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Using Artificial Intelligence to Identify Strategic Mortgage Default Attitudes

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Listed:
  • Jackson T. Anderson
  • Julia Freybote
  • David Lucus
  • Michael J. Seiler
  • Lauren Simon

Abstract

Previous studies have yielded ambiguous results regarding the impact of income and financial experience on the decision of residential borrowers to strategically default. One explanation for these findings is the presence of an interaction effect between income and financial experience, which was ignored in earlier studies. We hypothesize that borrowers with a greater ability to assess the financial benefits of strategic default, due to a higher financial experience, and overcome the financial consequences of it, due to higher incomes, have fewer negative attitudes towards strategic default. We capture negative attitudes of US homeowners (borrowers) by measuring their anger towards residential borrowers who decided to strategically default. In particular, we (1) ask them to self-report their anger and (2) measure their anger with artificial intelligence (AI)-based emotion recognition software. We find evidence for the hypothesized interaction effect: Borrowers with higher incomes and greater financial experience, particularly regarding value of money, savings, and investments, have fewer negative attitudes towards strategic default. Our results have implications for mortgage lenders. While borrowers with higher incomes and more financial experience are likely to have a lower economic default risk, their strategic default risk may be higher.

Suggested Citation

  • Jackson T. Anderson & Julia Freybote & David Lucus & Michael J. Seiler & Lauren Simon, 2022. "Using Artificial Intelligence to Identify Strategic Mortgage Default Attitudes," Journal of Real Estate Research, Taylor & Francis Journals, vol. 44(3), pages 429-445, July.
    • Handle: RePEc:taf:rjerxx:v:44:y:2022:i:3:p:429-445
    DOI: 10.1080/08965803.2021.2009621
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