Affiliations: [a] Division of Consumer and Community Affairs, Federal
Reserve Board, 1709 New York Ave, NW, Washington, DC, USA. Tel.: +1 202 973
7352; E-mail: [email protected]
Abstract: Testing for discrimination in mortgage lending requires classifying consumers
into treatment and control groups. Classification is quite complicated, because
Home Mortgage Disclosure Act (HMDA) data, the primary source of data for these
analyses, contain information on the ethnicity, race, and gender for both
primary and coapplicants. In addition, applicants have the option of reporting
multiple races. Using these detailed data to construct standard groups, such as
"Black," "Hispanic," and "White," requires subjective decisions on how to
aggregate applications. This study uses a data-driven approach to classify
applications, minimizing subjectivity. Using HMDA data, as well as data from a
recent examination conducted by the Office of the Comptroller of the Currency,
we disaggregated applications into the most basic subsets possible. Our
objectives are to better understand the characteristics of applicants, analyze
variation in denial rates across underlying subsets of applications, and develop
a data-driven classification strategy feasible for fair lending analyses.
