Classifying Applicants for Fair Lending Analyses: What Do the Data Have to Say?
by Jason Dietrich
Testing for discrimination in mortgage lending requires classifying consumers into treatment groups and control groups. Although this may seem like a straightforward task, it is actually quite complicated. 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 co-applicants. In addition, applicants have the option of reporting up to five races. Using these detailed data to construct the standard groups, such as "Black," "Hispanic," and " White," requires subjective decisions on how to appropriately 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 the HMDA data allowed. 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 that could be used during fair lending analyses.
Any whole or partial reproduction of material in this paper should include the following citation: Jason Dietrich, "Classifying Applicants for Fair Lending Analyses: What Do the Data Have to Say?" Office of the Comptroller of the Currency, Economics Working Paper 2009-4, August 2009.