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Impact of mortgage soft information in loan pricing on default prediction using machine learning

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  • Thi Mai Luong
  • Harald Scheule
  • Nitya Wanzare

Abstract

We analyze the impact of soft information on US mortgages for default prediction and provide a new measure for lender soft information that is based on the interest rates offered to borrowers and incremental to public hard information. Hard and soft information provide for a variation in annual default probabilities of approximately 3%. Soft information has a lesser impact over time and time since origination. Lenders rely more on soft information for high‐risk borrowers. Our study evidences the importance of soft information collected at loan origination.

Suggested Citation

  • Thi Mai Luong & Harald Scheule & Nitya Wanzare, 2023. "Impact of mortgage soft information in loan pricing on default prediction using machine learning," International Review of Finance, International Review of Finance Ltd., vol. 23(1), pages 158-186, March.
    • Handle: RePEc:bla:irvfin:v:23:y:2023:i:1:p:158-186
    DOI: 10.1111/irfi.12392
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