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The Importance of Lender Heterogeneity in Mortgage Lending
Abstract
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.
... The standard model also typically includes banking institution, county, and year fixed-effects in acknowledgement that there may institution-, year-, or location-specific heterogeneity known to influence loan origination decisions that are not captured by the dataset being used ( Harrison, 2001 ). ...
... Lang and Nakamura (1993) argues that the volume of home sales is negatively related to uncertainty in home values, which is a disincentive for lending activity, and thus one should observe a positive association between home sales volume in a previous period and current lending activity. This has been consistently confirmed empirically ( Harrison, 2001 ;Calem, 1996 ;Ling and Wachter, 1998 ). Along similar lines, Avery et al. (1999) and Blackburn and Vermilyea (2007) argue that there are economies of scale associated with processing loans in a local area, such that institutional uncertainty about the quality of neighborhoods and property values is reduced. ...
This research explores whether banks strategically leverage regulatory rules for the Community Reinvestment Act that fix a neighborhood's eligibility status over a decade based on a neighborhood's economic trajectory over that decade. Using 2004–2011 Home Mortgage Disclosure Act (HMDA) data, we find that banks approve loans more frequently in those neighborhoods that are most rapidly improving, and that this effect is stronger if the neighborhoods are CRA-eligible low- and moderate-income (LMI) tracts. We find the “moving up” CRA premium ranges in magnitude from a 2 to 13 percent reduction in the likelihood an application is not approved. These results suggest that banks learn which neighborhoods are most rapidly improving and funnel activity to those places to reduce default risk while complying with the fair lending regulation. The results imply a potential unanticipated consequence of the regulation is that it changes the distribution of resources within the target population.
... Several studies have empirically investigated the existence and significance of informational externalities in mortgage underwriting and have generally provided supportive evidence for Lang and Nakamura's main prediction (see, for example, Calem, 1996, Ling and Wachter, 1998, Avery et al., 1999, Harrison, 2001, and Blackburn and Vermilea, 2007. 2 Blackburn and Vermilea (2007) is, perhaps, the most comprehensive empirical study that tests the information-based theory and, in the same time, the existence of redlining based on neighborhood minority status. They use proprietary data from 8 large banks to account for detailed applicant/loan characteristics, include a comprehensive list of neighborhood risk variables, and recognize the role of lender heterogeneity by allowing for lender-specific specifications for the explanatory variables in their accept/reject models (the only study to do so in the redlining literature). ...
... In these studies, number of sales is often proxied with number of originations. SeeBlackburn and Vermilea (2007) tandHarrison (2001) for more detailed reviews of previous studies. ...
Theories of rational redlining suggest a low housing sales volume should lead to greater uncertainty in house price appraisals, making the mortgage loan riskier, less attractive to lenders, and consequently more likely to be denied or charged a higher price. Previous empirical tests of this “information externality” theory are limited solely to the denial decision, and are based on US data from the 1990s. The mortgage bubble of the early 2000s offers a unique framework for revisiting the topic. First, widespread securitization enabled banks to sell their loans, making them unaccountable for loan riskiness. Second, underwriting was extended to applicants previously deemed too risky, with pricing serving as the mechanism of compensating investors for the increased risk. Using rich loan-level data from US banks, we test whether the effect of information externalities (if any) shifted from underwriting to pricing during the mortgage bubble. We also test for any evidence of discriminatory redlining. The information externality theory has important policy implications. It can justify government intervention even if discriminatory redlining is absent. Additionally, it implies that if low sale volume areas have higher minority concentrations, omission of information variables may lead to the misidentification of economically rational redlining as discriminatory redlining. Our findings provide evidence of a shift of the importance of information externalities from underwriting to pricing decisions, although even in pricing the economic importance of information externalities is small for a majority of borrowers. We find no evidence of discriminatory redlining, and strong evidence of the importance of individual creditworthiness factors.
... The unit of analysis is also central to distinguishing process-based from outcome-based redlining. Studies of process-based redlining (Harrison 2001;Holloway 1998;Holloway and Wyly 2001;Ling and Wachter 1998;Reibel 2000;Schill and Wachter 1993;Tootell 1996;Wyly 2002) employ individual-level data, whereas studies of outcome-based redlining (Avery, Beeson, and Sniderman 1999;see Schill and Wachter 1993 for a review) use aggregate data (Turner and Skidmore 1999). ...
... When lending institutions redline an area, they avoid lending to whole spatially contiguous areas, not just small blocks with similar characteristics scattered throughout a city. In their search for more subtle forms of discrimination against certain areas, researchers more recently have looked for statistical associations between racial composition and mortgage outcomes, without the same concern for spatial relationships (Avery, Beeson, and Sniderman 1999;Harrison 2001;Holloway 1998;Holloway and Wyly 2001;Ling and Wachter 1998;Phillips-Patrick and Rossi 1996;Reibel 2000;Schill and Wachter 1993;Shlay 1989;Tootell 1996;Wyly 2002). If, after controlling for a host of individual and neighborhood-level factors, areas with higher percentages of racial minorities have higher mortgage denial rates, there is evidence of redlining. ...
Despite widespread belief that redlining contributed to disinvestment in cities, there has been little empirical analysis of historical lending patterns. The lack of appropriate data and clear definitions of redlining has contributed to this void. This article reviews definitions and methods that have emerged from research on lending in recent years and considers how they can be applied to research on historical redlining. Address-level mortgage data from Philadelphia from the 1940s are analyzed using spatial regression, “hot spot” analysis, and surface interpolation.
Employing multiple definitions of redlining that focus on process and outcome, as well as spatial and statistical relationships in lending, the analyses result in a series of map layers that indicate where redlining may have occurred. In addition to providing some evidence of lending discrimination, this article promotes an explicitly spatial view of redlining that has conceptual and methodological implications for research on contemporary and historical redlining.
... Accordingly, properties located in thin, informationally opaque market segments are more risky to lenders. Calem (1996), Wachter (1998), andHarrison (2001) all provide empirical support for this information externality hypothesis within the mortgage underwriting process. In tangentially related work on mortgage terminations, Noordewier et al. (2001) find the semi-variance of appraisal estimates is directly related to increased default risk on loans within a single lender's nationwide portfolio of loans, while LaCour-Little and Malpezzi (2003) demonstrate deviations between appraised values and estimates from hedonic models of property value are associated with increased default risk for a sample of loans within the relatively volatile Alaskan housing market. ...
Using a sample of 26,892 rate quotes on home purchase loan applications, the current paper investigates interstate variation in residential mortgage interest rates. More specifically, we find posted rate quotes by lenders are directly related to measures of foreclosure process risk including the length of time required to complete foreclosure proceedings within a jurisdiction and the presence (and length) of statutory redemption
periods. Mortgage rates are also found to be contingent upon differential underwriting fees and conditions, housing appreciation and volatility measures, and the competitive nature of the economic marketplace in which each lender operates. In contrast to the previous literature, we find the judicial foreclosure process requirements exert little to no impact on observable mortgage interest rate quotes after controlling for these
additional dimensions of risk.
... We define the property neighborhood as the census block group (CBG) and determine the market liquidity by counting the number of sales transactions that occurred in the 12 months prior to the transaction date. We classify neighborhoods with ten or fewer sales over the previous year as having "low" liquidity, neighborhoods with between 11 and 30 sales 12 See Calem (1996), Ling and Wachter (1998), Avery et al. (1999), Harrison (2001), and Blackburn and Vermilyea (2007), among others for evidence supporting the role of transaction volume in providing information. In addition, He and Wang (1995) examine the relation between trading volume and information in the stock market. ...
The financial crisis of 2007 and 2008 spawned numerous regulations and policies designed to mitigate or eliminate future crises. This paper examines the effect of a regulatory action (the Home Valuation Code of Conduct) that was designed to reduce the incidence of inflated collateral valuation. We identify the causal impact of the regulation using a diff-in-diff identification strategy and confirm that the regulation reduced inflated valuations by 43% in the large lender sample. We find a much smaller effect for small lenders, a placebo sample, who were granted exceptions since it was considered cost prohibitive for them to comply.
... Accordingly, properties located in thin, informationally opaque market segments are more risky to lenders. Calem (1996), Wachter (1998), andHarrison (2001) all provide empirical support for this information externality hypothesis within the mortgage underwriting process. In tangentially related work on mortgage terminations, Noordewier et al. (2001) find the semi-variance of appraisal estimates is directly related to increased default risk on loans within a single lender's nationwide portfolio of loans, while LaCour-Little and Malpezzi (2003) demonstrate deviations between appraised values and estimates from hedonic models of property value are associated with increased default risk for a sample of loans within the relatively volatile Alaskan housing market. ...
Using a sample of 26,892 rate quotes on home purchase loan applications, the current paper investigates interstate variation in residential mortgage interest rates. More specifically, we find posted rate quotes by lenders are directly related to measures of foreclosure process risk including the length of time required to complete foreclosure proceedings within a jurisdiction and the presence (and length) of statutory redemption periods. Mortgage rates are also found to be contingent upon differential underwriting fees and conditions, housing appreciation and volatility measures, and the competitive nature of the economic marketplace in which each lender operates. In contrast to the previous literature, we find judicial foreclosure process requirements exert little to no impact on observable mortgage interest rate quotes after controlling for these additional dimensions of risk.
... 6 In the related context of Federal Housing Administration (FHA) home mortgages, Wachter has pointed out that FHA insurance fostered perverse incentives for lenders to foreclose on insured loans as soon as possible because the sooner the loans were prepaid, the greater the return (1980). 7 Possibly, this ability to exploit information asymmetries to the detriment of investors may affect the premise, first advanced by Lang and Nakamura, that economies of scale rise in neighborhood lending as lenders obtain more and more private information about neighborhoods and borrowers, whether through increased scale of operation or otherwise (Avery, Beeson, and Sniderman 1999;Harrison 2001;Lang and Nakamura 1993). 8 In addition, RMBS investors are concerned with liquidity risk (the risk of an illiquid resale market) and interest rate risk. ...
In this article, we examine the contention that the secondary market will exert sufficient market discipline to drive predatory home loan lenders from the subprime marketplace. Using a so‐called lemons model, we identify the potential risks that investors encounter if they buy securities backed by predatory home loans. We then explain how structured finance, deal provisions, pricing mechanisms, and legal protections shield investors from much of the risk that those loans entail.While the secondary market does impose some discipline on the subprime home loan market, it is not enough to bring predatory lending to a halt. We provide rationales for imposing liability on the assignees of predatory loans and describe the parameters of our proposed assignee liability legislation.
... They find no links between secondary market activities, by the 4 Lang and Nakamura [23] develop a model of mortgage lending that shows that, because of higher uncertainty, mortgage applications for properties located in neighborhoods with thin markets will be deemed riskier than applications from neighborhoods with high transaction volumes ("thick markets"). Many studies have since found empirical evidence in support of the theory, including Harrison [19], Calem [13], and Ling and Wachter [24]. GSEs or others, and sales prices in these neighborhoods and only a limited relationship between secondary market activities and sales volumes. ...
Recent years have witnessed ongoing research and policy debate regarding the effects on lower-income and underserved housing markets of the affordable housing goals set by government-sponsored enterprises (GSEs). While the GSEs were established to provide liquidity to mortgage markets and to mitigate severe cyclical fluctuations in housing, those entities are intended as well to support the provision of mortgage credit and the attainment of homeownership in lower-income and minority communities. Indeed, federal regulators have devoted much attention of late to the performance of Fannie Mae and Freddie Mac in promoting the flow of funds to, and hence the widespread availability of mortgage credit among, targeted and underserved communities.
The Federal Housing Enterprise Financial Safety and Soundness Act of 1992 (GSE act) raised the level of support that the GSEs are required to provide to lower-income and minority communities and authorized the secretary of the Department of Housing and Urban Development (HUD) to establish "affordable housing goals" for the GSEs. According to those goals, a defined proportion of each GSE's annual loan purchases must derive from the following:
The GSE act defines lower-income borrowers (for purposes of the low-moderate-income goal) as those having incomes less than the metropolitan area median income. Under the geographically targeted goal, lower-income neighborhoods are defined as those having median incomes less than 90 percent of the area median income, and high-minority neighborhoods are defined as those having a minority population that is at least 30 percent of the total population and a median income of less than 120 percent of the area median. For the special affordable goal, very low-income borrowers are those with incomes of less than 60 percent of the area median income. The special affordable goal also includes borrowers living in low-income areas with incomes less than 80 percent of the area median income.
The goals specify a required percentage of GSE loan purchases in each category. The specific percentages are adjusted periodically, as market conditions shift. The most recent HUD rules, set in November 2004 for purchase activity from 2005 through 2008, established the low-moderate-income goal in a range from 52 to 56 percent of total GSE purchases, the geographically targeted goal in a range from 37 to 39 percent, and the special affordable goal in a range from 22 to 27 percent. These categories are not mutually exclusive, so a single loan purchase can count toward multiple goals. Table 1 indicates how the HUD-specified affordable housing goal loan-purchase thresholds for the housing GSEs have evolved over time.
In this paper, we seek to determine whether the GSE mortgage-purchase goals are associated with improved housing conditions and homeownership attainment among targeted communities that are the focus of the GSE act and the HUD affordable housing goals. More generally, we seek to assess the effects of the GSE mortgage-purchase goals on the geographic distribution of GSE mortgage-purchase activity and to evaluate whether GSE mortgage purchases are associated with improved housing outcomes. This is done using a standard ordinary least squares framework as well as a two-stage least squares framework that accounts for potential endogeneity issues. Finally, the analysis seeks to corroborate whether the credit quality and performance of FHA-insured home mortgages deteriorated subsequent to enactment of the GSE mortgage-purchase goals. Such deterioration in the credit composition and performance of the FHA-insured mortgage pool could result from improved outreach and lending to underserved, lower-income, and minority borrowers on the part of conforming lenders, consistent with the objectives of the GSE affordable housing goals for home-loan purchases.
In the first test, we find that, after accounting for the endogeneity of GSE loan-purchase activity, the GSEs appear to increase their purchase intensity significantly in neighborhoods targeted by the GSE...
... Both studies employ data exclusively from 1990, a period of economic recession (particularly in real estate markets), during which Lang andNakamura (1990, 1993) hypothesize that information effects should be most pronounced. Harrison (1999) overcomes the deficiencies inherent in these single-period analyses and provides the first multiperiod evidence of the existence, persistence, and economic significance of information externalities in home mortgage lending. Specifically, he examines the Pinellas County (St. ...
Financial theory, supported by recent empirical evidence, suggests that property transactions in a particular market area generate information that makes similar future transactions in that area less risky for prospective lenders. Specifically, infor- mation from home sales helps appraisers develop more precise value estimates, which reduce the uncertainty (risk) faced by lenders and, in turn, may increase acceptance rates and the flow of funds to the given market area. Interestingly, Federal housing policy efforts have been noticeably absent in the assessment and correction of these potential mortgage market imperfections known as information externalities. Using a sample of government-sponsored enterprise (GSE) purchasing activities across 12 Florida counties, we find both Fannie Mae and Freddie Mac are more active in neighborhoods with historically low transaction volume than they are in other neighborhoods. In addition, the results of our investigation are generally con- sistent with the previous literature suggesting Fannie Mae outperformed Freddie Mac in historically underserved market segments in 1993-95. Finally, when the analysis is restricted to loans with a high imputed payment relative to borrowers' income, we again find that Fannie Mae and Freddie Mac are more active in neigh- borhoods with historically low transaction volume. These findings are consistent with the view that GSE activity mitigates information externalities, at least within our 12-county sample.
... Calem (1996) regresses the average mortgage approval rate in a subdivision of a county on the number of previous year's housing transactions finding a positive empirical relationship. Similarly, Ling and Wachter (1998) and Harrison (2001) model mortgage application approval using the number of housing transactions in each census tract in Dade County (Miami) and Pinellas County (St. ...
[eng] Transportation costs and monopoly location in presence of regional disparities. . This article aims at analysing the impact of the level of transportation costs on the location choice of a monopolist. We consider two asymmetric regions. The heterogeneity of space lies in both regional incomes and population sizes: the first region is endowed with wide income spreads allocated among few consumers whereas the second one is highly populated however not as wealthy. Among the results, we show that a low transportation costs induces the firm to exploit size effects through locating in the most populated region. Moreover, a small transport cost decrease may induce a net welfare loss, thus allowing for regional development policies which do not rely on inter-regional transportation infrastructures. cost decrease may induce a net welfare loss, thus allowing for regional development policies which do not rely on inter-regional transportation infrastructures. [fre] Cet article d�veloppe une statique comparative de l'impact de diff�rents sc�narios d'investissement (projet d'infrastructure conduisant � une baisse mod�r�e ou � une forte baisse du co�t de transport inter-r�gional) sur le choix de localisation d'une entreprise en situation de monopole, au sein d'un espace int�gr� compos� de deux r�gions aux populations et revenus h�t�rog�nes. La premi�re r�gion, faiblement peupl�e, pr�sente de fortes disparit�s de revenus, tandis que la seconde, plus homog�ne en termes de revenu, repr�sente un march� potentiel plus �tendu. On montre que l'h�t�rog�n�it� des revenus constitue la force dominante du mod�le lorsque le sc�nario d'investissement privil�gi� par les politiques publiques conduit � des gains substantiels du point de vue du co�t de transport entre les deux r�gions. L'effet de richesse, lorsqu'il est associ� � une forte disparit� des revenus, n'incite pas l'entreprise � exploiter son pouvoir de march� au d�triment de la r�gion l
... The majority of the empirical studies [Calem 1996, Ling and Wachter 1998, Avery et. al. 1999, Harrison 2001] find evidence 2 S-W [1981] defines credit-rationing as a situation where, (a) among observationally equivalent credit applicants some receive credit and others do not or (b) there are identifiable groups of applicants who, with a given supply of credit, are unable to obtain loans at any interest rate, even though with a larger supply of credit they would. 3 Using a model of mortgage default, Brueckner [2000] shows that in the equilibrium safe borrowers cannot obtain the large and high-LTV mortgages at a fair price because such mortgages are not offered in the market. ...
Credit-rationing model similar to Stiglitz and Weiss [1981] is combined with the information externality model of Lang and Nakamura [1993] to examine the properties of mortgage markets characterized by both adverse selection and information externalities. In a credit-rationing model, additional information increases lenders ability to distinguish risks, which leads to increased supply of credit. According to Lang and Nakamura, larger supply of credit leads to additional market activities and therefore, greater information. The combination of these two propositions leads to a general equilibrium model. This paper describes properties of this general equilibrium model. The paper provides another sufficient condition in which credit rationing falls with information. In that, external information improves the accuracy of equity-risk assessments of properties, which reduces credit rationing. Contrary to intuition, this increased accuracy raises the mortgage interest rate. This allows clarifying the trade offs associated with reduced credit rationing and the quality of applicant pool.
... All analyses of residential and commercial mortgage default of which we are aware ignore the characteristics of the originating lenders and focus solely on the characteristics of the loan and borrower. However, some recent evidence suggests that cross-sectional variation in lender characteristics explains a significant proportion of the variation in various mortgage lending 21 See, for example, Harrison (1998) and its references. ...
Option-based models of mortgage default posit that the central measure of default risk is the loan-to-value (LTV) ratio. We argue, however, that an unrecognized problem with extending the basic option model to existing multifamily and commercial mortgages is that key variables in the option model are endogenous to the loan origination and property sale process. This endogeneity implies, among other things, that no empirical relationship may be observed between default and LTV. Since lenders may require lower LTVs in order to mitigate risk, mortgages with low and moderate LTVs may be as likely to default as those with high LTVs. Mindful of this risk endogeneity and its empirical implications, we examine the default experience of 495 fixed-rate multifamily mortgage loans securitized by the Resolution Trust Corporation (RTC) and the Federal Deposit Insurance Corporation (FDIC) during the period 1991-1996. The extensive nature of the data supports multivariate analysis of default incidence in a number of respects not possible in previous studies. Consistent with our expectations, we find that LTV evidences no relationship to default incidence, while the strongest predictors of default are property characteristics, including three-digit ZIP code location and initial cash flow as reflected in the debt coverage ratio. The latter results are particularly interesting in that they dominated the influence of postorigination changes in the local economy. Copyright 2002 by the American Real Estate and Urban Economics Association..
This article examines the effect of high foreclosure rates on postrecession lending. Our hypothesis is that high neighborhood foreclosure rates will have a significant and positive effect on the likelihood of mortgage loan denial. In a case study on Toledo, Ohio, we explore the role of foreclosure activity, race, and racial disparities in lending practices and how they differ across neighborhoods. Our results suggest that applicants in high-foreclosure neighborhoods have a greater likelihood of loan denial (ceteris paribus). We also find that minority applicants face a higher probability of loan denial in high-foreclosure minority neighborhoods. Overall, the results depict highly variable lending practices where race seems to make a difference albeit in a small subset of neighborhoods deeply affected by the foreclosure crisis. There is also some indication of a chilling effect on minority loan applicants in Toledo during the postrecession period.
There is a vast literature on discrepancies in consumer credit related to race and ethnicity. I explore a pattern that was first identified in aggregate data by Han, Keys, and Li (2011) in their study of credit access: Blacks were approximately 27% less likely to receive offers from credit card lenders during the sample period, even after controlling for variables such as credit history, household income, and local economic conditions. Hispanics were 17% less likely to receive an offer, after including controls. The discrepancy is robust to lender-specific regressions and the inclusion of a large number of explanatory variables. My findings imply that marketing is an important area for analysis of discrimination in consumer credit. Due to the likely need for confidential information in further analysis, investigation by an appropriate regulatory agency such as the Consumer Financial Protection Bureau would be useful.
An accurate and reliable model is critical for analyzing racial/ethnic and socioeconomic inequality in the spatial distribution of higher-priced/subprime mortgages. Employing a spatial error model, this research analyzes spatial autocorrelation in higher-priced mortgages in the Philadelphia and Chicago MSAs. The empirical results show that higher-priced mortgages occur disproportionately on tracts with a higher proportion of African–American and Hispanic residents, lower income, and higher investment risks, as well as tracts in fringe suburbs. The results imply that the government needs to enforce fair lending policies to alleviate social inequality arising from subprime lending. The paper also demonstrates the difference of subprime mortgage market dynamics between two MSAs, suggesting that local governments need to consider local demographic and socioeconomic characteristics when they design housing policy interventions. Furthermore, the paper compares the results of the OLS model and the spatial error model, highlighting that the spatial error model can yield better model fitness and more accurate estimate coefficients. Analyzing spatial autocorrelation is a methodological innovation in mortgage studies, which can advance our understanding of the spatial distribution of higher-priced mortgages across neighborhoods within a MSA.
For two generations, urbanists have analyzed how residential mortgage lending reflects and reinforces inner‐city inequality. Yet the basic dichotomies of this literature have been eroded by parallel developments in community organizing, public policy, and restructuring of financial services. Securitization, institutional structure, and increasingly sophisticated market segmentation have altered the relationship between mortgage capital and the inner city, redrawing patterns of exclusionary redlining into more complicated, stratified inclusion into prime and subprime reinvestment flows.In this article, we analyze lending dynamics in neighborhoods at the nexus between gentrified reinvestment and enduring poverty in 23 large U.S. cities. A strong, sustained resurgence of capital investment is woven together with enduring racial‐ethnic exclusion that cannot be blamed on borrower deficiencies. Institutional restructuring and secondary‐market linkages reinforce newer class and racial‐ethnic inequalities through subprime segmentation: Lenders’ willingness to serve black borrowers, for instance, is becoming closely associated with subprime specialization.
One response to the incentives provided by the Community Reinvestment Act of 1977 (CRA) has been for lenders and community groups to enter into CRA agreements, which involve pledges to provide prescribed levels of service to targeted neighborhoods. This article examines whether lenders actually change their behavior after entering into these agreements. Using data on CRA agreements and on mortgage lending, we find that institutions increase their lending activity with each year an agreement is in force and that increased lending persists after an agreement expires. Additional analysis shows that agreements that include provisions for mortgage counseling and technical assistance are associated with increased targeted lending. By contrast, agreements with provisions requiring small business counseling and technical assistance and periodic meetings by review committees are associated with somewhat depressed lending levels. Further research is needed to draw definitive implications from this second set of results.
This paper assesses the impact of the Community Reinvestment Act (CRA), a law mandating that banks help meet the credit needs of lower income borrowers and neighborhoods. To measure the law’s effect on lending to targeted groups since 1994, I take advantage of discontinuous targeting rules and abrupt changes in neighborhood target status. On average, the CRA appears to have had little impact, including during the mid 2000’s when lending to lower-income areas soared. I do find a significant effect during the late 1990’s and early 2000’s in large MSAs – when and where the CRA may have been most binding for banks. I take advantage of this episode to test how CRA-induced lending affects overall credit supply; that is lending by both regulated banks and unregulated nonbanks. The results are consistent with the notion that government intervention in credit markets may be justified on the grounds that information externalities exist and can depress credit supply.
Despite the depth and breadth of U.S. credit markets, low- and moderate-income communities and minority borrowers have not historically enjoyed full access to credit. The Community Reinvestment Act (CRA) was enacted in 1977 to help overcome barriers to credit that these groups faced. Scholars have long leveled numerous critiques against CRA as unnecessary, ineffectual, costly, and lawless. Many have argued that CRA should be eliminated. By contrast, I contend that market failures and discrimination justify governmental intervention and that CRA is a reasonable policy response to these problems. Using recent empirical evidence, I demonstrate that over the last decade CRA has enhanced access to credit for low-income, moderate-income, and minority borrowers at relatively low cost, consistent with the theory that CRA is helping to overcome market failures. I argue that the form of CRA's legal directive, more akin to a standard, is preferable to more rules-based approaches, on grounds of both efficiency and legitimacy. Comparing CRA to other credit market regulations and subsidies, I argue that CRA is a reasonably effective response to market failures and should not be abandoned. In sum, contrary to previous legal scholarship, I contend that CRA is justified, has resulted in progress, and should be retained.
This study investigates factors affecting changes in the disparity of home mortgage denial rates between white and minority loan applicants in the U.S. during the period 1991–1997. We develop a two-stage least-squares regression model that incorporates applicant-level characteristics, neighborhood characteristics, regional economic data, and bank-specific data as explanatory variables. Some have argued that mortgage lenders were under increasing pressure from industry regulators to extend additional credit to minorities and low-income groups during the period under study. The model includes each institution's periodic CRA rating as a proxy for regulatory influence. An alternative explanation is that market forces, such as improvements in economic conditions and in bank financial condition and performance, affected default loss estimates and credit standards in a way that disproportionally benefited minority and low-income applicants. The empirical findings are consistent with the latter hypothesis. We conclude that policy makers should consider the impact of market factors when assessing the allocation of mortgage credit in a particular demographic market. The findings also underscore the importance of controlling for lender assessments of credit risk when evaluating compliance with CRA and fair lending statutes.
We develop three empirical models to identify the impact of Community Reinvestment Act (CRA) agreements on the mortgage lending behavior of small banking institutions during the period 1990–1997. CRA agreements are pledges banking institutions make to extend levels of credit to targeted populations and are often used by institutions to reaffirm their commitment to the goals of the CRA. We hypothesize that CRA agreements increase the level of competition for mortgage loans in the targeted area, which in turn causes a reduction in the quantity of mortgage credit to be supplied by community banks. Consistent with the quantity hypothesis, the results show that CRA agreements are associated with less mortgage lending, including lending in lower-income communities (CRA lending) and in minority communities (minority lending), by small community lenders. Evidence does not support a second hypothesis – that community banks respond to the increased competition by providing credit to riskier individuals.
This study evaluates the effects of the GSE mortgage purchase goals on homeownership and housing conditions among communities that are the focus of the 1992 GSE Act and the HUD affordable housing goals. To identify GSE effects, the test framework exploits differences in the definition of lower-income and underserved neighborhoods under the 1992 GSE Act, which specifies loan purchase goals for the GSEs, and the 1977 Community Reinvestment Act, which governs loan origination activity among the federally-insured depository institutions. Research finding suggest little efficacy of the GSE home loan purchase goals in elevating the homeownership and housing conditions of targeted and underserved neighborhoods, in turn suggesting the importance of ongoing federal policy focus in achievement of affordable housing objectives.
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.
American mortgage markets, once arenas of discrimination by exclusion, now operate as venues of segmentation and discrimination by inclusion: credit is widely available, but its terms vary enormously. One market segment involves sophisticated predatory practices in which certain groups of borrowers are targeted for high-cost credit that strips out home equity and worsens the risks of delinquency, default, and foreclosure. Unfortunately, it has become more difficult to measure inequalities of predatory lending: race – ethnicity and gender are ‘disappearing’ from the main public data source used to study, organize, and mobilize on issues of lending inequalities. In this paper, we present a mixed-methods case study of statistical representation of homeowners and homebuyers marginalized by race, ethnicity, and gender. A theoretical examination of official data-collection practices is followed by a discussion of alternative meanings of racial – ethnic and gender nondisclosure. Interviews with a sample of homeowners and homebuyers in the Washington, DC, area reveal some respondent ambivalence about the details of data-collection practices, but provide no consistent support for the idea that nonreporting is solely a matter of individual choice. Econometric analyses indicate that nondisclosure is driven primarily by lending-industry practices, with the strongest disparate impacts in African-American suburbs. Predatory lending is producing ambivalent spaces of racial – ethnic and gender invisibility, requiring new strategies in the reinvestment movement.
Theories of rational redlining suggest a low volume of sales should lead to greater uncertainty in house price appraisals, making the mortgage loan less attractive to lenders. This paper represents the first test of this “information externality” theory using a well-specified model of lending. In our preferred model, information externalities are relevant but the marginal effect diminishes quickly, with only about 10 percent of applications materially disadvantaged by a low volume of sales. Our results also support the presence of bank-level economies of scale to reviewing applications in a given area, with increased bank-level applications associated with higher acceptance rates.
Recent data released pursuant to the 1989 amendments to the Home Mortgage Disclosure Act (HMDA) which show large disparities in mortgage lending between minority and non-minority neighborhoods, have refocused the attention of policy makers, lenders, community advocates and academics on possible racial discrimination in the home loan market. In this paper, we review the existing literature on redlining. Many of the methodological shortcomings of the previous studies can be remedied by using post-1989 HMDA data to examine whether lender acceptance or rejection of mortgage applications is related to racial and ethnic neighborhood composition. We test two models of the lender’s decision to accept or reject loan applicants, one including and one without variables that proxy for neighborhood risk using data for Boston and Philadelphia. With proxies for neighborhood risk included, the results do not support the hypothesis that financial institutions redline neighborhoods in these two cities.
Introduction to the Logistic Regression Model Multiple Logistic Regression Interpretation of the Fitted Logistic Regression Model Model-Building Strategies and Methods for Logistic Regression Assessing the Fit of the Model Application of Logistic Regression with Different Sampling Models Logistic Regression for Matched Case-Control Studies Special Topics References Index.
Several studies have used single-equation models of the mortgage lending process to test for discrimination. Unfortunately, mortgages are heteroge- neous financial instruments, and the mortgage lending is complex, with adjustments made on several margins. We consider various single-equation models that have been proposed in the literature and find that each is seriously flawed as a test for discrimination. We conclude that unbiased tests for discrimination require, at a minimum, multiple-equation models estimated by econometric techniques capable of dealing with simultaneous equations or sequential selectivity with limited dependent variables. Even if maximum-likelihood techniques for making inferences about the parameters of these complex models can be devised, serious questions of specification remain. We do not know enough about the microfoundations of risk in mortgage lending to determine what variables belong in our models, and research should concentrate on this issue first.
Black-owned and white-owned banks are studied to test for lending discrimination based on applicant''s race. Using single-equation models the rndings suggest that black-owned banks may utilize applicant race in the mortgage granting decision. The result holds in a naive HMDA model and in an enhanced HMDA model with bank-specirc, demographic, and neighborhood characteristics added.
Disparities in mortgage lending patterns between minority and nonminority neighborhoods have refocused attention on the Community Reinvestment Act (CRA), a statute designed to encourage lending by financial institutions to nearby lower income neighborhoods. Geographic disparities may derive from discrimination, neighborhood and borrower attributes, as well as regulation itself. This article examines possible spatial impacts of the CRA. Tests for differential lender screening across regulated and nonregulated institutions in five metropolitan areas provide no consistent findings of regulatory effects. The article also tests whether lower income and minority applicants are more likely to be accepted when they apply for loans in lower income and minority neighborhoods. Using data for Boston, evidence is found for concentration effects that may result from institutional factors, information economies, or regulation. Copyright 1994 by Kluwer Academic Publishers
Black-owned and white-owned banks are studied to test for lending discrimination based on applicant's race. Using single-equation models the rndings suggest that black-owned banks may utilize applicant race in the mortgage granting decision. The result holds in a naive HMDA model and in an enhanced HMDA model with bank-specirc, demographic, and neighborhood characteristics added.
An evaluation of the feasibility of using Home Mortgage Disclosure Act (HMDA) data to form quantitative measures of fair lending for use in enforcement. The authors find that cross-lender differences in minority and low-income originations primarily reflect differences in application rates, not in approval rates, and that most of the variation in lender behavior cannot be attributed to variance in applicant characteristics reported in the HMDA data or to differences in the geographic markets served by the lenders.
Much of the controversy about whether mortgage lenders discriminate against minorities can be explained in terms of the confusion about how to define discrimination. Based on the legal definition, careful studies of loan denial rates, such as that done by the Federal Reserve Bank of Boston, represent an appropriate method for testing for discrimination by lenders. Based on that study, it is quite clear that lenders discriminate. The fact that minorities have higher default rates on average than whites is irrelevant to the interpretation of the race coefficient in such models. Nonetheless, more research on and discussion about the relationship between the race of the applicant and delinquencies, defaults, and losses would be desirable. Copyright 1998 by American Economic Association.
Geographic homeownership disparities have led to regulations meant to encourage mortgage lending in low-and moderate-income areas. This study examines the regulations and uses metropolitan areas as organizing units to assess how the mortgage market affects homeownership rates for four racial groups. The use of an econometric procedure that can account for the complicating properties of the data produces several policy-relevant results. Increases in mortgage rejection rates for African Americans tend to increase homeownership, suggesting that when minority applications are encouraged instead of prescreened, their mortgage rejection rate might increase, but so would the volume of successful home purchases. An increased mortgage-expense-to-income ratio deters homeownership for all races but has several times greater effect on minorities than on whites. The effect of rent-to-mortgage-expense ratio is insignificant for whites and Asians, whereas for African Americans and Hispanics the variable decreases homeownership. This result appears consis-tent with a lack of affordable housing, a major public policy concern.
This study examines the performance of home purchase loans originated by a major depository institution in Philadelphia under a flexible lending program between 1988 and 1994. We examine long-term delinquency in relation to neighborhood housing market conditions, borrower credit-history scores and other factors. We find that likelihood of delinquency declines with increasing neighborhood housing market activity. Also, likelihood of delinquency is greater for borrowers with low credit-history scores and those with high ratios of housing expense to income, and when the property is unusually expensive for the neighborhood where it is located.
When explanatory variable data in a regression model are drawn from a population with grouped structure, the regression errors are often correlated within groups. Error component and random coefficient regression models are considered as models of the intraclass correlation. This paper analyzes several empirical examples to investigate the applicability of random effects models and the consequences of inappropriately using ordinary least squares (OLS) estimation in the presence of random group effects. The principal findings are that the assumption of independent errors is usually incorrect and the unadjusted OLS standard errors often have a substantial downward bias, suggesting a considerable danger of spurious regression.
This paper addresses the question of whether economic incentives exist for mortgage lenders to avoid or to minimize mortgage originations in neighborhoods inhabited primarily by low-income racial minorities. The Option Pricing Model is utilized to determine what mortgage borrower characteristics affect the market value of the mortgage contracts. It is found that existing laws do not enable mortgage lenders to vary either origination prices or mortgage terms so as to adjust for differences in the market values of mortgages. As a result, incentives are created for both the mortgage lender and the mortgage insurer to avoid originations and underwritings in areas with relatively high default probabilities. Various changes in mortgage lending regulations are suggested to eliminate these incentives, and the effects of alternative programs to subsidize mortgage borrowers with relatively high default probabilities are considered.
This paper investigates whether reliable house price indices can be constructed using a limited set of descriptive variables on a large number of observations. Four primary index methods are compared: (1) median sale price, (2) restricted hedonic, (3) repeat sales, and (4) the assessed-value technique. The paper examines the precision and accuracy of each of the alternative indices.
We use individual-level mortgage application data to empirically test for scale economies in neighborhood lending, and find evidence of significant economies associated with the scale of operation of individual lenders in a neighborhood. The inability to exploit these economies of scale is found to explain a substantial portion of the higher denial rates observed in low-income and minority neighborhoods, where the markets are generally thin. This is consistent with arguments that lenders rationally redline these neighborhoods, and suggest that policies that encourage lenders to share information, or to specialize in lending in these neighborhoods should increase credit flows to these neighborhoods.
Recent studies suggest that price differentials between what whites and blacks pay for housing are largely a function of changes in supply and demand in the two submarkets. These studies, however, estimate models that assume a unified housing market. As a result, imputed prices of housing attributes cannot vary with location, and the analyses obscure important racial price differentials. Based on a more realistic and complex housing market theory, the model described in this paper indicates that housing prices are substantially higher in the ghetto and transition areas than in white areas, and that within the same area blacks must pay more than whites for equivalent housing.
Historically, lenders have been accused of 'redlining' minority neighborhoods as well as refusing to lend to minority applicants. Considerable bank regulation is designed to prevent both actions. However, the strong correlation between race and neighborhood makes it difficult to distinguish the impact of geographic discrimination from the effects of racial discrimination. Previous studies have failed to untangle these two influences, in part, because of severe omitted variable bias. The data set in this paper allows the distinct effects of race and geography to be identified and it shows that the evidence for redlining is weak. Copyright 1996, the President and Fellows of Harvard College and the Massachusetts Institute of Technology.
We present our efforts to develop bank-specific models to test for the presence of mortgage lending discrimination. We discuss the potential for selection and simultaneity biases and delineate the conditions under which a single-equation model is appropriate. The results from three national banks demonstrate that, by incorporating the specific underwriting guidelines of each bank, our alternative approach significantly improves the ability of the model to explain the outcomes of the mortgage lending decision process when compared to a single generic specification applied across all banks. Our results also demonstrate the difficulties encountered in attempting to incorporate the specifics of a bank's underwriting criteria and the remaining potential for omitted-variables problems. Copyright American Real Estate and Urban Economics Association.
This study conducts a cross-sectional analysis of U.S. metropolitan counties to inquire into the factors affecting white and minority mortgage loan approval rates during 1990–1991. In particular, evidence is sought on whether minority loan applicants are denied credit more frequently than white applicants because of information externalities. Within each county, all predominantly minority, low- or moderate-income census tracts are groupted together, and then regression equations are estimated across counties and tract groupings. Separate approval rate equations are estimated for conventional and federally insured (FHA or VA) home purchase loans. In addition, a regression equation for the percentage of applicants applying for federally insured loans is estimated.
Statistical analyses of mortgage redlining at the neighborhood level have fueled the debate over the existence of racial redlining in mortgage lending, both "proving" and "disproving" that redlining exists, depending upon the type of model used. In this paper, we compare results of different statistical models using data for the Washington, DC metropolitan area to determine their usefulness in providing statistical evidence on this issue. After demonstrating the sensitivity of single-equation models to specification error, we estimate a simultaneous equations model of mortgage credit flows. This model makes it possible to analyze differences in the supply and demand for mortgage credit by the racial composition of the community. We conclude that most, if not all, statistical evidence of racial redlining based on aggregate loan data is at best inconclusive, and more likely, misleading.
This paper considers the empirical assessment of the relationship between prices and number of firms in local markets in geographic or, more generally, characteristic space and its use as evidence in merger cases. It outlines a structural, semi-nonparametric econometric model of competition in such markets, examines its testable implications in terms of price-concentration relationships, and demonstrates that the model is non-parametrically identified. This general approach to price-concentration analysis in differentiated product markets is illustrated in a small-scale application to cinemas in the UK. The application highlights the main decision points faced by an authority when assessing the weight that can be attached to this type of analysis as evidence. --
The results of this study indicate that minority applicants, on average, do have greater debt burdens, higher loan-to-value ratios, and weaker credit histories and they are less likely to buy single-family homes than white applicants, and that these disadvantages do account for a large portion of the difference in denial rates. Including the additional information on applicant and property characteristics reduces the disparity between minority and white denials from the originally reported ratio of 2.7 to 1 to roughly 1.6 to 1. But these factors do not wholly eliminate the disparity, since the adjusted ratio implies that even after controlling for financial, employment, and neighborhood characteristics, black and Hispanic mortgage applicants in the Boston metropolitan area are roughly 60 percent more likely to be turned down than whites. This discrepancy means that minority applicants with the same economic and property characteristics as white applicants would experience a denial rate of 17 percent rather than the actual white denial rate of 11 percent. Thus, in the end, a statistically significant gap remains, which is associated with race. ; The information gathered in this survey provides some insight into how this outcome emerges. Many observers believe that no rational lender would turn down a perfectly good application simply because the applicant is a member of a minority group. The results of this survey confirm this perception; minorities with unblemished credentials are almost (97 percent) certain of being approved. But the majority of borrowers - both white and minority - are not perfect, and lenders have considerable discretion over the extent to which they consider these imperfections as well as compensating factors.
A dynamic credit market is modeled in which the production and processing of information leads to alternating and persistent periods of growth and contraction. In the model, the number of projects engaged in determines the precision of estimates of future returns obtained from current investment. Shocks to underlying returns to risky projects are magnified and prolonged by the learning process. Multiple equilibria may exist and the magnification effect may be even larger since shocks can move the credit market to a new equilibrium. The allocation of credit is generally not a constrained Pareto-efficient allocation.
The Home Mortgage Disclosure Act was enacted to monitor minority and low-income access to the mortgage market. The data collected for this purpose show that minorities are more than twice as likely to be denied a mortgage as whites. Yet variables correlated with both race and creditworthiness were omitted from these data, making any conclusion about race's role in mortgage lending impossible. The Federal Reserve Bank of Boston collected additional variables important to the mortgage lending decision and found that race continued to play an important, though significantly diminished, role in the decision to grant a mortgage. Copyright 1996 by American Economic Association.
We develop a model of mortgage redlining which captures the dynamic information gathering which is implied by the use of appraisals in mortgage granting. In our model, the precision of appraisals depends on the quantity of previous home sales. In turn, the precision of appraisals influences current home sales, since when appraisals are inaccurate, lenders require larger down payments. There is thus a dynamic information externality in which past purchases influence current purchases. As a consequence, differential mortgage lending behavior will be sub-optimal and the appearance of redlining may be justifiably subject to corrective action.
The diagnostic tools examined in this article are applicable to regressions estimated with panel data or cross-sectional data drawn from a population with grouped structure. The diagnostic tools considered include (a) tests for the existence of group effects under both fixed and random effects models, (b) checks for outlying groups, and (c) specification tests for comparing the fixed and random effects models. A group-specific counterpart to the studentized residual is introduced. The methods are illustrated using a hedonic housing price regression.
Market Responses to Federal Examinations of Commercial Banking Firms
- M Flannery
- , J Houston
Bank-Specific Determinants of Community Reinvestment Act (CRA) Performance, Working Paper
- D M Harrison
Government intervention in the mortgage market
- Masulis
A Study of the Effectiveness of CRA Ratings in Capturing Discrimination in Mortgage Lending
- B C Moore