Do Black-Owned Banks Discriminate against Black Borrowers?

Black-owned and white-owned banks are studied to test for lending discrimination based on applicant's race. Using single-equation models the rndings suggest that black-owned banks may utilize applicant race in the mortgage granting decision. The result holds in a naive HMDA model and in an enhanced HMDA model with bank-specirc, demographic, and neighborhood characteristics added.     

Is Race an Important Factor in Bank–Customer Preferences? The Case of Mortgage Lending

This paper tests whether there is a preference by borrowers and lenders of a particular ethnic or racial group that may predispose them to borrow and lend from each other. Using a narrow geographic area called zip code clusters, this paper groups minority-owned and non-minority-owned banks to allow for a homogenous applicant pool from which both borrower and lender preferences can be determined. This study compares Asian-owned, black-owned and Hispanic-owned banks versus white-owned banks that are located in the same zip code and adjacent zip codes. The results show that borrowing preferences exist between all applicant groups and banks from the same racial classification. In addition, some cross-racial borrowing preferences exist. The results show that across sample lending preferences exist for both black and white applicants with black-owned banks and white applicants with white-owned banks that are located in the same area as Asian-owned banks. Also, the results of the within sample lending preference model shows that white-owned banks have a preference for white applicants, at the expense of Hispanic applicants and both white-owned and Asian-owned banks exhibit a preference for Asian applicants.     

Do lenders discriminate against low-income borrowers?

Summary and Conclusion The purpose of this paper is determine whether the use of a narrow geographic definitions of the market served by banks results in conclusions that are different from those found if a broader geographic definitions is used. Using ZIP code clusters for a sample of low-income applicants, we find results that are different from those when the traditional MSA sampling technique is used.