Mortgage Applications Research Articles

Banks' Liability Structure and Mortgage Lending During the Financial Crisis

We examine the impact of banks' exposure to market liquidity shocks through wholesale funding on their supply of credit during the financial crisis. We focus on mortgage lending since a comparison across heterogeneous categories of credit could be easily tainted by confounding demand factors. We exploit the time variation in market liquidity and the cross-section variation in wholesale funding by banks to study relative changes in bank rejection rates using comprehensive loan-level data on mortgage applications while controlling for bank, borrower, and regional characteristics. Banks that were more reliant on wholesale funding curtailed their credit significantly more than retail-funded banks during the crisis. The demand for mortgage credit, on the other hand, declined evenly across wholesale and retail banks. We also exploit the discrete fall in loan liquidity of jumbo loans (loans that cannot be purchased by the GSEs) and show that the relative decline in the supply of credit by wholesale banks was significantly more pronounced above the jumbo cutoff. This and several other robustness checks, including a matching exercise in which we eliminate imbalances in observable applicant characteristics between these banks, provide strong evidence that the crisis has led to a sharper contraction in the supply of credit by wholesale funded banks and highlight the role of liquidity conservation. We explore the aggregate implications of our findings by exploiting the heterogeneity in mortgage funding across U.S. MSAs and find that wholesale funding was a strong and significant predictor of a sharper decline in overall mortgage credit at the MSA level.

Banks' Liability Structure and Mortgage Lending During the Financial Crisis

We examine the impact of banks’ exposure to market liquidity shocks through wholesale funding on their supply of credit during the financial crisis in the United States. We focus on mortgage lending to minimize the impact of confounding demand factors that could potentially be large when comparing banks’ overall lending across heterogeneous categories of credit. The disaggregated data on mortgage applications that we use allows us to study the time variations in banks’ decisions to grant mortgage loans, while controlling for bank, borrower, and regional characteristics. The wealth of data also allows us to carry out matching exercises that eliminate imbalances in observable applicant characteristics between wholesale and retail banks, as well as various other robustness tests. We find that banks that were more reliant on wholesale funding curtailed their credit significantly more than retail-funded banks during the crisis. The demand for mortgage credit, on the other hand, declined evenly across wholesale and retail banks. To understand the aggregate implications of our findings, we exploit the heterogeneity in mortgage funding across U.S. Metropolitan Statistical Areas (MSAs) and find that wholesale funding was a strong and significant predictor of a sharper decline in overall mortgage credit at the MSA level.

In Mood for a Loan: The Causal Effect of Sentiment on Credit Origination

We study how loan officers’ mood affects their decisions on mortgage applications. Motivated by psychological evidence, we use three mood proxies: (1) outcomes of key sporting events such as the Super Bowl, (2) outcomes of the American Idol competition, and (3) days around major holidays. Our identification exploits the variation in daily loan approvals in each county, while observing the volume and quality of reviewed applications. Positive sentiment events are associated with a 4.5% higher loan approval rate in affected counties, and negative sentiment events have the opposite effect of a smaller magnitude. The effect is stronger for marginal quality applications, where loan officers have more discretion. The extra loans approved on high-sentiment days experience higher defaults. Overall, our evidence suggests that mood fluctuations affect decisions of financial experts and generate long-lasting real effects.

Banks' Liability Structure and Mortgage Lending During the Financial Crisis

We examine the impact of banks’ exposure to market liquidity shocks through wholesale funding on their supply of credit during the financial crisis in the United States. We focus on mortgage lending to minimize the impact of confounding demand factors that could potentially be large when comparing banks’ overall lending across heterogeneous categories of credit. The disaggregated data on mortgage applications that we use allows us to study the time variations in banks’ decisions to grant mortgage loans, while controlling for bank, borrower, and regional characteristics. The wealth of data also allows us to carry out matching exercises that eliminate imbalances in observable applicant characteristics between wholesale and retail banks, as well as various other robustness tests. We find that banks that were more reliant on wholesale funding curtailed their credit significantly more than retail-funded banks during the crisis. The demand for mortgage credit, on the other hand, declined evenly across wholesale and retail banks. To understand the aggregate implications of our findings, we exploit the heterogeneity in mortgage funding across U.S. Metropolitan Statistical Areas (MSAs) and find that wholesale funding was a strong and significant predictor of a sharper decline in overall mortgage credit at the MSA level.

TARP Consequences: Lending and Risk Taking

We use loan-level data on 25 million mortgage applications and 28 thousand corporate loans to study the effect of the Troubled Asset Relief Program (TARP) on credit availability. Contrary to the program’s declared objective, we find no evidence of greater credit origination by TARP participants relative to nonparticipants with similar characteristics. These results are consistent across retail and corporate lending and hold after controlling for loan demand and the selection of TARP recipients. Despite no changes in overall lending, recipient banks approve riskier loans and shift capital toward investments in risky securities. Overall, we evaluate the efficacy of government capital provisions and document their unintended consequences, consistent with models of moral hazard.

Residential Mortgage Default Behaviour in Hong Kong

Defaults in residential mortgages could be very costly and hazardous to market stability. This paper sets out to inform homebuyers, lenders and policy makers of the determinants of default, and constructs a mortgage default model to assist them in making mortgage applications, advancing loans, or implementing policies to maintain market stability. Residential default behaviour from 1998 to 2007 is studied and a model is constructed by means of Autoregressive Multiple Linear Regression. The results show that the lag term of default rate, gross mortgage rate, current loan-to-value ratio, change in debt-to-income ratio and Consumer Price Index are positively correlated with default rate; however, property price appreciation and change in the Hang Seng Index have a negative relationship with default rate.

The credit crunch of 2007: what went wrong? Why? What lessons can be learned?

The credit crunch of 2007: what went wrong? Why? What lessons can be learned?

The Subprime Crisis and House Price Appreciation

This paper argues that econometric analysis of housing price indexes before 2006 generated forecasts of future long-term price growth and low estimated probabilities of extreme price decreases. These forecasts of future increases in home-loan collateral values may have affected both the demand and the supply of mortgages. Standard time series models using repeat-sales indices suggested that positive trends had a long half-life. Expectations based on such models supported expectations that could lead to an asset bubble. Analysis of data from the HMDA loan data base and LoanPerformance at the MSA level and at the loan level substantiates both supply and demand effects of past price trends in housing markets, particularly with respect to subprime mortgage applications and approvals. At the MSA level, past home price increases are associated with higher subprime applications and loan to value ratios. Approval probability of subprime loans was not affected by higher loan to value ratios. At the loan level, the approval probability of subprime applications is also positively associated with past home price appreciation. These results differ for prime mortgages.

Giving Credit Where Credit is Due? The Community Reinvestment Act and Mortgage Lending in Lower-Income Neighborhoods

I identify and quantify the mortgage supply effect of the Community Reinvestment Act (CRA), a law mandating that banks help provide credit in lower-income neighborhoods, by exploiting a discontinuity in the selection rule determining which census tracts CRA targets. Using a comprehensive source of micro data on MSA mortgage applications, I find that CRA affects bank lending primarily in large MSA's, where banks are most scrutinized. The analysis indicates that CRA's effect on bank originations was about 4% between 1994 and 1996, and expanded to 8% in 1997-2002, consistent with the timing of a reform strengthening CRA. I provide some evidence that marginal loans go to atypical, potentially higher-risk borrowers. The results also indicate net "crowd-in": lending to targeted tracts by unregulated institutions rises in post-reform years, in particular to those areas that have had relatively low home purchase volume in the recent past, consistent with a model of information externalities in credit markets. Finally, using changes in tract eligibility status following the release of Census 2000 data as an additional source of variation, I find that CRA increased bank lending to newly targeted tracts in large MSA's by 4-5% in 2004 and 2005.

The Varying Effects of Predatory Lending Laws on High-Cost Mortgage Applications

Federal, state, and local predatory lending laws are designed to restrict and in some cases prohibit certain types of high-cost mortgage credit in the subprime market. Empirical evidence using the spatial variation in these laws shows that the aggregate flow of high-cost mortgage credit can increase, decrease, or be unchanged after these laws are enacted. Although it may seem counterintuitive to find that a law that prohibits lending could be associated with more lending, it is hypothesized that a law may reduce the cost of sorting honest loans from dishonest loans and lessen borrowers' fears of predation, thus stimulating the high-cost mortgage market.

Foreclosing on Opportunity: State Laws and Mortgage Credit

Foreclosure laws govern the rights of borrowers and lenders when borrowers default on mortgages. 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. I examine empirically the effect of the laws on equilibrium loan size. I exploit the rich geographic information available in the 1994 and 1995 Home Mortgage Disclosure Act data to compare mortgage applications for properties located in census tracts that border each other, yet are located in different states. Using semiparametric estimation methods, I find that defaulter-friendly foreclosure laws are correlated with a four percent to six percent decrease in loan size. This result suggests that defaulter-friendly foreclosure laws impose costs on borrowers at the time of loan origination.

Life insurance and mortgage application in adults with congenital heart disease.

To compare the outcome of life insurance and mortgage applications of adults with congenital heart disease (CHD) with controls and at different severities of CHD. Two hundred and ninety-nine adult CHD patients underwent a questionnaire-based interview by a trained nurse. They were asked to give an identical questionnaire to a friend to act as a control. One hundred and seventy-seven controls replied. CHD patients were classified into three categories based on severity. Comparisons were made between matched controls and between different severities of CHD. Similar proportions of the CHD group (59%) had applied for life insurance as matched controls (56%). Compared to controls, significantly more of the adults with CHD who had applied for life insurance have been refused (34 vs 4%, P < 0.0001) or asked to pay extra (37 vs 6%, P = 0.0002). Mortgage application rate was also similar in both groups with more of the CHD patients refused than matched controls (20 vs 3%, P = 0.0004). These differences in both life insurance and mortgage remain significant when the cases and controls are matched by employment status and NYHA functional class. There was no significant difference in life insurance and mortgage application outcome between the groups of mild, significant and complex CHD. Adults with CHD are significantly more likely to have difficulty obtaining life insurance or a mortgage than controls. Refusal rates appear to be independent of the severity of CHD. This suggests that the label of CHD may have a negative impact despite the lesion being minor and that the outcome of an individual application is difficult to predict based on the severity of the CHD. The increasing numbers of adults with CHD suggest that this problem is likely to increase and needs to be addressed as it can have a major impact on the patient's quality of life.

Redlining, the Community Reinvestment Act, and private mortgage insurance

This paper examines whether neighborhood racial or income composition influences a lender's treatment of mortgage applications. Recent studies have found little evidence of differential treatment based on either the racial or income composition of the neighborhood, once the specification accounts for neighborhood risk factors. This paper suggests that lenders may favor applicants from Community Reinvestment Act (CRA)-protected neighborhoods if they obtain private mortgage insurance (PMI) and that this behavior may mask lender redlining of low-income and minority neighborhoods. For loan applicants who are not covered by PMI, this paper finds strong evidence that applications for units in low-income neighborhoods are less likely to be approved, and some evidence that applications for units in minority neighborhoods are less likely to be approved, regardless of the race of the applicant. This pattern is not visible in earlier studies because lenders appear to treat applications from these neighborhoods more favorably when the applicant obtains PMI.

Foreclosing on Opportunity: State Laws and Mortgage Credit

Foreclosure laws govern the rights of borrowers and lenders when borrowers default on mortgages. 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. I examine empirically the effect of the laws on equilibrium loan size. I exploit the rich geographic information available in the 1994 and 1995 Home Mortgage Disclosure Act data to compare mortgage applications for properties located in census tracts that border each other, yet are located in different states. Using semiparametric estimation methods, I find that defaulter-friendly foreclosure laws are correlated with a four percent to six percent decrease in loan size. This result suggests that defaulter-friendly foreclosure laws impose costs on borrowers at the time of loan origination.

Foreclosing on Opportunity: State Laws and Mortgage Credit

Foreclosure laws govern the rights of borrowers and lenders when borrowers default on mortgages. 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. I examine empirically the effect of the laws on equilibrium loan size. I exploit the rich geographic information available in the 1994 and 1995 Home Mortgage Disclosure Act data to compare mortgage applications for properties located in census tracts that border each other, yet are located in different states. Using semiparametric estimation methods, I find that defaulter-friendly foreclosure laws are correlated with a four percent to six percent decrease in loan size. This result suggests that defaulter-friendly foreclosure laws impose costs on borrowers at the time of loan origination.

Mortgage lending and the entrepreneur

While existing literature assumes that financial institutions penalize entrepreneurs because of this group's perceived higher income risk, this paper contends the opposite, owing to, for example, potentially profitable customer relationships that financial institutions might have with entrepreneurs and the stylized fact that these institutions provide more flexible guidelines to the home mortgage applications of entrepreneurs. The 1995 National Microdata file of the American Housing Survey is employed to empirically address and provide support for this contention.

The Disappearance of Race in Mortgage Lending

In the past 25 years, housing researchers, governmental regulators, industry advocates, and community activists have all relied on a unified analytical infrastructure that was developed in the 1970s to monitor racial and geographic inequalities in U.S. housing markets. Since the mid-1990s, however, several developments in the housing finance sector have undermined a key element of this system: data collected under the auspices of the Home Mortgage Disclosure Act of 1975. The second-largest racial-ethnic group among mortgage loan applicants is now officially designated as “information not provided.” In this article, we analyze the causes and geography of this disappearance and its consequences for civil rights enforcement, academic research on redlining and discrimination, and community activism. Our analysis is simultaneously a cautionary narrative of the steady erosion of a valuable public data system and a strategic intervention intended to salvage the empirical contributions of the data for interdisciplinary research, community right-to-know purposes, and public policy development. Using Atlanta, Georgia, as a case study, we marshal a suite of multivariate techniques to disentangle the geographic, individual, and institutional factors responsible for the rise in unreported applications. We also devise a simple method to estimate the “true” racial and ethnic composition of the pool of unknown applicants. The contributions of this endeavor are mediated by the political and epistemological problems of racialization and categorical reification.

Looking the Other Way: A Critique of the Fair-Lending Enforcement System and a Plan to Fix it

In 2001, the homeownership rate in the United States reached 67.8 percent – an all-time high. The benefits of homeownership were not evenly spread across ethnic groups, however. In fact, the homeownership rate was 74.3 percent for non-Hispanic whites, 48.4 percent for non-Hispanic blacks, and 47.3 percent for Hispanics (U.S. Department of Housing and Urban Development 2002, Table 29). These homeownership gaps undoubtedly have many causes, but one of the key suspects is discrimination in mortgage lending. The vast majority of households cannot buy a house without a mortgage loan, and discriminatory barriers to obtaining a mortgage could have a dramatic impact on homeownership. A hint about the possible role of discrimination in mortgage lending comes from data collected under the Home Mortgage Disclosure Act (HMDA), which records the ethnicity of the applicant and the disposition of the application for virtually all the mortgage applications filed in the United States. In 2000, black applicants were twice as likely as white applicants to be turned down for a loan, and Hispanic applicants were 41 percent more likely to be turned down (FFIEC 2001b). These loan-approved disparities do not prove that blacks and Hispanics face discrimination in mortgage lending, because they do not account for possible differences in loan features or borrower creditworthiness across groups. Nevertheless, the differences are so dramatic that they focus attention on the possibility that this type of discrimination might exist. The purpose of this policy brief is to explore the possibility that mortgage lending discrimination contributes to ethnic disparities in homeownership, to evaluate the current fair-lending enforcement system, and to propose reforms in that system to make it more effective in uncovering – and, ultimately, eliminating – mortgage lending discrimination.

The Importance of Lender Heterogeneity in Mortgage Lending

Previous studies of the mortgage lending process, which fail to adequately account for inter-lender differences, suffer from a potentially serious omitted-variables bias. Using a sample of 23,094 home purchase mortgage applications from Pinellas County (St. Petersburg) Florida, over the 1993–1995 interval, I demonstrate that the inclusion of lender characteristics enhances the predictive power of accept/reject decision models of mortgage origination. In addition, the inclusion of lender attributes is shown to substantially influence the coefficient estimates of additional model parameters of interest, such as the applicant race and neighborhood racial composition variables. Finally, this study provides unique and supportive evidence regarding the existence and persistence of information externalities in home purchase mortgage markets.

Automated Underwriting and the Profitability of Mortgage Securitization

This paper develops a game-theoretic model of mortgage securitization, which is then used to examine a potential effect of automated underwriting. The paper's primary supposition is that automated underwriting lowers the costs to competitive mortgage originators and a monopolist securitizer of identifying mortgage applicants who are good credit risks. Faced with lower underwriting costs, originators will screen a larger number of mortgage applicants in the hopes of holding more good risks in their portfolios and passing through more bad risks to the securitizer. This mounting adverse-selection problem causes the securitizer's expected revenues to decline; this effect can outweigh the cost-saving benefit of automated underwriting, causing the securitizer's return on equity to fall.