2010 pilot study 1950s with basements

Pilot Study: Use of Regression to Identify, Quantify and Interpret Property Values In Louisville, KY

Prepared for

Donna Hunt Chief Deputy of the Jefferson County PVA

By

Margaret Maginnis

May 2010

An Analysis of Sales in 2000-2009 for 1950s Housing Stock in Jefferson County, Kentucky

1 08/18/2015

The Research Question: 1. What is the effect on sale price of 1950s housing stock in Louisville, KY when a finished basement is added? Does it matter how much of the basement is finished? Is there a point of diminishing returns? What is the effect of location in different neighborhoods? Using a simple linear regression model, sale values of 1950s housing for the period 2001 through 2009 were examined based on selected characteristics.

Pilot Study: Property Values of 1950s Housing stock

Introduction

The regression analysis identified lot size, number of stories, finished size, number of bathrooms, finished basement area, garages and neighborhood location to be the most significant characteristics to impact a home’s sale price. We found that having some portion of a basement finished certainly added value to the home, but that value diminished when the basement was more than 3/4s finished. The most significant indicator of home value was often the neighborhood in which the home was located, as the results show in the following write up.

Findings

2 08/18/2015

Source Data: PVA 3/25/2010

Limitations of the Data and Next Steps Limitations of the analysis included insufficient data on housing characteristics such as square footage of porches, decks, and garages. The lack of such detailed information prevents the model from being as robust and reliable it might be otherwise. Next steps would be to obtain more detailed data from the REMF_CHAR database and rerun the rgressions. In order to do this, we need to obtain a variable ‘dictionary’ of the codes used in the REMF_CHAR database. Using the REMF_Master merged with the REMF_char data, we could rerun the regressions and compare predicted values to actual sales values.


The initial query of parcels from the REMF Master consisted of all single-family houses - a database of 218,376 records with information on PVA assessments and sales, but no information on housing characteristics. These records were then linked to valid sales for the period 2001 through 2009 with a resulting file of approximately 50,000 records containing detailed information on housing characteristics. The parameters for the pilot study were 1950s-era single-family housing with full basements. The decision to use 1950s housing was predicated on the fact that there is a large supply of homes from that era in Louisville and the sample would be relatively homogeneous. In fact, Louisville Metro has 45,848 homes built during the 1950s. Of these, approximately 49% (22,504) include some sort of basement, with an average size (basement) of 700 square feet. After merging the data with valid sales, the number of homes built in the 1950s with full-sized basements comprised slightly over 4,200 records. Selecting for single-family residences with full basements, built in the 1950s, with valid sale transactions that occurred between 2001-2009, the final study included 1,481 records. Records were examined first in Excel, then frequencies, comparison of means and regression models were run in SPSS. The following section highlights the exploratory phase of analysis.

Methodology

3 08/18/2015



Exploratory Results

Typical characteristics of 1950s housing stock include:

* quarter acre (or less) lot * 1 to 1.5 stories

* 1 bathroom * small front porch or stoop

* 1200 sq ft with full basement * one-third of the basement area finished

* detached garage

4 08/18/2015


Pilot Analysis: Property Values of 1950s Housing stock

Exploratory Results Figure 1. Location of homes included in the analysis. Most of the 1950s housing used in this analysis was built directly inside the Watterson Expressway or just outside of it. In the Southwest portion of the city, many homes were built in and just south of Shively. In the East End, 1950-era homes were built inside the Watterson in the smaller incorporated cities of Kingsley and Wellington, St Mathews, Brownsboro Village, Indian Hills, Rolling Fields, Beechwood and Woodlawn Park, and in the Louisville neighborhoods of Brownsboro-Zorn, Clifton, Rock Creek, Gardiner Lane, Highlands-Douglas, Hawthorne, Belknap and Bowman. In addition, homes built in the 1950s just east of the Watterson in St Regis Park, J-Town and Bon Air are included.

N = 1,481

5 08/18/2015



Exploratory Results

Figure 3. Median Sale Price by Year; 1950s Houses with Full Basement.

Between 2001 and 2009, the median cost of a 1950s home rose approximately 17%.

$115,000

$119,950

$126,275$127,500

$132,500

$135,100

$137,500

$130,070

$134,000

$100,000

$105,000

$110,000

$115,000

$120,000

$125,000

$130,000

$135,000

$140,000

2001 2002 2003 2004 2005 2006 2007 2008 2009

Median Sale Price by Year of Sale

6 08/18/2015


N=546

N=496

N=624 N=539

N=573

N=471

N=299

N=394

N=310

Average number of sales per year = 473 Total number of sales of 1950s housing with basements = 4,252


Exploratory Results

To look further at the data, we divided the records into 3 categories according to the percent of finished basement. We then examined sale price by year based on those three categories.

Sale Year

Homes with less

than 30% finished

basement

Homes

between 30%

and 75%

finished

basement

Homes with

more than 75%

finished

basements

2001 $117,000.00 $115,000.00 $117,500.00

2002 $118,750.00 $122,900.00 $119,000.00

2003 $127,250.00 $127,450.00 $116,000.00

2004 $123,000.00 $131,000.00 $135,200.00

2005 $130,000.00 $137,500.00 $139,000.00

2006 $132,000.00 $137,500.00 $139,830.00

2007 $134,200.00 $140,000.00 $143,750.00

2008 $123,900.00 $138,500.00 $126,320.00

2009 $129,950.00 $141,750.00 $132,400.00

Median Sale Price

Although sale prices started to fall by 2008, between 2001 and 2009, median home prices rose 23% for homes with 1/3 to 3/4 finished basement, and 13% for those with full-finished basements (i.e., 75% or more finished).

7

Frequency Percent

1 794 53.6

2 534 36.1

3 153 10.3

Total 1481 100.0

Valid

Above Grade Price per Square Foot, 1950s Homes with Full

Finished Basements

Sale Year

Homes with less

than 30% finished

basement

Homes

between 30%

and 75%

finished

basement

Homes with

more than 75%

finished

basements

2001 $91.71 $95.17 $94.56

2002 $95.87 $102.13 $99.78

2003 $100.05 $102.82 $98.79

2004 $102.34 $106.99 $114.52

2005 $104.67 $112.24 $114.13

2006 $105.47 $111.44 $116.70

2007 $110.86 $113.70 $119.93

2008 $108.20 $115.36 $127.09

2009 $107.92 $112.32 $110.80

Average Sale Price Per Square Foot (Above Grade)

Homes with 1/3 to 3/4s of the basement finished did better in the marketplace in 2001-2003, but this trend began to change in 2004 when finished basements fetched a higher price per square foot (until 2009).

08/18/2015



Exploratory Results

Average Sale Price per Square Foot in 1950s Homes with Full Basements,

Varying by Percent of Basement Finished

Figure 4. Average sale price per square foot in 1950s homes with full basements.

While sales were higher for houses with 75% or more of the basement finished, Sale prices for 1950s homes with a third to three-quarters finished basement rose at a higher rate, on average 3% annually until 2008. Homes with less than 30% of the basement finished-the majority of the study sample-rose steadily until 2008 when prices began to fall.

$95$100 $99

$115 $114$117

$120

$127

$111

$95

$102$103

$107$112 $111

$114 $115 $112

$92$96

$100 $102$105 $105

$111 $108 $108

$60

$70

$80

$90

$100

$110

$120

$130

$140

2001 2002 2003 2004 2005 2006 2007 2008 2009

> 75% finished basement

30%-75% finished basement

< 30% finished basement

N = 1,481

8 08/18/2015



Statistical Modeling in Valuation

AVMs incorporating mass appraisal models use data based on geographical/neighborhood identity and finds appropriate ‘comparable’ properties. In the case of assessor’s models, often more than 20 independent variables are compared against the selling price of a home. Regression is often run to derive values relative to a specific home. The process usually includes all properties in the given area because the assessor’s job is to properly value all properties and distribute the tax burden evenly. Some AVMs on the other hand value properties one at a time, like a fee appraisor. The process may be progressive wherein the valuation algorithm is data-driven and starts with the identification of the subject property. Or the process can be retrospective, based on predetermined valuation equations (much like assessor’s models.) Pros and Cons to both: 1) Prospective method is cumbersome and blind to problems, but is also dynamic and can be more current than the retrospective method. 2) The retrospective method has the advantage that it can be verified up to a point. Outliers can be seen in advance

before the information is released to the public.

The following things need to be explained in detail in the appraisal report whenever AVM output is used: 1. number of sales 2. sales not used and reasons why 3. sample size(does the sample represent the whole population or market?

4. method used to derive value---regression, artificial intelligence, expert system, etc. 5. independent variables tested, used and not used in the model 6. area analyzed 7. statistics that measure model accuracy 8. outcome measures (independent/dependant values) 9. clear rationale of the model 10. any other information that may affect reliability of the model. ( I.e., source of sales data, source of property data,

description of editing process)

NOTE: It’s more important to have the simplest most straightforward model with only a small set of variables that can reliably predict value.

9 08/18/2015


Automated Valuation Models (AVMs) and Appraisal

10 08/18/2015




Location, size of home, number of bedrooms and bathrooms are the most important variables used to determine sale

value. Other primary variables may include year built, house style, subdivision, number of car spaces, lot size and basement finished square footage. However, the particular set of primary variables will differ from market to market.

Most buyers have an upper-limit price constraint, AND a certain minimum level of amenities they prefer. Examples would

be a preference for quality, view, new kitchen and/or yard size.

Primary Variables

Secondary Variables

Fireplaces, garage type, pool and air conditioning may be considered secondary variables important enough to the homebuyer to include in a model. These variables have some market impact but are less often significant in a regression model. They can be important though when there is little difference in variation among the primary variables.

For example, if a neighborhood has homes all built within a 2- year period and all between 1400-1600 square feet of living

area , then 2 primary variables can be excluded from the model ---age and gross living area---and other factors such as size of garage, fireplaces, or floor plan may be included.

Other Variables A third set of variables such as location of the laundry area, guest closet, fencing, flooring, patio or deck may influence some buyers or have small value relative to the overall decision to buy. Variables at this third level tend to be subjective or calculated by another method: construction quality, physical condition or functional utility for example.

11 08/18/2015




The modeling process should always be under the constraints of the appraisal process, not the other way around. The

analytical process should be placed within other appraisal processes not over them. This way, the statistical analysis and modeling process can correctly become a tool and a marketable product for appraisers. For users, the appraisal

product is more precise, more robust and more comprehensive. One rule of analysis is any phenomenon under scrutiny must be ‘typical’, meaning that one would expect to find that

people behave in a pattern that is roughly repeatable and quantifiable. If 100 people purchasing 100 different homes in the same area purchased those homes for unique reasons, it would be impossible to quantify the major factors contributing to the real estate value.

Mass appraisal approaches offer the ability to approximate a multiple bid process. (see p 44 in “A guide to Appraisal

Valuation Modeling”). The county assessor usually builds mass appraisal models with wide nets—many variables. This is partly because the county assessor’s goal is to distribute the tax burden equitably. The problem with models based on the methodologies used by assessors arises when properties are not valued equitably---for example, if one neighborhood is undervalued in comparison to a similar neighborhood in the same jurisdiction. Owners of the former neighborhood would pay less property tax than those in the latter neighborhood.

In contrast, appraisal models for individual properties must focus on variables most relevant to that property, therefore

only variables that significantly affect the market are included---that is, variables that approximate the market mechanism determining real estate value in a specific market.

The best approach would combine human interface in market definition and variable selection with appraisal theory on

top of statistical methods.

Modeling and Appraisal


Regression Results

Following the exploratory analysis, two regression models were run. The first model included a dependent variable of sale price, and independent variables of lot size, above-grade square footage of the home, and the presence of enclosed porches, open porches, decks, and/or attached and detached 1- and 2-car garages.

The descriptive statistics in Model 1 indicate that the average sale price of a 1950s home with full basement over the years 2001 through 2009 was around $135,000. Lot sizes are almost ¼ acre (0.205), the finished size above grade of the home on average was approximately 1,298 square feet.

12 08/18/2015


R Square

Change F Change df1 df2

Sig. F

Change

1 .808 .652 .650 26393.437 .652 275.826 10 1470 .000 1.531

Model Summaryb

Model

R R Square

Adjusted

R Square

Std. Error

of the

Estimate

Change Statistics

Durbin-

Watson

Mean

Std.

Deviation N

consider_3 135283.41 44611.390 1481

land_siz_1 .205557 .0787794 1481

finsize 1297.94 343.270 1481

num2baths .30 .479 1481

num3baths 1.40 .562 1481

att1car .06 .238 1481

att2car .05 .215 1481

det2car .26 .438 1481

det1car .00 .000 1481

enclosdporch .08 .266 1481

openporch .25 .432 1481

deck .09 .282 1481

Descriptive Statistics

Model 1


Regression Results

The variables in Model 1 that are significant and help to explain the sale price of 1950s homes include those circled in column 1 at right, with t-scores greater than 1.96

13 08/18/2015


Standardiz

ed

Coefficient

s

B Std. Error Beta

(Constant) -3105.351 3030.683 -1.025 .306

land_siz_1 89799.752 10161.408 .159 8.837 .000

finsize 75.327 2.685 .580 28.057 .000

num2baths 8558.955 1561.101 .092 5.483 .000

num3baths 13492.596 1483.860 .170 9.093 .000

att1car 2311.670 3008.007 .012 .769 .442

att2car 13087.060 3622.831 .063 3.612 .000

det2car 2060.475 1615.259 .020 1.276 .202

enclosdporch -738.237 2670.203 -.004 -.276 .782

openporch -2239.453 1602.111 -.022 -1.398 .162

deck 1008.074 2443.259 .006 .413 .680

Model

Unstandardized Coefficients

t Sig.

1

Model 1


Regression Results

14 08/18/2015

Model 2 was run with the same dependent variable of sale price, and the same independent variables, but with urban neighborhoods added to the model as independent variables. With neighborhoods added the model is a better fit for the data and explains 79% of results.


R Square

Change F Change df1 df2

Sig. F

Change

.894 .799 .791 20398.864 .799 99.219 57 1423 .000 1.592

Model Summaryb

Model

R R Square

Adjusted

R Square

Std. Error

of the

Estimate

Change Statistics Durbin-

WatsonModel 2

Mean

Std.

Deviation N

consider_3 135283.41 44611.390 1481

land_siz_1 .205557 .0787794 1481

finsize 1297.94 343.270 1481

num2baths .30 .479 1481

num3baths 1.40 .562 1481

att1car .06 .238 1481

att2car .05 .215 1481

det2car .26 .438 1481

det1car .00 .000 1481

enclosdporch .08 .266 1481

openporch .25 .432 1481

deck .09 .282 1481

Descriptive Statistics


Regression Results

15 08/18/2015


Those neighborhoods that significantly impacted sale price include the following: Algonquin, Auburndale, Audubon, Avondale, Bashford Manor, Beechmont, Belknap, Bon Air, Bowman, Brownsboro, Camp Taylor, Cherokee Gardens, Cherokee Seneca, Chickasaw, Cloverleaf, Hazelwood, Highland-Douglas, Jacobs, Kenwood Hill, Klondike, Park DuValle, Poplar Level, Portland, Prestonia, Rock creek, St Joseph, Shawnee, South Louisville, Southland Park, Southside, Taylor-Berry and Tyler Park.

Std. Deviation

consider_3 44611.390

land_siz_1 .0787794

finsize 343.270

bsmtfin 378.557

num2baths .479

num3baths .562

att1car .238

att2car .215

det2car .438

det1car .000

enclosdporch .266

openporch .432

deck .282

N1_ALGONGUIN .03674

N2_AUBURNDALE .06354

N3_AUDUBON .21083

N4_AVONDALE .28888

N5_BASHFORD .23522

N6_BEECHMONT .13137

N7_BELKNAP .14320

N8_BONAIR .37999

N9_BOWMAN .15613

N10_BROWNSBOR .12887

N11_CAMPTAYLOR .07774

N12_CHGARDENS .05192

N13_CHSENECA .02598

N14_CHICKASAW .10341

N15_CLIFTONHEIG .08968

N16_CLOVERLEAF .21228

N17_CRESCENTHIL .07774

N18_DEERPARK .05803

N19_GARDINERLA .14092

N20_GERMANTOW .05803

N21_HAWTHORNE .14320

N22_HAYFIELDDUN .08968

N23_HAZELWOOD .08192

N24_HILANDDOUG .09680

N25_HIKESPOINT .24510

N26_IROQUOIS .12101

N27_IROQUOISPAR .18582

N28_JACOBS .07332

N29_KENWOODHIL .17892

N30_KLONDIKE .26983

N31_MERRIWETHE .02598

N32_PARKDUVALL .03674

N33_POPLARLEVE .18067

N34_PORTLAND .03674

N35_PRESTONIA .10341

N36_REMAINDERCI .08589

N37_ROCKCREEK .16018

N38_SAINTJOSEPH .08589

N39_SCHNITZELBU .03674

N40_SHAWNEE .07332

N41_SOUTHLOUISV .03674

N42_SOUTHLANDP .12101

N43_SOUTHSIDE .10341

N44_TAYLORBERR .08192

N45_TYLERPARK .04498

N46_WYANDOTTE .07774

Neighborhood

Value Added to Sale

Based on

Neighborhood

Statiscal

Significance

Algonquin ($66,149.94) -4.400

Auburndale ($48,268.75) -5.122

Audubon ($27,039.55) -5.688

Avondale ($28,999.92) -6.551

Bashford Manor ($41,439.70) -8.966

Beechmont ($50,318.16) -8.788

Belknap ($2,000.51) -.368

Bon Air ($33,844.03) -7.865

Bowman ($286.11) -.053

Brownsboro $4,719.28 .793

Camp Taylor ($43,575.47) -5.445

Cherokee Gardens $59,442.41 5.371

Cherokee Seneca $68,079.00 3.216

Chickasaw ($65,220.18) -9.954

Clifton Heights ($27,168.59) -3.796

Cloverleaf ($43,784.27) -9.068

Crescent Hill ($8,853.17) -1.112

Deer Park ($12,972.11) -1.292

Gardiner Lane ($13,474.14) -2.438

Germantown ($25,508.95) -2.514

Hawthorne ($5,521.38) -1.015

Hayfield ($16,631.36) -2.294

Hazelwood ($58,071.34) -7.483

Highlands-Douglas $50,696.49 7.358

Hikes Point ($29,495.77) -6.343

Iroquois ($53,334.72) -8.915

Iroquois Park ($41,383.43) -8.274

Jacobs ($61,531.29) -7.382

Kenwood Hill ($51,625.35) -10.220

Klondike ($34,016.74) -7.499

Merriwether ($48,865.57) -2.345

Park DuValle ($84,682.05) -5.637

Poplar Level ($33,128.37) -6.638

Portland ($66,064.49) -4.390

Prestonia ($48,620.94) -7.440

Remainder of City ($38,082.08) -5.155

Rock Creek $19,665.12 3.716

Saint Joseph ($31,967.95) -4.329

Schnitzelberg ($21,206.71) -1.404

Shawnee ($65,017.71) -7.808

SouthLouisville ($60,125.89) -3.988

Southland Park ($52,110.55) -8.518

Southside ($54,525.69) -8.297

Taylor-Berry ($64,244.56) -8.364

Tyler Park $52,843.32 4.224

Wyandotte ($62,111.66) -7.801



Regression Results

16

112222234556889991010111112121416161622222526303131373949505369708795117136259

CHSENECAMERRIWETHER

ALGONGUINPARKDUVALLE

PORTLAND

SCHNITZELBURGSOUTHLOUISVILLE

TYLERPARK

CHGARDENSDEERPARK

GERMANTOWN

AUBURNDALEJACOBS

SHAWNEECAMPTAYLOR

CRESCENTHILLWYANDOTTEHAZELWOOD

TAYLORBERRYREMAINDERCITY

SAINTJOSEPHCLIFTONHEIGHTS

HAYFIELDDUNDEEHILANDDOUGLAS

CHICKASAW

PRESTONIASOUTHSIDE

IROQUOIS

SOUTHLANDPARKBROWNSBORO

BEECHMONT

GARDINERLANEBELKNAP

HAWTHORNE

BOWMANROCKCREEK

KENWOODHILL

POPLARLEVELIROQUOISPARK

AUDUBONCLOVERLEAF

BASHFORDHIKESPOINT

KLONDIKEAVONDALE

BONAIR

N= 1,454

08/18/2015 Figure 5. Number of Homes Sold by Neighborhood 2001-2009

The Bar Chart in Figure 5

shows the neighborhoods

listed according to the

number of homes sold

between 2001 and 2009.

Given houses with similar

characteristics, the sale price could be

roughly $68,000 more than the

median of $135,000 in the Cherokee

Seneca neighborhood, and

$66,000 less in neighborhoods such

as the Algonquin Neighborhood.


Regression Results

The results of the regression in Model 1 suggest that the lot size contributes $66,500 per acre to the total property value. The above grade finished size of the home is worth $56 per square foot, a half-bathroom would be $6,000, with additional half baths at $1500-$2000. A full bathroom is worth $11,000, with additional full baths roughly $3000-$4000. Basement area is worth $37 per square foot and finished basements add $5-$10 per square foot to the overall cost

17 08/18/2015


Land value usually accounts for approximately 20% of total cost so the $66,000 is very high for a median price of %135,000.

According to the 2003 Marshall & Swift Cost Handbook, this was approximately $15 per square foot.

Model 1. Interpretation of the data:

Independent variables that influence market price. Market Value

Lot size $66,501

Number of stories $12,412

Finished size of home per square foot (above grade) $56

Half-Bathroom $6,373

Full Bathroom $11,037

Basement area per square foot $37

Finished basement area per square foot $5

Attached garage per square foot $16

Detached garage per square foot $7

SaleYear $3,334

08/18/2015 18

Conclusions

This study found that having some portion of the basement finished certainly added value to the home, but that value diminished when the basement was more than 3/4s finished. The more significant indicator of home value was neighborhood. However, these regression models are flawed without the use of more refined variables such as detailed housing characteristics weighted to the Louisville Market Area. Automated Mass Appraisal Systems such as ProVal do not make this information available to property Valuation researchers thus hampering the strength and value of our models.

Government & Nonprofit

2010 pilot study 1950s with basements