Policy Issues Concerning Racial and Ethnic Differences in Home Loan Rejection Rates

Transcription

1 Policy Issues Concerning Racial and Journal Ethnic Differences of Housing in Research Home Loan Volume Rejection 6, Issue Rates1 115 Fannie Mae All Rights Reserved. Policy Issues Concerning Racial and Ethnic Differences in Home Loan Rejection Rates Brent W. Ambrose, William T. Hughes, Jr., and Patrick Simmons* Abstract Policy-making institutions associated with the mortgage lending market are facing increased pressure to respond to studies demonstrating wide disparity in loan rejection rates between white and minority applicants. Because the disparity is commonly attributed to discrimination, calls are going out to regulators and policy makers to address the issue. Unfortunately, not all the causes of the disparity are easily detected or fully understood, so policy makers must compare options without complete information. Despite the potential for imposing substantial costs on the market by implementing ill-considered and overly restrictive interventions, government inaction could lead to large costs in efficiency and equity if there is widespread discrimination in the market. To guide consideration of alternative policies for addressing the disparity, this article develops a framework based on a model of the underwriting process that illustrates potential sources of disparity. The article also discusses costs and benefits of policy options for reducing the disparity. Keywords: Racial discrimination; Mortgage underwriting; Home lending policy; Minorities Introduction The issue of discrimination in mortgage lending has received considerable attention in response to the publicity surrounding the reporting requirements imposed on financial institutions by the Home Mortgage Disclosure Act (HMDA). In the fall of 1991, the Board of Governors of the Federal Reserve Board released a summary study of the 1990 HMDA data (Canner and Smith 1991). The study identified large differences between loan application rejection rates for white and African-American applicants and thereby spurred renewed interest in the issue of mortgage discrimination. Since the release of the study, the press, the Federal Reserve, the U.S. Department of Justice, and individual banks have focused increased attention on racial and ethnic differences in mortgage lending outcomes and discrimination in the mortgage market. This article focuses on possible public sector responses to differences in loan rejection rates. 1 * Brent W. Ambrose is Assistant Professor of Finance at the University of Wisconsin Milwaukee. William T. Hughes, Jr., is Assistant Professor of Finance at Louisiana State University. Patrick Simmons is Manager of Housing Demography at the Fannie Mae Office of Housing Research. The authors wish to thank Mark Griffiths, John Goering, Isaac Megbolugbe, Eric Weiss, and two anonymous reviewers for their helpful comments and advice on an earlier draft of this article. 1 Other lending variables, such as application rates or the volume of loan originations, could also be examined. In fact, it has been suggested that the most important consideration in addressing lending disparities for African Americans is low loan application rates, not high loan rejection rates (Schnare 1994). In focusing on rejection rate differences, we do not minimize the importance of the preapplication stage of the mortgage lending process, including the need to ensure that discrimination does not exist at this stage and to expand outreach efforts to increase the number of minority applicants. But simply increasing the rate of applications

2 116 Brent W. Ambrose, William T. Hughes, Jr., and Patrick Simmons Evidence clearly demonstrates that white and minority loan applicant groups experience different rejection rates. 2 The Federal Reserve study reported rejection rates of 34 percent for African-American applicants and 14 percent for white applicants (Canner and Smith 1991), so the rejection ratio, calculated by dividing the African- American rejection rate by the white rejection rate, is The Federal Reserve study, however, made no attempt to understand what portion of the interracial difference in rejections was attributable to interracial differences in applicant, property, or loan-term characteristics. A more recent study of lenders in the Boston area conducted by the Federal Reserve Bank of Boston (the Boston Fed) accounts for many such factors and shows that minority applicants on average demonstrate lower incomes, higher debt levels, and less favorable credit histories than white applicants. Even after controlling for these factors, however, the Boston Fed study reported rejection rates of 17 percent for minority applicants and 11 percent for white applicants, for a rejection ratio of 1.55 (Munnell et al. 1992). A similar study conducted by Siskin and Cupingood (1993) as part of the Department of Justice s investigation of Decatur Federal Savings and Loan found that one-quarter of African-American applicants rejected for conventional fixed-rate loans and one-half of African-American applicants rejected for conventional adjustable-rate loans would have been accepted had the lender evaluated them using the same standards it applied to white applicants (Turner 1993). These studies demonstrate a difference in rejection rates across racial groups and indicate that some of the difference is explained by risk characteristics that are correlated with race. Whether the residual difference is attributable to lending discrimination remains the subject of considerable debate. 3 Muth (1986) has argued that racially motivated discrimination will be competed away by profit-maximizing firms if the mortgage market is informationally efficient and functions properly. Thus, the disparity in rejection rates must be attributable to some external social factors, such as lack of economic opportunities, that cause minority groups to exhibit higher credit risk. By this argument, the disparity in rejection rates is a symptom of some greater underlying social problem and not necessarily a product of the lending process. The opposing argument posits that discrimination occurs in the lending process and is responsible for part of the observed differences in loan rejection rates. Several scholars have concluded that the Boston Fed, Decatur Federal, and other studies provide a strong case for the existence of discrimination in the home mortgage market (e.g., Cloud and Galster 1993; Galster 1993a; Yinger 1993). from minority groups will not improve equity in lending outcomes. That is, loan products and services must meet the needs and preferences of minority groups that may be underserved by the market. 2 Not all minority groups have higher rejection rates than whites. Although HMDA data have consistently revealed substantial differences in white African-American, white-hispanic, and white American Indian/ Native Alaskan rejection rates, the same is not true for Asians/Pacific Islanders. In 1993, overall mortgage rejection rates for conventional loans were 15 percent for both whites and Asians/Pacific Islanders (Just- Released HMDA Data 1994). 3 See Carr and Megbolugbe (1993b) for a review of the debate surrounding the Boston Fed study.

3 Policy Issues Concerning Racial and Ethnic Differences in Home Loan Rejection Rates 117 Rather than engaging in the continuing debate, this article discusses policy options available to address the disparity in rejection rates. A major challenge facing policy makers is to select the appropriate intervention or mix of interventions without complete knowledge of the underlying causes of the observed differences. Although public policies are often adopted with incomplete information, inappropriate public interventions in the mortgage market could produce substantial market distortions and significant societal costs. Yet inaction could lead to large costs in efficiency and equity if there is widespread discrimination in the market. In acknowledging this policy-making quandary, this article examines potential costs and benefits of alternative policies as a first step in informing the adoption of strategies designed to address racial and ethnic differences in loan rejection rates. The article begins by developing a conceptual model of the loan underwriting process and then uses the model as a tool for identifying factors that contribute to observed differences. Included is a discussion of how various types of discrimination and the legitimate consideration of risk and return contribute to differences in rejection rates. After discussing the factors contributing to observed differences, the article considers the general types of costs and benefits associated with various policy interventions. It then describes selected policies targeted at each of the factors. The following types of interventions are considered: quotas, antidiscrimination programs, interest rate supplements, direct federal lending, and federal loan guarantees. The article provides qualitative assessments of the costs and benefits of these interventions and offers suggestions for future research. Identifying the Sources of Differences in Loan Rejection Rates Before examining policies that might decrease observed differences in loan rejection rates, it is necessary to examine how legitimate underwriting procedures and illegal discrimination might affect the outcome of the mortgage underwriting process. Yinger (1993) provides a succinct analytic definition of illegal personal discrimination based on the following equation: 4 R = f ( A, P, T, M, N ), (1) 4 Fair housing and lending statutes prohibit two major types of lending discrimination. The first type, termed personal discrimination, is based on characteristics of the applicant, such as race and sex, that are protected by law. The second, commonly called redlining, is based on the racial composition of the neighborhood of the property. Our study is limited to consideration of personal discrimination against racial and ethnic minorities. The form of equation (1) suggests a single-equation model of the underwriting process; indeed, the Boston Fed and Decatur Federal studies employed such models (Munnell et al. 1992; Siskin and Cupingood 1993). However, as Rachlis and Yezer (1993) point out, the lender s decision might more appropriately be modeled as a system of equations, with several of the independent variables of the single-equation model determined endogenously. Specifically, it is likely that some loan-term variables are endogenous to the lending decision process.

4 118 Brent W. Ambrose, William T. Hughes, Jr., and Patrick Simmons where R = the probability that a loan is rejected; A, P, and T = the applicant, property, and loan-term characteristics, respectively, used by the lender in the underwriting process to predict the return on the loan; M = the minority status of the applicant; and N = the racial/ethnic composition of the neighborhood where the property is located. According to Yinger, illegal personal discrimination occurs whenever loan applications from a minority group are more likely to be rejected than applications from the majority group, controlling for expected return. Following this definition and referring to equation (1), personal discrimination occurs whenever ]R/]M > 0. Before discussing further how discrimination contributes to interracial differences in loan rejection rates, it is useful to define the three types of personal discrimination that might distort the lending process (Board of Governors of the Federal Reserve System 1991; Carr and Megbolugbe 1993a; Galster 1991). 5 The first, termed blatant discrimination, involves explicit use of a protected characteristic such as race in establishing or implementing lending policies. This form of discrimination is the easiest to detect and challenge in court and is therefore the rarest form. The second type of discrimination involves disparate treatment of equally qualified mortgage applicants on the basis of one applicant s membership in a legally protected class of individuals. For example, a lender might overlook past credit difficulties or waive a requirement for a white applicant but not for a minority applicant. 6 A prima facie case of disparate treatment discrimination is demonstrated when all the following conditions hold (Board of Governors of the Federal Reserve System 1991): 1. The plaintiff is a member of one of the groups protected under the federal fair lending laws. 2. The plaintiff applied for and was qualified for credit under the creditor s creditgranting standards. 3. The plaintiff, though qualified, was denied credit. 5 Title VIII of the Civil Rights Act of 1968 (the Fair Housing Act) and the Equal Credit Opportunity Act prohibit discrimination on the basis of race and other protected characteristics in credit transactions related to residential real estate. The Civil Rights Acts of 1866 and 1870 have also been interpreted to prohibit discrimination in lending (Board of Governors of the Federal Reserve System 1991). 6 A particular type of disparate treatment discrimination that is the subject of later discussion is statistical discrimination, which Yinger (1993, 4 5) describes as follows: Lenders may believe, based on past experience, that minority applicants have poorer unobserved qualifications than others on average, but using this knowledge to deny a loan to a minority applicant is a form of discrimination, called statistical discrimination. In other words, it is discrimination to turn down a loan to a minority applicant on the basis of information that is valid for minorities on average but that may not be valid for that individual. This type of behavior may be economically rational, but it still is discrimination.

5 Policy Issues Concerning Racial and Ethnic Differences in Home Loan Rejection Rates Other applicants who had the same qualifications as the plaintiff were granted credit. The third type of discrimination is adverse impact. A prima facie case of adverse impact discrimination is established when the plaintiff demonstrates that a seemingly innocuous policy or practice disproportionately denies credit to a protected class of individuals. To disprove a claim of discrimination, a defendant must demonstrate that the policy or practice is related to a business necessity. In mortgage underwriting, a guideline that disproportionately leads to rejections of minorities and is not related to loan return would be termed discriminatory under the adverse impact rule. Keeping these definitions in mind and returning to the issue of interracial differences in loan rejection rates, the major factors contributing to observed differences may be identified by referring to equation (1) and to figure 1. Variables A, P, and T from equation (1) fall into one of two categories: those that are good predictors of loan return and those that are not. Those that are good predictors are legitimate underwriting variables as long as they are not applied differentially on the basis of race. Because minority and white applicants, on average, bring different A, P, and T endowments to the underwriting process, the legitimate and legal application of these underwriting factors contributes to observed aggregate differences in rejection rates (arrows A and B in figure 1). Certain A, P, and T variables that are not good predictors of loan return may also be used in underwriting. Like the good predictors, the poor predictors can interact with intergroup differences in endowments to produce interracial differences in rejection rates (arrows C and F in figure 1). In this case, however, because the variables do not serve a legitimate business purpose (i.e., prediction of loan return), their use in underwriting constitutes adverse impact discrimination. 7 Finally, assuming that the functional form of equation (1) is appropriately specified and that all A, P, and T variables used in the lending decision are included, a positive and significant coefficient on the M variable indicates that individual minorities have been victims of disparate treatment in the underwriting process. 8 Given that these forms of discrimination lead to rejection of minority applicants who would have received loans if they were white, they also contribute to aggregate interracial differences in loan rejection rates (arrows D and E in figure 1). 9 7 Note that the adverse impact form of discrimination can exist even when ]R/]M = 0. In other words, Yinger s analytic definition expressed in equation (1) and the condition ]R/]M > 0 correspond to the disparate treatment form of discrimination. 8 If the model is applied across lenders or metropolitan areas, two additional assumptions must apply: There is no tendency for minorities to apply disproportionately to lenders that have stricter underwriting standards, and minority applicants are not concentrated in metropolitan areas where lenders apply stricter underwriting standards. 9 Although equation (1) expresses the probability that an individual applicant will be rejected for a mortgage loan, figure 1 and most of the discussion in this section relate to differences in rejection rates between populations. If the estimation error were zero, then substitution of a group s mean values of A, P, and T into equation (1) would yield the group s observed rejection rate.

6 120 Brent W. Ambrose, William T. Hughes, Jr., and Patrick Simmons Figure 1. Conceptual Framework for Understanding Racial and Ethnic Differences in Loan Rejection Rates Lending Process Output Observed differences in rejection rates B D E F Legitimate A, P, and T factors considered in the underwriting process Blatant discrimination Disparate treatment Adverse impact Underwriting Discrimination A C Intergroup differences in average applicant characteristics (e.g., income, wealth, debt levels, collateral value) Lending Process Inputs Underwriting Process

7 Policy Issues Concerning Racial and Ethnic Differences in Home Loan Rejection Rates 121 Benefits and Costs of Policy Interventions Before discussing specific interventions targeted at the factors discussed above, it is useful to consider the benefits and costs that may be associated with the interventions. This section provides some general insights into the benefits and costs. Although costs are discussed at greater length, the reader should not draw conclusions from the immediate discussion about the relative magnitudes of benefits and costs associated with interventions. Benefits As previously stated, several recent studies have provided evidence that suggest the existence of racial discrimination in the lending decision-making process. To the extent that discrimination permanently prevents some qualified minority households from receiving credit and achieving homeownership, reducing the disparity in home loan rejection rates could have several economic and social benefits for households, communities, and society. 10 Most of the work to date on the benefits of homeownership has examined the economic and psychological benefits to families and individuals. Kain and Quigley (1975, 150) concluded that homeownership has been the most important method of wealth accumulation for low- and middle-income families in the postwar period. Other economic benefits include the greater consumption and amenity value of homeownership (Megbolugbe and Linneman 1993). Analysts have also cited the psychological benefits of homeownership for families, including increased life satisfaction, a greater sense of control, and higher self-esteem. 11 The broader community and societal benefits of homeownership have received less research attention. Despite the lack of empirical evidence for these benefits, homeownership has long been a national policy goal (Mitchell 1985; Stegman et al. 1991). Homeownership is generally viewed as a positive influence on community and political involvement, as a force for political stability, and as a major stimulus for growth in construction and related industries (Megbolugbe and Linneman 1993). Other potential benefits of public policies designed to reduce differences in loan rejection rates depend on the specific intervention applied. For example, an intervention that achieves greater equality in rejection rates by reducing discrimination also advances the public policy objectives of protecting basic civil rights and providing equal access to housing and mortgage credit. To the extent that loans are currently approved for 10 Even if discrimination does not prevent minority applicants from eventually obtaining mortgage loans and attaining homeownership, it imposes added search costs on its victims. Therefore, even if interventions do not reduce interracial differences in homeownership rates but only make the credit search process easier and less expensive, they can provide substantial economic benefits to minority credit applicants. 11 Rohe and Stegman (1994) found evidence only for improved life satisfaction in a study of low-income renters who became homeowners. Their study provides a good review of the previous literature on the relationships between homeownership and self-esteem, perceived control, and life satisfaction.

8 122 Brent W. Ambrose, William T. Hughes, Jr., and Patrick Simmons white applicants who are less credit worthy than minority applicants, eliminating discrimination would also promote more efficient allocation of mortgage credit. Finally, a policy that works to reduce racial and ethnic differences in loan rejection rates could reduce interracial/ethnic inequities in the distribution of wealth. Whether interventions in the mortgage lending process are the best or even a reasonable method for achieving this equity objective is debatable. Costs The costs of public policy interventions designed to reduce differences in loan rejection rates can be divided into four categories: organization, implementation, measurement and enforcement, and market efficiency. Organization. Organizational costs arise from the debate over where a particular intervention starts and stops. They are not the costs of debating whether action should be taken, nor are they the costs associated with the choice of which action to take, which are termed sunk costs. Rather, organizational costs as defined here are associated with establishing limits, determining the party in charge of measurement and enforcement, and deciding on the remedial action to be taken in case of a violation. Once the choice of policy action is made, the institution imposing the action must establish the administrative features of the action. For instance, the institution must determine whether all or only some participants in the market will be targets of the policy. In addition, the institution must determine the level of restriction to be imposed and select an appropriate enforcement agency. Finally, rules must be established for detecting violators and determining penalties. Implementation. After the procedural elements of a selected policy are determined, a policy organization must be charged with implementing the intervention. The costs of implementation are both educational and structural. Educational costs are inevitable because the affected participants must understand the new policy and the procedures necessary for compliance. Structural costs may be far greater than educational costs if a new administrative or institutional structure is needed. In other words, policy actions requiring the creation of entirely new programs need a structural framework in which the program can operate. The mortgage insurance program developed under the Federal Housing Administration (FHA) is an example of a structure developed for a new program. When FHA was established and charged with insuring primary mortgages, a framework had to be developed in which the insurance program could function. An entirely new organization was formed to process incoming documents and to underwrite mortgage applications. Personnel, equipment, procedures, and organizational components had to be put in place before FHA could begin operation. Furthermore, operating costs were initially budgeted from general funds, which would later be replaced by collected premiums. But actions attempting to cause a wealth transfer may not generate premiums to support long-term operation and therefore may require continued funding.

9 Policy Issues Concerning Racial and Ethnic Differences in Home Loan Rejection Rates 123 Measurement and Enforcement. Market restrictions do not remain in effect unless procedures for measuring the compliance of participants are in place and methods of enforcement are established. By definition, the market has failed to impose the required outcome through its normal operation. Therefore, the prevention of continued market failure requires ongoing monitoring and correction of noncompliance, which demands continuous investment. The costs of monitoring are paid by both the monitoring institution and the monitored organizations. The monitoring institution requires personnel and equipment, while the monitored firms require additional staff for internal tracking. Furthermore, data collection and reporting activities necessarily lead to higher nonproduction costs for the lenders. Ultimately, these costs are passed on to the general population. Enforcement costs are also passed on to the public. Litigation costs will be enormous, given the ultimate effect on a lender found guilty of violating the requirements. For example, Decatur Federal Savings and Loan recently paid a $1 million settlement in a lending discrimination case (Turner 1993). Enforcement and litigation costs may be recouped through penalties assessed on organizations found to be in violation of the policy. In any case, the increased risk of litigation and the potential costs levied on lenders will force firms to increase their operating margins. Market Efficiency. The most difficult cost to measure and potentially the greatest single cost is the lost efficiency in market operation caused by restrictions. Costs of organization, implementation, and measurement and enforcement can be considered direct costs. They may require extensive investigation but can usually be estimated with some level of confidence. The implicit costs of reduced market efficiency are far less quantifiable. An unrestricted, perfectly competitive capital market operates with utmost efficiency in providing capital to the most productive activities. In a truly efficient market, lending decisions are not based on noneconomic factors such as borrower race. An efficient market may, however, allocate capital in socially unacceptable ways if race is highly correlated with economic factors. Placing restrictions on the operation of such a market will reduce efficiency by redirecting capital to less productive activities. The costs of reducing market efficiency in an already regulated capital market are virtually unmeasurable. An experiment would be necessary to determine the costs of redirecting capital. Under such a scenario, production could be measured given free and restricted operation. The difference in productivity would determine the overall costs of reduced market efficiency. Unfortunately, experiments are extremely limited in the social sciences. Therefore, some estimation procedure must be applied. We will come back to estimation procedures after discussing some specific policy options. Selected Policy Options Policy options abound for addressing racial and ethnic differences in loan rejection rates. What follows is a discussion of major options for addressing interracial differences: quotas, antidiscrimination programs, interest rate supplements, federal lending programs, and mortgage guarantees. Van Horne (1984) provides a fuller discussion of the

10 124 Brent W. Ambrose, William T. Hughes, Jr., and Patrick Simmons economic impacts of some of these options. The list of options is not intended to be exhaustive. Rather, the options and their associated evaluation techniques merely represent some of the policy approaches available and procedures necessary to evaluate suggestions as they arise. Cost categories and policy options are summarized in table 1. The prospective benefits of these options are similar in that they all should increase credit availability and possibly homeownership rates for minority households. Therefore, our discussion focuses on costs. If a policy intervention might confer unique benefits, such benefits are briefly noted. Table 1. Outline of Costs Associated with Proposals to Reduce Racial and Ethnic Differences in Rejection Rates Measurement and Market Method Organization Implementation Enforcement Efficiency Quotas Low Low Moderate High Antidiscrimination: statistical analysis Low Moderate High Low Antidiscrimination: mortgage scoring Moderate Moderate Moderate Low Interest rate subsidy High High Low Moderate Federal lending High High Low Low Guarantees Moderate High Low Moderate Quotas One policy option available to regulators is the imposition of quotas. Quotas impose an external restriction on market operations and thus directly force equality or some fixed relationship between the rejection rates of whites and minorities. Rather than attempting directly to change the underwriting process or to alter expected minority loan returns (through guarantees or interest rate subsidies), quotas simply mandate specific lending outcomes. While quotas have not received and likely never will receive serious policy consideration, requiring a specific relationship between minority applications and minority loan approvals or between minority and white rejection rates is a straightforward method of addressing the observed differences. Forcing a predetermined number of minority loans will quickly bring rejection rates in line with the underlying requirement. Organizational costs for such a policy would be low. The only organizational requirements would be the identification of participants restricted by the quotas and a set of benchmarks for acceptable lending levels. The process of quota regulation would begin with the formulation of guidelines for loan acceptance regardless of applicant characteristics. Implementation of a quota policy

11 Policy Issues Concerning Racial and Ethnic Differences in Home Loan Rejection Rates 125 would also be relatively straightforward and inexpensive. Education would require only documentation of new guidelines and a brief instructional period to ensure individual knowledge of the new limitations. Implementation costs would be further minimized by the fact that no new structural arrangement would be required. As mentioned earlier, structural costs are often the greatest portion of costs associated with implementation. Measurement of compliance would be easy given the reporting requirements already in place. Institutions could be monitored through the examination of currently required records. For example, HMDA has established guidelines for lender reporting of loan characteristics. The only necessary adjustments would be requirements for racial ratios. These requirements could then be directly compared with currently reported ratios. Penalties would have to be established for institutions failing to meet the requirements. One important source of enforcement costs associated with quotas is litigation. If past legal actions related to quotas in hiring and residential integration are any indication, the establishment of race-based or ethnicity-based quotas could generate substantial litigation. 12 As Kushner (1989, 1114) notes, the U.S. Supreme Court has restricted affirmative action quotas designed to promote residential integration and considers affirmative action quotas to be acceptable only as a remedy for past discrimination, temporary relief to achieve the racial composition one would expect absent past bias, or preferences in pursuit of a compelling state interest. The courts might question the use of lending quotas to remedy differences in rejection rates, particularly if much of the difference is due to past discrimination outside the mortgage market, in such areas as employment and education. All of the above costs can be considered direct costs, and most can be estimated from previous programs established to regulate financial institutions. It is anticipated that direct costs, with the possible exception of litigation costs, would be low as compared with implicit costs. Costs associated with reduced market efficiency, though implicit, can be substantial. A quota policy, for example, would impose restrictions on a competitive market and require the market itself to distribute costs. If the current market operates efficiently, then quotas could force capital from the most productive activities to less efficient activities, thereby causing a wealth transfer from the excluded productive producers to the newly included group. Uncertainty surrounds the assignment of implicit costs. The market in general pays the costs of reduced productivity, whereas individuals constrained by the reallocation of capital remain unidentified. To the extent that quotas would make mortgage credit more accessible to qualified minority applicants and increase minority homeownership, they would produce many of the economic and social benefits associated with homeownership. But these benefits 12 With the recent election of a Republican Congress, race-based remedies have become an even more sensitive issue. Rep. Henry J. Hyde (R-IL), chair of the House Judiciary Committee, was recently quoted in the Washington Post as follows: We seem farther apart than we ever have been despite years of busing [and] coerced integration. What we can do is take a look at quotas and preferences and how they measure up to the law (Mariano 1995, E-1). In the same article, William Mellor, president and general counsel of the Institute for Justice, indicated that his organization was going to push for a Civil Rights Act of 1995, which would [forbid] the federal government from engaging in racial or gender preferences in programs, contracts or employment.

12 126 Brent W. Ambrose, William T. Hughes, Jr., and Patrick Simmons would be tempered by the fact that quotas may not end racial discrimination if it exists in lending. Quotas would require lending institutions to originate loans to a predetermined number of minority applicants. If lending institutions currently engage in discrimination, then the quota might be set below the actual number of qualified minority applicants. In this situation, the quota would improve lending to minorities but not eliminate discrimination. Removing Discrimination from Loan Underwriting 13 Rather than forcing outcomes on the housing credit market as quotas do, fair lending policies would target the underwriting process. By focusing on the underwriting process and eliminating market distortions caused by illegal discrimination, fair lending policies would eliminate a portion of the observed differences in rejection rates. Beyond meeting the credit needs of qualified minority applicants and producing benefits associated with expanded homeownership, increased fair lending enforcement would promote the public policy goal of protecting the civil rights of minority applicants. Both the disparate treatment (arrow E in figure 1) and the adverse impact (arrows C and F) forms of discrimination could be the targets of fair lending enforcement. (We assume that blatant discrimination is rare and that current enforcement efforts are sufficient to discourage it.) This section considers two interventions designed to remove discrimination from the underwriting process: statistical analysis of lender files and requiring lenders to use explicit mortgage-scoring models. While both interventions are targeted directly to the disparate treatment form of discrimination, mortgage-scoring models could have the additional benefit of reducing adverse impact discrimination. Statistical Analysis of Lender Files. To date, virtually all enforcement efforts have focused on detecting and penalizing the disparate treatment form of discrimination. Traditionally, enforcement has involved examining lender fair housing compliance programs and written documentation in lender files to detect credit worthy minority applicants who were denied loans but were as qualified as white applicants approved for loans. With the release of the Boston Fed and Decatur Federal studies, however, interest in the use of multivariate statistical analysis of lender application files has increased. This approach, which involves complex statistical techniques such as the application of logit regression to large data sets, offers some potentially important benefits. First, studies based on this approach have provided the strongest evidence of discrimination in the mortgage lending process. Even though this approach does not conclusively demonstrate the existence of discrimination, it does decrease the likelihood of imposing significant market efficiency costs on an efficiently operating market. In addition, this 13 It should be noted from the outset that antidiscrimination activities can be categorized along several dimensions: pattern and practice versus individual victim forms of discrimination; disparate treatment versus adverse impact discrimination; personal discrimination versus redlining; and discrimination at various stages of the lending process (e.g., outreach/marketing, preapplication, underwriting, treatment of borrowers after loan approval). The antidiscrimination initiative could vary substantially depending on the dimensions involved. Discussing all possible initiatives covering all of these dimensions is beyond the scope of this article and could be the subject of an entire article. The initiatives discussed in this section correspond roughly to the following dimensions: pattern and practice, personal, and the underwriting stage. The discussion considers both disparate treatment and adverse impact forms of discrimination.

13 Policy Issues Concerning Racial and Ethnic Differences in Home Loan Rejection Rates 127 approach has the potential to be much more successful than traditional reviews of written documentation in detecting illegal acts of discrimination. 14 Monitoring and enforcement costs for a statistical analysis enforcement program would be substantial. A Department of Justice official estimated that an investigation similar to that conducted at Decatur Federal would take six to nine months and cost $300,000 to $500,000 (Turner 1993). Although this cost estimate involves other components beyond the statistical analysis, the Decatur experience suggests that statistical analysis enforcement would be extremely expensive. In addition, the Decatur Federal case was settled out of court; the litigation costs associated with a statistical analysis program could add substantially to enforcement costs. Structural costs associated with statistical enforcement would likely be small because the existing infrastructure of the financial institution regulatory agencies, the U.S. Department of Housing and Urban Development (HUD), and the Department of Justice could be used to implement the policy. Educational costs, by contrast, would likely be substantial because of the complexity of the statistical approach and the need for expertise in applying and interpreting results. The extensive educational costs associated with the statistical approach are suggested in the following statement by an official from the Office of the Comptroller of the Currency: The statistical techniques used by the Federal Reserve Bank of Boston in its Boston study and by the Justice Department in its Atlanta investigation secured convincing evidence of the existence of discriminatory loan denials. However, these techniques are of virtually no use to the hundreds of HUD, state and local complaint investigators and thousands of Federal and state bank examiners who bear the vast bulk of the responsibility for enforcing lending discrimination laws. These professionals do not have the time, resources, or generally the expertise for investigations like those in Boston and Atlanta. (Riedman 1993, 3 4) Finally, although advanced statistical techniques are designed to isolate the effect of race on the lending decision process, several remaining methodological problems raise concerns that the techniques could still suggest violations when none exist, thereby creating market inefficiencies by imposing restrictions on nondiscriminating lenders. Two of the most commonly cited difficulties associated with this approach are using the correct functional form of the regression equation and ensuring that all variables relevant to the underwriting process are specified. Also, because of the sample size restrictions imposed by the statistical technique, the approach could not be applied to small lenders who originate few or no minority loans. Mortgage-Scoring Models. Another means of attacking disparate treatment in mortgage underwriting would be to require lenders to adopt explicit mortgage-scoring models for evaluating loan applications. Under this regulatory scheme, lenders would be required to use credit-scoring models based on historical loan performance criteria not related to legally protected characteristics of mortgage applicants (Galster 1993b). These models 14 In the Decatur Federal case, Department of Justice investigators began with a traditional approach based on a detailed review of written documentation contained in a sample of loan files. Even after reviewing 500 files, investigators could come to no conclusion regarding discrimination (Turner 1993).

14 128 Brent W. Ambrose, William T. Hughes, Jr., and Patrick Simmons would be used to develop a score for each applicant, for comparison with an established cutoff for loan approval. Such a scheme would help eliminate differential treatment by limiting underwriter discretion in disposing of loan applications. As noted by Galster (1993b), such a scheme could simplify the examination process, thereby reducing monitoring and enforcement costs: 1. Small and large institutions could be judged by the same criteria. 2. No subjective decision by the examiner is needed. Scores are either correctly generated or not, and decisions are consistently based on the scores or not. Educational costs of implementing a mortgage-scoring system would, however, be considerable. First, much basic research must be performed to develop adequate and defensible mortgage-scoring models. Second, individual institutions would need to develop some expertise either to create their own models or to select models offered by outside vendors. Finally, mortgage underwriters, loan officers, examiners, and investigators would have to be retrained. In contrast to educational costs, structural costs of a mortgage-scoring system would be modest in that existing financial institution regulatory agencies, HUD, and the Department of Justice are already experienced in and prepared to conduct examinations and investigations of fair lending violations. But organizational costs would arise as a result of debate over who should be covered by a regulation requiring mortgage-scoring systems. One final concern with underwriting systems based on mortgage-scoring models is that the models rigidity could lead to minority rejections because of greater idiosyncrasies in the credit characteristics of minority applicants. Rather than suggesting that mortgagescoring models are inherently flawed, this concern may simply point out that additional credit counseling and consumer education programs would be needed for minority applicants (Galster 1993b). Further, the development of mortgage-scoring models based solely on factors related to loan return (i.e., business necessity) could reduce adverse impact discrimination. If such discrimination is prevalent and appropriate mortgagescoring models can be developed, the benefits in increased market efficiency and expanded homeownership would be substantial. Interest Rate Supplements The government can change capital flows through the use of interest rate supplements or subsidies. For example, lenders making loans to a targeted borrowing group could receive supplemental interest payments. By increasing the expected total return for loans made to minority borrowers, supplemental interest payments could help compensate for intergroup differences in endowments, providing a disincentive for discrimination. (This approach is directed at arrows A-B, C-F, and E in figure 1.) Applicants currently denied loans would receive further consideration if they qualified for the subsidy. By providing an external supplement to any loan for which an applicant qualifies, this approach leaves the selection decision in the hands of individual institutions. Lenders

15 Policy Issues Concerning Racial and Ethnic Differences in Home Loan Rejection Rates 129 would continue to approve loan applications on a risk-and-return basis, but the return on minority loans would be enhanced. Such a policy alters the decision process of market participants without imposing a decision structure or defined outcome. Therefore, market efficiency costs are far lower than those associated with a policy that restricts lending activity. As Van Horne (1984) points out, however, the use of interest rate subsidies does have a second-order effect on the market in that the government must either increase its borrowing or raise taxes to pay for the subsidy. The direct costs of such a program would be staggering. Not only would initial organizational costs be high because of the complexity of lender contracting, but organizational costs would continue indefinitely because of the dynamic nature of lending. Accordingly, the qualification requirements and the magnitude of the subsidy would change over time. In addition, the supplement would have to be altered as interest rates change. Both educational and structural costs of implementation would also be enormous. The more complex the program, the greater the education required for implementation. A program of interest rate subsidies would also require a structural framework. New institutions would have to be developed to assess market conditions and to provide subsidies to qualified individuals. Further, the cost of providing the interest rate supplement would be huge and ongoing. Measurement and enforcement are not issues for such a program. To the extent that the program is targeted specifically by applicant race or ethnicity, however, substantial litigation could result, as in the case of quotas. Federal Lending The federal government could eliminate personal discrimination in the underwriting process by establishing a federal mortgage originator charged with originating loans solely to minorities. Any minority applicant suspecting discrimination in a loan evaluation could turn to the federal originator as a lender of last resort. In addition to providing an alternative in cases of discrimination (arrow E in figure 1), such a program could help compensate for intergroup differences in endowments by setting less stringent underwriting guidelines (arrows A-B). A program of this nature would place responsibility for the determination and implementation of underwriting criteria directly on the policy makers. A federal lending program would entail tremendous organizational and structural costs. In fact, the direct costs would resemble those associated with interest rate subsidies and could be determined through an investigation of previous federal programs. Monitoring and enforcement would not be necessary in an arrangement of this type, because the enforcer would be the sole participant. But, as with other race/ethnicity-based remedies, litigation could be a major problem. Market efficiency would be less altered under a federal mortgage originator than under other, more restrictive programs. If any effect on market efficiency occurred, it would take the form of increased efficiency in response to the entry into the market of a new competitor. On the other hand, if the supply of capital were relatively inelastic, then the introduction of increased borrowing by the federal government would affect the overall

16 130 Brent W. Ambrose, William T. Hughes, Jr., and Patrick Simmons market efficiency by shifting the flow of capital away from more efficient uses. However, the decision process of lenders would not be obstructed directly by restrictions, although introducing a new participant might change the lending decision making. Guarantees Another possible policy option is the federal government s provision of mortgage guarantees. Under a national program, lenders would submit minority applications for a mortgage guarantee. The government guarantee would change the risk profile of qualified borrowers, thereby helping to compensate for intergroup differences in endowments. Given that the federal government is a default-free borrower, lending to individuals who have a government guarantee is considered default free. Borrowers otherwise unable to secure financing because of perceived risk would be acceptable to lenders if a guarantee were provided. This approach would also provide a disincentive against statistical discrimination by protecting lenders against unobservable risks that they may associate with minority applicants. The use of government guarantees to influence the flow of capital in the market has a long and varied history. 15 One of the best-known mortgage guarantee programs is that of the U.S. Department of Veterans Affairs (VA), formerly the Veterans Administration. A guarantee program for minority mortgage loan applicants could be developed in parallel with the VA program and thereby avoid many of the pitfalls associated with a new policy. Indeed, regulators rarely have the opportunity to examine market reaction to a new policy based on the implementation of similar policies. Nevertheless, one direct cost of a minority loan guarantee program not associated with the VA program is the litigation cost previously discussed in connection with quotas, interest subsidy programs, and direct federal loans. Research on Measurement There are several methods for measuring the costs and benefits of policy options. Many of the costs can be estimated from a simple examination of the costs associated with similar policies already in place. Other costs, such as lost market efficiency, require sophisticated estimation techniques and many assumptions. In either case, informed decisions require knowledge of the potential costs and benefits of proposed policy options. Cost Measurement Organizational costs can be estimated by reviewing the organizational costs of previous national programs. Such costs range from virtually nothing, as with quotas, to extremely high costs, as in federal lending programs. Fortunately, organizational costs regardless of magnitude can be estimated from similar programs implemented in the recent past. 15 Van Horne (1984) points out that the government has used loan guarantee programs to help veterans (Veterans Administration loans), defense contractors during World War II (V-loans), large private corporations (Lockheed, Chrysler, and Wheeling-Pittsburgh Steel), and the export of U.S. goods and services (Export- Import Bank).

17 Policy Issues Concerning Racial and Ethnic Differences in Home Loan Rejection Rates 131 As with organizational costs, estimation of the costs associated with implementation and enforcement is relatively straightforward. Market Efficiency and Benefit Measurement To determine the effectiveness of policy options, researchers need a tool that allows them to measure the costs and benefits. One tool that is widely used in tax policy research and could be applied to mortgage lending policy research is simulation. In a simulation model, the researcher builds a general model of a specific sector of the economy, such as multifamily real estate investment, to determine the impact of changing tax variables on value while holding constant all other variables. Critics of simple static tax models contend that the models do not truly reflect the overall market reaction to proposed policy changes. In response, researchers have developed more sophisticated models that examine entire industries in an attempt to estimate the impact of tax policy changes on value and on other general economic factors that also influence value. 16 In general, economic research uses three types of simulation models: the static presentvalue model, the partial equilibrium model, and the general equilibrium model. The static present-value model is a single-firm or project-type model that isolates the effect of policy changes by holding all other factors constant. It is based on the idea that a project s value equals its discounted cash flows. The present-value model also assumes that an individual firm s reactions to changes in policies are independent of other participants actions. Clearly, this assumption is not realistic. The partial equilibrium model attempts to model the supply and demand for a particular segment of the economy or for a particular industry. In real estate research, the partial equilibrium model is widely used to study the mortgage market. It estimates the impact of policies on particular institutions (such as government-sponsored enterprises or banks) with respect to how the policies affect supply and demand. Thus, researchers can construct models of specific sectors of the mortgage lending industry to estimate the reaction of entire industry sectors to various proposals. Finally, the general equilibrium model attempts to simulate the entire market by analyzing the effect of proposed policies on the entire mortgage market. In such a simulation, the impact of policy choices is estimated for all institutions as well as for all sets of individuals. The benefit of such models is their ability to allow interaction between market participants in response to policy changes. Their drawback is their complexity. Structuring simulation models is beyond the scope of this article and is left to future research. In any event, we present several factors that any model of the mortgage market should incorporate to analyze fully the impact of race on mortgage lending. First, the simulation model should be a general model reflecting all aspects of the mortgage market. It should incorporate the fundamental relationships between the supply and demand for mortgage funds and interest rates. Previous studies have examined such relationships (see Bookstaber 1982; Chen and Ling 1989; Clauretie 1973; Green and Shoven 1986; Halloran and Yawitz 1979; Heuson 1988; Muth 1962; Page 1964; Sharp 1989). For example, the models should recognize that programs or policies that 16 See Ambrose and Ross (1993) for a discussion of the various simulation models used in tax policy research.

18 132 Brent W. Ambrose, William T. Hughes, Jr., and Patrick Simmons increase the demand for mortgage funds will place upward pressure on interest rates. In addition, the models should recognize the relationship between the flow of funds in the mortgage market and other capital markets. Thus, attempts to increase the flow of funds to the mortgage market may lead to reduced capital in other sectors of the economy. Second, the models should explicitly recognize the assumptions used to parameterize the models. Market costs are influenced by assumptions regarding the nature of funds in the mortgage market. For example, if researchers assume that the amount of capital in the mortgage market is fixed, then programs that increase borrowing to a particular minority group will result in a corresponding decrease in mortgage funds to all other borrowers. Under this assumption, if funds were originally allocated to the most efficient use, any policy that altered the market s capital allocation would result in a net loss to society because of inefficient use of funds. On the other hand, if researchers assume that the supply of funds is not fixed, then a policy option may not produce a net loss to society. Using a simulation model to analyze the impact of policy alternatives on the mortgage market offers some distinct advantages. For example, simulation models allow the researcher to isolate the impact of the specific policy option in a controlled environment. Thus, policy makers can study the consequences of their actions before policy implementation. Simulation also offers a distinct cost advantage over actual collection of data. The disadvantage of relying on simulation models is that the conclusions drawn from them depend on the underlying assumptions. One additional note on estimating the benefits of policies designed to reduce differences in rejection rates relates to the marginal increase in homeownership triggered by such policies. The number of additional homeowners that will result from a decrease in minority home loan rejection rates remains an unknown. Furthermore, to the extent that policies reallocate credit away from white borrowers who are currently receiving loans, one offsetting effect may be reduced homeownership among white borrowers. Finally, as previously noted, much more research must be performed to enumerate the benefits of homeownership to households, communities, and society. Conclusion Despite the persistent debate surrounding the causes of racial and ethnic differences in loan rejection rates, it is clear that substantial differences persist despite current government policies. The causes of these differences are numerous, varied, and complex, as are the public policy options for addressing them. Compounding this policy-making quandary is the lack of information on the relative importance of the various causes and incomplete knowledge of the costs and benefits of different policy interventions. Significant among these knowledge deficiencies are the uncertain connections between improved minority acceptance rates and increases in homeownership, the dearth of research on the community and societal benefits of homeownership, the often-unknown motivations behind discriminatory actions, and the need for simulation models to estimate the market impacts of interventions. These areas are worthy of extensive additional inquiry.

19 Policy Issues Concerning Racial and Ethnic Differences in Home Loan Rejection Rates 133 The potential costs associated with both action and inaction dictate that policy makers move cautiously in considering interventions targeted to addressing observed rejection rate disparities. Taking action without additional information could create substantial market risk and lead to misguided programs. Such misguided efforts can impose significant costs and burdens on particular segments of the market, which, in turn, can result in a net loss to society in market efficiency. Balanced against the potential cost of misguided interventions, however, is the cost of inaction in a discriminatory market. If the lending discrimination suggested by the Boston Fed and Decatur Federal studies is representative of general market conditions, then inaction will perpetuate widespread market distortions and economic inequities between whites and minorities. While a single article cannot hope to sort through the full range of mortgage lending complexities and thereby remedy information deficiencies with the rigor required for sound policy making, this article has nonetheless established a framework that outlines the types of costs and benefits associated with a limited menu of policy options. The policy analysis framework is linked to a conceptual model of the potential causes of rejection rate disparities. It therefore provides an explicit guide for selecting policies not only on the basis of costs and benefits but also on the basis of assumptions regarding causation and establishes a useful point of departure for future decision making and policy research. References Ambrose, Brent W., and John P. Ross Changes in the Tax Code and Multifamily Property Values. Working paper. U.S. Department of Housing and Urban Development. Board of Governors of the Federal Reserve System A Feasibility Study on the Application of the Testing Methodology to the Detection of Discrimination in Mortgage Lending. Washington, DC. Bookstaber, Richard Interest Rate Hedging for the Mortgage Banker: The Effect of Interest Rate Futures and Loan Commitments on Portfolio Return Distributions. Review of Financial Management 1(1): Canner, Glenn B., and Dolores S. Smith Home Mortgage Disclosure Act: Expanded Data on Residential Lending. Federal Reserve Bulletin 77(11): Carr, James H., and Isaac F. Megbolugbe. 1993a. Another Look at the Boston Fed s Mortgage Discrimination Study. Housing Research News 1(2):1 8. Carr, James H., and Isaac F. Megbolugbe. 1993b. The Federal Reserve Bank of Boston Study on Mortgage Lending Revisited. Journal of Housing Research 4(2): Chen, Andrew, and David Ling Optimal Mortgage Refinancing with Stochastic Interest Rates. AREUEA Journal 17(3): Clauretie, Terrence M Interest Rates, the Business Demand for Funds, and the Residential Mortgage Market: A Sectoral Econometric Study. Journal of Finance 28(5): Cloud, Cathy, and George Galster What Do We Know about Racial Discrimination in Mortgage Markets? Review of Black Political Economy 22(1): Galster, George A Statistical Perspective on Illegal Discrimination in Lending. Washington, DC: American Bankers Association.

20 134 Brent W. Ambrose, William T. Hughes, Jr., and Patrick Simmons Galster, George C. 1993a. The Facts of Lending Discrimination Cannot Be Argued Away by Examining Default Rates. Housing Policy Debate 4(1): Galster, George C. 1993b. Future Directions in Mortgage Discrimination Research and Enforcement. Paper read at Home Mortgage Lending and Discrimination: Research and Enforcement Conference, May 18 19, Washington, DC. Green, Jerry, and John B. Shoven The Effects of Interest Rates on Mortgage Prepayments. Journal of Money, Credit, and Banking 18(1): Halloran, John A., and Jess B. Yawitz The Effect of Mortgage Form on Borrower Interest Rate Risk: A Portfolio Theory Approach. Journal of Banking and Finance 3(2): Heuson, Andrea J Managing the Short-Term Interest Rate Exposure Inherent in Adjustable-Rate Mortgage Loans. AREUEA Journal 16(2): Just-Released HMDA Data for 1993 Show Mixed Results: Disparity Rates, Reasons for Denial Differ Little from CRA/HMDA Update, November, pp Kain, John F., and John M. Quigley Housing Markets and Racial Discrimination: A Microeconomic Analysis. New York: National Bureau of Economic Research. Kushner, James A The Fair Housing Amendments Act of 1988: The Second Generation of Fair Housing. Vanderbilt Law Review 42: Mariano, Ann Fair Housing Laws under Siege on Hill. Washington Post, February 11, p. E-1. Megbolugbe, Isaac F., and Peter D. Linneman Home Ownership. Urban Studies 30 (4/5): Mitchell, J. Paul Historical Overview of Federal Policy: Encouraging Homeownership. In Federal Housing Policy and Programs: Past and Present, ed. J. Paul Mitchell, New Brunswick, NJ: Center for Urban Policy Research. Munnell, Alicia H., Lynn E. Browne, James McEneaney, and Geoffrey M. B. Tootell Mortgage Lending in Boston: Interpreting HMDA Data. Working Paper Federal Reserve Bank of Boston. Muth, Richard F Interest Rates, Contract Terms, and the Allocation of Mortgage Funds. Journal of Finance 17(1): Muth, Richard F The Causes of Housing Segregation. In U.S. Commission on Civil Rights, Issues in Housing Discrimination, Washington, DC: U.S. Government Printing Office. Page, Alfred N The Variation of Mortgage Interest Rates. Journal of Business 37(3): Rachlis, Mitchell B., and Anthony M. J. Yezer Serious Flaws in Statistical Tests for Discrimination in Mortgage Markets. Journal of Housing Research 4(2): Riedman, Larry Perspectives from a Regulatory Agency. Paper read at Home Mortgage Lending and Discrimination: Research and Enforcement Conference, May 18 19, Washington, DC. Rohe, William M., and Michael A. Stegman The Effects of Homeownership on the Self- Esteem, Perceived Control, and Life Satisfaction of Low-Income People. Journal of the American Planning Association 60(2):