason we offer a third level of expected loss reduction of 60 which is the

midpoint between our two loss reduction estimates This estimate captures the expected direct loss

reduction suggested by the second stage of our hurdle model but still recognizes that in some areas

the number of claims is reduced by the FBC This appears to be a reasonable assumption and

provides a BCA ratio of 396 for the ISO sample and 302 for all residential

The ISO data are net of deductibles so our BCA thus far only includes losses compensated by

the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion

also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the

ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all

loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA

values from a low of 237 to a high of 793

Payback of the FBC

Finally we use our BCA results to calculate a payback period for the investment of stronger

codes To convert our BCA ratio to a payback period we simply divide our 50-year planning

horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss

and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years

This is important for gauging potential political support or non-support for enactment of the new

codes Payback periods that approach the typical mortgage term 30 years would in theory be

difficult to achieve and that is not what our analysis indicates for the FBC

VI - Concluding Comments

In the aftermath of Hurricane Andrew which had exposed not only poor building

construction but also poor building code enforcement the state of Florida enacted statewide

building code changes that wrested away building code adoption control from individual localities

26

With full implementation of the statewide building code associated expectations are that

windstorm losses from extreme events such as hurricanes should be reduced moving forward

There have been a few studies confirming these expectations following the 2004 and 2005

hurricane season In this article we further verify and quantify these findings and expand the

existing building code risk reduction research in several important ways

Overall we empirically test the statewide implementation of a building code in reducing

wind related damages in Florida controlling for other relevant wind hazard exposure and

vulnerability characteristics from a traditional risk assessment perspective Our results show the

strong effect the statewide FBC had on losses from wind storms during this timeframe From the

treatment variable that measures implementation of the statewide codes the post 2000 year of

construction losses are shown to be reduced by as much as 72 percent consistent with other

previous findings

Finally we have conducted a BCA of the FBC to determine if expected benefits exceed

the cost of implementation Using a direct estimate for mitigated losses and an estimate that

includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public

policy from an economic perspective This result is close to that recommended by the multi-hazard

mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a

statewide BCA Importantly this information is essential in generating political and consumer

support for such building code public policy implementation

For example the economic effectiveness results shown here have implications for ongoing

policy discussions about reforming building codes from a national US perspective Moore OK

independently adopted enhanced building codes after its third violent tornado in 14 years killed 24

including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)

27

Construction practices in North Texas were brought under scrutiny after the December 2015

tornado revealed inadequate construction including an elementary school whose exterior walls

failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House

announced initiatives to increase community resilience with building codes as a major component

of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748

and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better

construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397

would provide tax credits for owners andor contractors who use techniques designed for resiliency

in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one

recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to

encourage states to use current building codes (Vaughn and Turner 2014)

Future research in the BCA of the FBC will further inform the public policy debate on

enhanced building codes The issue has national implications as other states find that wind hazards

impact them as well We have sufficient wind data to examine how the BCA performs under

different wind hazards Additionally it will be important to consider how future economic

development affects the BCA as well as varying climate change scenarios As the FBC is

mandatory for all new construction a statewide analysis was appropriate But individual

homeowners in older homes can invest in the retrofit of their home and qualify for discounts on

their homeowners insurance This topic is deserving of a robust analysis Although our BCA is

statewide regions within the state will likely have a spectrum of results For instance the ARA

2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their

analysis did not use realized loss data so confirmation of how the BCA varies between those

regions would be an important contribution Finally our sensitivity analysis was limited to two

28

variables reduction in future loss and the inclusion of deductibles Additional work will highlight

other variables that could modify the results

29

Appendix

We use this appendix to conduct more detailed analysis on several topics First selection

of the model specification using a regression discontinuity approach Second we provide an in

depth examination of the relationship between structure age and losses Third we perform a

Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an

alternative specification to see if our RD results are similar followed by regressions to examine

the year to year consistency of our Post FBC result Next we run a regression on claims to verify

the difference between our direct reduction result and our full reduction result Finally we perform

a regression on homes built to the SFBC which had adopted enhanced building codes in advance

of the FBC to assess the effect of earlier adoption of enhanced construction

Regression Discontinuity

Regression Discontinuity (RD) applies when an observation receives a treatment in our case

homes built under the FBC based on a rating variable in our case age of the structure at the year

of observation So for observations in 2005 homes built post 2000 received the treatment

adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify

how observations on either side of the implementation of the FBC (2000) perform in suffering loss

from windstorms The treatment variable is a function of the age of the home and age affects loss

in ways not related to the FBC such as depreciation and differences in materials and construction

practices across time To account for both the effect of age on loss as well as the implementation

of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment

The data we have from ISO is aggregated loss data by zip code and decade of construction So

we cannot get an annualized age To approach a true age we set the year built for each decade of

construction at the beginning of the decade then subtract that from the year of each observation to

get an approximate agexiv

30

To find the best specification we began with a simpler model which used a series of

categorical variables for each decade of construction to examine the effect of the code compared

to the omitted decade This method would approximate the changes in materials and construction

practices but was less effective in controlling for depreciation But it would give us a first

approximation of the code effect that we used as a benchmark when testing the best RD

specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970

with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When

the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When

either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81

A rough approximation of the codersquos effect from this approach would suggest a reduction in the

mid 70 percent range

Insert Table 1 ndash Appendix Here

Next we used a standard procedure with RD to search for the best way to include the rating

variable This process creates specifications that include age in increasing polynomials and

interacted with the treatment variable The goal is to find the specification with the lowest AIC

that comes close to the benchmark value of the treatment variable

Insert Tables 2 and 3 ndash Appendix Here

We did this first with regressions that limited the co-variates then with our full model In both

sets AIC reaches a minimum on the specification with age and age squared The interaction model

after that increases the AIC then the AIC goes down again with a cubed model and its interaction

model with the overall lowest AIC found on the cubed interaction model But we chose not to

use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order

models the magnitude of the treatment variable in the models with just polynomials compared to

31

the corresponding interaction models were close with the interaction models providing a larger

magnitude When the cubed models were added the magnitude jumped where the polynomial

cubed model went down well below our benchmark and the interaction model went up above our

benchmark We felt this made use of the cubed model inappropriate So we now need to choose

between the squared model and the one with the interaction terms The squared model (Model 4)

had a lower AIC and the interaction variables on the interaction model (Model 5) were not

significant so we chose to use the squared model without the interaction term This model gave a

magnitude for the treatment variable of a 72 reduction somewhat lower than the expected

magnitude in the mid 70rsquos percent The general form of the model is

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1

of the observations on either end of our data sorted by loss Our treatment variable Post FBC

remains highly significant with a coefficient value of -117 which compares favorably to our

coefficient value of -126 when the entire sample is used

Structure Age and Wind Losses

Our study is similar to recent studies on the effect of energy efficiency building codes

adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that

better insulation caulking and more efficient HVAC systems would result in lower energy

consumption But the change in energy consumption is less than engineering estimates projected

Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for

homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study

32

may be confounding age with vintage and found a decrease in energy use related to the home

simply being new rather than the change in building code Indeed Kotchen (2015) revisited the

question with data 10 years older and found the effect on electricity had disappeared while the

reduction in natural gas use increased Something is occurring in energy use unrelated to the code

and could be explained by residents changing their use of energy as they adapt to their new home

Residents of an energy efficient home can undermine the intent of lower energy use by using the

efficient design to heat and cool their homes with a motivation toward increased comfort at the

same energy cost rather than energy savings Our study does not have the behavioral component

found in the case of energy efficiency In our application the construction elements that make the

structure able to withstand high winds are installed when the home is built and lie ldquobehind the

wallsrdquo making it unlikely for individual preferences to alter the homes performance against the

threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC

homes due to the code or simply an artifact of new versus old construction when confronted with

a windstorm

To first address our analysis of age versus the FBC we rerun our base regression but limit

our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more

than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14

years during the highest loss year of 2004 Since this is a comparison between two adjacent

decades on either side of our cut point of year 2000 we remove age and age squared Results are

shown in Table 4-Appendix

Insert Table 4-Appendix Here

The coefficient on Post FBC is still negative highly significant with a magnitude very close to

what we saw with the entire database and the age variables This result suggests that the code

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

11

Additional Data

We have 2000 and 2010 demographic data from the decennial census at the ZIP code level

for population area (in square miles) of the ZIP median household income and housing counts

Population growth across the decade is not even so we use building permits to help estimate

intervening years Each year is interpolated from decennial data for population and total housing

counts with an allocation factor based on the number of building permits for each ZIP and each

year Building permits are collected from census by place codes so we must re-allocate to ZIP

codes To convert from place to ZIP code we use allocation factors based on 2010 housing counts

provided by MABLE a service of the Missouri Census Data Center (MABLE 2015) For

example if a municipality has two ZIP codes with 60 of the homes in one and the remaining

40 in the other MABLE would use those percentages as the allocation factors from the

municipality to its corresponding ZIP codes In unincorporated areas we use allocation factors

from county to ZIP from the same service For median household income a straight-line

interpolation method is used adjusted for changes in the consumer price index (CPI-U) to 2010

CPI data are from the Bureau of Labor Statistics

Several factors were utilized to represent the overall geographic hazard risk of a ZIP code

The distance of the centroid of the ZIP to the coast was calculated to account for the overall

distance to the coast of each ZIP code Following Dehring and Halek (2013) dummy variables

that signifies whether a ZIP code contains a coastal construction control line (CCCL) were created

(1 equals CCCL in place) to account for stricter building codes in these areas Finally following

the 2005 hurricane season there was a significant increase in the number of policies underwritten

by Citizens the state-run wind-pool and insurer of last resort (Florida Catastrophic Storm Risk

Management Center 2013) Areas with large percentages of insured policies underwritten by

12

Citizens could represent inherently higher windstorm risk We spatially matched our Florida ZIP

codes to the Florida house districts and took the percentage of Citizens policies of the number of

occupied housing units as of December 31 2011 (Florida Catastrophic Storm Risk Management

Center 2013) Given the potential for adverse selection or offloading of high risk policies by the

private market in these areas it is unclear whether higher Citizensrsquo market penetration would lead

to a positive relationship with losses due to the higher risk or a negative relationship with private

losses as many of the bad risks have been transferred to the residual wind pool

IV Econometric Methodology

Better construction limits loss from windstorms through two channels first the direct effect

of decreasing loss on homes that experience damage and second through fewer claims on better

built homes Our data from ISO is aggregated at the ZIP codedecade of construction level So a

ZIP code where all homes experienced damage would have varying levels of damage between

homes built before and after implementation of the FBC Other ZIP codes may have damage for

older homes but little to no damage for homes built post FBC Our first challenge was to use

models that would provide an estimate of the full effect of the FBC lower levels of damage plus

the effect of fewer claims then an estimate for the direct effect alone To accomplish this we

employ two models The first includes all observations even if no claims have been filed and

second a hurdle model where the first stage models the probability of experiencing a loss and the

second stage isolates only the observations where a loss has been experienced

Base Model

The regression model is a semi-log ordinary least squares (OLS) fixed effects (time and

space) model with the natural log of loss as the dependent variable The base level of observation

is ZIP codeyeardecade of construction Explanatory variables include insurance information

13

(exposures and premiums) construction type demographic data based on the ZIP code measures

of the ZIP code hazard risk (how close to the coast the ZIP code is etc) and hazard data

concerning the wind speed and duration

Our test of the FBC creates a discontinuity that must be accounted for in the model All

observations with decade of construction post 2000 are considered under the new building code

regime But that dummy variable is a function of structure age so we employ a regression

discontinuity (RD) analysis to determine the best specification to estimate the effect of the FBC

allowing for the effect that structure age has on damage Intuitively structure age should increase

loss as older homes depreciate across their life making them more vulnerable to wind storms But

the effect of structure age is more than depreciation Over time construction practices and

materials used have changed which also affect how a structure responds to the stress of a violent

wind storm Indeed after Hurricane Andrew in 1992 it was noted that inferior construction

practices of the 1970rsquos and 1980rsquos had exacerbated the losses (Fronstin and Holtmann 1994 Keith

and Rose 1994)

This suggests that the effect of age is non-linear so a model that includes age as a

polynomial would be reasonable Determining the best specification requires testing a series of

models that include age as a polynomial andor interacted with our treatment variable Post FBC

(Lee and Lemieux 2010) (Jacob and Zhu 2012) The full analysis to choose our specification is

included in the Appendix The model that provided the best tradeoff between bias and precision

based on the AIC adds age and its square with the functional form

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

where the variable definitions are given in Table 3

14

Insert Table 3 Here

A positive sign is expected for both wind variables indicating that as wind speeds increase

andor the ZIP code is exposed to high winds for an extended period of time losses will increase

Post FBC construction should decrease loss so a negative sign is expected

Hurdle Model

One problem potentially encountered in attempting to model losses is there may be a

separate process occurring in the data that determines whether a loss is realized at all which could

affect the estimate of overall losses To address this issue hurdle models are used as they divide

the analysis into two stages We use a hurdle model to find the direct effect of the FBC The first

stage models the probability that a loss occurs and the second stage models the loss using only

observations that sustained a loss The dependent variable in the first stage equals one if there was

a loss and zero otherwise This binary dependent variable is then regressed against variables that

would affect the probability that a loss occurred Its form is

(2a)

Loss or No Loss = β0 + β1 Max Wind + β2 Wind Duration + β3 Population Density

+ β4 Post FBC

We expect that both wind variables max wind speed and duration as well as population

density will increase the probability of a loss Post FBC construction however should decrease

the probability of a loss

The second stage in the hurdle model is the same as Equation 1 with the exception that

only observations with a loss are included There are 19107 observations for the second stage and

its form is

15

(2b)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Model Validity

Regression models are limited by available data to understand how the dependent variable

varies as explanatory variables change If important variables are left out of the model some bias

can be expected This omitted variable bias is a common problem encountered with econometric

models Kuminoff et al 2010 found that one of the best approaches to reducing omitted variable

bias is to employ a spatial fixed effects model To accomplish this we use individual ZIP dummy

variables as a spatial fixed effect and dummy variables for each year in our data to control for

changes that may be related to time not otherwise controlled for within our co-variates These

dummy variables will contain all across-group variation leaving the remainder of the model to

contain the within-group variation (Greene 2003)

A second challenge to the validity of our model is another common problem

heteroscedasticity For Equation 1 we use clustered standard errors at the ZIP code through Proc

GLM in SAS Our hurdle model (Eq 2a and 2b) utilizes Proc Qlim which has a separate statement

(Hetero) that we invoked to model the error variance

V Regression Results

Our first regression (Equation 1) serves as a base from which we examine the effect of

basic explanatory variables on loss The results from this regression can be found in Regression

Table 4

Insert Table 4 Here

16

The performance of our regression model is satisfactory in terms of the performance of the

explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and

the coefficient on our treatment variable Post FBC is -126 and highly significant

Overall our results show the strong effect the statewide FBC had on losses from wind

storms during this timeframe Using the results from the regression in Table 4 the coefficient on

the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses

than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the

results from a study conducted by the Insurance Institute for Business and Home Safety after

Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60

percent less likely to suffer damage at all and those that were damaged sustained 42 percent less

damage than older homes Our result of 72 percent lower damage reflects both those attributes as

our data included ZIP codeyearYOC observations that suffered damage as well as those that did

not

Our variables to measure the effect of wind hazard are wind speed and duration For both

variables we have a positive sign and each is highly significant Higher wind speed and higher

duration of high wind speeds increases damage and thus loss The remaining variables perform as

expected

Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In

the first stage variables such as Max Wind and Wind Duration significantly increase the

probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post

FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower

for homes built after new building codes were adopted In the second stage we see that our wind

variables continue to significantly increase the size of the loss and our treatment variable Post

17

FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now

lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy

we see that losses are reduced by about 47 as opposed to 72 when all observations are

includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better

construction reduces loss in two ways First it lowers claims and reduces the amount of a loss

when a claim is filedviii

Model Evaluation

To evaluate our model we used the second stage of the hurdle models and broke our data

into two groups The first group represents 90 of the data randomly selected and was used to

run the model and collect parameter estimates The second group is an out of sample control group

to test the validity of the model Parameter estimates from the first group are applied to the control

group which gave us a predicted loss for each observation in the control group that can be

compared to the actual loss for each observation in the control group We then regressed the

predicted loss from the control group against the actual loss

Insert Figure 2 Here

Figure 2 plots the predicted loss against the actual loss and provides the fitted line with

95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears

to do a good job of predicting most losses

Robustness of Table 4 Base Model Results

To test the robustness of our results we run three separate analyses 1) We first run a

regression with few co-variates 2) As wind design speeds have been used as a proxy for building

code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss

18

analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking

Florida in 2004 and 2005

Regressions using Few Co-Variates

Additional relevant co-variates add precision to a model But the value of the focus

variable should be apparent with a smaller set So we ran a model with only insured customer

based variables EHY and paid premiums leaving out all other demographic and hazard related

variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and

significance

Regressions Using Design Speed

The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads

for Buildings and Other Structures published by the American Society of Civil Engineers and the

Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds

for most regions of the United States and their territories These reference wind speeds are used in

calculations to determine design wind pressures for the primary structure of a building and the

cladding and components attached to a building These calculations take into account the building

geometry the importance of a building the exposuresurrounding terrain and other parameters that

influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design

speeds as a proxy for building code strength and we similarly add this as an additional control in

our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps

were provided by the Applied Technology Council (ATC) Although this version of the wind

speed map was not utilized during the period under consideration the relative values in general

between two locations would be the sameix

19

We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded

form from the ATC and spatially joined the values to our Florida ZIP codes We then further

categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat

4 and Cat 5 hurricane levels

Insert Table 5 Here

The regression adds two dummy variables first for ZIP codes whose design speed exceeds

the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed

reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables

for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that

communities in higher wind zones may take further measures in local codes However the effect

is not significant Notably our variable for Post FBC construction maintains its negative sign

magnitude and significance

Regressions Limited to 2004 and 2005

Our next regression also shown in Table 5 is limited to observations that occurred during

the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and

2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not

surprisingly the magnitude on wind speed increases while maintaining its significance and the

magnitude on age does the same But the effect of the FBC remains the same a 72 reduction

Summary of Results on the FBC

We have collected a comprehensive set of data on insured paid losses from 2001 to 2010

windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity

model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of

the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction

20

in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to

determine if the policy is one that is cost effective

VI Benefit and Costs of the FBC

Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a

number of cases the economic effectiveness of the improved building codes has not been as well

documented especially from a statewide implementation perspective The multi-hazard

mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose

et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar

spent) was appropriate for process activity grant spending related to improved building codes

However this information was gathered from a limited number of studies (mainly earthquake

oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in

generating it and the ratio provided due to improvement would not be the same as those for

adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted

an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for

ten related counties and determined that the net present value of the revisions was $7 billion or

benefit-cost ratio greater than 1 Importantly though this study did not have access to actual

building code damage reduction data to utilize in the analysis In 2002 Applied Research

Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC

for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how

the three types of FBC built houses would perform in probabilistic hurricane scenarios compared

to the same houses built under the previous code Given the probabilistic nature of the analysis

average annual losses were generated that demonstrated post-FBC housing having loss reductions

54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were

21

then compared to their estimated cost impacts of the FBC for these housing types with at least

break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and

above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al

(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss

reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results

ranged from 041 to 183 for the retrofit mitigation activities to existing housing

We propose a BCA that differs from earlier work in several important ways First we use

realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of

how much loss can be reduced by the FBC Second our loss data spans 10 years which include a

combination of major hurricanes and smaller wind storms

BenefitCost Methodology

The elements of a BCA requires three inputs 1) an estimate of the added cost to implement

the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related

storms from our realized ISO loss data and then from a statewide catastrophe model estimate and

3) the percentage of expected loss that will be mitigated due to implementation of the FBC We

first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis

but use the full reduction in loss which includes the value of reduced claims Finally our ISO data

is paid losses and does not include deductibles so we add an estimate for deductibles

Additional Cost

In their 2002 benefit-cost comparison study of the enactment of the FBC for three related

housing types three actual sample homes were built to the FBC to evaluate the change in

construction costs (ARA 2002) For the purposes of code implementation the state was divided

into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region

22

(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account

for cost differences between the two regions

In the WBDR an added requirement is impact protection to windows and doors to reduce

damage from flying debris Along the coast and much of South Florida is classified as the WBDR

The N-WBDR is mainly classified in the interior of the state where impact protection is not

required Importantly the study provided a range of added costs for the N-WBDR and the WBDR

Three counties in South Florida Dade Broward and Monroe were under the South Florida

Building Code (SFBC) prior to the implementation of the FBC According to the ARA study

(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges

of incremental cost per square foot for the N-WBDR and WBDR along with the percent of

residential units that reside in each area This allows a calculation of a weighted average added

cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost

per square foot of $166 The cost compares favorably with a similar building code enhancement

adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in

2013 Consulting engineers and the Moore Association of Homebuilders estimated the code

enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is

1960 square feet which means that on average the FBC increases construction cost by $3254 per

structurexii

Insert Table 6 Here

Benefit of the FBC

Benefits stemming from the FBC are the expected reduction in losses from windstorms during

the life of the home We first find an average annual loss (AAL) use that number to estimate

losses for the next 50 years and then find the present value of those losses in 2010 Here we are

23

assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the

next 50 years A wealth of literature suggests the potential for changes to hurricane activity over

the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty

on future changes in wind hazard on the scale of a single state we choose to assume a stationary

climate

Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes

built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our

data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of

$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year

Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses

per structure of $21474

Finally we use parameter estimates from our regression for the Post FBC dummy variable

(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC

The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post

FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what

would be a reduction of $10093 from the projected $21474 in future losses

Insert Table 7 Here

BenefitCost Analysis

Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio

of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the

statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an

economically effective public policy following from our ISO loss data and results

Insert Table 8 Here

24

Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years

This relatively short timeframe makes it difficult to truly approximate an AAL as would be

provided from a probabilistically based catastrophe model that generates an AAL from thousands

of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state

of Florida to estimate an average annual wind loss for all residential properties in Florida of

approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL

estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion

with deductibles) Using this aggregate AAL and the number of residential units in Florida based

on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses

net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and

an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as

before derived from our regression results 47 we find $7710 of reduced loss from the projected

$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added

$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective

building code public policy

We run two additional analyses on our BCA results Our estimate of expected loss

reduction comes from the second stage of the hurdle model This is an estimate of the direct loss

reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the

number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests

as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which

includes the reduced magnitude of loss from affected homes and the reduction in claims for Post

FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table

8) However a 72 loss reduction seems too dramatic an expectation when planning so far in

25

advance For that reason we offer a third level of expected loss reduction of 60 which is the

midpoint between our two loss reduction estimates This estimate captures the expected direct loss

reduction suggested by the second stage of our hurdle model but still recognizes that in some areas

the number of claims is reduced by the FBC This appears to be a reasonable assumption and

provides a BCA ratio of 396 for the ISO sample and 302 for all residential

The ISO data are net of deductibles so our BCA thus far only includes losses compensated by

the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion

also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the

ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all

loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA

values from a low of 237 to a high of 793

Payback of the FBC

Finally we use our BCA results to calculate a payback period for the investment of stronger

codes To convert our BCA ratio to a payback period we simply divide our 50-year planning

horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss

and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years

This is important for gauging potential political support or non-support for enactment of the new

codes Payback periods that approach the typical mortgage term 30 years would in theory be

difficult to achieve and that is not what our analysis indicates for the FBC

VI - Concluding Comments

In the aftermath of Hurricane Andrew which had exposed not only poor building

construction but also poor building code enforcement the state of Florida enacted statewide

building code changes that wrested away building code adoption control from individual localities

26

With full implementation of the statewide building code associated expectations are that

windstorm losses from extreme events such as hurricanes should be reduced moving forward

There have been a few studies confirming these expectations following the 2004 and 2005

hurricane season In this article we further verify and quantify these findings and expand the

existing building code risk reduction research in several important ways

Overall we empirically test the statewide implementation of a building code in reducing

wind related damages in Florida controlling for other relevant wind hazard exposure and

vulnerability characteristics from a traditional risk assessment perspective Our results show the

strong effect the statewide FBC had on losses from wind storms during this timeframe From the

treatment variable that measures implementation of the statewide codes the post 2000 year of

construction losses are shown to be reduced by as much as 72 percent consistent with other

previous findings

Finally we have conducted a BCA of the FBC to determine if expected benefits exceed

the cost of implementation Using a direct estimate for mitigated losses and an estimate that

includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public

policy from an economic perspective This result is close to that recommended by the multi-hazard

mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a

statewide BCA Importantly this information is essential in generating political and consumer

support for such building code public policy implementation

For example the economic effectiveness results shown here have implications for ongoing

policy discussions about reforming building codes from a national US perspective Moore OK

independently adopted enhanced building codes after its third violent tornado in 14 years killed 24

including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)

27

Construction practices in North Texas were brought under scrutiny after the December 2015

tornado revealed inadequate construction including an elementary school whose exterior walls

failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House

announced initiatives to increase community resilience with building codes as a major component

of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748

and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better

construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397

would provide tax credits for owners andor contractors who use techniques designed for resiliency

in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one

recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to

encourage states to use current building codes (Vaughn and Turner 2014)

Future research in the BCA of the FBC will further inform the public policy debate on

enhanced building codes The issue has national implications as other states find that wind hazards

impact them as well We have sufficient wind data to examine how the BCA performs under

different wind hazards Additionally it will be important to consider how future economic

development affects the BCA as well as varying climate change scenarios As the FBC is

mandatory for all new construction a statewide analysis was appropriate But individual

homeowners in older homes can invest in the retrofit of their home and qualify for discounts on

their homeowners insurance This topic is deserving of a robust analysis Although our BCA is

statewide regions within the state will likely have a spectrum of results For instance the ARA

2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their

analysis did not use realized loss data so confirmation of how the BCA varies between those

regions would be an important contribution Finally our sensitivity analysis was limited to two

28

variables reduction in future loss and the inclusion of deductibles Additional work will highlight

other variables that could modify the results

29

Appendix

We use this appendix to conduct more detailed analysis on several topics First selection

of the model specification using a regression discontinuity approach Second we provide an in

depth examination of the relationship between structure age and losses Third we perform a

Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an

alternative specification to see if our RD results are similar followed by regressions to examine

the year to year consistency of our Post FBC result Next we run a regression on claims to verify

the difference between our direct reduction result and our full reduction result Finally we perform

a regression on homes built to the SFBC which had adopted enhanced building codes in advance

of the FBC to assess the effect of earlier adoption of enhanced construction

Regression Discontinuity

Regression Discontinuity (RD) applies when an observation receives a treatment in our case

homes built under the FBC based on a rating variable in our case age of the structure at the year

of observation So for observations in 2005 homes built post 2000 received the treatment

adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify

how observations on either side of the implementation of the FBC (2000) perform in suffering loss

from windstorms The treatment variable is a function of the age of the home and age affects loss

in ways not related to the FBC such as depreciation and differences in materials and construction

practices across time To account for both the effect of age on loss as well as the implementation

of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment

The data we have from ISO is aggregated loss data by zip code and decade of construction So

we cannot get an annualized age To approach a true age we set the year built for each decade of

construction at the beginning of the decade then subtract that from the year of each observation to

get an approximate agexiv

30

To find the best specification we began with a simpler model which used a series of

categorical variables for each decade of construction to examine the effect of the code compared

to the omitted decade This method would approximate the changes in materials and construction

practices but was less effective in controlling for depreciation But it would give us a first

approximation of the code effect that we used as a benchmark when testing the best RD

specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970

with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When

the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When

either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81

A rough approximation of the codersquos effect from this approach would suggest a reduction in the

mid 70 percent range

Insert Table 1 ndash Appendix Here

Next we used a standard procedure with RD to search for the best way to include the rating

variable This process creates specifications that include age in increasing polynomials and

interacted with the treatment variable The goal is to find the specification with the lowest AIC

that comes close to the benchmark value of the treatment variable

Insert Tables 2 and 3 ndash Appendix Here

We did this first with regressions that limited the co-variates then with our full model In both

sets AIC reaches a minimum on the specification with age and age squared The interaction model

after that increases the AIC then the AIC goes down again with a cubed model and its interaction

model with the overall lowest AIC found on the cubed interaction model But we chose not to

use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order

models the magnitude of the treatment variable in the models with just polynomials compared to

31

the corresponding interaction models were close with the interaction models providing a larger

magnitude When the cubed models were added the magnitude jumped where the polynomial

cubed model went down well below our benchmark and the interaction model went up above our

benchmark We felt this made use of the cubed model inappropriate So we now need to choose

between the squared model and the one with the interaction terms The squared model (Model 4)

had a lower AIC and the interaction variables on the interaction model (Model 5) were not

significant so we chose to use the squared model without the interaction term This model gave a

magnitude for the treatment variable of a 72 reduction somewhat lower than the expected

magnitude in the mid 70rsquos percent The general form of the model is

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1

of the observations on either end of our data sorted by loss Our treatment variable Post FBC

remains highly significant with a coefficient value of -117 which compares favorably to our

coefficient value of -126 when the entire sample is used

Structure Age and Wind Losses

Our study is similar to recent studies on the effect of energy efficiency building codes

adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that

better insulation caulking and more efficient HVAC systems would result in lower energy

consumption But the change in energy consumption is less than engineering estimates projected

Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for

homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study

32

may be confounding age with vintage and found a decrease in energy use related to the home

simply being new rather than the change in building code Indeed Kotchen (2015) revisited the

question with data 10 years older and found the effect on electricity had disappeared while the

reduction in natural gas use increased Something is occurring in energy use unrelated to the code

and could be explained by residents changing their use of energy as they adapt to their new home

Residents of an energy efficient home can undermine the intent of lower energy use by using the

efficient design to heat and cool their homes with a motivation toward increased comfort at the

same energy cost rather than energy savings Our study does not have the behavioral component

found in the case of energy efficiency In our application the construction elements that make the

structure able to withstand high winds are installed when the home is built and lie ldquobehind the

wallsrdquo making it unlikely for individual preferences to alter the homes performance against the

threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC

homes due to the code or simply an artifact of new versus old construction when confronted with

a windstorm

To first address our analysis of age versus the FBC we rerun our base regression but limit

our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more

than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14

years during the highest loss year of 2004 Since this is a comparison between two adjacent

decades on either side of our cut point of year 2000 we remove age and age squared Results are

shown in Table 4-Appendix

Insert Table 4-Appendix Here

The coefficient on Post FBC is still negative highly significant with a magnitude very close to

what we saw with the entire database and the age variables This result suggests that the code

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

12

Citizens could represent inherently higher windstorm risk We spatially matched our Florida ZIP

codes to the Florida house districts and took the percentage of Citizens policies of the number of

occupied housing units as of December 31 2011 (Florida Catastrophic Storm Risk Management

Center 2013) Given the potential for adverse selection or offloading of high risk policies by the

private market in these areas it is unclear whether higher Citizensrsquo market penetration would lead

to a positive relationship with losses due to the higher risk or a negative relationship with private

losses as many of the bad risks have been transferred to the residual wind pool

IV Econometric Methodology

Better construction limits loss from windstorms through two channels first the direct effect

of decreasing loss on homes that experience damage and second through fewer claims on better

built homes Our data from ISO is aggregated at the ZIP codedecade of construction level So a

ZIP code where all homes experienced damage would have varying levels of damage between

homes built before and after implementation of the FBC Other ZIP codes may have damage for

older homes but little to no damage for homes built post FBC Our first challenge was to use

models that would provide an estimate of the full effect of the FBC lower levels of damage plus

the effect of fewer claims then an estimate for the direct effect alone To accomplish this we

employ two models The first includes all observations even if no claims have been filed and

second a hurdle model where the first stage models the probability of experiencing a loss and the

second stage isolates only the observations where a loss has been experienced

Base Model

The regression model is a semi-log ordinary least squares (OLS) fixed effects (time and

space) model with the natural log of loss as the dependent variable The base level of observation

is ZIP codeyeardecade of construction Explanatory variables include insurance information

13

(exposures and premiums) construction type demographic data based on the ZIP code measures

of the ZIP code hazard risk (how close to the coast the ZIP code is etc) and hazard data

concerning the wind speed and duration

Our test of the FBC creates a discontinuity that must be accounted for in the model All

observations with decade of construction post 2000 are considered under the new building code

regime But that dummy variable is a function of structure age so we employ a regression

discontinuity (RD) analysis to determine the best specification to estimate the effect of the FBC

allowing for the effect that structure age has on damage Intuitively structure age should increase

loss as older homes depreciate across their life making them more vulnerable to wind storms But

the effect of structure age is more than depreciation Over time construction practices and

materials used have changed which also affect how a structure responds to the stress of a violent

wind storm Indeed after Hurricane Andrew in 1992 it was noted that inferior construction

practices of the 1970rsquos and 1980rsquos had exacerbated the losses (Fronstin and Holtmann 1994 Keith

and Rose 1994)

This suggests that the effect of age is non-linear so a model that includes age as a

polynomial would be reasonable Determining the best specification requires testing a series of

models that include age as a polynomial andor interacted with our treatment variable Post FBC

(Lee and Lemieux 2010) (Jacob and Zhu 2012) The full analysis to choose our specification is

included in the Appendix The model that provided the best tradeoff between bias and precision

based on the AIC adds age and its square with the functional form

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

where the variable definitions are given in Table 3

14

Insert Table 3 Here

A positive sign is expected for both wind variables indicating that as wind speeds increase

andor the ZIP code is exposed to high winds for an extended period of time losses will increase

Post FBC construction should decrease loss so a negative sign is expected

Hurdle Model

One problem potentially encountered in attempting to model losses is there may be a

separate process occurring in the data that determines whether a loss is realized at all which could

affect the estimate of overall losses To address this issue hurdle models are used as they divide

the analysis into two stages We use a hurdle model to find the direct effect of the FBC The first

stage models the probability that a loss occurs and the second stage models the loss using only

observations that sustained a loss The dependent variable in the first stage equals one if there was

a loss and zero otherwise This binary dependent variable is then regressed against variables that

would affect the probability that a loss occurred Its form is

(2a)

Loss or No Loss = β0 + β1 Max Wind + β2 Wind Duration + β3 Population Density

+ β4 Post FBC

We expect that both wind variables max wind speed and duration as well as population

density will increase the probability of a loss Post FBC construction however should decrease

the probability of a loss

The second stage in the hurdle model is the same as Equation 1 with the exception that

only observations with a loss are included There are 19107 observations for the second stage and

its form is

15

(2b)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Model Validity

Regression models are limited by available data to understand how the dependent variable

varies as explanatory variables change If important variables are left out of the model some bias

can be expected This omitted variable bias is a common problem encountered with econometric

models Kuminoff et al 2010 found that one of the best approaches to reducing omitted variable

bias is to employ a spatial fixed effects model To accomplish this we use individual ZIP dummy

variables as a spatial fixed effect and dummy variables for each year in our data to control for

changes that may be related to time not otherwise controlled for within our co-variates These

dummy variables will contain all across-group variation leaving the remainder of the model to

contain the within-group variation (Greene 2003)

A second challenge to the validity of our model is another common problem

heteroscedasticity For Equation 1 we use clustered standard errors at the ZIP code through Proc

GLM in SAS Our hurdle model (Eq 2a and 2b) utilizes Proc Qlim which has a separate statement

(Hetero) that we invoked to model the error variance

V Regression Results

Our first regression (Equation 1) serves as a base from which we examine the effect of

basic explanatory variables on loss The results from this regression can be found in Regression

Table 4

Insert Table 4 Here

16

The performance of our regression model is satisfactory in terms of the performance of the

explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and

the coefficient on our treatment variable Post FBC is -126 and highly significant

Overall our results show the strong effect the statewide FBC had on losses from wind

storms during this timeframe Using the results from the regression in Table 4 the coefficient on

the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses

than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the

results from a study conducted by the Insurance Institute for Business and Home Safety after

Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60

percent less likely to suffer damage at all and those that were damaged sustained 42 percent less

damage than older homes Our result of 72 percent lower damage reflects both those attributes as

our data included ZIP codeyearYOC observations that suffered damage as well as those that did

not

Our variables to measure the effect of wind hazard are wind speed and duration For both

variables we have a positive sign and each is highly significant Higher wind speed and higher

duration of high wind speeds increases damage and thus loss The remaining variables perform as

expected

Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In

the first stage variables such as Max Wind and Wind Duration significantly increase the

probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post

FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower

for homes built after new building codes were adopted In the second stage we see that our wind

variables continue to significantly increase the size of the loss and our treatment variable Post

17

FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now

lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy

we see that losses are reduced by about 47 as opposed to 72 when all observations are

includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better

construction reduces loss in two ways First it lowers claims and reduces the amount of a loss

when a claim is filedviii

Model Evaluation

To evaluate our model we used the second stage of the hurdle models and broke our data

into two groups The first group represents 90 of the data randomly selected and was used to

run the model and collect parameter estimates The second group is an out of sample control group

to test the validity of the model Parameter estimates from the first group are applied to the control

group which gave us a predicted loss for each observation in the control group that can be

compared to the actual loss for each observation in the control group We then regressed the

predicted loss from the control group against the actual loss

Insert Figure 2 Here

Figure 2 plots the predicted loss against the actual loss and provides the fitted line with

95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears

to do a good job of predicting most losses

Robustness of Table 4 Base Model Results

To test the robustness of our results we run three separate analyses 1) We first run a

regression with few co-variates 2) As wind design speeds have been used as a proxy for building

code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss

18

analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking

Florida in 2004 and 2005

Regressions using Few Co-Variates

Additional relevant co-variates add precision to a model But the value of the focus

variable should be apparent with a smaller set So we ran a model with only insured customer

based variables EHY and paid premiums leaving out all other demographic and hazard related

variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and

significance

Regressions Using Design Speed

The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads

for Buildings and Other Structures published by the American Society of Civil Engineers and the

Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds

for most regions of the United States and their territories These reference wind speeds are used in

calculations to determine design wind pressures for the primary structure of a building and the

cladding and components attached to a building These calculations take into account the building

geometry the importance of a building the exposuresurrounding terrain and other parameters that

influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design

speeds as a proxy for building code strength and we similarly add this as an additional control in

our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps

were provided by the Applied Technology Council (ATC) Although this version of the wind

speed map was not utilized during the period under consideration the relative values in general

between two locations would be the sameix

19

We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded

form from the ATC and spatially joined the values to our Florida ZIP codes We then further

categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat

4 and Cat 5 hurricane levels

Insert Table 5 Here

The regression adds two dummy variables first for ZIP codes whose design speed exceeds

the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed

reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables

for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that

communities in higher wind zones may take further measures in local codes However the effect

is not significant Notably our variable for Post FBC construction maintains its negative sign

magnitude and significance

Regressions Limited to 2004 and 2005

Our next regression also shown in Table 5 is limited to observations that occurred during

the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and

2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not

surprisingly the magnitude on wind speed increases while maintaining its significance and the

magnitude on age does the same But the effect of the FBC remains the same a 72 reduction

Summary of Results on the FBC

We have collected a comprehensive set of data on insured paid losses from 2001 to 2010

windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity

model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of

the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction

20

in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to

determine if the policy is one that is cost effective

VI Benefit and Costs of the FBC

Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a

number of cases the economic effectiveness of the improved building codes has not been as well

documented especially from a statewide implementation perspective The multi-hazard

mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose

et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar

spent) was appropriate for process activity grant spending related to improved building codes

However this information was gathered from a limited number of studies (mainly earthquake

oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in

generating it and the ratio provided due to improvement would not be the same as those for

adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted

an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for

ten related counties and determined that the net present value of the revisions was $7 billion or

benefit-cost ratio greater than 1 Importantly though this study did not have access to actual

building code damage reduction data to utilize in the analysis In 2002 Applied Research

Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC

for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how

the three types of FBC built houses would perform in probabilistic hurricane scenarios compared

to the same houses built under the previous code Given the probabilistic nature of the analysis

average annual losses were generated that demonstrated post-FBC housing having loss reductions

54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were

21

then compared to their estimated cost impacts of the FBC for these housing types with at least

break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and

above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al

(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss

reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results

ranged from 041 to 183 for the retrofit mitigation activities to existing housing

We propose a BCA that differs from earlier work in several important ways First we use

realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of

how much loss can be reduced by the FBC Second our loss data spans 10 years which include a

combination of major hurricanes and smaller wind storms

BenefitCost Methodology

The elements of a BCA requires three inputs 1) an estimate of the added cost to implement

the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related

storms from our realized ISO loss data and then from a statewide catastrophe model estimate and

3) the percentage of expected loss that will be mitigated due to implementation of the FBC We

first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis

but use the full reduction in loss which includes the value of reduced claims Finally our ISO data

is paid losses and does not include deductibles so we add an estimate for deductibles

Additional Cost

In their 2002 benefit-cost comparison study of the enactment of the FBC for three related

housing types three actual sample homes were built to the FBC to evaluate the change in

construction costs (ARA 2002) For the purposes of code implementation the state was divided

into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region

22

(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account

for cost differences between the two regions

In the WBDR an added requirement is impact protection to windows and doors to reduce

damage from flying debris Along the coast and much of South Florida is classified as the WBDR

The N-WBDR is mainly classified in the interior of the state where impact protection is not

required Importantly the study provided a range of added costs for the N-WBDR and the WBDR

Three counties in South Florida Dade Broward and Monroe were under the South Florida

Building Code (SFBC) prior to the implementation of the FBC According to the ARA study

(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges

of incremental cost per square foot for the N-WBDR and WBDR along with the percent of

residential units that reside in each area This allows a calculation of a weighted average added

cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost

per square foot of $166 The cost compares favorably with a similar building code enhancement

adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in

2013 Consulting engineers and the Moore Association of Homebuilders estimated the code

enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is

1960 square feet which means that on average the FBC increases construction cost by $3254 per

structurexii

Insert Table 6 Here

Benefit of the FBC

Benefits stemming from the FBC are the expected reduction in losses from windstorms during

the life of the home We first find an average annual loss (AAL) use that number to estimate

losses for the next 50 years and then find the present value of those losses in 2010 Here we are

23

assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the

next 50 years A wealth of literature suggests the potential for changes to hurricane activity over

the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty

on future changes in wind hazard on the scale of a single state we choose to assume a stationary

climate

Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes

built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our

data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of

$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year

Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses

per structure of $21474

Finally we use parameter estimates from our regression for the Post FBC dummy variable

(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC

The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post

FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what

would be a reduction of $10093 from the projected $21474 in future losses

Insert Table 7 Here

BenefitCost Analysis

Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio

of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the

statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an

economically effective public policy following from our ISO loss data and results

Insert Table 8 Here

24

Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years

This relatively short timeframe makes it difficult to truly approximate an AAL as would be

provided from a probabilistically based catastrophe model that generates an AAL from thousands

of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state

of Florida to estimate an average annual wind loss for all residential properties in Florida of

approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL

estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion

with deductibles) Using this aggregate AAL and the number of residential units in Florida based

on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses

net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and

an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as

before derived from our regression results 47 we find $7710 of reduced loss from the projected

$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added

$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective

building code public policy

We run two additional analyses on our BCA results Our estimate of expected loss

reduction comes from the second stage of the hurdle model This is an estimate of the direct loss

reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the

number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests

as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which

includes the reduced magnitude of loss from affected homes and the reduction in claims for Post

FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table

8) However a 72 loss reduction seems too dramatic an expectation when planning so far in

25

advance For that reason we offer a third level of expected loss reduction of 60 which is the

midpoint between our two loss reduction estimates This estimate captures the expected direct loss

reduction suggested by the second stage of our hurdle model but still recognizes that in some areas

the number of claims is reduced by the FBC This appears to be a reasonable assumption and

provides a BCA ratio of 396 for the ISO sample and 302 for all residential

The ISO data are net of deductibles so our BCA thus far only includes losses compensated by

the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion

also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the

ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all

loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA

values from a low of 237 to a high of 793

Payback of the FBC

Finally we use our BCA results to calculate a payback period for the investment of stronger

codes To convert our BCA ratio to a payback period we simply divide our 50-year planning

horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss

and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years

This is important for gauging potential political support or non-support for enactment of the new

codes Payback periods that approach the typical mortgage term 30 years would in theory be

difficult to achieve and that is not what our analysis indicates for the FBC

VI - Concluding Comments

In the aftermath of Hurricane Andrew which had exposed not only poor building

construction but also poor building code enforcement the state of Florida enacted statewide

building code changes that wrested away building code adoption control from individual localities

26

With full implementation of the statewide building code associated expectations are that

windstorm losses from extreme events such as hurricanes should be reduced moving forward

There have been a few studies confirming these expectations following the 2004 and 2005

hurricane season In this article we further verify and quantify these findings and expand the

existing building code risk reduction research in several important ways

Overall we empirically test the statewide implementation of a building code in reducing

wind related damages in Florida controlling for other relevant wind hazard exposure and

vulnerability characteristics from a traditional risk assessment perspective Our results show the

strong effect the statewide FBC had on losses from wind storms during this timeframe From the

treatment variable that measures implementation of the statewide codes the post 2000 year of

construction losses are shown to be reduced by as much as 72 percent consistent with other

previous findings

Finally we have conducted a BCA of the FBC to determine if expected benefits exceed

the cost of implementation Using a direct estimate for mitigated losses and an estimate that

includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public

policy from an economic perspective This result is close to that recommended by the multi-hazard

mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a

statewide BCA Importantly this information is essential in generating political and consumer

support for such building code public policy implementation

For example the economic effectiveness results shown here have implications for ongoing

policy discussions about reforming building codes from a national US perspective Moore OK

independently adopted enhanced building codes after its third violent tornado in 14 years killed 24

including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)

27

Construction practices in North Texas were brought under scrutiny after the December 2015

tornado revealed inadequate construction including an elementary school whose exterior walls

failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House

announced initiatives to increase community resilience with building codes as a major component

of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748

and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better

construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397

would provide tax credits for owners andor contractors who use techniques designed for resiliency

in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one

recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to

encourage states to use current building codes (Vaughn and Turner 2014)

Future research in the BCA of the FBC will further inform the public policy debate on

enhanced building codes The issue has national implications as other states find that wind hazards

impact them as well We have sufficient wind data to examine how the BCA performs under

different wind hazards Additionally it will be important to consider how future economic

development affects the BCA as well as varying climate change scenarios As the FBC is

mandatory for all new construction a statewide analysis was appropriate But individual

homeowners in older homes can invest in the retrofit of their home and qualify for discounts on

their homeowners insurance This topic is deserving of a robust analysis Although our BCA is

statewide regions within the state will likely have a spectrum of results For instance the ARA

2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their

analysis did not use realized loss data so confirmation of how the BCA varies between those

regions would be an important contribution Finally our sensitivity analysis was limited to two

28

variables reduction in future loss and the inclusion of deductibles Additional work will highlight

other variables that could modify the results

29

Appendix

We use this appendix to conduct more detailed analysis on several topics First selection

of the model specification using a regression discontinuity approach Second we provide an in

depth examination of the relationship between structure age and losses Third we perform a

Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an

alternative specification to see if our RD results are similar followed by regressions to examine

the year to year consistency of our Post FBC result Next we run a regression on claims to verify

the difference between our direct reduction result and our full reduction result Finally we perform

a regression on homes built to the SFBC which had adopted enhanced building codes in advance

of the FBC to assess the effect of earlier adoption of enhanced construction

Regression Discontinuity

Regression Discontinuity (RD) applies when an observation receives a treatment in our case

homes built under the FBC based on a rating variable in our case age of the structure at the year

of observation So for observations in 2005 homes built post 2000 received the treatment

adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify

how observations on either side of the implementation of the FBC (2000) perform in suffering loss

from windstorms The treatment variable is a function of the age of the home and age affects loss

in ways not related to the FBC such as depreciation and differences in materials and construction

practices across time To account for both the effect of age on loss as well as the implementation

of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment

The data we have from ISO is aggregated loss data by zip code and decade of construction So

we cannot get an annualized age To approach a true age we set the year built for each decade of

construction at the beginning of the decade then subtract that from the year of each observation to

get an approximate agexiv

30

To find the best specification we began with a simpler model which used a series of

categorical variables for each decade of construction to examine the effect of the code compared

to the omitted decade This method would approximate the changes in materials and construction

practices but was less effective in controlling for depreciation But it would give us a first

approximation of the code effect that we used as a benchmark when testing the best RD

specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970

with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When

the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When

either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81

A rough approximation of the codersquos effect from this approach would suggest a reduction in the

mid 70 percent range

Insert Table 1 ndash Appendix Here

Next we used a standard procedure with RD to search for the best way to include the rating

variable This process creates specifications that include age in increasing polynomials and

interacted with the treatment variable The goal is to find the specification with the lowest AIC

that comes close to the benchmark value of the treatment variable

Insert Tables 2 and 3 ndash Appendix Here

We did this first with regressions that limited the co-variates then with our full model In both

sets AIC reaches a minimum on the specification with age and age squared The interaction model

after that increases the AIC then the AIC goes down again with a cubed model and its interaction

model with the overall lowest AIC found on the cubed interaction model But we chose not to

use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order

models the magnitude of the treatment variable in the models with just polynomials compared to

31

the corresponding interaction models were close with the interaction models providing a larger

magnitude When the cubed models were added the magnitude jumped where the polynomial

cubed model went down well below our benchmark and the interaction model went up above our

benchmark We felt this made use of the cubed model inappropriate So we now need to choose

between the squared model and the one with the interaction terms The squared model (Model 4)

had a lower AIC and the interaction variables on the interaction model (Model 5) were not

significant so we chose to use the squared model without the interaction term This model gave a

magnitude for the treatment variable of a 72 reduction somewhat lower than the expected

magnitude in the mid 70rsquos percent The general form of the model is

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1

of the observations on either end of our data sorted by loss Our treatment variable Post FBC

remains highly significant with a coefficient value of -117 which compares favorably to our

coefficient value of -126 when the entire sample is used

Structure Age and Wind Losses

Our study is similar to recent studies on the effect of energy efficiency building codes

adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that

better insulation caulking and more efficient HVAC systems would result in lower energy

consumption But the change in energy consumption is less than engineering estimates projected

Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for

homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study

32

may be confounding age with vintage and found a decrease in energy use related to the home

simply being new rather than the change in building code Indeed Kotchen (2015) revisited the

question with data 10 years older and found the effect on electricity had disappeared while the

reduction in natural gas use increased Something is occurring in energy use unrelated to the code

and could be explained by residents changing their use of energy as they adapt to their new home

Residents of an energy efficient home can undermine the intent of lower energy use by using the

efficient design to heat and cool their homes with a motivation toward increased comfort at the

same energy cost rather than energy savings Our study does not have the behavioral component

found in the case of energy efficiency In our application the construction elements that make the

structure able to withstand high winds are installed when the home is built and lie ldquobehind the

wallsrdquo making it unlikely for individual preferences to alter the homes performance against the

threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC

homes due to the code or simply an artifact of new versus old construction when confronted with

a windstorm

To first address our analysis of age versus the FBC we rerun our base regression but limit

our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more

than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14

years during the highest loss year of 2004 Since this is a comparison between two adjacent

decades on either side of our cut point of year 2000 we remove age and age squared Results are

shown in Table 4-Appendix

Insert Table 4-Appendix Here

The coefficient on Post FBC is still negative highly significant with a magnitude very close to

what we saw with the entire database and the age variables This result suggests that the code

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

13

(exposures and premiums) construction type demographic data based on the ZIP code measures

of the ZIP code hazard risk (how close to the coast the ZIP code is etc) and hazard data

concerning the wind speed and duration

Our test of the FBC creates a discontinuity that must be accounted for in the model All

observations with decade of construction post 2000 are considered under the new building code

regime But that dummy variable is a function of structure age so we employ a regression

discontinuity (RD) analysis to determine the best specification to estimate the effect of the FBC

allowing for the effect that structure age has on damage Intuitively structure age should increase

loss as older homes depreciate across their life making them more vulnerable to wind storms But

the effect of structure age is more than depreciation Over time construction practices and

materials used have changed which also affect how a structure responds to the stress of a violent

wind storm Indeed after Hurricane Andrew in 1992 it was noted that inferior construction

practices of the 1970rsquos and 1980rsquos had exacerbated the losses (Fronstin and Holtmann 1994 Keith

and Rose 1994)

This suggests that the effect of age is non-linear so a model that includes age as a

polynomial would be reasonable Determining the best specification requires testing a series of

models that include age as a polynomial andor interacted with our treatment variable Post FBC

(Lee and Lemieux 2010) (Jacob and Zhu 2012) The full analysis to choose our specification is

included in the Appendix The model that provided the best tradeoff between bias and precision

based on the AIC adds age and its square with the functional form

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

where the variable definitions are given in Table 3

14

Insert Table 3 Here

A positive sign is expected for both wind variables indicating that as wind speeds increase

andor the ZIP code is exposed to high winds for an extended period of time losses will increase

Post FBC construction should decrease loss so a negative sign is expected

Hurdle Model

One problem potentially encountered in attempting to model losses is there may be a

separate process occurring in the data that determines whether a loss is realized at all which could

affect the estimate of overall losses To address this issue hurdle models are used as they divide

the analysis into two stages We use a hurdle model to find the direct effect of the FBC The first

stage models the probability that a loss occurs and the second stage models the loss using only

observations that sustained a loss The dependent variable in the first stage equals one if there was

a loss and zero otherwise This binary dependent variable is then regressed against variables that

would affect the probability that a loss occurred Its form is

(2a)

Loss or No Loss = β0 + β1 Max Wind + β2 Wind Duration + β3 Population Density

+ β4 Post FBC

We expect that both wind variables max wind speed and duration as well as population

density will increase the probability of a loss Post FBC construction however should decrease

the probability of a loss

The second stage in the hurdle model is the same as Equation 1 with the exception that

only observations with a loss are included There are 19107 observations for the second stage and

its form is

15

(2b)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Model Validity

Regression models are limited by available data to understand how the dependent variable

varies as explanatory variables change If important variables are left out of the model some bias

can be expected This omitted variable bias is a common problem encountered with econometric

models Kuminoff et al 2010 found that one of the best approaches to reducing omitted variable

bias is to employ a spatial fixed effects model To accomplish this we use individual ZIP dummy

variables as a spatial fixed effect and dummy variables for each year in our data to control for

changes that may be related to time not otherwise controlled for within our co-variates These

dummy variables will contain all across-group variation leaving the remainder of the model to

contain the within-group variation (Greene 2003)

A second challenge to the validity of our model is another common problem

heteroscedasticity For Equation 1 we use clustered standard errors at the ZIP code through Proc

GLM in SAS Our hurdle model (Eq 2a and 2b) utilizes Proc Qlim which has a separate statement

(Hetero) that we invoked to model the error variance

V Regression Results

Our first regression (Equation 1) serves as a base from which we examine the effect of

basic explanatory variables on loss The results from this regression can be found in Regression

Table 4

Insert Table 4 Here

16

The performance of our regression model is satisfactory in terms of the performance of the

explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and

the coefficient on our treatment variable Post FBC is -126 and highly significant

Overall our results show the strong effect the statewide FBC had on losses from wind

storms during this timeframe Using the results from the regression in Table 4 the coefficient on

the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses

than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the

results from a study conducted by the Insurance Institute for Business and Home Safety after

Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60

percent less likely to suffer damage at all and those that were damaged sustained 42 percent less

damage than older homes Our result of 72 percent lower damage reflects both those attributes as

our data included ZIP codeyearYOC observations that suffered damage as well as those that did

not

Our variables to measure the effect of wind hazard are wind speed and duration For both

variables we have a positive sign and each is highly significant Higher wind speed and higher

duration of high wind speeds increases damage and thus loss The remaining variables perform as

expected

Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In

the first stage variables such as Max Wind and Wind Duration significantly increase the

probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post

FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower

for homes built after new building codes were adopted In the second stage we see that our wind

variables continue to significantly increase the size of the loss and our treatment variable Post

17

FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now

lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy

we see that losses are reduced by about 47 as opposed to 72 when all observations are

includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better

construction reduces loss in two ways First it lowers claims and reduces the amount of a loss

when a claim is filedviii

Model Evaluation

To evaluate our model we used the second stage of the hurdle models and broke our data

into two groups The first group represents 90 of the data randomly selected and was used to

run the model and collect parameter estimates The second group is an out of sample control group

to test the validity of the model Parameter estimates from the first group are applied to the control

group which gave us a predicted loss for each observation in the control group that can be

compared to the actual loss for each observation in the control group We then regressed the

predicted loss from the control group against the actual loss

Insert Figure 2 Here

Figure 2 plots the predicted loss against the actual loss and provides the fitted line with

95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears

to do a good job of predicting most losses

Robustness of Table 4 Base Model Results

To test the robustness of our results we run three separate analyses 1) We first run a

regression with few co-variates 2) As wind design speeds have been used as a proxy for building

code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss

18

analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking

Florida in 2004 and 2005

Regressions using Few Co-Variates

Additional relevant co-variates add precision to a model But the value of the focus

variable should be apparent with a smaller set So we ran a model with only insured customer

based variables EHY and paid premiums leaving out all other demographic and hazard related

variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and

significance

Regressions Using Design Speed

The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads

for Buildings and Other Structures published by the American Society of Civil Engineers and the

Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds

for most regions of the United States and their territories These reference wind speeds are used in

calculations to determine design wind pressures for the primary structure of a building and the

cladding and components attached to a building These calculations take into account the building

geometry the importance of a building the exposuresurrounding terrain and other parameters that

influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design

speeds as a proxy for building code strength and we similarly add this as an additional control in

our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps

were provided by the Applied Technology Council (ATC) Although this version of the wind

speed map was not utilized during the period under consideration the relative values in general

between two locations would be the sameix

19

We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded

form from the ATC and spatially joined the values to our Florida ZIP codes We then further

categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat

4 and Cat 5 hurricane levels

Insert Table 5 Here

The regression adds two dummy variables first for ZIP codes whose design speed exceeds

the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed

reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables

for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that

communities in higher wind zones may take further measures in local codes However the effect

is not significant Notably our variable for Post FBC construction maintains its negative sign

magnitude and significance

Regressions Limited to 2004 and 2005

Our next regression also shown in Table 5 is limited to observations that occurred during

the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and

2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not

surprisingly the magnitude on wind speed increases while maintaining its significance and the

magnitude on age does the same But the effect of the FBC remains the same a 72 reduction

Summary of Results on the FBC

We have collected a comprehensive set of data on insured paid losses from 2001 to 2010

windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity

model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of

the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction

20

in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to

determine if the policy is one that is cost effective

VI Benefit and Costs of the FBC

Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a

number of cases the economic effectiveness of the improved building codes has not been as well

documented especially from a statewide implementation perspective The multi-hazard

mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose

et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar

spent) was appropriate for process activity grant spending related to improved building codes

However this information was gathered from a limited number of studies (mainly earthquake

oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in

generating it and the ratio provided due to improvement would not be the same as those for

adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted

an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for

ten related counties and determined that the net present value of the revisions was $7 billion or

benefit-cost ratio greater than 1 Importantly though this study did not have access to actual

building code damage reduction data to utilize in the analysis In 2002 Applied Research

Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC

for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how

the three types of FBC built houses would perform in probabilistic hurricane scenarios compared

to the same houses built under the previous code Given the probabilistic nature of the analysis

average annual losses were generated that demonstrated post-FBC housing having loss reductions

54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were

21

then compared to their estimated cost impacts of the FBC for these housing types with at least

break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and

above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al

(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss

reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results

ranged from 041 to 183 for the retrofit mitigation activities to existing housing

We propose a BCA that differs from earlier work in several important ways First we use

realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of

how much loss can be reduced by the FBC Second our loss data spans 10 years which include a

combination of major hurricanes and smaller wind storms

BenefitCost Methodology

The elements of a BCA requires three inputs 1) an estimate of the added cost to implement

the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related

storms from our realized ISO loss data and then from a statewide catastrophe model estimate and

3) the percentage of expected loss that will be mitigated due to implementation of the FBC We

first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis

but use the full reduction in loss which includes the value of reduced claims Finally our ISO data

is paid losses and does not include deductibles so we add an estimate for deductibles

Additional Cost

In their 2002 benefit-cost comparison study of the enactment of the FBC for three related

housing types three actual sample homes were built to the FBC to evaluate the change in

construction costs (ARA 2002) For the purposes of code implementation the state was divided

into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region

22

(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account

for cost differences between the two regions

In the WBDR an added requirement is impact protection to windows and doors to reduce

damage from flying debris Along the coast and much of South Florida is classified as the WBDR

The N-WBDR is mainly classified in the interior of the state where impact protection is not

required Importantly the study provided a range of added costs for the N-WBDR and the WBDR

Three counties in South Florida Dade Broward and Monroe were under the South Florida

Building Code (SFBC) prior to the implementation of the FBC According to the ARA study

(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges

of incremental cost per square foot for the N-WBDR and WBDR along with the percent of

residential units that reside in each area This allows a calculation of a weighted average added

cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost

per square foot of $166 The cost compares favorably with a similar building code enhancement

adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in

2013 Consulting engineers and the Moore Association of Homebuilders estimated the code

enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is

1960 square feet which means that on average the FBC increases construction cost by $3254 per

structurexii

Insert Table 6 Here

Benefit of the FBC

Benefits stemming from the FBC are the expected reduction in losses from windstorms during

the life of the home We first find an average annual loss (AAL) use that number to estimate

losses for the next 50 years and then find the present value of those losses in 2010 Here we are

23

assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the

next 50 years A wealth of literature suggests the potential for changes to hurricane activity over

the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty

on future changes in wind hazard on the scale of a single state we choose to assume a stationary

climate

Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes

built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our

data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of

$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year

Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses

per structure of $21474

Finally we use parameter estimates from our regression for the Post FBC dummy variable

(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC

The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post

FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what

would be a reduction of $10093 from the projected $21474 in future losses

Insert Table 7 Here

BenefitCost Analysis

Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio

of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the

statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an

economically effective public policy following from our ISO loss data and results

Insert Table 8 Here

24

Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years

This relatively short timeframe makes it difficult to truly approximate an AAL as would be

provided from a probabilistically based catastrophe model that generates an AAL from thousands

of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state

of Florida to estimate an average annual wind loss for all residential properties in Florida of

approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL

estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion

with deductibles) Using this aggregate AAL and the number of residential units in Florida based

on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses

net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and

an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as

before derived from our regression results 47 we find $7710 of reduced loss from the projected

$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added

$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective

building code public policy

We run two additional analyses on our BCA results Our estimate of expected loss

reduction comes from the second stage of the hurdle model This is an estimate of the direct loss

reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the

number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests

as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which

includes the reduced magnitude of loss from affected homes and the reduction in claims for Post

FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table

8) However a 72 loss reduction seems too dramatic an expectation when planning so far in

25

advance For that reason we offer a third level of expected loss reduction of 60 which is the

midpoint between our two loss reduction estimates This estimate captures the expected direct loss

reduction suggested by the second stage of our hurdle model but still recognizes that in some areas

the number of claims is reduced by the FBC This appears to be a reasonable assumption and

provides a BCA ratio of 396 for the ISO sample and 302 for all residential

The ISO data are net of deductibles so our BCA thus far only includes losses compensated by

the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion

also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the

ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all

loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA

values from a low of 237 to a high of 793

Payback of the FBC

Finally we use our BCA results to calculate a payback period for the investment of stronger

codes To convert our BCA ratio to a payback period we simply divide our 50-year planning

horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss

and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years

This is important for gauging potential political support or non-support for enactment of the new

codes Payback periods that approach the typical mortgage term 30 years would in theory be

difficult to achieve and that is not what our analysis indicates for the FBC

VI - Concluding Comments

In the aftermath of Hurricane Andrew which had exposed not only poor building

construction but also poor building code enforcement the state of Florida enacted statewide

building code changes that wrested away building code adoption control from individual localities

26

With full implementation of the statewide building code associated expectations are that

windstorm losses from extreme events such as hurricanes should be reduced moving forward

There have been a few studies confirming these expectations following the 2004 and 2005

hurricane season In this article we further verify and quantify these findings and expand the

existing building code risk reduction research in several important ways

Overall we empirically test the statewide implementation of a building code in reducing

wind related damages in Florida controlling for other relevant wind hazard exposure and

vulnerability characteristics from a traditional risk assessment perspective Our results show the

strong effect the statewide FBC had on losses from wind storms during this timeframe From the

treatment variable that measures implementation of the statewide codes the post 2000 year of

construction losses are shown to be reduced by as much as 72 percent consistent with other

previous findings

Finally we have conducted a BCA of the FBC to determine if expected benefits exceed

the cost of implementation Using a direct estimate for mitigated losses and an estimate that

includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public

policy from an economic perspective This result is close to that recommended by the multi-hazard

mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a

statewide BCA Importantly this information is essential in generating political and consumer

support for such building code public policy implementation

For example the economic effectiveness results shown here have implications for ongoing

policy discussions about reforming building codes from a national US perspective Moore OK

independently adopted enhanced building codes after its third violent tornado in 14 years killed 24

including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)

27

Construction practices in North Texas were brought under scrutiny after the December 2015

tornado revealed inadequate construction including an elementary school whose exterior walls

failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House

announced initiatives to increase community resilience with building codes as a major component

of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748

and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better

construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397

would provide tax credits for owners andor contractors who use techniques designed for resiliency

in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one

recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to

encourage states to use current building codes (Vaughn and Turner 2014)

Future research in the BCA of the FBC will further inform the public policy debate on

enhanced building codes The issue has national implications as other states find that wind hazards

impact them as well We have sufficient wind data to examine how the BCA performs under

different wind hazards Additionally it will be important to consider how future economic

development affects the BCA as well as varying climate change scenarios As the FBC is

mandatory for all new construction a statewide analysis was appropriate But individual

homeowners in older homes can invest in the retrofit of their home and qualify for discounts on

their homeowners insurance This topic is deserving of a robust analysis Although our BCA is

statewide regions within the state will likely have a spectrum of results For instance the ARA

2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their

analysis did not use realized loss data so confirmation of how the BCA varies between those

regions would be an important contribution Finally our sensitivity analysis was limited to two

28

variables reduction in future loss and the inclusion of deductibles Additional work will highlight

other variables that could modify the results

29

Appendix

We use this appendix to conduct more detailed analysis on several topics First selection

of the model specification using a regression discontinuity approach Second we provide an in

depth examination of the relationship between structure age and losses Third we perform a

Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an

alternative specification to see if our RD results are similar followed by regressions to examine

the year to year consistency of our Post FBC result Next we run a regression on claims to verify

the difference between our direct reduction result and our full reduction result Finally we perform

a regression on homes built to the SFBC which had adopted enhanced building codes in advance

of the FBC to assess the effect of earlier adoption of enhanced construction

Regression Discontinuity

Regression Discontinuity (RD) applies when an observation receives a treatment in our case

homes built under the FBC based on a rating variable in our case age of the structure at the year

of observation So for observations in 2005 homes built post 2000 received the treatment

adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify

how observations on either side of the implementation of the FBC (2000) perform in suffering loss

from windstorms The treatment variable is a function of the age of the home and age affects loss

in ways not related to the FBC such as depreciation and differences in materials and construction

practices across time To account for both the effect of age on loss as well as the implementation

of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment

The data we have from ISO is aggregated loss data by zip code and decade of construction So

we cannot get an annualized age To approach a true age we set the year built for each decade of

construction at the beginning of the decade then subtract that from the year of each observation to

get an approximate agexiv

30

To find the best specification we began with a simpler model which used a series of

categorical variables for each decade of construction to examine the effect of the code compared

to the omitted decade This method would approximate the changes in materials and construction

practices but was less effective in controlling for depreciation But it would give us a first

approximation of the code effect that we used as a benchmark when testing the best RD

specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970

with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When

the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When

either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81

A rough approximation of the codersquos effect from this approach would suggest a reduction in the

mid 70 percent range

Insert Table 1 ndash Appendix Here

Next we used a standard procedure with RD to search for the best way to include the rating

variable This process creates specifications that include age in increasing polynomials and

interacted with the treatment variable The goal is to find the specification with the lowest AIC

that comes close to the benchmark value of the treatment variable

Insert Tables 2 and 3 ndash Appendix Here

We did this first with regressions that limited the co-variates then with our full model In both

sets AIC reaches a minimum on the specification with age and age squared The interaction model

after that increases the AIC then the AIC goes down again with a cubed model and its interaction

model with the overall lowest AIC found on the cubed interaction model But we chose not to

use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order

models the magnitude of the treatment variable in the models with just polynomials compared to

31

the corresponding interaction models were close with the interaction models providing a larger

magnitude When the cubed models were added the magnitude jumped where the polynomial

cubed model went down well below our benchmark and the interaction model went up above our

benchmark We felt this made use of the cubed model inappropriate So we now need to choose

between the squared model and the one with the interaction terms The squared model (Model 4)

had a lower AIC and the interaction variables on the interaction model (Model 5) were not

significant so we chose to use the squared model without the interaction term This model gave a

magnitude for the treatment variable of a 72 reduction somewhat lower than the expected

magnitude in the mid 70rsquos percent The general form of the model is

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1

of the observations on either end of our data sorted by loss Our treatment variable Post FBC

remains highly significant with a coefficient value of -117 which compares favorably to our

coefficient value of -126 when the entire sample is used

Structure Age and Wind Losses

Our study is similar to recent studies on the effect of energy efficiency building codes

adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that

better insulation caulking and more efficient HVAC systems would result in lower energy

consumption But the change in energy consumption is less than engineering estimates projected

Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for

homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study

32

may be confounding age with vintage and found a decrease in energy use related to the home

simply being new rather than the change in building code Indeed Kotchen (2015) revisited the

question with data 10 years older and found the effect on electricity had disappeared while the

reduction in natural gas use increased Something is occurring in energy use unrelated to the code

and could be explained by residents changing their use of energy as they adapt to their new home

Residents of an energy efficient home can undermine the intent of lower energy use by using the

efficient design to heat and cool their homes with a motivation toward increased comfort at the

same energy cost rather than energy savings Our study does not have the behavioral component

found in the case of energy efficiency In our application the construction elements that make the

structure able to withstand high winds are installed when the home is built and lie ldquobehind the

wallsrdquo making it unlikely for individual preferences to alter the homes performance against the

threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC

homes due to the code or simply an artifact of new versus old construction when confronted with

a windstorm

To first address our analysis of age versus the FBC we rerun our base regression but limit

our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more

than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14

years during the highest loss year of 2004 Since this is a comparison between two adjacent

decades on either side of our cut point of year 2000 we remove age and age squared Results are

shown in Table 4-Appendix

Insert Table 4-Appendix Here

The coefficient on Post FBC is still negative highly significant with a magnitude very close to

what we saw with the entire database and the age variables This result suggests that the code

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

14

Insert Table 3 Here

A positive sign is expected for both wind variables indicating that as wind speeds increase

andor the ZIP code is exposed to high winds for an extended period of time losses will increase

Post FBC construction should decrease loss so a negative sign is expected

Hurdle Model

One problem potentially encountered in attempting to model losses is there may be a

separate process occurring in the data that determines whether a loss is realized at all which could

affect the estimate of overall losses To address this issue hurdle models are used as they divide

the analysis into two stages We use a hurdle model to find the direct effect of the FBC The first

stage models the probability that a loss occurs and the second stage models the loss using only

observations that sustained a loss The dependent variable in the first stage equals one if there was

a loss and zero otherwise This binary dependent variable is then regressed against variables that

would affect the probability that a loss occurred Its form is

(2a)

Loss or No Loss = β0 + β1 Max Wind + β2 Wind Duration + β3 Population Density

+ β4 Post FBC

We expect that both wind variables max wind speed and duration as well as population

density will increase the probability of a loss Post FBC construction however should decrease

the probability of a loss

The second stage in the hurdle model is the same as Equation 1 with the exception that

only observations with a loss are included There are 19107 observations for the second stage and

its form is

15

(2b)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Model Validity

Regression models are limited by available data to understand how the dependent variable

varies as explanatory variables change If important variables are left out of the model some bias

can be expected This omitted variable bias is a common problem encountered with econometric

models Kuminoff et al 2010 found that one of the best approaches to reducing omitted variable

bias is to employ a spatial fixed effects model To accomplish this we use individual ZIP dummy

variables as a spatial fixed effect and dummy variables for each year in our data to control for

changes that may be related to time not otherwise controlled for within our co-variates These

dummy variables will contain all across-group variation leaving the remainder of the model to

contain the within-group variation (Greene 2003)

A second challenge to the validity of our model is another common problem

heteroscedasticity For Equation 1 we use clustered standard errors at the ZIP code through Proc

GLM in SAS Our hurdle model (Eq 2a and 2b) utilizes Proc Qlim which has a separate statement

(Hetero) that we invoked to model the error variance

V Regression Results

Our first regression (Equation 1) serves as a base from which we examine the effect of

basic explanatory variables on loss The results from this regression can be found in Regression

Table 4

Insert Table 4 Here

16

The performance of our regression model is satisfactory in terms of the performance of the

explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and

the coefficient on our treatment variable Post FBC is -126 and highly significant

Overall our results show the strong effect the statewide FBC had on losses from wind

storms during this timeframe Using the results from the regression in Table 4 the coefficient on

the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses

than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the

results from a study conducted by the Insurance Institute for Business and Home Safety after

Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60

percent less likely to suffer damage at all and those that were damaged sustained 42 percent less

damage than older homes Our result of 72 percent lower damage reflects both those attributes as

our data included ZIP codeyearYOC observations that suffered damage as well as those that did

not

Our variables to measure the effect of wind hazard are wind speed and duration For both

variables we have a positive sign and each is highly significant Higher wind speed and higher

duration of high wind speeds increases damage and thus loss The remaining variables perform as

expected

Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In

the first stage variables such as Max Wind and Wind Duration significantly increase the

probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post

FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower

for homes built after new building codes were adopted In the second stage we see that our wind

variables continue to significantly increase the size of the loss and our treatment variable Post

17

FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now

lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy

we see that losses are reduced by about 47 as opposed to 72 when all observations are

includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better

construction reduces loss in two ways First it lowers claims and reduces the amount of a loss

when a claim is filedviii

Model Evaluation

To evaluate our model we used the second stage of the hurdle models and broke our data

into two groups The first group represents 90 of the data randomly selected and was used to

run the model and collect parameter estimates The second group is an out of sample control group

to test the validity of the model Parameter estimates from the first group are applied to the control

group which gave us a predicted loss for each observation in the control group that can be

compared to the actual loss for each observation in the control group We then regressed the

predicted loss from the control group against the actual loss

Insert Figure 2 Here

Figure 2 plots the predicted loss against the actual loss and provides the fitted line with

95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears

to do a good job of predicting most losses

Robustness of Table 4 Base Model Results

To test the robustness of our results we run three separate analyses 1) We first run a

regression with few co-variates 2) As wind design speeds have been used as a proxy for building

code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss

18

analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking

Florida in 2004 and 2005

Regressions using Few Co-Variates

Additional relevant co-variates add precision to a model But the value of the focus

variable should be apparent with a smaller set So we ran a model with only insured customer

based variables EHY and paid premiums leaving out all other demographic and hazard related

variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and

significance

Regressions Using Design Speed

The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads

for Buildings and Other Structures published by the American Society of Civil Engineers and the

Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds

for most regions of the United States and their territories These reference wind speeds are used in

calculations to determine design wind pressures for the primary structure of a building and the

cladding and components attached to a building These calculations take into account the building

geometry the importance of a building the exposuresurrounding terrain and other parameters that

influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design

speeds as a proxy for building code strength and we similarly add this as an additional control in

our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps

were provided by the Applied Technology Council (ATC) Although this version of the wind

speed map was not utilized during the period under consideration the relative values in general

between two locations would be the sameix

19

We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded

form from the ATC and spatially joined the values to our Florida ZIP codes We then further

categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat

4 and Cat 5 hurricane levels

Insert Table 5 Here

The regression adds two dummy variables first for ZIP codes whose design speed exceeds

the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed

reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables

for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that

communities in higher wind zones may take further measures in local codes However the effect

is not significant Notably our variable for Post FBC construction maintains its negative sign

magnitude and significance

Regressions Limited to 2004 and 2005

Our next regression also shown in Table 5 is limited to observations that occurred during

the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and

2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not

surprisingly the magnitude on wind speed increases while maintaining its significance and the

magnitude on age does the same But the effect of the FBC remains the same a 72 reduction

Summary of Results on the FBC

We have collected a comprehensive set of data on insured paid losses from 2001 to 2010

windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity

model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of

the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction

20

in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to

determine if the policy is one that is cost effective

VI Benefit and Costs of the FBC

Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a

number of cases the economic effectiveness of the improved building codes has not been as well

documented especially from a statewide implementation perspective The multi-hazard

mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose

et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar

spent) was appropriate for process activity grant spending related to improved building codes

However this information was gathered from a limited number of studies (mainly earthquake

oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in

generating it and the ratio provided due to improvement would not be the same as those for

adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted

an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for

ten related counties and determined that the net present value of the revisions was $7 billion or

benefit-cost ratio greater than 1 Importantly though this study did not have access to actual

building code damage reduction data to utilize in the analysis In 2002 Applied Research

Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC

for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how

the three types of FBC built houses would perform in probabilistic hurricane scenarios compared

to the same houses built under the previous code Given the probabilistic nature of the analysis

average annual losses were generated that demonstrated post-FBC housing having loss reductions

54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were

21

then compared to their estimated cost impacts of the FBC for these housing types with at least

break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and

above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al

(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss

reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results

ranged from 041 to 183 for the retrofit mitigation activities to existing housing

We propose a BCA that differs from earlier work in several important ways First we use

realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of

how much loss can be reduced by the FBC Second our loss data spans 10 years which include a

combination of major hurricanes and smaller wind storms

BenefitCost Methodology

The elements of a BCA requires three inputs 1) an estimate of the added cost to implement

the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related

storms from our realized ISO loss data and then from a statewide catastrophe model estimate and

3) the percentage of expected loss that will be mitigated due to implementation of the FBC We

first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis

but use the full reduction in loss which includes the value of reduced claims Finally our ISO data

is paid losses and does not include deductibles so we add an estimate for deductibles

Additional Cost

In their 2002 benefit-cost comparison study of the enactment of the FBC for three related

housing types three actual sample homes were built to the FBC to evaluate the change in

construction costs (ARA 2002) For the purposes of code implementation the state was divided

into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region

22

(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account

for cost differences between the two regions

In the WBDR an added requirement is impact protection to windows and doors to reduce

damage from flying debris Along the coast and much of South Florida is classified as the WBDR

The N-WBDR is mainly classified in the interior of the state where impact protection is not

required Importantly the study provided a range of added costs for the N-WBDR and the WBDR

Three counties in South Florida Dade Broward and Monroe were under the South Florida

Building Code (SFBC) prior to the implementation of the FBC According to the ARA study

(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges

of incremental cost per square foot for the N-WBDR and WBDR along with the percent of

residential units that reside in each area This allows a calculation of a weighted average added

cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost

per square foot of $166 The cost compares favorably with a similar building code enhancement

adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in

2013 Consulting engineers and the Moore Association of Homebuilders estimated the code

enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is

1960 square feet which means that on average the FBC increases construction cost by $3254 per

structurexii

Insert Table 6 Here

Benefit of the FBC

Benefits stemming from the FBC are the expected reduction in losses from windstorms during

the life of the home We first find an average annual loss (AAL) use that number to estimate

losses for the next 50 years and then find the present value of those losses in 2010 Here we are

23

assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the

next 50 years A wealth of literature suggests the potential for changes to hurricane activity over

the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty

on future changes in wind hazard on the scale of a single state we choose to assume a stationary

climate

Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes

built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our

data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of

$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year

Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses

per structure of $21474

Finally we use parameter estimates from our regression for the Post FBC dummy variable

(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC

The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post

FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what

would be a reduction of $10093 from the projected $21474 in future losses

Insert Table 7 Here

BenefitCost Analysis

Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio

of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the

statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an

economically effective public policy following from our ISO loss data and results

Insert Table 8 Here

24

Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years

This relatively short timeframe makes it difficult to truly approximate an AAL as would be

provided from a probabilistically based catastrophe model that generates an AAL from thousands

of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state

of Florida to estimate an average annual wind loss for all residential properties in Florida of

approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL

estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion

with deductibles) Using this aggregate AAL and the number of residential units in Florida based

on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses

net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and

an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as

before derived from our regression results 47 we find $7710 of reduced loss from the projected

$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added

$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective

building code public policy

We run two additional analyses on our BCA results Our estimate of expected loss

reduction comes from the second stage of the hurdle model This is an estimate of the direct loss

reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the

number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests

as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which

includes the reduced magnitude of loss from affected homes and the reduction in claims for Post

FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table

8) However a 72 loss reduction seems too dramatic an expectation when planning so far in

25

advance For that reason we offer a third level of expected loss reduction of 60 which is the

midpoint between our two loss reduction estimates This estimate captures the expected direct loss

reduction suggested by the second stage of our hurdle model but still recognizes that in some areas

the number of claims is reduced by the FBC This appears to be a reasonable assumption and

provides a BCA ratio of 396 for the ISO sample and 302 for all residential

The ISO data are net of deductibles so our BCA thus far only includes losses compensated by

the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion

also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the

ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all

loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA

values from a low of 237 to a high of 793

Payback of the FBC

Finally we use our BCA results to calculate a payback period for the investment of stronger

codes To convert our BCA ratio to a payback period we simply divide our 50-year planning

horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss

and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years

This is important for gauging potential political support or non-support for enactment of the new

codes Payback periods that approach the typical mortgage term 30 years would in theory be

difficult to achieve and that is not what our analysis indicates for the FBC

VI - Concluding Comments

In the aftermath of Hurricane Andrew which had exposed not only poor building

construction but also poor building code enforcement the state of Florida enacted statewide

building code changes that wrested away building code adoption control from individual localities

26

With full implementation of the statewide building code associated expectations are that

windstorm losses from extreme events such as hurricanes should be reduced moving forward

There have been a few studies confirming these expectations following the 2004 and 2005

hurricane season In this article we further verify and quantify these findings and expand the

existing building code risk reduction research in several important ways

Overall we empirically test the statewide implementation of a building code in reducing

wind related damages in Florida controlling for other relevant wind hazard exposure and

vulnerability characteristics from a traditional risk assessment perspective Our results show the

strong effect the statewide FBC had on losses from wind storms during this timeframe From the

treatment variable that measures implementation of the statewide codes the post 2000 year of

construction losses are shown to be reduced by as much as 72 percent consistent with other

previous findings

Finally we have conducted a BCA of the FBC to determine if expected benefits exceed

the cost of implementation Using a direct estimate for mitigated losses and an estimate that

includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public

policy from an economic perspective This result is close to that recommended by the multi-hazard

mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a

statewide BCA Importantly this information is essential in generating political and consumer

support for such building code public policy implementation

For example the economic effectiveness results shown here have implications for ongoing

policy discussions about reforming building codes from a national US perspective Moore OK

independently adopted enhanced building codes after its third violent tornado in 14 years killed 24

including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)

27

Construction practices in North Texas were brought under scrutiny after the December 2015

tornado revealed inadequate construction including an elementary school whose exterior walls

failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House

announced initiatives to increase community resilience with building codes as a major component

of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748

and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better

construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397

would provide tax credits for owners andor contractors who use techniques designed for resiliency

in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one

recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to

encourage states to use current building codes (Vaughn and Turner 2014)

Future research in the BCA of the FBC will further inform the public policy debate on

enhanced building codes The issue has national implications as other states find that wind hazards

impact them as well We have sufficient wind data to examine how the BCA performs under

different wind hazards Additionally it will be important to consider how future economic

development affects the BCA as well as varying climate change scenarios As the FBC is

mandatory for all new construction a statewide analysis was appropriate But individual

homeowners in older homes can invest in the retrofit of their home and qualify for discounts on

their homeowners insurance This topic is deserving of a robust analysis Although our BCA is

statewide regions within the state will likely have a spectrum of results For instance the ARA

2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their

analysis did not use realized loss data so confirmation of how the BCA varies between those

regions would be an important contribution Finally our sensitivity analysis was limited to two

28

variables reduction in future loss and the inclusion of deductibles Additional work will highlight

other variables that could modify the results

29

Appendix

We use this appendix to conduct more detailed analysis on several topics First selection

of the model specification using a regression discontinuity approach Second we provide an in

depth examination of the relationship between structure age and losses Third we perform a

Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an

alternative specification to see if our RD results are similar followed by regressions to examine

the year to year consistency of our Post FBC result Next we run a regression on claims to verify

the difference between our direct reduction result and our full reduction result Finally we perform

a regression on homes built to the SFBC which had adopted enhanced building codes in advance

of the FBC to assess the effect of earlier adoption of enhanced construction

Regression Discontinuity

Regression Discontinuity (RD) applies when an observation receives a treatment in our case

homes built under the FBC based on a rating variable in our case age of the structure at the year

of observation So for observations in 2005 homes built post 2000 received the treatment

adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify

how observations on either side of the implementation of the FBC (2000) perform in suffering loss

from windstorms The treatment variable is a function of the age of the home and age affects loss

in ways not related to the FBC such as depreciation and differences in materials and construction

practices across time To account for both the effect of age on loss as well as the implementation

of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment

The data we have from ISO is aggregated loss data by zip code and decade of construction So

we cannot get an annualized age To approach a true age we set the year built for each decade of

construction at the beginning of the decade then subtract that from the year of each observation to

get an approximate agexiv

30

To find the best specification we began with a simpler model which used a series of

categorical variables for each decade of construction to examine the effect of the code compared

to the omitted decade This method would approximate the changes in materials and construction

practices but was less effective in controlling for depreciation But it would give us a first

approximation of the code effect that we used as a benchmark when testing the best RD

specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970

with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When

the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When

either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81

A rough approximation of the codersquos effect from this approach would suggest a reduction in the

mid 70 percent range

Insert Table 1 ndash Appendix Here

Next we used a standard procedure with RD to search for the best way to include the rating

variable This process creates specifications that include age in increasing polynomials and

interacted with the treatment variable The goal is to find the specification with the lowest AIC

that comes close to the benchmark value of the treatment variable

Insert Tables 2 and 3 ndash Appendix Here

We did this first with regressions that limited the co-variates then with our full model In both

sets AIC reaches a minimum on the specification with age and age squared The interaction model

after that increases the AIC then the AIC goes down again with a cubed model and its interaction

model with the overall lowest AIC found on the cubed interaction model But we chose not to

use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order

models the magnitude of the treatment variable in the models with just polynomials compared to

31

the corresponding interaction models were close with the interaction models providing a larger

magnitude When the cubed models were added the magnitude jumped where the polynomial

cubed model went down well below our benchmark and the interaction model went up above our

benchmark We felt this made use of the cubed model inappropriate So we now need to choose

between the squared model and the one with the interaction terms The squared model (Model 4)

had a lower AIC and the interaction variables on the interaction model (Model 5) were not

significant so we chose to use the squared model without the interaction term This model gave a

magnitude for the treatment variable of a 72 reduction somewhat lower than the expected

magnitude in the mid 70rsquos percent The general form of the model is

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1

of the observations on either end of our data sorted by loss Our treatment variable Post FBC

remains highly significant with a coefficient value of -117 which compares favorably to our

coefficient value of -126 when the entire sample is used

Structure Age and Wind Losses

Our study is similar to recent studies on the effect of energy efficiency building codes

adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that

better insulation caulking and more efficient HVAC systems would result in lower energy

consumption But the change in energy consumption is less than engineering estimates projected

Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for

homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study

32

may be confounding age with vintage and found a decrease in energy use related to the home

simply being new rather than the change in building code Indeed Kotchen (2015) revisited the

question with data 10 years older and found the effect on electricity had disappeared while the

reduction in natural gas use increased Something is occurring in energy use unrelated to the code

and could be explained by residents changing their use of energy as they adapt to their new home

Residents of an energy efficient home can undermine the intent of lower energy use by using the

efficient design to heat and cool their homes with a motivation toward increased comfort at the

same energy cost rather than energy savings Our study does not have the behavioral component

found in the case of energy efficiency In our application the construction elements that make the

structure able to withstand high winds are installed when the home is built and lie ldquobehind the

wallsrdquo making it unlikely for individual preferences to alter the homes performance against the

threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC

homes due to the code or simply an artifact of new versus old construction when confronted with

a windstorm

To first address our analysis of age versus the FBC we rerun our base regression but limit

our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more

than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14

years during the highest loss year of 2004 Since this is a comparison between two adjacent

decades on either side of our cut point of year 2000 we remove age and age squared Results are

shown in Table 4-Appendix

Insert Table 4-Appendix Here

The coefficient on Post FBC is still negative highly significant with a magnitude very close to

what we saw with the entire database and the age variables This result suggests that the code

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

15

(2b)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Model Validity

Regression models are limited by available data to understand how the dependent variable

varies as explanatory variables change If important variables are left out of the model some bias

can be expected This omitted variable bias is a common problem encountered with econometric

models Kuminoff et al 2010 found that one of the best approaches to reducing omitted variable

bias is to employ a spatial fixed effects model To accomplish this we use individual ZIP dummy

variables as a spatial fixed effect and dummy variables for each year in our data to control for

changes that may be related to time not otherwise controlled for within our co-variates These

dummy variables will contain all across-group variation leaving the remainder of the model to

contain the within-group variation (Greene 2003)

A second challenge to the validity of our model is another common problem

heteroscedasticity For Equation 1 we use clustered standard errors at the ZIP code through Proc

GLM in SAS Our hurdle model (Eq 2a and 2b) utilizes Proc Qlim which has a separate statement

(Hetero) that we invoked to model the error variance

V Regression Results

Our first regression (Equation 1) serves as a base from which we examine the effect of

basic explanatory variables on loss The results from this regression can be found in Regression

Table 4

Insert Table 4 Here

16

The performance of our regression model is satisfactory in terms of the performance of the

explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and

the coefficient on our treatment variable Post FBC is -126 and highly significant

Overall our results show the strong effect the statewide FBC had on losses from wind

storms during this timeframe Using the results from the regression in Table 4 the coefficient on

the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses

than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the

results from a study conducted by the Insurance Institute for Business and Home Safety after

Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60

percent less likely to suffer damage at all and those that were damaged sustained 42 percent less

damage than older homes Our result of 72 percent lower damage reflects both those attributes as

our data included ZIP codeyearYOC observations that suffered damage as well as those that did

not

Our variables to measure the effect of wind hazard are wind speed and duration For both

variables we have a positive sign and each is highly significant Higher wind speed and higher

duration of high wind speeds increases damage and thus loss The remaining variables perform as

expected

Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In

the first stage variables such as Max Wind and Wind Duration significantly increase the

probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post

FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower

for homes built after new building codes were adopted In the second stage we see that our wind

variables continue to significantly increase the size of the loss and our treatment variable Post

17

FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now

lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy

we see that losses are reduced by about 47 as opposed to 72 when all observations are

includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better

construction reduces loss in two ways First it lowers claims and reduces the amount of a loss

when a claim is filedviii

Model Evaluation

To evaluate our model we used the second stage of the hurdle models and broke our data

into two groups The first group represents 90 of the data randomly selected and was used to

run the model and collect parameter estimates The second group is an out of sample control group

to test the validity of the model Parameter estimates from the first group are applied to the control

group which gave us a predicted loss for each observation in the control group that can be

compared to the actual loss for each observation in the control group We then regressed the

predicted loss from the control group against the actual loss

Insert Figure 2 Here

Figure 2 plots the predicted loss against the actual loss and provides the fitted line with

95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears

to do a good job of predicting most losses

Robustness of Table 4 Base Model Results

To test the robustness of our results we run three separate analyses 1) We first run a

regression with few co-variates 2) As wind design speeds have been used as a proxy for building

code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss

18

analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking

Florida in 2004 and 2005

Regressions using Few Co-Variates

Additional relevant co-variates add precision to a model But the value of the focus

variable should be apparent with a smaller set So we ran a model with only insured customer

based variables EHY and paid premiums leaving out all other demographic and hazard related

variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and

significance

Regressions Using Design Speed

The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads

for Buildings and Other Structures published by the American Society of Civil Engineers and the

Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds

for most regions of the United States and their territories These reference wind speeds are used in

calculations to determine design wind pressures for the primary structure of a building and the

cladding and components attached to a building These calculations take into account the building

geometry the importance of a building the exposuresurrounding terrain and other parameters that

influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design

speeds as a proxy for building code strength and we similarly add this as an additional control in

our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps

were provided by the Applied Technology Council (ATC) Although this version of the wind

speed map was not utilized during the period under consideration the relative values in general

between two locations would be the sameix

19

We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded

form from the ATC and spatially joined the values to our Florida ZIP codes We then further

categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat

4 and Cat 5 hurricane levels

Insert Table 5 Here

The regression adds two dummy variables first for ZIP codes whose design speed exceeds

the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed

reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables

for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that

communities in higher wind zones may take further measures in local codes However the effect

is not significant Notably our variable for Post FBC construction maintains its negative sign

magnitude and significance

Regressions Limited to 2004 and 2005

Our next regression also shown in Table 5 is limited to observations that occurred during

the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and

2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not

surprisingly the magnitude on wind speed increases while maintaining its significance and the

magnitude on age does the same But the effect of the FBC remains the same a 72 reduction

Summary of Results on the FBC

We have collected a comprehensive set of data on insured paid losses from 2001 to 2010

windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity

model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of

the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction

20

in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to

determine if the policy is one that is cost effective

VI Benefit and Costs of the FBC

Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a

number of cases the economic effectiveness of the improved building codes has not been as well

documented especially from a statewide implementation perspective The multi-hazard

mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose

et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar

spent) was appropriate for process activity grant spending related to improved building codes

However this information was gathered from a limited number of studies (mainly earthquake

oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in

generating it and the ratio provided due to improvement would not be the same as those for

adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted

an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for

ten related counties and determined that the net present value of the revisions was $7 billion or

benefit-cost ratio greater than 1 Importantly though this study did not have access to actual

building code damage reduction data to utilize in the analysis In 2002 Applied Research

Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC

for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how

the three types of FBC built houses would perform in probabilistic hurricane scenarios compared

to the same houses built under the previous code Given the probabilistic nature of the analysis

average annual losses were generated that demonstrated post-FBC housing having loss reductions

54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were

21

then compared to their estimated cost impacts of the FBC for these housing types with at least

break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and

above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al

(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss

reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results

ranged from 041 to 183 for the retrofit mitigation activities to existing housing

We propose a BCA that differs from earlier work in several important ways First we use

realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of

how much loss can be reduced by the FBC Second our loss data spans 10 years which include a

combination of major hurricanes and smaller wind storms

BenefitCost Methodology

The elements of a BCA requires three inputs 1) an estimate of the added cost to implement

the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related

storms from our realized ISO loss data and then from a statewide catastrophe model estimate and

3) the percentage of expected loss that will be mitigated due to implementation of the FBC We

first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis

but use the full reduction in loss which includes the value of reduced claims Finally our ISO data

is paid losses and does not include deductibles so we add an estimate for deductibles

Additional Cost

In their 2002 benefit-cost comparison study of the enactment of the FBC for three related

housing types three actual sample homes were built to the FBC to evaluate the change in

construction costs (ARA 2002) For the purposes of code implementation the state was divided

into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region

22

(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account

for cost differences between the two regions

In the WBDR an added requirement is impact protection to windows and doors to reduce

damage from flying debris Along the coast and much of South Florida is classified as the WBDR

The N-WBDR is mainly classified in the interior of the state where impact protection is not

required Importantly the study provided a range of added costs for the N-WBDR and the WBDR

Three counties in South Florida Dade Broward and Monroe were under the South Florida

Building Code (SFBC) prior to the implementation of the FBC According to the ARA study

(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges

of incremental cost per square foot for the N-WBDR and WBDR along with the percent of

residential units that reside in each area This allows a calculation of a weighted average added

cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost

per square foot of $166 The cost compares favorably with a similar building code enhancement

adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in

2013 Consulting engineers and the Moore Association of Homebuilders estimated the code

enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is

1960 square feet which means that on average the FBC increases construction cost by $3254 per

structurexii

Insert Table 6 Here

Benefit of the FBC

Benefits stemming from the FBC are the expected reduction in losses from windstorms during

the life of the home We first find an average annual loss (AAL) use that number to estimate

losses for the next 50 years and then find the present value of those losses in 2010 Here we are

23

assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the

next 50 years A wealth of literature suggests the potential for changes to hurricane activity over

the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty

on future changes in wind hazard on the scale of a single state we choose to assume a stationary

climate

Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes

built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our

data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of

$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year

Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses

per structure of $21474

Finally we use parameter estimates from our regression for the Post FBC dummy variable

(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC

The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post

FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what

would be a reduction of $10093 from the projected $21474 in future losses

Insert Table 7 Here

BenefitCost Analysis

Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio

of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the

statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an

economically effective public policy following from our ISO loss data and results

Insert Table 8 Here

24

Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years

This relatively short timeframe makes it difficult to truly approximate an AAL as would be

provided from a probabilistically based catastrophe model that generates an AAL from thousands

of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state

of Florida to estimate an average annual wind loss for all residential properties in Florida of

approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL

estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion

with deductibles) Using this aggregate AAL and the number of residential units in Florida based

on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses

net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and

an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as

before derived from our regression results 47 we find $7710 of reduced loss from the projected

$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added

$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective

building code public policy

We run two additional analyses on our BCA results Our estimate of expected loss

reduction comes from the second stage of the hurdle model This is an estimate of the direct loss

reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the

number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests

as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which

includes the reduced magnitude of loss from affected homes and the reduction in claims for Post

FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table

8) However a 72 loss reduction seems too dramatic an expectation when planning so far in

25

advance For that reason we offer a third level of expected loss reduction of 60 which is the

midpoint between our two loss reduction estimates This estimate captures the expected direct loss

reduction suggested by the second stage of our hurdle model but still recognizes that in some areas

the number of claims is reduced by the FBC This appears to be a reasonable assumption and

provides a BCA ratio of 396 for the ISO sample and 302 for all residential

The ISO data are net of deductibles so our BCA thus far only includes losses compensated by

the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion

also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the

ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all

loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA

values from a low of 237 to a high of 793

Payback of the FBC

Finally we use our BCA results to calculate a payback period for the investment of stronger

codes To convert our BCA ratio to a payback period we simply divide our 50-year planning

horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss

and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years

This is important for gauging potential political support or non-support for enactment of the new

codes Payback periods that approach the typical mortgage term 30 years would in theory be

difficult to achieve and that is not what our analysis indicates for the FBC

VI - Concluding Comments

In the aftermath of Hurricane Andrew which had exposed not only poor building

construction but also poor building code enforcement the state of Florida enacted statewide

building code changes that wrested away building code adoption control from individual localities

26

With full implementation of the statewide building code associated expectations are that

windstorm losses from extreme events such as hurricanes should be reduced moving forward

There have been a few studies confirming these expectations following the 2004 and 2005

hurricane season In this article we further verify and quantify these findings and expand the

existing building code risk reduction research in several important ways

Overall we empirically test the statewide implementation of a building code in reducing

wind related damages in Florida controlling for other relevant wind hazard exposure and

vulnerability characteristics from a traditional risk assessment perspective Our results show the

strong effect the statewide FBC had on losses from wind storms during this timeframe From the

treatment variable that measures implementation of the statewide codes the post 2000 year of

construction losses are shown to be reduced by as much as 72 percent consistent with other

previous findings

Finally we have conducted a BCA of the FBC to determine if expected benefits exceed

the cost of implementation Using a direct estimate for mitigated losses and an estimate that

includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public

policy from an economic perspective This result is close to that recommended by the multi-hazard

mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a

statewide BCA Importantly this information is essential in generating political and consumer

support for such building code public policy implementation

For example the economic effectiveness results shown here have implications for ongoing

policy discussions about reforming building codes from a national US perspective Moore OK

independently adopted enhanced building codes after its third violent tornado in 14 years killed 24

including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)

27

Construction practices in North Texas were brought under scrutiny after the December 2015

tornado revealed inadequate construction including an elementary school whose exterior walls

failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House

announced initiatives to increase community resilience with building codes as a major component

of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748

and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better

construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397

would provide tax credits for owners andor contractors who use techniques designed for resiliency

in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one

recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to

encourage states to use current building codes (Vaughn and Turner 2014)

Future research in the BCA of the FBC will further inform the public policy debate on

enhanced building codes The issue has national implications as other states find that wind hazards

impact them as well We have sufficient wind data to examine how the BCA performs under

different wind hazards Additionally it will be important to consider how future economic

development affects the BCA as well as varying climate change scenarios As the FBC is

mandatory for all new construction a statewide analysis was appropriate But individual

homeowners in older homes can invest in the retrofit of their home and qualify for discounts on

their homeowners insurance This topic is deserving of a robust analysis Although our BCA is

statewide regions within the state will likely have a spectrum of results For instance the ARA

2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their

analysis did not use realized loss data so confirmation of how the BCA varies between those

regions would be an important contribution Finally our sensitivity analysis was limited to two

28

variables reduction in future loss and the inclusion of deductibles Additional work will highlight

other variables that could modify the results

29

Appendix

We use this appendix to conduct more detailed analysis on several topics First selection

of the model specification using a regression discontinuity approach Second we provide an in

depth examination of the relationship between structure age and losses Third we perform a

Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an

alternative specification to see if our RD results are similar followed by regressions to examine

the year to year consistency of our Post FBC result Next we run a regression on claims to verify

the difference between our direct reduction result and our full reduction result Finally we perform

a regression on homes built to the SFBC which had adopted enhanced building codes in advance

of the FBC to assess the effect of earlier adoption of enhanced construction

Regression Discontinuity

Regression Discontinuity (RD) applies when an observation receives a treatment in our case

homes built under the FBC based on a rating variable in our case age of the structure at the year

of observation So for observations in 2005 homes built post 2000 received the treatment

adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify

how observations on either side of the implementation of the FBC (2000) perform in suffering loss

from windstorms The treatment variable is a function of the age of the home and age affects loss

in ways not related to the FBC such as depreciation and differences in materials and construction

practices across time To account for both the effect of age on loss as well as the implementation

of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment

The data we have from ISO is aggregated loss data by zip code and decade of construction So

we cannot get an annualized age To approach a true age we set the year built for each decade of

construction at the beginning of the decade then subtract that from the year of each observation to

get an approximate agexiv

30

To find the best specification we began with a simpler model which used a series of

categorical variables for each decade of construction to examine the effect of the code compared

to the omitted decade This method would approximate the changes in materials and construction

practices but was less effective in controlling for depreciation But it would give us a first

approximation of the code effect that we used as a benchmark when testing the best RD

specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970

with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When

the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When

either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81

A rough approximation of the codersquos effect from this approach would suggest a reduction in the

mid 70 percent range

Insert Table 1 ndash Appendix Here

Next we used a standard procedure with RD to search for the best way to include the rating

variable This process creates specifications that include age in increasing polynomials and

interacted with the treatment variable The goal is to find the specification with the lowest AIC

that comes close to the benchmark value of the treatment variable

Insert Tables 2 and 3 ndash Appendix Here

We did this first with regressions that limited the co-variates then with our full model In both

sets AIC reaches a minimum on the specification with age and age squared The interaction model

after that increases the AIC then the AIC goes down again with a cubed model and its interaction

model with the overall lowest AIC found on the cubed interaction model But we chose not to

use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order

models the magnitude of the treatment variable in the models with just polynomials compared to

31

the corresponding interaction models were close with the interaction models providing a larger

magnitude When the cubed models were added the magnitude jumped where the polynomial

cubed model went down well below our benchmark and the interaction model went up above our

benchmark We felt this made use of the cubed model inappropriate So we now need to choose

between the squared model and the one with the interaction terms The squared model (Model 4)

had a lower AIC and the interaction variables on the interaction model (Model 5) were not

significant so we chose to use the squared model without the interaction term This model gave a

magnitude for the treatment variable of a 72 reduction somewhat lower than the expected

magnitude in the mid 70rsquos percent The general form of the model is

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1

of the observations on either end of our data sorted by loss Our treatment variable Post FBC

remains highly significant with a coefficient value of -117 which compares favorably to our

coefficient value of -126 when the entire sample is used

Structure Age and Wind Losses

Our study is similar to recent studies on the effect of energy efficiency building codes

adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that

better insulation caulking and more efficient HVAC systems would result in lower energy

consumption But the change in energy consumption is less than engineering estimates projected

Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for

homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study

32

may be confounding age with vintage and found a decrease in energy use related to the home

simply being new rather than the change in building code Indeed Kotchen (2015) revisited the

question with data 10 years older and found the effect on electricity had disappeared while the

reduction in natural gas use increased Something is occurring in energy use unrelated to the code

and could be explained by residents changing their use of energy as they adapt to their new home

Residents of an energy efficient home can undermine the intent of lower energy use by using the

efficient design to heat and cool their homes with a motivation toward increased comfort at the

same energy cost rather than energy savings Our study does not have the behavioral component

found in the case of energy efficiency In our application the construction elements that make the

structure able to withstand high winds are installed when the home is built and lie ldquobehind the

wallsrdquo making it unlikely for individual preferences to alter the homes performance against the

threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC

homes due to the code or simply an artifact of new versus old construction when confronted with

a windstorm

To first address our analysis of age versus the FBC we rerun our base regression but limit

our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more

than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14

years during the highest loss year of 2004 Since this is a comparison between two adjacent

decades on either side of our cut point of year 2000 we remove age and age squared Results are

shown in Table 4-Appendix

Insert Table 4-Appendix Here

The coefficient on Post FBC is still negative highly significant with a magnitude very close to

what we saw with the entire database and the age variables This result suggests that the code

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

16

The performance of our regression model is satisfactory in terms of the performance of the

explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and

the coefficient on our treatment variable Post FBC is -126 and highly significant

Overall our results show the strong effect the statewide FBC had on losses from wind

storms during this timeframe Using the results from the regression in Table 4 the coefficient on

the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses

than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the

results from a study conducted by the Insurance Institute for Business and Home Safety after

Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60

percent less likely to suffer damage at all and those that were damaged sustained 42 percent less

damage than older homes Our result of 72 percent lower damage reflects both those attributes as

our data included ZIP codeyearYOC observations that suffered damage as well as those that did

not

Our variables to measure the effect of wind hazard are wind speed and duration For both

variables we have a positive sign and each is highly significant Higher wind speed and higher

duration of high wind speeds increases damage and thus loss The remaining variables perform as

expected

Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In

the first stage variables such as Max Wind and Wind Duration significantly increase the

probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post

FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower

for homes built after new building codes were adopted In the second stage we see that our wind

variables continue to significantly increase the size of the loss and our treatment variable Post

17

FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now

lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy

we see that losses are reduced by about 47 as opposed to 72 when all observations are

includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better

construction reduces loss in two ways First it lowers claims and reduces the amount of a loss

when a claim is filedviii

Model Evaluation

To evaluate our model we used the second stage of the hurdle models and broke our data

into two groups The first group represents 90 of the data randomly selected and was used to

run the model and collect parameter estimates The second group is an out of sample control group

to test the validity of the model Parameter estimates from the first group are applied to the control

group which gave us a predicted loss for each observation in the control group that can be

compared to the actual loss for each observation in the control group We then regressed the

predicted loss from the control group against the actual loss

Insert Figure 2 Here

Figure 2 plots the predicted loss against the actual loss and provides the fitted line with

95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears

to do a good job of predicting most losses

Robustness of Table 4 Base Model Results

To test the robustness of our results we run three separate analyses 1) We first run a

regression with few co-variates 2) As wind design speeds have been used as a proxy for building

code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss

18

analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking

Florida in 2004 and 2005

Regressions using Few Co-Variates

Additional relevant co-variates add precision to a model But the value of the focus

variable should be apparent with a smaller set So we ran a model with only insured customer

based variables EHY and paid premiums leaving out all other demographic and hazard related

variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and

significance

Regressions Using Design Speed

The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads

for Buildings and Other Structures published by the American Society of Civil Engineers and the

Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds

for most regions of the United States and their territories These reference wind speeds are used in

calculations to determine design wind pressures for the primary structure of a building and the

cladding and components attached to a building These calculations take into account the building

geometry the importance of a building the exposuresurrounding terrain and other parameters that

influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design

speeds as a proxy for building code strength and we similarly add this as an additional control in

our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps

were provided by the Applied Technology Council (ATC) Although this version of the wind

speed map was not utilized during the period under consideration the relative values in general

between two locations would be the sameix

19

We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded

form from the ATC and spatially joined the values to our Florida ZIP codes We then further

categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat

4 and Cat 5 hurricane levels

Insert Table 5 Here

The regression adds two dummy variables first for ZIP codes whose design speed exceeds

the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed

reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables

for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that

communities in higher wind zones may take further measures in local codes However the effect

is not significant Notably our variable for Post FBC construction maintains its negative sign

magnitude and significance

Regressions Limited to 2004 and 2005

Our next regression also shown in Table 5 is limited to observations that occurred during

the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and

2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not

surprisingly the magnitude on wind speed increases while maintaining its significance and the

magnitude on age does the same But the effect of the FBC remains the same a 72 reduction

Summary of Results on the FBC

We have collected a comprehensive set of data on insured paid losses from 2001 to 2010

windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity

model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of

the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction

20

in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to

determine if the policy is one that is cost effective

VI Benefit and Costs of the FBC

Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a

number of cases the economic effectiveness of the improved building codes has not been as well

documented especially from a statewide implementation perspective The multi-hazard

mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose

et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar

spent) was appropriate for process activity grant spending related to improved building codes

However this information was gathered from a limited number of studies (mainly earthquake

oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in

generating it and the ratio provided due to improvement would not be the same as those for

adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted

an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for

ten related counties and determined that the net present value of the revisions was $7 billion or

benefit-cost ratio greater than 1 Importantly though this study did not have access to actual

building code damage reduction data to utilize in the analysis In 2002 Applied Research

Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC

for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how

the three types of FBC built houses would perform in probabilistic hurricane scenarios compared

to the same houses built under the previous code Given the probabilistic nature of the analysis

average annual losses were generated that demonstrated post-FBC housing having loss reductions

54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were

21

then compared to their estimated cost impacts of the FBC for these housing types with at least

break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and

above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al

(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss

reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results

ranged from 041 to 183 for the retrofit mitigation activities to existing housing

We propose a BCA that differs from earlier work in several important ways First we use

realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of

how much loss can be reduced by the FBC Second our loss data spans 10 years which include a

combination of major hurricanes and smaller wind storms

BenefitCost Methodology

The elements of a BCA requires three inputs 1) an estimate of the added cost to implement

the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related

storms from our realized ISO loss data and then from a statewide catastrophe model estimate and

3) the percentage of expected loss that will be mitigated due to implementation of the FBC We

first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis

but use the full reduction in loss which includes the value of reduced claims Finally our ISO data

is paid losses and does not include deductibles so we add an estimate for deductibles

Additional Cost

In their 2002 benefit-cost comparison study of the enactment of the FBC for three related

housing types three actual sample homes were built to the FBC to evaluate the change in

construction costs (ARA 2002) For the purposes of code implementation the state was divided

into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region

22

(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account

for cost differences between the two regions

In the WBDR an added requirement is impact protection to windows and doors to reduce

damage from flying debris Along the coast and much of South Florida is classified as the WBDR

The N-WBDR is mainly classified in the interior of the state where impact protection is not

required Importantly the study provided a range of added costs for the N-WBDR and the WBDR

Three counties in South Florida Dade Broward and Monroe were under the South Florida

Building Code (SFBC) prior to the implementation of the FBC According to the ARA study

(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges

of incremental cost per square foot for the N-WBDR and WBDR along with the percent of

residential units that reside in each area This allows a calculation of a weighted average added

cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost

per square foot of $166 The cost compares favorably with a similar building code enhancement

adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in

2013 Consulting engineers and the Moore Association of Homebuilders estimated the code

enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is

1960 square feet which means that on average the FBC increases construction cost by $3254 per

structurexii

Insert Table 6 Here

Benefit of the FBC

Benefits stemming from the FBC are the expected reduction in losses from windstorms during

the life of the home We first find an average annual loss (AAL) use that number to estimate

losses for the next 50 years and then find the present value of those losses in 2010 Here we are

23

assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the

next 50 years A wealth of literature suggests the potential for changes to hurricane activity over

the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty

on future changes in wind hazard on the scale of a single state we choose to assume a stationary

climate

Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes

built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our

data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of

$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year

Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses

per structure of $21474

Finally we use parameter estimates from our regression for the Post FBC dummy variable

(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC

The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post

FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what

would be a reduction of $10093 from the projected $21474 in future losses

Insert Table 7 Here

BenefitCost Analysis

Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio

of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the

statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an

economically effective public policy following from our ISO loss data and results

Insert Table 8 Here

24

Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years

This relatively short timeframe makes it difficult to truly approximate an AAL as would be

provided from a probabilistically based catastrophe model that generates an AAL from thousands

of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state

of Florida to estimate an average annual wind loss for all residential properties in Florida of

approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL

estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion

with deductibles) Using this aggregate AAL and the number of residential units in Florida based

on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses

net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and

an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as

before derived from our regression results 47 we find $7710 of reduced loss from the projected

$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added

$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective

building code public policy

We run two additional analyses on our BCA results Our estimate of expected loss

reduction comes from the second stage of the hurdle model This is an estimate of the direct loss

reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the

number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests

as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which

includes the reduced magnitude of loss from affected homes and the reduction in claims for Post

FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table

8) However a 72 loss reduction seems too dramatic an expectation when planning so far in

25

advance For that reason we offer a third level of expected loss reduction of 60 which is the

midpoint between our two loss reduction estimates This estimate captures the expected direct loss

reduction suggested by the second stage of our hurdle model but still recognizes that in some areas

the number of claims is reduced by the FBC This appears to be a reasonable assumption and

provides a BCA ratio of 396 for the ISO sample and 302 for all residential

The ISO data are net of deductibles so our BCA thus far only includes losses compensated by

the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion

also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the

ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all

loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA

values from a low of 237 to a high of 793

Payback of the FBC

Finally we use our BCA results to calculate a payback period for the investment of stronger

codes To convert our BCA ratio to a payback period we simply divide our 50-year planning

horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss

and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years

This is important for gauging potential political support or non-support for enactment of the new

codes Payback periods that approach the typical mortgage term 30 years would in theory be

difficult to achieve and that is not what our analysis indicates for the FBC

VI - Concluding Comments

In the aftermath of Hurricane Andrew which had exposed not only poor building

construction but also poor building code enforcement the state of Florida enacted statewide

building code changes that wrested away building code adoption control from individual localities

26

With full implementation of the statewide building code associated expectations are that

windstorm losses from extreme events such as hurricanes should be reduced moving forward

There have been a few studies confirming these expectations following the 2004 and 2005

hurricane season In this article we further verify and quantify these findings and expand the

existing building code risk reduction research in several important ways

Overall we empirically test the statewide implementation of a building code in reducing

wind related damages in Florida controlling for other relevant wind hazard exposure and

vulnerability characteristics from a traditional risk assessment perspective Our results show the

strong effect the statewide FBC had on losses from wind storms during this timeframe From the

treatment variable that measures implementation of the statewide codes the post 2000 year of

construction losses are shown to be reduced by as much as 72 percent consistent with other

previous findings

Finally we have conducted a BCA of the FBC to determine if expected benefits exceed

the cost of implementation Using a direct estimate for mitigated losses and an estimate that

includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public

policy from an economic perspective This result is close to that recommended by the multi-hazard

mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a

statewide BCA Importantly this information is essential in generating political and consumer

support for such building code public policy implementation

For example the economic effectiveness results shown here have implications for ongoing

policy discussions about reforming building codes from a national US perspective Moore OK

independently adopted enhanced building codes after its third violent tornado in 14 years killed 24

including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)

27

Construction practices in North Texas were brought under scrutiny after the December 2015

tornado revealed inadequate construction including an elementary school whose exterior walls

failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House

announced initiatives to increase community resilience with building codes as a major component

of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748

and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better

construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397

would provide tax credits for owners andor contractors who use techniques designed for resiliency

in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one

recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to

encourage states to use current building codes (Vaughn and Turner 2014)

Future research in the BCA of the FBC will further inform the public policy debate on

enhanced building codes The issue has national implications as other states find that wind hazards

impact them as well We have sufficient wind data to examine how the BCA performs under

different wind hazards Additionally it will be important to consider how future economic

development affects the BCA as well as varying climate change scenarios As the FBC is

mandatory for all new construction a statewide analysis was appropriate But individual

homeowners in older homes can invest in the retrofit of their home and qualify for discounts on

their homeowners insurance This topic is deserving of a robust analysis Although our BCA is

statewide regions within the state will likely have a spectrum of results For instance the ARA

2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their

analysis did not use realized loss data so confirmation of how the BCA varies between those

regions would be an important contribution Finally our sensitivity analysis was limited to two

28

variables reduction in future loss and the inclusion of deductibles Additional work will highlight

other variables that could modify the results

29

Appendix

We use this appendix to conduct more detailed analysis on several topics First selection

of the model specification using a regression discontinuity approach Second we provide an in

depth examination of the relationship between structure age and losses Third we perform a

Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an

alternative specification to see if our RD results are similar followed by regressions to examine

the year to year consistency of our Post FBC result Next we run a regression on claims to verify

the difference between our direct reduction result and our full reduction result Finally we perform

a regression on homes built to the SFBC which had adopted enhanced building codes in advance

of the FBC to assess the effect of earlier adoption of enhanced construction

Regression Discontinuity

Regression Discontinuity (RD) applies when an observation receives a treatment in our case

homes built under the FBC based on a rating variable in our case age of the structure at the year

of observation So for observations in 2005 homes built post 2000 received the treatment

adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify

how observations on either side of the implementation of the FBC (2000) perform in suffering loss

from windstorms The treatment variable is a function of the age of the home and age affects loss

in ways not related to the FBC such as depreciation and differences in materials and construction

practices across time To account for both the effect of age on loss as well as the implementation

of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment

The data we have from ISO is aggregated loss data by zip code and decade of construction So

we cannot get an annualized age To approach a true age we set the year built for each decade of

construction at the beginning of the decade then subtract that from the year of each observation to

get an approximate agexiv

30

To find the best specification we began with a simpler model which used a series of

categorical variables for each decade of construction to examine the effect of the code compared

to the omitted decade This method would approximate the changes in materials and construction

practices but was less effective in controlling for depreciation But it would give us a first

approximation of the code effect that we used as a benchmark when testing the best RD

specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970

with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When

the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When

either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81

A rough approximation of the codersquos effect from this approach would suggest a reduction in the

mid 70 percent range

Insert Table 1 ndash Appendix Here

Next we used a standard procedure with RD to search for the best way to include the rating

variable This process creates specifications that include age in increasing polynomials and

interacted with the treatment variable The goal is to find the specification with the lowest AIC

that comes close to the benchmark value of the treatment variable

Insert Tables 2 and 3 ndash Appendix Here

We did this first with regressions that limited the co-variates then with our full model In both

sets AIC reaches a minimum on the specification with age and age squared The interaction model

after that increases the AIC then the AIC goes down again with a cubed model and its interaction

model with the overall lowest AIC found on the cubed interaction model But we chose not to

use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order

models the magnitude of the treatment variable in the models with just polynomials compared to

31

the corresponding interaction models were close with the interaction models providing a larger

magnitude When the cubed models were added the magnitude jumped where the polynomial

cubed model went down well below our benchmark and the interaction model went up above our

benchmark We felt this made use of the cubed model inappropriate So we now need to choose

between the squared model and the one with the interaction terms The squared model (Model 4)

had a lower AIC and the interaction variables on the interaction model (Model 5) were not

significant so we chose to use the squared model without the interaction term This model gave a

magnitude for the treatment variable of a 72 reduction somewhat lower than the expected

magnitude in the mid 70rsquos percent The general form of the model is

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1

of the observations on either end of our data sorted by loss Our treatment variable Post FBC

remains highly significant with a coefficient value of -117 which compares favorably to our

coefficient value of -126 when the entire sample is used

Structure Age and Wind Losses

Our study is similar to recent studies on the effect of energy efficiency building codes

adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that

better insulation caulking and more efficient HVAC systems would result in lower energy

consumption But the change in energy consumption is less than engineering estimates projected

Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for

homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study

32

may be confounding age with vintage and found a decrease in energy use related to the home

simply being new rather than the change in building code Indeed Kotchen (2015) revisited the

question with data 10 years older and found the effect on electricity had disappeared while the

reduction in natural gas use increased Something is occurring in energy use unrelated to the code

and could be explained by residents changing their use of energy as they adapt to their new home

Residents of an energy efficient home can undermine the intent of lower energy use by using the

efficient design to heat and cool their homes with a motivation toward increased comfort at the

same energy cost rather than energy savings Our study does not have the behavioral component

found in the case of energy efficiency In our application the construction elements that make the

structure able to withstand high winds are installed when the home is built and lie ldquobehind the

wallsrdquo making it unlikely for individual preferences to alter the homes performance against the

threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC

homes due to the code or simply an artifact of new versus old construction when confronted with

a windstorm

To first address our analysis of age versus the FBC we rerun our base regression but limit

our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more

than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14

years during the highest loss year of 2004 Since this is a comparison between two adjacent

decades on either side of our cut point of year 2000 we remove age and age squared Results are

shown in Table 4-Appendix

Insert Table 4-Appendix Here

The coefficient on Post FBC is still negative highly significant with a magnitude very close to

what we saw with the entire database and the age variables This result suggests that the code

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

17

FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now

lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy

we see that losses are reduced by about 47 as opposed to 72 when all observations are

includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better

construction reduces loss in two ways First it lowers claims and reduces the amount of a loss

when a claim is filedviii

Model Evaluation

To evaluate our model we used the second stage of the hurdle models and broke our data

into two groups The first group represents 90 of the data randomly selected and was used to

run the model and collect parameter estimates The second group is an out of sample control group

to test the validity of the model Parameter estimates from the first group are applied to the control

group which gave us a predicted loss for each observation in the control group that can be

compared to the actual loss for each observation in the control group We then regressed the

predicted loss from the control group against the actual loss

Insert Figure 2 Here

Figure 2 plots the predicted loss against the actual loss and provides the fitted line with

95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears

to do a good job of predicting most losses

Robustness of Table 4 Base Model Results

To test the robustness of our results we run three separate analyses 1) We first run a

regression with few co-variates 2) As wind design speeds have been used as a proxy for building

code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss

18

analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking

Florida in 2004 and 2005

Regressions using Few Co-Variates

Additional relevant co-variates add precision to a model But the value of the focus

variable should be apparent with a smaller set So we ran a model with only insured customer

based variables EHY and paid premiums leaving out all other demographic and hazard related

variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and

significance

Regressions Using Design Speed

The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads

for Buildings and Other Structures published by the American Society of Civil Engineers and the

Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds

for most regions of the United States and their territories These reference wind speeds are used in

calculations to determine design wind pressures for the primary structure of a building and the

cladding and components attached to a building These calculations take into account the building

geometry the importance of a building the exposuresurrounding terrain and other parameters that

influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design

speeds as a proxy for building code strength and we similarly add this as an additional control in

our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps

were provided by the Applied Technology Council (ATC) Although this version of the wind

speed map was not utilized during the period under consideration the relative values in general

between two locations would be the sameix

19

We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded

form from the ATC and spatially joined the values to our Florida ZIP codes We then further

categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat

4 and Cat 5 hurricane levels

Insert Table 5 Here

The regression adds two dummy variables first for ZIP codes whose design speed exceeds

the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed

reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables

for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that

communities in higher wind zones may take further measures in local codes However the effect

is not significant Notably our variable for Post FBC construction maintains its negative sign

magnitude and significance

Regressions Limited to 2004 and 2005

Our next regression also shown in Table 5 is limited to observations that occurred during

the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and

2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not

surprisingly the magnitude on wind speed increases while maintaining its significance and the

magnitude on age does the same But the effect of the FBC remains the same a 72 reduction

Summary of Results on the FBC

We have collected a comprehensive set of data on insured paid losses from 2001 to 2010

windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity

model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of

the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction

20

in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to

determine if the policy is one that is cost effective

VI Benefit and Costs of the FBC

Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a

number of cases the economic effectiveness of the improved building codes has not been as well

documented especially from a statewide implementation perspective The multi-hazard

mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose

et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar

spent) was appropriate for process activity grant spending related to improved building codes

However this information was gathered from a limited number of studies (mainly earthquake

oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in

generating it and the ratio provided due to improvement would not be the same as those for

adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted

an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for

ten related counties and determined that the net present value of the revisions was $7 billion or

benefit-cost ratio greater than 1 Importantly though this study did not have access to actual

building code damage reduction data to utilize in the analysis In 2002 Applied Research

Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC

for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how

the three types of FBC built houses would perform in probabilistic hurricane scenarios compared

to the same houses built under the previous code Given the probabilistic nature of the analysis

average annual losses were generated that demonstrated post-FBC housing having loss reductions

54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were

21

then compared to their estimated cost impacts of the FBC for these housing types with at least

break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and

above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al

(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss

reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results

ranged from 041 to 183 for the retrofit mitigation activities to existing housing

We propose a BCA that differs from earlier work in several important ways First we use

realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of

how much loss can be reduced by the FBC Second our loss data spans 10 years which include a

combination of major hurricanes and smaller wind storms

BenefitCost Methodology

The elements of a BCA requires three inputs 1) an estimate of the added cost to implement

the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related

storms from our realized ISO loss data and then from a statewide catastrophe model estimate and

3) the percentage of expected loss that will be mitigated due to implementation of the FBC We

first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis

but use the full reduction in loss which includes the value of reduced claims Finally our ISO data

is paid losses and does not include deductibles so we add an estimate for deductibles

Additional Cost

In their 2002 benefit-cost comparison study of the enactment of the FBC for three related

housing types three actual sample homes were built to the FBC to evaluate the change in

construction costs (ARA 2002) For the purposes of code implementation the state was divided

into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region

22

(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account

for cost differences between the two regions

In the WBDR an added requirement is impact protection to windows and doors to reduce

damage from flying debris Along the coast and much of South Florida is classified as the WBDR

The N-WBDR is mainly classified in the interior of the state where impact protection is not

required Importantly the study provided a range of added costs for the N-WBDR and the WBDR

Three counties in South Florida Dade Broward and Monroe were under the South Florida

Building Code (SFBC) prior to the implementation of the FBC According to the ARA study

(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges

of incremental cost per square foot for the N-WBDR and WBDR along with the percent of

residential units that reside in each area This allows a calculation of a weighted average added

cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost

per square foot of $166 The cost compares favorably with a similar building code enhancement

adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in

2013 Consulting engineers and the Moore Association of Homebuilders estimated the code

enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is

1960 square feet which means that on average the FBC increases construction cost by $3254 per

structurexii

Insert Table 6 Here

Benefit of the FBC

Benefits stemming from the FBC are the expected reduction in losses from windstorms during

the life of the home We first find an average annual loss (AAL) use that number to estimate

losses for the next 50 years and then find the present value of those losses in 2010 Here we are

23

assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the

next 50 years A wealth of literature suggests the potential for changes to hurricane activity over

the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty

on future changes in wind hazard on the scale of a single state we choose to assume a stationary

climate

Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes

built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our

data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of

$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year

Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses

per structure of $21474

Finally we use parameter estimates from our regression for the Post FBC dummy variable

(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC

The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post

FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what

would be a reduction of $10093 from the projected $21474 in future losses

Insert Table 7 Here

BenefitCost Analysis

Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio

of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the

statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an

economically effective public policy following from our ISO loss data and results

Insert Table 8 Here

24

Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years

This relatively short timeframe makes it difficult to truly approximate an AAL as would be

provided from a probabilistically based catastrophe model that generates an AAL from thousands

of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state

of Florida to estimate an average annual wind loss for all residential properties in Florida of

approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL

estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion

with deductibles) Using this aggregate AAL and the number of residential units in Florida based

on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses

net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and

an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as

before derived from our regression results 47 we find $7710 of reduced loss from the projected

$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added

$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective

building code public policy

We run two additional analyses on our BCA results Our estimate of expected loss

reduction comes from the second stage of the hurdle model This is an estimate of the direct loss

reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the

number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests

as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which

includes the reduced magnitude of loss from affected homes and the reduction in claims for Post

FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table

8) However a 72 loss reduction seems too dramatic an expectation when planning so far in

25

advance For that reason we offer a third level of expected loss reduction of 60 which is the

midpoint between our two loss reduction estimates This estimate captures the expected direct loss

reduction suggested by the second stage of our hurdle model but still recognizes that in some areas

the number of claims is reduced by the FBC This appears to be a reasonable assumption and

provides a BCA ratio of 396 for the ISO sample and 302 for all residential

The ISO data are net of deductibles so our BCA thus far only includes losses compensated by

the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion

also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the

ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all

loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA

values from a low of 237 to a high of 793

Payback of the FBC

Finally we use our BCA results to calculate a payback period for the investment of stronger

codes To convert our BCA ratio to a payback period we simply divide our 50-year planning

horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss

and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years

This is important for gauging potential political support or non-support for enactment of the new

codes Payback periods that approach the typical mortgage term 30 years would in theory be

difficult to achieve and that is not what our analysis indicates for the FBC

VI - Concluding Comments

In the aftermath of Hurricane Andrew which had exposed not only poor building

construction but also poor building code enforcement the state of Florida enacted statewide

building code changes that wrested away building code adoption control from individual localities

26

With full implementation of the statewide building code associated expectations are that

windstorm losses from extreme events such as hurricanes should be reduced moving forward

There have been a few studies confirming these expectations following the 2004 and 2005

hurricane season In this article we further verify and quantify these findings and expand the

existing building code risk reduction research in several important ways

Overall we empirically test the statewide implementation of a building code in reducing

wind related damages in Florida controlling for other relevant wind hazard exposure and

vulnerability characteristics from a traditional risk assessment perspective Our results show the

strong effect the statewide FBC had on losses from wind storms during this timeframe From the

treatment variable that measures implementation of the statewide codes the post 2000 year of

construction losses are shown to be reduced by as much as 72 percent consistent with other

previous findings

Finally we have conducted a BCA of the FBC to determine if expected benefits exceed

the cost of implementation Using a direct estimate for mitigated losses and an estimate that

includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public

policy from an economic perspective This result is close to that recommended by the multi-hazard

mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a

statewide BCA Importantly this information is essential in generating political and consumer

support for such building code public policy implementation

For example the economic effectiveness results shown here have implications for ongoing

policy discussions about reforming building codes from a national US perspective Moore OK

independently adopted enhanced building codes after its third violent tornado in 14 years killed 24

including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)

27

Construction practices in North Texas were brought under scrutiny after the December 2015

tornado revealed inadequate construction including an elementary school whose exterior walls

failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House

announced initiatives to increase community resilience with building codes as a major component

of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748

and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better

construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397

would provide tax credits for owners andor contractors who use techniques designed for resiliency

in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one

recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to

encourage states to use current building codes (Vaughn and Turner 2014)

Future research in the BCA of the FBC will further inform the public policy debate on

enhanced building codes The issue has national implications as other states find that wind hazards

impact them as well We have sufficient wind data to examine how the BCA performs under

different wind hazards Additionally it will be important to consider how future economic

development affects the BCA as well as varying climate change scenarios As the FBC is

mandatory for all new construction a statewide analysis was appropriate But individual

homeowners in older homes can invest in the retrofit of their home and qualify for discounts on

their homeowners insurance This topic is deserving of a robust analysis Although our BCA is

statewide regions within the state will likely have a spectrum of results For instance the ARA

2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their

analysis did not use realized loss data so confirmation of how the BCA varies between those

regions would be an important contribution Finally our sensitivity analysis was limited to two

28

variables reduction in future loss and the inclusion of deductibles Additional work will highlight

other variables that could modify the results

29

Appendix

We use this appendix to conduct more detailed analysis on several topics First selection

of the model specification using a regression discontinuity approach Second we provide an in

depth examination of the relationship between structure age and losses Third we perform a

Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an

alternative specification to see if our RD results are similar followed by regressions to examine

the year to year consistency of our Post FBC result Next we run a regression on claims to verify

the difference between our direct reduction result and our full reduction result Finally we perform

a regression on homes built to the SFBC which had adopted enhanced building codes in advance

of the FBC to assess the effect of earlier adoption of enhanced construction

Regression Discontinuity

Regression Discontinuity (RD) applies when an observation receives a treatment in our case

homes built under the FBC based on a rating variable in our case age of the structure at the year

of observation So for observations in 2005 homes built post 2000 received the treatment

adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify

how observations on either side of the implementation of the FBC (2000) perform in suffering loss

from windstorms The treatment variable is a function of the age of the home and age affects loss

in ways not related to the FBC such as depreciation and differences in materials and construction

practices across time To account for both the effect of age on loss as well as the implementation

of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment

The data we have from ISO is aggregated loss data by zip code and decade of construction So

we cannot get an annualized age To approach a true age we set the year built for each decade of

construction at the beginning of the decade then subtract that from the year of each observation to

get an approximate agexiv

30

To find the best specification we began with a simpler model which used a series of

categorical variables for each decade of construction to examine the effect of the code compared

to the omitted decade This method would approximate the changes in materials and construction

practices but was less effective in controlling for depreciation But it would give us a first

approximation of the code effect that we used as a benchmark when testing the best RD

specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970

with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When

the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When

either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81

A rough approximation of the codersquos effect from this approach would suggest a reduction in the

mid 70 percent range

Insert Table 1 ndash Appendix Here

Next we used a standard procedure with RD to search for the best way to include the rating

variable This process creates specifications that include age in increasing polynomials and

interacted with the treatment variable The goal is to find the specification with the lowest AIC

that comes close to the benchmark value of the treatment variable

Insert Tables 2 and 3 ndash Appendix Here

We did this first with regressions that limited the co-variates then with our full model In both

sets AIC reaches a minimum on the specification with age and age squared The interaction model

after that increases the AIC then the AIC goes down again with a cubed model and its interaction

model with the overall lowest AIC found on the cubed interaction model But we chose not to

use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order

models the magnitude of the treatment variable in the models with just polynomials compared to

31

the corresponding interaction models were close with the interaction models providing a larger

magnitude When the cubed models were added the magnitude jumped where the polynomial

cubed model went down well below our benchmark and the interaction model went up above our

benchmark We felt this made use of the cubed model inappropriate So we now need to choose

between the squared model and the one with the interaction terms The squared model (Model 4)

had a lower AIC and the interaction variables on the interaction model (Model 5) were not

significant so we chose to use the squared model without the interaction term This model gave a

magnitude for the treatment variable of a 72 reduction somewhat lower than the expected

magnitude in the mid 70rsquos percent The general form of the model is

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1

of the observations on either end of our data sorted by loss Our treatment variable Post FBC

remains highly significant with a coefficient value of -117 which compares favorably to our

coefficient value of -126 when the entire sample is used

Structure Age and Wind Losses

Our study is similar to recent studies on the effect of energy efficiency building codes

adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that

better insulation caulking and more efficient HVAC systems would result in lower energy

consumption But the change in energy consumption is less than engineering estimates projected

Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for

homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study

32

may be confounding age with vintage and found a decrease in energy use related to the home

simply being new rather than the change in building code Indeed Kotchen (2015) revisited the

question with data 10 years older and found the effect on electricity had disappeared while the

reduction in natural gas use increased Something is occurring in energy use unrelated to the code

and could be explained by residents changing their use of energy as they adapt to their new home

Residents of an energy efficient home can undermine the intent of lower energy use by using the

efficient design to heat and cool their homes with a motivation toward increased comfort at the

same energy cost rather than energy savings Our study does not have the behavioral component

found in the case of energy efficiency In our application the construction elements that make the

structure able to withstand high winds are installed when the home is built and lie ldquobehind the

wallsrdquo making it unlikely for individual preferences to alter the homes performance against the

threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC

homes due to the code or simply an artifact of new versus old construction when confronted with

a windstorm

To first address our analysis of age versus the FBC we rerun our base regression but limit

our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more

than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14

years during the highest loss year of 2004 Since this is a comparison between two adjacent

decades on either side of our cut point of year 2000 we remove age and age squared Results are

shown in Table 4-Appendix

Insert Table 4-Appendix Here

The coefficient on Post FBC is still negative highly significant with a magnitude very close to

what we saw with the entire database and the age variables This result suggests that the code

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

18

analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking

Florida in 2004 and 2005

Regressions using Few Co-Variates

Additional relevant co-variates add precision to a model But the value of the focus

variable should be apparent with a smaller set So we ran a model with only insured customer

based variables EHY and paid premiums leaving out all other demographic and hazard related

variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and

significance

Regressions Using Design Speed

The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads

for Buildings and Other Structures published by the American Society of Civil Engineers and the

Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds

for most regions of the United States and their territories These reference wind speeds are used in

calculations to determine design wind pressures for the primary structure of a building and the

cladding and components attached to a building These calculations take into account the building

geometry the importance of a building the exposuresurrounding terrain and other parameters that

influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design

speeds as a proxy for building code strength and we similarly add this as an additional control in

our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps

were provided by the Applied Technology Council (ATC) Although this version of the wind

speed map was not utilized during the period under consideration the relative values in general

between two locations would be the sameix

19

We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded

form from the ATC and spatially joined the values to our Florida ZIP codes We then further

categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat

4 and Cat 5 hurricane levels

Insert Table 5 Here

The regression adds two dummy variables first for ZIP codes whose design speed exceeds

the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed

reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables

for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that

communities in higher wind zones may take further measures in local codes However the effect

is not significant Notably our variable for Post FBC construction maintains its negative sign

magnitude and significance

Regressions Limited to 2004 and 2005

Our next regression also shown in Table 5 is limited to observations that occurred during

the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and

2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not

surprisingly the magnitude on wind speed increases while maintaining its significance and the

magnitude on age does the same But the effect of the FBC remains the same a 72 reduction

Summary of Results on the FBC

We have collected a comprehensive set of data on insured paid losses from 2001 to 2010

windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity

model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of

the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction

20

in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to

determine if the policy is one that is cost effective

VI Benefit and Costs of the FBC

Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a

number of cases the economic effectiveness of the improved building codes has not been as well

documented especially from a statewide implementation perspective The multi-hazard

mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose

et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar

spent) was appropriate for process activity grant spending related to improved building codes

However this information was gathered from a limited number of studies (mainly earthquake

oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in

generating it and the ratio provided due to improvement would not be the same as those for

adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted

an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for

ten related counties and determined that the net present value of the revisions was $7 billion or

benefit-cost ratio greater than 1 Importantly though this study did not have access to actual

building code damage reduction data to utilize in the analysis In 2002 Applied Research

Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC

for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how

the three types of FBC built houses would perform in probabilistic hurricane scenarios compared

to the same houses built under the previous code Given the probabilistic nature of the analysis

average annual losses were generated that demonstrated post-FBC housing having loss reductions

54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were

21

then compared to their estimated cost impacts of the FBC for these housing types with at least

break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and

above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al

(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss

reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results

ranged from 041 to 183 for the retrofit mitigation activities to existing housing

We propose a BCA that differs from earlier work in several important ways First we use

realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of

how much loss can be reduced by the FBC Second our loss data spans 10 years which include a

combination of major hurricanes and smaller wind storms

BenefitCost Methodology

The elements of a BCA requires three inputs 1) an estimate of the added cost to implement

the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related

storms from our realized ISO loss data and then from a statewide catastrophe model estimate and

3) the percentage of expected loss that will be mitigated due to implementation of the FBC We

first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis

but use the full reduction in loss which includes the value of reduced claims Finally our ISO data

is paid losses and does not include deductibles so we add an estimate for deductibles

Additional Cost

In their 2002 benefit-cost comparison study of the enactment of the FBC for three related

housing types three actual sample homes were built to the FBC to evaluate the change in

construction costs (ARA 2002) For the purposes of code implementation the state was divided

into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region

22

(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account

for cost differences between the two regions

In the WBDR an added requirement is impact protection to windows and doors to reduce

damage from flying debris Along the coast and much of South Florida is classified as the WBDR

The N-WBDR is mainly classified in the interior of the state where impact protection is not

required Importantly the study provided a range of added costs for the N-WBDR and the WBDR

Three counties in South Florida Dade Broward and Monroe were under the South Florida

Building Code (SFBC) prior to the implementation of the FBC According to the ARA study

(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges

of incremental cost per square foot for the N-WBDR and WBDR along with the percent of

residential units that reside in each area This allows a calculation of a weighted average added

cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost

per square foot of $166 The cost compares favorably with a similar building code enhancement

adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in

2013 Consulting engineers and the Moore Association of Homebuilders estimated the code

enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is

1960 square feet which means that on average the FBC increases construction cost by $3254 per

structurexii

Insert Table 6 Here

Benefit of the FBC

Benefits stemming from the FBC are the expected reduction in losses from windstorms during

the life of the home We first find an average annual loss (AAL) use that number to estimate

losses for the next 50 years and then find the present value of those losses in 2010 Here we are

23

assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the

next 50 years A wealth of literature suggests the potential for changes to hurricane activity over

the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty

on future changes in wind hazard on the scale of a single state we choose to assume a stationary

climate

Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes

built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our

data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of

$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year

Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses

per structure of $21474

Finally we use parameter estimates from our regression for the Post FBC dummy variable

(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC

The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post

FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what

would be a reduction of $10093 from the projected $21474 in future losses

Insert Table 7 Here

BenefitCost Analysis

Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio

of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the

statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an

economically effective public policy following from our ISO loss data and results

Insert Table 8 Here

24

Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years

This relatively short timeframe makes it difficult to truly approximate an AAL as would be

provided from a probabilistically based catastrophe model that generates an AAL from thousands

of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state

of Florida to estimate an average annual wind loss for all residential properties in Florida of

approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL

estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion

with deductibles) Using this aggregate AAL and the number of residential units in Florida based

on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses

net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and

an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as

before derived from our regression results 47 we find $7710 of reduced loss from the projected

$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added

$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective

building code public policy

We run two additional analyses on our BCA results Our estimate of expected loss

reduction comes from the second stage of the hurdle model This is an estimate of the direct loss

reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the

number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests

as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which

includes the reduced magnitude of loss from affected homes and the reduction in claims for Post

FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table

8) However a 72 loss reduction seems too dramatic an expectation when planning so far in

25

advance For that reason we offer a third level of expected loss reduction of 60 which is the

midpoint between our two loss reduction estimates This estimate captures the expected direct loss

reduction suggested by the second stage of our hurdle model but still recognizes that in some areas

the number of claims is reduced by the FBC This appears to be a reasonable assumption and

provides a BCA ratio of 396 for the ISO sample and 302 for all residential

The ISO data are net of deductibles so our BCA thus far only includes losses compensated by

the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion

also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the

ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all

loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA

values from a low of 237 to a high of 793

Payback of the FBC

Finally we use our BCA results to calculate a payback period for the investment of stronger

codes To convert our BCA ratio to a payback period we simply divide our 50-year planning

horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss

and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years

This is important for gauging potential political support or non-support for enactment of the new

codes Payback periods that approach the typical mortgage term 30 years would in theory be

difficult to achieve and that is not what our analysis indicates for the FBC

VI - Concluding Comments

In the aftermath of Hurricane Andrew which had exposed not only poor building

construction but also poor building code enforcement the state of Florida enacted statewide

building code changes that wrested away building code adoption control from individual localities

26

With full implementation of the statewide building code associated expectations are that

windstorm losses from extreme events such as hurricanes should be reduced moving forward

There have been a few studies confirming these expectations following the 2004 and 2005

hurricane season In this article we further verify and quantify these findings and expand the

existing building code risk reduction research in several important ways

Overall we empirically test the statewide implementation of a building code in reducing

wind related damages in Florida controlling for other relevant wind hazard exposure and

vulnerability characteristics from a traditional risk assessment perspective Our results show the

strong effect the statewide FBC had on losses from wind storms during this timeframe From the

treatment variable that measures implementation of the statewide codes the post 2000 year of

construction losses are shown to be reduced by as much as 72 percent consistent with other

previous findings

Finally we have conducted a BCA of the FBC to determine if expected benefits exceed

the cost of implementation Using a direct estimate for mitigated losses and an estimate that

includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public

policy from an economic perspective This result is close to that recommended by the multi-hazard

mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a

statewide BCA Importantly this information is essential in generating political and consumer

support for such building code public policy implementation

For example the economic effectiveness results shown here have implications for ongoing

policy discussions about reforming building codes from a national US perspective Moore OK

independently adopted enhanced building codes after its third violent tornado in 14 years killed 24

including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)

27

Construction practices in North Texas were brought under scrutiny after the December 2015

tornado revealed inadequate construction including an elementary school whose exterior walls

failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House

announced initiatives to increase community resilience with building codes as a major component

of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748

and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better

construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397

would provide tax credits for owners andor contractors who use techniques designed for resiliency

in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one

recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to

encourage states to use current building codes (Vaughn and Turner 2014)

Future research in the BCA of the FBC will further inform the public policy debate on

enhanced building codes The issue has national implications as other states find that wind hazards

impact them as well We have sufficient wind data to examine how the BCA performs under

different wind hazards Additionally it will be important to consider how future economic

development affects the BCA as well as varying climate change scenarios As the FBC is

mandatory for all new construction a statewide analysis was appropriate But individual

homeowners in older homes can invest in the retrofit of their home and qualify for discounts on

their homeowners insurance This topic is deserving of a robust analysis Although our BCA is

statewide regions within the state will likely have a spectrum of results For instance the ARA

2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their

analysis did not use realized loss data so confirmation of how the BCA varies between those

regions would be an important contribution Finally our sensitivity analysis was limited to two

28

variables reduction in future loss and the inclusion of deductibles Additional work will highlight

other variables that could modify the results

29

Appendix

We use this appendix to conduct more detailed analysis on several topics First selection

of the model specification using a regression discontinuity approach Second we provide an in

depth examination of the relationship between structure age and losses Third we perform a

Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an

alternative specification to see if our RD results are similar followed by regressions to examine

the year to year consistency of our Post FBC result Next we run a regression on claims to verify

the difference between our direct reduction result and our full reduction result Finally we perform

a regression on homes built to the SFBC which had adopted enhanced building codes in advance

of the FBC to assess the effect of earlier adoption of enhanced construction

Regression Discontinuity

Regression Discontinuity (RD) applies when an observation receives a treatment in our case

homes built under the FBC based on a rating variable in our case age of the structure at the year

of observation So for observations in 2005 homes built post 2000 received the treatment

adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify

how observations on either side of the implementation of the FBC (2000) perform in suffering loss

from windstorms The treatment variable is a function of the age of the home and age affects loss

in ways not related to the FBC such as depreciation and differences in materials and construction

practices across time To account for both the effect of age on loss as well as the implementation

of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment

The data we have from ISO is aggregated loss data by zip code and decade of construction So

we cannot get an annualized age To approach a true age we set the year built for each decade of

construction at the beginning of the decade then subtract that from the year of each observation to

get an approximate agexiv

30

To find the best specification we began with a simpler model which used a series of

categorical variables for each decade of construction to examine the effect of the code compared

to the omitted decade This method would approximate the changes in materials and construction

practices but was less effective in controlling for depreciation But it would give us a first

approximation of the code effect that we used as a benchmark when testing the best RD

specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970

with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When

the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When

either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81

A rough approximation of the codersquos effect from this approach would suggest a reduction in the

mid 70 percent range

Insert Table 1 ndash Appendix Here

Next we used a standard procedure with RD to search for the best way to include the rating

variable This process creates specifications that include age in increasing polynomials and

interacted with the treatment variable The goal is to find the specification with the lowest AIC

that comes close to the benchmark value of the treatment variable

Insert Tables 2 and 3 ndash Appendix Here

We did this first with regressions that limited the co-variates then with our full model In both

sets AIC reaches a minimum on the specification with age and age squared The interaction model

after that increases the AIC then the AIC goes down again with a cubed model and its interaction

model with the overall lowest AIC found on the cubed interaction model But we chose not to

use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order

models the magnitude of the treatment variable in the models with just polynomials compared to

31

the corresponding interaction models were close with the interaction models providing a larger

magnitude When the cubed models were added the magnitude jumped where the polynomial

cubed model went down well below our benchmark and the interaction model went up above our

benchmark We felt this made use of the cubed model inappropriate So we now need to choose

between the squared model and the one with the interaction terms The squared model (Model 4)

had a lower AIC and the interaction variables on the interaction model (Model 5) were not

significant so we chose to use the squared model without the interaction term This model gave a

magnitude for the treatment variable of a 72 reduction somewhat lower than the expected

magnitude in the mid 70rsquos percent The general form of the model is

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1

of the observations on either end of our data sorted by loss Our treatment variable Post FBC

remains highly significant with a coefficient value of -117 which compares favorably to our

coefficient value of -126 when the entire sample is used

Structure Age and Wind Losses

Our study is similar to recent studies on the effect of energy efficiency building codes

adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that

better insulation caulking and more efficient HVAC systems would result in lower energy

consumption But the change in energy consumption is less than engineering estimates projected

Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for

homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study

32

may be confounding age with vintage and found a decrease in energy use related to the home

simply being new rather than the change in building code Indeed Kotchen (2015) revisited the

question with data 10 years older and found the effect on electricity had disappeared while the

reduction in natural gas use increased Something is occurring in energy use unrelated to the code

and could be explained by residents changing their use of energy as they adapt to their new home

Residents of an energy efficient home can undermine the intent of lower energy use by using the

efficient design to heat and cool their homes with a motivation toward increased comfort at the

same energy cost rather than energy savings Our study does not have the behavioral component

found in the case of energy efficiency In our application the construction elements that make the

structure able to withstand high winds are installed when the home is built and lie ldquobehind the

wallsrdquo making it unlikely for individual preferences to alter the homes performance against the

threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC

homes due to the code or simply an artifact of new versus old construction when confronted with

a windstorm

To first address our analysis of age versus the FBC we rerun our base regression but limit

our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more

than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14

years during the highest loss year of 2004 Since this is a comparison between two adjacent

decades on either side of our cut point of year 2000 we remove age and age squared Results are

shown in Table 4-Appendix

Insert Table 4-Appendix Here

The coefficient on Post FBC is still negative highly significant with a magnitude very close to

what we saw with the entire database and the age variables This result suggests that the code

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

19

We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded

form from the ATC and spatially joined the values to our Florida ZIP codes We then further

categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat

4 and Cat 5 hurricane levels

Insert Table 5 Here

The regression adds two dummy variables first for ZIP codes whose design speed exceeds

the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed

reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables

for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that

communities in higher wind zones may take further measures in local codes However the effect

is not significant Notably our variable for Post FBC construction maintains its negative sign

magnitude and significance

Regressions Limited to 2004 and 2005

Our next regression also shown in Table 5 is limited to observations that occurred during

the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and

2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not

surprisingly the magnitude on wind speed increases while maintaining its significance and the

magnitude on age does the same But the effect of the FBC remains the same a 72 reduction

Summary of Results on the FBC

We have collected a comprehensive set of data on insured paid losses from 2001 to 2010

windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity

model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of

the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction

20

in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to

determine if the policy is one that is cost effective

VI Benefit and Costs of the FBC

Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a

number of cases the economic effectiveness of the improved building codes has not been as well

documented especially from a statewide implementation perspective The multi-hazard

mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose

et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar

spent) was appropriate for process activity grant spending related to improved building codes

However this information was gathered from a limited number of studies (mainly earthquake

oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in

generating it and the ratio provided due to improvement would not be the same as those for

adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted

an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for

ten related counties and determined that the net present value of the revisions was $7 billion or

benefit-cost ratio greater than 1 Importantly though this study did not have access to actual

building code damage reduction data to utilize in the analysis In 2002 Applied Research

Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC

for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how

the three types of FBC built houses would perform in probabilistic hurricane scenarios compared

to the same houses built under the previous code Given the probabilistic nature of the analysis

average annual losses were generated that demonstrated post-FBC housing having loss reductions

54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were

21

then compared to their estimated cost impacts of the FBC for these housing types with at least

break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and

above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al

(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss

reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results

ranged from 041 to 183 for the retrofit mitigation activities to existing housing

We propose a BCA that differs from earlier work in several important ways First we use

realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of

how much loss can be reduced by the FBC Second our loss data spans 10 years which include a

combination of major hurricanes and smaller wind storms

BenefitCost Methodology

The elements of a BCA requires three inputs 1) an estimate of the added cost to implement

the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related

storms from our realized ISO loss data and then from a statewide catastrophe model estimate and

3) the percentage of expected loss that will be mitigated due to implementation of the FBC We

first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis

but use the full reduction in loss which includes the value of reduced claims Finally our ISO data

is paid losses and does not include deductibles so we add an estimate for deductibles

Additional Cost

In their 2002 benefit-cost comparison study of the enactment of the FBC for three related

housing types three actual sample homes were built to the FBC to evaluate the change in

construction costs (ARA 2002) For the purposes of code implementation the state was divided

into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region

22

(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account

for cost differences between the two regions

In the WBDR an added requirement is impact protection to windows and doors to reduce

damage from flying debris Along the coast and much of South Florida is classified as the WBDR

The N-WBDR is mainly classified in the interior of the state where impact protection is not

required Importantly the study provided a range of added costs for the N-WBDR and the WBDR

Three counties in South Florida Dade Broward and Monroe were under the South Florida

Building Code (SFBC) prior to the implementation of the FBC According to the ARA study

(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges

of incremental cost per square foot for the N-WBDR and WBDR along with the percent of

residential units that reside in each area This allows a calculation of a weighted average added

cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost

per square foot of $166 The cost compares favorably with a similar building code enhancement

adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in

2013 Consulting engineers and the Moore Association of Homebuilders estimated the code

enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is

1960 square feet which means that on average the FBC increases construction cost by $3254 per

structurexii

Insert Table 6 Here

Benefit of the FBC

Benefits stemming from the FBC are the expected reduction in losses from windstorms during

the life of the home We first find an average annual loss (AAL) use that number to estimate

losses for the next 50 years and then find the present value of those losses in 2010 Here we are

23

assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the

next 50 years A wealth of literature suggests the potential for changes to hurricane activity over

the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty

on future changes in wind hazard on the scale of a single state we choose to assume a stationary

climate

Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes

built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our

data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of

$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year

Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses

per structure of $21474

Finally we use parameter estimates from our regression for the Post FBC dummy variable

(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC

The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post

FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what

would be a reduction of $10093 from the projected $21474 in future losses

Insert Table 7 Here

BenefitCost Analysis

Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio

of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the

statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an

economically effective public policy following from our ISO loss data and results

Insert Table 8 Here

24

Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years

This relatively short timeframe makes it difficult to truly approximate an AAL as would be

provided from a probabilistically based catastrophe model that generates an AAL from thousands

of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state

of Florida to estimate an average annual wind loss for all residential properties in Florida of

approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL

estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion

with deductibles) Using this aggregate AAL and the number of residential units in Florida based

on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses

net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and

an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as

before derived from our regression results 47 we find $7710 of reduced loss from the projected

$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added

$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective

building code public policy

We run two additional analyses on our BCA results Our estimate of expected loss

reduction comes from the second stage of the hurdle model This is an estimate of the direct loss

reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the

number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests

as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which

includes the reduced magnitude of loss from affected homes and the reduction in claims for Post

FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table

8) However a 72 loss reduction seems too dramatic an expectation when planning so far in

25

advance For that reason we offer a third level of expected loss reduction of 60 which is the

midpoint between our two loss reduction estimates This estimate captures the expected direct loss

reduction suggested by the second stage of our hurdle model but still recognizes that in some areas

the number of claims is reduced by the FBC This appears to be a reasonable assumption and

provides a BCA ratio of 396 for the ISO sample and 302 for all residential

The ISO data are net of deductibles so our BCA thus far only includes losses compensated by

the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion

also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the

ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all

loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA

values from a low of 237 to a high of 793

Payback of the FBC

Finally we use our BCA results to calculate a payback period for the investment of stronger

codes To convert our BCA ratio to a payback period we simply divide our 50-year planning

horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss

and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years

This is important for gauging potential political support or non-support for enactment of the new

codes Payback periods that approach the typical mortgage term 30 years would in theory be

difficult to achieve and that is not what our analysis indicates for the FBC

VI - Concluding Comments

In the aftermath of Hurricane Andrew which had exposed not only poor building

construction but also poor building code enforcement the state of Florida enacted statewide

building code changes that wrested away building code adoption control from individual localities

26

With full implementation of the statewide building code associated expectations are that

windstorm losses from extreme events such as hurricanes should be reduced moving forward

There have been a few studies confirming these expectations following the 2004 and 2005

hurricane season In this article we further verify and quantify these findings and expand the

existing building code risk reduction research in several important ways

Overall we empirically test the statewide implementation of a building code in reducing

wind related damages in Florida controlling for other relevant wind hazard exposure and

vulnerability characteristics from a traditional risk assessment perspective Our results show the

strong effect the statewide FBC had on losses from wind storms during this timeframe From the

treatment variable that measures implementation of the statewide codes the post 2000 year of

construction losses are shown to be reduced by as much as 72 percent consistent with other

previous findings

Finally we have conducted a BCA of the FBC to determine if expected benefits exceed

the cost of implementation Using a direct estimate for mitigated losses and an estimate that

includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public

policy from an economic perspective This result is close to that recommended by the multi-hazard

mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a

statewide BCA Importantly this information is essential in generating political and consumer

support for such building code public policy implementation

For example the economic effectiveness results shown here have implications for ongoing

policy discussions about reforming building codes from a national US perspective Moore OK

independently adopted enhanced building codes after its third violent tornado in 14 years killed 24

including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)

27

Construction practices in North Texas were brought under scrutiny after the December 2015

tornado revealed inadequate construction including an elementary school whose exterior walls

failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House

announced initiatives to increase community resilience with building codes as a major component

of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748

and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better

construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397

would provide tax credits for owners andor contractors who use techniques designed for resiliency

in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one

recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to

encourage states to use current building codes (Vaughn and Turner 2014)

Future research in the BCA of the FBC will further inform the public policy debate on

enhanced building codes The issue has national implications as other states find that wind hazards

impact them as well We have sufficient wind data to examine how the BCA performs under

different wind hazards Additionally it will be important to consider how future economic

development affects the BCA as well as varying climate change scenarios As the FBC is

mandatory for all new construction a statewide analysis was appropriate But individual

homeowners in older homes can invest in the retrofit of their home and qualify for discounts on

their homeowners insurance This topic is deserving of a robust analysis Although our BCA is

statewide regions within the state will likely have a spectrum of results For instance the ARA

2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their

analysis did not use realized loss data so confirmation of how the BCA varies between those

regions would be an important contribution Finally our sensitivity analysis was limited to two

28

variables reduction in future loss and the inclusion of deductibles Additional work will highlight

other variables that could modify the results

29

Appendix

We use this appendix to conduct more detailed analysis on several topics First selection

of the model specification using a regression discontinuity approach Second we provide an in

depth examination of the relationship between structure age and losses Third we perform a

Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an

alternative specification to see if our RD results are similar followed by regressions to examine

the year to year consistency of our Post FBC result Next we run a regression on claims to verify

the difference between our direct reduction result and our full reduction result Finally we perform

a regression on homes built to the SFBC which had adopted enhanced building codes in advance

of the FBC to assess the effect of earlier adoption of enhanced construction

Regression Discontinuity

Regression Discontinuity (RD) applies when an observation receives a treatment in our case

homes built under the FBC based on a rating variable in our case age of the structure at the year

of observation So for observations in 2005 homes built post 2000 received the treatment

adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify

how observations on either side of the implementation of the FBC (2000) perform in suffering loss

from windstorms The treatment variable is a function of the age of the home and age affects loss

in ways not related to the FBC such as depreciation and differences in materials and construction

practices across time To account for both the effect of age on loss as well as the implementation

of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment

The data we have from ISO is aggregated loss data by zip code and decade of construction So

we cannot get an annualized age To approach a true age we set the year built for each decade of

construction at the beginning of the decade then subtract that from the year of each observation to

get an approximate agexiv

30

To find the best specification we began with a simpler model which used a series of

categorical variables for each decade of construction to examine the effect of the code compared

to the omitted decade This method would approximate the changes in materials and construction

practices but was less effective in controlling for depreciation But it would give us a first

approximation of the code effect that we used as a benchmark when testing the best RD

specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970

with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When

the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When

either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81

A rough approximation of the codersquos effect from this approach would suggest a reduction in the

mid 70 percent range

Insert Table 1 ndash Appendix Here

Next we used a standard procedure with RD to search for the best way to include the rating

variable This process creates specifications that include age in increasing polynomials and

interacted with the treatment variable The goal is to find the specification with the lowest AIC

that comes close to the benchmark value of the treatment variable

Insert Tables 2 and 3 ndash Appendix Here

We did this first with regressions that limited the co-variates then with our full model In both

sets AIC reaches a minimum on the specification with age and age squared The interaction model

after that increases the AIC then the AIC goes down again with a cubed model and its interaction

model with the overall lowest AIC found on the cubed interaction model But we chose not to

use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order

models the magnitude of the treatment variable in the models with just polynomials compared to

31

the corresponding interaction models were close with the interaction models providing a larger

magnitude When the cubed models were added the magnitude jumped where the polynomial

cubed model went down well below our benchmark and the interaction model went up above our

benchmark We felt this made use of the cubed model inappropriate So we now need to choose

between the squared model and the one with the interaction terms The squared model (Model 4)

had a lower AIC and the interaction variables on the interaction model (Model 5) were not

significant so we chose to use the squared model without the interaction term This model gave a

magnitude for the treatment variable of a 72 reduction somewhat lower than the expected

magnitude in the mid 70rsquos percent The general form of the model is

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1

of the observations on either end of our data sorted by loss Our treatment variable Post FBC

remains highly significant with a coefficient value of -117 which compares favorably to our

coefficient value of -126 when the entire sample is used

Structure Age and Wind Losses

Our study is similar to recent studies on the effect of energy efficiency building codes

adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that

better insulation caulking and more efficient HVAC systems would result in lower energy

consumption But the change in energy consumption is less than engineering estimates projected

Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for

homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study

32

may be confounding age with vintage and found a decrease in energy use related to the home

simply being new rather than the change in building code Indeed Kotchen (2015) revisited the

question with data 10 years older and found the effect on electricity had disappeared while the

reduction in natural gas use increased Something is occurring in energy use unrelated to the code

and could be explained by residents changing their use of energy as they adapt to their new home

Residents of an energy efficient home can undermine the intent of lower energy use by using the

efficient design to heat and cool their homes with a motivation toward increased comfort at the

same energy cost rather than energy savings Our study does not have the behavioral component

found in the case of energy efficiency In our application the construction elements that make the

structure able to withstand high winds are installed when the home is built and lie ldquobehind the

wallsrdquo making it unlikely for individual preferences to alter the homes performance against the

threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC

homes due to the code or simply an artifact of new versus old construction when confronted with

a windstorm

To first address our analysis of age versus the FBC we rerun our base regression but limit

our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more

than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14

years during the highest loss year of 2004 Since this is a comparison between two adjacent

decades on either side of our cut point of year 2000 we remove age and age squared Results are

shown in Table 4-Appendix

Insert Table 4-Appendix Here

The coefficient on Post FBC is still negative highly significant with a magnitude very close to

what we saw with the entire database and the age variables This result suggests that the code

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

20

in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to

determine if the policy is one that is cost effective

VI Benefit and Costs of the FBC

Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a

number of cases the economic effectiveness of the improved building codes has not been as well

documented especially from a statewide implementation perspective The multi-hazard

mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose

et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar

spent) was appropriate for process activity grant spending related to improved building codes

However this information was gathered from a limited number of studies (mainly earthquake

oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in

generating it and the ratio provided due to improvement would not be the same as those for

adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted

an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for

ten related counties and determined that the net present value of the revisions was $7 billion or

benefit-cost ratio greater than 1 Importantly though this study did not have access to actual

building code damage reduction data to utilize in the analysis In 2002 Applied Research

Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC

for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how

the three types of FBC built houses would perform in probabilistic hurricane scenarios compared

to the same houses built under the previous code Given the probabilistic nature of the analysis

average annual losses were generated that demonstrated post-FBC housing having loss reductions

54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were

21

then compared to their estimated cost impacts of the FBC for these housing types with at least

break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and

above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al

(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss

reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results

ranged from 041 to 183 for the retrofit mitigation activities to existing housing

We propose a BCA that differs from earlier work in several important ways First we use

realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of

how much loss can be reduced by the FBC Second our loss data spans 10 years which include a

combination of major hurricanes and smaller wind storms

BenefitCost Methodology

The elements of a BCA requires three inputs 1) an estimate of the added cost to implement

the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related

storms from our realized ISO loss data and then from a statewide catastrophe model estimate and

3) the percentage of expected loss that will be mitigated due to implementation of the FBC We

first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis

but use the full reduction in loss which includes the value of reduced claims Finally our ISO data

is paid losses and does not include deductibles so we add an estimate for deductibles

Additional Cost

In their 2002 benefit-cost comparison study of the enactment of the FBC for three related

housing types three actual sample homes were built to the FBC to evaluate the change in

construction costs (ARA 2002) For the purposes of code implementation the state was divided

into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region

22

(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account

for cost differences between the two regions

In the WBDR an added requirement is impact protection to windows and doors to reduce

damage from flying debris Along the coast and much of South Florida is classified as the WBDR

The N-WBDR is mainly classified in the interior of the state where impact protection is not

required Importantly the study provided a range of added costs for the N-WBDR and the WBDR

Three counties in South Florida Dade Broward and Monroe were under the South Florida

Building Code (SFBC) prior to the implementation of the FBC According to the ARA study

(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges

of incremental cost per square foot for the N-WBDR and WBDR along with the percent of

residential units that reside in each area This allows a calculation of a weighted average added

cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost

per square foot of $166 The cost compares favorably with a similar building code enhancement

adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in

2013 Consulting engineers and the Moore Association of Homebuilders estimated the code

enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is

1960 square feet which means that on average the FBC increases construction cost by $3254 per

structurexii

Insert Table 6 Here

Benefit of the FBC

Benefits stemming from the FBC are the expected reduction in losses from windstorms during

the life of the home We first find an average annual loss (AAL) use that number to estimate

losses for the next 50 years and then find the present value of those losses in 2010 Here we are

23

assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the

next 50 years A wealth of literature suggests the potential for changes to hurricane activity over

the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty

on future changes in wind hazard on the scale of a single state we choose to assume a stationary

climate

Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes

built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our

data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of

$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year

Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses

per structure of $21474

Finally we use parameter estimates from our regression for the Post FBC dummy variable

(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC

The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post

FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what

would be a reduction of $10093 from the projected $21474 in future losses

Insert Table 7 Here

BenefitCost Analysis

Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio

of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the

statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an

economically effective public policy following from our ISO loss data and results

Insert Table 8 Here

24

Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years

This relatively short timeframe makes it difficult to truly approximate an AAL as would be

provided from a probabilistically based catastrophe model that generates an AAL from thousands

of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state

of Florida to estimate an average annual wind loss for all residential properties in Florida of

approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL

estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion

with deductibles) Using this aggregate AAL and the number of residential units in Florida based

on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses

net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and

an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as

before derived from our regression results 47 we find $7710 of reduced loss from the projected

$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added

$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective

building code public policy

We run two additional analyses on our BCA results Our estimate of expected loss

reduction comes from the second stage of the hurdle model This is an estimate of the direct loss

reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the

number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests

as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which

includes the reduced magnitude of loss from affected homes and the reduction in claims for Post

FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table

8) However a 72 loss reduction seems too dramatic an expectation when planning so far in

25

advance For that reason we offer a third level of expected loss reduction of 60 which is the

midpoint between our two loss reduction estimates This estimate captures the expected direct loss

reduction suggested by the second stage of our hurdle model but still recognizes that in some areas

the number of claims is reduced by the FBC This appears to be a reasonable assumption and

provides a BCA ratio of 396 for the ISO sample and 302 for all residential

The ISO data are net of deductibles so our BCA thus far only includes losses compensated by

the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion

also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the

ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all

loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA

values from a low of 237 to a high of 793

Payback of the FBC

Finally we use our BCA results to calculate a payback period for the investment of stronger

codes To convert our BCA ratio to a payback period we simply divide our 50-year planning

horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss

and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years

This is important for gauging potential political support or non-support for enactment of the new

codes Payback periods that approach the typical mortgage term 30 years would in theory be

difficult to achieve and that is not what our analysis indicates for the FBC

VI - Concluding Comments

In the aftermath of Hurricane Andrew which had exposed not only poor building

construction but also poor building code enforcement the state of Florida enacted statewide

building code changes that wrested away building code adoption control from individual localities

26

With full implementation of the statewide building code associated expectations are that

windstorm losses from extreme events such as hurricanes should be reduced moving forward

There have been a few studies confirming these expectations following the 2004 and 2005

hurricane season In this article we further verify and quantify these findings and expand the

existing building code risk reduction research in several important ways

Overall we empirically test the statewide implementation of a building code in reducing

wind related damages in Florida controlling for other relevant wind hazard exposure and

vulnerability characteristics from a traditional risk assessment perspective Our results show the

strong effect the statewide FBC had on losses from wind storms during this timeframe From the

treatment variable that measures implementation of the statewide codes the post 2000 year of

construction losses are shown to be reduced by as much as 72 percent consistent with other

previous findings

Finally we have conducted a BCA of the FBC to determine if expected benefits exceed

the cost of implementation Using a direct estimate for mitigated losses and an estimate that

includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public

policy from an economic perspective This result is close to that recommended by the multi-hazard

mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a

statewide BCA Importantly this information is essential in generating political and consumer

support for such building code public policy implementation

For example the economic effectiveness results shown here have implications for ongoing

policy discussions about reforming building codes from a national US perspective Moore OK

independently adopted enhanced building codes after its third violent tornado in 14 years killed 24

including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)

27

Construction practices in North Texas were brought under scrutiny after the December 2015

tornado revealed inadequate construction including an elementary school whose exterior walls

failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House

announced initiatives to increase community resilience with building codes as a major component

of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748

and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better

construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397

would provide tax credits for owners andor contractors who use techniques designed for resiliency

in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one

recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to

encourage states to use current building codes (Vaughn and Turner 2014)

Future research in the BCA of the FBC will further inform the public policy debate on

enhanced building codes The issue has national implications as other states find that wind hazards

impact them as well We have sufficient wind data to examine how the BCA performs under

different wind hazards Additionally it will be important to consider how future economic

development affects the BCA as well as varying climate change scenarios As the FBC is

mandatory for all new construction a statewide analysis was appropriate But individual

homeowners in older homes can invest in the retrofit of their home and qualify for discounts on

their homeowners insurance This topic is deserving of a robust analysis Although our BCA is

statewide regions within the state will likely have a spectrum of results For instance the ARA

2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their

analysis did not use realized loss data so confirmation of how the BCA varies between those

regions would be an important contribution Finally our sensitivity analysis was limited to two

28

variables reduction in future loss and the inclusion of deductibles Additional work will highlight

other variables that could modify the results

29

Appendix

We use this appendix to conduct more detailed analysis on several topics First selection

of the model specification using a regression discontinuity approach Second we provide an in

depth examination of the relationship between structure age and losses Third we perform a

Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an

alternative specification to see if our RD results are similar followed by regressions to examine

the year to year consistency of our Post FBC result Next we run a regression on claims to verify

the difference between our direct reduction result and our full reduction result Finally we perform

a regression on homes built to the SFBC which had adopted enhanced building codes in advance

of the FBC to assess the effect of earlier adoption of enhanced construction

Regression Discontinuity

Regression Discontinuity (RD) applies when an observation receives a treatment in our case

homes built under the FBC based on a rating variable in our case age of the structure at the year

of observation So for observations in 2005 homes built post 2000 received the treatment

adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify

how observations on either side of the implementation of the FBC (2000) perform in suffering loss

from windstorms The treatment variable is a function of the age of the home and age affects loss

in ways not related to the FBC such as depreciation and differences in materials and construction

practices across time To account for both the effect of age on loss as well as the implementation

of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment

The data we have from ISO is aggregated loss data by zip code and decade of construction So

we cannot get an annualized age To approach a true age we set the year built for each decade of

construction at the beginning of the decade then subtract that from the year of each observation to

get an approximate agexiv

30

To find the best specification we began with a simpler model which used a series of

categorical variables for each decade of construction to examine the effect of the code compared

to the omitted decade This method would approximate the changes in materials and construction

practices but was less effective in controlling for depreciation But it would give us a first

approximation of the code effect that we used as a benchmark when testing the best RD

specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970

with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When

the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When

either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81

A rough approximation of the codersquos effect from this approach would suggest a reduction in the

mid 70 percent range

Insert Table 1 ndash Appendix Here

Next we used a standard procedure with RD to search for the best way to include the rating

variable This process creates specifications that include age in increasing polynomials and

interacted with the treatment variable The goal is to find the specification with the lowest AIC

that comes close to the benchmark value of the treatment variable

Insert Tables 2 and 3 ndash Appendix Here

We did this first with regressions that limited the co-variates then with our full model In both

sets AIC reaches a minimum on the specification with age and age squared The interaction model

after that increases the AIC then the AIC goes down again with a cubed model and its interaction

model with the overall lowest AIC found on the cubed interaction model But we chose not to

use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order

models the magnitude of the treatment variable in the models with just polynomials compared to

31

the corresponding interaction models were close with the interaction models providing a larger

magnitude When the cubed models were added the magnitude jumped where the polynomial

cubed model went down well below our benchmark and the interaction model went up above our

benchmark We felt this made use of the cubed model inappropriate So we now need to choose

between the squared model and the one with the interaction terms The squared model (Model 4)

had a lower AIC and the interaction variables on the interaction model (Model 5) were not

significant so we chose to use the squared model without the interaction term This model gave a

magnitude for the treatment variable of a 72 reduction somewhat lower than the expected

magnitude in the mid 70rsquos percent The general form of the model is

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1

of the observations on either end of our data sorted by loss Our treatment variable Post FBC

remains highly significant with a coefficient value of -117 which compares favorably to our

coefficient value of -126 when the entire sample is used

Structure Age and Wind Losses

Our study is similar to recent studies on the effect of energy efficiency building codes

adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that

better insulation caulking and more efficient HVAC systems would result in lower energy

consumption But the change in energy consumption is less than engineering estimates projected

Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for

homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study

32

may be confounding age with vintage and found a decrease in energy use related to the home

simply being new rather than the change in building code Indeed Kotchen (2015) revisited the

question with data 10 years older and found the effect on electricity had disappeared while the

reduction in natural gas use increased Something is occurring in energy use unrelated to the code

and could be explained by residents changing their use of energy as they adapt to their new home

Residents of an energy efficient home can undermine the intent of lower energy use by using the

efficient design to heat and cool their homes with a motivation toward increased comfort at the

same energy cost rather than energy savings Our study does not have the behavioral component

found in the case of energy efficiency In our application the construction elements that make the

structure able to withstand high winds are installed when the home is built and lie ldquobehind the

wallsrdquo making it unlikely for individual preferences to alter the homes performance against the

threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC

homes due to the code or simply an artifact of new versus old construction when confronted with

a windstorm

To first address our analysis of age versus the FBC we rerun our base regression but limit

our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more

than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14

years during the highest loss year of 2004 Since this is a comparison between two adjacent

decades on either side of our cut point of year 2000 we remove age and age squared Results are

shown in Table 4-Appendix

Insert Table 4-Appendix Here

The coefficient on Post FBC is still negative highly significant with a magnitude very close to

what we saw with the entire database and the age variables This result suggests that the code

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

21

then compared to their estimated cost impacts of the FBC for these housing types with at least

break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and

above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al

(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss

reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results

ranged from 041 to 183 for the retrofit mitigation activities to existing housing

We propose a BCA that differs from earlier work in several important ways First we use

realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of

how much loss can be reduced by the FBC Second our loss data spans 10 years which include a

combination of major hurricanes and smaller wind storms

BenefitCost Methodology

The elements of a BCA requires three inputs 1) an estimate of the added cost to implement

the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related

storms from our realized ISO loss data and then from a statewide catastrophe model estimate and

3) the percentage of expected loss that will be mitigated due to implementation of the FBC We

first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis

but use the full reduction in loss which includes the value of reduced claims Finally our ISO data

is paid losses and does not include deductibles so we add an estimate for deductibles

Additional Cost

In their 2002 benefit-cost comparison study of the enactment of the FBC for three related

housing types three actual sample homes were built to the FBC to evaluate the change in

construction costs (ARA 2002) For the purposes of code implementation the state was divided

into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region

22

(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account

for cost differences between the two regions

In the WBDR an added requirement is impact protection to windows and doors to reduce

damage from flying debris Along the coast and much of South Florida is classified as the WBDR

The N-WBDR is mainly classified in the interior of the state where impact protection is not

required Importantly the study provided a range of added costs for the N-WBDR and the WBDR

Three counties in South Florida Dade Broward and Monroe were under the South Florida

Building Code (SFBC) prior to the implementation of the FBC According to the ARA study

(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges

of incremental cost per square foot for the N-WBDR and WBDR along with the percent of

residential units that reside in each area This allows a calculation of a weighted average added

cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost

per square foot of $166 The cost compares favorably with a similar building code enhancement

adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in

2013 Consulting engineers and the Moore Association of Homebuilders estimated the code

enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is

1960 square feet which means that on average the FBC increases construction cost by $3254 per

structurexii

Insert Table 6 Here

Benefit of the FBC

Benefits stemming from the FBC are the expected reduction in losses from windstorms during

the life of the home We first find an average annual loss (AAL) use that number to estimate

losses for the next 50 years and then find the present value of those losses in 2010 Here we are

23

assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the

next 50 years A wealth of literature suggests the potential for changes to hurricane activity over

the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty

on future changes in wind hazard on the scale of a single state we choose to assume a stationary

climate

Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes

built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our

data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of

$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year

Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses

per structure of $21474

Finally we use parameter estimates from our regression for the Post FBC dummy variable

(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC

The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post

FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what

would be a reduction of $10093 from the projected $21474 in future losses

Insert Table 7 Here

BenefitCost Analysis

Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio

of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the

statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an

economically effective public policy following from our ISO loss data and results

Insert Table 8 Here

24

Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years

This relatively short timeframe makes it difficult to truly approximate an AAL as would be

provided from a probabilistically based catastrophe model that generates an AAL from thousands

of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state

of Florida to estimate an average annual wind loss for all residential properties in Florida of

approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL

estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion

with deductibles) Using this aggregate AAL and the number of residential units in Florida based

on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses

net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and

an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as

before derived from our regression results 47 we find $7710 of reduced loss from the projected

$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added

$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective

building code public policy

We run two additional analyses on our BCA results Our estimate of expected loss

reduction comes from the second stage of the hurdle model This is an estimate of the direct loss

reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the

number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests

as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which

includes the reduced magnitude of loss from affected homes and the reduction in claims for Post

FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table

8) However a 72 loss reduction seems too dramatic an expectation when planning so far in

25

advance For that reason we offer a third level of expected loss reduction of 60 which is the

midpoint between our two loss reduction estimates This estimate captures the expected direct loss

reduction suggested by the second stage of our hurdle model but still recognizes that in some areas

the number of claims is reduced by the FBC This appears to be a reasonable assumption and

provides a BCA ratio of 396 for the ISO sample and 302 for all residential

The ISO data are net of deductibles so our BCA thus far only includes losses compensated by

the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion

also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the

ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all

loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA

values from a low of 237 to a high of 793

Payback of the FBC

Finally we use our BCA results to calculate a payback period for the investment of stronger

codes To convert our BCA ratio to a payback period we simply divide our 50-year planning

horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss

and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years

This is important for gauging potential political support or non-support for enactment of the new

codes Payback periods that approach the typical mortgage term 30 years would in theory be

difficult to achieve and that is not what our analysis indicates for the FBC

VI - Concluding Comments

In the aftermath of Hurricane Andrew which had exposed not only poor building

construction but also poor building code enforcement the state of Florida enacted statewide

building code changes that wrested away building code adoption control from individual localities

26

With full implementation of the statewide building code associated expectations are that

windstorm losses from extreme events such as hurricanes should be reduced moving forward

There have been a few studies confirming these expectations following the 2004 and 2005

hurricane season In this article we further verify and quantify these findings and expand the

existing building code risk reduction research in several important ways

Overall we empirically test the statewide implementation of a building code in reducing

wind related damages in Florida controlling for other relevant wind hazard exposure and

vulnerability characteristics from a traditional risk assessment perspective Our results show the

strong effect the statewide FBC had on losses from wind storms during this timeframe From the

treatment variable that measures implementation of the statewide codes the post 2000 year of

construction losses are shown to be reduced by as much as 72 percent consistent with other

previous findings

Finally we have conducted a BCA of the FBC to determine if expected benefits exceed

the cost of implementation Using a direct estimate for mitigated losses and an estimate that

includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public

policy from an economic perspective This result is close to that recommended by the multi-hazard

mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a

statewide BCA Importantly this information is essential in generating political and consumer

support for such building code public policy implementation

For example the economic effectiveness results shown here have implications for ongoing

policy discussions about reforming building codes from a national US perspective Moore OK

independently adopted enhanced building codes after its third violent tornado in 14 years killed 24

including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)

27

Construction practices in North Texas were brought under scrutiny after the December 2015

tornado revealed inadequate construction including an elementary school whose exterior walls

failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House

announced initiatives to increase community resilience with building codes as a major component

of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748

and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better

construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397

would provide tax credits for owners andor contractors who use techniques designed for resiliency

in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one

recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to

encourage states to use current building codes (Vaughn and Turner 2014)

Future research in the BCA of the FBC will further inform the public policy debate on

enhanced building codes The issue has national implications as other states find that wind hazards

impact them as well We have sufficient wind data to examine how the BCA performs under

different wind hazards Additionally it will be important to consider how future economic

development affects the BCA as well as varying climate change scenarios As the FBC is

mandatory for all new construction a statewide analysis was appropriate But individual

homeowners in older homes can invest in the retrofit of their home and qualify for discounts on

their homeowners insurance This topic is deserving of a robust analysis Although our BCA is

statewide regions within the state will likely have a spectrum of results For instance the ARA

2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their

analysis did not use realized loss data so confirmation of how the BCA varies between those

regions would be an important contribution Finally our sensitivity analysis was limited to two

28

variables reduction in future loss and the inclusion of deductibles Additional work will highlight

other variables that could modify the results

29

Appendix

We use this appendix to conduct more detailed analysis on several topics First selection

of the model specification using a regression discontinuity approach Second we provide an in

depth examination of the relationship between structure age and losses Third we perform a

Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an

alternative specification to see if our RD results are similar followed by regressions to examine

the year to year consistency of our Post FBC result Next we run a regression on claims to verify

the difference between our direct reduction result and our full reduction result Finally we perform

a regression on homes built to the SFBC which had adopted enhanced building codes in advance

of the FBC to assess the effect of earlier adoption of enhanced construction

Regression Discontinuity

Regression Discontinuity (RD) applies when an observation receives a treatment in our case

homes built under the FBC based on a rating variable in our case age of the structure at the year

of observation So for observations in 2005 homes built post 2000 received the treatment

adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify

how observations on either side of the implementation of the FBC (2000) perform in suffering loss

from windstorms The treatment variable is a function of the age of the home and age affects loss

in ways not related to the FBC such as depreciation and differences in materials and construction

practices across time To account for both the effect of age on loss as well as the implementation

of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment

The data we have from ISO is aggregated loss data by zip code and decade of construction So

we cannot get an annualized age To approach a true age we set the year built for each decade of

construction at the beginning of the decade then subtract that from the year of each observation to

get an approximate agexiv

30

To find the best specification we began with a simpler model which used a series of

categorical variables for each decade of construction to examine the effect of the code compared

to the omitted decade This method would approximate the changes in materials and construction

practices but was less effective in controlling for depreciation But it would give us a first

approximation of the code effect that we used as a benchmark when testing the best RD

specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970

with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When

the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When

either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81

A rough approximation of the codersquos effect from this approach would suggest a reduction in the

mid 70 percent range

Insert Table 1 ndash Appendix Here

Next we used a standard procedure with RD to search for the best way to include the rating

variable This process creates specifications that include age in increasing polynomials and

interacted with the treatment variable The goal is to find the specification with the lowest AIC

that comes close to the benchmark value of the treatment variable

Insert Tables 2 and 3 ndash Appendix Here

We did this first with regressions that limited the co-variates then with our full model In both

sets AIC reaches a minimum on the specification with age and age squared The interaction model

after that increases the AIC then the AIC goes down again with a cubed model and its interaction

model with the overall lowest AIC found on the cubed interaction model But we chose not to

use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order

models the magnitude of the treatment variable in the models with just polynomials compared to

31

the corresponding interaction models were close with the interaction models providing a larger

magnitude When the cubed models were added the magnitude jumped where the polynomial

cubed model went down well below our benchmark and the interaction model went up above our

benchmark We felt this made use of the cubed model inappropriate So we now need to choose

between the squared model and the one with the interaction terms The squared model (Model 4)

had a lower AIC and the interaction variables on the interaction model (Model 5) were not

significant so we chose to use the squared model without the interaction term This model gave a

magnitude for the treatment variable of a 72 reduction somewhat lower than the expected

magnitude in the mid 70rsquos percent The general form of the model is

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1

of the observations on either end of our data sorted by loss Our treatment variable Post FBC

remains highly significant with a coefficient value of -117 which compares favorably to our

coefficient value of -126 when the entire sample is used

Structure Age and Wind Losses

Our study is similar to recent studies on the effect of energy efficiency building codes

adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that

better insulation caulking and more efficient HVAC systems would result in lower energy

consumption But the change in energy consumption is less than engineering estimates projected

Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for

homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study

32

may be confounding age with vintage and found a decrease in energy use related to the home

simply being new rather than the change in building code Indeed Kotchen (2015) revisited the

question with data 10 years older and found the effect on electricity had disappeared while the

reduction in natural gas use increased Something is occurring in energy use unrelated to the code

and could be explained by residents changing their use of energy as they adapt to their new home

Residents of an energy efficient home can undermine the intent of lower energy use by using the

efficient design to heat and cool their homes with a motivation toward increased comfort at the

same energy cost rather than energy savings Our study does not have the behavioral component

found in the case of energy efficiency In our application the construction elements that make the

structure able to withstand high winds are installed when the home is built and lie ldquobehind the

wallsrdquo making it unlikely for individual preferences to alter the homes performance against the

threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC

homes due to the code or simply an artifact of new versus old construction when confronted with

a windstorm

To first address our analysis of age versus the FBC we rerun our base regression but limit

our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more

than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14

years during the highest loss year of 2004 Since this is a comparison between two adjacent

decades on either side of our cut point of year 2000 we remove age and age squared Results are

shown in Table 4-Appendix

Insert Table 4-Appendix Here

The coefficient on Post FBC is still negative highly significant with a magnitude very close to

what we saw with the entire database and the age variables This result suggests that the code

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

22

(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account

for cost differences between the two regions

In the WBDR an added requirement is impact protection to windows and doors to reduce

damage from flying debris Along the coast and much of South Florida is classified as the WBDR

The N-WBDR is mainly classified in the interior of the state where impact protection is not

required Importantly the study provided a range of added costs for the N-WBDR and the WBDR

Three counties in South Florida Dade Broward and Monroe were under the South Florida

Building Code (SFBC) prior to the implementation of the FBC According to the ARA study

(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges

of incremental cost per square foot for the N-WBDR and WBDR along with the percent of

residential units that reside in each area This allows a calculation of a weighted average added

cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost

per square foot of $166 The cost compares favorably with a similar building code enhancement

adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in

2013 Consulting engineers and the Moore Association of Homebuilders estimated the code

enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is

1960 square feet which means that on average the FBC increases construction cost by $3254 per

structurexii

Insert Table 6 Here

Benefit of the FBC

Benefits stemming from the FBC are the expected reduction in losses from windstorms during

the life of the home We first find an average annual loss (AAL) use that number to estimate

losses for the next 50 years and then find the present value of those losses in 2010 Here we are

23

assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the

next 50 years A wealth of literature suggests the potential for changes to hurricane activity over

the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty

on future changes in wind hazard on the scale of a single state we choose to assume a stationary

climate

Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes

built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our

data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of

$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year

Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses

per structure of $21474

Finally we use parameter estimates from our regression for the Post FBC dummy variable

(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC

The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post

FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what

would be a reduction of $10093 from the projected $21474 in future losses

Insert Table 7 Here

BenefitCost Analysis

Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio

of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the

statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an

economically effective public policy following from our ISO loss data and results

Insert Table 8 Here

24

Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years

This relatively short timeframe makes it difficult to truly approximate an AAL as would be

provided from a probabilistically based catastrophe model that generates an AAL from thousands

of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state

of Florida to estimate an average annual wind loss for all residential properties in Florida of

approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL

estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion

with deductibles) Using this aggregate AAL and the number of residential units in Florida based

on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses

net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and

an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as

before derived from our regression results 47 we find $7710 of reduced loss from the projected

$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added

$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective

building code public policy

We run two additional analyses on our BCA results Our estimate of expected loss

reduction comes from the second stage of the hurdle model This is an estimate of the direct loss

reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the

number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests

as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which

includes the reduced magnitude of loss from affected homes and the reduction in claims for Post

FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table

8) However a 72 loss reduction seems too dramatic an expectation when planning so far in

25

advance For that reason we offer a third level of expected loss reduction of 60 which is the

midpoint between our two loss reduction estimates This estimate captures the expected direct loss

reduction suggested by the second stage of our hurdle model but still recognizes that in some areas

the number of claims is reduced by the FBC This appears to be a reasonable assumption and

provides a BCA ratio of 396 for the ISO sample and 302 for all residential

The ISO data are net of deductibles so our BCA thus far only includes losses compensated by

the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion

also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the

ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all

loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA

values from a low of 237 to a high of 793

Payback of the FBC

Finally we use our BCA results to calculate a payback period for the investment of stronger

codes To convert our BCA ratio to a payback period we simply divide our 50-year planning

horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss

and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years

This is important for gauging potential political support or non-support for enactment of the new

codes Payback periods that approach the typical mortgage term 30 years would in theory be

difficult to achieve and that is not what our analysis indicates for the FBC

VI - Concluding Comments

In the aftermath of Hurricane Andrew which had exposed not only poor building

construction but also poor building code enforcement the state of Florida enacted statewide

building code changes that wrested away building code adoption control from individual localities

26

With full implementation of the statewide building code associated expectations are that

windstorm losses from extreme events such as hurricanes should be reduced moving forward

There have been a few studies confirming these expectations following the 2004 and 2005

hurricane season In this article we further verify and quantify these findings and expand the

existing building code risk reduction research in several important ways

Overall we empirically test the statewide implementation of a building code in reducing

wind related damages in Florida controlling for other relevant wind hazard exposure and

vulnerability characteristics from a traditional risk assessment perspective Our results show the

strong effect the statewide FBC had on losses from wind storms during this timeframe From the

treatment variable that measures implementation of the statewide codes the post 2000 year of

construction losses are shown to be reduced by as much as 72 percent consistent with other

previous findings

Finally we have conducted a BCA of the FBC to determine if expected benefits exceed

the cost of implementation Using a direct estimate for mitigated losses and an estimate that

includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public

policy from an economic perspective This result is close to that recommended by the multi-hazard

mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a

statewide BCA Importantly this information is essential in generating political and consumer

support for such building code public policy implementation

For example the economic effectiveness results shown here have implications for ongoing

policy discussions about reforming building codes from a national US perspective Moore OK

independently adopted enhanced building codes after its third violent tornado in 14 years killed 24

including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)

27

Construction practices in North Texas were brought under scrutiny after the December 2015

tornado revealed inadequate construction including an elementary school whose exterior walls

failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House

announced initiatives to increase community resilience with building codes as a major component

of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748

and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better

construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397

would provide tax credits for owners andor contractors who use techniques designed for resiliency

in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one

recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to

encourage states to use current building codes (Vaughn and Turner 2014)

Future research in the BCA of the FBC will further inform the public policy debate on

enhanced building codes The issue has national implications as other states find that wind hazards

impact them as well We have sufficient wind data to examine how the BCA performs under

different wind hazards Additionally it will be important to consider how future economic

development affects the BCA as well as varying climate change scenarios As the FBC is

mandatory for all new construction a statewide analysis was appropriate But individual

homeowners in older homes can invest in the retrofit of their home and qualify for discounts on

their homeowners insurance This topic is deserving of a robust analysis Although our BCA is

statewide regions within the state will likely have a spectrum of results For instance the ARA

2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their

analysis did not use realized loss data so confirmation of how the BCA varies between those

regions would be an important contribution Finally our sensitivity analysis was limited to two

28

variables reduction in future loss and the inclusion of deductibles Additional work will highlight

other variables that could modify the results

29

Appendix

We use this appendix to conduct more detailed analysis on several topics First selection

of the model specification using a regression discontinuity approach Second we provide an in

depth examination of the relationship between structure age and losses Third we perform a

Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an

alternative specification to see if our RD results are similar followed by regressions to examine

the year to year consistency of our Post FBC result Next we run a regression on claims to verify

the difference between our direct reduction result and our full reduction result Finally we perform

a regression on homes built to the SFBC which had adopted enhanced building codes in advance

of the FBC to assess the effect of earlier adoption of enhanced construction

Regression Discontinuity

Regression Discontinuity (RD) applies when an observation receives a treatment in our case

homes built under the FBC based on a rating variable in our case age of the structure at the year

of observation So for observations in 2005 homes built post 2000 received the treatment

adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify

how observations on either side of the implementation of the FBC (2000) perform in suffering loss

from windstorms The treatment variable is a function of the age of the home and age affects loss

in ways not related to the FBC such as depreciation and differences in materials and construction

practices across time To account for both the effect of age on loss as well as the implementation

of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment

The data we have from ISO is aggregated loss data by zip code and decade of construction So

we cannot get an annualized age To approach a true age we set the year built for each decade of

construction at the beginning of the decade then subtract that from the year of each observation to

get an approximate agexiv

30

To find the best specification we began with a simpler model which used a series of

categorical variables for each decade of construction to examine the effect of the code compared

to the omitted decade This method would approximate the changes in materials and construction

practices but was less effective in controlling for depreciation But it would give us a first

approximation of the code effect that we used as a benchmark when testing the best RD

specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970

with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When

the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When

either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81

A rough approximation of the codersquos effect from this approach would suggest a reduction in the

mid 70 percent range

Insert Table 1 ndash Appendix Here

Next we used a standard procedure with RD to search for the best way to include the rating

variable This process creates specifications that include age in increasing polynomials and

interacted with the treatment variable The goal is to find the specification with the lowest AIC

that comes close to the benchmark value of the treatment variable

Insert Tables 2 and 3 ndash Appendix Here

We did this first with regressions that limited the co-variates then with our full model In both

sets AIC reaches a minimum on the specification with age and age squared The interaction model

after that increases the AIC then the AIC goes down again with a cubed model and its interaction

model with the overall lowest AIC found on the cubed interaction model But we chose not to

use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order

models the magnitude of the treatment variable in the models with just polynomials compared to

31

the corresponding interaction models were close with the interaction models providing a larger

magnitude When the cubed models were added the magnitude jumped where the polynomial

cubed model went down well below our benchmark and the interaction model went up above our

benchmark We felt this made use of the cubed model inappropriate So we now need to choose

between the squared model and the one with the interaction terms The squared model (Model 4)

had a lower AIC and the interaction variables on the interaction model (Model 5) were not

significant so we chose to use the squared model without the interaction term This model gave a

magnitude for the treatment variable of a 72 reduction somewhat lower than the expected

magnitude in the mid 70rsquos percent The general form of the model is

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1

of the observations on either end of our data sorted by loss Our treatment variable Post FBC

remains highly significant with a coefficient value of -117 which compares favorably to our

coefficient value of -126 when the entire sample is used

Structure Age and Wind Losses

Our study is similar to recent studies on the effect of energy efficiency building codes

adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that

better insulation caulking and more efficient HVAC systems would result in lower energy

consumption But the change in energy consumption is less than engineering estimates projected

Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for

homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study

32

may be confounding age with vintage and found a decrease in energy use related to the home

simply being new rather than the change in building code Indeed Kotchen (2015) revisited the

question with data 10 years older and found the effect on electricity had disappeared while the

reduction in natural gas use increased Something is occurring in energy use unrelated to the code

and could be explained by residents changing their use of energy as they adapt to their new home

Residents of an energy efficient home can undermine the intent of lower energy use by using the

efficient design to heat and cool their homes with a motivation toward increased comfort at the

same energy cost rather than energy savings Our study does not have the behavioral component

found in the case of energy efficiency In our application the construction elements that make the

structure able to withstand high winds are installed when the home is built and lie ldquobehind the

wallsrdquo making it unlikely for individual preferences to alter the homes performance against the

threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC

homes due to the code or simply an artifact of new versus old construction when confronted with

a windstorm

To first address our analysis of age versus the FBC we rerun our base regression but limit

our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more

than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14

years during the highest loss year of 2004 Since this is a comparison between two adjacent

decades on either side of our cut point of year 2000 we remove age and age squared Results are

shown in Table 4-Appendix

Insert Table 4-Appendix Here

The coefficient on Post FBC is still negative highly significant with a magnitude very close to

what we saw with the entire database and the age variables This result suggests that the code

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

23

assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the

next 50 years A wealth of literature suggests the potential for changes to hurricane activity over

the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty

on future changes in wind hazard on the scale of a single state we choose to assume a stationary

climate

Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes

built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our

data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of

$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year

Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses

per structure of $21474

Finally we use parameter estimates from our regression for the Post FBC dummy variable

(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC

The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post

FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what

would be a reduction of $10093 from the projected $21474 in future losses

Insert Table 7 Here

BenefitCost Analysis

Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio

of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the

statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an

economically effective public policy following from our ISO loss data and results

Insert Table 8 Here

24

Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years

This relatively short timeframe makes it difficult to truly approximate an AAL as would be

provided from a probabilistically based catastrophe model that generates an AAL from thousands

of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state

of Florida to estimate an average annual wind loss for all residential properties in Florida of

approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL

estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion

with deductibles) Using this aggregate AAL and the number of residential units in Florida based

on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses

net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and

an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as

before derived from our regression results 47 we find $7710 of reduced loss from the projected

$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added

$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective

building code public policy

We run two additional analyses on our BCA results Our estimate of expected loss

reduction comes from the second stage of the hurdle model This is an estimate of the direct loss

reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the

number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests

as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which

includes the reduced magnitude of loss from affected homes and the reduction in claims for Post

FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table

8) However a 72 loss reduction seems too dramatic an expectation when planning so far in

25

advance For that reason we offer a third level of expected loss reduction of 60 which is the

midpoint between our two loss reduction estimates This estimate captures the expected direct loss

reduction suggested by the second stage of our hurdle model but still recognizes that in some areas

the number of claims is reduced by the FBC This appears to be a reasonable assumption and

provides a BCA ratio of 396 for the ISO sample and 302 for all residential

The ISO data are net of deductibles so our BCA thus far only includes losses compensated by

the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion

also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the

ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all

loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA

values from a low of 237 to a high of 793

Payback of the FBC

Finally we use our BCA results to calculate a payback period for the investment of stronger

codes To convert our BCA ratio to a payback period we simply divide our 50-year planning

horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss

and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years

This is important for gauging potential political support or non-support for enactment of the new

codes Payback periods that approach the typical mortgage term 30 years would in theory be

difficult to achieve and that is not what our analysis indicates for the FBC

VI - Concluding Comments

In the aftermath of Hurricane Andrew which had exposed not only poor building

construction but also poor building code enforcement the state of Florida enacted statewide

building code changes that wrested away building code adoption control from individual localities

26

With full implementation of the statewide building code associated expectations are that

windstorm losses from extreme events such as hurricanes should be reduced moving forward

There have been a few studies confirming these expectations following the 2004 and 2005

hurricane season In this article we further verify and quantify these findings and expand the

existing building code risk reduction research in several important ways

Overall we empirically test the statewide implementation of a building code in reducing

wind related damages in Florida controlling for other relevant wind hazard exposure and

vulnerability characteristics from a traditional risk assessment perspective Our results show the

strong effect the statewide FBC had on losses from wind storms during this timeframe From the

treatment variable that measures implementation of the statewide codes the post 2000 year of

construction losses are shown to be reduced by as much as 72 percent consistent with other

previous findings

Finally we have conducted a BCA of the FBC to determine if expected benefits exceed

the cost of implementation Using a direct estimate for mitigated losses and an estimate that

includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public

policy from an economic perspective This result is close to that recommended by the multi-hazard

mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a

statewide BCA Importantly this information is essential in generating political and consumer

support for such building code public policy implementation

For example the economic effectiveness results shown here have implications for ongoing

policy discussions about reforming building codes from a national US perspective Moore OK

independently adopted enhanced building codes after its third violent tornado in 14 years killed 24

including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)

27

Construction practices in North Texas were brought under scrutiny after the December 2015

tornado revealed inadequate construction including an elementary school whose exterior walls

failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House

announced initiatives to increase community resilience with building codes as a major component

of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748

and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better

construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397

would provide tax credits for owners andor contractors who use techniques designed for resiliency

in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one

recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to

encourage states to use current building codes (Vaughn and Turner 2014)

Future research in the BCA of the FBC will further inform the public policy debate on

enhanced building codes The issue has national implications as other states find that wind hazards

impact them as well We have sufficient wind data to examine how the BCA performs under

different wind hazards Additionally it will be important to consider how future economic

development affects the BCA as well as varying climate change scenarios As the FBC is

mandatory for all new construction a statewide analysis was appropriate But individual

homeowners in older homes can invest in the retrofit of their home and qualify for discounts on

their homeowners insurance This topic is deserving of a robust analysis Although our BCA is

statewide regions within the state will likely have a spectrum of results For instance the ARA

2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their

analysis did not use realized loss data so confirmation of how the BCA varies between those

regions would be an important contribution Finally our sensitivity analysis was limited to two

28

variables reduction in future loss and the inclusion of deductibles Additional work will highlight

other variables that could modify the results

29

Appendix

We use this appendix to conduct more detailed analysis on several topics First selection

of the model specification using a regression discontinuity approach Second we provide an in

depth examination of the relationship between structure age and losses Third we perform a

Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an

alternative specification to see if our RD results are similar followed by regressions to examine

the year to year consistency of our Post FBC result Next we run a regression on claims to verify

the difference between our direct reduction result and our full reduction result Finally we perform

a regression on homes built to the SFBC which had adopted enhanced building codes in advance

of the FBC to assess the effect of earlier adoption of enhanced construction

Regression Discontinuity

Regression Discontinuity (RD) applies when an observation receives a treatment in our case

homes built under the FBC based on a rating variable in our case age of the structure at the year

of observation So for observations in 2005 homes built post 2000 received the treatment

adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify

how observations on either side of the implementation of the FBC (2000) perform in suffering loss

from windstorms The treatment variable is a function of the age of the home and age affects loss

in ways not related to the FBC such as depreciation and differences in materials and construction

practices across time To account for both the effect of age on loss as well as the implementation

of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment

The data we have from ISO is aggregated loss data by zip code and decade of construction So

we cannot get an annualized age To approach a true age we set the year built for each decade of

construction at the beginning of the decade then subtract that from the year of each observation to

get an approximate agexiv

30

To find the best specification we began with a simpler model which used a series of

categorical variables for each decade of construction to examine the effect of the code compared

to the omitted decade This method would approximate the changes in materials and construction

practices but was less effective in controlling for depreciation But it would give us a first

approximation of the code effect that we used as a benchmark when testing the best RD

specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970

with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When

the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When

either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81

A rough approximation of the codersquos effect from this approach would suggest a reduction in the

mid 70 percent range

Insert Table 1 ndash Appendix Here

Next we used a standard procedure with RD to search for the best way to include the rating

variable This process creates specifications that include age in increasing polynomials and

interacted with the treatment variable The goal is to find the specification with the lowest AIC

that comes close to the benchmark value of the treatment variable

Insert Tables 2 and 3 ndash Appendix Here

We did this first with regressions that limited the co-variates then with our full model In both

sets AIC reaches a minimum on the specification with age and age squared The interaction model

after that increases the AIC then the AIC goes down again with a cubed model and its interaction

model with the overall lowest AIC found on the cubed interaction model But we chose not to

use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order

models the magnitude of the treatment variable in the models with just polynomials compared to

31

the corresponding interaction models were close with the interaction models providing a larger

magnitude When the cubed models were added the magnitude jumped where the polynomial

cubed model went down well below our benchmark and the interaction model went up above our

benchmark We felt this made use of the cubed model inappropriate So we now need to choose

between the squared model and the one with the interaction terms The squared model (Model 4)

had a lower AIC and the interaction variables on the interaction model (Model 5) were not

significant so we chose to use the squared model without the interaction term This model gave a

magnitude for the treatment variable of a 72 reduction somewhat lower than the expected

magnitude in the mid 70rsquos percent The general form of the model is

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1

of the observations on either end of our data sorted by loss Our treatment variable Post FBC

remains highly significant with a coefficient value of -117 which compares favorably to our

coefficient value of -126 when the entire sample is used

Structure Age and Wind Losses

Our study is similar to recent studies on the effect of energy efficiency building codes

adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that

better insulation caulking and more efficient HVAC systems would result in lower energy

consumption But the change in energy consumption is less than engineering estimates projected

Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for

homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study

32

may be confounding age with vintage and found a decrease in energy use related to the home

simply being new rather than the change in building code Indeed Kotchen (2015) revisited the

question with data 10 years older and found the effect on electricity had disappeared while the

reduction in natural gas use increased Something is occurring in energy use unrelated to the code

and could be explained by residents changing their use of energy as they adapt to their new home

Residents of an energy efficient home can undermine the intent of lower energy use by using the

efficient design to heat and cool their homes with a motivation toward increased comfort at the

same energy cost rather than energy savings Our study does not have the behavioral component

found in the case of energy efficiency In our application the construction elements that make the

structure able to withstand high winds are installed when the home is built and lie ldquobehind the

wallsrdquo making it unlikely for individual preferences to alter the homes performance against the

threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC

homes due to the code or simply an artifact of new versus old construction when confronted with

a windstorm

To first address our analysis of age versus the FBC we rerun our base regression but limit

our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more

than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14

years during the highest loss year of 2004 Since this is a comparison between two adjacent

decades on either side of our cut point of year 2000 we remove age and age squared Results are

shown in Table 4-Appendix

Insert Table 4-Appendix Here

The coefficient on Post FBC is still negative highly significant with a magnitude very close to

what we saw with the entire database and the age variables This result suggests that the code

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

24

Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years

This relatively short timeframe makes it difficult to truly approximate an AAL as would be

provided from a probabilistically based catastrophe model that generates an AAL from thousands

of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state

of Florida to estimate an average annual wind loss for all residential properties in Florida of

approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL

estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion

with deductibles) Using this aggregate AAL and the number of residential units in Florida based

on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses

net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and

an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as

before derived from our regression results 47 we find $7710 of reduced loss from the projected

$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added

$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective

building code public policy

We run two additional analyses on our BCA results Our estimate of expected loss

reduction comes from the second stage of the hurdle model This is an estimate of the direct loss

reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the

number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests

as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which

includes the reduced magnitude of loss from affected homes and the reduction in claims for Post

FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table

8) However a 72 loss reduction seems too dramatic an expectation when planning so far in

25

advance For that reason we offer a third level of expected loss reduction of 60 which is the

midpoint between our two loss reduction estimates This estimate captures the expected direct loss

reduction suggested by the second stage of our hurdle model but still recognizes that in some areas

the number of claims is reduced by the FBC This appears to be a reasonable assumption and

provides a BCA ratio of 396 for the ISO sample and 302 for all residential

The ISO data are net of deductibles so our BCA thus far only includes losses compensated by

the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion

also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the

ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all

loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA

values from a low of 237 to a high of 793

Payback of the FBC

Finally we use our BCA results to calculate a payback period for the investment of stronger

codes To convert our BCA ratio to a payback period we simply divide our 50-year planning

horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss

and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years

This is important for gauging potential political support or non-support for enactment of the new

codes Payback periods that approach the typical mortgage term 30 years would in theory be

difficult to achieve and that is not what our analysis indicates for the FBC

VI - Concluding Comments

In the aftermath of Hurricane Andrew which had exposed not only poor building

construction but also poor building code enforcement the state of Florida enacted statewide

building code changes that wrested away building code adoption control from individual localities

26

With full implementation of the statewide building code associated expectations are that

windstorm losses from extreme events such as hurricanes should be reduced moving forward

There have been a few studies confirming these expectations following the 2004 and 2005

hurricane season In this article we further verify and quantify these findings and expand the

existing building code risk reduction research in several important ways

Overall we empirically test the statewide implementation of a building code in reducing

wind related damages in Florida controlling for other relevant wind hazard exposure and

vulnerability characteristics from a traditional risk assessment perspective Our results show the

strong effect the statewide FBC had on losses from wind storms during this timeframe From the

treatment variable that measures implementation of the statewide codes the post 2000 year of

construction losses are shown to be reduced by as much as 72 percent consistent with other

previous findings

Finally we have conducted a BCA of the FBC to determine if expected benefits exceed

the cost of implementation Using a direct estimate for mitigated losses and an estimate that

includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public

policy from an economic perspective This result is close to that recommended by the multi-hazard

mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a

statewide BCA Importantly this information is essential in generating political and consumer

support for such building code public policy implementation

For example the economic effectiveness results shown here have implications for ongoing

policy discussions about reforming building codes from a national US perspective Moore OK

independently adopted enhanced building codes after its third violent tornado in 14 years killed 24

including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)

27

Construction practices in North Texas were brought under scrutiny after the December 2015

tornado revealed inadequate construction including an elementary school whose exterior walls

failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House

announced initiatives to increase community resilience with building codes as a major component

of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748

and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better

construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397

would provide tax credits for owners andor contractors who use techniques designed for resiliency

in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one

recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to

encourage states to use current building codes (Vaughn and Turner 2014)

Future research in the BCA of the FBC will further inform the public policy debate on

enhanced building codes The issue has national implications as other states find that wind hazards

impact them as well We have sufficient wind data to examine how the BCA performs under

different wind hazards Additionally it will be important to consider how future economic

development affects the BCA as well as varying climate change scenarios As the FBC is

mandatory for all new construction a statewide analysis was appropriate But individual

homeowners in older homes can invest in the retrofit of their home and qualify for discounts on

their homeowners insurance This topic is deserving of a robust analysis Although our BCA is

statewide regions within the state will likely have a spectrum of results For instance the ARA

2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their

analysis did not use realized loss data so confirmation of how the BCA varies between those

regions would be an important contribution Finally our sensitivity analysis was limited to two

28

variables reduction in future loss and the inclusion of deductibles Additional work will highlight

other variables that could modify the results

29

Appendix

We use this appendix to conduct more detailed analysis on several topics First selection

of the model specification using a regression discontinuity approach Second we provide an in

depth examination of the relationship between structure age and losses Third we perform a

Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an

alternative specification to see if our RD results are similar followed by regressions to examine

the year to year consistency of our Post FBC result Next we run a regression on claims to verify

the difference between our direct reduction result and our full reduction result Finally we perform

a regression on homes built to the SFBC which had adopted enhanced building codes in advance

of the FBC to assess the effect of earlier adoption of enhanced construction

Regression Discontinuity

Regression Discontinuity (RD) applies when an observation receives a treatment in our case

homes built under the FBC based on a rating variable in our case age of the structure at the year

of observation So for observations in 2005 homes built post 2000 received the treatment

adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify

how observations on either side of the implementation of the FBC (2000) perform in suffering loss

from windstorms The treatment variable is a function of the age of the home and age affects loss

in ways not related to the FBC such as depreciation and differences in materials and construction

practices across time To account for both the effect of age on loss as well as the implementation

of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment

The data we have from ISO is aggregated loss data by zip code and decade of construction So

we cannot get an annualized age To approach a true age we set the year built for each decade of

construction at the beginning of the decade then subtract that from the year of each observation to

get an approximate agexiv

30

To find the best specification we began with a simpler model which used a series of

categorical variables for each decade of construction to examine the effect of the code compared

to the omitted decade This method would approximate the changes in materials and construction

practices but was less effective in controlling for depreciation But it would give us a first

approximation of the code effect that we used as a benchmark when testing the best RD

specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970

with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When

the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When

either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81

A rough approximation of the codersquos effect from this approach would suggest a reduction in the

mid 70 percent range

Insert Table 1 ndash Appendix Here

Next we used a standard procedure with RD to search for the best way to include the rating

variable This process creates specifications that include age in increasing polynomials and

interacted with the treatment variable The goal is to find the specification with the lowest AIC

that comes close to the benchmark value of the treatment variable

Insert Tables 2 and 3 ndash Appendix Here

We did this first with regressions that limited the co-variates then with our full model In both

sets AIC reaches a minimum on the specification with age and age squared The interaction model

after that increases the AIC then the AIC goes down again with a cubed model and its interaction

model with the overall lowest AIC found on the cubed interaction model But we chose not to

use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order

models the magnitude of the treatment variable in the models with just polynomials compared to

31

the corresponding interaction models were close with the interaction models providing a larger

magnitude When the cubed models were added the magnitude jumped where the polynomial

cubed model went down well below our benchmark and the interaction model went up above our

benchmark We felt this made use of the cubed model inappropriate So we now need to choose

between the squared model and the one with the interaction terms The squared model (Model 4)

had a lower AIC and the interaction variables on the interaction model (Model 5) were not

significant so we chose to use the squared model without the interaction term This model gave a

magnitude for the treatment variable of a 72 reduction somewhat lower than the expected

magnitude in the mid 70rsquos percent The general form of the model is

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1

of the observations on either end of our data sorted by loss Our treatment variable Post FBC

remains highly significant with a coefficient value of -117 which compares favorably to our

coefficient value of -126 when the entire sample is used

Structure Age and Wind Losses

Our study is similar to recent studies on the effect of energy efficiency building codes

adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that

better insulation caulking and more efficient HVAC systems would result in lower energy

consumption But the change in energy consumption is less than engineering estimates projected

Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for

homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study

32

may be confounding age with vintage and found a decrease in energy use related to the home

simply being new rather than the change in building code Indeed Kotchen (2015) revisited the

question with data 10 years older and found the effect on electricity had disappeared while the

reduction in natural gas use increased Something is occurring in energy use unrelated to the code

and could be explained by residents changing their use of energy as they adapt to their new home

Residents of an energy efficient home can undermine the intent of lower energy use by using the

efficient design to heat and cool their homes with a motivation toward increased comfort at the

same energy cost rather than energy savings Our study does not have the behavioral component

found in the case of energy efficiency In our application the construction elements that make the

structure able to withstand high winds are installed when the home is built and lie ldquobehind the

wallsrdquo making it unlikely for individual preferences to alter the homes performance against the

threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC

homes due to the code or simply an artifact of new versus old construction when confronted with

a windstorm

To first address our analysis of age versus the FBC we rerun our base regression but limit

our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more

than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14

years during the highest loss year of 2004 Since this is a comparison between two adjacent

decades on either side of our cut point of year 2000 we remove age and age squared Results are

shown in Table 4-Appendix

Insert Table 4-Appendix Here

The coefficient on Post FBC is still negative highly significant with a magnitude very close to

what we saw with the entire database and the age variables This result suggests that the code

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

25

advance For that reason we offer a third level of expected loss reduction of 60 which is the

midpoint between our two loss reduction estimates This estimate captures the expected direct loss

reduction suggested by the second stage of our hurdle model but still recognizes that in some areas

the number of claims is reduced by the FBC This appears to be a reasonable assumption and

provides a BCA ratio of 396 for the ISO sample and 302 for all residential

The ISO data are net of deductibles so our BCA thus far only includes losses compensated by

the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion

also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the

ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all

loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA

values from a low of 237 to a high of 793

Payback of the FBC

Finally we use our BCA results to calculate a payback period for the investment of stronger

codes To convert our BCA ratio to a payback period we simply divide our 50-year planning

horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss

and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years

This is important for gauging potential political support or non-support for enactment of the new

codes Payback periods that approach the typical mortgage term 30 years would in theory be

difficult to achieve and that is not what our analysis indicates for the FBC

VI - Concluding Comments

In the aftermath of Hurricane Andrew which had exposed not only poor building

construction but also poor building code enforcement the state of Florida enacted statewide

building code changes that wrested away building code adoption control from individual localities

26

With full implementation of the statewide building code associated expectations are that

windstorm losses from extreme events such as hurricanes should be reduced moving forward

There have been a few studies confirming these expectations following the 2004 and 2005

hurricane season In this article we further verify and quantify these findings and expand the

existing building code risk reduction research in several important ways

Overall we empirically test the statewide implementation of a building code in reducing

wind related damages in Florida controlling for other relevant wind hazard exposure and

vulnerability characteristics from a traditional risk assessment perspective Our results show the

strong effect the statewide FBC had on losses from wind storms during this timeframe From the

treatment variable that measures implementation of the statewide codes the post 2000 year of

construction losses are shown to be reduced by as much as 72 percent consistent with other

previous findings

Finally we have conducted a BCA of the FBC to determine if expected benefits exceed

the cost of implementation Using a direct estimate for mitigated losses and an estimate that

includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public

policy from an economic perspective This result is close to that recommended by the multi-hazard

mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a

statewide BCA Importantly this information is essential in generating political and consumer

support for such building code public policy implementation

For example the economic effectiveness results shown here have implications for ongoing

policy discussions about reforming building codes from a national US perspective Moore OK

independently adopted enhanced building codes after its third violent tornado in 14 years killed 24

including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)

27

Construction practices in North Texas were brought under scrutiny after the December 2015

tornado revealed inadequate construction including an elementary school whose exterior walls

failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House

announced initiatives to increase community resilience with building codes as a major component

of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748

and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better

construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397

would provide tax credits for owners andor contractors who use techniques designed for resiliency

in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one

recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to

encourage states to use current building codes (Vaughn and Turner 2014)

Future research in the BCA of the FBC will further inform the public policy debate on

enhanced building codes The issue has national implications as other states find that wind hazards

impact them as well We have sufficient wind data to examine how the BCA performs under

different wind hazards Additionally it will be important to consider how future economic

development affects the BCA as well as varying climate change scenarios As the FBC is

mandatory for all new construction a statewide analysis was appropriate But individual

homeowners in older homes can invest in the retrofit of their home and qualify for discounts on

their homeowners insurance This topic is deserving of a robust analysis Although our BCA is

statewide regions within the state will likely have a spectrum of results For instance the ARA

2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their

analysis did not use realized loss data so confirmation of how the BCA varies between those

regions would be an important contribution Finally our sensitivity analysis was limited to two

28

variables reduction in future loss and the inclusion of deductibles Additional work will highlight

other variables that could modify the results

29

Appendix

We use this appendix to conduct more detailed analysis on several topics First selection

of the model specification using a regression discontinuity approach Second we provide an in

depth examination of the relationship between structure age and losses Third we perform a

Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an

alternative specification to see if our RD results are similar followed by regressions to examine

the year to year consistency of our Post FBC result Next we run a regression on claims to verify

the difference between our direct reduction result and our full reduction result Finally we perform

a regression on homes built to the SFBC which had adopted enhanced building codes in advance

of the FBC to assess the effect of earlier adoption of enhanced construction

Regression Discontinuity

Regression Discontinuity (RD) applies when an observation receives a treatment in our case

homes built under the FBC based on a rating variable in our case age of the structure at the year

of observation So for observations in 2005 homes built post 2000 received the treatment

adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify

how observations on either side of the implementation of the FBC (2000) perform in suffering loss

from windstorms The treatment variable is a function of the age of the home and age affects loss

in ways not related to the FBC such as depreciation and differences in materials and construction

practices across time To account for both the effect of age on loss as well as the implementation

of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment

The data we have from ISO is aggregated loss data by zip code and decade of construction So

we cannot get an annualized age To approach a true age we set the year built for each decade of

construction at the beginning of the decade then subtract that from the year of each observation to

get an approximate agexiv

30

To find the best specification we began with a simpler model which used a series of

categorical variables for each decade of construction to examine the effect of the code compared

to the omitted decade This method would approximate the changes in materials and construction

practices but was less effective in controlling for depreciation But it would give us a first

approximation of the code effect that we used as a benchmark when testing the best RD

specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970

with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When

the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When

either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81

A rough approximation of the codersquos effect from this approach would suggest a reduction in the

mid 70 percent range

Insert Table 1 ndash Appendix Here

Next we used a standard procedure with RD to search for the best way to include the rating

variable This process creates specifications that include age in increasing polynomials and

interacted with the treatment variable The goal is to find the specification with the lowest AIC

that comes close to the benchmark value of the treatment variable

Insert Tables 2 and 3 ndash Appendix Here

We did this first with regressions that limited the co-variates then with our full model In both

sets AIC reaches a minimum on the specification with age and age squared The interaction model

after that increases the AIC then the AIC goes down again with a cubed model and its interaction

model with the overall lowest AIC found on the cubed interaction model But we chose not to

use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order

models the magnitude of the treatment variable in the models with just polynomials compared to

31

the corresponding interaction models were close with the interaction models providing a larger

magnitude When the cubed models were added the magnitude jumped where the polynomial

cubed model went down well below our benchmark and the interaction model went up above our

benchmark We felt this made use of the cubed model inappropriate So we now need to choose

between the squared model and the one with the interaction terms The squared model (Model 4)

had a lower AIC and the interaction variables on the interaction model (Model 5) were not

significant so we chose to use the squared model without the interaction term This model gave a

magnitude for the treatment variable of a 72 reduction somewhat lower than the expected

magnitude in the mid 70rsquos percent The general form of the model is

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1

of the observations on either end of our data sorted by loss Our treatment variable Post FBC

remains highly significant with a coefficient value of -117 which compares favorably to our

coefficient value of -126 when the entire sample is used

Structure Age and Wind Losses

Our study is similar to recent studies on the effect of energy efficiency building codes

adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that

better insulation caulking and more efficient HVAC systems would result in lower energy

consumption But the change in energy consumption is less than engineering estimates projected

Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for

homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study

32

may be confounding age with vintage and found a decrease in energy use related to the home

simply being new rather than the change in building code Indeed Kotchen (2015) revisited the

question with data 10 years older and found the effect on electricity had disappeared while the

reduction in natural gas use increased Something is occurring in energy use unrelated to the code

and could be explained by residents changing their use of energy as they adapt to their new home

Residents of an energy efficient home can undermine the intent of lower energy use by using the

efficient design to heat and cool their homes with a motivation toward increased comfort at the

same energy cost rather than energy savings Our study does not have the behavioral component

found in the case of energy efficiency In our application the construction elements that make the

structure able to withstand high winds are installed when the home is built and lie ldquobehind the

wallsrdquo making it unlikely for individual preferences to alter the homes performance against the

threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC

homes due to the code or simply an artifact of new versus old construction when confronted with

a windstorm

To first address our analysis of age versus the FBC we rerun our base regression but limit

our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more

than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14

years during the highest loss year of 2004 Since this is a comparison between two adjacent

decades on either side of our cut point of year 2000 we remove age and age squared Results are

shown in Table 4-Appendix

Insert Table 4-Appendix Here

The coefficient on Post FBC is still negative highly significant with a magnitude very close to

what we saw with the entire database and the age variables This result suggests that the code

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

26

With full implementation of the statewide building code associated expectations are that

windstorm losses from extreme events such as hurricanes should be reduced moving forward

There have been a few studies confirming these expectations following the 2004 and 2005

hurricane season In this article we further verify and quantify these findings and expand the

existing building code risk reduction research in several important ways

Overall we empirically test the statewide implementation of a building code in reducing

wind related damages in Florida controlling for other relevant wind hazard exposure and

vulnerability characteristics from a traditional risk assessment perspective Our results show the

strong effect the statewide FBC had on losses from wind storms during this timeframe From the

treatment variable that measures implementation of the statewide codes the post 2000 year of

construction losses are shown to be reduced by as much as 72 percent consistent with other

previous findings

Finally we have conducted a BCA of the FBC to determine if expected benefits exceed

the cost of implementation Using a direct estimate for mitigated losses and an estimate that

includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public

policy from an economic perspective This result is close to that recommended by the multi-hazard

mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a

statewide BCA Importantly this information is essential in generating political and consumer

support for such building code public policy implementation

For example the economic effectiveness results shown here have implications for ongoing

policy discussions about reforming building codes from a national US perspective Moore OK

independently adopted enhanced building codes after its third violent tornado in 14 years killed 24

including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)

27

Construction practices in North Texas were brought under scrutiny after the December 2015

tornado revealed inadequate construction including an elementary school whose exterior walls

failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House

announced initiatives to increase community resilience with building codes as a major component

of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748

and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better

construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397

would provide tax credits for owners andor contractors who use techniques designed for resiliency

in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one

recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to

encourage states to use current building codes (Vaughn and Turner 2014)

Future research in the BCA of the FBC will further inform the public policy debate on

enhanced building codes The issue has national implications as other states find that wind hazards

impact them as well We have sufficient wind data to examine how the BCA performs under

different wind hazards Additionally it will be important to consider how future economic

development affects the BCA as well as varying climate change scenarios As the FBC is

mandatory for all new construction a statewide analysis was appropriate But individual

homeowners in older homes can invest in the retrofit of their home and qualify for discounts on

their homeowners insurance This topic is deserving of a robust analysis Although our BCA is

statewide regions within the state will likely have a spectrum of results For instance the ARA

2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their

analysis did not use realized loss data so confirmation of how the BCA varies between those

regions would be an important contribution Finally our sensitivity analysis was limited to two

28

variables reduction in future loss and the inclusion of deductibles Additional work will highlight

other variables that could modify the results

29

Appendix

We use this appendix to conduct more detailed analysis on several topics First selection

of the model specification using a regression discontinuity approach Second we provide an in

depth examination of the relationship between structure age and losses Third we perform a

Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an

alternative specification to see if our RD results are similar followed by regressions to examine

the year to year consistency of our Post FBC result Next we run a regression on claims to verify

the difference between our direct reduction result and our full reduction result Finally we perform

a regression on homes built to the SFBC which had adopted enhanced building codes in advance

of the FBC to assess the effect of earlier adoption of enhanced construction

Regression Discontinuity

Regression Discontinuity (RD) applies when an observation receives a treatment in our case

homes built under the FBC based on a rating variable in our case age of the structure at the year

of observation So for observations in 2005 homes built post 2000 received the treatment

adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify

how observations on either side of the implementation of the FBC (2000) perform in suffering loss

from windstorms The treatment variable is a function of the age of the home and age affects loss

in ways not related to the FBC such as depreciation and differences in materials and construction

practices across time To account for both the effect of age on loss as well as the implementation

of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment

The data we have from ISO is aggregated loss data by zip code and decade of construction So

we cannot get an annualized age To approach a true age we set the year built for each decade of

construction at the beginning of the decade then subtract that from the year of each observation to

get an approximate agexiv

30

To find the best specification we began with a simpler model which used a series of

categorical variables for each decade of construction to examine the effect of the code compared

to the omitted decade This method would approximate the changes in materials and construction

practices but was less effective in controlling for depreciation But it would give us a first

approximation of the code effect that we used as a benchmark when testing the best RD

specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970

with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When

the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When

either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81

A rough approximation of the codersquos effect from this approach would suggest a reduction in the

mid 70 percent range

Insert Table 1 ndash Appendix Here

Next we used a standard procedure with RD to search for the best way to include the rating

variable This process creates specifications that include age in increasing polynomials and

interacted with the treatment variable The goal is to find the specification with the lowest AIC

that comes close to the benchmark value of the treatment variable

Insert Tables 2 and 3 ndash Appendix Here

We did this first with regressions that limited the co-variates then with our full model In both

sets AIC reaches a minimum on the specification with age and age squared The interaction model

after that increases the AIC then the AIC goes down again with a cubed model and its interaction

model with the overall lowest AIC found on the cubed interaction model But we chose not to

use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order

models the magnitude of the treatment variable in the models with just polynomials compared to

31

the corresponding interaction models were close with the interaction models providing a larger

magnitude When the cubed models were added the magnitude jumped where the polynomial

cubed model went down well below our benchmark and the interaction model went up above our

benchmark We felt this made use of the cubed model inappropriate So we now need to choose

between the squared model and the one with the interaction terms The squared model (Model 4)

had a lower AIC and the interaction variables on the interaction model (Model 5) were not

significant so we chose to use the squared model without the interaction term This model gave a

magnitude for the treatment variable of a 72 reduction somewhat lower than the expected

magnitude in the mid 70rsquos percent The general form of the model is

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1

of the observations on either end of our data sorted by loss Our treatment variable Post FBC

remains highly significant with a coefficient value of -117 which compares favorably to our

coefficient value of -126 when the entire sample is used

Structure Age and Wind Losses

Our study is similar to recent studies on the effect of energy efficiency building codes

adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that

better insulation caulking and more efficient HVAC systems would result in lower energy

consumption But the change in energy consumption is less than engineering estimates projected

Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for

homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study

32

may be confounding age with vintage and found a decrease in energy use related to the home

simply being new rather than the change in building code Indeed Kotchen (2015) revisited the

question with data 10 years older and found the effect on electricity had disappeared while the

reduction in natural gas use increased Something is occurring in energy use unrelated to the code

and could be explained by residents changing their use of energy as they adapt to their new home

Residents of an energy efficient home can undermine the intent of lower energy use by using the

efficient design to heat and cool their homes with a motivation toward increased comfort at the

same energy cost rather than energy savings Our study does not have the behavioral component

found in the case of energy efficiency In our application the construction elements that make the

structure able to withstand high winds are installed when the home is built and lie ldquobehind the

wallsrdquo making it unlikely for individual preferences to alter the homes performance against the

threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC

homes due to the code or simply an artifact of new versus old construction when confronted with

a windstorm

To first address our analysis of age versus the FBC we rerun our base regression but limit

our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more

than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14

years during the highest loss year of 2004 Since this is a comparison between two adjacent

decades on either side of our cut point of year 2000 we remove age and age squared Results are

shown in Table 4-Appendix

Insert Table 4-Appendix Here

The coefficient on Post FBC is still negative highly significant with a magnitude very close to

what we saw with the entire database and the age variables This result suggests that the code

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

27

Construction practices in North Texas were brought under scrutiny after the December 2015

tornado revealed inadequate construction including an elementary school whose exterior walls

failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House

announced initiatives to increase community resilience with building codes as a major component

of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748

and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better

construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397

would provide tax credits for owners andor contractors who use techniques designed for resiliency

in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one

recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to

encourage states to use current building codes (Vaughn and Turner 2014)

Future research in the BCA of the FBC will further inform the public policy debate on

enhanced building codes The issue has national implications as other states find that wind hazards

impact them as well We have sufficient wind data to examine how the BCA performs under

different wind hazards Additionally it will be important to consider how future economic

development affects the BCA as well as varying climate change scenarios As the FBC is

mandatory for all new construction a statewide analysis was appropriate But individual

homeowners in older homes can invest in the retrofit of their home and qualify for discounts on

their homeowners insurance This topic is deserving of a robust analysis Although our BCA is

statewide regions within the state will likely have a spectrum of results For instance the ARA

2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their

analysis did not use realized loss data so confirmation of how the BCA varies between those

regions would be an important contribution Finally our sensitivity analysis was limited to two

28

variables reduction in future loss and the inclusion of deductibles Additional work will highlight

other variables that could modify the results

29

Appendix

We use this appendix to conduct more detailed analysis on several topics First selection

of the model specification using a regression discontinuity approach Second we provide an in

depth examination of the relationship between structure age and losses Third we perform a

Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an

alternative specification to see if our RD results are similar followed by regressions to examine

the year to year consistency of our Post FBC result Next we run a regression on claims to verify

the difference between our direct reduction result and our full reduction result Finally we perform

a regression on homes built to the SFBC which had adopted enhanced building codes in advance

of the FBC to assess the effect of earlier adoption of enhanced construction

Regression Discontinuity

Regression Discontinuity (RD) applies when an observation receives a treatment in our case

homes built under the FBC based on a rating variable in our case age of the structure at the year

of observation So for observations in 2005 homes built post 2000 received the treatment

adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify

how observations on either side of the implementation of the FBC (2000) perform in suffering loss

from windstorms The treatment variable is a function of the age of the home and age affects loss

in ways not related to the FBC such as depreciation and differences in materials and construction

practices across time To account for both the effect of age on loss as well as the implementation

of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment

The data we have from ISO is aggregated loss data by zip code and decade of construction So

we cannot get an annualized age To approach a true age we set the year built for each decade of

construction at the beginning of the decade then subtract that from the year of each observation to

get an approximate agexiv

30

To find the best specification we began with a simpler model which used a series of

categorical variables for each decade of construction to examine the effect of the code compared

to the omitted decade This method would approximate the changes in materials and construction

practices but was less effective in controlling for depreciation But it would give us a first

approximation of the code effect that we used as a benchmark when testing the best RD

specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970

with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When

the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When

either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81

A rough approximation of the codersquos effect from this approach would suggest a reduction in the

mid 70 percent range

Insert Table 1 ndash Appendix Here

Next we used a standard procedure with RD to search for the best way to include the rating

variable This process creates specifications that include age in increasing polynomials and

interacted with the treatment variable The goal is to find the specification with the lowest AIC

that comes close to the benchmark value of the treatment variable

Insert Tables 2 and 3 ndash Appendix Here

We did this first with regressions that limited the co-variates then with our full model In both

sets AIC reaches a minimum on the specification with age and age squared The interaction model

after that increases the AIC then the AIC goes down again with a cubed model and its interaction

model with the overall lowest AIC found on the cubed interaction model But we chose not to

use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order

models the magnitude of the treatment variable in the models with just polynomials compared to

31

the corresponding interaction models were close with the interaction models providing a larger

magnitude When the cubed models were added the magnitude jumped where the polynomial

cubed model went down well below our benchmark and the interaction model went up above our

benchmark We felt this made use of the cubed model inappropriate So we now need to choose

between the squared model and the one with the interaction terms The squared model (Model 4)

had a lower AIC and the interaction variables on the interaction model (Model 5) were not

significant so we chose to use the squared model without the interaction term This model gave a

magnitude for the treatment variable of a 72 reduction somewhat lower than the expected

magnitude in the mid 70rsquos percent The general form of the model is

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1

of the observations on either end of our data sorted by loss Our treatment variable Post FBC

remains highly significant with a coefficient value of -117 which compares favorably to our

coefficient value of -126 when the entire sample is used

Structure Age and Wind Losses

Our study is similar to recent studies on the effect of energy efficiency building codes

adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that

better insulation caulking and more efficient HVAC systems would result in lower energy

consumption But the change in energy consumption is less than engineering estimates projected

Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for

homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study

32

may be confounding age with vintage and found a decrease in energy use related to the home

simply being new rather than the change in building code Indeed Kotchen (2015) revisited the

question with data 10 years older and found the effect on electricity had disappeared while the

reduction in natural gas use increased Something is occurring in energy use unrelated to the code

and could be explained by residents changing their use of energy as they adapt to their new home

Residents of an energy efficient home can undermine the intent of lower energy use by using the

efficient design to heat and cool their homes with a motivation toward increased comfort at the

same energy cost rather than energy savings Our study does not have the behavioral component

found in the case of energy efficiency In our application the construction elements that make the

structure able to withstand high winds are installed when the home is built and lie ldquobehind the

wallsrdquo making it unlikely for individual preferences to alter the homes performance against the

threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC

homes due to the code or simply an artifact of new versus old construction when confronted with

a windstorm

To first address our analysis of age versus the FBC we rerun our base regression but limit

our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more

than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14

years during the highest loss year of 2004 Since this is a comparison between two adjacent

decades on either side of our cut point of year 2000 we remove age and age squared Results are

shown in Table 4-Appendix

Insert Table 4-Appendix Here

The coefficient on Post FBC is still negative highly significant with a magnitude very close to

what we saw with the entire database and the age variables This result suggests that the code

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

28

variables reduction in future loss and the inclusion of deductibles Additional work will highlight

other variables that could modify the results

29

Appendix

We use this appendix to conduct more detailed analysis on several topics First selection

of the model specification using a regression discontinuity approach Second we provide an in

depth examination of the relationship between structure age and losses Third we perform a

Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an

alternative specification to see if our RD results are similar followed by regressions to examine

the year to year consistency of our Post FBC result Next we run a regression on claims to verify

the difference between our direct reduction result and our full reduction result Finally we perform

a regression on homes built to the SFBC which had adopted enhanced building codes in advance

of the FBC to assess the effect of earlier adoption of enhanced construction

Regression Discontinuity

Regression Discontinuity (RD) applies when an observation receives a treatment in our case

homes built under the FBC based on a rating variable in our case age of the structure at the year

of observation So for observations in 2005 homes built post 2000 received the treatment

adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify

how observations on either side of the implementation of the FBC (2000) perform in suffering loss

from windstorms The treatment variable is a function of the age of the home and age affects loss

in ways not related to the FBC such as depreciation and differences in materials and construction

practices across time To account for both the effect of age on loss as well as the implementation

of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment

The data we have from ISO is aggregated loss data by zip code and decade of construction So

we cannot get an annualized age To approach a true age we set the year built for each decade of

construction at the beginning of the decade then subtract that from the year of each observation to

get an approximate agexiv

30

To find the best specification we began with a simpler model which used a series of

categorical variables for each decade of construction to examine the effect of the code compared

to the omitted decade This method would approximate the changes in materials and construction

practices but was less effective in controlling for depreciation But it would give us a first

approximation of the code effect that we used as a benchmark when testing the best RD

specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970

with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When

the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When

either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81

A rough approximation of the codersquos effect from this approach would suggest a reduction in the

mid 70 percent range

Insert Table 1 ndash Appendix Here

Next we used a standard procedure with RD to search for the best way to include the rating

variable This process creates specifications that include age in increasing polynomials and

interacted with the treatment variable The goal is to find the specification with the lowest AIC

that comes close to the benchmark value of the treatment variable

Insert Tables 2 and 3 ndash Appendix Here

We did this first with regressions that limited the co-variates then with our full model In both

sets AIC reaches a minimum on the specification with age and age squared The interaction model

after that increases the AIC then the AIC goes down again with a cubed model and its interaction

model with the overall lowest AIC found on the cubed interaction model But we chose not to

use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order

models the magnitude of the treatment variable in the models with just polynomials compared to

31

the corresponding interaction models were close with the interaction models providing a larger

magnitude When the cubed models were added the magnitude jumped where the polynomial

cubed model went down well below our benchmark and the interaction model went up above our

benchmark We felt this made use of the cubed model inappropriate So we now need to choose

between the squared model and the one with the interaction terms The squared model (Model 4)

had a lower AIC and the interaction variables on the interaction model (Model 5) were not

significant so we chose to use the squared model without the interaction term This model gave a

magnitude for the treatment variable of a 72 reduction somewhat lower than the expected

magnitude in the mid 70rsquos percent The general form of the model is

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1

of the observations on either end of our data sorted by loss Our treatment variable Post FBC

remains highly significant with a coefficient value of -117 which compares favorably to our

coefficient value of -126 when the entire sample is used

Structure Age and Wind Losses

Our study is similar to recent studies on the effect of energy efficiency building codes

adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that

better insulation caulking and more efficient HVAC systems would result in lower energy

consumption But the change in energy consumption is less than engineering estimates projected

Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for

homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study

32

may be confounding age with vintage and found a decrease in energy use related to the home

simply being new rather than the change in building code Indeed Kotchen (2015) revisited the

question with data 10 years older and found the effect on electricity had disappeared while the

reduction in natural gas use increased Something is occurring in energy use unrelated to the code

and could be explained by residents changing their use of energy as they adapt to their new home

Residents of an energy efficient home can undermine the intent of lower energy use by using the

efficient design to heat and cool their homes with a motivation toward increased comfort at the

same energy cost rather than energy savings Our study does not have the behavioral component

found in the case of energy efficiency In our application the construction elements that make the

structure able to withstand high winds are installed when the home is built and lie ldquobehind the

wallsrdquo making it unlikely for individual preferences to alter the homes performance against the

threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC

homes due to the code or simply an artifact of new versus old construction when confronted with

a windstorm

To first address our analysis of age versus the FBC we rerun our base regression but limit

our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more

than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14

years during the highest loss year of 2004 Since this is a comparison between two adjacent

decades on either side of our cut point of year 2000 we remove age and age squared Results are

shown in Table 4-Appendix

Insert Table 4-Appendix Here

The coefficient on Post FBC is still negative highly significant with a magnitude very close to

what we saw with the entire database and the age variables This result suggests that the code

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

29

Appendix

We use this appendix to conduct more detailed analysis on several topics First selection

of the model specification using a regression discontinuity approach Second we provide an in

depth examination of the relationship between structure age and losses Third we perform a

Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an

alternative specification to see if our RD results are similar followed by regressions to examine

the year to year consistency of our Post FBC result Next we run a regression on claims to verify

the difference between our direct reduction result and our full reduction result Finally we perform

a regression on homes built to the SFBC which had adopted enhanced building codes in advance

of the FBC to assess the effect of earlier adoption of enhanced construction

Regression Discontinuity

Regression Discontinuity (RD) applies when an observation receives a treatment in our case

homes built under the FBC based on a rating variable in our case age of the structure at the year

of observation So for observations in 2005 homes built post 2000 received the treatment

adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify

how observations on either side of the implementation of the FBC (2000) perform in suffering loss

from windstorms The treatment variable is a function of the age of the home and age affects loss

in ways not related to the FBC such as depreciation and differences in materials and construction

practices across time To account for both the effect of age on loss as well as the implementation

of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment

The data we have from ISO is aggregated loss data by zip code and decade of construction So

we cannot get an annualized age To approach a true age we set the year built for each decade of

construction at the beginning of the decade then subtract that from the year of each observation to

get an approximate agexiv

30

To find the best specification we began with a simpler model which used a series of

categorical variables for each decade of construction to examine the effect of the code compared

to the omitted decade This method would approximate the changes in materials and construction

practices but was less effective in controlling for depreciation But it would give us a first

approximation of the code effect that we used as a benchmark when testing the best RD

specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970

with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When

the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When

either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81

A rough approximation of the codersquos effect from this approach would suggest a reduction in the

mid 70 percent range

Insert Table 1 ndash Appendix Here

Next we used a standard procedure with RD to search for the best way to include the rating

variable This process creates specifications that include age in increasing polynomials and

interacted with the treatment variable The goal is to find the specification with the lowest AIC

that comes close to the benchmark value of the treatment variable

Insert Tables 2 and 3 ndash Appendix Here

We did this first with regressions that limited the co-variates then with our full model In both

sets AIC reaches a minimum on the specification with age and age squared The interaction model

after that increases the AIC then the AIC goes down again with a cubed model and its interaction

model with the overall lowest AIC found on the cubed interaction model But we chose not to

use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order

models the magnitude of the treatment variable in the models with just polynomials compared to

31

the corresponding interaction models were close with the interaction models providing a larger

magnitude When the cubed models were added the magnitude jumped where the polynomial

cubed model went down well below our benchmark and the interaction model went up above our

benchmark We felt this made use of the cubed model inappropriate So we now need to choose

between the squared model and the one with the interaction terms The squared model (Model 4)

had a lower AIC and the interaction variables on the interaction model (Model 5) were not

significant so we chose to use the squared model without the interaction term This model gave a

magnitude for the treatment variable of a 72 reduction somewhat lower than the expected

magnitude in the mid 70rsquos percent The general form of the model is

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1

of the observations on either end of our data sorted by loss Our treatment variable Post FBC

remains highly significant with a coefficient value of -117 which compares favorably to our

coefficient value of -126 when the entire sample is used

Structure Age and Wind Losses

Our study is similar to recent studies on the effect of energy efficiency building codes

adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that

better insulation caulking and more efficient HVAC systems would result in lower energy

consumption But the change in energy consumption is less than engineering estimates projected

Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for

homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study

32

may be confounding age with vintage and found a decrease in energy use related to the home

simply being new rather than the change in building code Indeed Kotchen (2015) revisited the

question with data 10 years older and found the effect on electricity had disappeared while the

reduction in natural gas use increased Something is occurring in energy use unrelated to the code

and could be explained by residents changing their use of energy as they adapt to their new home

Residents of an energy efficient home can undermine the intent of lower energy use by using the

efficient design to heat and cool their homes with a motivation toward increased comfort at the

same energy cost rather than energy savings Our study does not have the behavioral component

found in the case of energy efficiency In our application the construction elements that make the

structure able to withstand high winds are installed when the home is built and lie ldquobehind the

wallsrdquo making it unlikely for individual preferences to alter the homes performance against the

threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC

homes due to the code or simply an artifact of new versus old construction when confronted with

a windstorm

To first address our analysis of age versus the FBC we rerun our base regression but limit

our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more

than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14

years during the highest loss year of 2004 Since this is a comparison between two adjacent

decades on either side of our cut point of year 2000 we remove age and age squared Results are

shown in Table 4-Appendix

Insert Table 4-Appendix Here

The coefficient on Post FBC is still negative highly significant with a magnitude very close to

what we saw with the entire database and the age variables This result suggests that the code

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

30

To find the best specification we began with a simpler model which used a series of

categorical variables for each decade of construction to examine the effect of the code compared

to the omitted decade This method would approximate the changes in materials and construction

practices but was less effective in controlling for depreciation But it would give us a first

approximation of the code effect that we used as a benchmark when testing the best RD

specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970

with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When

the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When

either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81

A rough approximation of the codersquos effect from this approach would suggest a reduction in the

mid 70 percent range

Insert Table 1 ndash Appendix Here

Next we used a standard procedure with RD to search for the best way to include the rating

variable This process creates specifications that include age in increasing polynomials and

interacted with the treatment variable The goal is to find the specification with the lowest AIC

that comes close to the benchmark value of the treatment variable

Insert Tables 2 and 3 ndash Appendix Here

We did this first with regressions that limited the co-variates then with our full model In both

sets AIC reaches a minimum on the specification with age and age squared The interaction model

after that increases the AIC then the AIC goes down again with a cubed model and its interaction

model with the overall lowest AIC found on the cubed interaction model But we chose not to

use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order

models the magnitude of the treatment variable in the models with just polynomials compared to

31

the corresponding interaction models were close with the interaction models providing a larger

magnitude When the cubed models were added the magnitude jumped where the polynomial

cubed model went down well below our benchmark and the interaction model went up above our

benchmark We felt this made use of the cubed model inappropriate So we now need to choose

between the squared model and the one with the interaction terms The squared model (Model 4)

had a lower AIC and the interaction variables on the interaction model (Model 5) were not

significant so we chose to use the squared model without the interaction term This model gave a

magnitude for the treatment variable of a 72 reduction somewhat lower than the expected

magnitude in the mid 70rsquos percent The general form of the model is

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1

of the observations on either end of our data sorted by loss Our treatment variable Post FBC

remains highly significant with a coefficient value of -117 which compares favorably to our

coefficient value of -126 when the entire sample is used

Structure Age and Wind Losses

Our study is similar to recent studies on the effect of energy efficiency building codes

adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that

better insulation caulking and more efficient HVAC systems would result in lower energy

consumption But the change in energy consumption is less than engineering estimates projected

Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for

homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study

32

may be confounding age with vintage and found a decrease in energy use related to the home

simply being new rather than the change in building code Indeed Kotchen (2015) revisited the

question with data 10 years older and found the effect on electricity had disappeared while the

reduction in natural gas use increased Something is occurring in energy use unrelated to the code

and could be explained by residents changing their use of energy as they adapt to their new home

Residents of an energy efficient home can undermine the intent of lower energy use by using the

efficient design to heat and cool their homes with a motivation toward increased comfort at the

same energy cost rather than energy savings Our study does not have the behavioral component

found in the case of energy efficiency In our application the construction elements that make the

structure able to withstand high winds are installed when the home is built and lie ldquobehind the

wallsrdquo making it unlikely for individual preferences to alter the homes performance against the

threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC

homes due to the code or simply an artifact of new versus old construction when confronted with

a windstorm

To first address our analysis of age versus the FBC we rerun our base regression but limit

our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more

than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14

years during the highest loss year of 2004 Since this is a comparison between two adjacent

decades on either side of our cut point of year 2000 we remove age and age squared Results are

shown in Table 4-Appendix

Insert Table 4-Appendix Here

The coefficient on Post FBC is still negative highly significant with a magnitude very close to

what we saw with the entire database and the age variables This result suggests that the code

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

31

the corresponding interaction models were close with the interaction models providing a larger

magnitude When the cubed models were added the magnitude jumped where the polynomial

cubed model went down well below our benchmark and the interaction model went up above our

benchmark We felt this made use of the cubed model inappropriate So we now need to choose

between the squared model and the one with the interaction terms The squared model (Model 4)

had a lower AIC and the interaction variables on the interaction model (Model 5) were not

significant so we chose to use the squared model without the interaction term This model gave a

magnitude for the treatment variable of a 72 reduction somewhat lower than the expected

magnitude in the mid 70rsquos percent The general form of the model is

(1)

Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1

of the observations on either end of our data sorted by loss Our treatment variable Post FBC

remains highly significant with a coefficient value of -117 which compares favorably to our

coefficient value of -126 when the entire sample is used

Structure Age and Wind Losses

Our study is similar to recent studies on the effect of energy efficiency building codes

adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that

better insulation caulking and more efficient HVAC systems would result in lower energy

consumption But the change in energy consumption is less than engineering estimates projected

Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for

homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study

32

may be confounding age with vintage and found a decrease in energy use related to the home

simply being new rather than the change in building code Indeed Kotchen (2015) revisited the

question with data 10 years older and found the effect on electricity had disappeared while the

reduction in natural gas use increased Something is occurring in energy use unrelated to the code

and could be explained by residents changing their use of energy as they adapt to their new home

Residents of an energy efficient home can undermine the intent of lower energy use by using the

efficient design to heat and cool their homes with a motivation toward increased comfort at the

same energy cost rather than energy savings Our study does not have the behavioral component

found in the case of energy efficiency In our application the construction elements that make the

structure able to withstand high winds are installed when the home is built and lie ldquobehind the

wallsrdquo making it unlikely for individual preferences to alter the homes performance against the

threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC

homes due to the code or simply an artifact of new versus old construction when confronted with

a windstorm

To first address our analysis of age versus the FBC we rerun our base regression but limit

our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more

than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14

years during the highest loss year of 2004 Since this is a comparison between two adjacent

decades on either side of our cut point of year 2000 we remove age and age squared Results are

shown in Table 4-Appendix

Insert Table 4-Appendix Here

The coefficient on Post FBC is still negative highly significant with a magnitude very close to

what we saw with the entire database and the age variables This result suggests that the code

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

32

may be confounding age with vintage and found a decrease in energy use related to the home

simply being new rather than the change in building code Indeed Kotchen (2015) revisited the

question with data 10 years older and found the effect on electricity had disappeared while the

reduction in natural gas use increased Something is occurring in energy use unrelated to the code

and could be explained by residents changing their use of energy as they adapt to their new home

Residents of an energy efficient home can undermine the intent of lower energy use by using the

efficient design to heat and cool their homes with a motivation toward increased comfort at the

same energy cost rather than energy savings Our study does not have the behavioral component

found in the case of energy efficiency In our application the construction elements that make the

structure able to withstand high winds are installed when the home is built and lie ldquobehind the

wallsrdquo making it unlikely for individual preferences to alter the homes performance against the

threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC

homes due to the code or simply an artifact of new versus old construction when confronted with

a windstorm

To first address our analysis of age versus the FBC we rerun our base regression but limit

our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more

than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14

years during the highest loss year of 2004 Since this is a comparison between two adjacent

decades on either side of our cut point of year 2000 we remove age and age squared Results are

shown in Table 4-Appendix

Insert Table 4-Appendix Here

The coefficient on Post FBC is still negative highly significant with a magnitude very close to

what we saw with the entire database and the age variables This result suggests that the code

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

33

change did have an impact at least compared to homes built in the 1990rsquos Next we run a model

which tests for vintage effects This model has dummy variables for each decade omitting the

Post FBC dummy to examine how changing construction practices and materials across time have

impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category

Results are also shown in Table 4-App Compared to the Post FBC construction the decades of

the 1970rsquos and 1980rsquos show the worst performance

Our final test on age compares loss by structure age and is found on Figure 1-App For

this graph we show how loss for similar aged homes varies by decade of construction where the

Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and

Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till

now Instead of average loss we use the natural log of average loss in order to fit the graph Post

FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages

losses are lower for Post FBC In this way we illustrate how the loss performance for homes with

similar vintage and age compare with the only change being the code Consider the high point of

the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high

point on the orange line which are Post FBC homes with an age of 4 years facing the same threat

The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with

a high of 707 or an average loss of $1176

Insert Figure 1-Appendix Here

Balance Test

To further test the reliability of our FBC result we perform a balance test on either side of

our cut point year 2000 First we do a simple test of two means on demographic features by ZIP

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

34

code before and after the year 2000 for several periods to see how time has altered the differences

Results are shown in Table 5-Appendix

Insert Table 5-Appendix Here

The table shows that there is little difference between the demographic characteristics of

the ZIP codes until you get to data prior to 1970 We then test the impact those differences may

have on our results by running a series of regressions using categorical dummy variables for

decades rather than including age as a separate variable Here there are 3 regressions the full

data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to

see how the FBC performance changes relative to the most recent decade between our full model

and recent time frames Those results are in Table 6-Appendix

Insert Table 6-Appendix Here

This analysis shows that differences in observations across time have little effect on our treatment

variable

Alternative Specification

Our reported models in Table 4 use structure age as an added variable in a specification

based on a discontinuity between age and our treatment variable Another way to approach this

would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to

examine the effect of the FBC against the most recent decade Then run the same regression but

use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results

Insert Table 7-Appendix Here

Using this specification to examine the effect of the FBC we get a 66 reduction in the full model

and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

35

and not earlier decades with lower performance these results compare well to our results in the

models using structure age reported in Table 4

Year to Year Consistency of our Post FBC Result

As a final examination of our model we run regressions on each year separately to see how

the Post FBC variable changes from year to year While we do not have loss data prior to the

implementation of the FBC necessary to do a falsification test we can examine if the code lost its

significance or changed signs across the years of our study Also we approached this from the

reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable

associated with the decade experiencing some of the worst results from wind storms the 1980rsquos

Insert Table 8-Appendix Here

Insert Table 9-Appendix Here

The Post FBC variable maintains its sign and significance in each of the ten years ranging

from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When

we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected

reverse effect posting positive and significant results across all ten years

Effect of the FBC on Claims

The main difference between the effect of the FBC between our full and hurdle model is

the full model includes all observations regardless of whether a claim has been filed and the second

stage of the hurdle model includes only observations that had a claim So we should be able to

test the difference in the coefficient on the FBC by running an analysis on claims To do this we

use the same equation as Equation 1 except that the dependent variable is not the natural log of

loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes

count data Therefore we use a regression model appropriate for count data Further there is

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

36

evidence of overdispersion so rather than use a Poisson regression we employ a Negative

Binomial model with the form

(3)

Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +

β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens

+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared

+ Vector of dummy variables for year + Vector of dummy variables for ZIP code

Table 10-Appendix reports the results

Insert Table 10-Appendix Here

Our treatment variable is negative highly significant and shows a reduction of 35 in claims due

to the FBC Assuming the average loss from an avoided claim would have been equal to average

losses from reported claims this result infers a full loss reduction of 72 from the direct loss

reduction of 47 There is enough variability with this assumption to question the apparent

precision in the estimate of full loss reduction to what our model suggests And we are not trying

to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most

of the difference between our direct loss reduction estimate of the FBC and our full loss reduction

of the FBC can be explained by a reduction in claims for homes built to the FBC

SFBC Regressions

Three counties Dade Broward and Monroe adopted the South Florida Building Code as

early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to

include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those

counties would have a more homogeneous stock of resilient housing providing a muted effect of

the FBC and a smaller difference between the direct and full effect of the FBC To test this we

ran our full regression and hurdle regression on observations that are in those counties alone This

reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

37

Insert Table 11-Appendix Here

On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3

counties On the second stage of the hurdle model we find that the effect of the FBC is reduced

from 47 statewide to 20 and this result does not attain significance These results suggest

that homes in Dade Broward and Monroe counties perform as expected if stronger construction

had been adopted prior to the FBC

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

38

References

Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction

Benefit Comparison Study

Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study

Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf

Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of

Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal

Emergency Management Agency ATC-117

Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the

Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014

(DOI 101175WCAS-D-13-000241)

Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling

high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change

doi 101007s10584-013-0954-6

Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land

Economics 89(4) 597-613

Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence

from Building Codes Available at SSRN 2314665

Dixon R (2009) Florida Building Commission Presentation Available at -

httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009

0917_DixonFLBldgCodepdf

Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South

Florida Building Code Risk Analysis 16(1) 81-91

Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property

Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature

Available from

httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-

FSU20Storm20Risk20Centerpdf

Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from

Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct

Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ

39

Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy

Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June

1980 pp 474-475

Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-

based assessment of hurricane risk and estimates of potential insured losses for the state of

Florida Natural Hazards Review 12 no 4 (2011) 171-176

Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample

Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of

Economic and Social Measurement 5 (4) 475-92

Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61

Insurance Institute for Business and Home Safety (IBHS) (2004) Hurricane Charley Executive

Summary Available at httpdisastersafetyorgwp-contentuploadshurricane_charleypdf

(last accessed February 10 2016)

Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates

Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-

releasesnew-ibhs-report-rates-building-codes-18-coastal-states (last accessed February 10

2016)

Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide

to Regression Discontinuityrdquo MDRC July 2012 Available online at

httpmdrcorgpublicationpractical-guide-regression-discontinuity

Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving

Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and

Statistics Vol 95 No 1 pp 34-49 March 2013

Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims

Data Proceedings 11th American Conference on Wind Engineering

Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available

online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash

The-Role-of-Wind-Duration-in-Damage-Estimation

Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal

February 2014 Available at

httpwwwclaimsjournalcomnewsnational20140228245314htm

(last accessed February 12 2016)

Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South

Florida Journal of Performance of Constructed Facilities 8(3) 178-191

40

Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes

Reduce Residential Energy Consumptionrdquo working paper June 2016

Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic

Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental

Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November

2010

Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and

Response Upper SaddleRiver NJ Wharton School Publishing

Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of

the American Academy of Political and Social Science604(1) 208-227

Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru

and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and

Atmospheric Physics 100(1-4) 3-22

Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in

Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010

Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from

Californiardquo working paper November 2015

Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic

Correspondence Engine Web application accessed June 2015 at

httpmcdcmissourieduwebsasgeocorr[90|2k|12]html

McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing

Costs and Risks of Extreme Weather Events A Ceres Report

Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor

Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343

Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under

Climate Change A Growing Challenge for the US A Ceres Report

Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent

Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study

Documentation Prepared for the Federal Emergency Management Agency of the US

Department of Homeland Security by the Applied Technology Council under contract to the

Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC

NARR 2015 National Centers for Environmental PredictionNational Weather

ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American

Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric

41

Research Computational and Information Systems Laboratory

httprdaucaredudatasetsds6080 Accessed May 22 2015

National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based

on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance

Insurance and Real Estate Report Available at

httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP

pdf (accessed January 2016)

Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient

Claims Journal Available at

httpwwwclaimsjournalcomnewsnational20150708264405htm

Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R

Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation

grants Natural hazards review 8(4) pp97-111

Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation

BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society

April 2015

Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses

and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics

53 (1 2) 45-55

Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado

Wreckagerdquo Dallas Morning News December 30 2015

Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving

Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash

6133

Vaughan E J Turner (2014) The Value and Impact of Building Codes Available

httpwwwcoalition4safetyorgtoolkithtml

Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G

Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and

climate change WIREs Climate Change 7 65-89 doi101002wcc371

Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind

speed and storm size Environmental Research Letters 96 064019

42

Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year

Windstorm Windstorm Avg Wind Number of

Incurred Losses Incurred Loss Per Earned House claims per

Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY

2001 $ 41758462 11377 $ 3670 869645 131

2002 $ 13664281 3656 $ 3737 952238 38

2003 $ 12527758 3085 $ 4061 1024566 30

2004 $ 3715877513 207905 $ 17873 991491 2097

2005 $ 1261591875 77901 $ 16195 1029461 757

2006 $ 12217068 1479 $ 8260 739962 20

2007 $ 52296497 2059 $ 25399 711885 29

2008 $ 41420175 5860 $ 7068 685920 85

2009 $ 17681332 2297 $ 7698 694412 33

2010 $ 9604188 1386 $ 6929 669770 21

Averages all years $ 517863915 31701 $ 10089 836935 324

excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48

Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)

Average Claims Per EHY Average Loss Per EHY

Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified

2001 14 05 07 $ 45 $ 20 $ 29

2002 04 01 03 $ 14 $ 4 $ 11

2003 04 02 02 $ 10 $ 6 $ 10

2004 206 104 185 $ 3605 $ 1211 $ 2701

2005 82 37 71 $ 1116 $ 433 $ 841

2006 03 01 01 $ 26 $ 6 $ 4

2007 04 01 03 $ 40 $ 14 $ 19

2008 10 05 05 $ 73 $ 29 $ 18

2009 04 02 03 $ 29 $ 7 $ 21

2010 03 01 04 $ 18 $ 4 $ 17

Total 34 15 31 $ 512 $ 168 $ 405

43

Table 3 - Variable Definitions

Variable Description

Intercept

EHY Number of customers by ZIP decade of construction and by year

Premiums Natural log of total insurance premiums Adjusted to 2010 dollars

BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year

Income Natural log of Median Household Income CPI adjusted to 2010

Population Density Population divided by the size of the ZIP code in miles by ZIP and year

Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year

CCCL Equals 1 if the ZIP code has a construction control line

Distance Natural log of the mean distance in miles to the nearest coast

Citizens Percent of insurance customers using the state insurer Citizens

Max Wind Maximum wind speed

Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours

Post FBC Equals 1 if the observation was for homes built in the 2000s

Age Year minus the beginning of the decade of construction

Age Sq Age Squared

Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane

Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane

44

Regression Table 4 ndash Base Models

Zip Code Fixed Effects Dummies Have Been Suppressed

Full Model Hurdle Model

Parameter Estimate Clustered

Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -262515 003503 First

Max Wind 0156383 000266 Stage

Wind Duration 0042673 0007991

Population Density

-000498 0002246

Post FBC -018365 0016166

Obs 69442

AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second

EHY 0035960515 00039 0002734 0000531 Stage

Premiums 0731719781 00269 0399849 0014034

BrickMasonry 0312210902 01413 0900063 0081676

Income -0214963136 0069 007255 0046157

Unit Density -0000084471 00002 -000052 0000159

CCCL 0092215125 00799 0187263 0051162

Distance 0168890828 0017 0079778 0010192

Citizens -1474742952 01257 -078342 0089688

Max Wind 0256278789 00179 0248594 0013452

Wind Duration 016693209 0042 0089406 0014077

Post FBC -126098448 00707 -063402 005657

Age 0015411882 00027 0011001 0002548

Age Sq -0002087834 00002 -000135 0000277

Obs 69442 19107

Adj R Squared 04643

45

Table 5 ndash Robustness Tests

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|

Intercept -44716798 -8628364 -8662343632 -195375973

EHY 00397957 0035961 003592987 000414663

Premiums 06911625 073172 0732205042 155455262

BrickMasonry 0312211 0378693342 494600107

Income -0214963 -0206314713 -069789714

Unit Density -8447E-05 -0000056364 -0001762

CCCL 0092215 0089157134 -024189863

Distance 0168891 0166864719 021309203

Citizens -1474743 -1447225912 -304176511

Max Wind 0256279 0255880813 053083086

Wind Duration 0166932 016814728 000796048

Post FBC -12504886 -1260984 -1261543925 -127291057

Age 00162403 0015412 0015359444 004154843

Age Sq -00021287 -0002088 -0002084084 -000492109

Design CAT4 -0034039886

Design CAT5 -0076779624

Obs 69442 69442 69442 14149

Adj R Squared 04436 04643 04643 05255

46

Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC

Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct

N-WBDR Cost 023 128 076 01745 0131748

WBDR-(lt140 mph) Cost 106 167 137 04206 0574119

WBDR-(gt140 mph) Cost 135 249 192 03445 066144

SFBC 000 000 000 00604 0

Weighted Avg $137

2010 CPI Adj $166

Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC

Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year

ISO Sample 1960 166 3254 479732808 1029461 466 21474

With Deductibles 1960 166 3254 801153789 1029461 778 35852

Statewide 1960 166 3254 3155658466 8863057 356 16405

With Deductibles 1960 166 3254 5269949638 8863057 594 27373

Table 8 ndash BenefitCost Ratios

Per Unit Cost

FBC Damage

Reduction 47

FBC Damage

Reduction 60

FBC Damage

Reduction 72

BCA 47 Reduction

BCA 60 Reduction

BCA 72 Reduction

ISO Sample 3254 10093 12884 15461 310 396 475

With Deductibles 3254 16850 21511 25813 518 661 793

All Florida 3254 7710 9843 11812 237 302 363

With Deductibles 3254 12865 16424 19709 395 505 606

47

Table 1-Appendix

1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept 0427553

-7942695 -7940978 -8628364

EHY 0036632 0036632 0036632 0035961

Premiums 0718244 0718244 0718244 073172

BrickMasonry 0310039 0310039 0310039 0312211

Income -0229136 -0229136 -0229136 -0214963

Unit Density -0000035524

-0000035524

-0000035524

-0000084471

CCCL 0099362 0099362 0099362 0092215

Distance 0168051 0168051 0168051 0168891

Citizens -1493938 -1493938 -1493938 -1474743

Max Wind 0256727 0256727 0256727 0256279

Wind Duration 0166741 0166741 0166741 0166932

Post FBC -1072119 -1682877 -1684593 -1260984

d_1990

-0610758 -0612475

d_1980 0610758

-0001716

d_1970 0612475 0001716

d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341

pre_1950 -0112074 -0722832 -0724548 Age

0015412

Age Sq

-0002088

AIC 231513 231513 231513 231659

48

Table 2 ndash Appendix

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988

EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506

Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454

Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633

Age

-0007237 -0007307 001624 0016124 0063445 0070627

Age Sq

-0002129 -0002119 -001207 -0013457

Age Cu

587E-05 6652E-05

Age_PostFBC 0022066

-0006069

1042536

Agesq_PostFBC

0009971

-2328348

Agecu_PostFBC

0140286

AIC 234499 234390 234389 234279 234283 234223 234177

Model1 uses Post FBC and no age variable

Model2 uses Post FBC and age

Model3 uses Post FBC age and age interacted with Post FBC

Model4 uses Post FBC age and age squared

Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC

Model6 uses Post FBC age age squared and age cubed

Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC

49

Table 3 ndash Appendix

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127

EHY 003424 0034993 003485 0035961 0035889 0036392 0036671

Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426

BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482

Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407

Unit Density -7935E-05

-586E-05

-5982E-05

-845E-05

-8468E-05

-58E-05

-551E-05

CCCL 012243

0096304 0095483 0092215 0091992 0089874 0091186

Distance 017593 0169206 0169129 0168891 0168879 0167171 016703

Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534

Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214

Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622

Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842

Age

-0007626 -000772 0015412 0015125 0064521 007053

Age Sq

-0002088 -0002065 -001244 -0013596

AgeCu

612E-05 6766E-05

Age_PostFBC 0028294 0002751

1022203

Agesq_PostFBC 0008043

-2269315

Agecu_PostFBC

0136632

AIC 231894 231770 231766 231659 231662 231595 231552

50

Table 4-Appendix

1990-2010 Full Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388

Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013

Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131

CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653

Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152

pre_1950 0960045042 00475102

d_1950 1319252383 00508302

d_1960 1433696307 00489112

d_1970 1684593481 00482689

d_1980 1682877105 0048269

d_1990 1072118969 00483608

Post FBC -122639772 00520538

Obs 17906 69442

Adj R Squared 04675 04655

51

Table 5-Appendix

Balance Test

1990-2010

1980-2010

1970-2010

1960-2010

1950-2010

1900-2010

BrickMasonry

Mobile

Income

Home Value

Unit Density

CCCL

Distance

Citizens

Rejection of the Hypothesis that the means are equal α=1 α=05 α=01

Table 6-Appendix

Balance Test ndash Decade Dummies

1900-2010 1970-2010 1980-2010

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655

EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153

Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194

BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122

Income -0229136499 00436795 -009497743 006848399 0045469518 008095252

Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015

CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606

Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488

Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691

Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713

Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001

pre_1950 -0112073927 00506211

d_1950 0247133413 00526112

d_1960 0361577338 00500973

d_1970 0612474511 00488438 0575621494 005331684

d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286

d_1990

Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279

Obs 69442 35507

26729

Adj R Squared 04654 04778 048

52

Table 7-Appendix

Full Model Hurdle Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -262563 0035028 First

Max_Wind 0156425 000266 Stage

Wind Duration 0042652 0007989

Population Density -000501 0002246

Post FBC -018364 0016165

Obs 69442

AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second

EHY 0036631987 00007388 0003094 0000536 Stage

Premiums 0718244372 00100833 0386122 0014285

BrickMasonry 0310039307 00775013 0910896 0081548

Income -0229136499 00436795 0082266 0046119

Unit Density -0000035524 00001131 -000047 0000159

CCCL 0099361591 00484053 018571 005109

Distance 0168051018 00096458 0078816 0010177

Citizens -1493938439 00804653 -080097 0089598

Max Wind 0256726978 00087826 0250276 0013364

Wind Duration 016674127 00196152 0089513 001408

Pre 1950 -0112073927 00506211 -003 0062288

d_1950 0247133413 00526112 0079901 005082

d_1960 0361577338 00500973 016725 0043534

d_1970 0612474511 00488438 0247777 0039334

d_1980 0610758136 00484157 0289707 0037518

d_1990

Post FBC -1072118969 00483608 -06069 0048224

Obs 69442 19107

Adj R Squared 04654

53

Table 8-Appendix

2001 2002 2003 2004 2005

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383

EHY 0046815496 0049755016 0046129696 -000549296 001407418

Premiums 0902225848 0520713952 0541260712 177809555 137222708

BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961

Income -0556333367 007673894 -0333566059 -139739466 -001458013

Unit Density -000273761 0000017044 -0000399979 -000624441 000229285

CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961

Distance -0006196654 0146642336 0074095408 001597389 027645125

Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859

Max Wind 0189251023 02218324 -0028507713 062796853 033670019

Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226

Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475

Age 0024430912 0007695322 0018112422 005900484 002379329

Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583

Obs 7404 7315 7172 7138 7011

Adj R Squared 04659 03911 03599 06072 04469

2006 2007 2008 2009 2010

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759

EHY 0029382684 0038563888 004035125 0040966039 0038016928

Premiums 0410656129 0355927665 087161791 0526462478 02894645

BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283

Income -0098853673 -0243879192 029287347 0444050006 022370289

Unit Density 0000300877 0000720442 000118661 0001595448 0000576067

CCCL -027184702 0306014726 06309048 0038276012 -002689181

Distance 0081513275 0195383664 035499478 021933669 0163507604

Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008

Max Wind 0055728801 0201000209 014525329 017495008 -001688058

Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648

Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859

Age 0006187693 001016534 001631759 -001596812 -002182466

Age Sq -0000687056 -000118586 -000152884 0000907348 000112213

Obs 6719 6643 6575 6570 6895

Adj R Squared 0197 02293 03667 02803 02262

54

Table 9-Appendix

2001 2002 2003 2004 2005

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304

EHY 0047913193 0050579977 0046738464 -000511538 001472664

Premiums 0933434158 0540773786 0554312075 178778901 139820098

BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317

Income -0592068944 0052289191 -0345142584 -140252136 -002535229

Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385

CCCL 0743282837 -009598118 0007668609 -040039481 -002349054

Distance -0004011689 014827054 0076206078 001718914 028091933

Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783

Max Wind 0186392922 0219937725 -0029594557 062788723 03369511

Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395

d_1980 0882524305 0733952915 0820252047 048813821 072461562

Age 005656422 0033984684 0045371148 007917294 007253832

Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145

Obs 7404 7315 7172 7138 7011

Adj R Squared 04663 03917 03615 06072 04438

2006 2007 2008 2009 2010

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249

EHY 0029291524 0038176189 003980162 003937994 0036637581

Premiums 0430840225 0373067836 089118372 05725051 0338993543

BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629

Income -011246799 -0246875105 028804568 043007102 0198547424

Unit Density 0000308373 0000729796 000115155 000148608 0000544974

CCCL -0270035498 0310339493 06391466 00571657 -001174278

Distance 0084719416 0198463554 035735414 022474189 0168702153

Citizens -07322541 -0654042742 -309502993 -025940821 -05347339

Max Wind 0055528386 0201585869 014451638 017175987 -002013935

Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523

d_1980 0483661036 0599607 028523853 045083377 0431080232

Age 0041843078 0047004839 004563723 004699644 0024861386

Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913

Obs 6719 6643 6575 6570 6895

Adj R Squared 01923 02258 03648 02663 02178

55

Table 10-Appendix

Claims Regression

Parameter Estimate Std Err Pr gt ChiSq

Intercept -125027 0189

EHY 00031 00004

Premiums 09238 00091

BrickMasonry 04034 00589

Income -04719 00319

Unit Density -00007 00001

CCCL 0049 00343

Distance 01448 00068

Citizens -10523 00567

Max Wind 01721 00056

Wind Duration -00017 00101

Post FBC -04247 00366

Age 00375 00018

Age Sq -00043 00002

Obs 69442

Pseudo R Squared 029

56

Table 11-Appendix

SFBC Hurdle Regression

Full Model Hurdle Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -38419 0110688 First

Max Wind 0249099 0008523 Stage

Wind Duration -017305 0034269

Pop Density -00021 0004094

Post FBC -026859 0045551

Obs 2201

AIC 17362

Intercept -642410358 07694634 -1735 1612104 Second

EHY 003777857 0001882 -000198 0001279 Stage

Premiums 058526953 00206452 0651733 0031265

BrickMasonry 051703099 03228598 -08406 0521577

Income -024791541 00885833 -024543 0119458

Unit Density -000024465 00001635 -000108 0000324

CCCL 004187952 01058532 0083285 0147401

Distance 013910546 00256963 007182 0035699

Citizens -105795182 01382522 -087333 0192635

Max Wind 009254137 00352015 0207402 0075261

Wind Duration -005698597 00853374 -020077 0083082

Post FBC -033082693 01292625 -02288 016678

Age 002653867 00055593 0044771 0007671

Age Sq -000286273 00005216 -000527 0000887

Obs 10001

10001

Adj R Squared 05309

57

Figure Titles

Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned

house years

Figure 2 Regressions of predicted loss versus actual loss for model validation

Figure 1-App LN of Avg Loss by Structure Age

Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years

0

10

20

30

40

50

60

70

80

90

100

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Pre2000 YOC Post2000 YOC Unclassified YOC

58

Figure 2 Regressions of predicted loss versus actual loss for model validation

59

Figure 1-App

000

100

200

300

400

500

600

700

800

900

1000

1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981

LN o

f A

vg L

oss

Structure Age

LN of Avg Loss by Structure Age

2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969

1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929

60

i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC

x A state provided map of the region can be found here

httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png

xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard

43

Table 3 - Variable Definitions

Variable Description

Intercept

EHY Number of customers by ZIP decade of construction and by year

Premiums Natural log of total insurance premiums Adjusted to 2010 dollars

BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year

Income Natural log of Median Household Income CPI adjusted to 2010

Population Density Population divided by the size of the ZIP code in miles by ZIP and year

Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year

CCCL Equals 1 if the ZIP code has a construction control line

Distance Natural log of the mean distance in miles to the nearest coast

Citizens Percent of insurance customers using the state insurer Citizens

Max Wind Maximum wind speed

Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours

Post FBC Equals 1 if the observation was for homes built in the 2000s

Age Year minus the beginning of the decade of construction

Age Sq Age Squared

Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane

Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane

44

Regression Table 4 ndash Base Models

Zip Code Fixed Effects Dummies Have Been Suppressed

Full Model Hurdle Model

Parameter Estimate Clustered

Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -262515 003503 First

Max Wind 0156383 000266 Stage

Wind Duration 0042673 0007991

Population Density

-000498 0002246

Post FBC -018365 0016166

Obs 69442

AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second

EHY 0035960515 00039 0002734 0000531 Stage

Premiums 0731719781 00269 0399849 0014034

BrickMasonry 0312210902 01413 0900063 0081676

Income -0214963136 0069 007255 0046157

Unit Density -0000084471 00002 -000052 0000159

CCCL 0092215125 00799 0187263 0051162

Distance 0168890828 0017 0079778 0010192

Citizens -1474742952 01257 -078342 0089688

Max Wind 0256278789 00179 0248594 0013452

Wind Duration 016693209 0042 0089406 0014077

Post FBC -126098448 00707 -063402 005657

Age 0015411882 00027 0011001 0002548

Age Sq -0002087834 00002 -000135 0000277

Obs 69442 19107

Adj R Squared 04643

45

Table 5 ndash Robustness Tests

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|

Intercept -44716798 -8628364 -8662343632 -195375973

EHY 00397957 0035961 003592987 000414663

Premiums 06911625 073172 0732205042 155455262

BrickMasonry 0312211 0378693342 494600107

Income -0214963 -0206314713 -069789714

Unit Density -8447E-05 -0000056364 -0001762

CCCL 0092215 0089157134 -024189863

Distance 0168891 0166864719 021309203

Citizens -1474743 -1447225912 -304176511

Max Wind 0256279 0255880813 053083086

Wind Duration 0166932 016814728 000796048

Post FBC -12504886 -1260984 -1261543925 -127291057

Age 00162403 0015412 0015359444 004154843

Age Sq -00021287 -0002088 -0002084084 -000492109

Design CAT4 -0034039886

Design CAT5 -0076779624

Obs 69442 69442 69442 14149

Adj R Squared 04436 04643 04643 05255

46

Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC

Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct

N-WBDR Cost 023 128 076 01745 0131748

WBDR-(lt140 mph) Cost 106 167 137 04206 0574119

WBDR-(gt140 mph) Cost 135 249 192 03445 066144

SFBC 000 000 000 00604 0

Weighted Avg $137

2010 CPI Adj $166

Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC

Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year

ISO Sample 1960 166 3254 479732808 1029461 466 21474

With Deductibles 1960 166 3254 801153789 1029461 778 35852

Statewide 1960 166 3254 3155658466 8863057 356 16405

With Deductibles 1960 166 3254 5269949638 8863057 594 27373

Table 8 ndash BenefitCost Ratios

Per Unit Cost

FBC Damage

Reduction 47

FBC Damage

Reduction 60

FBC Damage

Reduction 72

BCA 47 Reduction

BCA 60 Reduction

BCA 72 Reduction

ISO Sample 3254 10093 12884 15461 310 396 475

With Deductibles 3254 16850 21511 25813 518 661 793

All Florida 3254 7710 9843 11812 237 302 363

With Deductibles 3254 12865 16424 19709 395 505 606

47

Table 1-Appendix

1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept 0427553

-7942695 -7940978 -8628364

EHY 0036632 0036632 0036632 0035961

Premiums 0718244 0718244 0718244 073172

BrickMasonry 0310039 0310039 0310039 0312211

Income -0229136 -0229136 -0229136 -0214963

Unit Density -0000035524

-0000035524

-0000035524

-0000084471

CCCL 0099362 0099362 0099362 0092215

Distance 0168051 0168051 0168051 0168891

Citizens -1493938 -1493938 -1493938 -1474743

Max Wind 0256727 0256727 0256727 0256279

Wind Duration 0166741 0166741 0166741 0166932

Post FBC -1072119 -1682877 -1684593 -1260984

d_1990

-0610758 -0612475

d_1980 0610758

-0001716

d_1970 0612475 0001716

d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341

pre_1950 -0112074 -0722832 -0724548 Age

0015412

Age Sq

-0002088

AIC 231513 231513 231513 231659

48

Table 2 ndash Appendix

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988

EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506

Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454

Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633

Age

-0007237 -0007307 001624 0016124 0063445 0070627

Age Sq

-0002129 -0002119 -001207 -0013457

Age Cu

587E-05 6652E-05

Age_PostFBC 0022066

-0006069

1042536

Agesq_PostFBC

0009971

-2328348

Agecu_PostFBC

0140286

AIC 234499 234390 234389 234279 234283 234223 234177

Model1 uses Post FBC and no age variable

Model2 uses Post FBC and age

Model3 uses Post FBC age and age interacted with Post FBC

Model4 uses Post FBC age and age squared

Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC

Model6 uses Post FBC age age squared and age cubed

Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC

49

Table 3 ndash Appendix

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127

EHY 003424 0034993 003485 0035961 0035889 0036392 0036671

Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426

BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482

Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407

Unit Density -7935E-05

-586E-05

-5982E-05

-845E-05

-8468E-05

-58E-05

-551E-05

CCCL 012243

0096304 0095483 0092215 0091992 0089874 0091186

Distance 017593 0169206 0169129 0168891 0168879 0167171 016703

Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534

Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214

Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622

Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842

Age

-0007626 -000772 0015412 0015125 0064521 007053

Age Sq

-0002088 -0002065 -001244 -0013596

AgeCu

612E-05 6766E-05

Age_PostFBC 0028294 0002751

1022203

Agesq_PostFBC 0008043

-2269315

Agecu_PostFBC

0136632

AIC 231894 231770 231766 231659 231662 231595 231552

50

Table 4-Appendix

1990-2010 Full Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388

Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013

Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131

CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653

Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152

pre_1950 0960045042 00475102

d_1950 1319252383 00508302

d_1960 1433696307 00489112

d_1970 1684593481 00482689

d_1980 1682877105 0048269

d_1990 1072118969 00483608

Post FBC -122639772 00520538

Obs 17906 69442

Adj R Squared 04675 04655

51

Table 5-Appendix

Balance Test

1990-2010

1980-2010

1970-2010

1960-2010

1950-2010

1900-2010

BrickMasonry

Mobile

Income

Home Value

Unit Density

CCCL

Distance

Citizens

Rejection of the Hypothesis that the means are equal α=1 α=05 α=01

Table 6-Appendix

Balance Test ndash Decade Dummies

1900-2010 1970-2010 1980-2010

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655

EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153

Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194

BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122

Income -0229136499 00436795 -009497743 006848399 0045469518 008095252

Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015

CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606

Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488

Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691

Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713

Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001

pre_1950 -0112073927 00506211

d_1950 0247133413 00526112

d_1960 0361577338 00500973

d_1970 0612474511 00488438 0575621494 005331684

d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286

d_1990

Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279

Obs 69442 35507

26729

Adj R Squared 04654 04778 048

52

Table 7-Appendix

Full Model Hurdle Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -262563 0035028 First

Max_Wind 0156425 000266 Stage

Wind Duration 0042652 0007989

Population Density -000501 0002246

Post FBC -018364 0016165

Obs 69442

AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second

EHY 0036631987 00007388 0003094 0000536 Stage

Premiums 0718244372 00100833 0386122 0014285

BrickMasonry 0310039307 00775013 0910896 0081548

Income -0229136499 00436795 0082266 0046119

Unit Density -0000035524 00001131 -000047 0000159

CCCL 0099361591 00484053 018571 005109

Distance 0168051018 00096458 0078816 0010177

Citizens -1493938439 00804653 -080097 0089598

Max Wind 0256726978 00087826 0250276 0013364

Wind Duration 016674127 00196152 0089513 001408

Pre 1950 -0112073927 00506211 -003 0062288

d_1950 0247133413 00526112 0079901 005082

d_1960 0361577338 00500973 016725 0043534

d_1970 0612474511 00488438 0247777 0039334

d_1980 0610758136 00484157 0289707 0037518

d_1990

Post FBC -1072118969 00483608 -06069 0048224

Obs 69442 19107

Adj R Squared 04654

53

Table 8-Appendix

2001 2002 2003 2004 2005

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383

EHY 0046815496 0049755016 0046129696 -000549296 001407418

Premiums 0902225848 0520713952 0541260712 177809555 137222708

BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961

Income -0556333367 007673894 -0333566059 -139739466 -001458013

Unit Density -000273761 0000017044 -0000399979 -000624441 000229285

CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961

Distance -0006196654 0146642336 0074095408 001597389 027645125

Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859

Max Wind 0189251023 02218324 -0028507713 062796853 033670019

Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226

Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475

Age 0024430912 0007695322 0018112422 005900484 002379329

Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583

Obs 7404 7315 7172 7138 7011

Adj R Squared 04659 03911 03599 06072 04469

2006 2007 2008 2009 2010

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759

EHY 0029382684 0038563888 004035125 0040966039 0038016928

Premiums 0410656129 0355927665 087161791 0526462478 02894645

BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283

Income -0098853673 -0243879192 029287347 0444050006 022370289

Unit Density 0000300877 0000720442 000118661 0001595448 0000576067

CCCL -027184702 0306014726 06309048 0038276012 -002689181

Distance 0081513275 0195383664 035499478 021933669 0163507604

Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008

Max Wind 0055728801 0201000209 014525329 017495008 -001688058

Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648

Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859

Age 0006187693 001016534 001631759 -001596812 -002182466

Age Sq -0000687056 -000118586 -000152884 0000907348 000112213

Obs 6719 6643 6575 6570 6895

Adj R Squared 0197 02293 03667 02803 02262

54

Table 9-Appendix

2001 2002 2003 2004 2005

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304

EHY 0047913193 0050579977 0046738464 -000511538 001472664

Premiums 0933434158 0540773786 0554312075 178778901 139820098

BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317

Income -0592068944 0052289191 -0345142584 -140252136 -002535229

Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385

CCCL 0743282837 -009598118 0007668609 -040039481 -002349054

Distance -0004011689 014827054 0076206078 001718914 028091933

Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783

Max Wind 0186392922 0219937725 -0029594557 062788723 03369511

Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395

d_1980 0882524305 0733952915 0820252047 048813821 072461562

Age 005656422 0033984684 0045371148 007917294 007253832

Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145

Obs 7404 7315 7172 7138 7011

Adj R Squared 04663 03917 03615 06072 04438

2006 2007 2008 2009 2010

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249

EHY 0029291524 0038176189 003980162 003937994 0036637581

Premiums 0430840225 0373067836 089118372 05725051 0338993543

BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629

Income -011246799 -0246875105 028804568 043007102 0198547424

Unit Density 0000308373 0000729796 000115155 000148608 0000544974

CCCL -0270035498 0310339493 06391466 00571657 -001174278

Distance 0084719416 0198463554 035735414 022474189 0168702153

Citizens -07322541 -0654042742 -309502993 -025940821 -05347339

Max Wind 0055528386 0201585869 014451638 017175987 -002013935

Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523

d_1980 0483661036 0599607 028523853 045083377 0431080232

Age 0041843078 0047004839 004563723 004699644 0024861386

Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913

Obs 6719 6643 6575 6570 6895

Adj R Squared 01923 02258 03648 02663 02178

55

Table 10-Appendix

Claims Regression

Parameter Estimate Std Err Pr gt ChiSq

Intercept -125027 0189

EHY 00031 00004

Premiums 09238 00091

BrickMasonry 04034 00589

Income -04719 00319

Unit Density -00007 00001

CCCL 0049 00343

Distance 01448 00068

Citizens -10523 00567

Max Wind 01721 00056

Wind Duration -00017 00101

Post FBC -04247 00366

Age 00375 00018

Age Sq -00043 00002

Obs 69442

Pseudo R Squared 029

56

Table 11-Appendix

SFBC Hurdle Regression

Full Model Hurdle Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -38419 0110688 First

Max Wind 0249099 0008523 Stage

Wind Duration -017305 0034269

Pop Density -00021 0004094

Post FBC -026859 0045551

Obs 2201

AIC 17362

Intercept -642410358 07694634 -1735 1612104 Second

EHY 003777857 0001882 -000198 0001279 Stage

Premiums 058526953 00206452 0651733 0031265

BrickMasonry 051703099 03228598 -08406 0521577

Income -024791541 00885833 -024543 0119458

Unit Density -000024465 00001635 -000108 0000324

CCCL 004187952 01058532 0083285 0147401

Distance 013910546 00256963 007182 0035699

Citizens -105795182 01382522 -087333 0192635

Max Wind 009254137 00352015 0207402 0075261

Wind Duration -005698597 00853374 -020077 0083082

Post FBC -033082693 01292625 -02288 016678

Age 002653867 00055593 0044771 0007671

Age Sq -000286273 00005216 -000527 0000887

Obs 10001

10001

Adj R Squared 05309

57

Figure Titles

Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned

house years

Figure 2 Regressions of predicted loss versus actual loss for model validation

Figure 1-App LN of Avg Loss by Structure Age

Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years

0

10

20

30

40

50

60

70

80

90

100

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Pre2000 YOC Post2000 YOC Unclassified YOC

58

Figure 2 Regressions of predicted loss versus actual loss for model validation

59

Figure 1-App

000

100

200

300

400

500

600

700

800

900

1000

1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981

LN o

f A

vg L

oss

Structure Age

LN of Avg Loss by Structure Age

2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969

1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929

60

i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC

x A state provided map of the region can be found here

httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png

xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard

44

Regression Table 4 ndash Base Models

Zip Code Fixed Effects Dummies Have Been Suppressed

Full Model Hurdle Model

Parameter Estimate Clustered

Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -262515 003503 First

Max Wind 0156383 000266 Stage

Wind Duration 0042673 0007991

Population Density

-000498 0002246

Post FBC -018365 0016166

Obs 69442

AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second

EHY 0035960515 00039 0002734 0000531 Stage

Premiums 0731719781 00269 0399849 0014034

BrickMasonry 0312210902 01413 0900063 0081676

Income -0214963136 0069 007255 0046157

Unit Density -0000084471 00002 -000052 0000159

CCCL 0092215125 00799 0187263 0051162

Distance 0168890828 0017 0079778 0010192

Citizens -1474742952 01257 -078342 0089688

Max Wind 0256278789 00179 0248594 0013452

Wind Duration 016693209 0042 0089406 0014077

Post FBC -126098448 00707 -063402 005657

Age 0015411882 00027 0011001 0002548

Age Sq -0002087834 00002 -000135 0000277

Obs 69442 19107

Adj R Squared 04643

45

Table 5 ndash Robustness Tests

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|

Intercept -44716798 -8628364 -8662343632 -195375973

EHY 00397957 0035961 003592987 000414663

Premiums 06911625 073172 0732205042 155455262

BrickMasonry 0312211 0378693342 494600107

Income -0214963 -0206314713 -069789714

Unit Density -8447E-05 -0000056364 -0001762

CCCL 0092215 0089157134 -024189863

Distance 0168891 0166864719 021309203

Citizens -1474743 -1447225912 -304176511

Max Wind 0256279 0255880813 053083086

Wind Duration 0166932 016814728 000796048

Post FBC -12504886 -1260984 -1261543925 -127291057

Age 00162403 0015412 0015359444 004154843

Age Sq -00021287 -0002088 -0002084084 -000492109

Design CAT4 -0034039886

Design CAT5 -0076779624

Obs 69442 69442 69442 14149

Adj R Squared 04436 04643 04643 05255

46

Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC

Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct

N-WBDR Cost 023 128 076 01745 0131748

WBDR-(lt140 mph) Cost 106 167 137 04206 0574119

WBDR-(gt140 mph) Cost 135 249 192 03445 066144

SFBC 000 000 000 00604 0

Weighted Avg $137

2010 CPI Adj $166

Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC

Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year

ISO Sample 1960 166 3254 479732808 1029461 466 21474

With Deductibles 1960 166 3254 801153789 1029461 778 35852

Statewide 1960 166 3254 3155658466 8863057 356 16405

With Deductibles 1960 166 3254 5269949638 8863057 594 27373

Table 8 ndash BenefitCost Ratios

Per Unit Cost

FBC Damage

Reduction 47

FBC Damage

Reduction 60

FBC Damage

Reduction 72

BCA 47 Reduction

BCA 60 Reduction

BCA 72 Reduction

ISO Sample 3254 10093 12884 15461 310 396 475

With Deductibles 3254 16850 21511 25813 518 661 793

All Florida 3254 7710 9843 11812 237 302 363

With Deductibles 3254 12865 16424 19709 395 505 606

47

Table 1-Appendix

1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept 0427553

-7942695 -7940978 -8628364

EHY 0036632 0036632 0036632 0035961

Premiums 0718244 0718244 0718244 073172

BrickMasonry 0310039 0310039 0310039 0312211

Income -0229136 -0229136 -0229136 -0214963

Unit Density -0000035524

-0000035524

-0000035524

-0000084471

CCCL 0099362 0099362 0099362 0092215

Distance 0168051 0168051 0168051 0168891

Citizens -1493938 -1493938 -1493938 -1474743

Max Wind 0256727 0256727 0256727 0256279

Wind Duration 0166741 0166741 0166741 0166932

Post FBC -1072119 -1682877 -1684593 -1260984

d_1990

-0610758 -0612475

d_1980 0610758

-0001716

d_1970 0612475 0001716

d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341

pre_1950 -0112074 -0722832 -0724548 Age

0015412

Age Sq

-0002088

AIC 231513 231513 231513 231659

48

Table 2 ndash Appendix

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988

EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506

Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454

Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633

Age

-0007237 -0007307 001624 0016124 0063445 0070627

Age Sq

-0002129 -0002119 -001207 -0013457

Age Cu

587E-05 6652E-05

Age_PostFBC 0022066

-0006069

1042536

Agesq_PostFBC

0009971

-2328348

Agecu_PostFBC

0140286

AIC 234499 234390 234389 234279 234283 234223 234177

Model1 uses Post FBC and no age variable

Model2 uses Post FBC and age

Model3 uses Post FBC age and age interacted with Post FBC

Model4 uses Post FBC age and age squared

Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC

Model6 uses Post FBC age age squared and age cubed

Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC

49

Table 3 ndash Appendix

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127

EHY 003424 0034993 003485 0035961 0035889 0036392 0036671

Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426

BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482

Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407

Unit Density -7935E-05

-586E-05

-5982E-05

-845E-05

-8468E-05

-58E-05

-551E-05

CCCL 012243

0096304 0095483 0092215 0091992 0089874 0091186

Distance 017593 0169206 0169129 0168891 0168879 0167171 016703

Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534

Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214

Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622

Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842

Age

-0007626 -000772 0015412 0015125 0064521 007053

Age Sq

-0002088 -0002065 -001244 -0013596

AgeCu

612E-05 6766E-05

Age_PostFBC 0028294 0002751

1022203

Agesq_PostFBC 0008043

-2269315

Agecu_PostFBC

0136632

AIC 231894 231770 231766 231659 231662 231595 231552

50

Table 4-Appendix

1990-2010 Full Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388

Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013

Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131

CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653

Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152

pre_1950 0960045042 00475102

d_1950 1319252383 00508302

d_1960 1433696307 00489112

d_1970 1684593481 00482689

d_1980 1682877105 0048269

d_1990 1072118969 00483608

Post FBC -122639772 00520538

Obs 17906 69442

Adj R Squared 04675 04655

51

Table 5-Appendix

Balance Test

1990-2010

1980-2010

1970-2010

1960-2010

1950-2010

1900-2010

BrickMasonry

Mobile

Income

Home Value

Unit Density

CCCL

Distance

Citizens

Rejection of the Hypothesis that the means are equal α=1 α=05 α=01

Table 6-Appendix

Balance Test ndash Decade Dummies

1900-2010 1970-2010 1980-2010

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655

EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153

Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194

BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122

Income -0229136499 00436795 -009497743 006848399 0045469518 008095252

Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015

CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606

Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488

Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691

Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713

Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001

pre_1950 -0112073927 00506211

d_1950 0247133413 00526112

d_1960 0361577338 00500973

d_1970 0612474511 00488438 0575621494 005331684

d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286

d_1990

Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279

Obs 69442 35507

26729

Adj R Squared 04654 04778 048

52

Table 7-Appendix

Full Model Hurdle Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -262563 0035028 First

Max_Wind 0156425 000266 Stage

Wind Duration 0042652 0007989

Population Density -000501 0002246

Post FBC -018364 0016165

Obs 69442

AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second

EHY 0036631987 00007388 0003094 0000536 Stage

Premiums 0718244372 00100833 0386122 0014285

BrickMasonry 0310039307 00775013 0910896 0081548

Income -0229136499 00436795 0082266 0046119

Unit Density -0000035524 00001131 -000047 0000159

CCCL 0099361591 00484053 018571 005109

Distance 0168051018 00096458 0078816 0010177

Citizens -1493938439 00804653 -080097 0089598

Max Wind 0256726978 00087826 0250276 0013364

Wind Duration 016674127 00196152 0089513 001408

Pre 1950 -0112073927 00506211 -003 0062288

d_1950 0247133413 00526112 0079901 005082

d_1960 0361577338 00500973 016725 0043534

d_1970 0612474511 00488438 0247777 0039334

d_1980 0610758136 00484157 0289707 0037518

d_1990

Post FBC -1072118969 00483608 -06069 0048224

Obs 69442 19107

Adj R Squared 04654

53

Table 8-Appendix

2001 2002 2003 2004 2005

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383

EHY 0046815496 0049755016 0046129696 -000549296 001407418

Premiums 0902225848 0520713952 0541260712 177809555 137222708

BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961

Income -0556333367 007673894 -0333566059 -139739466 -001458013

Unit Density -000273761 0000017044 -0000399979 -000624441 000229285

CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961

Distance -0006196654 0146642336 0074095408 001597389 027645125

Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859

Max Wind 0189251023 02218324 -0028507713 062796853 033670019

Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226

Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475

Age 0024430912 0007695322 0018112422 005900484 002379329

Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583

Obs 7404 7315 7172 7138 7011

Adj R Squared 04659 03911 03599 06072 04469

2006 2007 2008 2009 2010

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759

EHY 0029382684 0038563888 004035125 0040966039 0038016928

Premiums 0410656129 0355927665 087161791 0526462478 02894645

BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283

Income -0098853673 -0243879192 029287347 0444050006 022370289

Unit Density 0000300877 0000720442 000118661 0001595448 0000576067

CCCL -027184702 0306014726 06309048 0038276012 -002689181

Distance 0081513275 0195383664 035499478 021933669 0163507604

Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008

Max Wind 0055728801 0201000209 014525329 017495008 -001688058

Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648

Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859

Age 0006187693 001016534 001631759 -001596812 -002182466

Age Sq -0000687056 -000118586 -000152884 0000907348 000112213

Obs 6719 6643 6575 6570 6895

Adj R Squared 0197 02293 03667 02803 02262

54

Table 9-Appendix

2001 2002 2003 2004 2005

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304

EHY 0047913193 0050579977 0046738464 -000511538 001472664

Premiums 0933434158 0540773786 0554312075 178778901 139820098

BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317

Income -0592068944 0052289191 -0345142584 -140252136 -002535229

Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385

CCCL 0743282837 -009598118 0007668609 -040039481 -002349054

Distance -0004011689 014827054 0076206078 001718914 028091933

Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783

Max Wind 0186392922 0219937725 -0029594557 062788723 03369511

Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395

d_1980 0882524305 0733952915 0820252047 048813821 072461562

Age 005656422 0033984684 0045371148 007917294 007253832

Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145

Obs 7404 7315 7172 7138 7011

Adj R Squared 04663 03917 03615 06072 04438

2006 2007 2008 2009 2010

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249

EHY 0029291524 0038176189 003980162 003937994 0036637581

Premiums 0430840225 0373067836 089118372 05725051 0338993543

BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629

Income -011246799 -0246875105 028804568 043007102 0198547424

Unit Density 0000308373 0000729796 000115155 000148608 0000544974

CCCL -0270035498 0310339493 06391466 00571657 -001174278

Distance 0084719416 0198463554 035735414 022474189 0168702153

Citizens -07322541 -0654042742 -309502993 -025940821 -05347339

Max Wind 0055528386 0201585869 014451638 017175987 -002013935

Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523

d_1980 0483661036 0599607 028523853 045083377 0431080232

Age 0041843078 0047004839 004563723 004699644 0024861386

Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913

Obs 6719 6643 6575 6570 6895

Adj R Squared 01923 02258 03648 02663 02178

55

Table 10-Appendix

Claims Regression

Parameter Estimate Std Err Pr gt ChiSq

Intercept -125027 0189

EHY 00031 00004

Premiums 09238 00091

BrickMasonry 04034 00589

Income -04719 00319

Unit Density -00007 00001

CCCL 0049 00343

Distance 01448 00068

Citizens -10523 00567

Max Wind 01721 00056

Wind Duration -00017 00101

Post FBC -04247 00366

Age 00375 00018

Age Sq -00043 00002

Obs 69442

Pseudo R Squared 029

56

Table 11-Appendix

SFBC Hurdle Regression

Full Model Hurdle Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -38419 0110688 First

Max Wind 0249099 0008523 Stage

Wind Duration -017305 0034269

Pop Density -00021 0004094

Post FBC -026859 0045551

Obs 2201

AIC 17362

Intercept -642410358 07694634 -1735 1612104 Second

EHY 003777857 0001882 -000198 0001279 Stage

Premiums 058526953 00206452 0651733 0031265

BrickMasonry 051703099 03228598 -08406 0521577

Income -024791541 00885833 -024543 0119458

Unit Density -000024465 00001635 -000108 0000324

CCCL 004187952 01058532 0083285 0147401

Distance 013910546 00256963 007182 0035699

Citizens -105795182 01382522 -087333 0192635

Max Wind 009254137 00352015 0207402 0075261

Wind Duration -005698597 00853374 -020077 0083082

Post FBC -033082693 01292625 -02288 016678

Age 002653867 00055593 0044771 0007671

Age Sq -000286273 00005216 -000527 0000887

Obs 10001

10001

Adj R Squared 05309

57

Figure Titles

Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned

house years

Figure 2 Regressions of predicted loss versus actual loss for model validation

Figure 1-App LN of Avg Loss by Structure Age

Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years

0

10

20

30

40

50

60

70

80

90

100

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Pre2000 YOC Post2000 YOC Unclassified YOC

58

Figure 2 Regressions of predicted loss versus actual loss for model validation

59

Figure 1-App

000

100

200

300

400

500

600

700

800

900

1000

1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981

LN o

f A

vg L

oss

Structure Age

LN of Avg Loss by Structure Age

2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969

1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929

60

i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC

x A state provided map of the region can be found here

httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png

xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard

45

Table 5 ndash Robustness Tests

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|

Intercept -44716798 -8628364 -8662343632 -195375973

EHY 00397957 0035961 003592987 000414663

Premiums 06911625 073172 0732205042 155455262

BrickMasonry 0312211 0378693342 494600107

Income -0214963 -0206314713 -069789714

Unit Density -8447E-05 -0000056364 -0001762

CCCL 0092215 0089157134 -024189863

Distance 0168891 0166864719 021309203

Citizens -1474743 -1447225912 -304176511

Max Wind 0256279 0255880813 053083086

Wind Duration 0166932 016814728 000796048

Post FBC -12504886 -1260984 -1261543925 -127291057

Age 00162403 0015412 0015359444 004154843

Age Sq -00021287 -0002088 -0002084084 -000492109

Design CAT4 -0034039886

Design CAT5 -0076779624

Obs 69442 69442 69442 14149

Adj R Squared 04436 04643 04643 05255

46

Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC

Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct

N-WBDR Cost 023 128 076 01745 0131748

WBDR-(lt140 mph) Cost 106 167 137 04206 0574119

WBDR-(gt140 mph) Cost 135 249 192 03445 066144

SFBC 000 000 000 00604 0

Weighted Avg $137

2010 CPI Adj $166

Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC

Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year

ISO Sample 1960 166 3254 479732808 1029461 466 21474

With Deductibles 1960 166 3254 801153789 1029461 778 35852

Statewide 1960 166 3254 3155658466 8863057 356 16405

With Deductibles 1960 166 3254 5269949638 8863057 594 27373

Table 8 ndash BenefitCost Ratios

Per Unit Cost

FBC Damage

Reduction 47

FBC Damage

Reduction 60

FBC Damage

Reduction 72

BCA 47 Reduction

BCA 60 Reduction

BCA 72 Reduction

ISO Sample 3254 10093 12884 15461 310 396 475

With Deductibles 3254 16850 21511 25813 518 661 793

All Florida 3254 7710 9843 11812 237 302 363

With Deductibles 3254 12865 16424 19709 395 505 606

47

Table 1-Appendix

1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept 0427553

-7942695 -7940978 -8628364

EHY 0036632 0036632 0036632 0035961

Premiums 0718244 0718244 0718244 073172

BrickMasonry 0310039 0310039 0310039 0312211

Income -0229136 -0229136 -0229136 -0214963

Unit Density -0000035524

-0000035524

-0000035524

-0000084471

CCCL 0099362 0099362 0099362 0092215

Distance 0168051 0168051 0168051 0168891

Citizens -1493938 -1493938 -1493938 -1474743

Max Wind 0256727 0256727 0256727 0256279

Wind Duration 0166741 0166741 0166741 0166932

Post FBC -1072119 -1682877 -1684593 -1260984

d_1990

-0610758 -0612475

d_1980 0610758

-0001716

d_1970 0612475 0001716

d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341

pre_1950 -0112074 -0722832 -0724548 Age

0015412

Age Sq

-0002088

AIC 231513 231513 231513 231659

48

Table 2 ndash Appendix

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988

EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506

Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454

Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633

Age

-0007237 -0007307 001624 0016124 0063445 0070627

Age Sq

-0002129 -0002119 -001207 -0013457

Age Cu

587E-05 6652E-05

Age_PostFBC 0022066

-0006069

1042536

Agesq_PostFBC

0009971

-2328348

Agecu_PostFBC

0140286

AIC 234499 234390 234389 234279 234283 234223 234177

Model1 uses Post FBC and no age variable

Model2 uses Post FBC and age

Model3 uses Post FBC age and age interacted with Post FBC

Model4 uses Post FBC age and age squared

Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC

Model6 uses Post FBC age age squared and age cubed

Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC

49

Table 3 ndash Appendix

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127

EHY 003424 0034993 003485 0035961 0035889 0036392 0036671

Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426

BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482

Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407

Unit Density -7935E-05

-586E-05

-5982E-05

-845E-05

-8468E-05

-58E-05

-551E-05

CCCL 012243

0096304 0095483 0092215 0091992 0089874 0091186

Distance 017593 0169206 0169129 0168891 0168879 0167171 016703

Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534

Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214

Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622

Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842

Age

-0007626 -000772 0015412 0015125 0064521 007053

Age Sq

-0002088 -0002065 -001244 -0013596

AgeCu

612E-05 6766E-05

Age_PostFBC 0028294 0002751

1022203

Agesq_PostFBC 0008043

-2269315

Agecu_PostFBC

0136632

AIC 231894 231770 231766 231659 231662 231595 231552

50

Table 4-Appendix

1990-2010 Full Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388

Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013

Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131

CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653

Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152

pre_1950 0960045042 00475102

d_1950 1319252383 00508302

d_1960 1433696307 00489112

d_1970 1684593481 00482689

d_1980 1682877105 0048269

d_1990 1072118969 00483608

Post FBC -122639772 00520538

Obs 17906 69442

Adj R Squared 04675 04655

51

Table 5-Appendix

Balance Test

1990-2010

1980-2010

1970-2010

1960-2010

1950-2010

1900-2010

BrickMasonry

Mobile

Income

Home Value

Unit Density

CCCL

Distance

Citizens

Rejection of the Hypothesis that the means are equal α=1 α=05 α=01

Table 6-Appendix

Balance Test ndash Decade Dummies

1900-2010 1970-2010 1980-2010

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655

EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153

Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194

BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122

Income -0229136499 00436795 -009497743 006848399 0045469518 008095252

Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015

CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606

Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488

Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691

Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713

Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001

pre_1950 -0112073927 00506211

d_1950 0247133413 00526112

d_1960 0361577338 00500973

d_1970 0612474511 00488438 0575621494 005331684

d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286

d_1990

Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279

Obs 69442 35507

26729

Adj R Squared 04654 04778 048

52

Table 7-Appendix

Full Model Hurdle Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -262563 0035028 First

Max_Wind 0156425 000266 Stage

Wind Duration 0042652 0007989

Population Density -000501 0002246

Post FBC -018364 0016165

Obs 69442

AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second

EHY 0036631987 00007388 0003094 0000536 Stage

Premiums 0718244372 00100833 0386122 0014285

BrickMasonry 0310039307 00775013 0910896 0081548

Income -0229136499 00436795 0082266 0046119

Unit Density -0000035524 00001131 -000047 0000159

CCCL 0099361591 00484053 018571 005109

Distance 0168051018 00096458 0078816 0010177

Citizens -1493938439 00804653 -080097 0089598

Max Wind 0256726978 00087826 0250276 0013364

Wind Duration 016674127 00196152 0089513 001408

Pre 1950 -0112073927 00506211 -003 0062288

d_1950 0247133413 00526112 0079901 005082

d_1960 0361577338 00500973 016725 0043534

d_1970 0612474511 00488438 0247777 0039334

d_1980 0610758136 00484157 0289707 0037518

d_1990

Post FBC -1072118969 00483608 -06069 0048224

Obs 69442 19107

Adj R Squared 04654

53

Table 8-Appendix

2001 2002 2003 2004 2005

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383

EHY 0046815496 0049755016 0046129696 -000549296 001407418

Premiums 0902225848 0520713952 0541260712 177809555 137222708

BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961

Income -0556333367 007673894 -0333566059 -139739466 -001458013

Unit Density -000273761 0000017044 -0000399979 -000624441 000229285

CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961

Distance -0006196654 0146642336 0074095408 001597389 027645125

Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859

Max Wind 0189251023 02218324 -0028507713 062796853 033670019

Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226

Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475

Age 0024430912 0007695322 0018112422 005900484 002379329

Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583

Obs 7404 7315 7172 7138 7011

Adj R Squared 04659 03911 03599 06072 04469

2006 2007 2008 2009 2010

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759

EHY 0029382684 0038563888 004035125 0040966039 0038016928

Premiums 0410656129 0355927665 087161791 0526462478 02894645

BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283

Income -0098853673 -0243879192 029287347 0444050006 022370289

Unit Density 0000300877 0000720442 000118661 0001595448 0000576067

CCCL -027184702 0306014726 06309048 0038276012 -002689181

Distance 0081513275 0195383664 035499478 021933669 0163507604

Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008

Max Wind 0055728801 0201000209 014525329 017495008 -001688058

Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648

Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859

Age 0006187693 001016534 001631759 -001596812 -002182466

Age Sq -0000687056 -000118586 -000152884 0000907348 000112213

Obs 6719 6643 6575 6570 6895

Adj R Squared 0197 02293 03667 02803 02262

54

Table 9-Appendix

2001 2002 2003 2004 2005

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304

EHY 0047913193 0050579977 0046738464 -000511538 001472664

Premiums 0933434158 0540773786 0554312075 178778901 139820098

BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317

Income -0592068944 0052289191 -0345142584 -140252136 -002535229

Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385

CCCL 0743282837 -009598118 0007668609 -040039481 -002349054

Distance -0004011689 014827054 0076206078 001718914 028091933

Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783

Max Wind 0186392922 0219937725 -0029594557 062788723 03369511

Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395

d_1980 0882524305 0733952915 0820252047 048813821 072461562

Age 005656422 0033984684 0045371148 007917294 007253832

Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145

Obs 7404 7315 7172 7138 7011

Adj R Squared 04663 03917 03615 06072 04438

2006 2007 2008 2009 2010

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249

EHY 0029291524 0038176189 003980162 003937994 0036637581

Premiums 0430840225 0373067836 089118372 05725051 0338993543

BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629

Income -011246799 -0246875105 028804568 043007102 0198547424

Unit Density 0000308373 0000729796 000115155 000148608 0000544974

CCCL -0270035498 0310339493 06391466 00571657 -001174278

Distance 0084719416 0198463554 035735414 022474189 0168702153

Citizens -07322541 -0654042742 -309502993 -025940821 -05347339

Max Wind 0055528386 0201585869 014451638 017175987 -002013935

Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523

d_1980 0483661036 0599607 028523853 045083377 0431080232

Age 0041843078 0047004839 004563723 004699644 0024861386

Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913

Obs 6719 6643 6575 6570 6895

Adj R Squared 01923 02258 03648 02663 02178

55

Table 10-Appendix

Claims Regression

Parameter Estimate Std Err Pr gt ChiSq

Intercept -125027 0189

EHY 00031 00004

Premiums 09238 00091

BrickMasonry 04034 00589

Income -04719 00319

Unit Density -00007 00001

CCCL 0049 00343

Distance 01448 00068

Citizens -10523 00567

Max Wind 01721 00056

Wind Duration -00017 00101

Post FBC -04247 00366

Age 00375 00018

Age Sq -00043 00002

Obs 69442

Pseudo R Squared 029

56

Table 11-Appendix

SFBC Hurdle Regression

Full Model Hurdle Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -38419 0110688 First

Max Wind 0249099 0008523 Stage

Wind Duration -017305 0034269

Pop Density -00021 0004094

Post FBC -026859 0045551

Obs 2201

AIC 17362

Intercept -642410358 07694634 -1735 1612104 Second

EHY 003777857 0001882 -000198 0001279 Stage

Premiums 058526953 00206452 0651733 0031265

BrickMasonry 051703099 03228598 -08406 0521577

Income -024791541 00885833 -024543 0119458

Unit Density -000024465 00001635 -000108 0000324

CCCL 004187952 01058532 0083285 0147401

Distance 013910546 00256963 007182 0035699

Citizens -105795182 01382522 -087333 0192635

Max Wind 009254137 00352015 0207402 0075261

Wind Duration -005698597 00853374 -020077 0083082

Post FBC -033082693 01292625 -02288 016678

Age 002653867 00055593 0044771 0007671

Age Sq -000286273 00005216 -000527 0000887

Obs 10001

10001

Adj R Squared 05309

57

Figure Titles

Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned

house years

Figure 2 Regressions of predicted loss versus actual loss for model validation

Figure 1-App LN of Avg Loss by Structure Age

Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years

0

10

20

30

40

50

60

70

80

90

100

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Pre2000 YOC Post2000 YOC Unclassified YOC

58

Figure 2 Regressions of predicted loss versus actual loss for model validation

59

Figure 1-App

000

100

200

300

400

500

600

700

800

900

1000

1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981

LN o

f A

vg L

oss

Structure Age

LN of Avg Loss by Structure Age

2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969

1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929

60

i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC

x A state provided map of the region can be found here

httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png

xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard

46

Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC

Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct

N-WBDR Cost 023 128 076 01745 0131748

WBDR-(lt140 mph) Cost 106 167 137 04206 0574119

WBDR-(gt140 mph) Cost 135 249 192 03445 066144

SFBC 000 000 000 00604 0

Weighted Avg $137

2010 CPI Adj $166

Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC

Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year

ISO Sample 1960 166 3254 479732808 1029461 466 21474

With Deductibles 1960 166 3254 801153789 1029461 778 35852

Statewide 1960 166 3254 3155658466 8863057 356 16405

With Deductibles 1960 166 3254 5269949638 8863057 594 27373

Table 8 ndash BenefitCost Ratios

Per Unit Cost

FBC Damage

Reduction 47

FBC Damage

Reduction 60

FBC Damage

Reduction 72

BCA 47 Reduction

BCA 60 Reduction

BCA 72 Reduction

ISO Sample 3254 10093 12884 15461 310 396 475

With Deductibles 3254 16850 21511 25813 518 661 793

All Florida 3254 7710 9843 11812 237 302 363

With Deductibles 3254 12865 16424 19709 395 505 606

47

Table 1-Appendix

1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept 0427553

-7942695 -7940978 -8628364

EHY 0036632 0036632 0036632 0035961

Premiums 0718244 0718244 0718244 073172

BrickMasonry 0310039 0310039 0310039 0312211

Income -0229136 -0229136 -0229136 -0214963

Unit Density -0000035524

-0000035524

-0000035524

-0000084471

CCCL 0099362 0099362 0099362 0092215

Distance 0168051 0168051 0168051 0168891

Citizens -1493938 -1493938 -1493938 -1474743

Max Wind 0256727 0256727 0256727 0256279

Wind Duration 0166741 0166741 0166741 0166932

Post FBC -1072119 -1682877 -1684593 -1260984

d_1990

-0610758 -0612475

d_1980 0610758

-0001716

d_1970 0612475 0001716

d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341

pre_1950 -0112074 -0722832 -0724548 Age

0015412

Age Sq

-0002088

AIC 231513 231513 231513 231659

48

Table 2 ndash Appendix

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988

EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506

Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454

Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633

Age

-0007237 -0007307 001624 0016124 0063445 0070627

Age Sq

-0002129 -0002119 -001207 -0013457

Age Cu

587E-05 6652E-05

Age_PostFBC 0022066

-0006069

1042536

Agesq_PostFBC

0009971

-2328348

Agecu_PostFBC

0140286

AIC 234499 234390 234389 234279 234283 234223 234177

Model1 uses Post FBC and no age variable

Model2 uses Post FBC and age

Model3 uses Post FBC age and age interacted with Post FBC

Model4 uses Post FBC age and age squared

Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC

Model6 uses Post FBC age age squared and age cubed

Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC

49

Table 3 ndash Appendix

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127

EHY 003424 0034993 003485 0035961 0035889 0036392 0036671

Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426

BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482

Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407

Unit Density -7935E-05

-586E-05

-5982E-05

-845E-05

-8468E-05

-58E-05

-551E-05

CCCL 012243

0096304 0095483 0092215 0091992 0089874 0091186

Distance 017593 0169206 0169129 0168891 0168879 0167171 016703

Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534

Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214

Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622

Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842

Age

-0007626 -000772 0015412 0015125 0064521 007053

Age Sq

-0002088 -0002065 -001244 -0013596

AgeCu

612E-05 6766E-05

Age_PostFBC 0028294 0002751

1022203

Agesq_PostFBC 0008043

-2269315

Agecu_PostFBC

0136632

AIC 231894 231770 231766 231659 231662 231595 231552

50

Table 4-Appendix

1990-2010 Full Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388

Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013

Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131

CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653

Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152

pre_1950 0960045042 00475102

d_1950 1319252383 00508302

d_1960 1433696307 00489112

d_1970 1684593481 00482689

d_1980 1682877105 0048269

d_1990 1072118969 00483608

Post FBC -122639772 00520538

Obs 17906 69442

Adj R Squared 04675 04655

51

Table 5-Appendix

Balance Test

1990-2010

1980-2010

1970-2010

1960-2010

1950-2010

1900-2010

BrickMasonry

Mobile

Income

Home Value

Unit Density

CCCL

Distance

Citizens

Rejection of the Hypothesis that the means are equal α=1 α=05 α=01

Table 6-Appendix

Balance Test ndash Decade Dummies

1900-2010 1970-2010 1980-2010

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655

EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153

Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194

BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122

Income -0229136499 00436795 -009497743 006848399 0045469518 008095252

Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015

CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606

Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488

Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691

Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713

Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001

pre_1950 -0112073927 00506211

d_1950 0247133413 00526112

d_1960 0361577338 00500973

d_1970 0612474511 00488438 0575621494 005331684

d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286

d_1990

Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279

Obs 69442 35507

26729

Adj R Squared 04654 04778 048

52

Table 7-Appendix

Full Model Hurdle Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -262563 0035028 First

Max_Wind 0156425 000266 Stage

Wind Duration 0042652 0007989

Population Density -000501 0002246

Post FBC -018364 0016165

Obs 69442

AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second

EHY 0036631987 00007388 0003094 0000536 Stage

Premiums 0718244372 00100833 0386122 0014285

BrickMasonry 0310039307 00775013 0910896 0081548

Income -0229136499 00436795 0082266 0046119

Unit Density -0000035524 00001131 -000047 0000159

CCCL 0099361591 00484053 018571 005109

Distance 0168051018 00096458 0078816 0010177

Citizens -1493938439 00804653 -080097 0089598

Max Wind 0256726978 00087826 0250276 0013364

Wind Duration 016674127 00196152 0089513 001408

Pre 1950 -0112073927 00506211 -003 0062288

d_1950 0247133413 00526112 0079901 005082

d_1960 0361577338 00500973 016725 0043534

d_1970 0612474511 00488438 0247777 0039334

d_1980 0610758136 00484157 0289707 0037518

d_1990

Post FBC -1072118969 00483608 -06069 0048224

Obs 69442 19107

Adj R Squared 04654

53

Table 8-Appendix

2001 2002 2003 2004 2005

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383

EHY 0046815496 0049755016 0046129696 -000549296 001407418

Premiums 0902225848 0520713952 0541260712 177809555 137222708

BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961

Income -0556333367 007673894 -0333566059 -139739466 -001458013

Unit Density -000273761 0000017044 -0000399979 -000624441 000229285

CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961

Distance -0006196654 0146642336 0074095408 001597389 027645125

Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859

Max Wind 0189251023 02218324 -0028507713 062796853 033670019

Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226

Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475

Age 0024430912 0007695322 0018112422 005900484 002379329

Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583

Obs 7404 7315 7172 7138 7011

Adj R Squared 04659 03911 03599 06072 04469

2006 2007 2008 2009 2010

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759

EHY 0029382684 0038563888 004035125 0040966039 0038016928

Premiums 0410656129 0355927665 087161791 0526462478 02894645

BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283

Income -0098853673 -0243879192 029287347 0444050006 022370289

Unit Density 0000300877 0000720442 000118661 0001595448 0000576067

CCCL -027184702 0306014726 06309048 0038276012 -002689181

Distance 0081513275 0195383664 035499478 021933669 0163507604

Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008

Max Wind 0055728801 0201000209 014525329 017495008 -001688058

Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648

Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859

Age 0006187693 001016534 001631759 -001596812 -002182466

Age Sq -0000687056 -000118586 -000152884 0000907348 000112213

Obs 6719 6643 6575 6570 6895

Adj R Squared 0197 02293 03667 02803 02262

54

Table 9-Appendix

2001 2002 2003 2004 2005

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304

EHY 0047913193 0050579977 0046738464 -000511538 001472664

Premiums 0933434158 0540773786 0554312075 178778901 139820098

BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317

Income -0592068944 0052289191 -0345142584 -140252136 -002535229

Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385

CCCL 0743282837 -009598118 0007668609 -040039481 -002349054

Distance -0004011689 014827054 0076206078 001718914 028091933

Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783

Max Wind 0186392922 0219937725 -0029594557 062788723 03369511

Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395

d_1980 0882524305 0733952915 0820252047 048813821 072461562

Age 005656422 0033984684 0045371148 007917294 007253832

Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145

Obs 7404 7315 7172 7138 7011

Adj R Squared 04663 03917 03615 06072 04438

2006 2007 2008 2009 2010

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249

EHY 0029291524 0038176189 003980162 003937994 0036637581

Premiums 0430840225 0373067836 089118372 05725051 0338993543

BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629

Income -011246799 -0246875105 028804568 043007102 0198547424

Unit Density 0000308373 0000729796 000115155 000148608 0000544974

CCCL -0270035498 0310339493 06391466 00571657 -001174278

Distance 0084719416 0198463554 035735414 022474189 0168702153

Citizens -07322541 -0654042742 -309502993 -025940821 -05347339

Max Wind 0055528386 0201585869 014451638 017175987 -002013935

Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523

d_1980 0483661036 0599607 028523853 045083377 0431080232

Age 0041843078 0047004839 004563723 004699644 0024861386

Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913

Obs 6719 6643 6575 6570 6895

Adj R Squared 01923 02258 03648 02663 02178

55

Table 10-Appendix

Claims Regression

Parameter Estimate Std Err Pr gt ChiSq

Intercept -125027 0189

EHY 00031 00004

Premiums 09238 00091

BrickMasonry 04034 00589

Income -04719 00319

Unit Density -00007 00001

CCCL 0049 00343

Distance 01448 00068

Citizens -10523 00567

Max Wind 01721 00056

Wind Duration -00017 00101

Post FBC -04247 00366

Age 00375 00018

Age Sq -00043 00002

Obs 69442

Pseudo R Squared 029

56

Table 11-Appendix

SFBC Hurdle Regression

Full Model Hurdle Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -38419 0110688 First

Max Wind 0249099 0008523 Stage

Wind Duration -017305 0034269

Pop Density -00021 0004094

Post FBC -026859 0045551

Obs 2201

AIC 17362

Intercept -642410358 07694634 -1735 1612104 Second

EHY 003777857 0001882 -000198 0001279 Stage

Premiums 058526953 00206452 0651733 0031265

BrickMasonry 051703099 03228598 -08406 0521577

Income -024791541 00885833 -024543 0119458

Unit Density -000024465 00001635 -000108 0000324

CCCL 004187952 01058532 0083285 0147401

Distance 013910546 00256963 007182 0035699

Citizens -105795182 01382522 -087333 0192635

Max Wind 009254137 00352015 0207402 0075261

Wind Duration -005698597 00853374 -020077 0083082

Post FBC -033082693 01292625 -02288 016678

Age 002653867 00055593 0044771 0007671

Age Sq -000286273 00005216 -000527 0000887

Obs 10001

10001

Adj R Squared 05309

57

Figure Titles

Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned

house years

Figure 2 Regressions of predicted loss versus actual loss for model validation

Figure 1-App LN of Avg Loss by Structure Age

Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years

0

10

20

30

40

50

60

70

80

90

100

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Pre2000 YOC Post2000 YOC Unclassified YOC

58

Figure 2 Regressions of predicted loss versus actual loss for model validation

59

Figure 1-App

000

100

200

300

400

500

600

700

800

900

1000

1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981

LN o

f A

vg L

oss

Structure Age

LN of Avg Loss by Structure Age

2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969

1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929

60

i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC

x A state provided map of the region can be found here

httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png

xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard

47

Table 1-Appendix

1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept 0427553

-7942695 -7940978 -8628364

EHY 0036632 0036632 0036632 0035961

Premiums 0718244 0718244 0718244 073172

BrickMasonry 0310039 0310039 0310039 0312211

Income -0229136 -0229136 -0229136 -0214963

Unit Density -0000035524

-0000035524

-0000035524

-0000084471

CCCL 0099362 0099362 0099362 0092215

Distance 0168051 0168051 0168051 0168891

Citizens -1493938 -1493938 -1493938 -1474743

Max Wind 0256727 0256727 0256727 0256279

Wind Duration 0166741 0166741 0166741 0166932

Post FBC -1072119 -1682877 -1684593 -1260984

d_1990

-0610758 -0612475

d_1980 0610758

-0001716

d_1970 0612475 0001716

d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341

pre_1950 -0112074 -0722832 -0724548 Age

0015412

Age Sq

-0002088

AIC 231513 231513 231513 231659

48

Table 2 ndash Appendix

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988

EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506

Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454

Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633

Age

-0007237 -0007307 001624 0016124 0063445 0070627

Age Sq

-0002129 -0002119 -001207 -0013457

Age Cu

587E-05 6652E-05

Age_PostFBC 0022066

-0006069

1042536

Agesq_PostFBC

0009971

-2328348

Agecu_PostFBC

0140286

AIC 234499 234390 234389 234279 234283 234223 234177

Model1 uses Post FBC and no age variable

Model2 uses Post FBC and age

Model3 uses Post FBC age and age interacted with Post FBC

Model4 uses Post FBC age and age squared

Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC

Model6 uses Post FBC age age squared and age cubed

Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC

49

Table 3 ndash Appendix

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127

EHY 003424 0034993 003485 0035961 0035889 0036392 0036671

Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426

BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482

Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407

Unit Density -7935E-05

-586E-05

-5982E-05

-845E-05

-8468E-05

-58E-05

-551E-05

CCCL 012243

0096304 0095483 0092215 0091992 0089874 0091186

Distance 017593 0169206 0169129 0168891 0168879 0167171 016703

Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534

Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214

Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622

Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842

Age

-0007626 -000772 0015412 0015125 0064521 007053

Age Sq

-0002088 -0002065 -001244 -0013596

AgeCu

612E-05 6766E-05

Age_PostFBC 0028294 0002751

1022203

Agesq_PostFBC 0008043

-2269315

Agecu_PostFBC

0136632

AIC 231894 231770 231766 231659 231662 231595 231552

50

Table 4-Appendix

1990-2010 Full Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388

Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013

Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131

CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653

Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152

pre_1950 0960045042 00475102

d_1950 1319252383 00508302

d_1960 1433696307 00489112

d_1970 1684593481 00482689

d_1980 1682877105 0048269

d_1990 1072118969 00483608

Post FBC -122639772 00520538

Obs 17906 69442

Adj R Squared 04675 04655

51

Table 5-Appendix

Balance Test

1990-2010

1980-2010

1970-2010

1960-2010

1950-2010

1900-2010

BrickMasonry

Mobile

Income

Home Value

Unit Density

CCCL

Distance

Citizens

Rejection of the Hypothesis that the means are equal α=1 α=05 α=01

Table 6-Appendix

Balance Test ndash Decade Dummies

1900-2010 1970-2010 1980-2010

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655

EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153

Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194

BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122

Income -0229136499 00436795 -009497743 006848399 0045469518 008095252

Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015

CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606

Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488

Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691

Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713

Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001

pre_1950 -0112073927 00506211

d_1950 0247133413 00526112

d_1960 0361577338 00500973

d_1970 0612474511 00488438 0575621494 005331684

d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286

d_1990

Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279

Obs 69442 35507

26729

Adj R Squared 04654 04778 048

52

Table 7-Appendix

Full Model Hurdle Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -262563 0035028 First

Max_Wind 0156425 000266 Stage

Wind Duration 0042652 0007989

Population Density -000501 0002246

Post FBC -018364 0016165

Obs 69442

AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second

EHY 0036631987 00007388 0003094 0000536 Stage

Premiums 0718244372 00100833 0386122 0014285

BrickMasonry 0310039307 00775013 0910896 0081548

Income -0229136499 00436795 0082266 0046119

Unit Density -0000035524 00001131 -000047 0000159

CCCL 0099361591 00484053 018571 005109

Distance 0168051018 00096458 0078816 0010177

Citizens -1493938439 00804653 -080097 0089598

Max Wind 0256726978 00087826 0250276 0013364

Wind Duration 016674127 00196152 0089513 001408

Pre 1950 -0112073927 00506211 -003 0062288

d_1950 0247133413 00526112 0079901 005082

d_1960 0361577338 00500973 016725 0043534

d_1970 0612474511 00488438 0247777 0039334

d_1980 0610758136 00484157 0289707 0037518

d_1990

Post FBC -1072118969 00483608 -06069 0048224

Obs 69442 19107

Adj R Squared 04654

53

Table 8-Appendix

2001 2002 2003 2004 2005

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383

EHY 0046815496 0049755016 0046129696 -000549296 001407418

Premiums 0902225848 0520713952 0541260712 177809555 137222708

BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961

Income -0556333367 007673894 -0333566059 -139739466 -001458013

Unit Density -000273761 0000017044 -0000399979 -000624441 000229285

CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961

Distance -0006196654 0146642336 0074095408 001597389 027645125

Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859

Max Wind 0189251023 02218324 -0028507713 062796853 033670019

Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226

Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475

Age 0024430912 0007695322 0018112422 005900484 002379329

Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583

Obs 7404 7315 7172 7138 7011

Adj R Squared 04659 03911 03599 06072 04469

2006 2007 2008 2009 2010

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759

EHY 0029382684 0038563888 004035125 0040966039 0038016928

Premiums 0410656129 0355927665 087161791 0526462478 02894645

BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283

Income -0098853673 -0243879192 029287347 0444050006 022370289

Unit Density 0000300877 0000720442 000118661 0001595448 0000576067

CCCL -027184702 0306014726 06309048 0038276012 -002689181

Distance 0081513275 0195383664 035499478 021933669 0163507604

Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008

Max Wind 0055728801 0201000209 014525329 017495008 -001688058

Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648

Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859

Age 0006187693 001016534 001631759 -001596812 -002182466

Age Sq -0000687056 -000118586 -000152884 0000907348 000112213

Obs 6719 6643 6575 6570 6895

Adj R Squared 0197 02293 03667 02803 02262

54

Table 9-Appendix

2001 2002 2003 2004 2005

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304

EHY 0047913193 0050579977 0046738464 -000511538 001472664

Premiums 0933434158 0540773786 0554312075 178778901 139820098

BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317

Income -0592068944 0052289191 -0345142584 -140252136 -002535229

Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385

CCCL 0743282837 -009598118 0007668609 -040039481 -002349054

Distance -0004011689 014827054 0076206078 001718914 028091933

Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783

Max Wind 0186392922 0219937725 -0029594557 062788723 03369511

Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395

d_1980 0882524305 0733952915 0820252047 048813821 072461562

Age 005656422 0033984684 0045371148 007917294 007253832

Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145

Obs 7404 7315 7172 7138 7011

Adj R Squared 04663 03917 03615 06072 04438

2006 2007 2008 2009 2010

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249

EHY 0029291524 0038176189 003980162 003937994 0036637581

Premiums 0430840225 0373067836 089118372 05725051 0338993543

BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629

Income -011246799 -0246875105 028804568 043007102 0198547424

Unit Density 0000308373 0000729796 000115155 000148608 0000544974

CCCL -0270035498 0310339493 06391466 00571657 -001174278

Distance 0084719416 0198463554 035735414 022474189 0168702153

Citizens -07322541 -0654042742 -309502993 -025940821 -05347339

Max Wind 0055528386 0201585869 014451638 017175987 -002013935

Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523

d_1980 0483661036 0599607 028523853 045083377 0431080232

Age 0041843078 0047004839 004563723 004699644 0024861386

Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913

Obs 6719 6643 6575 6570 6895

Adj R Squared 01923 02258 03648 02663 02178

55

Table 10-Appendix

Claims Regression

Parameter Estimate Std Err Pr gt ChiSq

Intercept -125027 0189

EHY 00031 00004

Premiums 09238 00091

BrickMasonry 04034 00589

Income -04719 00319

Unit Density -00007 00001

CCCL 0049 00343

Distance 01448 00068

Citizens -10523 00567

Max Wind 01721 00056

Wind Duration -00017 00101

Post FBC -04247 00366

Age 00375 00018

Age Sq -00043 00002

Obs 69442

Pseudo R Squared 029

56

Table 11-Appendix

SFBC Hurdle Regression

Full Model Hurdle Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -38419 0110688 First

Max Wind 0249099 0008523 Stage

Wind Duration -017305 0034269

Pop Density -00021 0004094

Post FBC -026859 0045551

Obs 2201

AIC 17362

Intercept -642410358 07694634 -1735 1612104 Second

EHY 003777857 0001882 -000198 0001279 Stage

Premiums 058526953 00206452 0651733 0031265

BrickMasonry 051703099 03228598 -08406 0521577

Income -024791541 00885833 -024543 0119458

Unit Density -000024465 00001635 -000108 0000324

CCCL 004187952 01058532 0083285 0147401

Distance 013910546 00256963 007182 0035699

Citizens -105795182 01382522 -087333 0192635

Max Wind 009254137 00352015 0207402 0075261

Wind Duration -005698597 00853374 -020077 0083082

Post FBC -033082693 01292625 -02288 016678

Age 002653867 00055593 0044771 0007671

Age Sq -000286273 00005216 -000527 0000887

Obs 10001

10001

Adj R Squared 05309

57

Figure Titles

Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned

house years

Figure 2 Regressions of predicted loss versus actual loss for model validation

Figure 1-App LN of Avg Loss by Structure Age

Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years

0

10

20

30

40

50

60

70

80

90

100

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Pre2000 YOC Post2000 YOC Unclassified YOC

58

Figure 2 Regressions of predicted loss versus actual loss for model validation

59

Figure 1-App

000

100

200

300

400

500

600

700

800

900

1000

1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981

LN o

f A

vg L

oss

Structure Age

LN of Avg Loss by Structure Age

2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969

1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929

60

i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC

x A state provided map of the region can be found here

httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png

xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard

48

Table 2 ndash Appendix

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988

EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506

Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454

Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633

Age

-0007237 -0007307 001624 0016124 0063445 0070627

Age Sq

-0002129 -0002119 -001207 -0013457

Age Cu

587E-05 6652E-05

Age_PostFBC 0022066

-0006069

1042536

Agesq_PostFBC

0009971

-2328348

Agecu_PostFBC

0140286

AIC 234499 234390 234389 234279 234283 234223 234177

Model1 uses Post FBC and no age variable

Model2 uses Post FBC and age

Model3 uses Post FBC age and age interacted with Post FBC

Model4 uses Post FBC age and age squared

Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC

Model6 uses Post FBC age age squared and age cubed

Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC

49

Table 3 ndash Appendix

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127

EHY 003424 0034993 003485 0035961 0035889 0036392 0036671

Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426

BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482

Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407

Unit Density -7935E-05

-586E-05

-5982E-05

-845E-05

-8468E-05

-58E-05

-551E-05

CCCL 012243

0096304 0095483 0092215 0091992 0089874 0091186

Distance 017593 0169206 0169129 0168891 0168879 0167171 016703

Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534

Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214

Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622

Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842

Age

-0007626 -000772 0015412 0015125 0064521 007053

Age Sq

-0002088 -0002065 -001244 -0013596

AgeCu

612E-05 6766E-05

Age_PostFBC 0028294 0002751

1022203

Agesq_PostFBC 0008043

-2269315

Agecu_PostFBC

0136632

AIC 231894 231770 231766 231659 231662 231595 231552

50

Table 4-Appendix

1990-2010 Full Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388

Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013

Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131

CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653

Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152

pre_1950 0960045042 00475102

d_1950 1319252383 00508302

d_1960 1433696307 00489112

d_1970 1684593481 00482689

d_1980 1682877105 0048269

d_1990 1072118969 00483608

Post FBC -122639772 00520538

Obs 17906 69442

Adj R Squared 04675 04655

51

Table 5-Appendix

Balance Test

1990-2010

1980-2010

1970-2010

1960-2010

1950-2010

1900-2010

BrickMasonry

Mobile

Income

Home Value

Unit Density

CCCL

Distance

Citizens

Rejection of the Hypothesis that the means are equal α=1 α=05 α=01

Table 6-Appendix

Balance Test ndash Decade Dummies

1900-2010 1970-2010 1980-2010

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655

EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153

Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194

BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122

Income -0229136499 00436795 -009497743 006848399 0045469518 008095252

Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015

CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606

Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488

Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691

Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713

Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001

pre_1950 -0112073927 00506211

d_1950 0247133413 00526112

d_1960 0361577338 00500973

d_1970 0612474511 00488438 0575621494 005331684

d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286

d_1990

Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279

Obs 69442 35507

26729

Adj R Squared 04654 04778 048

52

Table 7-Appendix

Full Model Hurdle Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -262563 0035028 First

Max_Wind 0156425 000266 Stage

Wind Duration 0042652 0007989

Population Density -000501 0002246

Post FBC -018364 0016165

Obs 69442

AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second

EHY 0036631987 00007388 0003094 0000536 Stage

Premiums 0718244372 00100833 0386122 0014285

BrickMasonry 0310039307 00775013 0910896 0081548

Income -0229136499 00436795 0082266 0046119

Unit Density -0000035524 00001131 -000047 0000159

CCCL 0099361591 00484053 018571 005109

Distance 0168051018 00096458 0078816 0010177

Citizens -1493938439 00804653 -080097 0089598

Max Wind 0256726978 00087826 0250276 0013364

Wind Duration 016674127 00196152 0089513 001408

Pre 1950 -0112073927 00506211 -003 0062288

d_1950 0247133413 00526112 0079901 005082

d_1960 0361577338 00500973 016725 0043534

d_1970 0612474511 00488438 0247777 0039334

d_1980 0610758136 00484157 0289707 0037518

d_1990

Post FBC -1072118969 00483608 -06069 0048224

Obs 69442 19107

Adj R Squared 04654

53

Table 8-Appendix

2001 2002 2003 2004 2005

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383

EHY 0046815496 0049755016 0046129696 -000549296 001407418

Premiums 0902225848 0520713952 0541260712 177809555 137222708

BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961

Income -0556333367 007673894 -0333566059 -139739466 -001458013

Unit Density -000273761 0000017044 -0000399979 -000624441 000229285

CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961

Distance -0006196654 0146642336 0074095408 001597389 027645125

Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859

Max Wind 0189251023 02218324 -0028507713 062796853 033670019

Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226

Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475

Age 0024430912 0007695322 0018112422 005900484 002379329

Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583

Obs 7404 7315 7172 7138 7011

Adj R Squared 04659 03911 03599 06072 04469

2006 2007 2008 2009 2010

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759

EHY 0029382684 0038563888 004035125 0040966039 0038016928

Premiums 0410656129 0355927665 087161791 0526462478 02894645

BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283

Income -0098853673 -0243879192 029287347 0444050006 022370289

Unit Density 0000300877 0000720442 000118661 0001595448 0000576067

CCCL -027184702 0306014726 06309048 0038276012 -002689181

Distance 0081513275 0195383664 035499478 021933669 0163507604

Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008

Max Wind 0055728801 0201000209 014525329 017495008 -001688058

Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648

Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859

Age 0006187693 001016534 001631759 -001596812 -002182466

Age Sq -0000687056 -000118586 -000152884 0000907348 000112213

Obs 6719 6643 6575 6570 6895

Adj R Squared 0197 02293 03667 02803 02262

54

Table 9-Appendix

2001 2002 2003 2004 2005

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304

EHY 0047913193 0050579977 0046738464 -000511538 001472664

Premiums 0933434158 0540773786 0554312075 178778901 139820098

BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317

Income -0592068944 0052289191 -0345142584 -140252136 -002535229

Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385

CCCL 0743282837 -009598118 0007668609 -040039481 -002349054

Distance -0004011689 014827054 0076206078 001718914 028091933

Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783

Max Wind 0186392922 0219937725 -0029594557 062788723 03369511

Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395

d_1980 0882524305 0733952915 0820252047 048813821 072461562

Age 005656422 0033984684 0045371148 007917294 007253832

Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145

Obs 7404 7315 7172 7138 7011

Adj R Squared 04663 03917 03615 06072 04438

2006 2007 2008 2009 2010

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249

EHY 0029291524 0038176189 003980162 003937994 0036637581

Premiums 0430840225 0373067836 089118372 05725051 0338993543

BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629

Income -011246799 -0246875105 028804568 043007102 0198547424

Unit Density 0000308373 0000729796 000115155 000148608 0000544974

CCCL -0270035498 0310339493 06391466 00571657 -001174278

Distance 0084719416 0198463554 035735414 022474189 0168702153

Citizens -07322541 -0654042742 -309502993 -025940821 -05347339

Max Wind 0055528386 0201585869 014451638 017175987 -002013935

Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523

d_1980 0483661036 0599607 028523853 045083377 0431080232

Age 0041843078 0047004839 004563723 004699644 0024861386

Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913

Obs 6719 6643 6575 6570 6895

Adj R Squared 01923 02258 03648 02663 02178

55

Table 10-Appendix

Claims Regression

Parameter Estimate Std Err Pr gt ChiSq

Intercept -125027 0189

EHY 00031 00004

Premiums 09238 00091

BrickMasonry 04034 00589

Income -04719 00319

Unit Density -00007 00001

CCCL 0049 00343

Distance 01448 00068

Citizens -10523 00567

Max Wind 01721 00056

Wind Duration -00017 00101

Post FBC -04247 00366

Age 00375 00018

Age Sq -00043 00002

Obs 69442

Pseudo R Squared 029

56

Table 11-Appendix

SFBC Hurdle Regression

Full Model Hurdle Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -38419 0110688 First

Max Wind 0249099 0008523 Stage

Wind Duration -017305 0034269

Pop Density -00021 0004094

Post FBC -026859 0045551

Obs 2201

AIC 17362

Intercept -642410358 07694634 -1735 1612104 Second

EHY 003777857 0001882 -000198 0001279 Stage

Premiums 058526953 00206452 0651733 0031265

BrickMasonry 051703099 03228598 -08406 0521577

Income -024791541 00885833 -024543 0119458

Unit Density -000024465 00001635 -000108 0000324

CCCL 004187952 01058532 0083285 0147401

Distance 013910546 00256963 007182 0035699

Citizens -105795182 01382522 -087333 0192635

Max Wind 009254137 00352015 0207402 0075261

Wind Duration -005698597 00853374 -020077 0083082

Post FBC -033082693 01292625 -02288 016678

Age 002653867 00055593 0044771 0007671

Age Sq -000286273 00005216 -000527 0000887

Obs 10001

10001

Adj R Squared 05309

57

Figure Titles

Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned

house years

Figure 2 Regressions of predicted loss versus actual loss for model validation

Figure 1-App LN of Avg Loss by Structure Age

Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years

0

10

20

30

40

50

60

70

80

90

100

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Pre2000 YOC Post2000 YOC Unclassified YOC

58

Figure 2 Regressions of predicted loss versus actual loss for model validation

59

Figure 1-App

000

100

200

300

400

500

600

700

800

900

1000

1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981

LN o

f A

vg L

oss

Structure Age

LN of Avg Loss by Structure Age

2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969

1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929

60

i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC

x A state provided map of the region can be found here

httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png

xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard

49

Table 3 ndash Appendix

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127

EHY 003424 0034993 003485 0035961 0035889 0036392 0036671

Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426

BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482

Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407

Unit Density -7935E-05

-586E-05

-5982E-05

-845E-05

-8468E-05

-58E-05

-551E-05

CCCL 012243

0096304 0095483 0092215 0091992 0089874 0091186

Distance 017593 0169206 0169129 0168891 0168879 0167171 016703

Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534

Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214

Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622

Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842

Age

-0007626 -000772 0015412 0015125 0064521 007053

Age Sq

-0002088 -0002065 -001244 -0013596

AgeCu

612E-05 6766E-05

Age_PostFBC 0028294 0002751

1022203

Agesq_PostFBC 0008043

-2269315

Agecu_PostFBC

0136632

AIC 231894 231770 231766 231659 231662 231595 231552

50

Table 4-Appendix

1990-2010 Full Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388

Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013

Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131

CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653

Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152

pre_1950 0960045042 00475102

d_1950 1319252383 00508302

d_1960 1433696307 00489112

d_1970 1684593481 00482689

d_1980 1682877105 0048269

d_1990 1072118969 00483608

Post FBC -122639772 00520538

Obs 17906 69442

Adj R Squared 04675 04655

51

Table 5-Appendix

Balance Test

1990-2010

1980-2010

1970-2010

1960-2010

1950-2010

1900-2010

BrickMasonry

Mobile

Income

Home Value

Unit Density

CCCL

Distance

Citizens

Rejection of the Hypothesis that the means are equal α=1 α=05 α=01

Table 6-Appendix

Balance Test ndash Decade Dummies

1900-2010 1970-2010 1980-2010

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655

EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153

Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194

BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122

Income -0229136499 00436795 -009497743 006848399 0045469518 008095252

Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015

CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606

Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488

Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691

Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713

Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001

pre_1950 -0112073927 00506211

d_1950 0247133413 00526112

d_1960 0361577338 00500973

d_1970 0612474511 00488438 0575621494 005331684

d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286

d_1990

Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279

Obs 69442 35507

26729

Adj R Squared 04654 04778 048

52

Table 7-Appendix

Full Model Hurdle Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -262563 0035028 First

Max_Wind 0156425 000266 Stage

Wind Duration 0042652 0007989

Population Density -000501 0002246

Post FBC -018364 0016165

Obs 69442

AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second

EHY 0036631987 00007388 0003094 0000536 Stage

Premiums 0718244372 00100833 0386122 0014285

BrickMasonry 0310039307 00775013 0910896 0081548

Income -0229136499 00436795 0082266 0046119

Unit Density -0000035524 00001131 -000047 0000159

CCCL 0099361591 00484053 018571 005109

Distance 0168051018 00096458 0078816 0010177

Citizens -1493938439 00804653 -080097 0089598

Max Wind 0256726978 00087826 0250276 0013364

Wind Duration 016674127 00196152 0089513 001408

Pre 1950 -0112073927 00506211 -003 0062288

d_1950 0247133413 00526112 0079901 005082

d_1960 0361577338 00500973 016725 0043534

d_1970 0612474511 00488438 0247777 0039334

d_1980 0610758136 00484157 0289707 0037518

d_1990

Post FBC -1072118969 00483608 -06069 0048224

Obs 69442 19107

Adj R Squared 04654

53

Table 8-Appendix

2001 2002 2003 2004 2005

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383

EHY 0046815496 0049755016 0046129696 -000549296 001407418

Premiums 0902225848 0520713952 0541260712 177809555 137222708

BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961

Income -0556333367 007673894 -0333566059 -139739466 -001458013

Unit Density -000273761 0000017044 -0000399979 -000624441 000229285

CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961

Distance -0006196654 0146642336 0074095408 001597389 027645125

Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859

Max Wind 0189251023 02218324 -0028507713 062796853 033670019

Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226

Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475

Age 0024430912 0007695322 0018112422 005900484 002379329

Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583

Obs 7404 7315 7172 7138 7011

Adj R Squared 04659 03911 03599 06072 04469

2006 2007 2008 2009 2010

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759

EHY 0029382684 0038563888 004035125 0040966039 0038016928

Premiums 0410656129 0355927665 087161791 0526462478 02894645

BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283

Income -0098853673 -0243879192 029287347 0444050006 022370289

Unit Density 0000300877 0000720442 000118661 0001595448 0000576067

CCCL -027184702 0306014726 06309048 0038276012 -002689181

Distance 0081513275 0195383664 035499478 021933669 0163507604

Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008

Max Wind 0055728801 0201000209 014525329 017495008 -001688058

Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648

Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859

Age 0006187693 001016534 001631759 -001596812 -002182466

Age Sq -0000687056 -000118586 -000152884 0000907348 000112213

Obs 6719 6643 6575 6570 6895

Adj R Squared 0197 02293 03667 02803 02262

54

Table 9-Appendix

2001 2002 2003 2004 2005

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304

EHY 0047913193 0050579977 0046738464 -000511538 001472664

Premiums 0933434158 0540773786 0554312075 178778901 139820098

BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317

Income -0592068944 0052289191 -0345142584 -140252136 -002535229

Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385

CCCL 0743282837 -009598118 0007668609 -040039481 -002349054

Distance -0004011689 014827054 0076206078 001718914 028091933

Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783

Max Wind 0186392922 0219937725 -0029594557 062788723 03369511

Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395

d_1980 0882524305 0733952915 0820252047 048813821 072461562

Age 005656422 0033984684 0045371148 007917294 007253832

Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145

Obs 7404 7315 7172 7138 7011

Adj R Squared 04663 03917 03615 06072 04438

2006 2007 2008 2009 2010

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249

EHY 0029291524 0038176189 003980162 003937994 0036637581

Premiums 0430840225 0373067836 089118372 05725051 0338993543

BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629

Income -011246799 -0246875105 028804568 043007102 0198547424

Unit Density 0000308373 0000729796 000115155 000148608 0000544974

CCCL -0270035498 0310339493 06391466 00571657 -001174278

Distance 0084719416 0198463554 035735414 022474189 0168702153

Citizens -07322541 -0654042742 -309502993 -025940821 -05347339

Max Wind 0055528386 0201585869 014451638 017175987 -002013935

Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523

d_1980 0483661036 0599607 028523853 045083377 0431080232

Age 0041843078 0047004839 004563723 004699644 0024861386

Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913

Obs 6719 6643 6575 6570 6895

Adj R Squared 01923 02258 03648 02663 02178

55

Table 10-Appendix

Claims Regression

Parameter Estimate Std Err Pr gt ChiSq

Intercept -125027 0189

EHY 00031 00004

Premiums 09238 00091

BrickMasonry 04034 00589

Income -04719 00319

Unit Density -00007 00001

CCCL 0049 00343

Distance 01448 00068

Citizens -10523 00567

Max Wind 01721 00056

Wind Duration -00017 00101

Post FBC -04247 00366

Age 00375 00018

Age Sq -00043 00002

Obs 69442

Pseudo R Squared 029

56

Table 11-Appendix

SFBC Hurdle Regression

Full Model Hurdle Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -38419 0110688 First

Max Wind 0249099 0008523 Stage

Wind Duration -017305 0034269

Pop Density -00021 0004094

Post FBC -026859 0045551

Obs 2201

AIC 17362

Intercept -642410358 07694634 -1735 1612104 Second

EHY 003777857 0001882 -000198 0001279 Stage

Premiums 058526953 00206452 0651733 0031265

BrickMasonry 051703099 03228598 -08406 0521577

Income -024791541 00885833 -024543 0119458

Unit Density -000024465 00001635 -000108 0000324

CCCL 004187952 01058532 0083285 0147401

Distance 013910546 00256963 007182 0035699

Citizens -105795182 01382522 -087333 0192635

Max Wind 009254137 00352015 0207402 0075261

Wind Duration -005698597 00853374 -020077 0083082

Post FBC -033082693 01292625 -02288 016678

Age 002653867 00055593 0044771 0007671

Age Sq -000286273 00005216 -000527 0000887

Obs 10001

10001

Adj R Squared 05309

57

Figure Titles

Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned

house years

Figure 2 Regressions of predicted loss versus actual loss for model validation

Figure 1-App LN of Avg Loss by Structure Age

Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years

0

10

20

30

40

50

60

70

80

90

100

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Pre2000 YOC Post2000 YOC Unclassified YOC

58

Figure 2 Regressions of predicted loss versus actual loss for model validation

59

Figure 1-App

000

100

200

300

400

500

600

700

800

900

1000

1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981

LN o

f A

vg L

oss

Structure Age

LN of Avg Loss by Structure Age

2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969

1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929

60

i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC

x A state provided map of the region can be found here

httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png

xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard

50

Table 4-Appendix

1990-2010 Full Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388

Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013

Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131

CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653

Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152

pre_1950 0960045042 00475102

d_1950 1319252383 00508302

d_1960 1433696307 00489112

d_1970 1684593481 00482689

d_1980 1682877105 0048269

d_1990 1072118969 00483608

Post FBC -122639772 00520538

Obs 17906 69442

Adj R Squared 04675 04655

51

Table 5-Appendix

Balance Test

1990-2010

1980-2010

1970-2010

1960-2010

1950-2010

1900-2010

BrickMasonry

Mobile

Income

Home Value

Unit Density

CCCL

Distance

Citizens

Rejection of the Hypothesis that the means are equal α=1 α=05 α=01

Table 6-Appendix

Balance Test ndash Decade Dummies

1900-2010 1970-2010 1980-2010

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655

EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153

Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194

BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122

Income -0229136499 00436795 -009497743 006848399 0045469518 008095252

Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015

CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606

Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488

Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691

Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713

Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001

pre_1950 -0112073927 00506211

d_1950 0247133413 00526112

d_1960 0361577338 00500973

d_1970 0612474511 00488438 0575621494 005331684

d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286

d_1990

Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279

Obs 69442 35507

26729

Adj R Squared 04654 04778 048

52

Table 7-Appendix

Full Model Hurdle Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -262563 0035028 First

Max_Wind 0156425 000266 Stage

Wind Duration 0042652 0007989

Population Density -000501 0002246

Post FBC -018364 0016165

Obs 69442

AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second

EHY 0036631987 00007388 0003094 0000536 Stage

Premiums 0718244372 00100833 0386122 0014285

BrickMasonry 0310039307 00775013 0910896 0081548

Income -0229136499 00436795 0082266 0046119

Unit Density -0000035524 00001131 -000047 0000159

CCCL 0099361591 00484053 018571 005109

Distance 0168051018 00096458 0078816 0010177

Citizens -1493938439 00804653 -080097 0089598

Max Wind 0256726978 00087826 0250276 0013364

Wind Duration 016674127 00196152 0089513 001408

Pre 1950 -0112073927 00506211 -003 0062288

d_1950 0247133413 00526112 0079901 005082

d_1960 0361577338 00500973 016725 0043534

d_1970 0612474511 00488438 0247777 0039334

d_1980 0610758136 00484157 0289707 0037518

d_1990

Post FBC -1072118969 00483608 -06069 0048224

Obs 69442 19107

Adj R Squared 04654

53

Table 8-Appendix

2001 2002 2003 2004 2005

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383

EHY 0046815496 0049755016 0046129696 -000549296 001407418

Premiums 0902225848 0520713952 0541260712 177809555 137222708

BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961

Income -0556333367 007673894 -0333566059 -139739466 -001458013

Unit Density -000273761 0000017044 -0000399979 -000624441 000229285

CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961

Distance -0006196654 0146642336 0074095408 001597389 027645125

Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859

Max Wind 0189251023 02218324 -0028507713 062796853 033670019

Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226

Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475

Age 0024430912 0007695322 0018112422 005900484 002379329

Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583

Obs 7404 7315 7172 7138 7011

Adj R Squared 04659 03911 03599 06072 04469

2006 2007 2008 2009 2010

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759

EHY 0029382684 0038563888 004035125 0040966039 0038016928

Premiums 0410656129 0355927665 087161791 0526462478 02894645

BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283

Income -0098853673 -0243879192 029287347 0444050006 022370289

Unit Density 0000300877 0000720442 000118661 0001595448 0000576067

CCCL -027184702 0306014726 06309048 0038276012 -002689181

Distance 0081513275 0195383664 035499478 021933669 0163507604

Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008

Max Wind 0055728801 0201000209 014525329 017495008 -001688058

Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648

Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859

Age 0006187693 001016534 001631759 -001596812 -002182466

Age Sq -0000687056 -000118586 -000152884 0000907348 000112213

Obs 6719 6643 6575 6570 6895

Adj R Squared 0197 02293 03667 02803 02262

54

Table 9-Appendix

2001 2002 2003 2004 2005

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304

EHY 0047913193 0050579977 0046738464 -000511538 001472664

Premiums 0933434158 0540773786 0554312075 178778901 139820098

BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317

Income -0592068944 0052289191 -0345142584 -140252136 -002535229

Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385

CCCL 0743282837 -009598118 0007668609 -040039481 -002349054

Distance -0004011689 014827054 0076206078 001718914 028091933

Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783

Max Wind 0186392922 0219937725 -0029594557 062788723 03369511

Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395

d_1980 0882524305 0733952915 0820252047 048813821 072461562

Age 005656422 0033984684 0045371148 007917294 007253832

Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145

Obs 7404 7315 7172 7138 7011

Adj R Squared 04663 03917 03615 06072 04438

2006 2007 2008 2009 2010

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249

EHY 0029291524 0038176189 003980162 003937994 0036637581

Premiums 0430840225 0373067836 089118372 05725051 0338993543

BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629

Income -011246799 -0246875105 028804568 043007102 0198547424

Unit Density 0000308373 0000729796 000115155 000148608 0000544974

CCCL -0270035498 0310339493 06391466 00571657 -001174278

Distance 0084719416 0198463554 035735414 022474189 0168702153

Citizens -07322541 -0654042742 -309502993 -025940821 -05347339

Max Wind 0055528386 0201585869 014451638 017175987 -002013935

Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523

d_1980 0483661036 0599607 028523853 045083377 0431080232

Age 0041843078 0047004839 004563723 004699644 0024861386

Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913

Obs 6719 6643 6575 6570 6895

Adj R Squared 01923 02258 03648 02663 02178

55

Table 10-Appendix

Claims Regression

Parameter Estimate Std Err Pr gt ChiSq

Intercept -125027 0189

EHY 00031 00004

Premiums 09238 00091

BrickMasonry 04034 00589

Income -04719 00319

Unit Density -00007 00001

CCCL 0049 00343

Distance 01448 00068

Citizens -10523 00567

Max Wind 01721 00056

Wind Duration -00017 00101

Post FBC -04247 00366

Age 00375 00018

Age Sq -00043 00002

Obs 69442

Pseudo R Squared 029

56

Table 11-Appendix

SFBC Hurdle Regression

Full Model Hurdle Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -38419 0110688 First

Max Wind 0249099 0008523 Stage

Wind Duration -017305 0034269

Pop Density -00021 0004094

Post FBC -026859 0045551

Obs 2201

AIC 17362

Intercept -642410358 07694634 -1735 1612104 Second

EHY 003777857 0001882 -000198 0001279 Stage

Premiums 058526953 00206452 0651733 0031265

BrickMasonry 051703099 03228598 -08406 0521577

Income -024791541 00885833 -024543 0119458

Unit Density -000024465 00001635 -000108 0000324

CCCL 004187952 01058532 0083285 0147401

Distance 013910546 00256963 007182 0035699

Citizens -105795182 01382522 -087333 0192635

Max Wind 009254137 00352015 0207402 0075261

Wind Duration -005698597 00853374 -020077 0083082

Post FBC -033082693 01292625 -02288 016678

Age 002653867 00055593 0044771 0007671

Age Sq -000286273 00005216 -000527 0000887

Obs 10001

10001

Adj R Squared 05309

57

Figure Titles

Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned

house years

Figure 2 Regressions of predicted loss versus actual loss for model validation

Figure 1-App LN of Avg Loss by Structure Age

Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years

0

10

20

30

40

50

60

70

80

90

100

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Pre2000 YOC Post2000 YOC Unclassified YOC

58

Figure 2 Regressions of predicted loss versus actual loss for model validation

59

Figure 1-App

000

100

200

300

400

500

600

700

800

900

1000

1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981

LN o

f A

vg L

oss

Structure Age

LN of Avg Loss by Structure Age

2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969

1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929

60

i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC

x A state provided map of the region can be found here

httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png

xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard

51

Table 5-Appendix

Balance Test

1990-2010

1980-2010

1970-2010

1960-2010

1950-2010

1900-2010

BrickMasonry

Mobile

Income

Home Value

Unit Density

CCCL

Distance

Citizens

Rejection of the Hypothesis that the means are equal α=1 α=05 α=01

Table 6-Appendix

Balance Test ndash Decade Dummies

1900-2010 1970-2010 1980-2010

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655

EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153

Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194

BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122

Income -0229136499 00436795 -009497743 006848399 0045469518 008095252

Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015

CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606

Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488

Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691

Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713

Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001

pre_1950 -0112073927 00506211

d_1950 0247133413 00526112

d_1960 0361577338 00500973

d_1970 0612474511 00488438 0575621494 005331684

d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286

d_1990

Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279

Obs 69442 35507

26729

Adj R Squared 04654 04778 048

52

Table 7-Appendix

Full Model Hurdle Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -262563 0035028 First

Max_Wind 0156425 000266 Stage

Wind Duration 0042652 0007989

Population Density -000501 0002246

Post FBC -018364 0016165

Obs 69442

AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second

EHY 0036631987 00007388 0003094 0000536 Stage

Premiums 0718244372 00100833 0386122 0014285

BrickMasonry 0310039307 00775013 0910896 0081548

Income -0229136499 00436795 0082266 0046119

Unit Density -0000035524 00001131 -000047 0000159

CCCL 0099361591 00484053 018571 005109

Distance 0168051018 00096458 0078816 0010177

Citizens -1493938439 00804653 -080097 0089598

Max Wind 0256726978 00087826 0250276 0013364

Wind Duration 016674127 00196152 0089513 001408

Pre 1950 -0112073927 00506211 -003 0062288

d_1950 0247133413 00526112 0079901 005082

d_1960 0361577338 00500973 016725 0043534

d_1970 0612474511 00488438 0247777 0039334

d_1980 0610758136 00484157 0289707 0037518

d_1990

Post FBC -1072118969 00483608 -06069 0048224

Obs 69442 19107

Adj R Squared 04654

53

Table 8-Appendix

2001 2002 2003 2004 2005

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383

EHY 0046815496 0049755016 0046129696 -000549296 001407418

Premiums 0902225848 0520713952 0541260712 177809555 137222708

BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961

Income -0556333367 007673894 -0333566059 -139739466 -001458013

Unit Density -000273761 0000017044 -0000399979 -000624441 000229285

CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961

Distance -0006196654 0146642336 0074095408 001597389 027645125

Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859

Max Wind 0189251023 02218324 -0028507713 062796853 033670019

Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226

Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475

Age 0024430912 0007695322 0018112422 005900484 002379329

Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583

Obs 7404 7315 7172 7138 7011

Adj R Squared 04659 03911 03599 06072 04469

2006 2007 2008 2009 2010

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759

EHY 0029382684 0038563888 004035125 0040966039 0038016928

Premiums 0410656129 0355927665 087161791 0526462478 02894645

BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283

Income -0098853673 -0243879192 029287347 0444050006 022370289

Unit Density 0000300877 0000720442 000118661 0001595448 0000576067

CCCL -027184702 0306014726 06309048 0038276012 -002689181

Distance 0081513275 0195383664 035499478 021933669 0163507604

Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008

Max Wind 0055728801 0201000209 014525329 017495008 -001688058

Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648

Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859

Age 0006187693 001016534 001631759 -001596812 -002182466

Age Sq -0000687056 -000118586 -000152884 0000907348 000112213

Obs 6719 6643 6575 6570 6895

Adj R Squared 0197 02293 03667 02803 02262

54

Table 9-Appendix

2001 2002 2003 2004 2005

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304

EHY 0047913193 0050579977 0046738464 -000511538 001472664

Premiums 0933434158 0540773786 0554312075 178778901 139820098

BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317

Income -0592068944 0052289191 -0345142584 -140252136 -002535229

Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385

CCCL 0743282837 -009598118 0007668609 -040039481 -002349054

Distance -0004011689 014827054 0076206078 001718914 028091933

Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783

Max Wind 0186392922 0219937725 -0029594557 062788723 03369511

Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395

d_1980 0882524305 0733952915 0820252047 048813821 072461562

Age 005656422 0033984684 0045371148 007917294 007253832

Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145

Obs 7404 7315 7172 7138 7011

Adj R Squared 04663 03917 03615 06072 04438

2006 2007 2008 2009 2010

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249

EHY 0029291524 0038176189 003980162 003937994 0036637581

Premiums 0430840225 0373067836 089118372 05725051 0338993543

BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629

Income -011246799 -0246875105 028804568 043007102 0198547424

Unit Density 0000308373 0000729796 000115155 000148608 0000544974

CCCL -0270035498 0310339493 06391466 00571657 -001174278

Distance 0084719416 0198463554 035735414 022474189 0168702153

Citizens -07322541 -0654042742 -309502993 -025940821 -05347339

Max Wind 0055528386 0201585869 014451638 017175987 -002013935

Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523

d_1980 0483661036 0599607 028523853 045083377 0431080232

Age 0041843078 0047004839 004563723 004699644 0024861386

Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913

Obs 6719 6643 6575 6570 6895

Adj R Squared 01923 02258 03648 02663 02178

55

Table 10-Appendix

Claims Regression

Parameter Estimate Std Err Pr gt ChiSq

Intercept -125027 0189

EHY 00031 00004

Premiums 09238 00091

BrickMasonry 04034 00589

Income -04719 00319

Unit Density -00007 00001

CCCL 0049 00343

Distance 01448 00068

Citizens -10523 00567

Max Wind 01721 00056

Wind Duration -00017 00101

Post FBC -04247 00366

Age 00375 00018

Age Sq -00043 00002

Obs 69442

Pseudo R Squared 029

56

Table 11-Appendix

SFBC Hurdle Regression

Full Model Hurdle Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -38419 0110688 First

Max Wind 0249099 0008523 Stage

Wind Duration -017305 0034269

Pop Density -00021 0004094

Post FBC -026859 0045551

Obs 2201

AIC 17362

Intercept -642410358 07694634 -1735 1612104 Second

EHY 003777857 0001882 -000198 0001279 Stage

Premiums 058526953 00206452 0651733 0031265

BrickMasonry 051703099 03228598 -08406 0521577

Income -024791541 00885833 -024543 0119458

Unit Density -000024465 00001635 -000108 0000324

CCCL 004187952 01058532 0083285 0147401

Distance 013910546 00256963 007182 0035699

Citizens -105795182 01382522 -087333 0192635

Max Wind 009254137 00352015 0207402 0075261

Wind Duration -005698597 00853374 -020077 0083082

Post FBC -033082693 01292625 -02288 016678

Age 002653867 00055593 0044771 0007671

Age Sq -000286273 00005216 -000527 0000887

Obs 10001

10001

Adj R Squared 05309

57

Figure Titles

Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned

house years

Figure 2 Regressions of predicted loss versus actual loss for model validation

Figure 1-App LN of Avg Loss by Structure Age

Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years

0

10

20

30

40

50

60

70

80

90

100

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Pre2000 YOC Post2000 YOC Unclassified YOC

58

Figure 2 Regressions of predicted loss versus actual loss for model validation

59

Figure 1-App

000

100

200

300

400

500

600

700

800

900

1000

1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981

LN o

f A

vg L

oss

Structure Age

LN of Avg Loss by Structure Age

2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969

1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929

60

i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC

x A state provided map of the region can be found here

httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png

xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard

52

Table 7-Appendix

Full Model Hurdle Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -262563 0035028 First

Max_Wind 0156425 000266 Stage

Wind Duration 0042652 0007989

Population Density -000501 0002246

Post FBC -018364 0016165

Obs 69442

AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second

EHY 0036631987 00007388 0003094 0000536 Stage

Premiums 0718244372 00100833 0386122 0014285

BrickMasonry 0310039307 00775013 0910896 0081548

Income -0229136499 00436795 0082266 0046119

Unit Density -0000035524 00001131 -000047 0000159

CCCL 0099361591 00484053 018571 005109

Distance 0168051018 00096458 0078816 0010177

Citizens -1493938439 00804653 -080097 0089598

Max Wind 0256726978 00087826 0250276 0013364

Wind Duration 016674127 00196152 0089513 001408

Pre 1950 -0112073927 00506211 -003 0062288

d_1950 0247133413 00526112 0079901 005082

d_1960 0361577338 00500973 016725 0043534

d_1970 0612474511 00488438 0247777 0039334

d_1980 0610758136 00484157 0289707 0037518

d_1990

Post FBC -1072118969 00483608 -06069 0048224

Obs 69442 19107

Adj R Squared 04654

53

Table 8-Appendix

2001 2002 2003 2004 2005

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383

EHY 0046815496 0049755016 0046129696 -000549296 001407418

Premiums 0902225848 0520713952 0541260712 177809555 137222708

BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961

Income -0556333367 007673894 -0333566059 -139739466 -001458013

Unit Density -000273761 0000017044 -0000399979 -000624441 000229285

CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961

Distance -0006196654 0146642336 0074095408 001597389 027645125

Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859

Max Wind 0189251023 02218324 -0028507713 062796853 033670019

Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226

Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475

Age 0024430912 0007695322 0018112422 005900484 002379329

Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583

Obs 7404 7315 7172 7138 7011

Adj R Squared 04659 03911 03599 06072 04469

2006 2007 2008 2009 2010

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759

EHY 0029382684 0038563888 004035125 0040966039 0038016928

Premiums 0410656129 0355927665 087161791 0526462478 02894645

BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283

Income -0098853673 -0243879192 029287347 0444050006 022370289

Unit Density 0000300877 0000720442 000118661 0001595448 0000576067

CCCL -027184702 0306014726 06309048 0038276012 -002689181

Distance 0081513275 0195383664 035499478 021933669 0163507604

Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008

Max Wind 0055728801 0201000209 014525329 017495008 -001688058

Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648

Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859

Age 0006187693 001016534 001631759 -001596812 -002182466

Age Sq -0000687056 -000118586 -000152884 0000907348 000112213

Obs 6719 6643 6575 6570 6895

Adj R Squared 0197 02293 03667 02803 02262

54

Table 9-Appendix

2001 2002 2003 2004 2005

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304

EHY 0047913193 0050579977 0046738464 -000511538 001472664

Premiums 0933434158 0540773786 0554312075 178778901 139820098

BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317

Income -0592068944 0052289191 -0345142584 -140252136 -002535229

Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385

CCCL 0743282837 -009598118 0007668609 -040039481 -002349054

Distance -0004011689 014827054 0076206078 001718914 028091933

Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783

Max Wind 0186392922 0219937725 -0029594557 062788723 03369511

Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395

d_1980 0882524305 0733952915 0820252047 048813821 072461562

Age 005656422 0033984684 0045371148 007917294 007253832

Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145

Obs 7404 7315 7172 7138 7011

Adj R Squared 04663 03917 03615 06072 04438

2006 2007 2008 2009 2010

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249

EHY 0029291524 0038176189 003980162 003937994 0036637581

Premiums 0430840225 0373067836 089118372 05725051 0338993543

BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629

Income -011246799 -0246875105 028804568 043007102 0198547424

Unit Density 0000308373 0000729796 000115155 000148608 0000544974

CCCL -0270035498 0310339493 06391466 00571657 -001174278

Distance 0084719416 0198463554 035735414 022474189 0168702153

Citizens -07322541 -0654042742 -309502993 -025940821 -05347339

Max Wind 0055528386 0201585869 014451638 017175987 -002013935

Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523

d_1980 0483661036 0599607 028523853 045083377 0431080232

Age 0041843078 0047004839 004563723 004699644 0024861386

Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913

Obs 6719 6643 6575 6570 6895

Adj R Squared 01923 02258 03648 02663 02178

55

Table 10-Appendix

Claims Regression

Parameter Estimate Std Err Pr gt ChiSq

Intercept -125027 0189

EHY 00031 00004

Premiums 09238 00091

BrickMasonry 04034 00589

Income -04719 00319

Unit Density -00007 00001

CCCL 0049 00343

Distance 01448 00068

Citizens -10523 00567

Max Wind 01721 00056

Wind Duration -00017 00101

Post FBC -04247 00366

Age 00375 00018

Age Sq -00043 00002

Obs 69442

Pseudo R Squared 029

56

Table 11-Appendix

SFBC Hurdle Regression

Full Model Hurdle Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -38419 0110688 First

Max Wind 0249099 0008523 Stage

Wind Duration -017305 0034269

Pop Density -00021 0004094

Post FBC -026859 0045551

Obs 2201

AIC 17362

Intercept -642410358 07694634 -1735 1612104 Second

EHY 003777857 0001882 -000198 0001279 Stage

Premiums 058526953 00206452 0651733 0031265

BrickMasonry 051703099 03228598 -08406 0521577

Income -024791541 00885833 -024543 0119458

Unit Density -000024465 00001635 -000108 0000324

CCCL 004187952 01058532 0083285 0147401

Distance 013910546 00256963 007182 0035699

Citizens -105795182 01382522 -087333 0192635

Max Wind 009254137 00352015 0207402 0075261

Wind Duration -005698597 00853374 -020077 0083082

Post FBC -033082693 01292625 -02288 016678

Age 002653867 00055593 0044771 0007671

Age Sq -000286273 00005216 -000527 0000887

Obs 10001

10001

Adj R Squared 05309

57

Figure Titles

Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned

house years

Figure 2 Regressions of predicted loss versus actual loss for model validation

Figure 1-App LN of Avg Loss by Structure Age

Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years

0

10

20

30

40

50

60

70

80

90

100

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Pre2000 YOC Post2000 YOC Unclassified YOC

58

Figure 2 Regressions of predicted loss versus actual loss for model validation

59

Figure 1-App

000

100

200

300

400

500

600

700

800

900

1000

1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981

LN o

f A

vg L

oss

Structure Age

LN of Avg Loss by Structure Age

2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969

1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929

60

i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC

x A state provided map of the region can be found here

httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png

xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard

53

Table 8-Appendix

2001 2002 2003 2004 2005

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383

EHY 0046815496 0049755016 0046129696 -000549296 001407418

Premiums 0902225848 0520713952 0541260712 177809555 137222708

BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961

Income -0556333367 007673894 -0333566059 -139739466 -001458013

Unit Density -000273761 0000017044 -0000399979 -000624441 000229285

CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961

Distance -0006196654 0146642336 0074095408 001597389 027645125

Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859

Max Wind 0189251023 02218324 -0028507713 062796853 033670019

Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226

Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475

Age 0024430912 0007695322 0018112422 005900484 002379329

Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583

Obs 7404 7315 7172 7138 7011

Adj R Squared 04659 03911 03599 06072 04469

2006 2007 2008 2009 2010

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759

EHY 0029382684 0038563888 004035125 0040966039 0038016928

Premiums 0410656129 0355927665 087161791 0526462478 02894645

BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283

Income -0098853673 -0243879192 029287347 0444050006 022370289

Unit Density 0000300877 0000720442 000118661 0001595448 0000576067

CCCL -027184702 0306014726 06309048 0038276012 -002689181

Distance 0081513275 0195383664 035499478 021933669 0163507604

Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008

Max Wind 0055728801 0201000209 014525329 017495008 -001688058

Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648

Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859

Age 0006187693 001016534 001631759 -001596812 -002182466

Age Sq -0000687056 -000118586 -000152884 0000907348 000112213

Obs 6719 6643 6575 6570 6895

Adj R Squared 0197 02293 03667 02803 02262

54

Table 9-Appendix

2001 2002 2003 2004 2005

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304

EHY 0047913193 0050579977 0046738464 -000511538 001472664

Premiums 0933434158 0540773786 0554312075 178778901 139820098

BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317

Income -0592068944 0052289191 -0345142584 -140252136 -002535229

Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385

CCCL 0743282837 -009598118 0007668609 -040039481 -002349054

Distance -0004011689 014827054 0076206078 001718914 028091933

Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783

Max Wind 0186392922 0219937725 -0029594557 062788723 03369511

Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395

d_1980 0882524305 0733952915 0820252047 048813821 072461562

Age 005656422 0033984684 0045371148 007917294 007253832

Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145

Obs 7404 7315 7172 7138 7011

Adj R Squared 04663 03917 03615 06072 04438

2006 2007 2008 2009 2010

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249

EHY 0029291524 0038176189 003980162 003937994 0036637581

Premiums 0430840225 0373067836 089118372 05725051 0338993543

BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629

Income -011246799 -0246875105 028804568 043007102 0198547424

Unit Density 0000308373 0000729796 000115155 000148608 0000544974

CCCL -0270035498 0310339493 06391466 00571657 -001174278

Distance 0084719416 0198463554 035735414 022474189 0168702153

Citizens -07322541 -0654042742 -309502993 -025940821 -05347339

Max Wind 0055528386 0201585869 014451638 017175987 -002013935

Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523

d_1980 0483661036 0599607 028523853 045083377 0431080232

Age 0041843078 0047004839 004563723 004699644 0024861386

Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913

Obs 6719 6643 6575 6570 6895

Adj R Squared 01923 02258 03648 02663 02178

55

Table 10-Appendix

Claims Regression

Parameter Estimate Std Err Pr gt ChiSq

Intercept -125027 0189

EHY 00031 00004

Premiums 09238 00091

BrickMasonry 04034 00589

Income -04719 00319

Unit Density -00007 00001

CCCL 0049 00343

Distance 01448 00068

Citizens -10523 00567

Max Wind 01721 00056

Wind Duration -00017 00101

Post FBC -04247 00366

Age 00375 00018

Age Sq -00043 00002

Obs 69442

Pseudo R Squared 029

56

Table 11-Appendix

SFBC Hurdle Regression

Full Model Hurdle Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -38419 0110688 First

Max Wind 0249099 0008523 Stage

Wind Duration -017305 0034269

Pop Density -00021 0004094

Post FBC -026859 0045551

Obs 2201

AIC 17362

Intercept -642410358 07694634 -1735 1612104 Second

EHY 003777857 0001882 -000198 0001279 Stage

Premiums 058526953 00206452 0651733 0031265

BrickMasonry 051703099 03228598 -08406 0521577

Income -024791541 00885833 -024543 0119458

Unit Density -000024465 00001635 -000108 0000324

CCCL 004187952 01058532 0083285 0147401

Distance 013910546 00256963 007182 0035699

Citizens -105795182 01382522 -087333 0192635

Max Wind 009254137 00352015 0207402 0075261

Wind Duration -005698597 00853374 -020077 0083082

Post FBC -033082693 01292625 -02288 016678

Age 002653867 00055593 0044771 0007671

Age Sq -000286273 00005216 -000527 0000887

Obs 10001

10001

Adj R Squared 05309

57

Figure Titles

Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned

house years

Figure 2 Regressions of predicted loss versus actual loss for model validation

Figure 1-App LN of Avg Loss by Structure Age

Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years

0

10

20

30

40

50

60

70

80

90

100

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Pre2000 YOC Post2000 YOC Unclassified YOC

58

Figure 2 Regressions of predicted loss versus actual loss for model validation

59

Figure 1-App

000

100

200

300

400

500

600

700

800

900

1000

1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981

LN o

f A

vg L

oss

Structure Age

LN of Avg Loss by Structure Age

2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969

1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929

60

i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC

x A state provided map of the region can be found here

httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png

xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard

54

Table 9-Appendix

2001 2002 2003 2004 2005

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304

EHY 0047913193 0050579977 0046738464 -000511538 001472664

Premiums 0933434158 0540773786 0554312075 178778901 139820098

BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317

Income -0592068944 0052289191 -0345142584 -140252136 -002535229

Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385

CCCL 0743282837 -009598118 0007668609 -040039481 -002349054

Distance -0004011689 014827054 0076206078 001718914 028091933

Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783

Max Wind 0186392922 0219937725 -0029594557 062788723 03369511

Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395

d_1980 0882524305 0733952915 0820252047 048813821 072461562

Age 005656422 0033984684 0045371148 007917294 007253832

Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145

Obs 7404 7315 7172 7138 7011

Adj R Squared 04663 03917 03615 06072 04438

2006 2007 2008 2009 2010

Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|

Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249

EHY 0029291524 0038176189 003980162 003937994 0036637581

Premiums 0430840225 0373067836 089118372 05725051 0338993543

BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629

Income -011246799 -0246875105 028804568 043007102 0198547424

Unit Density 0000308373 0000729796 000115155 000148608 0000544974

CCCL -0270035498 0310339493 06391466 00571657 -001174278

Distance 0084719416 0198463554 035735414 022474189 0168702153

Citizens -07322541 -0654042742 -309502993 -025940821 -05347339

Max Wind 0055528386 0201585869 014451638 017175987 -002013935

Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523

d_1980 0483661036 0599607 028523853 045083377 0431080232

Age 0041843078 0047004839 004563723 004699644 0024861386

Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913

Obs 6719 6643 6575 6570 6895

Adj R Squared 01923 02258 03648 02663 02178

55

Table 10-Appendix

Claims Regression

Parameter Estimate Std Err Pr gt ChiSq

Intercept -125027 0189

EHY 00031 00004

Premiums 09238 00091

BrickMasonry 04034 00589

Income -04719 00319

Unit Density -00007 00001

CCCL 0049 00343

Distance 01448 00068

Citizens -10523 00567

Max Wind 01721 00056

Wind Duration -00017 00101

Post FBC -04247 00366

Age 00375 00018

Age Sq -00043 00002

Obs 69442

Pseudo R Squared 029

56

Table 11-Appendix

SFBC Hurdle Regression

Full Model Hurdle Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -38419 0110688 First

Max Wind 0249099 0008523 Stage

Wind Duration -017305 0034269

Pop Density -00021 0004094

Post FBC -026859 0045551

Obs 2201

AIC 17362

Intercept -642410358 07694634 -1735 1612104 Second

EHY 003777857 0001882 -000198 0001279 Stage

Premiums 058526953 00206452 0651733 0031265

BrickMasonry 051703099 03228598 -08406 0521577

Income -024791541 00885833 -024543 0119458

Unit Density -000024465 00001635 -000108 0000324

CCCL 004187952 01058532 0083285 0147401

Distance 013910546 00256963 007182 0035699

Citizens -105795182 01382522 -087333 0192635

Max Wind 009254137 00352015 0207402 0075261

Wind Duration -005698597 00853374 -020077 0083082

Post FBC -033082693 01292625 -02288 016678

Age 002653867 00055593 0044771 0007671

Age Sq -000286273 00005216 -000527 0000887

Obs 10001

10001

Adj R Squared 05309

57

Figure Titles

Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned

house years

Figure 2 Regressions of predicted loss versus actual loss for model validation

Figure 1-App LN of Avg Loss by Structure Age

Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years

0

10

20

30

40

50

60

70

80

90

100

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Pre2000 YOC Post2000 YOC Unclassified YOC

58

Figure 2 Regressions of predicted loss versus actual loss for model validation

59

Figure 1-App

000

100

200

300

400

500

600

700

800

900

1000

1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981

LN o

f A

vg L

oss

Structure Age

LN of Avg Loss by Structure Age

2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969

1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929

60

i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC

x A state provided map of the region can be found here

httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png

xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard

55

Table 10-Appendix

Claims Regression

Parameter Estimate Std Err Pr gt ChiSq

Intercept -125027 0189

EHY 00031 00004

Premiums 09238 00091

BrickMasonry 04034 00589

Income -04719 00319

Unit Density -00007 00001

CCCL 0049 00343

Distance 01448 00068

Citizens -10523 00567

Max Wind 01721 00056

Wind Duration -00017 00101

Post FBC -04247 00366

Age 00375 00018

Age Sq -00043 00002

Obs 69442

Pseudo R Squared 029

56

Table 11-Appendix

SFBC Hurdle Regression

Full Model Hurdle Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -38419 0110688 First

Max Wind 0249099 0008523 Stage

Wind Duration -017305 0034269

Pop Density -00021 0004094

Post FBC -026859 0045551

Obs 2201

AIC 17362

Intercept -642410358 07694634 -1735 1612104 Second

EHY 003777857 0001882 -000198 0001279 Stage

Premiums 058526953 00206452 0651733 0031265

BrickMasonry 051703099 03228598 -08406 0521577

Income -024791541 00885833 -024543 0119458

Unit Density -000024465 00001635 -000108 0000324

CCCL 004187952 01058532 0083285 0147401

Distance 013910546 00256963 007182 0035699

Citizens -105795182 01382522 -087333 0192635

Max Wind 009254137 00352015 0207402 0075261

Wind Duration -005698597 00853374 -020077 0083082

Post FBC -033082693 01292625 -02288 016678

Age 002653867 00055593 0044771 0007671

Age Sq -000286273 00005216 -000527 0000887

Obs 10001

10001

Adj R Squared 05309

57

Figure Titles

Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned

house years

Figure 2 Regressions of predicted loss versus actual loss for model validation

Figure 1-App LN of Avg Loss by Structure Age

Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years

0

10

20

30

40

50

60

70

80

90

100

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Pre2000 YOC Post2000 YOC Unclassified YOC

58

Figure 2 Regressions of predicted loss versus actual loss for model validation

59

Figure 1-App

000

100

200

300

400

500

600

700

800

900

1000

1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981

LN o

f A

vg L

oss

Structure Age

LN of Avg Loss by Structure Age

2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969

1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929

60

i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC

x A state provided map of the region can be found here

httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png

xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard

56

Table 11-Appendix

SFBC Hurdle Regression

Full Model Hurdle Model

Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|

Intercept -38419 0110688 First

Max Wind 0249099 0008523 Stage

Wind Duration -017305 0034269

Pop Density -00021 0004094

Post FBC -026859 0045551

Obs 2201

AIC 17362

Intercept -642410358 07694634 -1735 1612104 Second

EHY 003777857 0001882 -000198 0001279 Stage

Premiums 058526953 00206452 0651733 0031265

BrickMasonry 051703099 03228598 -08406 0521577

Income -024791541 00885833 -024543 0119458

Unit Density -000024465 00001635 -000108 0000324

CCCL 004187952 01058532 0083285 0147401

Distance 013910546 00256963 007182 0035699

Citizens -105795182 01382522 -087333 0192635

Max Wind 009254137 00352015 0207402 0075261

Wind Duration -005698597 00853374 -020077 0083082

Post FBC -033082693 01292625 -02288 016678

Age 002653867 00055593 0044771 0007671

Age Sq -000286273 00005216 -000527 0000887

Obs 10001

10001

Adj R Squared 05309

57

Figure Titles

Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned

house years

Figure 2 Regressions of predicted loss versus actual loss for model validation

Figure 1-App LN of Avg Loss by Structure Age

Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years

0

10

20

30

40

50

60

70

80

90

100

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Pre2000 YOC Post2000 YOC Unclassified YOC

58

Figure 2 Regressions of predicted loss versus actual loss for model validation

59

Figure 1-App

000

100

200

300

400

500

600

700

800

900

1000

1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981

LN o

f A

vg L

oss

Structure Age

LN of Avg Loss by Structure Age

2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969

1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929

60

i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC

x A state provided map of the region can be found here

httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png

xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard

57

Figure Titles

Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned

house years

Figure 2 Regressions of predicted loss versus actual loss for model validation

Figure 1-App LN of Avg Loss by Structure Age

Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years

0

10

20

30

40

50

60

70

80

90

100

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Pre2000 YOC Post2000 YOC Unclassified YOC

58

Figure 2 Regressions of predicted loss versus actual loss for model validation

59

Figure 1-App

000

100

200

300

400

500

600

700

800

900

1000

1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981

LN o

f A

vg L

oss

Structure Age

LN of Avg Loss by Structure Age

2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969

1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929

60

i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC

x A state provided map of the region can be found here

httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png

xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard

58

Figure 2 Regressions of predicted loss versus actual loss for model validation

59

Figure 1-App

000

100

200

300

400

500

600

700

800

900

1000

1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981

LN o

f A

vg L

oss

Structure Age

LN of Avg Loss by Structure Age

2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969

1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929

60

i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC

x A state provided map of the region can be found here

httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png

xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard

59

Figure 1-App

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1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981

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2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969

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60

i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC

x A state provided map of the region can be found here

httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png

xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard

60

i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC

x A state provided map of the region can be found here

httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png

xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard