Impact of mortgage soft information in loan pricing on default prediction using machine learning. (18th September 2022)

• Record Type: Journal Article
• Authors: Luong, Thi Mai; Scheule, Harald; Wanzare, Nitya
• Is Part Of: International review of finance. Volume 23:Number 1(2023)
• Page Start: 158
• Page End: 186
• Publication Date: 2022-09-18
• Subjects: credit risk -- default -- hard information -- lending -- mortgage -- prediction -- pricing -- soft information -- yield spreads; Finance -- Periodicals; Financial institutions -- Periodicals; 332.673
• DOI: 10.1111/irfi.12392
• Languages: English
• ISSNs: 1369-412X

Abstract

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.