WP 25-25 – We develop an algorithm to detect loan applicants who submit multiple applications in a loan-level dataset without personal identifiers. Our method detects applicants that submit multiple mortgage applications with 92.3 percent precision.
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Supersedes Working Paper 25-05 – 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 identifies cross-applicants with 92.3 percent precision.
