{"title":"Impact of mortgage soft information in loan pricing on default prediction using machine learning","authors":"Thi Mai Luong, Harald Scheule, Nitya Wanzare","doi":"10.1111/irfi.12392","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":46664,"journal":{"name":"International Review of Finance","volume":"23 1","pages":"158-186"},"PeriodicalIF":1.8000,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/irfi.12392","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Review of Finance","FirstCategoryId":"96","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/irfi.12392","RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
引用次数: 1
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.
期刊介绍：
The International Review of Finance (IRF) publishes high-quality research on all aspects of financial economics, including traditional areas such as asset pricing, corporate finance, market microstructure, financial intermediation and regulation, financial econometrics, financial engineering and risk management, as well as new areas such as markets and institutions of emerging market economies, especially those in the Asia-Pacific region. In addition, the Letters Section in IRF is a premium outlet of letter-length research in all fields of finance. The length of the articles in the Letters Section is limited to a maximum of eight journal pages.
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