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Working Paper
Constructing Applicants from Loan-Level Data: A Case Study of Mortgage Applications
We develop a clustering-based algorithm to detect loan applicants who submit multiple applications (“cross-applicants”) in a loan-level dataset without personal identifiers. A key innovation of our approach is a novel evaluation method that does not require labeled training data, allowing us to optimize the tuning parameters of our machine learning algorithm. By applying this methodology to Home Mortgage Disclosure Act (HMDA) data, we create a unique dataset that consolidates mortgage applications to the individual applicant level across the United States. Our preferred specification ...
Working Paper
Measuring Fairness in the U.S. Mortgage Market
Black Americans are both substantially more likely to have their mortgage application rejected and substantially more likely to default on their mortgages than White Americans. We take these stark inequalities as a starting point to ask the question: How fair or unfair is the U.S. mortgage market? We show that the answer to this question crucially depends on the definition of fairness. We consider six competing and widely used definitions of fairness and find that they lead to markedly different conclusions. We then combine these six definitions into a series of stylized facts that offer a ...