Impact of mortgage soft information in loan pricing on default prediction using machine learning |
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| Authors: | Thi Mai Luong Harald Scheule Nitya Wanzare |
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| Institution: | Finance Discipline Group, UTS Business School, University of Technology Sydney, Sydney, New South Wales, Australia |
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| 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. |
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| Keywords: | credit risk default hard information lending mortgage prediction pricing soft information yield spreads |
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