MORTGAGE LENDING IN BOSTON: A RECONSIDERATION OF THE EVIDENCE

I. INTRODUCTION Discrimination in the marketplace is often measured using statistical methods. In order for the results to be interpretable as some form of discrimination it is necessary that these methods be firmly based on economic theories as to how these markets function. The reason for this is that it is easy to confuse voluntary acts of segregation from involuntary acts of discrimination by the simple examination of statistical entrails. The former is presumably acceptable to society, whereas the latter is not. Any society that values its freedoms will want to ensure that a mis-classification of the former as evidence of the latter is avoided. Claims of racial discrimination in mortgage lending provide an excellent arena to illustrate the issues that arise in statistically measuring discrimination. In the United States data on mortgage applications has been collected for several years under the Home Mortgage Disclosure Act (HMDA). These data show a persistent tendency for blacks to be denied mortgages at a greater rate than whites, leading many political commentators to claim that they prove the existence and severity of racial discrimination. One problem with the statistical measurement of discrimination is that the available data are often inadequate. The HMDA data has been criticized for not collecting all of the information germane to the loan decision, such as the credit history or personal characteristics of the loan applicant and co-applicant. The argument is that blacks are denied loans more than whites because of these characteristics, rather than the color of their skin. Whether or not this, in turn, reflects prior or concurrent racial discrimination in other markets (e.g., in employment or housing or education) is an intriguing question with important implications for policy reform in mortgage lending regulations.(1) In an effort to address these concerns, the Federal Reserve Bank of Boston undertook to augment the basic HMDA data in the Boston area with ancillary data on loan applicants: see Munnell et al. [1992, 1996]. The vast bulk of these extra data were collected from the original loan application file at the bank of original application, indicating that the information was viewed by the bank as relevant to the loan decision. The Boston Fed study found that allowing for the characteristics of black applicants reduced the statistical importance of the applicant's race, but that some racial discrimination remained. Specifically, they find that black and Hispanic applicants are roughly 60% more likely to be turned down just because of their ethnic origin. The denial rate in this sample is about 17% for such applicants, rather than the 11% denial rate for "statistically equivalent" whites. Although the study has been the subject of some controversy in the popular press(2) and policy circles,(3) it remains an important effort to address these issues by directly collecting the primary data. There are, however, some problems with the way in which these data are interpreted by Munnell et al. [1992, 1996]. These problem concern their statistical methods, even if one accepts the economic model of mortgage lending decisions underlying their study. Simple corrections to their statistical procedures removes any evidence of discrimination. An important conclusion, echoing the Boston Fed study itself, is to point out the dangers of drawing inferences about discrimination based on incomplete data. Moreover, the simple statistical corrections considered here are the standard fare of textbooks and not from the fancy frontiers of econometrics. Hence it is likely to be useful in general to see why those textbook lessons are there by having them demonstrated explicitly in an important policy setting. II. REPLICATING THE BOSTON FED RESULTS The Boston Fed study began with the existing HMDA database for mortgage applications in the Boston area for 1990.(4) The Fed requested that banks augment their HMDA reports for all applications made by blacks and Hispanics, and by a random sample of applications made by whites. …