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The Impact of State Bankruptcy and Foreclosure Laws on Higher-Risk Lending: Evidence from FHA and Subprime Mortgage Originations

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The Impact of State Bankruptcy and Foreclosure Laws on Higher-Risk Lending Evidence from FHA and Subprime Mortgage Originations

Qianqian Cao Department of Economics

Syracuse University Syracuse, New York 13244-1090 Email: qcao02@maxwell.syr.edu

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Abstract

Mortgages made to borrowers with high credit risks are associated with high probabilities of default, foreclosure and bankruptcy. State foreclosure and bankruptcy provisions govern the rights of lenders and borrowers during foreclosure and bankruptcy proceedings and therefore impact on lenders’ exposure to credit risks. Therefore, the degree to which lenders would respond to those state legislations by adjusting their underwriting standards might differ across different risk segments of the mortgage market. This paper looks for evidence that lenders tend to respond to higher-risk environment by modifying the types of loans that are originated. This study draws upon state-level variations in foreclosure laws and bankruptcy provisions to analyze the impact of these policies on different types of loans that are available to prospective borrowers. The empirical analysis examines these mechanisms using the Home Mortgage Disclosure Act (HMDA) files from 2001 to 2008.

Findings from an ordered probit model of conventional-prime vs. FHA vs. subprime loans suggest that higher-risk loans are less likely to be originated in a state with judicial foreclosure requirement. Estimates also suggest that conventional prime loans are more likely to be

originated in a state with larger homestead exemption. Estimates from the probit model of FHA vs. subprime loans are consistent with the idea that FHA and subprime loans share a very similar clientele and are close substitutes.

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1 1. Introduction

The market shares1 for different types of mortgages have changed dramatically in the past decade with the boom and bust of the housing market (see Figure 1). From 2001 to 2006, Federal Housing Administration (FHA)2 lending dwindled to historically low shares of the mortgage market as subprime3 lending surged. With the onset of the 2007 financial crisis, the FHA lending soared due to the collapse of the subprime market and the pullback by conventional lenders. By 2008, the FHA origination comprised 19 percent of the mortgage market. This turbulent time provides an opportunity to study the mortgage market in general, specifically higher-risk mortgage lending, including subprime and FHA lending.

The FHA has historically been the major provider of low-down-payment loans4 and has provided mortgage credit to its targeted market of first-time and low income borrowers.

Subprime loans are loans made to borrowers with high credit risk, often because they have lower credit scores, little or no down payments, and/or incomplete income documentation. Even though subprime and FHA loans were initially developed under different market conditions and differ in some aspects5, they seem to share a very similar clientele compared to conventional prime mortgages—borrowers with higher credit risks. Not surprisingly, given their weaker credit histories and financial situations, higher-risk borrowers are more likely to default on their loans.

1Market shares reported in this paper are based on the number of loans and not the dollar amounts. The calculations of market shares are restricted to owner-occupied mortgage originations from Home Mortgage Disclosure Act (HMDA) data. Mortgages that backed by Department of Veterans Affairs or by the Farm Service Agency or Rural Housing Service are excluded. Detailed explanations will be provided in the Data section.

2 FHA loans are those loans backed by guarantees from the Federal Housing Administration. 3

Subprime loans are for persons with blemished or limited credit histories. The loans carry a higher rate of interest than prime loans to compensate for increased credit risk.

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The standard down payment requirement for a conventional home purchase loan was 20 percent during much of the 20th century. The traditional, minimum down payment of FHA loan was 3% in 2000 and 3.5% in 2011. 5 For instance, most FHA loans are fixed-rate mortgages, while the majority of subprime loans are adjustable-rate mortgages.

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2 To compensate for higher risks, the interest rates for FHA and subprime loans are substantially higher than those for conventional prime loans.

The growth of higher-risk lending provided the opportunity of homeownership to those who could not previously qualify for a mortgage. However, the expansion of lending to high-risk borrowers may also contribute to the current financial crisis by trapping millions of homeowners in higher-cost loans that might eventually lead to default, foreclosure, bankruptcy and other adverse events. This paper seeks to link higher-risk lending to broader market conditions where state policies may interact with lending decisions. State foreclosure and bankruptcy provisions have potential influence on lenders’ exposure to credit risks. Therefore, the degree to which lenders would respond to those state legislations by adjusting their underwriting standards might differ across different risk segments of the mortgage markets. The research question is to

examine the impact of foreclosure and bankruptcy provisions on different types of mortgages that are available to prospective borrowers. My focus is to look for evidence that lenders tend to respond to higher-risk environment by modifying the types of loans that are originated.

This paper is related to a number of studies that show the connections between legal environment, borrowers’ access to mortgage credit and transaction costs in mortgage outcomes. These studies can be broadly categorized into three literature strands. First, government

legislations affect market shares of different loans. The academic literature has confirmed the impact of government targeted programs (i.e. GSE6 affordable housing goals7 and CRA8-related

6 GSE stands for Government Sponsored Enterprise, and is commonly used to refer to Fannie Mae and Freddie Mac (together, the GSEs). The US Department of Housing and Urban Development (HUD) have targets for GSEs to purchase mortgages from the secondary market.

7 The affordable housing goals require the GSEs to have a certain proportion of their purchases be of mortgages made to lower-income borrowers, borrowers residing in lower-income communities and borrowers in certain high minority neighborhoods, and borrowers with low and very low income that love in low income areas.

8 CRA stands for the Community Reinvestment Act. It is a United States federal law designed to encourage

commercial banks and savings associations to help meet the needs of borrowers in all segments of their communities, including low- and moderate-income neighborhoods.

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3 lending) on the traditional domain of the government-insured programs (FHA loans) in the mortgage market. An and Bostic (2008) found evidence that more aggressive GSE purchases in targeted communities result in a significant retreat of FHA activities. Spader and Quercia (2010) showed the extent to which the origination of a CRA mortgage substitutes for FHA and subprime originations. Other research has shown that federal, state and local predatory lending laws affect the aggregate flow of subprime credit (e.g. Ho and Pennington-Cross (2007), and Bostic, Engel, McCoy, Pennington-Cross and Wachter (2008)).

Second, state bankruptcy and foreclosure laws affect mortgage default and foreclosure rates. Lin and White (2001) argued that state bankruptcy homestead exemption influence

borrowers’ incentives to default by delaying resolution of default and by determining the amount of assets that borrowers can retain after bankruptcy. Desai, Elliehausen and Steinbuks (2012) argued that the effects of bankruptcy exemptions and foreclosure laws on mortgage performance may not be the same across different risk segments of the mortgage market. They found that the effects of these legal provisions are larger for subprime than for prime mortgages and larger for adjustable rate mortgages than for fixed rate mortgages.

Third, state bankruptcy and foreclosure laws affect borrowers’ access to mortgage credit. Pence (2006) examined the impact of state foreclosure laws on equilibrium loan size. Many states protect borrowers by imposing restrictions on the foreclosure process; these restrictions, in turn, impose large costs on lenders. Lenders may respond to these higher costs by reducing loan supply; borrowers may respond to the protections imbedded in these laws by demanding larger mortgages. Cao (2011) studied the impact of state bankruptcy homestead exemptions on mortgage demand and supply. Her paper showed that while homestead exemptions protect mortgage borrowers ex post involved in bankruptcy proceedings, more generous exemption

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4 might encourage homeowners ex ante to retain more wealth in the form of home equity9 and thereby affect the loan amount requested. Mortgage lenders’ right to foreclose provides them with protections from the discharge of the debt in bankruptcy but the bankruptcy proceedings make it more costly to foreclose. However, bankruptcy also leaves borrowers with larger post-bankruptcy wealth which can be used to pay mortgage payments and avoid foreclosure. Therefore, homestead exemptions might have an ambiguous impact on mortgage lenders’ willingness to issue credit and thereby affect mortgage denials and originations.

This paper adds to the literature by examining the impact of state foreclosure and bankruptcy laws on mortgage outcomes. Instead of focusing on loan amount and loan denials, this paper looks into the impact of the variations in the state provisions on the types of mortgages that are issued. This paper also integrates the idea that the interactions between state legislations and mortgage choices are likely to be nonlinear10. Specifically, I hypothesize that lenders might respond to state foreclosure and bankruptcy provisions differently across different risk segments of the mortgage markets. The impact of those provisions might be more pronounced in the most risky segments of the mortgage market, i.e. FHA and subprime markets.

The core assumption of this paper is that higher-risk loans (subprime and FHA loans) costs more than conventional prime mortgages and mortgage applicants with different risk profiles fall into different markets. As outlined by Stiglitz and Weiss (1981), when borrowers display observable differences in risk attributes, competitive markets will charge higher risk borrowers higher loan rates. Such risk-based pricing has become increasingly common in the

9 There are two mechanisms through which mortgage borrowers could retain more wealth in the form of home equity. One is through the house value effect: holding the loan-to-value (LTV) ratio constant, borrowers buy more expensive homes and request larger loans. The other effect is through the LTV effect: holding house value constant, borrowers take out smaller loans and have smaller LTV ratios.

10 Desai, Elliehausen and Steinbuks (2012) had similar ideas with regard to the interactions among legal environment, mortgage contract choices and probabilities of default and foreclosure.

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5 mortgage market and has been a longstanding practice in markets for commercial and industrial loans. Since subprime lending standards are generally less stringent than conventional prime standards and, on some dimensions, less demanding than FHA lending standards, the costs of subprime loans are substantially higher than conventional prime loans and usually higher than FHA mortgages. In this regards, the three types of mortgages that are examined in this paper have an ordered ranking11 based on their risks and lending costs. For this reason, an ordered probit model will be used in the empirical work to test the impact of state policies on different types of mortgages.

There is also an interesting relationship between FHA and subprime loans. Since they serve a relatively similar clientele, they might be close substitutes. Therefore, in a separate specification, I run a simple probit model using only FHA and subprime loans. The idea is implicitly reflected in Figure 1 through the recent reversal in subprime and FHA market shares. Karikari, Voicu and Fang (2009) confirmed this notion and found that a sizeable number of subprime loan borrowers would have qualified for FHA loans. By focusing on differences in the impact of state legislations on FHA and subprime loans, this paper helps to understand the extent to which the two types of loans are good substitutes. The results also shed some light on the government’s effort to promote FHA loans as the subprime market has collapsed.

The data that I utilize was obtained from the Home Mortgage Disclosure Act (HMDA) from 2001 to 2008. The sample is restricted to loan originations that are FHA-eligible (i.e. meet the FHA loan limits12 for one-unit single-family housing). Subprime loans are identified using

11 Conventional prime loans are less risky than FHA loans and FHA loans are less risky than subprime loans. Conventional prime loans are less costly than FHA loans and even less costly than subprime loans.

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6 both the rate spread13 variable in HMDA and Department of Housing and Urban Development (HUD) Subprime Lender List. Separate models are run for each sample year. The year-by-year estimation of the models allows for differences in the impacts due to the changes14 in market conditions over the sample horizon. I also stratify my analysis into home purchase versus refinance loans. This is in part due to the fact that refinance loan borrowers have credit history and normally hold some positive net equity in the house. Therefore, these mortgage applicants may face different underwriting standards from home purchase borrowers.

Besides the policy variables that I am keen on, I also included borrower characteristics as well as a wide range of county and census level controls. Two of my control measures warrant special attention and are highlighted here (the other controls are described later in the paper). The first is the denial rate of conventional loan applications in the census tract a year ago. It is highly possible that borrowers turn to higher-risk loans because their conventional mortgage

applications have been turned down before or because they know it is difficult to be approved for conventional mortgages. Mian and Sufi (2009) found evidence of the expansion in mortgage credit to subprime ZIP codes15. They found that borrowers who live in the subprime ZIP codes are more likely to be denied credit prior to the expansion of subprime mortgages. It can be inferred that firms wanting to exploit people with unwarranted subprime loans might look where there is unsatisfied demand. I draw on this idea to offer some indirect evidence regarding the origins of the mortgage crisis.

13 Rate spread is the difference between the annual percentage rate (APR) on a mortgage and the yield on a Treasury security of comparable maturity.

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Those changes include dramatic shifts in mortgage underwriting standards, fluctuations in housing prices and differences in the overall mortgage market.

15 Subprime zip codes are defined in the paper as zip codes in the highest quartile in the national distribution based on the fraction of consumers with a credit score below 660 as of 1991.

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7 A second control variable that should be noted is the Herfindahl-Hirschman Index (HHI)16 of mortgage markets at the county level. The HHI captures the local market competition among lenders. As pointed out by Dick and Lehnert (2010), the effects of competition on lending standards are theoretically ambiguous.17 Partly due to the ambiguity, there is a sparse literature documenting the relationship between market competitions and lending standards. Dell’Ariccia, Igan and Laeven (2008) found that denial rates decreased more in areas with a larger number of competitors, indicating that there is a decline in lending standards associated with market competition. This paper adds to the literature by testing whether higher-risk loans are more easily originated in a more competitive market.

Results are largely consistent with the priors. Estimates from the ordered probit model of home purchase loan regression suggest that higher-risk loans are less likely to be originated in a state with judicial foreclosure requirement no matter under what market condition. Estimates also suggest that conventional prime loans are more likely to be originated in a state with a larger homestead exemption.

The coefficient estimate of the HHI also yields the expected result: higher-risk loans are more likely to be originated in a local market with more severe competition among lenders. This result indicates that market competition might lead to the easing of underwriting standards. In addition, the results suggest that higher-risk loans are more likely to be originated when the conventional applications are more likely to be turned down before. The results based on borrowers characteristics and census tract attributes provide strong evidence of mortgage

16The HHI equals to the sum of squared market shares of mortgage loans. The shares are based on the number of loan originated by a mortgage lender to the total number of mortgage loans originated in a county. Increase in the HHI generally indicates a decrease in competition and an increase of market power.

17 On the one hand, we might expect banks to loosen standards due to more competitive pressure. On the other hand, banks may not be able to afford the luxury of relaxing lending standards due to more competitive pressure, and in fact, may have to strengthen them.

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8 discrimination and redlining. Estimation results ofrefinance regressions generally follow a similar pattern to that of home purchase regression.

Estimates from the probit model of home purchase loan regression suggest that there is no clear pattern of the choices between FHA and subprime origination with respect to state foreclosure and bankruptcy laws under different market conditions. This result is consistent with the idea that subprime and FHA loans are close substitutes and dominate the mortgage markets for riskier borrowers.

As the final exercise, estimation results ofthe probit model based on refinance

regressions and the estimates are quite different from the home purchase regression. The findings suggest that subprime refinance loans are more likely to be originated than FHA loans in a state with a judicial foreclosure requirement. Estimates also suggest that subprime loans are more likely to be originated in a state with larger homestead exemption.

The plan for the rest of the paper is as follows. Section 2 develops the conceptual model used to clarify the impact of state laws on mortgage market outcomes. Section 3 describes my empirical strategy and model. Section 4 contains a description of the data. Section 5 reports on estimation results and Section 6 concludes.

2. Conceptual Model

Consider now a stylized model of mortgage origination outcome. Following the work by Ho and Pennington-Cross (2007), this study assumes higher-risk loans (subprime and FHA loans) costs more than conventional prime mortgages and mortgage applicants with different risk

profiles fall into different markets. Since subprime lending standards are generally less stringent than conventional prime and, on some dimensions, less demanding than FHA lending standards,

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9 the costs of subprime loans are substantially higher than conventional prime loans and usually higher than FHA mortgages. Therefore, the borrowers of subprime loans will tend to have higher than those of FHA loans and conventional prime loans.

Each loan applicant has a credit risk summarized by a single number (i.e. credit score). The credit risk is a monotonically increasing function of the borrower’s likelihood of default. Assuming mortgage lenders can observe the true credit risks of borrowers, they approve all loans applications with a credit risk that is lower than a uniform underwriting cut-offs. In this model, I assume that everyone who applies for a conventional prime loan and has a credit risk less than the corresponding cutoff and will be approved. 18 Borrowers with credit risk above the

conventional prime underwriting standards will be classified as FHA and subprime borrowers. As mentioned earlier in the Introduction, state judicial foreclosure provision requires lender to proceed through the courts to foreclose on a property. This makes the foreclosure process slower and more expensive, hence imposing extra costs on mortgage lenders. Since higher-risk borrowers are more likely to default than conventional prime loan borrowers,

mortgage lenders will tighten the underwriting standards of the higher-risk loans much more than that of their conventional prime counterparts. The judicial foreclosure requirement will make higher-risk loans less likely to be originated by lenders.

As discussed in Cao (2011), the homestead exemption is unambiguously beneficial to mortgage borrowers and is likely to increase the propensity of homeowners to retain wealth in the form of home equity. However, the impact of the homestead exemption on mortgage lenders is potentially ambiguous19. Empirical results of the paper indicated that the benefits of the

18 Although we do observe some rejections of prime applications in practice, empirical research has shown that subprime loans are rejected at a much higher rate than prime loans

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There is a tradeoff of mortgage lenders during the underwriting process caused by state bankruptcy provisions. On the one hand, the homestead exemption allows mortgage borrowers to shift resources from consumer debts

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10 “wealth effect” outweigh the costs of foreclosure delay for mortgage lenders. Mortgage lenders are less exposed to default risks under the protection of the homestead exemption. Therefore, mortgage lenders would be more willing to originate safer, conventional prime loans in a state with a more generous homestead exemption.

As emphasized earlier in the Introduction, subprime and FHA loans seem to share a very similar clientele compared to conventional prime mortgages—borrowers with higher credit risks. Allowing for the possibility that subprime and FHA loans are close substitutes, the impact of the foreclosure and bankruptcy laws on these two types of loans will be relatively similar.

In summary, the above analysis allows me to develop a set of testable hypotheses regarding the impact of the judicial foreclosure requirement and bankruptcy homestead exemption provision on higher-risk mortgage lending outcomes. First, I expect lenders to impose tighter underwriting standards on higher-risk loans in states with a judicial foreclosure requirement. Therefore, higher-risk loans are less likely to be originated in those areas, holding everything else equal. Second, conventional prime loans are more likely to be originated in the states that offer greater bankruptcy protection, which is measured by the bankruptcy homestead exemption. Finally, the laws will not have a significant impact on the lending activities between subprime loans and FHA loans since the two types of loans are close substitutes for each other.

3. Empirical Model

3.1. Identification Strategy

There are two primary challenges that arise in the empirical analysis. First, it is difficult towards mortgage debts. This effect is known as the “wealth effect,” since a more generous homestead exemption leaves borrowers with more funds to make their mortgage payments. Therefore, mortgage lenders are less likely to encounter mortgage default risks. On the other hand, the bankruptcy filing stops all collection efforts and delays the foreclosure process. Therefore, mortgage lenders are more likely to encounter additional transaction costs.

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11 to identify the impact of state foreclosure and bankruptcy policies on mortgage credit because the housing markets vary dramatically by region. For instance, the inflation-adjusted house prices have shown dramatically different growth around the country20. From 1979 through 1998, house prices rose 74% in Boston, 10% in Los Angeles, 11% in Chicago and fell 21% in Dallas. In a simple cross sectional regression, a regional shock to the housing market could be misinterpreted as an effect of the foreclosure or bankruptcy laws. The second challenge is the potential

endogeneity associated with judicial foreclosure requirement or the homestead exemption levels. If differences or changes in the state judicial foreclosure requirement or the exemption level are driven by unobserved local attributes, then the estimates produced will be biased.

Similar to Pence (2006), I control for the identification problems by focusing on the 55 urban areas that cross state boundaries. Urban areas are the focus of the paper because these areas are more populated and most likely to be affected by the policies. I assume that mortgage applications in the census tracts that are within the same urban areas but located in adjacent states are relatively similar. Therefore, mortgage applications in these census tracts are subject to different state foreclosure or bankruptcy provisions, but the proximity of the homes assures that they have similar unobserved local attributes. Also, by focusing on the border areas, which are only a small portion of each state, I can assume that the border areas are not large enough to drive the changes in the state-level policy, thus making the policy changes exogenous.

3.2. Estimation Equations

The discussion above suggests that there are two set models that can examined for both home purchase loans and refinance loans in each sample year. These regressions are (i) the impact of state laws on mortgage origination outcomes of conventional prime versus FHA loans

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12 versus subprime loans using an ordered probit model, (ii) the impact of state laws on mortgage origination outcomes of FHA loans versus subprime loans using a simple probit model.

My estimation equation of each sample year is as follow:

(3.1)

where t denotes the time period and j specifies the urban area to which the mortgage belongs. equals to 1 for conventional prime origination, 2 for FHA origination, and 3 for subprime origination in the first set of regressions. Similarly, is 0 for FHA origination and 1 for

subprime origination in the second set of regressions. is an indicator variable which equals one located in a state with judicial foreclosure requirement, is the homestead exemption in a specific period21, is the urban area fixed-effects, are a set of other relevant variables.

The urban area fixed effects control for time-invariant area characteristics, such as proximity to amenities. The year-by-year estimation of the models allows for differences in the impacts due to the changes in market conditions over the sample horizon. Those changes include dramatic shifts in mortgage underwriting standards, fluctuations in housing prices and

differences in the overall mortgage market. I also add an assortment of county- and tract- level variables in order to directly control for the within urban area time-varying, area-specific characteristics. The detailed explanations of these variables are in the Data section.

4. Data

4.1 Sources and variables

Following Pence (2006), I selected counties in the United States that are part of a

metropolitan statistical area (MSA) as defined by the Census Bureau; that lie along state borders;

21 Judicial foreclosure requirements generally do not vary over time. But the homestead exemption levels vary over time even after adjusting for inflations. More information will be provided in the Data section.

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13 and that share a border with another metropolitan22 county. Based on this selection criterion, I focused on 55 county groups that cross state boundaries which consist of 181 bordering counties.

The primary data used in the analysis was obtained from the Home Mortgage Disclosure Act (HMDA). Specifically, I drew upon the HMDA data for each year from 2001 to 2008. The HMDA data contains a rich set of information, including loan type, loan purpose, and borrower characteristics. The FHA loans are also identified in the HMDA data. As highlighted in the Introduction, I limited my sample to mortgages that meet the FHA loan limits23, so that the choices between mortgage products that I considered in this study will not be influenced by the limits24. I exclude other government insured loans, such as VA loans, because they are not

available to the general public, employ unique underwriting standards, and comprise only a small share of the market. Throughout the analysis, I retained only loan originations for

owner-occupied properties. Since the sample sizes are large, I selected a random sample of 50% of all originations each year.

Subprime loans are much more difficult to define. Mayer and Pence (2008) discussed three different definitions and data sources that can be used to identify subprime loans: securitized subprime loans in from the LoanPerformance25 data set, higher-priced loans

identified by rate spread variables26 in HMDA and the HUD subprime lender list27. They found that the factors associated with subprime lending are similar across all three measures of

22 It is worth mentioning the “metropolitan” requirement because the Home Mortgage Disclosure Act (HMDA) only requires lenders to report about mortgage lending in metropolitan areas.

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As of August 2011, the standard (floor) loan limit for areas where housing costs are relatively low is $271,050 for one-unit properties. The “ceiling” loan limit for higher cost areas is $729,750 for one-unit properties. FHA loan limits vary based on area median home price, but all will fall within the range of $271,050 and $729,750 for one unit properties.

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The limits on FHA loans have constrained its borrowers to lower-priced houses.

25 First American LoanPerformance, a subsidiary of First American Core Logic, Inc., provides information on securitized mortgages in subprime pools.

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The information started to be added to HMDA data in 2004. Mortgages with an APR three percentage points over the Treasury benchmark and for junior liens with an APR five percentage points over the benchmark are called “higher-priced” loans.

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14 subprime. Therefore, subprime loans are identified in this study using a combination of the higher-priced loans in HMDA and the HUD’s subprime lender list. From 2001 to 2003, the subprime loans are identified using the HUD’s subprime lender list only. Between 2004 and 2005, the subprime loans are identified as either been originated by a lender in the HUD’s subprime lender list or loans are higher-priced. From 2006 to 2008, the subprime loans are identified using rate spread variable only.

The key policy variables in this study are: judicial foreclosure variable28 and bankruptcy homestead exemption. First, I included one state foreclosure law variable29: whether judicial foreclosure process is required. The judicial foreclosure variable equals to one if the state

requires that lenders must proceed through the courts to foreclose on a property. This restriction imposes large costs on mortgage lenders. Since higher-risk loans are more likely to encounter default and foreclosure, lenders may respond by imposing tighter underwriting standards on subprime and FHA loans.

Second, I included one state bankruptcy homestead exemption variable whose effects on mortgage markets are examined in Cao (2011).There are substantial differences in the generosity of the homestead exemption across states and across time. The homestead exemption ranges from zero or a few thousand dollars to unlimited. For the simplicity, I top coded the unlimited exemption as $500.000.30 The homestead exemption also depends on the characteristics of debtors. Some states allow married couples to have a higher homestead exemption which usually doubles when they file jointly. Since HMDA does not have information about the applicant’s

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I drew upon the judicial foreclosure data from Elias (2009).

29 I did not include the deficiency judgment variable or statutory right of redemption variable. This is because deficiency judgment is not often pursued and borrowers rarely exercise the statutory right of redemption in practice. The results from Pence (2006) are consistent with these ideas. She found that there is not a statistical significant impact of the deficiency judgment or statutory right of redemption on mortgage size.

30I have converted the homestead exemptions to year 2008 dollars using the Consumer Price Index obtained from the Bureau of Labor Statistics.

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15 marital status, I assumed the applicants that have co-applicants are married. I doubled the

homestead exemptions for the applicants with co-applicants who live in states that allow this increase. Some states also specify larger exemptions for senior citizens, veterans, the disabled, heads of family and debtors with dependents. To make it simple, I ignored the possibilities that the debtors might qualify for these special treatments.

Also included in the models are several county-level controls. I obtained the yearly unemployment rate from the Bureau of Labor Statistics. Areas with high unemployment rates may be viewed as higher-risk locations by lenders. In addition, I calculated the Herfindahl-Hirschman Index (HHI)31 of mortgage markets at the county level using the HMDA data. As mentioned in the Introduction, this paper adds to the literature by linking market competitions and lending standards. Intense competition for mortgage business might lead to an excess of lending and a weakening of underwriting standards. Therefore, the more competitive the local mortgage industry, the more likely a higher-risk loan may be originated.

Data on neighborhood characteristics are obtained from the year 2000 census. The census data provide tract-level measures of the socio-demographic and economic variables. These include income, racial composition, education characteristics, unemployment, poverty status, population density, and characteristics of housing stocks. As emphasized in the Introduction, I also included the denial rate of conventional loan applications in the census tract in the previous year. It is likely that borrowers turn to higher-risk loans because their conventional mortgage applications have been turned down before or they know it is difficult to be approved of conventional mortgages. Therefore, I include this variation to capture this type of variation.

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4.2 Summary Statistics

Table 1 provides the summary statistics. The table indicates that 57 percent of mortgage originations in my sample are located in states that require a judicial foreclosure process. The average homestead exemption is about $88,682. The mean of HHI is around 0.05, indicating the market competition is relatively intense. The loan denial rate of conventional loan applications in the previous year is around 17%, which is consistent with market priors.

5. Estimation Results

5.1. Overview

Table 2a presents the estimation results ofthe ordered probit model, which includes the conventional prime loans, FHA loans and subprime loans, based on home purchase regressions. Table 2b presents the estimation results ofthe ordered probit model based on refinance

regressions. Table 3a presents the estimation results ofthe simple probit model, which includes only FHA loans and subprime loans, based on home purchase regressions. Table 3b presents the estimation results ofthe simple probit model based on refinance regressions. In all cases, t ratios are reported based on clustering at the census tract level32. All the regressions include urban area fixed effects and an extensive list of socioeconomics attributes at different geographic levels.

5.2 Ordered Probit Model of Conventional Prime vs. FHA vs. Subprime Originations

Estimates from the ordered probit model of home purchase loan regressions suggest that higher-risk loans are less likely to be originated in a state with judicial foreclosure requirement

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I did not cluster at the urban area level because house values differ dramatically within the same urban area and across neighborhood. Households living in the same census tract are more homogeneous with respect to income, wealth and other socioeconomic attributes. By clustering on the census tract level, I allow for arbitrary correlation across observations in the same census tract.

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17 no matter under what the market conditions are. This result is consistent with the prediction that lenders may respond to the extra cost imposed by the judicial foreclosure requirement by imposing tighter underwriting standards. However, the signal is less significant for year 2007 and year 2008. It is highly possible that after the collapse of the subprime market, lenders are cautious about all types of loans with respect to the additional cost posed by potential default and foreclosure. Therefore, we do not see differences among mortgage types with regard to the response of lenders to the foreclosure laws.

Estimates also suggest that conventional prime loans are more likely to be originated in a state with larger homestead exemption. This result is consistent with the idea that a more

generous homestead exemption protects more home equity from being taken away by unsecured creditors and leaves more funds for borrowers after filing for bankruptcy. Therefore, mortgage lenders are less exposed to default risks and therefore more willing to originate safer and less expensive loans.

The estimate of the HHI also yields the expected result: higher-risk loans are more likely to be originated in a local market with more severe competition among lenders. The regression coefficient indicates that market competition might lead to the easing of underwriting standards. The denial rate of conventional loan applications in the census tract a year ago also has the expected sign. Higher-risk loans are more likely to be originated when the conventional applications are more likely to be turned down before.

The county unemployment rate also has the anticipated sign. The higher the

unemployment rate, the more cautious the lenders will be and the more likely a safer loan will be originated.

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18 The borrower characteristic variables and census tract variables show strong evidence of both mortgage discrimination33 and redlining 34. Results suggest that higher-risk loans are more likely to be issued if the borrowers are African-American, Hispanic or female which is indicative of the mortgage discrimination based on race or gender. Results also suggest that higher-risk loans are more likely to be issued if the census tract has a higher percentage of African-American or Hispanic which is indicative of the mortgage redlining.

Table 2b presents the estimation results ofthe ordered probit model based on refinance regressions. In general, the pattern is similar to that of home purchase regression. It is expected that lenders may treat refinance borrowers differently from home purchase borrowers. However, the potential differences might not be reflected with regards to the mortgage choices in the ordered probit model.

5.3 Probit Model of FHA vs. Subprime Originations

Estimates from the probit model of home purchase loan regression suggest that there is no clear pattern of the choices between FHA and subprime origination with respect to state foreclosure and bankruptcy laws under different market conditions. This result is consistent with the idea that subprime and FHA loans are close substitutes and dominate the mortgage markets for riskier borrowers. Therefore, the impact of the foreclosure and bankruptcy laws on these two types of loans is anticipated to be similar.

The estimate of the HHI suggest that subprime loans are more likely to be originated in a local market with less competition among lenders except for those years of the subprime boom.

33

Discrimination is the practice of banks, governments or other lending institutions denying loans to one or more groups of people primarily on the basis of race, ethnic origin, sex or religion.

34Redlining is the practice whereby mortgage lenders figuratively draw a red line around minority neighborhoods and refuse to make mortgage loans available inside the red lined area.

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19 However, for the year 2005 and 2006, subprime loans were more likely to be originated in a local market with more intense competition among lenders. This result indicates that during the boom period of the subprime market, market competitions may have led to the easing of

underwriting standards of subprime mortgages. The denial rate of conventional loan applications in the census tract a year ago also has the expected sign. FHA loans are more likely to be

originated than subprime loans when the conventional applications are more likely to be turned down before.

Table 3b presents the estimation results ofthe probit model based on refinance regressions and the estimates are quite different from the home purchase regression. There is evidence that subprime refinance loans are more likely to be originated than FHA loans in a state with judicial foreclosure requirement. The effect is prominent for refinance loans but not for home purchase loans. This may be due to the fact that a large proportion of both FHA loans and subprime are refinance loans. The result is suggestive that lenders may not impose as tight underwriting standards on subprime loans as on FHA loan. This negligence of high risks associated with the subprime loans might contribute to the turmoil of the subprime market.

Estimates also suggest that subprime loans are more likely to be originated in a state with larger homestead exemption. This is suggestive that a more generous homestead exemption protects more home equity from being taken by unsecured creditors and leaves more funds for borrowers after filing for bankruptcy. Therefore, borrowers are more likely to choose higher risk subprime loans under the protection of the homestead exemption.

The estimate of the HHI suggests that FHA refinance loans are more likely to be originated in a local market with more intense competition among lenders under all market conditions. This is inconsistent with what I found for home purchase loan regression. However,

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20 it may suggest that market competitions might lead to the easing of underwriting standards of FHA mortgages more than subprime mortgages.

7. Conclusion

This paper provides empirical evidence on the impact of state foreclosure and bankruptcy laws on lenders’ exposure to credit risks in the context of U.S. mortgage markets over the 2001-2008 periods. This paper looks for evidence that lenders tend to respond to higher-risk

environment by modifying the types of loans that are issued. This paper contributes to the state policy and mortgage markets literature by focusing on the impact of state legislations on the origination of different mortgage types with different level of risks. I conduct the analysis using a border methodology by comparing mortgage origination for properties in 55 urban areas across state borders. The identification strategies help addresses the potential issues of controlling for unobserved variables and endogeneity problems.

Results are largely consistent with my priors. Estimates from the ordered probit model of home purchase loan regression suggest that higher-risk loans are less likely to be originated in a state with judicial foreclosure requirement no matter what the market conditions are. Estimates also suggest that conventional prime loans are more likely to be originated in a state with a larger homestead exemption. A more generous homestead exemption protects more home equity from being taken by unsecured creditors and leaves more funds for borrowers after filing for

bankruptcy. Therefore, mortgage lenders are less exposed to default risks and therefore more willing to originate safer and less expensive loans.

The estimate of the HHI suggests that higher-risk loans are more likely to be originated in a local market with more severe competition among lenders. This adds to the literature that

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21 market competition might lead to the easing of underwriting standards. Furthermore, higher-risk loans are more likely to be originated when the conventional applications are more likely to be turned down previously. The result offers some indirect evidence regarding the origins of the mortgage crisis. Finally, the results based on borrowers characteristics and census tract attributes provide strong evidence of mortgage discrimination and redlining.

Estimates from the probit model of home purchase loan regression suggest that there is no clear pattern of the choices between FHA and subprime origination with respect to state foreclosure and bankruptcy laws under different market conditions. This result is consistent with the idea that subprime and FHA loans are close substitutes and dominate the mortgage markets for riskier borrowers. Therefore, the government’s effort to promote FHA loans as the subprime market has collapsed is warranted. It is possible that FHA loans might be the answer and cure to the current subprime crisis with the ability to adapt to changings purposes under different market conditions35.

As the final perspective, result found that subprime refinance loans are more likely to be originated than FHA loans in a state with judicial foreclosure requirement. It is highly possible that the negligence of similar high risks associated with the subprime loans might contribute to the turmoil of the subprime market. Estimates also suggest that subprime loans are more likely to be originated in a state with larger homestead exemption. This suggests that borrowers are more likely to choose higher risk subprime loans under the protection of the homestead exemption.

35 Yezer and Pennington-Cross (2000) believe that FHA market share will be maintained and perhaps expanded in the new millennium with the ability to invent new purchases for itself.

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22 References

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Journal of Real Estate Finance and Economics, 30:2 (2005) 133-151.

An, Xudong and Raphael Bostic, "Have the Affordable Housing Goals been a Shield Against Subprime? Regulatory Incentive and the Extension of Mortgage Credit” (2005) mimeo. An, Xudong and Raphael Bostic, "GSE Activity, FHA Feedback, and Implications for the Efficacy of the Affordable Housing Goals,” Journal of Real Estate Finance and Economics, Vol. 36, No. 2, 2008.

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23 Ho, Giang, and Anthony Pennington-Cross,“The Varying Effects of Predatory Lending Laws on High-Cost Mortgage Applications,” Federal Reserve Bank of St. Louis Review, 2007, 89(1), 39-59.

Immergluck, Dan, “Foreclosed: High-risk Lending, Deregulations, and the Undermining of America’s Mortgage Market,” Ithaca, NY: Cornell University Press.

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Statistics, Vol.76, No.1, (Feb. 1994), pp.127-141.

Jaffee, Dwight and John Quigley, “Housing Policy, Subprime Mortgage Policy, and the Federal Housing Administration,” Institute of Business and Economic Research, University of California, Berkley, working paper.

Karikari, John, Ioan Voicu and Irene Fang,“FHA vs. Subprime Mortgage Originations: Is FHA the Answer to Subprime Lending,” Journal of Real Estate Finance and Economics, 43(4) 441-458, 2011.

Mayer, Christopher and Karen Pence, “Subprime Mortgages: What, Where, and to Whom,” Finance and Economics Discussion Series, 2008-29. Washington: Federal Reverse Board. Mian, Atif and Amir Sufi, “The Consequences of Mortgage Credit Expansion: Evidence from the U.S. Mortgage Default Crisis,” Quarterly Journal of Economics, Vol. 124, Issue 4,

November 2009.

Nichols, Joseph and Anthony Pennington-Cross,“Credit History and the FHA-Conventional Choice,” Real Estate Economics, 28(2), 307-336, 2000.

Nichols, Joseph, Anthony Pennington-Cross and Anthony Yezer,“Borrower Self-Selection, Underwriting Costs, and Subprime Mortgage Credit Supply,” Journal of Real Estate Finance and Economics, 30(2) 197-219, 2005.

Pence, Karen (2006), “Foreclosing on Opportunity: State Laws and Mortgage Credit,” Review of Economics and Statistics, 88, pp. 177-82.

Pennington-Cross, Anthony,“Credit History and the Performance of Prime and Nonprime Mortgages,” The Journal of Real Estate Finance and Economics, 27(3) 279-302, 2003. Pennington-Cross, Anthony and Souphala Chomsisengphet“Evolution of the Subprime Mortgage Market,” Federal Reserve Bank of St. Louis Review, 2006, 88(1), 31-56. Ross, Stephen L. and John Yinger (2002), The Color of Credit, MIT Press.

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24 Stiglitz, J., and A. Weiss (1981), “Credit Rationing in Markets with Imperfect Information,”

American Economic Review, Vol 71 (3), 393-410, June.

Spader, Jonathan and Roberto Quercia, “CRA Lending in a Changing Context: Evidence of Interaction with FHA and Subprime Origination,” Journal of Real Estate Finance and Economics,March 2010.

Yezer, Anthony and Anthony Pennington-Cross,“FHA in the New Millennium,” Journal of Housing Research, 11(2) 357-372, 2000

Zywicki, Todd and Joseph Adamson, “The Law & Economics of Subprime Lending,” George Mason Law & Economics Research Paper No. 08-17.

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25 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 2001 2002 2003 2004 2005 2006 2007 2008

Figure 1. Mortgage Originations by Market Segments, 2001-2008

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26 Table 1: Summary Statistics

Variables Mean Std. Dev.

Foreclosure must go through the courts 0.57 0.50

Homestead exemption level 88,682 155,134

Herfindahl-Hirschman Index of mortgage lenders 0.05 0.06

Unemployment rate in the current year 5.09 1.30

Denial rate of conventional loans 1 year ago 16.58 8.57

Percent age under 15 9.30 4.82

Percent age 15-29 18.45 5.72

percent age 30-54 39.46 4.68

Percent age 55 or more 20.81 7.21

Percent age 65 or more 11.98 5.80

Percent female 51.43 2.49

Percent Black 11.30 20.72

Percent Hispanic 6.91 12.53

Percent some high school 9.98 6.39

Percent high school graduates 28.90 11.00

Percent some college 21.81 6.02

Percent college or higher 39.31 17.86

Average income 100,509 47,225

Tract income relative to MSA 1.14 0.48

Percent unemployed 4.50 3.78

Average house value 189,410 123,451

Median rent 977.07 335.73

Loan amount of home purchase loans 214,696 469,850

Loan amount of refinance loans 200,402 367,730

Income 105,410 158,745

Black 0.08 0.28

Hispanic 0.02 0.14

Female 0.28 0.45

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27

Table 2a. Ordered Probit Model of Conventional-Prime(1) vs. FHA(2) vs. Subprime (3) Based on Home Purchase Originations (t-ratios based on standard errors clustered at the census tract level in parentheses)

2001 2002 2003 2004 2005 2006 2007 2008

Judicial foreclosure requirements -0.0529 -0.0856 -0.0579 -0.0390 -0.0308 -0.0599 -0.0135 -0.0039 (-3.05) (-5.19) (-3.37) (-3.08) (-2.45) (-4.48) (-1.02) (-0.26) Bankruptcy homestead exemption 3.69E-05 -0.0001 -0.0002 -0.0003 -0.0003 -0.0003 -0.0003 -0.0002 (0.73) (-3.01) (-4.73) (-9.82) (-8.21) (-7.40) (-6.97) (-4.49) Unemployment rate in the county -0.0521 -0.0354 -0.0116 -0.0229 -0.0129 -0.0239 0.0157 0.0127 (-8.86) (-6.77) (-2.42) (-4.95) (-2.44) (-4.06) (2.69) (2.37) Herfindahl-Hirschman Index of lenders -5.9726 -4.8710 -3.4956 -6.5306 -9.3113 -1.7589 -4.8865 -3.4737

(-13.56) (-10.13) (-7.31) (-13.50) (-13.52) (-14.83) (-8.98) (-8.49) Conventional loan denial rate one year ago 1.7016 2.3203 0.8183 2.3738 2.3445 2.4509 2.8285 2.3390 (21.57) (28.76) (5.57) (30.06) (25.34) (25.86) (31.91) (28.25) Percent aged 15-29 -0.1719 -0.3506 -0.4907 -0.7470 -0.9366 -1.0348 -0.7750 -0.4942 (-1.28) (-2.80) (-3.61) (-7.01) (-8.98) (-8.80) (-6.99) (-4.02) Percent aged 30-54 -1.1838 -1.1892 -1.7514 -1.5522 -1.9013 -1.9763 -1.4254 -1.0629 (-6.22) (-6.47) (-9.25) (-10.12) (-12.00) (-10.97) (-8.69) (-5.63) Percent aged 55-64 -1.8566 -1.4563 -1.7236 -0.4841 -0.5945 -0.7591 -0.4728 -1.0682 (-6.78) (-5.53) (-6.39) (-2.50) (-3.07) (-3.78) (-2.40) (-4.87) Percent aged 65+ 1.2635 0.8365 0.7916 -0.4444 -0.5422 -0.4002 -0.4550 0.1959 (4.14) (2.77) (2.63) (-1.87) (-2.37) (-1.65) (-1.93) (0.74) Percent female 1.0367 0.8149 0.4340 0.1127 0.3090 0.3541 -0.2185 0.9816 (5.27) (4.37) (2.27) (0.69) (1.77) (1.95) (-1.22) (4.98)

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28

Table 2a cont. Ordered Probit Model of Conventional-Prime(1) vs. FHA(2) vs. Subprime (3) Based on Home Purchase Originations (t-ratios based on standard errors clustered at the census tract level in parentheses)

2001 2002 2003 2004 2005 2006 2007 2008

Percent African-American 0.0299 -0.1636 0.0914 0.0050 0.1041 0.0981 -0.0439 -0.2629 (0.94) (-5.35) (2.84) (0.19) (3.71) (3.64) (-1.61) (-8.60)

Percent Hispanic 0.0916 0.1567 0.3588 0.7104 0.9308 0.7920 0.3615 -0.0389

(1.99) (3.49) (7.92) (18.02) (20.41) (18.34) (7.66) (-0.73) Percent some high schools 1.1966 0.6382 1.5559 0.9074 1.2520 1.0938 0.8472 0.8366 (9.37) (5.21) (11.22) (8.87) (10.59) (9.43) (7.40) (6.97) Percent high school graduates 0.7792 0.8528 1.1805 1.0731 1.2211 1.1724 1.1057 0.9089

(11.10) (12.76) (16.68) (19.43) (20.27) (19.65) (18.31) (13.72)

Percent some colleges 1.7441 1.7209 1.8571 1.4046 1.5007 1.4165 1.0531 1.4982

(15.81) (16.77) (16.89) (17.16) (16.44) (15.73) (11.82) (15.24) Average income( in 1,000 dollars) 4.69E-06 -0.0005 0.0011 4.01E-05 0.0006 -0.0009 4.09E-05 0.0007 (0.01) (-1.06) (2.05) (0.10) (1.50) (-2.11) (0.10) (1.43) Average house value( in 1,000 dollars) -0.0005 -0.0003 -0.0006 -0.0006 -0.0005 -0.0006 -0.0005 -0.0008

(-5.31) (-2.87) (-5.79) (-7.25) (-6.02) (-6.32) (-6.40) (-8.50) Median rent 8.02E-05 5.89 E-05 1.34 E-05 7.81 E-05 1.18 E-05 1.33 E-05 4.53 E-05 4.44 E-05

(3.46) (2.73) (6.16) (4.56) (6.74) (7.45) (2.42) (2.20) Percent unemployed -0.1102 -0.2262 -0.4685 -0.6450 -0.8173 -0.8920 -0.3827 -0.4131

(-0.75) (-1.70) (-3.60) (-5.52) (-6.22) (-6.17) (-2.83) (-2.63) Tract income relative to MSA -0.0200 -0.0273 -0.0541 0.0084 -0.0873 0.0474 0.0175 -0.0589 (-0.52) (-0.78) (-1.43) (0.28) (-2.85) (1.54) (0.55) (-1.51)

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29

Table 2a cont. Ordered Probit Model of Conventional-Prime(1) vs. FHA(2) vs. Subprime (3) Based on Home Purchase Originations (t-ratios based on standard errors clustered at the census tract level in parentheses)

2001 2002 2003 2004 2005 2006 2007 2008 African-American 0.4480 0.4933 0.4691 0.4622 0.5738 0.5439 0.4599 0.3730 (38.92) (44.44) (42.25) (52.25) (62.96) (59.74) (45.80) (33.17) Hispanic 0.4410 0.4803 0.4822 0.1563 0.2269 0.1790 0.0383 0.2127 (38.30) (41.81) (43.29) (5.19) (7.51) (5.39) (0.89) (4.48) Female 0.0444 0.0637 0.0557 0.0508 0.0362 0.0265 0.0207 0.0521 (6.70) (10.49) (9.11) (10.34) (7.86) (5.55) (3.70) (8.27) Co-applicants -0.0908 -0.1423 -0.1248 -0.2773 -0.3925 -0.3490 -0.1504 0.0773 (-11.62) (-23.89) (-18.83) (-54.65) (-80.18) (-67.46) (-25.70) (10.13) Income -0.0013 -0.0001 -0.0008 -0.0003 -0.0003 -6.30E-05 -0.0001 -0.0013 (-5.35) (-3.53) (-7.13) (-4.71) (-6.67) (-0.27) (-3.14) (-7.27)

Urban area fixed-effects 55 55 55 55 55 55 55 55

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30

Table 2b. Ordered Probit Model of Conventional-Prime(1) vs. FHA(2) vs. Subprime (3) Based on Refinance Originations (t-ratios based on standard errors clustered at the census tract level in parentheses)

2001 2002 2003 2004 2005 2006 2007 2008

Judicial foreclosure requirements -0.0623 -0.0689 -0.0408 -0.00612 -0.0214 -0.0646 -0.0271 0.0118 (-4.56) (-5.60) (-3.51) (-0.61) (-2.14) (-6.00) (-2.53) (0.95) Bankruptcy homestead exemption 9.81E-05 -6.36E-05 -0.0002 -0.0001 -0.0002 -7.28E-05 -8.84E-05 -6.48E-05

(2.47) (-2.03) (-4.98) (-5.43) (-8.71) (-2.94) (-3.12) (-1.82) Unemployment rate in the county -0.0228 -0.0273 -0.0124 -0.0188 -0.0044 -0.0076 0.0018 -0.0028 (-5.23) (-7.27) (-3.71) (-5.91) (-1.23) (-1.93) (0.41) (-0.62) Herfindahl-Hirschman Index of lenders -3.9234 -4.1162 -3.3735 -3.8988 -5.8291 -0.7070 -2.5679 -1.3800 (-12.37) (-13.39) (-13.53) (-11.49) (-11.74) (-8.93) (-6.94) (-4.21) Conventional loan denial rate one year ago 1.1618 1.8082 0.7054 2.1254 2.1700 2.2152 2.1118 2.1327 (19.65) (30.26) (5.44) (33.86) (34.64) (33.87) (32.46) (29.78) Percent aged 15-29 -0.7452 -0.7834 -0.8385 -0.5397 -0.8576 -0.6766 -0.5198 -0.6652 (-7.90) (-9.53) (-9.59) (-7.07) (-10.87) (-8.29) (-5.92) (-5.98) Percent aged 30-54 -1.3013 -1.4340 -1.6751 -0.9262 -1.3482 -1.0578 -1.0084 -1.5482 (-9.30) (-11.09) (-13.33) (-8.25) (-11.37) (-8.91) (-7.96) (-9.56) Percent aged 55-64 -0.9508 -0.6447 -1.2170 -1.0893 -1.3400 -1.8982 -1.2710 -1.5872 (-5.55) (-4.17) (-7.64) (-8.29) (-9.96) (-13.16) (-8.44) (-8.42) Percent aged 65+ 0.0938 -0.2044 0.0965 0.5125 0.4103 1.1986 0.6744 0.8797 (0.47) (-1.14) (0.55) (3.32) (2.61) (7.23) (3.69) (3.95) Percent female 0.4005 0.1900 0.4445 0.2990 0.2213 0.3220 0.4674 0.6159 (2.63) (1.38) (3.40) (2.50) (1.75) (2.69) (3.52) (3.63)

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31

Table 2b cont. Ordered Probit Model of Conventional-Prime(1) vs. FHA(2) vs. Subprime (3) Based on Refinance Originations (t-ratios based on standard errors clustered at the census tract level in parentheses)

2001 2002 2003 2004 2005 2006 2007 2008

Percent African-American 0.4648 0.2311 0.3193 0.1245 0.2309 0.1550 0.1212 -0.0867 (19.75) (9.93) (11.62) (6.30) (11.77) (8.74) (6.28) (-3.41)

Percent Hispanic 0.2507 0.2921 0.3494 0.2954 0.3408 0.3194 0.2162 -0.0375

(7.03) (9.02) (11.21) (10.56) (12.09) (11.50) (7.24) (-0.88) Percent some high schools 1.3486 0.9164 1.6140 0.6744 0.760 0.7482 0.8342 0.8698 (14.80) (10.55) (16.89) (8.78) (9.98) (9.89) (10.12) (8.20) Percent high school graduates 0.5898 0.5229 0.7788 0.7182 0.6370 0.6715 0.6531 0.7098

(11.45) (11.36) (15.93) (18.02) (15.91) (16.19) (14.70) (12.08)

Percent some colleges 0.5394 0.6698 1.1658 1.1922 1.2396 1.0789 1.0420 1.2422

(6.95) (9.60) (17.16) (20.01) (20.61) (17.21) (16.43) (14.73) Average income( in 1,000 dollars) -0.0009 -0.0010 -0.0010 -0.0017 -0.0014 -0.0005 0.0004 -0.0004 (-2.74) (-3.68) (-3.20) (-5.75) (-5.46) (-1.86) (1.45) (-0.91) Average house value( in 1,000 dollars) 0.0001 -0.0001 -0.0003 -0.0003 -0.0005 -0.0003 -0.0004 -0.0009 (0.84) (-1.14) (-5.30) (-4.76) (-8.65) (-5.99) (-7.15) (-9.57)

Median rent 0.0000 0.0000 0.0001 0.0000 0.0001 0.0001 0.0000 0.0000

(2.78) (3.19) (4.25) (2.38) (4.78) (6.59) (3.59) (2.64) Percent unemployed 0.3122 0.0728 -0.1681 -0.2177 -0.2918 -0.3266 -0.2337 -0.2418

(2.82) (0.72) (-1.86) (-2.35) (-3.22) (-3.65) (-2.28) (-1.94) Tract income relative to MSA 0.0691 0.0660 0.1127 0.1029 0.1025 0.0163 -0.0355 0.0248 (3.05) (3.30) (5.13) (4.83) (5.42) (0.79) (-1.66) (0.73)

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32

Table 2b cont. Ordered Probit Model of Conventional-Prime(1) vs. FHA(2) vs. Subprime (3) Based on Refinance Originations (t-ratios based on standard errors clustered at the census tract level in parentheses)

2001 2002 2003 2004 2005 2006 2007 2008 African-American 0.2914 0.3067 0.3521 0.2876 0.2984 0.3294 0.3266 0.4170 (23.06) (27.13) (38.96) (36.03) (35.85) (41.66) (37.52) (38.52) Hispanic 0.1507 0.2255 0.2622 0.1060 0.1086 0.1254 0.1125 0.1859 (10.85) (18.67) (27.55) (3.74) (3.73) (4.07) (3.12) (4.61) Female 0.0000 -0.0133 0.0771 0.1366 0.1120 0.0737 0.0898 0.1029 (0.01) (-2.40) (16.86) (33.71) (27.61) (17.80) (18.49) (16.84) Co-applicants -0.3623 -0.3474 -0.2048 -0.1516 -0.2005 -0.1939 -0.0768 0.0382 (-58.15) (-68.82) (-44.26) (-36.91) (-47.39) (-44.99) (-16.00) (5.53) Income -0.0010 -0.0008 -0.0011 -0.0006 -0.0005 -0.0001 -0.0002 -0.0016 (-5.84) (-9.93) (-10.12) (-10.64) (-8.21) (-4.70) (-4.50) (-9.10)

Urban area fixed-effects 55 55 55 55 55 55 55 55

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33

Table 3a. Probit Model of FHA(0) vs. Subprime(1) Based on Home Purchase Originations (t-ratios based on standard errors clustered at the census tract level in parentheses)

2001 2002 2003 2004 2005 2006 2007 2008

Judicial foreclosure requirements 0.0021 0.0375 -0.0663 0.0186 -0.0209 -0.122 -0.0574 0.0922 (0.05) (1.09) (-1.94) (0.72) (-0.82) (-4.37) (-2.02) (2.74) Bankruptcy homestead exemption 0.0002 -2.35E-05 1.34E-05 -0.0003 -8.27E-05 -2.33E-05 -5.87E-05 -0.0001

(2.15) (-0.27) (0.15) (-3.87) (-1.29) (-0.33) (-0.74) (-1.26) Unemployment rate in the county -0.0324 -0.0138 -0.00473 0.0041 0.0522 0.0265 0.0283 0.0127 (-2.81) (-1.41) (-0.51) (0.47) (6.70) (2.65) (2.47) (1.15) Herfindahl-Hirschman Index of lenders 2.2962 2.4831 4.4738 0.2958 -3.3158 -0.0360 3.9858 5.7539

(2.21) (2.96) (5.19) (0.31) (-3.10) (-0.17) (3.47) (7.37) Conventional loan denial rate one year ago -0.2160 -0.5798 -0.4598 -0.6755 -0.8546 -0.2704 0.3379 -0.1473

(-1.53) (-3.99) (-4.13) (-3.44) (-4.93) (-1.70) (2.00) (-0.90) Percent aged 15-29 -0.3338 -0.0885 0.5170 0.2748 -0.0785 -0.3142 -0.3620 -0.8649 (-1.24) (-0.29) (1.67) (1.11) (-0.36) (-1.37) (-1.46) (-3.30) Percent aged 30-54 0.0189 0.2857 0.6823 0.5558 0.2967 -0.2743 0.1598 -1.3585 (0.05) (0.75) (1.86) (1.70) (0.95) (-0.86) (0.45) (-3.38) Percent aged 55-64 2.3053 2.2380 1.8641 2.4029 0.9913 1.0688 1.2895 2.3913 (4.59) (3.68) (3.02) (5.46) (2.58) (2.84) (2.90) (5.38) Percent aged 65+ -2.6048 -2.3479 -1.1261 -2.1544 -0.6885 -1.2495 -1.4060 -3.5192 (-4.75) (-3.89) (-1.82) (-4.44) (-1.53) (-2.85) (-2.68) (-6.58) Percent female -1.6662 -1.4953 -1.0438 -1.2416 -1.3967 -0.6789 -1.4164 -1.8308 (-4.29) (-4.00) (-2.52) (-3.53) (-4.12) (-2.04) (-3.73) (-4.58)

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34

Table 3a cont. Probit Model of FHA(0) vs. Subprime(1) Based on Home Purchase Originations (t-ratios based on standard errors clustered at the census tract level in parentheses)

2001 2002 2003 2004 2005 2006 2007 2008

Percent African-American 0.6048 0.6374 0.7054 0.4615 0.4680 0.4232 0.4184 0.3451 (11.86) (11.61) (13.54) (9.96) (9.86) (9.71) (8.16) (6.04)

Percent Hispanic -0.3030 -0.1339 0.1043 0.1218 0.3706 0.6335 0.4343 -0.0769

(-3.53) (-1.49) (1.21) (1.75) (5.13) (8.52) (4.89) (-0.79) Percent some high schools 0.5819 0.3467 0.2696 0.2704 0.4752 0.3511 -0.0382 0.8833 (2.51) (1.55) (1.17) (1.21) (2.42) (1.86) (-0.18) (3.70) Percent high school graduates -0.5154 -0.4571 -0.1680 -0.3583 -0.1624 0.0786 -0.2344 0.1041

(-3.89) (-3.45) (-1.26) (-2.62) (-1.30) (0.67) (-1.70) (0.75) Percent some colleges -2.6755 -2.1385 -1.8143 -1.2444 -0.6095 -0.6340 -1.3313 -1.5658

(-12.91) (-9.71) (-8.61) (-6.80) (-3.68) (-3.66) (-7.28) (-7.42) Average income( in 1,000 dollars) 0.0109 0.0066 0.0018 0.0065 0.0049 0.0056 0.0085 0.0105 (8.55) (5.41) (1.42) (5.63) (4.24) (4.80) (6.64) (9.19) Average house value( in 1,000 dollars) 0.0009 0.0016 0.0011 0.0008 0.0008 0.0006 -0.0000 -0.0019

(3.96) (6.74) (4.22) (3.30) (3.78) (2.79) (-0.11) (-7.93)

Median rent -0.0000 0.0001 0.0001 0.0001 0.0001 0.0001 -0.0000 -0.0002

(-0.45) (2.44) (2.25) (1.58) (1.53) (2.34) (-0.16) (-4.42)

Percent unemployed 1.4319 0.6812 0.1426 0.0894 -0.6569 0.0354 0.4367 0.5870

(5.82) (2.69) (0.54) (0.30) (-2.13) (0.16) (1.73) (2.05) Tract income relative to MSA -0.5732 -0.4003 -0.1138 -0.4283 -0.5052 -0.4474 -0.4465 -0.3070

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35

Table 3a cont. Probit Model of FHA(0) vs. Subprime(1) Based on Home Purchase Originations (t-ratios based on standard errors clustered at the census tract level in parentheses)

2001 2002 2003 2004 2005 2006 2007 2008 African-American -0.0605 -0.0154 -0.0358 0.0947 0.1470 0.1654 0.0182 -0.1495 (-3.10) (-0.81) (-1.92) (6.79) (10.71) (10.67) (1.03) (-6.84) Hispanic -0.3675 -0.3069 -0.2185 -0.1087 0.1660 -0.0192 -0.1213 0.0423 (-15.96) (-15.18) (-11.51) (-1.98) (2.57) (-0.28) (-1.42) (0.34) Female -0.0574 -0.0671 0.0113 -0.0403 -0.0236 -0.0448 -0.0795 -0.0595 (-4.51) (-5.55) (0.96) (-3.34) (-2.50) (-4.59) (-6.79) (-4.60) Co-applicants -0.2051 -0.3169 -0.4477 -0.4779 -0.5962 -0.6047 -0.5498 -0.2709 (-13.14) (-25.42) (-32.15) (-16.58) (-42.86) (-43.88) (-43.24) (-19.73) Income 0.0015 4.68E-05 0.0083 0.0028 0.0069 0.0070 0.0044 0.0035 (2.92) (0.99) (20.31) (1.59) (9.82) (10.52) (13.65) (17.06)

Urban area fixed-effects 55 55 55 55 55 55 55 55

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36

Table 3b. Probit Model of FHA(0) vs. Subprime(1) Based on Refinance Originations (t-ratios based on standard errors clustered at the census tract level in parentheses)

2001 2002 2003 2004 2005 2006 2007 2008

Judicial foreclosure requirements 0.1843 0.1858 0.1146 0.1186 0.1113 0.0206 0.0725 0.1308 (5.14) (5.67) (4.03) (4.70) (3.99) (0.86) (3.16) (4.72) Bankruptcy homestead exemption -0.0001 -0.0008 -0.0002 -0.0001 -0.0002 -1.95E-05 -0.0002 -0.0001

(-1.40) (-2.03) (-2.99) (-1.83) (-3.06) (-0.30) (-2.50) (-0.17) Unemployment rate in the county -0.0265 -0.0463 -0.0122 -0.0124 0.0103 -0.0096 0.0160 0.0048 (-2.48) (-5.07) (-1.63) (-1.68) (1.14) (-0.97) (1.72) (0.56) Herfindahl-Hirschman Index of lenders 3.7774 2.0662 3.2238 3.7517 5.1564 0.2847 7.3092 8.5894

(4.12) (2.41) (3.61) (3.41) (2.69) (1.31) (6.72) (11.11) Conventional loan denial rate one year ago -0.2201 -0.4938 -0.2455 0.0973 -0.0501 0.3739 0.4697 0.7032 (-1.65) (-3.45) (-2.59) (0.70) (-0.31) (2.51) (3.38) (5.47) Percent aged 15-29 0.0322 -0.2679 -0.2330 -0.2907 -0.1493 -0.2449 0.1754 -0.1278 (0.13) (-1.06) (-0.98) (-1.40) (-0.60) (-1.10) (0.83) (-0.57) Percent aged 30-54 0.2274 -0.1473 0.6877 0.1104 0.3639 -0.1302 0.7111 -0.4213 (0.64) (-0.43) (2.17) (0.39) (1.08) (-0.44) (2.45) (-1.34) Percent aged 55-64 2.3993 1.7392 1.3241 1.4495 0.5815 0.8113 0.7192 1.4148 (4.95) (3.78) (2.96) (4.17) (1.51) (2.36) (2.26) (3.99) Percent aged 65+ -2.6202 -2.1566 -1.2067 -1.8835 -0.5726 -1.3465 -0.8038 -1.3380 (-5.02) (-4.28) (-2.52) (-4.64) (-1.29) (-3.29) (-2.14) (-3.16) Percent female -0.6066 -0.4530 -0.3725 -0.4294 -0.2247 0.1722 0.3266 -0.6076 (-1.49) (-1.22) (-1.10) (-1.43) (-0.63) (0.52) (1.07) (-1.85)

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37

Table 3b cont. Probit Model of FHA(0) vs. Subprime(1) Based on Refinance Originations (t-ratios based on standard errors clustered at the census tract level in parentheses)

2001 2002 2003 2004 2005 2006 2007 2008

Percent African-American 0.7765 0.5615 0.4896 0.2495 0.1596 0.1379 0.1926 0.1154 (14.55) (10.33) (11.03) (5.99) (3.47) (3.32) (4.93) (2.54)

Percent Hispanic 0.1168 0.0529 0.0727 -0.3130 -0.0680 0.0607 0.0440 0.1423

(1.42) (0.68) (1.04) (-4.77) (-0.89) (0.85) (0.67) (1.74) Percent some high schools 0.9971 0.7001 0.5638 0.2415 0.4496 0.5212 0.5841 0.7291

(4.40) (3.18) (2.94) (1.28) (2.17) (2.75) (3.34) (3.81) Percent high school graduates -0.2009 -0.2022 -0.2927 -0.2832 -0.3563 -0.5427 -0.6049 0.0469

(-1.57) (-1.62) (-2.66) (-2.73) (-3.03) (-5.13) (-6.10) (0.43) Percent some colleges -2.7481 -2.4089 -2.5453 -1.8753 -1.3574 -1.2494 -2.0861 -1.7965

(-14.16) (-12.60) (-14.82) (-12.25) (-8.19) (-8.11) (-14.26) (-10.78) Average income( in 1,000 dollars) 0.0032 0.0021 0.0014 -0.0012 0.0001 0.0021 0.0058 0.0085

(3.01) (1.93) (1.39) (-1.28) (0.12) (1.70) (5.50) (7.79) Average house value( in 1,000 dollars) 0.0016 0.0014 0.0019 0.0015 0.0014 0.0012 0.0015 0.0002

(7.96) (6.88) (10.41) (8.12) (6.64) (6.12) (7.67) (1.05)

Median rent -0.0000 0.0000 -0.0000 -0.0001 -0.0000 0.0000 -0.0000 -0.0001

(-0.77) (0.19) (-0.68) (-2.37) (-0.36) (0.67) (-0.94) (-3.16)

Percent unemployed 0.7852 1.0400 0.9902 0.4891 0.8127 0.5825 0.4184 0.5452

(2.94) (3.77) (4.09) (2.15) (3.22) (2.49) (2.00) (2.33) Tract income relative to MSA -0.1686 -0.0034 -0.0998 0.0364 -0.0412 -0.2484 -0.4577 -0.4467

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