The basic approach used by most studies of redlining using the process-based definition is to determine whether the probability that a loan application is denied is higher in minority neighborhoods than in white neighborhoods, all else equal. Thus, studies of redlining face the same key challenge as studies of discrimination, namely, to find a data set with adequate information on loans and applicants, including applicant credit history. Without this information, inferences about redlining, like inferences about discrimination, are likely to be subject to severe omitted-variable bias.
HMDA data, which do not contain information on applicant credit history, are therefore not adequate for isolating redlining. Indeed, HMDA data may be particularly unsuited for studying redlining, because it appears that lenders who are active in minority and low-income neighborhoods tend to attract appli- cants with relatively poor credit qualifications, based both on variables that are observed in the HMDA data and on variables that are not observed there. (See the discussion of Bostic and Canner 1997, later in this chapter.)
The first three studies of process-based redlining we review have access to information on applicants’ credit histories, which implies that they are based on the only data set with such information, namely, the Boston Fed Study’s data set. The final study is based on HMDA data combined with census tract data and data on house sales by tract.
Two articles, Tootell (1996a) and Hunter and Walker (1996), study redlin- ing using the Boston Fed Study’s data and a standard loan denial equation. Both authors add to this equation explanatory variables that describe the character- istics of the census tract in which the housing unit is located. These variables include the vacancy rate, the poverty rate, and the percentage of the popula- tion belonging to a minority group. None of these variables is statistically sig- nificant, and both studies conclude that there is no evidence of redlining in Boston.15The results in Tootell are particularly compelling because he con-
trols for the perceived risk to owners of home equity in a neighborhood, using variables that are not in the public-use version of the Boston Fed Study’s data.16
Another study based on the Boston Fed Study’s data set, Ross and Tootell (1998), examines a more complex model in which redlining is related to the market for private mortgage insurance (PMI). They examine redlining based both on the minority composition of a neighborhood and on the neighborhood’s median income. They find evidence of redlining against low-income census tracts, defined as having a median income at least one standard deviation below that of the MSA, when the applicant did not apply for PMI. The coefficient esti- mate was 0.56 (with a t-statistic of 2.52).17They also find some evidence that
applications from low-income tracts are favored when the applicant applies
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for PMI. In this case the coefficient was –1.96 (2.18). They find no evidence, however, that the probability of denial is higher in tracts with a minority per- centage above 30 percent than in other tracts.
Ross and Tootell (1998) suggest that lenders may be meeting their CRA oblig- ations to meet the credit needs of all members of the community from which a lender draws deposits at low risk by encouraging applicants from low-income tracts to apply for PMI. Because low-income tracts are seen as riskier, however, even after accounting for all the variables in the Boston Fed Study’s data set, the authors find that lenders are more likely to deny applicants from those tracts when the applicant does not apply for PMI. Ross and Tootell also conclude that their test for redlining based on minority status has little power in the Boston area. In the Boston Fed Study’s data set, the income and minority composition of tracts is highly correlated (a correlation coefficient of 0.7). Moreover, when the cut-off used to define a low-income tract is raised, the minority tract variable is statistically significant. In short, with this data set it is difficult to distinguish between income-based and minority status–based redlining.
Ross and Tootell obtain similar results for many different sets of explanatory variables and for several different models. In one alternative model they exclude cases in which the individual applied for PMI. In another they model the application for and receipt of PMI and allow this outcome to influence the lender’s loan denial decision.18 In both cases, their main result, that lenders
practice redlining against low-income neighborhoods, is upheld.
Finally, Ling and Wachter (1998) test a redlining hypothesis put forward by Lang and Nakamura (1993). The Lang-Nakamura hypothesis begins with the observation that lenders are uncertain about future developments in any particular neighborhood. Because they are risk averse, a greater degree of uncer- tainty about a neighborhood is associated, all else equal, with a higher proba- bility of denying applications for loans to buy houses in that neighborhood. Lenders gain information by observing house sales. So, controlling for other things, the probability of loan denial should decline as the number of house sales goes up. This hypothesis should be of interest to policymakers because it implies that the flow of funds to some neighborhoods, particularly low-income neighborhoods where few house sales take place, may be restricted by a lack of information, which is the type of problem that markets cannot solve. If it is true, therefore, this hypothesis may serve as a justification for the CRA or other policies to offset redlining.
Ling and Wachter (1998) test this hypothesis by combining HMDA loan approval data for Dade County, Florida, where Miami is located, with census data on neighborhood (i.e., tract) characteristics and data on house sales and sales prices from the Florida Department of Revenue. The resulting data set has an extensive set of neighborhood variables (such as median income, median education, median house value, and percentage of housing units that are owner- occupied), along with a variable to test the Lang-Nakamura hypothesis (namely, the share of owner-occupied housing units that sold over a three-year period), and a related variable (namely, the percentage change in the price of housing). The HMDA data contain several applicant characteristics but do not, of course, indicate applicant credit history. Thus, the Ling and Wachter regressions, while thought-provoking, may be subject to severe omitted-variable bias.19
OTHER EVIDENCE OF DISCRIMINATION
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Ling and Wachter (1998) find that, as predicted by Lang and Nakamura, the probability of loan acceptance increases with the share of houses that sell. It also increases with the rate of increase in housing prices. Ling and Wachter also point out, however, that an increase in sales could signal an upward shift in the demand for housing in a neighborhood, so that their results are also consistent with the view that lenders see less risk in neighborhoods where housing demand is on the rise. This alternative hypothesis does not imply a need for governmen- tal anti-redlining policies, so further research on this topic is clearly needed.