JOURNAL ARTICLE

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

  • Published In: International Review of Finance, 2023, v. 23, n. 1. P. 158 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Luong, Thi Mai; Scheule, Harald; Wanzare, Nitya 3 of 3

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. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Review of Finance. 2023/03, Vol. 23, Issue 1, p158
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2023
  • ISSN:1369-412X
  • DOI:10.1111/irfi.12392
  • Accession Number:162168069
  • Copyright Statement:Copyright of International Review of Finance is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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