Electronic copy available at: http://ssrn.com/abstract=1658008

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Flips, Flops and Foreclosures: Anatomy of a Real Estate Bubble

Craig A. Depken II UNC-Charlotte

Harris Hollans Auburn University

Steve Swidler* Auburn University

JEL Classification: G11, G21, R31 Key Words: Flipping, Mortgages, Foreclosure, Speculation, Real Estate

* Corresponding Author: Steve Swidler

Department of Finance 303 Lowder Business Bldg. Auburn University, AL 36849 swidler@auburn.edu (334)844-3014 (334)844-4960 fax

Electronic copy available at: http://ssrn.com/abstract=1658008

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Flips, Flops and Foreclosures: Anatomy of a Real Estate Bubble

Abstract

This paper examines the anatomy of a real estate bubble. In the process, we identify three

phases of the markets evolution: in the first phase, a large percentage of transactions are

speculative or flips causing prices to rapidly increase; in phase two, flipping loses its

profitability; and in phase three, there are an increasing number of foreclosures leading to falling

prices. An illustration of this anatomy is provided by the evolution of the Las Vegas

metropolitan housing market from 1994 through 2009. The descriptive analysis of the Las Vegas

market is augmented with causality tests which show that the percentage change in price was the

driving force behind all three phases in the markets evolution.

JEL Classifications: G11, G21, R31 Key Words: Flipping, Mortgages, Foreclosure, Speculation, Real Estate

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1. Introduction

Conventional wisdom in the United States blames the housing market as the first

domino that fell in the lead-up to the recession that began in early 2007 (see, e.g., Shiller,

2009). Some have alluded to a housing bubble that was unsustainable and which caused

individuals to have a false perception that housing prices would continue to increase, thereby

making it profitable to purchase more expensive homes or to speculate on residential real estate

in so-called flipping (see Wheaton and Nechayev, 2008). Indeed, the popularity of flipping

might be reflected in popular culture in which television shows such as Flip this house (A&E

network) and Flip that House (TLC) were amongst the most popular television shows in the

early 2000s. As markets have cooled off or even collapsed, these shows have since been replaced

by less bullish shows such as late night television infomercials selling foreclosed residential

condominiums.

This paper examines the anatomy of a real estate bubble. In the process, we identify three

phases of the markets evolution: in the first phase, a large percentage of transactions are

speculative or flips causing prices to rapidly increase; in phase two, flipping loses its

profitability and many individuals are caught holding the bag, (i.e., cannot resell their house at

a higher high price); and in phase three, there are an increasing number of foreclosures leading to

falling prices. Eventually properties held by banks (shadow inventory) must be sold or destroyed

before the market can recover and stabilize.

To illustrate a real estate bubble, we investigate the evolution of the Las Vegas

metropolitan housing market from 1994 through 2009. We begin with positive economic

analysis that is mainly descriptive in nature and graphically captures the three phases of the Las

Vegas real estate bubble. Our subsequent analysis formally investigates the extent to which flips,

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foreclosures and percentage change in price are related to each other. Granger Causality tests

imply that percentage change in price is the driving force behind flipping and foreclosure

activity, but that flips and foreclosures are not directly related to each other. Finally, normative

analysis of a real estate bubble suggests a number of policy propositions that might be

considered by lawmakers and real estate professionals.

While local housing markets follow idiosyncratic cycles, price trends include a

systematic component related to certain factors. For instance, the literature has established that

housing prices tend to increase as the local population increases, as new housing stock replaces

older homes, as local incomes increase, and as the supply of developable land decreases (see,

e.g., Brueckner, 1980 and Capozza and Helsley, 1989). On the other hand, (moderate)

recessions are not necessarily associated with a fall in housing prices but rather with a reduction

in the number of houses sold in any particular time period and an increase in the time-on-market

for existing houses. In other words, house prices tend to be sticky downward. Nevertheless, a

sufficiently severe recession might induce price decreases. For instance, Case and Quigley

(2008) find that, in the last half of 2006, sales activity slowed, but housing prices in Boston

declined only moderately at the beginning of the downturn. However, as the recession continued

to deepen, Boston housing prices at the end of 2009 were approximately 17% below their peak,

falling to their 2003 levels (as measured by the Case-Shiller index).

Although stable and increasing housing prices are frequently thought of as the norm, it is

possible for local housing markets to experience dramatic increases (and decreases) in price.

Whether such price volatility is generated by artificially restrained supply, for instance through

overly restrictive zoning or land-use policies, or through artificially enhanced demand, rapidly

escalating prices might induce individuals to speculate on residential properties in the form of

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flipping. Flipping entails purchasing a residential property, perhaps improving the property

through cosmetic or structural changes, and attempting to rapidly resell the property for a profit.

House flipping contributes to an increase in the demand for existing properties, thereby pushing

up price. However, house flipping might also be a rational response to other market signals such

as a rapidly increasing population or relatively easy credit for potential home-buyers (Wheaton

and Nechayev, 2008). Estimating the relative influence of these possible factors is mainly an

empirical exercise.

Factors that lead to a dramatic escalation in housing prices cannot be expected to last

forever. Thus, a tapering-off period follows during which price increases moderate, the profits

from flipping fall, and there is a decline in the proportion of sales that are flips. This reduced

exuberance might presage an actual, and potentially dramatic, decline in price. If this is the case,

those who attempted to participate in flipping toward the end of the exuberant period and

many who purchased at the peak of the market will find themselves holding a depreciating asset.

In this environment, the flipping period is followed by a period of flops and finally a

potential for foreclosures as some individuals find it in their best interest to default on their

mortgage rather than trying to sell the property for a loss. The subsequent analysis chronicles a

cycle of flips, flops and foreclosures in Clark County, Nevada, a district that essentially

comprises the Las Vegas metropolitan area.

2. Data and Definitions of Transaction Type

The data sample used in this study describes 541,373 separate residential property

transactions from Clark County, Nevada from 1994:q1 through 2009:q4, obtained from the Clark

County tax assessors office. The data describe, among other things, the transaction price, the

transaction type, the date of the transaction, and a unique parcel identifier.

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We are able to examine up to nine separate transactions for each parcel, although the vast

majority of properties have less than four transactions during the sample period. To facilitate the

use of such a large data set and to provide a level of aggregation that might inform policy

discussion, the analysis translates each transaction date into the appropriate quarter and year. The

subsequent analysis is then undertaken on a quarterly basis.

Each transfer of property is coded by the Clark County Tax Assessors office according

to the transaction (sale) type. There are three categories that we examine: i) Recorded Value

denoting an arms-length transaction (coded with an R), ii) Trustees Deed is the amount bid

at foreclosure auction on the trustees deed (coded with a T), and iii) Foreclosure is a transfer

indicating a resale after foreclosure (coded with an F). These three sale types constitute the

bulk of all transactions filed at the tax assessors office and are the most important categories for

the purposes of our investigation.

Table 1 lists the distribution of residential transactions by sales type. For the entire

sample period, there are 464,093 R transactions, 47,320 T transactions, and 29,960 F

transactions recorded. Examining tax records on a quarterly basis, total transactions trend

upwards and reach a peak of more than fifteen thousand sales in the third quarter of 2005. On a

percentage basis, arms-length transactions (R) constitute more than 95% of all sales through the

fourth quarter of 2006. House prices in Las Vegas (as measured by the Case-Shiller index)

reflected the vigorous sales activity of this period, and after large run-ups in 2004 and 2005,

prices eventually crested in the second quarter of 2006.

To put the figures in perspective, Figure 1 depicts the number of R, T and F transactions

for the period 2004:q1 through 2009:q4. As can be seen, arms-length transactions dominate the

distribution up until the end of 2006. In 2007, the F and T transactions begin to increase in

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number and eventually foreclosure activity constitutes the leading share of sales. The properties

in foreclosure combined with additional new and used properties on the market might be

expected to exert downward pressure on price and alter the expectations of potential buyers

about future price changes. In fact, the last three years of the sample period witnessed rapid

price declines in the Las Vegas market.

The dispersion of foreclosure activity is not evenly distributed across the 104 different

tax districts of Clark County. Table 2 depicts the R, T and F transactions for the twenty one tax

districts that constitute our sample and include all areas with more than 1,000 residential

transactions during the period of analysis. Of particular note is that the districts with the largest

foreclosure activity tend to have the lowest per capita income in the area. In particular,

foreclosures were more than 17% of total sales in the low income districts of North Las Vegas,

Sunrise Manor and Whitney.

It is important that the county assessor accurately characterize each transaction as the

data serves as a foundation for mass-appraisal models used to determine fair market value for

ad valorem tax purposes. As such, arms-length transactions denoted as R transactions in the data

serve as the benchmark for market value estimation. Frequently, arms-length transactions are

thought of as a sale between a willing seller and a willing buyer.

A trustees deed transaction (coded T) denotes a foreclosure sale and signifies that the

property either resides in the Real Estate Owned (REO) inventory of the lender or was purchased

at the foreclosure sale by an owner/investor. Typically the lender is the winning bidder at

auction and the recorded sale price of the T transaction represents the amount bid on the trustees

deed. A T transaction may also represent a deed-in-lieu of foreclosure, which entails the lender

repossessing the house without pursuing a foreclosure on the property, with the result that the

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homeowner loses whatever equity they have in the house. Presumably there is little or, more

likely, negative equity precipitating the transference of the property. In a deed-in-lieu of

foreclosure, the lender often agrees to not pursue the individual home owner for recourse, which

has arguably become easier under the recently passed rules of the Trouble Asset Relief Program

(TARP) and the American Recovery and Reinvestment Act (ARRA).

Early in the sample period, an F code denoted a deed-in-lieu of foreclosure transaction.

With the recent increase in foreclosure activity, the county changed an F transaction to mean that

the transfer of a property is a resale after foreclosure. The typical example of a recently coded F

transaction would be the sale of the house by the lender (who acquired the trustees deed through

a T transaction) to a new homeowner. If the sale price is thought to be different from market

value, the county then codes this as an F transaction.

Table 3 gives the flavor of foreclosure activity in Clark County and how the coding of

deed-in-lieu of foreclosure changed over the sample period. In the early part of the sample

(1994:q1-2006:q4), homes going into foreclosure were predominantly coded T transactions that

signaled transference of a trustees deed. In a typical quarter, nearly 161 homes were T

transactions, while 5 were coded F by the county and primarily denoted a deed-in-lieu of

foreclosure sale. Looking at the last three columns of Table 3, whether designated a T or an F

for homes going into foreclosure, virtually all subsequent sales were coded R by the county.

This implies that almost all houses were sold to the new homeowners at market value, i.e., the

county considered the transaction between the lender and new homeowner as an arms-length

sale.

Over the more recent sample period (2007:q1-2009:q4), Table 3 illustrates two important

changes that occurred. First, there were virtually no examples of a T (trustees deed) followed

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by an F (deed-in-lieu of foreclosure) in the early sample period. However, T then F becomes the

predominant foreclosure sequence of transactions moving into the latter sample period and

implies that for most of 2007-2009, T transactions included both trustees deed and deed-in-lieu

of foreclosure transactions. Moreover, an F transaction in 2007-2009 refers to a resale after

foreclosure. In the third column, the few hundred examples of F followed by R found in 2007-

2009 presumably denote properties that sold in an earlier period as a deed-in-lieu of foreclosure,

but then were coded as an arms-length transaction on the subsequent sale.

Of potentially more interest is the second change made in recent years. Whereas it has

already been noted that from 1994-2006 virtually all second sales in the foreclosure sequence

were considered R transactions, by 2009 more than 90% of second sales were coded F by the

county. This has important implications for housing valuation. In recent years, the county no

longer considered the vast majority of the foreclosure resales as an arms-length transaction, and

therefore, they would not likely be used in any tax valuation model or price index.

3. Property Flipping

As documented earlier, the rapid rise in housing prices from 2003-2005 corresponded to a

period that experienced a high number of sales. The county designated virtually all transactions

as arms-length (R). Many of these sales involved property flipping.

Depken, Hollans, and Swidler (2009), define a flip as the purchase of a home with the

intent of quickly reselling the property at a higher price. They examine all cases that involve two

arms-length transactions for a property within a two year window. Two years is a relevant time

frame as the Internal Revenue Service allows any capital gains to be excluded from taxable

income if the seller has used the home as his or her primary residence for two of the previous

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five years. This definition does not depend upon flipping motivation or economic profitability,

but rather focuses on the short-term investment horizon that is the epitome of house flipping.

House flipping is often depicted as purchasing property in poor condition at a discount,

renovating the house and then selling it at or near full market value. This is sometimes referred to

as a fix and flip and has been the basis for a number of reality television shows. However, this

is not the only situation in which a property might be ripe for flipping. Some properties might

be purchased at a discount due to forced circumstances such as relocation, divorce, or a pending

foreclosure. Such situations might provide for a nominally profitable opportunity if the house can

be resold at market value in a relatively short amount of time.

Given multiple transactions for a given property, it is possible to identify, ex post, which

transactions are the front-end of an eventual house flip, called the buy-side flip transaction, and

which are the back-end of an eventual house flip, called the sell-side flip transaction. This, in

turn, allows for the calculation of any economic profits due to flipping. House flipping, similar

to any speculative activity, is an inherently risky proposition, and higher risk suggests higher

expected return. Depken, Hollans, and Swidler (2009) find that flippers earned positive

economic profits in the Las Vegas residential housing market up through 2005, in large part,

reflecting sell-side flip prices that tended to be higher than other similar arms-length transactions.

The premium on the sell-side transaction might indicate improvement to the property not

captured in the tax records. However, the housing stock in Las Vegas is relatively young, and

many flips were of new or nearly new houses. It is unlikely that sell-side premiums were

primarily a reflection of home improvements.

Alternatively, a sales premium might obtain due to the circumstances of flipping. If

information is costly to obtain, flippers may have superior knowledge of the local real estate

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market. Since there is no pressure to relocate, flippers can take time to search for buyers offering

the highest price for the property. Still another possibility due to asymmetric information is that

sell-side flips might carry a premium because of illegal activity. For instance, appraisers might

collude with mortgage originators and the flippers broker to provide an inflated appraisal of the

flipped property. Inflated prices might then be the result of mortgage companies artificially

stimulating demand by qualifying buyers for more expensive homes. To limit predatory

flipping, the Department of Housing and Urban Development promulgated a set of guidelines in

2004 (amended in 2006) that prohibited Federal Housing Authority (FHA)-insured mortgage

financing for properties re-sold within 90 days. These guidelines, however, did little to stop

mortgage fraud that has been the focus of, Operation Stolen Dreams, an FBI campaign that

targets illegal flipping activity, loan origination schemes, and equity skimming

(http://www.fbi.gov/page2/june10/mortgage_061710.html).

To get a better idea of Las Vegas flipping activity during the sample period, Figure 2

depicts quarterly sell-side and buy-side flip transactions as a percentage of all arms-length sales.

As can be seen, buy-side and sell-side transactions comprise a small percentage of all

transactions until sometime after 2002 when the buy-side flip transactions break the ten percent

barrier. Eventually, the buy-side flip transactions peaks at approximately 23% of all arms-length

transactions in the first quarter of 2004 after which the percentage drops dramatically. The

falling number of buy-side flip transactions coincides with the declining profitably of flipping

and the eventual fall in housing prices (Depken, Hollans, and Swidler, 2009).

Figure 2 also exhibits a pattern of seasonality in the flip transactions data. Over the entire

sample period, there is no statistically meaningful difference between the percentage of buy-side

transactions in quarters 1, 2 or 3. However, there is a statistically significant decline in the

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percentage of buy-side transactions in the fourth quarter (by approximately 3.8 percent between

q1 and q4, p-value=.032). There exists a different but distinct pattern in sell-side flip transactions

as well: there is a positive and slightly significant increase in sell-side flip transactions in quarter

4 (by approximately 2.7 percent between q1 and q4, p-value=.098) although there is no

statistically meaningful difference between quarter 1 and quarter 2 or quarter 3.

When focus is restricted to the period starting with 2004:q1, the seasonal patterns

disappear for both buy and sell-side flip transactions. As can be seen in Figure 3, the percentage

of buy-side flip transactions falls below the percentage of sell-side flip transactions by the third

quarter of 2004, that is, buy-side flip transactions fall before the Las Vegas market experienced

dramatic decreases in prices and increases in foreclosure activity. Moreover, Figure 3 indicates

that flipping activity never actually reached zero; even during the dramatic downward adjustment

of the market after 2006 there were still some properties that speculators felt were potentially

profitable flips.

4. Descriptive Analysis of the Las Vegas Housing Bubble

Figure 4 depicts an evolution of flips, foreclosures, housing prices and price changes in

Clark County over the entire sample period. The bar graph above the x-axis represents the

percentage of all transactions identified ex post as one side of a flip (both buy-side and sell-side

each quarter). The bar graph below the horizontal axis illustrates the number of trustee deeds (T)

and foreclosures (F) as a percentage of total transactions. The percentage of flips and

foreclosures are then drawn against the median price of all arms-length transactions (solid line)

and the annualized percentage change in median price for each quarter (dashed line). In

chronicling the boom to bust housing cycle in the Las Vegas area, the graph can be neatly

divided into three distinct regimes.

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Flips 1994:q1 through 2005:q4

Between 1994 and 2000, the great majority of sales were arms-length transactions, with

relatively few flips or foreclosures. During this period, quarterly price changes were small

(approximating the nominal inflation rate), and the median transaction price in the Las Vegas

market remained relatively modest. After 2000, the percentage change in prices increased and

median prices followed accordingly. Seemingly in response to these price changes, flipping

activity also increased.

By 2004, flipping activity rose to roughly 40% of all housing transactions in Las Vegas,

and while perhaps not clear ex ante, flipping activity of this magnitude could not be sustained

forever. The artificial stimulus in demand from flipping, in turn, helped fuel an increase in new

homes built. The housing stock nearly doubled in Las Vegas from 2000 through 2008; however,

Clark Countys population grew only 33.9% (from 1.39 million to 1.87 million people) during

the same period. Flippers, home builders and (non-flip) sellers, eventually found it more

difficult to find buyers leading to the next stage of the bubble.

Flops 2006:q1 through 2007:q4

Figure 5 replicates the previous graph, but centers on the transition period between flips

and foreclosures. As flippers found it more difficult to locate buyers for their properties, price

increases attenuated and prices eventually peaked in 2006. In many cases, flips were no longer

profitable leading to economic losses and a fall in flipping activity. A reduction in flips occurred

because fewer investors decided to buy properties for flipping, and those that did could not

always sell them within two years. For many investors, 2006-2007 was a period of flops, and by

2008, Figure 5 shows that flipping activity had fallen to less than 5% of all transactions. This

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contributed to further price declines, and starting in 2008, foreclosures constituted the majority

of transactions in the Las Vegas area.

Foreclosures 2008:q1 through 2009:q4

In the final phase, flipping was not economically profitable as median prices continued to

decline, potential buyers were reluctant to purchase a home in anticipation of lower future prices

and more existing home owners found themselves underwater on their mortgage. Coupled with

an increasingly soft labor market and higher unemployment, the number of foreclosures

snowballed despite policy interventions such as mortgage restructuring, first-time homebuyer

tax credits and the Feds purchase of mortgage-backed securities leading to lower interest rates.

One more effect of the high foreclosure rate is a sharp increase in shadow inventory.

Figure 6 illustrates the stock of homes owned by the lender, where quarterly changes equal the

net flows of properties going into foreclosure minus REO inventory that has been resold. During

2004, with prices increasing rapidly, REO inventory was being sold faster than the (small)

number of new foreclosures and shadow inventory fell to its local minimum. However, as house

prices began to fall in 2007, the negative equity position of owners caused foreclosures to

escalate. Initially new foreclosures exceeded REO resales and shadow inventory reached its

peak at the beginning of 2009. In a situation where asset prices are falling dramatically, lenders

were trying to sell properties as quickly as possible. Given the brisk turnover of REO property

along with shadow inventory near historically high levels, there is further reason to believe that

housing prices will continue to decline in a soft Las Vegas housing market.

5. Establishing Causal Relationships

The fan graphs discussed in the previous section are suggestive of intertemporal

relationships between quarterly foreclosures (including deed-in-lieu of foreclosure), median

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price, the quarterly percentage change in median price, and quarterly flipping activity (both buy-

side and sell-side transactions). To establish whether intertemporal relationships actually exist,

we consider Granger causality tests between any two variables of interest. We do not consider

the level of median price explicitly as it is subsumed within the percentage change in median

price variable. While Granger causality methodology is a useful starting point to examine

intertemporal relationships, it is limited in its ability to identify structural relationships between

more than two variables at a time. Thus, with this methodology we simply seek to establish

whether there are unilateral, bilateral, or independent relationships between each variable dyad.

Grangers definition of causality (Granger, 1969) asserts that variable X causes variable

Y if past values of X and Y help explain the variation in current Y better than previous values of

Y itself. Granger Causality is predicated on a rather simple concept. A base-line is established by

using previous values of Y to explain the current value of Y. By adding lagged values of another

variable X, the subsequent model will explain at least as much of the variation in Y as the base-

line model. If the additional explanatory value of the lagged values of X more than outweigh the

lost degrees of freedom associated with the additional explanatory variables, variable X is said to

Granger Cause variable Y. It is possible that variable Y can also Granger cause variable X,

suggesting bilateral feedback between the two variables or a potential third variable that is

causing both X and Y. If neither X nor Y Granger cause each other then the two variables can be

considered independent in the Granger sense of causation even if the correlation between the two

variables is positive.

Before testing the interrelationships between the percentage of transactions that are part

of a flip, the percentage of transactions that are foreclosures, and the percentage change in

median price, it is important to establish the stationarity of each time series. Non-stationarity of

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one or more of the variables could lead to spurious results in any Granger causality test. To

establish stationarity, the first column of Table 4A reports Dickey-Fuller test statistics assuming

no drift for each variable. (Qualitatively similar results were obtained when allowing for drift).

As can be seen in all cases, the null hypothesis of a unit root, i.e., non-stationarity, cannot be

rejected.

It is difficult to reconcile how percentage of transactions that are flips or foreclosures can

actually be non-stationary in the long-run because the two variables are constrained from above

and below. One possibility is that the Dickey-Fuller tests are misleading because of structural

breaks in the data which make the series appear to be non-stationary. The methodology

developed by Zivot and Andrews (1992) tests for non-stationarity after controlling for any data-

determined structural break the procedure discovers. The final two columns of Table 4A report

the results of Zivot-Andrews tests assuming a single structural break in the data. The second

column shows that after controlling for a structural break, the three variables are all stationary.

In other words, the Dickey-Fuller results in column one are likely incorrect.

The final column in Table 4A reports the data-determined structural breaks for the three

variables. The structural breaks are remarkably aligned with anecdotal evidence of when these

characteristics of the Las Vegas housing market experienced fundamental change. For instance,

the percentage change in median price and the percentage of transactions that were flips reveal a

structural break during the first quarter of 2004, exactly when claims of many industry observers

suggested flipping experienced a fundamental increase in popularity. In addition, the percentage

of transactions that were foreclosures reveals a structural break in the second quarter of 2007,

very close to when industry observers suggest the housing market peaked. As median prices

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started to fall, highly leveraged home buyers began to find their mortgages underwater, leading

to a shift in the temporal pattern of foreclosures in the Las Vegas housing market.

The results presented in Table 4A suggest that, after controlling for the structural breaks

in the data, all three variables are stationary. Thus, the standard Granger Causality regression,

where the current value of the dependent variable is regressed on the lagged values of the

dependent and independent variables, is augmented with a dummy variable that takes a value of

zero before the structural break of the independent variable and one thereafter.

The results of the Granger Causality tests are reported in Table 4B. Each dyad between

the three variables involves a pair of Granger Causality tests. The first number in a cell uses only

one-quarter lagged values of both variables, and we label this as short term causation. The

second Granger causality statistic uses four quarters of lagged values of both variables and we

refer to this as longer term causation.

The Granger Causality tests in Table 4B reveal an intriguing set of relationships between

the percentage change in median price, the percentage of transactions that are flips, and the

percentage of transactions that are foreclosures. The first result is that there seems to be

independence between flips and foreclosures (i.e., there is no Granger Causality in either

direction). On the other hand, flips influence percentage change in median price in the longer

term, while foreclosures influence the percentage change in median price in the short term.

Finally, the last row in Table 4B shows that percentage change in median price influences the

other two variables over both one and four quarters.

Thus, while there is some feedback from flips and foreclosures on the percentage change

in price, it is evident that the driving force among these three variables is the percentage change

in price. One explanation for this is that percentage change in price influences the profitability

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(or loss) of flips and foreclosures. As prices increase, regardless of their level, individuals

attempt to reap profits from house flipping. On the other hand, as housing prices fall, more

individuals find their equity eroded and eventually find their mortgages underwater which might

ultimately lead to foreclosure.

A somewhat surprising result from these bivariate tests is the lack of a causal relationship

(in either direction) between flips and foreclosures. Conventional wisdom might suggest that

flips Granger Cause foreclosures, that is, those who bought in the sell-side flip transaction might

have overpaid for the property and be more likely to walk away once underwater. On the other

hand, foreclosures might be expected to influence flips as foreclosures lower the price on the

buy-side of the flip and make flips potentially more profitable. However, there is no causal

relationship between the two suggesting that they are independent of each other in the Las Vegas

market.

6. Policy Discussion and Conclusions

Looking backwards, it is easy to trace through the housing bubble in Las Vegas over the

last decade. The percentage change in price was the driving force behind a surge of flipping

activity that artificially boosted demand for housing in the metropolitan area. This, in turn,

ignited further price increases, and home builders responded by constructing more new homes.

Ultimately growth in the Las Vegas housing stock outstripped population growth and the

resulting moderation in price increases meant that many flips were no longer profitable. As

flipping activity slowed considerably, house prices began to fall. Eventually some homeowners

found their mortgages underwater and defaulted on their notes. These foreclosures led to a

further decline in prices causing more foreclosures in the area.

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That price changes drive foreclosures is consistent with Elul, et al (2010) and Bhutta, et

al (2010) who find that negative equity is a primary reason for default. Together, this work

suggests that loan modification programs will necessarily have limited success in curbing new

foreclosures, and steps must be taken to firm up prices. One possibility is to expand resources

like HUDs Neighborhood Stabilization Program (http://hudnsphelp.info/). This program shores

up demand of foreclosed properties by providing financial assistance to first-time home buyers.

Still another Neighborhood Stabilization Program influences supply of foreclosed homes by

granting funds to government entities for the purpose of demolishing blighted neighborhoods.

Detroit, for example, plans to knock down 3000 homes by September 2010 using federal

government funds. Moreover, Mayor Dave Bing has promised to tear down 10,000 structures in

his first term in office to right-size Detroit and align housing needs with a shrinking city

population (Kellogg, 2010).

Given that excess supply is part of the foreclosure problem, it is perhaps surprising that

new home building continues in Las Vegas. As one Las Vegas builder noted (Streitfeld, 2010),

Were building them because were selling them. Yet new home building continues to add to

the problem of excess supply, falling prices and foreclosed homes. In circumstances like these,

local government units might develop policies that encourage renovation of properties and

rehabilitation of neighborhoods. While declaring a temporary moratorium on new homes may

diminish local tax and permit revenues generated by new housing construction in the short run,

the offset is that city will not have to contend with the costs associated with abandoned homes

and blighted neighborhoods.

At least two other lessons can be derived from the Las Vegas housing market bubble.

First, flipping activity contributed to rising home prices, and given asymmetric information, it

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might be prudent to alert potential homebuyers of legal flipping activity. One way to do that is

to require MLS listings to include information on when the current owner bought the property

and whether the current owner lives in the home. This solution to the asymmetric information

problem is similar to the requirement in several states that sellers divulge information that they

are an agent/owner of a property. (See Levitt and Syverson, 2008, for market distortions related

to the agent/owner problem.)

The second lesson is that municipal governments must be consistent in their record

keeping. Clark County in changing the F transaction code from deed-in-lieu of foreclosure to

foreclosure resale makes it difficult to do any meaningful time comparisons. More importantly,

up to the last three years, foreclosure resales were coded as arms-length transactions (R). But in

the recent downturn, 90% of foreclosure resales were coded F. The upshot is that tax valuation

models and many price indexes will not include these F transactions. In the case of tax valuation

models, excluding foreclosure resales may seriously bias upward the countys assessment of

market value.

Finally, looking forward, it will be important to focus on underlying structural inter-

temporal relationships, perhaps with the use of vector autoregressive models. So, for example,

how did mortgage rates or easy credit influence prices, flipping activity and foreclosures? Or,

did pricing dynamics differ when prices were going up versus down? Answers to questions such

as these will be left for future research.

21

Table 1: Temporal Distribution of Residential Transaction Types in Las Vegas, NV

Quarter Total R Total T Total F Pct. T Pct. F Pct. R Quarter Total R Total T Total F Pct. T Pct. F Pct. R

1994q1 3514 102 3 2.82 0.08 97.1 2002q1 7537 364 8 4.6 0.1 95.3

1994q2 4422 77 4 1.71 0.09 98.2 2002q2 8724 447 7 4.87 0.08 95.05

1994q3 4116 79 5 1.88 0.12 98 2002q3 8884 431 16 4.62 0.17 95.21

1994q4 3868 83 4 2.1 0.1 97.8 2002q4 9180 392 6 4.09 0.06 95.85

1995q1 3202 85 1 2.59 0.03 97.38 2003q1 8619 450 8 4.96 0.09 94.95

1995q2 3935 72 7 1.79 0.17 98.04 2003q2 10762 466 10 4.15 0.09 95.76

1995q3 4134 82 3 1.94 0.07 97.99 2003q3 12537 461 10 3.54 0.08 96.38

1995q4 3977 84 4 2.07 0.1 97.83 2003q4 12354 363 7 2.85 0.06 97.09

1996q1 4120 85 2 2.02 0.05 97.93 2004q1 12513 342 6 2.66 0.05 97.29

1996q2 4730 68 5 1.42 0.1 98.48 2004q2 15432 99 14 0.64 0.09 99.27

1996q3 4593 92 2 1.96 0.04 98 2004q3 14635 85 13 0.58 0.09 99.33

1996q4 4755 92 7 1.9 0.14 97.96 2004q4 12907 39 8 0.3 0.06 99.64

1997q1 4064 140 4 3.33 0.1 96.57 2005q1 12360 34 11 0.27 0.09 99.64

1997q2 4867 116 20 2.32 0.4 97.28 2005q2 14970 25 3 0.17 0.02 99.81

1997q3 5049 155 4 2.98 0.08 96.94 2005q3 15453 21 2 0.14 0.01 99.85

1997q4 4923 146 8 2.88 0.16 96.96 2005q4 14302 42 6 0.29 0.04 99.67

1998q1 4469 178 6 3.83 0.13 96.04 2006q1 12414 74 10 0.59 0.08 99.33

1998q2 5669 197 4 3.36 0.07 96.57 2006q2 13267 128 3 0.96 0.02 99.02

1998q3 5645 236 7 4.01 0.12 95.87 2006q3 11741 238 10 1.99 0.08 97.93

1998q4 5727 231 3 3.88 0.05 96.07 2006q4 10511 314 18 2.9 0.17 96.93

1999q1 5406 263 8 4.63 0.14 95.23 2007q1 7801 646 29 7.62 0.34 92.04

1999q2 6656 253 3 3.66 0.04 96.3 2007q2 7451 980 76 11.52 0.89 87.59

1999q3 6453 282 8 4.18 0.12 95.7 2007q3 6103 1432 154 18.62 2 79.38

1999q4 6048 233 6 3.71 0.1 96.19 2007q4 5521 2231 213 28.01 2.67 69.32

2000q1 5654 288 5 4.84 0.08 95.08 2008q1 3515 3060 1607 37.4 19.64 42.96

2000q2 6850 254 9 3.57 0.13 96.3 2008q2 3828 4586 3095 39.85 26.89 33.26

2000q3 6586 319 9 4.61 0.13 95.26 2008q3 4046 5108 4244 38.13 31.68 30.19

2000q4 6756 276 6 3.92 0.09 95.99 2008q4 3755 4473 3861 37 31.94 31.06

2001q1 6600 328 6 4.73 0.09 95.18 2009q1 2787 4319 4127 38.45 36.74 24.81

2001q2 8134 300 5 3.55 0.06 96.39 2009q2 3461 3591 5643 28.29 44.45 27.26

2001q3 7668 309 5 3.87 0.06 96.07 2009q3 4205 4431 4914 32.7 36.27 31.03

2001q4 8086 356 9 4.21 0.11 95.68 2009q4 1842 1787 1639 33.92 31.11 34.97

*Quarterly data from 2009:q4 is not from the entire quarter.

22

Table 2: Tax Districts and the Frequency of Transaction Types (Full Sample)

Tax District Number Tax District Name

Total Transactions

R Transactions

T transactions

F transactions

Pct. T & F Transactions

50 Boulder City Library 2,556 2,437 82 37 4.66 107 Laughlin Town Big Bend Colorado River 1,154 1,060 71 23 8.15 125 Artesian Basin Fire 911 1,198 995 114 89 16.94 200 Las Vegas City 164,123 141,063 14,523 8,537 14.05 203 Las Vegas City Redevelopment 1,118 937 125 56 16.19 250 North Las Vegas City 52,169 43,055 5,652 3,462 17.47 254 North Las Vegas Library 31,815 25,911 3,399 2,505 18.56 340 Sunrise Manor 43,009 35,251 4,978 2,780 18.04 410 Winchester Town 2,169 1,885 193 91 13.09 417 Spring Valley Town 46,285 40,262 3,541 2,482 13.01 420 Summerlin Town Artesian Basin 10,468 9,623 478 367 8.07 470 Paradise Town 26,249 23,015 2,110 1,124 12.32 500 Henderson City 3,390 3,027 222 141 10.71 503 Henderson City Redevelopment 1,839 1,556 182 101 15.39 505 Henderson Artesian Basin 49,832 45,255 2,838 1,739 9.18 514 Henderson City Library Debt 4,144 3,878 153 113 6.42 516 Henderson Library Debt/Artesian Basin 18,868 17,137 1,020 711 9.17 521 Henderson City Redevelopment 521 4,522 3,765 514 243 16.74 570 Whitney Artesian Basin 12,419 10,196 1,341 882 17.90 635 Enterprise Fire Artesian Library 911 Manpower 59,379 49,355 5,626 4,398 16.88 901 Mesquite City 4,667 4,430 158 79 5.08

Totals 541,373 464,093 47,320 29,960

23

Table 3: Sales around Foreclosures

Quarter

T followed by an R

T followed by an F

F followed by an R

F followed by a T

F or T followed by an R

F or T followed by an F

F or T followed

by a T

Quarterly Average

1994:q1-2006:q4 160.8 0.2 5.0 0.3 165.7 0.2 2.6

2007:q1 174 14 6 1 180 14 11 2007:q2 288 33 4 2 292 33 19 2007:q3 268 86 3 1 271 86 13 2007:q4 370 119 3 2 373 120 16 2008:q1 96 1380 20 5 116 1382 30 2008:q2 85 2772 29 15 114 2779 65 2008:q3 114 3892 85 7 199 3905 43 2008:q4 116 3697 97 10 213 3700 58 2009:q1 126 3896 95 10 221 3902 31 2009:q2 79 5148 160 12 239 5158 53 2009:q3 95 4619 208 20 303 4626 65

2009:q4 76 1571 117 9 193 1571 42

Totals 1994:q1-2009:q4 10246 27237 1086 107 11332 27287 581

*Quarterly data from 2009:q4 is not from the entire quarter.

24

Table 4A: Stationarity Tests

Variable Dickey-Fuller Test Zivot-Andrews Test Break Point Percentage Flips -1.097 -5.555* 2004:q1 Percentage Foreclosures 1.274 -4.812* 2007:q2 Pct. Change in Median Price -1.887 -4.635** 2004:q1

P-values indicate rejection of the null hypothesis of a unit root (non-stationarity): * p

25

Figure 1: Distribution of Residential Property Transactions by Type and Quarter (2004:q1-2009:q4)

26

Figure 2: Percent Buy-Side and Sell-Side Flip Transactions (1994:q1-2009:q4)

27

Figure 3: Percent Buy-Side and Sell-Side Flip Transactions (2004:q1-2009:q4)

Buy-side flips in 2008 and 2009 do not cover entire two year flipping window.

28

Figure 4: Percentage of Transactions that were Flips, Percentage of Transactions Foreclosures, Quarterly Median Price and Percentage Change in Median Price (1994:q1-2009:q4)

29

Figure 5: Percentage of Transactions that were Flips, Percentage of Transactions Foreclosures, Quarterly Median Price and Percentage Change in Median Price (2004:q1-2009:q4)

30

Figure 6: Shadow Inventory and Median Prices (2000:q1-2009:q4)

1500

0020

0000

2500

0030

0000

3500

00M

edia

n P

rice

2000

4000

6000

8000

1000

012

000

Sha

dow

Inve

ntor

y

2000q1 2002q3 2005q1 2007q3 2010q1

Shadow Inventory Median Price

31

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