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Precision Without Labels: Detecting Cross-Applicants in Mortgage Data Using Unsupervised Learning

Author

Listed:
  • Hadi Elzayn

  • Simon Freyaldenhoven
  • Minchul Shin

Abstract

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% precision.

Suggested Citation

  • Hadi Elzayn & Simon Freyaldenhoven & Minchul Shin, 2025. "Precision Without Labels: Detecting Cross-Applicants in Mortgage Data Using Unsupervised Learning," Working Papers 25-25, Federal Reserve Bank of Philadelphia.
    • Handle: RePEc:fip:fedpwp:101559
    DOI: 10.21799/frbp.wp.2025.25
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    References listed on IDEAS

    as
    1. Yuliya Demyanyk & Otto Van Hemert, 2011. "Understanding the Subprime Mortgage Crisis," The Review of Financial Studies, Society for Financial Studies, vol. 24(6), pages 1848-1880.
    2. Christopher Mayer & Karen Pence & Shane M. Sherlund, 2009. "The Rise in Mortgage Defaults," Journal of Economic Perspectives, American Economic Association, vol. 23(1), pages 27-50, Winter.
    3. Bhutta, Neil & Hizmo, Aurel, 2020. "Paying Too Much? Borrower Sophistication and Overpayment in the US Mortgage Market," CEPR Discussion Papers 14924, C.E.P.R. Discussion Papers.
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