Publications:
Classifying Applicants for Fair Lending Analyses: What Do the Data Have to Say?
by Jason Dietrich
Abstract: 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 coapplicants. 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.
Disclaimer
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.
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