Foreclosures, House Prices, and the Real Economy*

Atif Mian
University of California, Berkeley and NBER
Amir Sufi
University of Chicago Booth School of Business and NBER
Francesco Trebbi
University of British Columbia, CIFAR, and NBER

December 2010

Abstract

A central idea in macroeconomic theory is that negative price effects from the leverage-induced
forced sale of durable goods can amplify negative shocks and reduce economic activity. We
examine this idea by estimating the effect of U.S. foreclosures in 2008 and 2009 on house prices,
residential investment, and durable consumption. We show that states that require judicial
process for a foreclosure sale have significantly lower rates of foreclosures relative to states thathave no such requirement. Using state laws requiring a judicial foreclosure as an instrument for
actual foreclosures, as well as a regression discontinuity design around state borders withdiffering foreclosure laws, we show that foreclosures have a large negative impact on house
prices. Foreclosures also lead to a significant decline in residential investment and durable
consumption. The magnitudes of the effects are large, suggesting that foreclosures have been an
important factor in weak house price, residential investment, and durable consumption patterns
during and after the Great Recession of 2007 to 2009.

*We thank Paul Beaudry, Christopher James, and seminar participants at Boston University, the University of
British Columbia, and UCLA for comments. We also thank the National Science Foundation and the Initiative on
Global Markets at the University of Chicago Booth School of Business for funding. Filipe Lacerda provided
excellent research assistance. Atif Mian: atif@haas.berkeley.edu; Amir Sufi: amir.sufi@chicagobooth.edu;
Francesco Trebbi: ftrebbi@interchange.ubc.ca.

Electronic copy available at: http://ssrn.com/abstract=1722195

An extensive body of research postulates that a levered economy is subject to large
swings in economic activity (e.g., Fisher (1933)). One of the key mechanisms through whichleverage is believed to amplify shocks is negative price effects from the leverage-induced forcedsale of durable goods (e.g., Shleifer and Vishny (1992), Kiyotaki and Moore (1997),
Krishnamurthy (2003, 2009), and Lorenzoni (2008)). This amplification can occur through avariety of channels including reduction in collateral value, balance sheet weakness, or negative
wealth effects. But the central conclusions from this literature are clear: first, the forced sale ofdurable goods can have negative price effects and, second, these negative price effects can leadto a significant decline in real economic activity.
We examine this idea in the context of the recent rise in foreclosures. The top left panelof Figure 1 shows that aggregate foreclosure filings in the U.S. increased from 750,000 in 2006
to almost 2.5 million in 2009. While we do not have data on foreclosures before 2006, themortgage default rate increased above 10% in 2009, which is more than twice as high as any year
since 1991. By any standard, the recent U.S. mortgage default and foreclosure crisis is ofunprecedented historical magnitude.
The sharp rise in foreclosures accompanied large drops in house prices, residential
investment, and durable consumption. As the top right panel of Figure 1 shows, nominal house
prices fell 35% from 2005 to 2009. The drop in residential investment from 2005 to 2009 shownin the bottom left was larger than any drop experienced in the post World War II era. The drop in
durable consumption is also large, but more comparable to recent recessions. While durable
consumption and residential investment are small components of overall GDP, they areespecially important in understanding macroeconomic fluctuations (Leamer, 2007).

1

Electronic copy available at: http://ssrn.com/abstract=1722195

This paper evaluates the effect of the recent foreclosure crisis on house prices, and thenexamines the amplification effect by estimating how foreclosures affect residential investment
and durable consumption. We utilize a micro-level data set covering the entire United States
until the end of 2009 with information on a number of variables of interest including house
prices, residential investment, auto sales, mortgage delinquencies, and foreclosures. We have allof these variables at the zip code-year level, with the exception of residential investment and auto
sales which are at the county-year level.
A study seeking to estimate the effect of foreclosures on house prices is confounded byconcerns of unobserved shocks and reverse causality. For example, an unobserved negative
shock can drive down house prices and increase delinquencies and foreclosures at the same time.
Further, reverse causality is a major concern given that a necessary condition for foreclosure isthat a borrower have negative equity (Deng, Quigley, and Van Order (2000), Bajari, Chu, andPark (2008)). Consequently, foreclosures and house prices will be strongly negatively correlated
in the data, even if foreclosures have no independent effect on house prices. An empiricalstrategy seeking to estimate the effects of foreclosures on house prices must employ plausibly
exogenous variation in foreclosures.
Our strategy relies on variation in foreclosures that is driven by state rules on whether aforeclosure must take place through the courts (a judicial foreclosure). In states that require a
judicial foreclosure, a lender must sue a borrower in court before conducting an auction to sell
the property. In states without this requirement, lenders have the right to sell the house after
providing only a notice of sale to the borrower (a non-judicial foreclosure). As first highlighted
in the economics literature by Pence (2006), the 21 states that require judicial foreclosure impose
substantial costs and time on lenders seeking to foreclose on a house.

2We begin by showing that there is indeed a very strong negative correlation between
actual foreclosures and whether a state requires a judicial foreclosure. States that require judicial
foreclosure have a rate of foreclosures per homeowner during 2008 and 2009 that is 3 percentage
points lower than states without, which translates to a 2/3 standard deviation and is more thanhalf of the mean (4.5% homeowners in foreclosure). Using data on mortgage delinquencies, weshow that states with judicial requirements have a much lower ratio of foreclosures to delinquentaccounts. In fact of the 14 states with the highest propensity to convert delinquent homes into
foreclosure sales, none require judicial foreclosure, and only 2 of the top 24 states require
judicial foreclosure.
While judicial requirement strongly predicts the foreclosure rate, it can only be alegitimate instrument for foreclosures if it is not correlated with other factors that may havecontributed to the severity of a recession in a state. We show that states with a judicial
foreclosure requirement are remarkably similar to other states in all attributes of interest except
the propensity to foreclose. For example, as of 2000 states that do and do not require judicial
foreclosure display no difference in the fraction of subprime borrowers, the fraction of lower
income residents, the unemployment rate, the minority share of the population, and the fractionof the residents living in urban areas. Similarly, there is no evidence of differential credit growthor differential house price growth between 2000 and 2005, and no difference in mortgage
delinquency rates during the mortgage default crisis. In other words, the rate at which
homeowners default on their homes is almost identical in states that do and do not require
judicial foreclosure. But the rate at which delinquencies progress into foreclosures issubstantially lower in judicial requirement states.

3

Using state laws requiring judicial foreclosure as an instrument for foreclosures, weestimate the effect of foreclosures on house prices. We find a large effect. Our state-levelbaseline estimate suggests that a one standard deviation increase in foreclosures in 2008 and2009 leads to 2/3 standard deviation lower house price growth over the same period.
Alternatively, moving from the median to the 90th percentile of the foreclosure per homeowner
distribution leads to 9% lower house price growth from 2007 to 2009.
Our estimate of the effect of foreclosures on house price growth is robust to extensive
controls for demographics and income differences across states. All specifications explicitly
control for the effect of mortgage delinquencies on house prices. In other words, our estimate
captures the incremental price effect of foreclosures above and beyond delinquencies. In
addition, the effect is similar when we conduct the analysis at the MSA-level and is robust to theuse of either the Fiserv Case Shiller Weiss or Zillow.com house price indices.
We also employ a zip code-level border regression discontinuity (RD) specification thatis similar to the specification that Pence (2006) uses for credit. This specification allows us tocompare zip codes that are very close to each other in geographical distance and observablecharacteristics. Consistent with the state level correlations, there is a sharp increase in theforeclosure rate as one crosses the border from a judicial requirement state into a state with nojudicial requirement. However, there is no similar jump in other observable variables as one
crosses the border. Focusing only on zip codes that are very close to the border between two
states that differ in judicial foreclosure requirement laws, we find similar two-stage least squares
estimates of the effect of foreclosures on house prices. The similarity of the results using the zip
code-level RD design mitigates omitted variable concerns in state- and CBSA-level regressions.

4

We then turn to residential investment and durable consumption. Employing a similar
two stage least squares estimation strategy, we find that a one standard deviation increase inforeclosures per homeowner leads to a 2/3 standard deviation decrease in permits for new
residential construction. Further, a one standard deviation increase in foreclosures leads to a 2/3
standard deviation decline in auto sales. The estimates are robust to controls for demographics
and income. Data on residential investment and auto sales are disaggregated only to the county
level. Nonetheless, we employ a similar border discontinuity strategy using county-level data andfind similar coefficient estimates, although the statistical power of the discontinuity estimation is
weak.
We use our microeconomic estimates to quantify the aggregate effects of foreclosure on
the macro-economy. Our estimates suggest that foreclosures were responsible for 15 to 30% of
the decline in residential investment from 2007 to 2009 and 20 to 40% of the decline in auto
sales over the same period. The details of this calculation are in Section V. B.
It is important to emphasize that we do not take a stand on whether foreclosures help tobring house prices, durable consumption, or residential investment closer to or further from theirlong-run socially efficient levels. For example, in the absence of foreclosures, house prices may
display downward rigidity given loss aversion (Genesove and Mayer (2001)). Alternatively,
house prices may be kept above their socially efficient level by government support. Further, it isconceivable that the declines we document would occur in the long run even in the absence of
foreclosures; it is also conceivable that states where foreclosure is relatively easy will experiencea faster housing recovery.
But our estimates suggest that foreclosures lead to more abrupt declines in theseoutcomes than would be observed in the absence of foreclosures, and these declines are likely to5be more painful in the midst of a severe recession. This is consistent with the amplification
mechanisms emphasized in Kiyotaki and Moore (1997) and Krishnamurthy (2003). We believethat these results demonstrate a direct connection between a financial friction--forced sales
induced by foreclosures--and a reduction in residential investment and durable consumption
during and after the recession of 2007 to 2009.
Our findings are most closely related to recent studies on foreclosures and house prices(Calomiris, Longhofer, and Miles (2008), Campbell, Giglio, and Pathak (2010), Foote, Gerardi,
and Willen (2008), Hartley (2010)). One advantage of our study relative to the existing literature
is comprehensiveness: our analysis covers the entire United States as opposed to one state or one
city and we examine foreclosures all the way through the end of 2009.1 We are also the first touse state laws on judicial requirement for foreclosure to identify the effect of foreclosures onhouse prices. The importance of an instrument for foreclosures is mentioned prominently in theliterature.2 Further, to the best of our knowledge, we are the first to examine the effect offoreclosures on real economic activity.3
The paper is organized as follows. In the next section, we discuss the data and summary
statistics. Section II presents the main suggestive correlations and Section III discusses
identification and the empirical strategy we employ. Sections IV and V present and discuss our
main empirical results on house prices, residential investment, and durable consumption. Section
VI concludes.
1

One important disadvantage is that many of these studies have individual level data on foreclosures and house
prices, whereas we have only zip code level data.
2
As Campbell, Giglio, and Pathak (2010) note, “…foreclosures are endogenous to house prices because
homeowners are more likely to default if they have negative equity, which is more likely as house prices fall.
Ideally, we would like an instrument that influences foreclosures but that does not influence house price except
through foreclosures; however, we have not been able to find such an instrument” (15).
3
The importance of precise estimates of the effect of foreclosures on real economy activity is highlighted by thelarge number of policy interventions that seek to reduce foreclosures, such as the Bush Administration’s Foreclosure
Prevention Act of 2008, the foreclosure moratoria in Maryland and California, and the Obama Administration'sHome Affordability Modification Program of 2009.
6

I. Data and Summary Statistics
A. Data
We use data from a number of sources. Foreclosure data from RealtyTrac.com, one of theleading foreclosure listing websites, are available to us at the zip code level at annual frequency
for 2006 through 2009. RealtyTrac.com collects data from legal documents that are submitted bylenders during the foreclosure process. There are five types of filings collected byRealtyTrac.com. The first two are filings that are done before a foreclosure auction: a notice ofdefault (NOD) and a lis pendens (LIS). Two of the filings are directly associated with aforeclosure auction: a notice of trustee sale (NTS) and a notice of foreclosure sale (NFS).
Finally, RealtyTrac.com collects information on whether the foreclosed home is purchased by
the lender at auction, or real-estate owned (REO).
For every zip code, we have the total number of filings for each of these five categories.
To avoid double-counting filings for the same property, RealtyTrac.com provided us totals forthe last filing in the process for a given property in a given year. For example, if a borrower
received a notice of default and a notice of trustee sale in the same year, RealtyTrac.com records
one notice of trustee sale for the property. Our measure of total foreclosures in a zip code is thetotal number of notices of trustee sale, foreclosure sales, or real estate owned(NTS+NFS+REO).4
Data on house prices at the zip code-quarter level are from Fiserv Case Shiller Weiss andZillow.com. An excellent description of the differences and similarities between FCSW and
Zillow.com is available in the appendix of Guerrieri, Hartley, and Hurst (2010), which we

4

We are grateful to Tyler White for providing us with detailed information on the foreclosure data fromRealtyTrac.com. Readers interested in acquiring the foreclosure data should contact tyler.white@realtytrac.com.
7

summarize here. Both FCSW and Zillow.com data are collected from underlying transactions
data based on deeds. FCSW uses a repeat sales methodology to capture the price growth ofproperties that are similar in characteristics. In contrast, Zillow.com combines the underlying
transactions data with a hedonic adjustment model that assigns values to homes based on
characteristics of the home. The hedonic model used by Zillow.com is not publicly available, but
is a function of the size of the home, the number of bedrooms, and the number of bathrooms.
New residential permit data is from the Census and is available at the county-annual
level. Auto sales data are from R.L. Polk and are available at the county-monthly frequency. For
more information on the R.L. Polk data, see Mian and Sufi (2010a).
We supplement foreclosure, house price, residential investment, and auto sales data withzip code-quarterly level information on delinquencies from Equifax.5 The Equifax data also
allow us to measure at the zip code level the fraction of borrowers that had credit scores below
660 as of 2000. Finally, we supplement the zip code level data with demographic informationfrom the 2000 Decennial Census.
Given the availability of variables at different levels of geographic aggregation, weconstruct final data sets at the state, CBSA, and zip code level. The underlying zip code level
data covers approximately 31,000 zip codes, which represent the entire United States. For the
purpose of aggregation, the zip codes are matched to states, counties, and CBSAs using a data set
from zip-codes.com.
The main restriction on the data is the availability of zip code house price indices.
Zillow.com zip code level house price data are available for 8,900 zip codes in our sample, and
FCSW house price data are available for 4,199 zip codes. Zip code level data are available fromone of these two sources for 9,213 zip codes. These zip codes represent 65% of the total U.S.
5

See Mian and Sufi (2009) and Mian, Sufi and Trebbi (2010) for more information on the Equifax data.
8

population, 81% of total home-related debt as of 2005, and 83% of total foreclosures in 2008 and2009. By far the largest observable difference between zip codes for which we do and do nothave data is whether the zip code is in an urban area. Almost 80% of zip codes for which wehave house price data available are in urban areas; only 19% of zip codes for which we do nothave house price data are in urban areas.

B. Summary Statistics
The top panel of Table 1 presents summary statistics of the state level data used in theanalysis. The average number of foreclosures per homeowner in 2008 and 2009 is 0.045. The
number of homeowners is approximated using the number of mortgage accounts as of 2005
according to Equifax. The median is significantly lower than the mean, which reflects a very
high number of foreclosures at the high end of the distribution. The number of 60 days past due
delinquent mortgage or home equity accounts per homeowners is 0.095, which implies anaverage pass-through from delinquency to foreclosure close to 50%.
Data on house prices and residential investment show the dramatic turn of events startingin 2006 and 2007. From 2007 to 2009, house prices dropped by 15 to 20% depending on the data
source. Residential investment at the state level dropped by 80% as measured by the Census dataon permits for new residential construction. Auto sales dropped by 41%.
Table 1 also presents summary statistics at the CBSA level. The patterns in foreclosures,delinquencies, house price growth, residential investment growth, and auto sales growth aresimilar. Table 1 also contains information on other important variables, including the increase in
the debt to income ratio from 2002 to 2005, the fraction of consumers that were subprime
borrowers as of 2000, and the unemployment rate as of 2000.

9

II. Correlations
A crucial insight from previous research is that house price declines are a necessary
condition for foreclosures. If a homeowner owns a house with positive equity but faces
significant liquidity constraints in making mortgage payments, she can either refinance to loosen
the constraint or sell the home to liquefy the positive equity position. However, she will notallow for the bank to foreclose if she has positive equity (Deng, Quigley, and Van Order (2000),
Bajari, Chu, and Park (2008)). This logic implies that foreclosures and house price growth will
be mechanically negatively correlated.
The results in Table 2 confirm this mechanical correlation. In columns 1 and 2, we report
estimates form an OLS specification of house price growth from 2007 to 2009 (or first quarter of2010 for Zillow.com) on foreclosures in 2008 and 2009. Even after controlling for mortgage
delinquencies, there is a strong negative correlation. The coefficient estimate in column 1 ofPanel A implies that a one standard deviation increase in foreclosures is associated with 5%
lower house price growth. The magnitude is similar using Zillow.com data. In column 2 we usean alternative functional form by regressing house price growth on foreclosures per delinquentaccount. The estimate implies that a one standard deviation increase in foreclosures perdelinquent account (0.22) is associated with 7% lower house price growth, which is more thanhalf a standard deviation.
Columns 3 and 4 show the correlation between house price growth and foreclosures at the
CBSA level. The columns report specifications from OLS regressions of house price growth on
foreclosures with the inclusion of state fixed effects. In columns 5 and 6, we examine thecorrelation at the zip code level with the inclusion of CBSA fixed effects. In all specifications,10

house price growth is strongly negatively correlated with house price growth. This correlation
holds when using variation in foreclosures across states, within states, and within CBSAs. In
terms of magnitudes, the coefficients for the within state and within CBSA regressions are
smaller than the cross-state specification. But distributional effects are similar.6
Figure 2 shows the scatter plot of house price growth against foreclosures at the statelevel. Arizona and Nevada have by far the largest number of foreclosures per homeowner andalso sharply lower house prices. However, the correlation is also strongly negative among theother states in the sample.
The results in Table 2 and Figure 2 confirm a strong negative correlation between
foreclosures and house price growth. This correlation is robust in variation that is within-CBSA,
within-state, and across state. It is difficult, however, to infer the direction of causality. Given
that a borrower must have negative equity in order to allow a foreclosure to occur, it would beshocking if there were anything but a strong negative correlation. An analysis seeking to estimate
the effect of foreclosures on house prices must utilize plausibly exogenous variation
foreclosures. We discuss our strategy in the next section.

III. Empirical Strategy
We utilize state laws that require judicial foreclosures as an instrumental variable for
actual foreclosures.7 In this section, we first provide background on judicial foreclosures andthen provide evidence on the legitimacy of the identification strategy.

6

The distributional effects are similar because the within-state and within-CBSA foreclosure variation is a larger
portion of the overall variation relative to house prices. For example, a one within-CBSA SD increase inforeclosures (0.08) is associated with half a within-CBSA SD decline in house price growth (0.04/0.09).
7
General information on the foreclosure process presented in this section comes from Pence (2003, 2006),
http://www.all-foreclosure.com/judicial.htm, http://en.wikipedia.org/wiki/Foreclosure, andhttp://www.calculatedriskblog.com/2007/04/foreclosure-sales-and-reo-for-ubernerds.html.
11

A. Judicial Foreclosure Requirement as an Instrument
A foreclosure represents a forced sale of a property by a lender with the purpose ofreimbursing the lender for the debt outstanding against the property. The process by which thelender executes the sale differs across states. One of the most important differences is whether astate requires that the sale be implemented through the courts. In states that require a judicial
foreclosure, lenders must file a notice with a judge providing evidence regarding the amount of
the debt, the delinquency of the debt, and why the delinquency should allow the lender to sell theproperty. This filing is typically called a lis pendens. The borrower is notified of the filing andhas a chance to respond. If the court finds that the lender is accurate in their claim, a property
will move to the auction stage of the process.
In a non-judicial foreclosure, the lender does not need court approval to auction aproperty. Lenders use rights that they have obtained in the original mortgage document allowing
sale of the property if the borrower is delinquent on the account. In a non-judicial foreclosure, alender sends a notice of default to the borrower, and the notice is typically also filed with thejurisdiction authority (i.e., county, municipality, etc.). If the borrower fails to pay the debt or
dispute the notice, a notice of sale is subsequently filed which begins the auction process.
A large body of evidence suggests that costs to lenders are substantially higher forjudicial versus non-judicial foreclosures (Wood (1997), Ciochetti (1997), Pence (2003),
Pennington-Cross (2004)). Websites covering the mechanics of foreclosure frequently cite that
judicial foreclosures are expensive for lenders. For example, on calculatedriskblog.com, one of12the main bloggers writes: “Non-judicial foreclosure is almost always faster and cheaper for thelender than a judicial foreclosure.” 8
The October 2010 announced foreclosure moratorium by JPMorgan-Chase, GMAC, andBank of America highlights the costs to lender in states that require judicial foreclosure. Givenproblems with the verification of documents, these servicers stopped all foreclosure activity instates that require judicial foreclosure.9
Figure 3 shows states that require judicial foreclosure shaded in dark gray. The
classification of states comes from RealtyTrac.com and follows closely the classification used byPence (2006) and the classification listed on all-foreclosures.com.10 While the majority of statesthat require judicial foreclosure are located in the upper Midwest and Northeast, there isgeographical variation outside this area.
One particular set of zip codes that we focus on in the empirical analysis includes thosethat are close to the border of two states that differ in whether judicial foreclosures are required.
We refer to these zip codes as the border discontinuity sample. To form this sample, we restrict
the sample to zip codes that meet the following three restrictions: (1) the zip code must haveavailable house price data from FCSW, (2) there must be zip codes across the nearest stateborder that also have house price data available, and (3) the state that is across the border musthave a different law regarding judicial foreclosures. These three restrictions leave us with 870
zip codes.
Table 3 lists the state borders that are included in the border discontinuity analysis, alongwith the number of zip codes as the sample is isolated to zip codes within 50, 25, and 5 miles of8

http://www.calculatedriskblog.com/2007/04/foreclosure-sales-and-reo-for-ubernerds.html
See http://www.nytimes.com/2010/10/08/business/08frozen.html .
10
The only states that differ across these three classifications are Massachusetts, Nebraska, Oklahoma, Rhode
Island, and Wisconsin.
913

the border. One disadvantage of the border discontinuity sample that is obvious from Table 3 isthat none of the states with the largest incidence of foreclosures are included (i.e., Arizona,
California, Florida, and Nevada).

B. Two-Stage Least Squares Specification
Our estimation of the effect of foreclosures on house prices, residential investment anddurable consumption is based on a two stage least squares specification of the following form:

2009

2007
0809

∗

0809

∗

Γ∗

(1)
Λ∗

(2)

Equation (2) represents the first stage. We regress foreclosures in 2008 and 2009 scaled by the
number of homeowners as of 2005 in geographical unit g (which can be a state or CBSA) on anindicator variable for whether the geographical unit is in a state s that requires judicial
foreclosure. If the level of analysis is the state level then the g subscript is redundant. The second
stage in equation (1) regresses the growth rate in outcome Y in geographical unit g from the endof 2007 to the end of 2009 on the predicted value of foreclosures from the first stage. Outcomes
include house prices, residential investment, and auto sales. The matrix X contains control
variables.
The specification outlined in (1) and (2) treats the variation in foreclosures induced bydifferences in state laws on judicial foreclosure as random, and uses this random variation to
examine house prices, residential investment, and durable consumption. One obvious drawbackfrom this approach is that we cannot back out the structural parameters of the full system of14

equations where each of these three outcome variables (house prices, residential investment, anddurable consumption) is allowed to affect one another. In other words, if foreclosures lead to a
reduction in residential investment in the two-stage least squares specification, we cannot discern
whether foreclosures directly affect residential investment, or whether foreclosures indirectly
affect residential investment through their effect on prices. Nonetheless, under the identifying
assumptions, we are able to use the specification to estimate the overall effect of foreclosures oneach of these outcomes.
A consistent estimate of the coefficientrequires two conditions. First, whether a state

requires judicial foreclosure must be correlated with the actual incidence of foreclosures. Second,the exclusion restriction must be met. The instrument must be uncorrelated with the error term in
the underlying relation between the outcome of interest and foreclosures. The next two
subsections discuss each of these two conditions.

C. Judicial Foreclosure Requirement and Actual Foreclosures
The evidence in this section overwhelming supports the argument that foreclosures areless likely in states that require judicial foreclosure. Table 4 presents regressions of foreclosureson an indicator variable for whether the state requires judicial foreclosure, which is a specificversion of the first stage shown above in equation (2). As column 1 shows, states with a judicial
foreclosure requirement have a foreclosure per homeowner ratio in 2008 and 2009 that is 0.030
lower, which represents 2/3 of the mean and 2/3 of a standard deviation of the left hand side
variable.
Further, column 2 shows that mortgage delinquencies display no strong correlation withwhether states require judicial foreclosure. The standard error is small, and we are able to reject15

at the 10% level the hypothesis that delinquencies per homeowner are a two-third standard
deviation lower in judicial foreclosure requirement states. The inclusion of delinquencies per
homeowner as a control variable does not materially change the lower foreclosure rate in judicial
states.
In column 4, we examine the pass-through rate, which we define to be the number offoreclosures scaled by the number of delinquent mortgage accounts. As the coefficient estimateshows, the pass-through rate to foreclosures is significantly lower in judicial foreclosure states.
The magnitude is large. Judicial states have a pass-through rate to foreclosure that is a fullstandard deviation lower than non-judicial states.
Figure 4 shows more evidence on the pass-through rate. In the left panel, we show theforeclosures per delinquent account ratio for every state. States shaded in black require judicial
foreclosure. The 14 states with the highest foreclosure to delinquent account ratios all allow nonjudicial foreclosure. Of the 27 states with the highest pass-through rate from delinquencies toforeclosures, only 3 require judicial foreclosure. The right panel of Figure 4 plots foreclosures
per homeowner against delinquencies per homeowner. Judicial states are plotted as triangles, andnon-judicial states are plotted as circles. Consistent with the left panel, there is a much lower
sensitivity of foreclosures to delinquencies in judicial states.
In order to isolate the sample to geographic areas that are similar, we plot in Figure 5 thepass-through rate for zip codes in the border discontinuity sample described in the above subsection. More specifically, to produce the plots in Figure 5, we estimate the following
specification:

∑

16

∗

(3)

whererepresents foreclosures per delinquent accountfor zip code z that is located near border b in state s within a 10 mile strip x of the border.11 The
specification includes fixed effects at the level of border-state-10 mile strip (Figure 5 represent the coefficient estimates ofon the indicators). The dots in, which are indicators foreach one mile on either side of the border, with negative values being in the state that requires
judicial foreclosure. These coefficient estimates represent the average foreclosures perdelinquent account ratio for one mile wide bands around the border of a judicial state and nonjudicial foreclosure state, after controlling for (border state*10 mile strip) fixed effects.
Figure 5 plots the estimates offor the foreclosures per delinquent account for 2006

through 2009. Consistent with the state level analysis in Figure 4, there is a very sharp jump in
the foreclosure to delinquent account ratio as one crosses the border from a judicial requirement
state into a non-judicial requirement state. This border discontinuity jump is weaker in 2006 andbecomes stronger through time.
As a final check we assessed the issue of possible weakness of our instrumental variable.
Weak instruments arise in the presence of low correlation between the included endogenous
variable and the instrument. The ensuing weak identification leads to IV statistics that are nonnormal even in large samples, and standard IV tests become unreliable in terms of size and bias.
The strong correlation between judicial proceedings on foreclosure rates at the state level isreassuring that the instrument is strong in state-level regressions. The issue of weak instruments,
however, could still potentially arise in CBSA or zip code-level analysis due to the fact that thelevel of variation we employ for the IV remains at the state-level. Reassuringly, in Tables 4 andfollowing, we generally observe F statistics above Stock and Yogo (2005) weak identification
11

The 10 mile strip indicator variables control non-parametrically for omitted variables among zip codes that areclose to one another and equidistant from the border. These are important given that some states border one anotherin very different geographical areas.
17critical values, rejecting the hypothesis that the IV is weak. However, on occasion, theinstrument displayed Kleibergen-Paap F statistics below the 10% maximal IV size, suggesting
the potential for the IV inference being misleading. We verified that all our results were robust to
weak instruments by employing the approach in Moreira (2003, 2009), which produces tests andconfidence sets with correct size when instruments are arbitrarily weak for the just-identified
case of a single endogenous variable (our specific instance).

D. Exclusion Restriction
The bottom panel of Table 4 shows that there are no obvious statistically significant
differences in observable covariates between judicial and non-judicial states. In particular, stateswith judicial foreclosure do not show a statistically significant difference in delinquency rates,
house price growth from 2002 to 2005, subprime fraction of the population, income,unemployment, poverty, racial demographics, education, or ruralness. The standard errors are
relatively small. For almost every single variable in Panel B, we can reject at the 10% level ofconfidence that judicial requirement states are different by a 3/4 standard deviation. The only
variable for which we cannot reject the difference is FCSW house price growth from 2002 to
2005, and this is due to a small sample of only 24 states.
In Figure 6, we examine the validity of the exclusion restriction using the zip code border
discontinuity sample. More specifically, Figure 6 shows whether zip codes on either side of theborder are different in terms of their delinquency rates, subprime borrowers, income, poverty
incidence, minority share, or education. The specification that produces these plots is analogous
to equation (3) with different outcome variables. As the coefficient estimates on the one-milebands show, there is no discernable jump in any of the observable variables at the border.

18

Perhaps the biggest concern for the exclusion restriction is the ex ante differential
incentives of lenders to supply credit in judicial versus non-judicial states. Given that lenders canmore easily foreclose on collateral in non-judicial states, they should be more willing to supply
credit for borrowers in those states. A potential concern is that the higher credit supply during the
housing boom in non-judicial states is responsible for the outcomes we find. Support for thisconcern comes from Pence (2006), who uses a census tract border discontinuity design in 1994
and 1995 data and finds that individual mortgages are 3 to 7% smaller in judicial versus nonjudicial states (see also Benmelech, Garmaise, and Moskowitz (2005) on commercial
mortgages).
We explore this concern using the zip code border discontinuity sample, which is similar
to the strategy used in Pence (2006). In Table 5, we report results from our estimation of thefollowing equation:∗(4)

where an outcome in zip code z near state border b in state s is regressed on a border-state-10mile strip fixed effect and the judicial foreclosure requirement indicator variable. In Panel A of
Table 5, we first replicate the first stage where the outcome variable is the foreclosure rate. As
column 1 shows, the foreclosure rate per homeowner is significantly lower in judicial states. The
magnitude of the effect is similar to the state level evidence in Table 4. Column 2 shows that theforeclosure per delinquency ratio is also much lower in zip codes on the judicial state side of theborder.
In column 3, we examine whether the average mortgage for home purchase in a zip codeis smaller if the zip code is in a judicial state. This specification is similar to the one reported in
19

Pence (2006) except we are using the average in a zip code instead of the underlying loans and
we are examining the 2005 loans instead of 1994 and 1995 loans. The mortgage data come from
HMDA. In column 4, we use an alternative left hand side variable, which is the total amount ofmortgages for home purchase in a zip code scaled by total income from the IRS in that zip code.
As the estimates in columns 3 and 4 show, we find no evidence that average loan sizes or total
lending are significantly lower in judicial states, despite the fact that ex post foreclosure rates are
significantly lower. The standard errors are sufficiently small that we can reject at the 10% level
the hypothesis that loans sizes or loans to income are 3/4 standard deviation lower in zip codeson the judicial state side of the border.
To further explore this issue, Panel B presents the same coefficients as in columns 3 and4 but for every year going back to 1992. While statistical power is clearly an issue, we find very
similar point estimates as Pence (2006) in the early part of the sample: lenders extended smallerand fewer loans to zip codes in judicial states. However, beginning in the middle 1990s andlasting throughout the housing boom, the coefficient estimates move toward zero and then turn
positive. In other words, lenders during the housing boom did not take into account the ex post
differences in foreclosure rates between judicial and non-judicial states.
We also isolate the sample to loans that were not sold to GSEs given the argument that
GSEs may not discriminate between judicial and non-judicial foreclosure states. The results are
similar. The standard errors across all specifications are small enough that we can reject at the
10% level of confidence that lenders extended loan amounts or loan sizes to judicial states in any
year from 2000 to 2004 that were 1/2 standard deviation lower than non-judicial states.
Why does the Pence (2006) result weaken over time? Or in other words, why did lenders
from 2000 to 2005 not extend more credit to borrowers in non-judicial states where the costs of20

foreclosure are lower? One potential reason is that, during the housing boom, lenders andintermediaries assigned a very low probability to states of the world in which house prices
declined substantially (Gerardi, Lehnert, Sherlund, and Willen (2008)). If lenders assigned a very
low probability to default states, then the loss given default likely played a small role in lending
decisions.
Another reason is lack of due diligence by purchasers of securitized mortgage backed
securities, who may not have fully understood the ex post differences in foreclosure rates acrossstates. Relatedly, most of the loans originated in general, i.e. the conforming loans, areguaranteed by the GSEs against default. There is no evidence that we know of that suggests that
GSE insurance premiums differ by the foreclosure laws in a given state. As a result, originators
would be indifferent between judicial and non-judicial states when it comes to evaluating thelosses given default in different states. Finally, we find that ease of foreclosure leads to larger
price declines. If banks ex-ante understand this general equilibrium effect of forced sales, they
will weigh the ease with which they can grab the delinquent home against the lower price they
get in the event of a sale.12 The net effect of these two forces may be neutral.

E. State Foreclosure Statutes in Further DetailState laws requiring foreclosures to take place through courts are only one of many legal
differences in mortgage markets across states. To assess the importance of the additional legal
differences, we employ the Rao and Walsh (2009) taxonomy of consumer protection clauses
included in state foreclosure statutes.13 Our goal is to examine whether other legal differences are

12

The house price drop due to foreclosures is an externality from the perspective of the individual decision of a bank
to foreclose or not. Thus, in the event of default, ex-post competition across banks will lead them to foreclose
without internalizing the impact on house prices.
13
We thank Christopher James for pointing us in this direction.
21

(1) responsible for our results on judicial foreclosure requirement and (2) important in their own
right.
Rao and Walsh (2009) list the following six pre-sale protections: Access to court review;
loss mitigation requirement before foreclosure; right to cure before acceleration; right to reinstate
before sale; personal service requirement for complaint or sale notice; and housing emergency
assistance fund. They also list four common post-sale protections: Right to redeem; deficiency
judgments; accounting of sale proceeds; prompt return of surplus. While some of these
dimensions correlate quite highly with judicial foreclosures (access to court review has a positive
correlation of 69%), others display almost no correlation (right to reinstate before sale has anegative correlation of -1 %).
In regressions some of which are reported in Appendix Table 1, we estimate augmented
versions of the four main specifications in the top panel of Table 4. We regress the outcomes ofinterest on an indicator variable for whether the state requires judicial foreclosure with theaddition of a discrete control variable taking value 1 if any of the ten consumer protection
clauses in Rao and Walsh (2009) is present in a strong form, 1/2 if present but weak, and 0 if
missing. We add each clause individually to the specification and the whole set of ten clauses
simultaneously. This latter case is reported in Appendix Table 1.
Examining the foreclosure per homeowner ratio in 2008 and 2009, the judicial
foreclosure indicator maintains its original size and significance in each of the ten augmented
specifications and in the specification with all clauses simultaneously (column 1). Foreclosure
rates appear significantly lower in judicial foreclosure states. The judicial foreclosure variable
eliminates the statistical significance of all of the other Rao and Walsh (2009) clauses except for
the “right to reinstate before sale” and the “housing emergency assistance fund.” The results are

22

similar if we control for delinquencies per homeowner in 2008 and 2009 and when the left-handside variable is the foreclosure per delinquency ratio in 2008 and 2009. In fact, the foreclosure
per delinquency ratio remains significantly lower in judicial foreclosure states and does not varysystematically with any additional protection clause.
Mortgage delinquencies do not display a correlation with whether states require judicial
foreclosure, and they also display no strong correlation with any of the ten consumer protection
clauses in Rao and Walsh (2009). We are unable to reject at the 10% level the hypothesis thatdelinquencies per homeowner are significantly different along any of these ten legal dimensions.
In sum, we find that the judicial foreclosure requirement is the most relevant legal difference for
explaining foreclosure rates and we find no evidence that any other legal difference is polluting
our first stage estimate.

IV. The Effect of Foreclosures on House Prices
In this section, we present results from our two-stage least squares estimation of theeffect of foreclosures on house prices. The first section utilizes state and CBSA level data for thefull sample, and the second section utilizes the zip code border discontinuity sample.
A. Full Sample
Figure 7 presents the reduced form version of our two-stage least squares estimation
strategy. It plots house price growth in states with and without a judicial foreclosure requirementfrom 2004 to the end of the sample period. For both the FCSW (top) and Zillow.com (bottom)
indices, there is a larger drop in house prices in states that do not require judicial foreclosure.
The magnitude of the relative decline is significantly larger using the FCSW index. For the
FCSW index, house prices in non-judicial states fell by 43% from the middle of 2006 to the23beginning of 2009. They fell by only 28% in judicial states. The top right panel plots thedifference over time. The drop using Zillow.com from the second quarter of 2007 to the third
quarter of 2009 is about 4%.14 Further, there is no systematic evidence of differential house pricetrends before the foreclosure crisis.
Table 6 presents the second stage estimates of the effect of foreclosures on house pricegrowth. Columns 1 through 3 focus on house price growth measured by Zillow.com from thefourth quarter of 2007 through the first quarter of 2010. As the estimates show, there is a strong
negative effect of foreclosures on house price growth. The estimates in columns 1 through 3
imply that a one standard deviation increase in foreclosures per homeowner in 2008 and 2009leads to an 8 to 12% relative drop in house price growth, which is 2/3 to a full standard deviation
decrease in house price growth. The estimate in column 2 implies that moving from the statewith median foreclosure rate to a state with the 90th percentile foreclosure rate leads to 9% lower
house price growth from 2007 to 2010.
The inclusion of control variables does not have a large effect on the magnitude of theestimates. These results are consistent with evidence in Section III that states with and without
judicial foreclosure requirement are similar on observable characteristics.
The estimates are similar for the FCSW house price measure. The statistical power isweaker, especially in column 6, given that FCSW data is available for only 24 states in thesample. The similarity in direction and magnitude of the coefficient estimates is reassuring given
the different methodologies used by FCSW and Zillow.com. Given that Zillow.com attempts to

14

In Appendix Figure 1, we replicate Figure 7 using publicly available data from the FHFA and the S&P Case
Shiller 20 MSA indices. For the S&P CS indices, we exclude three MSAs that cross the borders of states that differ
in their judicial foreclosure requirement laws (Chicago, IL; Charlotte, NC; and Washington, DC). The relative drop
in non-judicial states using the S&P CS publicly available data is 12%, and the relative drop using FHFA is 3%.
FHFA data excludes non-conforming (mostly subprime and jumbo loans) loans in its construction and hence tends
to underestimate house price changes driven by the mortgage crisis.
24

adjust for characteristics of the home, it is unlikely that our estimate is driven by the effect offoreclosures on upkeep alone.
In Table 7, we replicate the specifications using CBSA level data. While the variation in
judicial requirement for foreclosures in the first stage is at the state level, the CBSA levelanalysis allows us to control for other characteristics at a more granular level. This is aparticularly useful specification because CBSAs are formed in part because they are consideredby government agencies to be a geographical economic unit.
The estimates imply a negative effect of foreclosures on house prices that is statisticallysignificant at the 10% level in all specifications except for column 3. The magnitude of thecoefficient estimates is slightly smaller in the CBSA level analysis. The estimate in column 2
implies that a one standard deviation increase in foreclosures per homeowner leads to a 1/3
standard deviation lower house price growth.
While statistical power is strong enough to reject the null hypothesis of zero effect inmost specifications, standard errors are in general quite large. This reflects the fact that wecluster all standard errors at the state level given that our instrument varies only at the state level.
This is the main disadvantage of the judicial foreclosure requirement instrument.
The 2SLS magnitude of the effect of foreclosures on house price growth is similar using
the Zillow.com or FCSW indices. However, the reduced form graphs in Figure 7 suggest asmaller relative decline with the use of Zillow.com. This difference in reduced form and 2SLS
magnitude is driven by two effects. First, the reduced form graph does not condition on
delinquencies while 2SLS controls for delinquencies. Doing so does not change the reduced form
relationship for Zillow.com index, but decreases the reduced form difference between judicial
and non-judicial states for FCSW index by about 25 percent. Second, the first stage for FCSW

25

index is based on a smaller subset of states. These states turn out to have a stronger first stage.15
Since the 2SLS coefficient is based on the ratio of reduced form coefficient and first stage
coefficient, the 2SLS estimate based on FCSW index is similar to the one based on Zillow.com
index despite larger reduced form relationship based on FCSW index.

B. Zip Code Border Discontinuity
In this section, we estimate the effect of foreclosures on house prices using the zip code
border discontinuity sample described in Section III. The benefit of this methodology is that it
estimates the causal impact of foreclosures on house prices under the relatively mild assumptionof local continuity in omitted variables of interest. More specifically, Table 8 presents estimatesfrom the following two-stage least squares estimation:

0809

∗

Γ∗

∗

09

where

09

0809

(5)
Λ∗

(6)

is house price growth from 2008 to 2009 in a zip code z that is

located near border b in state s within a 10 mile strip x of the border. The specification includes
fixed effects at the level of border-state-10 mile strip (). The first stage in equation (6)regresses foreclosures in 2009 on an indicator variable for whether the state in which the zip
code is located requires judicial foreclosure. The second stage in equation (4) estimates the effectof foreclosures in 2009 on house price growth from 2008 to 2009.
One difference in the empirical specification for the border discontinuity sample is that
we focus only on foreclosures as of 2009. In the smaller sample, the first stage is weak for 2008.
15

The first stage is -0.03 for states with Zillow.com index, and -0.044 for states with FCSW index.
26

This is likely due to the fact that the foreclosure crisis in the border discontinuity sample wasmuted relative to the full sample in 2008; indeed, states with very high foreclosure rates as of2008 (e.g., Arizona, California, Florida, Nevada) are not included in the border discontinuity
sample.
The second stage specification reported in Panel A of Table 8 uses the entire sample ofzip codes in the border discontinuity sample. The coefficient estimate ofis negative andstatistically significant at the 1% level in column 1. In this subsample, the magnitude of theestimate in column 1 implies that a one standard deviation increase in foreclosures per
homeowner in 2009 leads to a one standard deviation lower house price growth from 2008 to
2009. This magnitude calculation is done using standard deviations that are within each stateborder-10 mile strip group. The congruence of the quantitative estimates of the RD with thestate-level analysis estimates is reassuring in terms of external validity of the RD results.
The statistical power of the estimate is slightly weaker with control variables. The results
using Zillow.com house price data are similar in magnitude and significant at the 10% level ofconfidence. While the coefficient estimate is twice as large, the within state-border-10 mile strip
group standard deviation in Zillow.com house price growth is also twice as large implying asimilar distributional effect.
In order to further homogenize the sample of zip codes in judicial and non-judicial states
and to tighten the identification of the foreclosure coefficient, we restrict the sample in Panels B
through D to zip codes that are within 50, 25, and 5 miles of the border. While the statisticalpower of the tests diminishes, the magnitudes are similar.

27

V. The Effect of Foreclosures on Residential Investment and Durable Consumption
A. Two-stage least squares estimates
The results in the above section document a large negative effect of foreclosures on house
prices. A central idea in macroeconomic research is that a sharp negative movement in therelative price of durable goods can amplify shocks and lead to a reduction in real economic
activity. This section explores this idea in the context of residential investment and durable
consumption.
As we emphasize in Section III, we do not attempt to separate the independent effects ofhouse price declines and foreclosures on residential investment and auto sales. In other words,
the effect of foreclosures on residential investment and auto sales could come either directlyfrom foreclosures or indirectly through the house price effects of foreclosures documented
above. Our empirical strategy is designed to detect the impact of an exogenous increase inforeclosures on house prices, residential investment, and auto sales. But we cannot identify theexact channels through which each of these variables affects each other, absent additional
identifying information.
In the left panel of Figure 8, we present the state-level correlation between residential
investment growth from 2007 to 2009 and foreclosures per homeowner in 2008 and 2009. Thereis a very strong negative correlation. Arizona and Nevada are the extreme states, but thecorrelation is quite strong even among the other states in the sample. The same is true of durable
consumption as measured by auto sales. More specifically, the right panel shows a strong
negative correlation between auto sales growth from 2007 to 2009 and foreclosures per
homeowner in 2008 and 2009.

28

Figure 9 presents the reduced form version of our two-stage least squares specification.
The top panel plots residential investment growth in non-judicial and judicial states from 2004 to2009 as measured by new residential construction permits collected by the Census. The data usedin the top panel are at the annual frequency.16 The top left graph is in natural log scale with thenatural log of the level of residential investment in 2004Q1 subtracted from the series.
Residential investment patterns were similar through 2007, at which point there was alarger drop in residential investment in non-judicial states through 2009. The significance of therelative decline appears muted given the very large overall decrease in residential investment inall states. However, in the top right panel we show the difference between non-judicial andjudicial states. Residential investment dropped by 8 percentage points more in non-judicial states
relative to judicial states from 2007 to 2008 and remained significantly lower in 2009.
The bottom panel of Figure 9 plots auto sales. It shows a smaller decline in auto sales instates that require judicial foreclosure. As the bottom right panel shows, auto sales in eachquarter from 2008Q2 to 2010Q2 were 5 to 10% lower in non-judicial versus judicial states
relative to their 2004Q1 respective values. It is important to note that both the residential
investment and auto sales data are flows, not stocks. So the cumulative difference over 2008 and2009 in auto sales and residential investment between judicial and non-judicial states is large.
The first three columns of Table 9 present the state-level two-stage least squares
estimates for residential investment as measured by new residential construction permits. The
estimate in column 2 on foreclosures per homeowner implies that a one standard deviation
increase in foreclosures leads to a 0.65 standard deviation decrease in residential investment

16

Permits for new residential construction are available from the Census at a monthly frequency. However, there are
two disadvantages with the monthly data. First, monthly data are available for only 2/3 of the underlying counties
for which the annual data are available. Second, the seasonal pattern in residential construction is so strong that it isdifficult to discern differences using data at a frequency less than annual.
29

growth from 2007 to 2009. Alternatively, moving from the median to the 90th percentile of the
distribution of foreclosures leads to 23 percentage points lower residential investment growthfrom 2007 to 2009. The CBSA level estimates imply a similar magnitude. The state level
estimates are sensitive to the inclusion of the full set of control variables in column 3, but arerobust when examined at the CBSA level even when standard errors are clustered at the statelevel.
Table 10 presents the corresponding results for auto sales. The estimate in column 2
implies that a one standard deviation increase in foreclosures leads to a 0.70 standard deviation
decrease in auto sales growth from 2007 to 2009. Alternatively, moving from the median to the90th percentile of the foreclosures distribution leads to 14 percentage points lower auto sales
growth from 2007 to 2009.
One concern with the results in Tables 9 and 10 is that omitted variables across states or
CBSAs pollute the two-stage least squares estimates. Unlike the house price data, we do not havezip code level information on auto sales or residential investment. However, the original datafrom the Census and R.L. Polk are at the county level. This allows for a less granular border
discontinuity design where the units of observation are counties that are very near the border of
two states that have differing foreclosure rules.
The exact specification isolates the sample to counties that are within 10 miles of theborder of two states which differ on whether judicial foreclosure is required. The specification
utilizes border fixed effects and is clustered at the border level. In other words, the two-stage
least squares estimates exploit variation in foreclosures for two counties right across the borderfrom another, where one county is in a state that requires judicial foreclosure while the othercounty is not.

30

The county-level border discontinuity second stage results are in Table 11. For residential
investment, the standard errors are quite large. The point estimates are similar in magnitude as
the CBSA level results in Table 9, but there is almost no precision. The results for auto sales
using the border discontinuity specification are similar in magnitude as the CBSA level results inTable109. The standard errors are smaller, and we can reject the null of no effect at the 10%
level in columns 4 and 5.

B. Macroeconomic Implications
We can use the estimates obtained in Tables 9 and 10 to inform the debate regarding theeffect of foreclosures on the macro-economy. However, it is critical to emphasize that themarginal effects estimated in Tables 9 and 10 are driven by variation in foreclosures that comesfrom the judicial foreclosure requirement in certain states. Given that the local average treatment
effect (LATE) is driven by this very specific source of variation, we urge caution in using the full
distribution of foreclosures to estimate aggregate impacts.17
Our strategy to estimate the aggregate effect of foreclosures relies only on the variation in
foreclosures that is driven by the judicial foreclosure requirement. This corresponds to the firststage estimate of the effect of judicial foreclosure requirement on foreclosures that is reported inTable 4 for the state level data and Table 5 for the zip code border discontinuity data. The
advantage of this approach is that it utilizes variation that can be explained with the first stage,and is therefore analogous to an "in-sample" treatment effect where judicial foreclosure
requirement states represent the control group.18 The estimates are close to -0.03 in both, which

17

For more on this issue, see Chapter 4 of Angrist and Pischke (2009).
An alternative approach would use the overall variation in foreclosures by picking states or CBSAs with very low
foreclosures as a control group or using a one standard deviation change in foreclosures in the cross-section.

18

31

implies that foreclosures per homeowner are 3 percentage points lower in judicial foreclosure
requirement states.
We multiply the coefficient estimates in Tables 9 and 10 with the 3 percentage point
difference in foreclosure rates to estimate the aggregate impact of foreclosures on residential
investment and auto sales. The state-level estimate in column 2 of Table 9 suggests that
residential investment growth from 2007 to 2009 was (-4.1*-0.03 =) 12 percentage points lower
in non-judicial versus judicial states. The average decline in the sample (from Table 1) is 77
percentage points, which implies that foreclosures can explain about 15% of the overall decline
in residential investment. A similar calculation using the CBSA-level estimate in column 5 ofTable 9 implies that foreclosures can explain 30% of the overall decline in residential
investment.
For auto sales, the estimate in column 2 of Table 10 implies that auto sales growth from2007 to 2009 was (-2.6*-0.03 =) 8 percentage points lower in non-judicial versus judicial
requirement states. The average decline in the sample (from Table 1) is 41 percentage points,
which implies that foreclosures can explain about 20% of the overall decline in auto sales. Using
the CBSA-level estimate in column 5 of Table 10 implies that foreclosures can explain 40% of
the overall decline in auto sales from 2007 to 2009.
Overall, our analysis implies that foreclosures can explain 15 to 30% of the dramatic
decline in residential investment from 2007 to 2009 and 20 to 40% of the dramatic decline in
auto sales over the same period. It is critical to emphasize that these effects represent the total
impact of foreclosures on outcomes of interest; these total effects may come about through manychannels. For example, foreclosures may reduce residential investment in distressed properties
through neglect of maintenance or debt overhang frictions. Foreclosures may also reduce32residential investment through their effect on house prices within a standard Tobin's Q
investment model.

VI. Conclusion
A large body of theoretical research in macroeconomics emphasizes how the leverageinduced forced sale of durable goods can (1) lead to negative price effects and (2) reduce
economic output. Many academics, policy-makers, and regulators have emphasized these models
in building an understanding of the recession of 2007 to 2009. Yet, to our knowledge, there islimited empirical evidence that directly links a specific financial friction to the real economy.
In this study, we bridge this gap by examining the price and real effects of foreclosures.
Using variation in foreclosure rates induced by state-specific laws on judicial requirements, weshow a large and statistically significant negative effect of foreclosures on house prices. Further,
we show that foreclosures lead to lower levels of durable consumption and residential
investment, each an important cyclical components of GDP.
Our estimates of the effect of foreclosures on residential investment and auto sales canpartially explain both the magnitude and length of the recession of 2007 to 2009. For example,
the sharp rise in foreclosures began relatively late in the recession and continues into 2010. If we
combine this fact with the finding in Leamer (2007) that residential investment is among themost powerful NIPA components leading the U.S. out of recession, one can conjecture that
foreclosures have likely contributed to the length of the recession and sluggishness of therecovery. Similar arguments apply to our findings on auto sales. Leamer (2007) identifies
durables as the consumer item with the largest negative average cumulative contribution toeconomic growth during recessions. Under the assumption that our results on auto sales extend

33

to the entire durable goods share of the economy (23.6 % of GDP in 2008), foreclosures can
explain the relatively sluggish growth in durables well into 2010. Given that the 2007 to 2009
recession and its aftermath have been closely related to depressed levels of durable consumption
and residential investment, our results highlight an important role for foreclosures and house
prices in understanding weakness in the economy.

34

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Table 1
Summary Statistics
This table presents summary statistics for the state and CBSA level data used in the analysis. Foreclosures aremeasured by RealtyTrac.com as new foreclosure filings. Delinquencies represent the number of delinquent accounts
60 days past due as measured by Equifax. The scalar homeowner represents the number of mortgage accounts as of2005 as measured by Equifax. Subprime consumer fraction is the fraction of consumers with a credit score less than
660 as measured by Equifax. Residential permits represent the value of permits for new residential construction as
measured by the Census. Auto sales are measured by R.L. Polk.
N

Mean

SD

10th

50th

90th

State level data
Foreclosures per homeowner, 2008 and 2009
Delinquencies per homeowner, 2008 and 2009
Zillow house price growth, 2002 to 2006
Zillow house price growth, 2006 to 2007
Zillow house price growth, 2007 to 2010q1
FCSW house price growth, 2002 to 2006FCSW house price growth, 2006 to 2007
FCSW house price growth, 2007 to 2009
Residential permits growth, 2002 to 2006
Residential permits growth, 2006 to 2007
Residential permits growth, 2007 to 2009
Auto sales growth, 2004 to 2006
Auto sales growth, 2006 to 2007
Auto sales growth, 2007 to 2009

51
51
43
45
46
24
24
24
51
51
51
51
51
51

0.045
0.095
0.336
-0.020-0.1440.365-0.070-0.2060.289-0.198-0.768-0.020-0.022-0.413

0.043
0.042
0.159
0.048
0.127
0.199
0.069
0.162
0.275
0.141
0.270
0.123
0.056
0.157

0.010
0.058
0.167
-0.085-0.261
0.097-0.196-0.476-0.071-0.339-1.082-0.116-0.104-0.578

0.035
0.086
0.328
-0.014-0.131
0.347-0.048-0.1780.245-0.191-0.726-0.046-0.019-0.399

0.090
0.133
0.592
0.043
0.013
0.675
-0.002-0.0660.656-0.037-0.4960.093
0.050-0.238CBSA level data
Foreclosures per homeowner, 2008 and 2009
Delinquencies per homeowner, 2008 and 2009
Zillow house price growth, 2002 to 2006
Zillow house price growth, 2006 to 2007
Zillow house price growth, 2007 to 2010q1
FCSW house price growth, 2002 to 2006FCSW house price growth, 2006 to 2007
FCSW house price growth, 2007 to 2009
Residential permits growth, 2002 to 2006
Residential permits growth, 2006 to 2007
Residential permits growth, 2007 to 2009
Auto sales growth, 2002 to 2006
Auto sales growth, 2006 to 2007
Auto sales growth, 2007 to 2009
New mortgages/income, 2005
Debt to income increase, 2002 to 2005
Subprime consumer fraction, 2000
Ln(Income, 2005)
Fraction with income less than 25K, 2005
Unemployment rate, 2000
Poverty fraction, 2000
Black fraction, 2000
Hispanic fraction, 2000
Less than high school education fraction, 2000
Urban fraction, 2000

958
958
296
312
320
121
120
120
946
947
945
958
958
958
958
958
958
958
958
958
958
958
958
958
958

0.034
0.092
0.367
-0.029-0.1800.385-0.098-0.2010.317-0.159-0.771-0.049-0.024-0.420
0.113
0.193
0.343
3.757
0.470
0.061
0.138
0.084
0.060
0.210
0.617

0.040
0.044
0.193
0.077
0.152
0.213
0.112
0.158
0.567
0.442
0.517
0.121
0.080
0.153
0.094
0.325
0.094
0.215
0.062
0.022
0.056
0.121
0.120
0.077
0.188

0.003
0.050
0.121
-0.141-0.3840.096-0.249-0.443-0.311-0.559-1.480-0.170-0.125-0.6240.038-0.087
0.236
3.542
0.401
0.038
0.079
0.002
0.005
0.127
0.362

0.022
0.083
0.353
-0.018-0.1630.396-0.058-0.1620.281-0.179-0.757-0.060-0.020-0.413
0.082
0.190
0.328
3.722
0.469
0.057
0.128
0.028
0.019
0.194
0.612

0.075
0.140
0.631
0.051
-0.0030.677
0.007-0.0421.010
0.264-0.1380.079
0.072-0.227
0.235
0.481
0.474
4.015
0.540
0.089
0.211
0.276
0.140
0.320
0.881

Table 2
Foreclosures and House Price Growth:
Correlations
This table presents correlations between foreclosures and house price growth. The specifications reported in columns 1 and 2 are estimated at the state level,
specifications reported in columns 3 and 4 are estimated at the CBSA level using state fixed effects, and specifications reported in columns 5 and 6 are
estimated at the zip code level using CBSA fixed effects. Standard errors in columns 3 and 4 are clustered at the state level. Standard errors in columns 5 and 6
are clustered at the CBSA level.

State level(1)
Foreclosures per homeowner, 2008 and 2009
Delinquencies per homeowner, 2008 and 2009-1.112**(0.310)-1.426**(0.302)

Foreclosures per delinquency, 2008 and 2009
Constant
N
R2

0.047*(0.021)
46
0.661

(1)

Delinquencies per homeowner, 2008 and 2009
Foreclosures per delinquency, 2008 and 2009

-0.806**(0.297)-0.800**(0.243)-0.276**(0.094)-0.035(0.035)46
0.224

State level

Foreclosures per homeowner, 2008 and 2009

Panel A: Zillow house price growth, 2007 to 2010q1
CBSA level with State FE
Zip code level with CBSA FE
(2)
(3)
(4)
(5)
(6)

-1.379**(0.474)-1.450**(0.436)-0.050**(0.014)320
0.716-0.104*(0.052)-0.458**(0.086)-0.246**(0.084)-0.081*(0.034)320
0.635-0.137**(0.012)
8,949
0.737

-0.040**(0.010)-0.189**(0.004)
8,788
0.682

Panel B: FCSW House price growth, 2007 to 2009
CBSA level with State FE
Zip code level with CBSA FE
(2)
(3)
(4)
(5)
(6)
-0.860*(0.402)-0.638**(0.199)-0.344*(0.143)
Constant
0.044-0.056-0.061**(0.037)(0.050)(0.013)
N
24
24
120
R2
0.754
0.269
0.871
**,*,+ Coefficient statistically different than zero at the 1%, 5%, and 10% confidence level, respectively-0.265**(0.094)-0.402**(0.077)-0.353(0.210)-0.050(0.090)120
0.783-0.157**(0.009)
4,098
0.858

-0.073**(0.020)-0.209**(0.009)
4,095
0.777

Table 3
List of Borders of States with Different Foreclosure Rules
This table shows the borders of states where the judicial foreclosure requirement laws differ. It also shows thenumber of zip codes in the sample that are near those borders. The total sample is restricted to zip codes for which
the border in question is the closest state border and for which FCSW house price data are available.
Total
BorderConnecticut - Rhode Island
Georgia - South Carolina
Illinois – Wisconsin
Massachusetts - New Hampshire
Massachusetts - Rhode Island
Michigan – Ohio
North Carolina - South Carolina

Number of zip codes
80
53
150
170
181
151
85

Within 50 miles of border
BorderConnecticut - Rhode Island
Georgia - South Carolina
Illinois – Wisconsin
Massachusetts - New Hampshire
Massachusetts - Rhode Island
Michigan – Ohio
North Carolina - South Carolina

Number of zip codes
80
12
150
170
180
117
76

Within 25 miles of border
BorderConnecticut - Rhode Island
Georgia - South Carolina
Illinois - Wisconsin
Massachusetts - New Hampshire
Massachusetts - Rhode Island
Michigan - Ohio
North Carolina - South Carolina

Number of zip codes
27
4
55
149
142
27
57

Within 5 miles of border
BorderConnecticut - Rhode Island
Georgia - South Carolina
Illinois - Wisconsin
Massachusetts - New Hampshire
Massachusetts - Rhode Island
Michigan - Ohio
North Carolina - South Carolina

Number of zip codes
2
2
8
36
34
6
12

Table 4
Judicial Foreclosure Requirement Instrument
Panel A presents coefficients from the first stage regression of foreclosures on whether a state requires a judicial
foreclosure. Each row of Panel B represents an univariate regression of the variable in the first column on whether astate requires a judicial foreclosure. Standard errors are heteroskedasticity-robust.
Panel A: First Stage
(1)
(2)
(3)
(4)
Foreclosures
Delinquencies
Foreclosures
Foreclosures perper homeownerper homeownerper homeownerdelinquency
08-09
08-09
08-09
08-09
Judicial foreclosure requirement-0.030**(0.010)-0.004(0.012)0.057**(0.009)51
0.116

0.096**(0.008)51
0.003

Delinquencies per homeowner, 08-09
Constant
N
R2-0.026**(0.006)
0.788**(0.143)-0.019(0.012)51
0.698

Panel B: Correlation with Other Observable Variables
JudicialN
foreclosure
requirement
Delinquencies per homeowner, 06-0.236**(0.048)0.464**(0.041)
51
0.277
R2

0.001451
0.003(0.004)
Delinquencies per homeowner, 09-0.002851
0.001(0.010)
Zillow house price growth, 2002 to 2005
0.029
45
0.007
(0.050)FCSW house price growth, 2002 to 2005
0.049
24
0.018
(0.073)
Debt to income increase, 2002 to 2005
-0.02651
0.007(0.042)
Subprime consumer fraction, 2000
-0.016151
0.014(0.018)Ln(Income, 2005)
0.033251
0.010(0.050)Fraction with income less than 25K, 2005
-0.004651
0.003(0.012)
Unemployment rate, 2000
-0.004651
0.029(0.004)
Poverty fraction, 2000
-0.007851
0.014(0.009)Black fraction, 2000
0.010351
0.002(0.030)
Hispanic fraction, 2000
0.005051
0.001(0.021)Less than high school education fraction, 2000
0.001351
0.000(0.012)
Urban fraction, 2000
0.026651
0.007(0.046)**,*,+ Coefficient statistically different than zero at the 1%, 5%, and 10% confidence level, respectively

Table 5
Ex Ante Credit Supply, Border Discontinuity Zip Codes
This table presents evidence on whether zip codes in judicial versus non-judicial states experience differential credit
supply before the foreclosure crisis in 2008 and 2009. The sample for all specifications is the border discontinuity
zip code sample, and all specifications include border-strip fixed effects. Columns 1 and 2 of Panel A replicate thefirst stage in the border discontinuity sample. Loan size is defined to be the average loan size of mortgages
originated for the purpose of home purchase in a zip code. Loans to income is the total amount of mortgages
originated for the purpose of home purchase scaled by total aggregate IRS income in the zip code. Non-GSE loans
are mortgages that are not sold to a GSE within a year of origination. In Panel B, each coefficient is from a separate
regression for the dependent variable in the column heading and the year in the row heading. Standard errors in all
specifications are clustered at the border-strip level.
Panel A: First Stage and 2005 Credit Supply Measures
(1)
(2)
(3)
(4)
Foreclosures
Foreclosures per Ln(loan size 05)
Loans/Income,per homeownerdelinquency
05
08-09
08-09
Judicial foreclosure requirement-0.033*(0.013)0.736**(0.097)0.001(0.010)870
0.709

Delinquencies per homeowner, 08-09
Constant
N
R2

Ln(loan size)

1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007

-0.188**(0.071)

0.045(0.061)-0.008(0.019)0.531**(0.040)869
0.498

5.101**(0.035)866
0.441

0.164**(0.011)866
0.256

Panel B: Coefficients by Year
Loans/Income

Non-GSE Loans/Income

Coeff

Std Error

Coeff

Std Error

Coeff

Std Error-0.0381-0.0365-0.0262-0.00120.02600.02450.04290.0576
0.07350.0841
0.0941
0.05460.05020.04520.0089-0.0204(0.054)(0.063)(0.066)(0.062)(0.074)(0.076)(0.071)(0.073)(0.072)(0.073)(0.084)(0.064)(0.054)(0.061)(0.056)(0.051)-0.0153-0.0245-0.0136-0.0127-0.0106-0.0084-0.0083-0.0058-0.00310.01010.01000.00740.0088-0.0081-0.0125-0.0064(0.010)(0.019)(0.018)(0.016)(0.020)(0.020)(0.025)(0.022)(0.017)(0.015)(0.016)(0.015)(0.019)(0.019)(0.018)(0.014)-0.0069-0.0079-0.0048-0.0061-0.0025-0.0017-0.00220.00040.00410.00730.00960.00670.0087-0.0060-0.0093-0.0025(0.005)(0.006)(0.009)(0.009)(0.011)(0.010)(0.012)(0.011)(0.010)(0.008)(0.009)(0.010)(0.016)(0.018)(0.015)(0.010)**,*,+ Coefficient statistically different than zero at the 1%, 5%, and 10% confidence level, respectively

Table 6
Foreclosures and House Prices, State-Level 2SLSThis table presents coefficients of the second stage of a 2SLS specification of house price growth on foreclosures.
The first stage, reported in Table 4, regresses foreclosures on whether a state has a judicial foreclosure requirement.
Standard errors are heteroskedasticity-robust.
Zillow house price growth, 07-10q1
(1)
(2)
(3)
Foreclosures per homeowner, 08-09-1.749*(0.791)-0.903(0.698)-1.642**(0.631)-0.099(0.599)-0.053(0.063)0.988**(0.220)FCSW house price growth, 07-09
(4)
(5)
(6)

-2.693**-1.457*-1.074+(0.973)(0.684)(0.580)Delinquencies per homeowner, 08-09
1.379-1.384*-0.158(2.150)(0.596)(0.599)House price growth, 02-06-0.251-0.126(0.155)(0.105)
House price growth, 06-07
0.665+
1.151*(0.372)(0.576)
Delinquencies squared, 08-09-3.730(5.048)New mortgages/income, 2005
0.712
(0.563)
Debt to income increase, 02-05-0.085(0.129)Subprime consumer fraction, 2000
-0.045(0.423)
Income, 2005
-0.222(0.172)income < 25K fraction, 2005
-0.960(0.918)
Unemployment rate, 2000
0.913
(1.868)Poverty fraction, 2000
0.747
(1.010)Black fraction, 2000
-0.178(0.198)Hispanic fraction, 2000
-0.138(0.246)< high school education fraction, 2000
0.388
(0.485)
Urban fraction, 2000
-0.083(0.134)
Constant
0.028-0.020
1.074
0.041
0.006
(0.032)(0.029)(1.003)(0.039)(0.045)
N
46
43
43
24
24
R2
0.643
0.746
0.751
0.753
0.835
**,*,+ Coefficient statistically different than zero at the 1%, 5%, and 10% confidence level, respectively-3.747(2.567)5.758(10.570)-0.347(0.351)
1.420(1.291)-9.955(24.139)
0.323(1.394)-0.119(0.244)-0.377(0.906)-0.738(0.463)-4.242(2.666)-6.236*(2.792)
3.117*(1.273)-0.507(0.547)0.975(1.100)1.270(1.095)-0.138(0.290)4.463+(2.639)24
0.846

Table 7
Foreclosures and House Prices, CBSA-Level 2SLSThis table presents coefficients of the second stage of a 2SLS specification of house price growth on foreclosures.
The first stage regresses foreclosures on whether a state has a judicial foreclosure requirement. Standard errors areheteroskedasticity-robust and clustered at the state level.
Zillow house price growth, 07-10q1
(1)
(2)
(3)
Foreclosures per homeowner, 08-09-1.159+(0.625)-1.172**(0.422)-1.176**(0.404)-0.720*(0.326)-0.217**(0.046)0.071(0.155)FCSW house price growth, 07-09
(4)
(5)
(6)

-0.736-1.238+-0.952+(0.642)(0.748)(0.570)Delinquencies per homeowner, 08-09-1.923**-1.045+-0.526(0.523)(0.591)(0.321)
House price growth, 02-06-0.148*-0.202*(0.061)(0.087)
House price growth, 06-07-0.2470.261+(0.178)(0.142)Delinquencies squared, 08-09
1.961**(0.751)
New mortgages/income, 2005
-0.204(0.168)Debt to income increase, 02-05-0.084**(0.026)
Subprime consumer fraction, 2000
0.333*(0.157)
Income, 2005
-0.151**(0.055)income < 25K fraction, 2005
-0.482(0.337)
Unemployment rate, 2000
-0.222(0.483)
Poverty fraction, 2000
-0.058(0.429)Black fraction, 2000
0.142*(0.072)
Hispanic fraction, 2000
0.161*(0.074)< high school education fraction, 2000
-0.177(0.183)
Urban fraction, 2000
0.103*(0.047)
Constant
0.010
0.037
0.786**
0.017
0.035(0.017)(0.025)(0.293)(0.037)(0.035)
N
320
296
296
120
120
R2
0.551
0.629
0.717
0.693
0.770
**,*,+ Coefficient statistically different than zero at the 1%, 5%, and 10% confidence level, respectively-2.370*(1.040)-0.475(0.711)-0.407**(0.090)0.065(0.225)0.614(1.427)0.449+(0.241)-0.096+(0.058)0.125(0.389)-0.236**(0.080)-1.104*(0.538)-0.486(0.743)
1.298*(0.638)-0.228(0.309)0.005(0.117)0.342(0.243)0.126(0.082)
1.222**(0.434)120
0.793

Table 8
Foreclosures and House Prices:
Zip Code Level 2SLS with Border Discontinuity
This table presents coefficient estimates from the zip code level second stage of a 2SLS specification of house prices
on foreclosures. Every specification includes fixed effects for 10 mile strips on either side of the border. The control
variables are house price growth from 2002 to 2006 and from 2006 to 2007 and delinquencies per homeowner from2008 to 2009. Standard errors are clustered at the 10 mile strip level.
Panel A: Full Sample of Zip Codes Near Border of Two States with Different Judicial Foreclosure Laws
(1)
(2)
(3)
(4)
FCSW house price growth 08-09
Zillow house price growth, 08-10q1
Foreclosures per homeowner, 09
Control variables?
N-1.050**(0.389)N862-1.004(0.836)
Y
862

Panel B: Within 50 Miles of Border(1)(2)
FCSW house price growth 08-09
Foreclosures per homeowner, 09
Control variables?
N-1.050**(0.389)N
776-1.072(0.935)
Y
776

Panel C: Within 25 Miles of Border(1)(2)
FCSW house price growth 08-09
Foreclosures per homeowner, 09
Control variables?
N-1.175*(0.480)N
458-1.596*(0.745)Y
458

Panel D: Within 5 Miles of Border(1)(2)
FCSW house price growth 08-09
Foreclosures per homeowner, 09-2.108+(1.189)N746-2.234+(1.232)
Y
746

(3)
(4)
Zillow house price growth, 08-10q1-2.110+(1.190)N
674-2.232+(1.270)
Y
674

(3)
(4)
Zillow house price growth, 08-10q1-2.234(1.415)N
415-1.742+(1.050)
Y
415

(3)
(4)
Zillow house price growth, 08-10q1-0.846**-1.202*-1.178-1.468+(0.323)(0.492)(0.967)(0.806)
Control variables?
N
Y
N
Y
N
96
96
82
82
**,*,+ Coefficient statistically different than zero at the 1%, 5%, and 10% confidence level, respectively

Table 9
Foreclosures and Residential Investment, 2SLSThis table presents coefficients of the second stage of a 2SLS specification of residential investment growth on
foreclosures. The first stage regresses foreclosures on whether a state has a judicial foreclosure requirement.
Standard errors are heteroskedasticity-robust. Standard errors in columns 4 through 6 are clustered at the state level.

(1)
Foreclosures per homeowner, 08-09-4.707*(2.117)-0.417(1.735)Residential Permits Growth, 2007 to 2009
State-level 2SLS
CBSA-level 2SLS
(2)
(3)
(4)
(5)
(6)
-4.132*(1.893)-0.896(1.537)-0.115(0.111)-0.040(0.188)-1.829-7.800*-6.656+(2.241)(3.815)(3.549)Delinquencies per homeowner, 08-09-11.171**
1.281-0.084(3.955)(2.183)(2.051)
Residential permits growth, 02-06-0.213-0.085*(0.229)(0.036)
Residential permits growth, 06-07-0.141-0.383**(0.244)(0.065)
Delinquencies squared, 08-09
28.695*(11.905)
New mortgages/income, 2005
-0.498(1.037)
Debt to income increase, 02-05-0.077(0.388)Subprime consumer fraction, 2000
-0.440(1.484)
Income, 2005
-0.425(0.578)income < 25K fraction, 2005
-1.641(3.303)
Unemployment rate, 2000
-6.763(4.555)
Poverty fraction, 2000
3.983
(3.585)
Black fraction, 2000
0.892
(0.663)
Hispanic fraction, 2000
0.326
(0.781)< high school education fraction, 2000
-0.306(1.671)
Urban fraction, 2000
0.437
(0.266)
Constant-0.515**-0.471**2.267-0.622**-0.570**(0.087)(0.077)(3.523)(0.091)(0.086)
N
51
51
51
945
943
R2
0.448
0.483
0.624
0.051
0.180
**,*,+ Coefficient statistically different than zero at the 1%, 5%, and 10% confidence level, respectively-6.107(3.822)-2.629(2.011)-0.069(0.046)-0.373**(0.067)
7.843(5.779)-0.345(0.580)0.087(0.055)0.584(0.398)-0.445*(0.199)0.018(0.944)0.366(1.547)-0.826(0.705)-0.207(0.263)0.058(0.266)-0.304(0.535)0.493**(0.138)0.912(1.000)943
0.233

Table 10
Foreclosures and Durable Consumption, 2SLSThis table presents coefficients of the second stage of a 2SLS specification of auto sales growth on foreclosures. The
first stage regresses foreclosures on whether a state has a judicial foreclosure requirement. Standard errors areheteroskedasticity-robust. Standard errors in columns 4 through 6 are clustered at the state level.

(1)
Foreclosures per homeowner, 08-09-2.342+(1.279)-0.441(1.052)

Auto Sales Growth, 2007 to 2009
State-level 2SLS
CBSA-level 2SLS
(2)
(3)
(4)
(5)
(6)
-2.643+(1.381)0.161(1.087)0.172(0.214)0.718(0.480)-2.939+-6.181*-5.489*(1.694)(2.936)(2.315)
Delinquencies per homeowner, 08-09-2.7282.055
1.598(4.297)(1.710)(1.338)
Auto sales growth, 04-06
0.517**-0.331*(0.134)(0.161)
Auto sales growth, 06-07
0.607-0.387*(0.482)(0.161)
Delinquencies squared, 08-09
13.318(10.894)
New mortgages/income, 2005
-0.693(0.795)
Debt to income increase, 02-05
0.437(0.301)
Subprime consumer fraction, 2000
-0.598(0.968)
Income, 2005
-0.273(0.337)
income < 25K fraction, 2005
-0.855(1.886)
Unemployment rate, 2000
1.113
(2.168)Poverty fraction, 2000
-0.034(1.712)Black fraction, 2000
0.355
(0.398)Hispanic fraction, 2000
-0.080(0.475)< high school education fraction, 2000
0.504
(1.056)
Urban fraction, 2000
0.123
(0.241)
Constant-0.265**-0.289**1.221-0.397**-0.405**(0.054)(0.054)(1.961)(0.066)(0.057)
N
51
51
51
958
958
R2
0.352
0.398
0.578
**,*,+ Coefficient statistically different than zero at the 1%, 5%, and 10% confidence level, respectively-4.268*(1.824)0.356(0.919)-0.201(0.120)-0.097(0.109)3.863+(2.075)-0.018(0.295)0.043(0.031)-0.255+(0.138)-0.148+(0.084)-0.012(0.378)0.274(0.465)-0.279(0.244)-0.105(0.106)-0.171*(0.084)-0.264(0.167)0.200**(0.049)0.258(0.418)958
0.145

Table 11
Foreclosures, Residential Investment, and Auto SalesCounty-Level 2SLS with Border Discontinuity
This table presents coefficients of the second stage of a 2SLS specification of residential investment growth or auto
sales on foreclosures. The sample includes only counties that are within 10 miles of the border of a state with the
opposite judicial foreclosure law. All regressions include border fixed effects. The first stage regresses foreclosureson whether a state has a judicial foreclosure requirement. Standard errors are heteroskedasticity-robust and clusteredat the border level.
Residential permits growth, 08-09
(1)
(2)
(3)
Foreclosures per homeowner, 09-9.961(18.905)-0.760(2.664)-4.267(4.506)-1.345(2.001)-0.067+(0.041)-0.078(0.114)

Auto sales growth, 08-09
(4)
(5)
(6)

-10.769-4.998+-3.963+-3.081(17.838)(2.833)(2.321)(2.074)
Delinquencies per homeowner, 08-09
3.965
0.050
-0.143-0.574(3.873)(0.430)(0.416)(0.734)
Permits growth, 02-06-0.179*0.003
0.008(0.088)(0.007)(0.009)Permits growth, 06-07-0.104-0.043**-0.038**(0.089)(0.015)(0.014)
Delinquencies squared, 08-09-23.803
1.682(19.042)(3.559)New mortgages/income, 2005
0.325
0.218
(1.483)(0.175)
Debt to income increase, 02-05
0.095
0.010
(0.139)(0.017)
Subprime consumer fraction, 2000
0.422
-0.333+(1.318)(0.196)
Income, 2005
-0.223-0.002(0.444)(0.082)income < 25K fraction, 2005
-1.1740.506(1.888)(0.311)
Unemployment rate, 2000
1.345
0.881+(3.586)(0.534)
Poverty fraction, 2000
-0.116-0.395(2.040)(0.284)
Black fraction, 2000
0.520
0.201
(0.906)(0.167)
Hispanic fraction, 2000
-0.2420.161(0.871)(0.131)< high school education fraction, 2000
-0.146-0.358+(1.247)(0.214)
Urban fraction, 2000
-0.0600.020(0.216)(0.034)
N
409
400
400
416
402
402
R2
0.001
0.062
0.063
-0.302-0.1260.066**,*,+ Coefficient statistically different than zero at the 1%, 5%, and 10% confidence level, respectively

Figure 1
Foreclosures, House Prices, Residential Investment, and Durable Consumption
The top left panel shows aggregate foreclosures from RealtyTrac.com and the household default rate from Equifax. House price growth in the top right panel isfrom S&P/Case Shiller. Residential investment and durable consumption growth in the bottom panels are from the NIPA.

1990

1995

2000

2005

2010

Household default rate, left axis
Foreclosures, right axis

.5
0
-.5

Household default rate
.02 .04 .06 .08 .1

1985

Growth since 2000Q4

House price growth.5
1 1.5 2 2.5
Foreclosures, millions

Foreclosures and Defaults

1985q1

2000q1

2005q1

2010q1

.5
0
-1

-.5

Growth since 2000Q4

.2
0
-.2

Growth since 2000Q4-.4-.61985q1

1995q1

Durable consumption growth.4

Residential investment growth

1990q1

1990q1

1995q1

2000q1

2005q1

2010q1

1985q1

1990q1

1995q1

2000q1

2005q1

2010q1

Figure 2
State Level Correlation of Foreclosures with House Price Growth
Fiserv CSW house price growth0.2

Zillow house price growth

CO

CT

KY

OR

-.2

WYPA
TXTN
NC
WI
NY
SC
AR
MA
NM
MO
HI
VA
DCOH
UT
NJ
AL
CT
WA
NHOR
MD
MN
IL ID
RI

House price growth 07-09

IA
CO

GA
CA
MI

NJ
MN
MD
WA
IL

RI

GA

TN

CA

FL

-.4

MI
AZ

FL

-.6

AZ

-.6

House price growth 07-10

NH

NY
IN

-.2

NE

DE
AK
OK
LA

OH

-.4

0

ND
WV
MT

NC
PA
WI
MA
SC

NV

0

.05

.1

.15

Foreclosures per homeowner 08-09

.2

NV

0

.05

.1

.15

Foreclosures per homeowner 08-09.2

Figure 3
States with Judicial Foreclosure Requirement
States shaded in dark gray require judicial foreclosure. The data come from RealtyTrac.com and are available at: http://www.realtytrac.com/foreclosurelaws/foreclosure-laws-comparison.asp

(0,1]
[0,0]

Figure 4
The Effect of Judicial Foreclosure Requirement on Actual Foreclosures

.15
.1
0

.05

OH
NH
DC
VA
TN
CA
OR
TX
MO
UT
RI
MN
WA
ID
IN
OK
HI
MA
FL
IL
AL
WI
KS
NE
CT
MS
NM
ME
NJ
KY
MD
MT
ND
NC
DE
SC
LA
IA
WY
PA
NY
WV
SD

.2

Foreclosures per mortgaged home 08-09

MI
AK
AR
GA
NV

.8
.6
.4

0

.1

.2

.3

VT

Delinquencies per mortgaged home 08-09

0

Foreclosures per delinquency, 08-09

CO

.2

1

AZ

The left panel plots the foreclosures per delinquent account ratio for 2008 and 2009 by state. States that require a judicial foreclosure are shown in black. The
right panel plots foreclosures against delinquencies, where the sample is split by whether the state requires a judicial foreclosure.

Judicial

Non-Judicial

Judicial

Non-Judicial

Judicial Fitted

Non-Judicial Fitted

Figure 5
Judicial Foreclosure Requirement and Actual Foreclosures
Zip Code Level Border Discontinuity

Foreclosures per delinquency - 2008
0
.1
.2
.3
.4

Foreclosures per delinquency - 2009
-.2
-.1
0
.1
.2

The figures plot the foreclosures per delinquency ratio by year for zip codes that are near borders where the judicial requirement regime changes across states.
We generate the graphs by regressing the foreclosures per delinquency on state-border-group FE and on 1-mile band distance-to-the-border dummies (where thedummies assume negative values for judicial states) and then plot the coefficients on the distance-to-the-border dummies. The border is at 0, the omitted
category.

0
Miles from border

50

-50

0
Miles from border

50

-50

0
Miles from border

50

-50

0
Miles from border

50

Foreclosures per delinquency - 2007
-.4
-.2
0
.2

Foreclosures per delinquency - 2006
-.2
-.1
0
.1
.2

-50

Figure 6
Zip Code Border Discontinuity: Other Variables

Under 660 Share-.1 -.05 0
.05
-.15

House Defaults 09-.06 -.04 -.02 0 .02 .04

The figures plot averages of other variables for zip codes that are near borders where the judicial requirement regime changes across states. We generate thegraphs by regressing the variable of interest on state-border-group FE and on 1-mile band distance-to-the-border dummies (where the dummies assume negativevalues for judicial states) and then plot the coefficients on the distance-to-the-border dummies. The border is at 0, the omitted category.

0
Miles from border

50

-50

0
Miles from border

50

-50

0
Miles from border

50

-50

0
Miles from border

50

-50

0
Miles from border

50

-50

0
Miles from border

50

-.1

White %age
0
.1

.2

Less than High School %age-.1 -.08 -.06 -.04 -.02 0-.1

Poverty %age-.05
0log IRS 2005 income-.1 0.1 .2 .3.05-50

Figure 7
Foreclosures and House Prices, Reduced Form
The figures plots house price growth in judicial and non-judicial states from 2004 to 2009. The averages are weighted by total population.

2004q1-.15

Difference, Non-judicial - judicial
-.1
-.05
0
.05

Natural Log Scale with 2004Q1 Subtracted
-.2
-.1
0
.1
.2
.3

FCSW House Price Growth

2005q3

2007q1

No judicial requirement

2008q3

2010q1

2004q1

2005q3

Judicial requirement

2007q1

2008q3

2010q1

Difference, Non-judicial - Judicial

2004q1-.03

Difference, Non-judicial - judicial
-.02
-.01
0
.01

Natural Log Scale with 2004Q1 Subtracted
0
.05
.1
.15
.2
.25

Zillow House Price Growth

2005q3

2007q1

No judicial requirement

2008q3

2010q1

Judicial requirement

2004q1

2005q3

2007q1

2008q3Difference, Non-judicial - Judicial

2010q1

Figure 8
State Level Correlation of Foreclosures with Residential Investment and Durable Consumption
Residential investment is measured by the value of new permits for new residential construction as collected by the Census. Auto sales data are from R.L. Polk.

0

Durable consumption growth0

Residential investment growth
OK

DC
ND
NE
SD
ND

NC

OR

NY
NE

MI
FL

CA

CO

AZ

IL

-1.5

MO
NH

WV

WI
NJ
PA
MTKY
CT
WY

IL

IN

OHRI
AR

MD
VA
MI
TX

LA
NC HI

UT
MS AL
WA
NM SC
DE
MN OR

-.6

GA

MA
KS AK

VTSD

-.4

Auto Sales growth, 2007-2009

-1

OK
DC
LA
AK
PADE
AR
NJ
MD
ME MAALWY KS MNNHVA
IN TX
RI
WI
OH
KYMS
MO
HI
NM
NY
WV
CT
MT
SC
WA UT
TN
ID

ME
IA

-.2

VT

TN

CO

FL

GA
CA

ID
AZ

-.8

NV

-2

Permits growth, 2007-2009

-.5

IA

NV

0

.05

.1

.15

Foreclosures per homeowner 08-09

.2

0

.05

.1

.15

Foreclosures per homeowner 08-09.2

Figure 9
Foreclosures, Residential Investment, and Durable Consumption, Reduced Form
The figures plot residential investment (top) and auto sales (bottom) growth in judicial and non-judicial states from 2004 to 2009. The averages are weighted bytotal population.

-.08

Difference, Non-judicial - judicial-.06 -.04 -.02
0.02

Natural Log Scale with 2004Q1 Subtracted-1.25 -1
-.75
-.5
-.25
0

Residential Investment Growth

2004

2005

2006

2007

No judicial requirement

2008

2009

2004

2005

Judicial requirement

2006

2007

2008

2009

Difference, Non-judicial - Judicial

2004q1

Difference, Non-judicial - judicial
-.1
-.05
0
.05
.1

Natural Log Scale with 2004Q1 Subtracted
-.6
-.4
-.2
0
.2

Auto Sales Growth

2005q3

2007q1

No judicial requirement

2008q3

2010q1

Judicial requirement

2004q1

2005q3

2007q1

2008q3Difference, Non-judicial - Judicial

2010q1

Appendix Table 1
Foreclosure Statutes in Detail
The table presents coefficients from the first stage regression of foreclosures on whether a state requires a judicial
foreclosure and all the consumer protection provisions reported in Rao and Walsh (2009). Standard errors areheteroskedasticity-robust.
First Stage
(1)
(2)
(3)
Foreclosures per
Foreclosures per
Delinquencies per
homeowner
delinquency
homeowner
08-09
08-09
08-09

Judicial foreclosure requirement
Delinquencies per homeowner, 08-09
Access to court review
Loss mitigation
Right to cure
Right to reinstate
Personal service requirement
Housing emergency fundRight to redeem
Deficiency judgment
Accounting for sale proceeds
Prompt return of surplus
Constant
N
R2-0.026**(0.007)0.799**(0.131)
0.011(0.008)-0.005(0.014)0.00001(0.009)-0.006(0.011)-0.015(0.01)
0.002(0.009)-0.016+(0.008)0.011(0.01)
0.009(0.014)-0.015(0.014)-0.021(0.013)51
0.745-0.205**(0.059)1.823**(0.649)0.096(0.073)0.026(0.121)-0.059(0.074)0.022(0.075)-0.118(0.072)0.016(0.076)-0.122+(0.066)0.06(0.076)-0.009(0.105)-0.105(0.104)0.309**(0.079)51
0.511-0.006(0.015)-0.017(0.013)0.064(0.04)-0.002(0.025)0.007(0.019)-0.002(0.013)-0.023(0.018)-0.013(0.019)-0.014(0.014)0.015(0.022)0.004(0.023)0.102**(0.011)51
0.163

Appendix Figure 1
Foreclosures and House Prices using Publicly Available Data, Reduced Form
The figures plots house price growth in judicial and non-judicial states from 2004 to 2009. The averages are weighted by total population.

2004q1-.15

Difference, Non-judicial - judicial
-.1
-.05
0
.05

Natural Log Scale with 2004Q1 Subtracted
-.2
-.1
0
.1
.2
.3

S&P CS 20 House Price Growth

2005q3

2007q1

No judicial requirement

2008q3

2010q1

2004q1

2005q3

Judicial requirement

2007q1

2008q3

2010q1

Difference, Non-judicial - Judicial

2004q1-.03

Difference, Non-judicial - judicial
-.02
-.01
0

Natural Log Scale with 2004Q1 Subtracted
0
.05
.1
.15
.2
.25

FHFA House Price Growth

2005q3

2007q1

No judicial requirement

2008q3

2010q1

Judicial requirement

2004q1

2005q3

2007q1

2008q3Difference, Non-judicial - Judicial

2010q1