Discrimination in Mortgage Lending Anthony M. Yezer







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Discrimination in

Mortgage Lending

Anthony M. Yezer




Mortgage market discrimination has been categorized as blatant treatment, differ-ential treatment, and adverse impact. Blatant treatment involves explicit refusal to lend or explicit lending policies that consider prohibited demographic character-istics, race, sex, and ethnicity. Differential treatment discrimination arises when lending criteria are applied differently based on prohibited characteristics, even though there is no explicit policy of discrimination. Such differential treatment, because it is implicit, is inferred by statistical analysis of lending outcomes. Adverse impact discrimination arises when lenders use lending criteria that are signific-antly correlated with prohibited characteristics when alternative criteria could be used. Of course, demographic characteristics are correlated with many economic characteristics, such as income and wealth, which are good predictors of credit-worthiness. Lenders are only allowed to use these factors if they can show that alternative economic indicators, not correlated with minority status, are not sufficient to predict credit risk in mortgage lending. The concept of statistical dis-crimination is closely related to adverse impact. Statistical discrimination arises when lenders use readily available characteristics that are correlated with minor-ity status in the underwriting process rather than using alternative information that might be more costly to collect.

Mortgage markets have several distinctive characteristics that must be under-stood before considering the possibility that there is discrimination against any particular category of mortgage applicant. The first task of this essay will be to review these distinct features. Armed with an understanding of the process of application, loan taking, underwriting, loan approval, closing, repackaging, and sale to the ultimate investor, we will be equipped to consider how discrimination

Edited by Richard J. Arnott, Daniel P. McMillen Copyright © 2006 by Blackwell Publishing Ltd


might arise and, more importantly, how to test for its existence. Thus far, concern with discrimination in mortgage lending, both as the object of academic inquiry and public policy action, has been concentrated in the United States. However, concern with equal access to credit is spreading to other countries, particularly in Europe and lessons learned in the USA may be valuable elsewhere.

The next section of this essay reviews important institutional characteristics of mortgage lending and mortgage markets that must be understood before modeling the lending process or testing for discrimination. Then the relation between this institutional structure and possibilities for discrimination is traced. Subsequently, alternative procedures that have been used to test for discrimination are presented and evaluated. For an excellent review article that covers the subject of this essay, see LaCour-Little (1999).












The mortgage lending process involves several agents, whose characteristics are essential to any discussion of discrimination in mortgage lending. Naturally, we begin with an applicant, a person seeking a mortgage in order to purchase or

refinance residential real estate. In the case of a new purchase, the applicant is often guided by a realtor, who identifies properties in a price range appropriate for the applicant and frequently intervenes in the mortgage application process. Applicants interact directly with loan originators, who take information supplied by the applicant and provide advice regarding alternative mortgage products, pricing, and underwriting policy. Loan originators often work on a commission basis and have a strong incentive to direct applicants toward mortgage products for which they are qualified. The originator works with the applicant to make sure that the information in the loan file meets standards set by the lender. One important decision is whether to apply for a government guaranteed loan – that is, FHA, VA, or Farmer’s Home – or a conventional mortgage, which will require private mortgage insurance (PMI) if the loan-to-value (LTV) ratio exceeds 0.8. A higher LTV increases credit risk in mortgage lending for two reasons. First, it makes default more likely, as borrowers have less equity at risk. Second, it increases expected loss in case of default, because owner’s equity helps to protect the lender against large losses.

In most cases, loan originators pass the partially completed application on to a loan processor, who assembles private information on the applicant’s financial and employment history that is obtained with the applicant’s formal permission. This private information includes bank and employment references, documenta-tion for other sources of income and debt service, and credit history, including the credit score. Under the Fair Credit Reporting Act, the USA has an elaborate system in which information on how promptly and completely individuals pay debts and any legal actions taken against them is collected in a uniform fashion for people living across the country. Concerns with privacy have made credit reporting a controversial issue in many countries. Credit history includes detailed


information on credit outstanding and payment history, as well as past debt problems such as write-offs and even bankruptcy. The loan processor also secures an estimate of the market value of the house. This is usually done by an appraiser, although increasingly automated appraisal techniques using statistical models of local house prices are used. The loan processor verifies the accuracy of informa-tion taken by the loan originator.

Once the loan processor has completed the application, an underwriter makes the decision to accept or reject, relaying that information back to the loan pro-cessor and originator. If the loan is initially rejected, the applicant, often with the assistance of the loan originator, may request that the loan be reconsidered based on extra information provided or modified loan terms. Cosigners may be added to the loan at this point. Applications that were initially rejected may be accepted by the underwriter upon reconsideration. The underwriter’s decision is based on credit risk associated with the application, including applicant income, assets, liabilities, and credit history; the appraisal; and provisions of the loan, particularly the LTV ratio and the presence of cosigners.

Underwriters generally do not consider the interest rate, discount points, and fees associated with the transaction, except insofar as these factors change the monthly payment to income or loan-to-value ratios used in underwriting. The total price of the mortgage includes discount points, fees, and interest rate. All those applications judged qualified by a given underwriter pay a similar price for credit except those with LTV > 0.8, who will usually pay mortgage insurance. Thus within the group of accepted applicants, credit risk may vary considerably although the applicants pay the same price for credit. This price discrimination in mortgage lending, in which the lowest-risk borrowers pay the same price as higher-risk borrowers, should not be confused with discrimination based on race or ethnicity.

The division of labor into loan originators paid commissions based on approved loans and loan processors and underwriters whose compensation is not tied to the approval decision solves an incentive problem for the lender. It is important to have loan originators directing applicants to appropriate loan products, but it is equally important that those with final responsibility for verifying informa-tion and underwriting do not have a financial incentive to approve unqualified applicants. Commissions paid to loan originators sometimes include a bonus payment, called an overage, based on the difference between the price paid by an applicant and a minimum acceptable price posted by the lender. Loan processors and underwriters are salaried employees, whose compensation is not based on the fraction of loans approved.

Rejected applicants whose loans are not approved, even upon reconsideration, are generally not offered alternative mortgage credit at higher rates. Instead, they must apply to other lenders, or sometimes to subsidiaries of the same lender. Thus nonprice rationing of mortgage credit has resulted in a market divided into

prime and subprime lenders. Prime, or A and A–, mortgage lending dominates the market. However, subprime lending is a significant activity and applicants rejected by prime lenders may turn to subprime lenders, where the cost of credit is higher but underwriting criteria, particularly tolerance for a blemished credit


history, are more lenient. The subprime market is not well understood or docu-mented. It includes many small lenders about whom little is known. Subprime lending is more commonly refinancing, as opposed to new-purchase mortgages, and lending for specialized types of housing, particularly manufactured housing. Although information on subprime lending is limited to selected samples of loans, it appears that credit scores are lower, the cost of credit higher, and rejec-tion rates higher in subprime lending. While the first two points may appear consistent with expectations, the higher rejection rate for subprime lenders is, at first, curious. If these lenders take greater credit risk, why do they reject more applicants? In fact, the answer is quite logical. Applicants are aware of differ-ences in lending criteria and the higher-risk applicants self-select into the subprime market. Typically, application costs are higher at prime than subprime lenders. High-risk applicants avoid lenders where the combination of high application cost and likely rejection outweigh the possibility of getting lower-cost credit. This self-selection by applicants is very important in the cost structure of mortgage lenders, because application fees cover only a fraction of the cost of underwriting and prime lenders cannot afford to have high rejection rates.

Thus far, this institutional description has given the impression that mortgage lending involves a single firm or organization. In practice, several different types of firms are involved in mortgage lending. First, the lender discussed is seldom the ultimate holder of the debt. Even before the mortgage is endorsed, lenders often secure commitments to purchase from the secondary market. Much of the credit risk associated with the mortgage passes on with this sale. Second, it is quite common for loan originators, either alone or with loan processors, to operate as independent mortgage brokers and to sell processed loans to lenders. The rela-tion between loan originator and lender ranges from one in which the originator only brings applicants to the lender for processing and underwriting to one in which processing and underwriting are delegated to the originator by the lender, who then purchases the loan, perhaps after doing secondary underwriting. In this case, the lender will generally record the loan as purchased or wholesale rather than originated or retail. The image of applicants interacting with loan originators, processors, and underwriters all employed and controlled by a single lender is simply not correct.

In the USA, government-insured mortgages are important to understanding mortgage supply. The primary program serving low-income applicants is FHA Section 203b mortgage insurance. Although all aspects of FHA loan originations, ranging from the loan originator through the underwriter and including the appraiser, were once performed inside FHA, today most FHA mortgage insur-ance is based on direct endorsement by private lenders under the supervision of the Department of Housing and Urban Development (HUD). FHA mortgage insurance allows higher loan-to-value and monthly-payment-to-income ratios than private mortgage insurance, making it particularly attractive for young, first-time homeowners. Today, it is generally assumed that FHA mortgage applicants are not subject to discrimination, but that was initially not the case. Indeed, the term

redlining, which is used to indicate the practice of limiting mortgage lending in high-minority or low-income communities, originated at a time, in the 1960s,


when red lines were literally found on maps in FHA area offices. FHA appraisals required an assessment of general neighborhood condition and the red lines indicated “less desirable” areas. Needless to say, both the red lines and the practice of assessing neighborhood condition when appraising individual properties were quickly dropped!

A final institutional aspect of mortgage lending is the rise of electronic loan application, whether by telephone or Internet, during the 1990s. Electronic lending eliminates the need for personal contact between the applicant and the loan originator and, in most cases, between the loan processor and the applicant. None of the standard information used in underwriting, including credit reports, tax records, payroll reports, and so on, contains information on the race or ethnicity of the applicant. Of course, names may reveal some information, particularly regarding the sex and marital status of applicants, but the only regular source of information on applicant characteristics in the completed loan file is the required HUD disclosure form, and applicants may decline to respond to this request for information by the government. Thus electronic application is essentially racially and ethnically blind.
















The discussion of the institutional characteristics of the mortgage market leaves us with two apparently contradictory views of the potential for discrimination, at least as manifest in the USA. First, the origins of the term “redlining” are based on a form of blatant discrimination in government-guaranteed mortgage insur-ance. Clearly, discrimination has existed in mortgage lending. Second, the rise and rapid growth of electronic application indicates that a significant segment of the mortgage market cannot be engaging in blatant or differential treatment dis-crimination, because credit decisions are made without observing the demographic characteristics of applicants. Furthermore, the possibility of adverse impact discrimination in electronic lending can be easily assessed as the underwriting criteria are apparent – indeed, underwriting is often automated.

These contradictory views of discrimination in mortgage lending are easily resolved. FHA redlining existed in the late 1960s. By the 1970s, FHA policies had been reversed and FHA underwriting criteria became more lenient in formerly redlined areas. The structure of the mortgage lending industry in the USA changed with the problems of the thrift industry, beginning in the late 1970s, and the increase in government attention to equal credit opportunity in mortgage lend-ing. Mortgage lending was transformed from local lending by depository institu-tions, using funds collected locally, into a national enterprise, with large mortgage banking firms originating mortgages across the country, for sale packaged together in mortgage-backed securities. Similar changes are occurring in countries around the world. The connection between local banking services and mortgage lending has been broken. The rise of electronic lending and statistical underwriting was simply a product of the competitive pressures that arose when mortgage lending


was transformed from a local to a national enterprise. Lenders did not move to the current remote, electronic system because it virtually eliminated the possibil-ity for discrimination in mortgage lending. Instead, market forces produced the transformation to lower-cost electronic systems. Elimination of direct contact between applicant and agents of the lender was a byproduct of the changing technology. It is paradoxical that the only way in which demographic informa-tion can enter the loan file is if the HUD declarainforma-tion form is filled out accurately by the applicant.

In spite of these changes in mortgage lending, the possibility for discrimina-tion still exists, but it is concentrated in segments of the market where there is significant personal contact between the loan originator and the applicant. Applic-ants fail to take advantage of electronic application techniques for a variety of reasons. Some may be uncomfortable with the impersonal nature of the trans-action or may lack information on application procedures. Others have special credit problems, including lack of a credit history or difficulty in verification of income, that require extensive contact with the loan originator. In such cases, the loan originator may serve as financial advisor to the applicant, providing credit counseling services. Individuals whose use of credit in the past has been insuffi-cient to generate a credit score or those with a history of repayment problems may work with loan originators for months to produce an application that has a chance of passing the scrutiny of an underwriter. For expositional purposes, such individuals will be termed at-risk applicants, because for them the opportunity for discrimination is certainly real and there is little prospect that electronic lending will be able to serve these individuals in the near future. Note that most of the subprime market should be considered to be at risk.










Various techniques have been proposed to test for discrimination in mortgage lending, ranging from tests applied to individual lenders or metropolitan areas. The purpose of this review is to present the rationale for each test, give examples of the types of results obtained, and discuss problems in interpreting the results. Initially, the mortgage lending discrimination literature proceeded as though mortgage application was analogous to employment application. Tests that had been successful in detecting employment discrimination were adapted to the case of lending discrimination. Unfortunately, the mortgage application process is quite complex and differs substantially from employment transactions. Finding adequate tests for lending discrimination has proved to be a very difficult task, and the analogy with employment discrimination has led to some unfortunate testing mistakes.

Most of the discussion of discrimination in mortgage lending concerns dis-crimination against individuals based on their minority status. However, it is also possible to argue that there is discrimination against geographical areas, based on minority composition of the neighborhood. For minority groups whose housing is highly segregated, the hypothesis of individual discrimination can


become confused with geographical discrimination. However, for dispersed groups it is possible to argue that there might be discrimination against minorities living in minority neighborhoods, but not for minorities living in white neighborhoods. Testing for geographical discrimination is analogous to testing for individual discrimination. In order to economize on space, the discussion concentrates on testing for individual discrimination.


Tests based on segregation

of applicants or loans

A classic result in the economics of discrimination is that discrimination tends to produce market segregation. In the case of mortgage markets, if minorities are charged higher prices and/or rejected more often at some lenders than others, they will tend to apply to the lenders who are offering more favorable terms. In labor markets, discrimination resulted in segregation of the workforce, a classic example being the rise of minority sports leagues in the presence of discrimina-tion by existing major leagues.

To implement the segregation test in mortgage markets, it is necessary to first identify a nondiscriminating sector. Based on the experience of the 1970s, when there was a sharp reversal in policy, the FHA has been used as the nondiscrim-inating sector. The test for discrimination through segregation then involves estimating an FHA participation equation. To test for discrimination by prime conventional mortgage lenders, the dependent variable would be binary, equal to zero if the household has a conventional mortgage and equal to unity if the household has an FHA mortgage. To implement such a test, a sample of house-holds from a given geographical area is needed. The financial characteristics of the households that might explain the choice of conventional versus FHA financing are necessary in addition to information on the minority status of the house-holds. The market segregation test then rests on the estimated coefficients of dummy variables that indicate minority status. If these variables are positive, this indicates that minorities are concentrated in FHA rather than conventional mort-gages in a fashion not accounted for by differences in their financial condition. When such tests have been implemented, the general finding is that, holding financial condition and even credit score constant, African-American households are more likely to have FHA mortgages than white households.

The best publicized tests for discrimination in mortgage lending were a series of articles by Dedman (1988), entitled “The Color of Money,” which resulted in subsequent television programs. These articles documented the relative lack of conventional lending by depository institutions in African-American neighbor-hoods. Differences for other minority households were generally not significant and were even reversed in the case of Asian households. Taken at face value, such tests seem consistent with the hypothesis of discrimination against African-American households.

There are two obvious problems with the type of test used in “The Color of Money.” First, the Home Mortgage Disclosure Act (HMDA) data used in the test


lack information on borrower creditworthiness and loan terms used in under-writing. These omissions render HMDA data inadequate to test for discrimina-tion in lending. Second, HMDA data, at this time, did not cover mortgage bankers, who made most use of the FHA loans. Given that African-American households tend to be differentially concentrated in the FHA sector, where required down payments are lower, it is not surprising that depository institutions in Atlanta were making relatively few loans to African-American households. Overall, “The Color of Money” is a very poor example of testing for discrimination in mortgage lending based on market segregation.

Unfortunately, the simplistic segregation test for discrimination produces even more misleading results when it is extended to choice of other types of mortgage products. If a mortgage choice equation is estimated for conventional prime versus subprime mortgages, the finding is that African-American households are, other factors held constant, more likely to choose subprime loans. Using the segregation test, this would imply that, relative to prime lenders, subprime lenders are less likely to discriminate against minorities. Such a result seems highly improbable in itself and calls into question the general validity of tests based on segregation.


Tests based on performance

An important result in the economics of discrimination, noted by Ferguson and Peters (1995), is that if members of a particular group are subject to higher per-formance standards, the marginal members of that group will perform better than the marginal members of other groups. When this test is applied to dis-crimination against minority athletes by professional sports teams, it compares the performance of the weakest member of a minority group to make a team with the performance of the weakest nonminority athlete. If equal standards of team membership are applied regardless of demographic considerations, then there should be no association between demographic characteristics and the ability of the last player to make the team.

When this argument is applied to mortgage lending, it generally takes the form of tests based on loan performance. Using loans endorsed by a particular lender or group of lenders, a single-equation statistical model of serious delinquency, default, or foreclosure is estimated. The simplest form of such models has a binary dependent variable equal to unity if the loan defaulted and equal to zero if it was paid off normally. The default model is estimated using information available to the lender at the time of endorsement, including all factors used in the under-writing process. In addition to variables reflecting the underunder-writing decision, demographic variables are added to reflect the protected status of the borrower. The test for discrimination based on loan performance is that the partial effect of minority status on default or foreclosure is negative; that is, if lending criteria are applied more rigidly to minorities, they should be, at the margin, less likely to be default. Estimates of loan performance models by Berkovec, Canner, Gabriel, and Hannan (1994) and by Martin and Hill (2000) show that default and foreclosure loss is lower for white borrowers. This indicates discrimination in


favor of African-American borrowers. Recall from the previous section that market segregation tests implemented for FHA-insured mortgages tend to find African-American applicants concentrated in FHA-insured mortgages, indicating potential discrimination. Thus the results of loan performance tests tend to indic-ate discrimination in just the opposite direction to that found in market segrega-tion tests! This contradicsegrega-tion in test results is even more remarkable because the segregation test indicates that African-American applicants appear segregated in FHA programs and the loan performance studies have generally been performed using FHA-insured mortgages.

How can we resolve the contradiction between the segregation and loan performance tests? One possibility is that there are omitted variables – that is, variables not observed in the loan file – which predict default and foreclosure and that these variables are correlated with race. The problem with an appeal to omitted variables is that such an argument can be used to invalidate any statistical test, because there are always omitted variables that are correlated with demo-graphic characteristics.

An alternative view of the loan performance test is that it should consider more than credit risk. Another aspect of loan performance is prepayment risk. If borrowers refinance aggressively when interest rates fall, then mortgage lending is less profitable. The empirical evidence is that groups that have higher credit risk tend to have lower rates of prepayment, and that the gain in profitability from lower refinancing rates approximately offsets higher default and foreclosure losses. Given the way in which mortgages are packaged and sold in securitized form, it is not clear that differences in prepayment risk on individual mortgages are always priced. Nevertheless, the implications of moving from credit risk to loan profitability, including prepayment, as the basis for loan performance testing are worth examining. If loan profitability is the basis for loan performance testing, then the tests may show no relation between minority status and profit-ability of the marginal loan.


Tests based on differential rejection rates

Tests for discrimination in mortgage lending based on the partial association between applicant rejection rates and minority status have received widespread attention. Bank regulators have used these tests to examine lenders for discrimina-tion against minority applicants and neighborhoods, as discussed in Tootell (1996). They are also used by plaintiffs in fair lending cases.

Differential rejection rate tests require data on mortgage applications to a given lender or group of lenders. Black, Schweitzer, and Mandell (1978) were the first to suggest a test based on a single-equation model of mortgage rejection. The dependent variable is equal to unity if the application is rejected and to zero if accepted. The regressors include variables affecting the underwriting decision as noted above. Demographic variables reflecting minority status are added to the equation to determine whether there is any partial effect of prohibited factors on the probability of rejection holding creditworthiness constant, as reflected in the variables used in the underwriting process.


Initial rejection rate tests were plagued by lack of information on applicant characteristics. Home Mortgage Disclosure Act (HMDA) data provided informa-tion on the acceptances and rejecinforma-tions, but credit history, loan-to-value ratio, and other factors important in underwriting were omitted. Rejection rate tests using only HMDA data affirmed the hypothesis of discrimination, but were heavily criticized because omitted variables used in underwriting were likely correlated with applicant demographics.

To remedy these charges of omitted variable bias, the Federal Reserve Bank of Boston (the Boston Fed) secured the cooperation of Boston-area banks in pro-viding access to individual loan files. To the extent possible, information in the loan files provided to underwriters was coded into a data set. Single-equation estimates of a rejection equation indicated that the demographic characteristics of the applicant had a partial influence on rejection, even after a good faith effort to consider all of the information in the loan file. Subsequent studies have found similar results and, in particular, estimated coefficients for African-American applicants tend to be positive and significant, indicating that race plays a role in rejection. These single-equation models have been used by bank regulators and by plaintiffs’ experts in mortgage lending discrimination cases.

Unfortunately, as noted in the discussion of the institutional characteristics of the mortgage market, applicants frequently work with realtors, and particularly with loan originators, to avoid rejection. In the Boston Fed study this was called “coaching” and it was suggested that coaching was applied differently based on race: “Similarly, if white applicants are more likely than minority applicants to be ‘coached’ when filling out the application, they will have stronger applica-tions than similarly situated minorities. In this case, the ratios and other financial information in the final application, which is the focus of this analysis, may find themselves to be the product of differential treatment. This study does not explore the extent to which coaching occurs . . .” (Munnell, Tootell, Browne & McEneaney 1992, p. 43; emphasis present in the original).

The problem with tests based on differential rejection rates is that these single-equation tests assume that applicants are not coached by loan officers and that they choose loan terms without regard to the likelihood of rejection. Horne (1994) has noted that 20 percent of the applications in the Boston Fed study were reviewed more than three times and that applicants clearly adjusted loan terms. If the applicants, particularly using information from the loan originator, choose loan terms to meet underwriting criteria, then the estimated coefficients of a single-equation model of rejection are biased and inconsistent. Furthermore, Yezer, Phillips, and Trost (1994) have shown that the direction of bias will tend to show discrimination against less affluent minority groups even when no differential treatment exists. Put another way, the bias is in the rejection equation test, not at the banks.

One possible solution to the bias in rejection equation tests suggested by Maddala and Trost (1982) and by Barth, Cordes, and Yezer (1980) is to formulate a simultaneous equations model in which the probability of rejection and the loan terms that applicants manipulate to avoid rejection are jointly determined by economic and demographic factors. There are two problems with a simultaneous


equations model. First, applicants adjust several loan terms, including loan-to-value ratio, monthly-payment-to-income ratio, term to maturity, and the presence of a cosigner. In some cases, applicants remedy problems in their credit history, by paying off or closing outstanding credit lines or removing incorrect informa-tion. Modeling so many jointly determined variables is not feasible (see Munnell, Tootell, Browne, and McEneaney 1996). Second, information used in the rejection equation comes from the credit files assembled by the loan processor. It is not easy to find the exogenous information, variables collected by lenders but not considered in the underwriting decision, that would be needed to identify the rejection equation.

Although tests based on differential rejection rates tend to produce false indications of differential treatment discrimination when none exists, they are still used. Typically, single-equation models find that single females have signi-ficantly lower rejection rates than single males. Does this indicate that lenders are discriminating against single males or that the test for discrimination is biased? One legitimate use of a biased test is as an initial screening device to show nondiscrimination. Any lender whose rejection rates are not associated with the minority status of the applicant is clearly not discriminating against minorities. Unfortunately, it is common to see studies that use any positive association between rejection rates and minority status as an indication of discrimination, rather than as an indication of bias in tests based on differential rejection rates.


Tests based on differential pricing

Given the difficulty of testing for discrimination based on differential rejection rates, the next logical step is to test for differences in actual pricing of mortgages. Once again, the hypothesis of discrimination would be based on an association between the minority status of the borrower and the mortgage price. A first problem with this approach is that prices are only observed for mortgages that are endorsed, and pricing to rejected applicants cannot be considered. The second problem is the complex nature of mortgage pricing. Even restricting the discus-sion to a single type of mortgage product, borrowers trade off discount points for an interest rate based on their expected prepayment strategy. Third, borrowers routinely try to time the market during the period between application and final endorsement of the mortgage by “floating” or reserving the option of adjusting the price downward if interest rates fall. The fourth problem arises from the institutional structure of the mortgage market. Loan originators may collect fees independent of the lender. Thus the mortgage price paid by the borrower may include a specific component of compensation for the loan originator. Fifth, the use of single-equation models of mortgage pricing is not appropriate because applicants adjust loan terms to manipulate the price that they will pay – that is, all the difficulties associated with the analysis of differential rejection rates also apply to mortgage pricing.

Because of the complexity of mortgage pricing and the lack of generally avail-able data on pricing, few tests of discrimination in pricing have been performed. Some researchers have attempted to find a shortcut to discrimination based on


pricing by using the concept of overages in the mortgage transaction. As noted in the institutional discussion, loan originators are often paid a commission. One basis of that commission is the difference between the price paid and a daily rate sheet supplied by the lender giving the minimum acceptable prices. Any surplus in price over the daily rate sheet is termed an overage and may determine the loan officer’s commission. One possible test for discrimination in loan pricing that appears to cut through much of the complexity is to examine the statistical relation between borrower characteristics and the amount of overages.

While it is tempting to regard the overage as a pure price premium paid by the borrower, this view is false. Loan originators use overages as compensation for extra effort needed to qualify marginal applicants. Without overages, loan origin-ators would have no incentive to work with such applicants. Also, overages are determined by the behavior of borrowers who try to time the market by preserv-ing the option to lock in an interest rate durpreserv-ing the period between application and endorsement. There is every reason to suspect that differences in floating behavior may be associated with borrower demographic characteristics in ways that have nothing to do with discrimination. The final overage paid by the rower is, in effect, determined by the loan originator, the lender, and the bor-rower through their behavior during the period when the option to lock in a final loan price is being exercised.

Given the complexity of mortgage loan pricing, it is not surprising that it has been largely neglected as a basis for testing for discrimination in mortgage lending.












While it has proved most difficult to find unbiased and unambiguous tests for differential treatment discrimination, the attempt to regulate adverse impact dis-crimination in mortgage lending has proved to be even more frustrating. Recall that adverse impact discrimination arises because minority status is naturally correlated with many economic characteristics that can be used in the lending decision. Lenders are required to choose, from among the economic variables, those that are least associated with minority status, provided that they predict default and foreclosure loss as well as other variables. For example, assume that two variables, “number of bank accounts” and “average bank balance in all accounts,” are close substitutes in predicting creditworthiness, but that number of accounts is negatively associated with minority status, while average balance is not. Adverse impact discrimination would occur if lenders used number of accounts in underwriting rather than average balance, if both variables could be readily observed. Lenders, under pressure from regulators to increase minority lending, have an incentive to eliminate adverse impact discrimination.

The paradox of adverse impact discrimination arises because government inaction and regulation have, thus far, impeded two possible approaches to the problem. One approach would involve a major government-sponsored research effort to identify the variables that could be used to identify creditworthiness with the least adverse impact on minorities. The problem with relying on private


lenders to develop this information is that credit-scoring schemes must be held as a trade secret in order to be valuable. Therefore, research on these schemes is generally not publicly available and it is difficult for lenders to determine what variables minimize adverse impact. Another approach would be to allow lenders to estimate separate credit-scoring equations for different demographic groups. This approach allows for the possibility that the relation between economic variables and credit risk for minority groups is different; that is, it introduces an element of diversity into the credit-scoring scheme. While either of these approaches would likely increase lending to minority borrowers and lower the problem of adverse impact discrimination, to the average person they appear to be discriminatory. Thus the problem of dealing with adverse impact discrimina-tion may be largely political. Separate credit-scoring schemes for minority groups appear unequal to the public. Paradoxically, it may be that equality of access to credit is furthered when underwriting is based on specially fitted credit risk equations.

Two other approaches to adverse impact discrimination that are not solutions at all have been proposed. One approach would require that lenders include demographic characteristics in their credit-scoring equations. Then credit score would be computed ignoring demographic characteristics. To the extent that an economic variable had an effect on credit risk but was strongly associated with minority status, this procedure would insure that the effect was not translated into the final credit score. The problem with this approach is that it biases the estimated coefficients of the credit-scoring equation and results in denial of credit to the creditworthy and granting of credit to higher-risk applicants.

A second proposed approach would reveal information on the details of the credit-scoring scheme to the applicant. This has been advanced as a fairness issue that potentially lessens adverse impact. Unfortunately, credit-scoring models pre-sume that applicants do not know the model used to evaluate their application. If applicants are sufficiently coached in the details of underwriting, they will behave in ways that invalidate the initial credit-scoring scheme. The argument here is directly analogous to giving out test questions before an examination. If students know the questions before the examination, they will modify their study accordingly and grading of the examination must be changed to recognize the additional student information.




Mortgage lending is a very complex process that presents a challenge for eco-nomic analysis. Discrimination in mortgage markets cannot be detected using techniques that have succeeded in labor markets. While there is evidence of past discrimination, it appears that regulators and lenders have taken steps to insure against future discrimination. The government program charged directly with insuring mortgage lending for low-income and minority homebuyers, FHA mort-gage insurance, has certainly remedied past failures and now lends aggressively to needy borrowers. Electronic lending virtually eliminates any possibility for


discrimination. Thus research on discrimination should focus on the applicant groups that have the least affinity with electronic commerce and on high-risk applicants for whom underwriting requires personal contact.

One promising frontier for research is adverse impact discrimination. Building efficient credit-scoring schemes that are appropriate for minority groups remains a challenge both for researchers and the political system. There is a common perception that separate credit-scoring systems are inherently unequal. But, para-doxically, it is likely that separate underwriting schemes for minority groups may be needed to provide equal opportunity access to mortgage credit.

Research and regulatory experience with discrimination in mortgage lending has thus far been concentrated in the USA. However, across the world, mortgage markets are changing in ways that often mimic the evolution of the industry in the USA, including the rise of mortgage securitization and electronic lending. It may be that the lessons reviewed in this essay about the complexity of mortgage lending and the effects of regulation may be valuable and applicable to many countries in the near future.


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