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Using Machine Learning to Predict Success or Failure in Chapter 13 Bankruptcy Cases

Agin, Warren - 2018

Obtaining a chapter 13 bankruptcy discharge is notoriously difficult. Past empirical studies conclude that only one …

Persistent link: https://www.econbiz.de/10012918451

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

Luong, Thi Mai; Scheule, Harald; Wanzare, Nitya - In: International review of finance : the official journal … 23 (2023) 1, pp. 158-186

Persistent link: https://www.econbiz.de/10014251350

A proportional hazards model of commercial mortgage default with originator bias

Ciochetti, Brian A.; Deng, Yongheng; Lee, Gail; … - In: The journal of real estate finance and economics 27 (2003) 1, pp. 5-23

Persistent link: https://www.econbiz.de/10001763275

A forecasting model for the likelihood of delinquency, default or prepayment : the case of Taiwan

Lin, Chin-tsai; Yang, Shih-yu - In: International journal of business 8 (2003) 2, pp. 203-211

Persistent link: https://www.econbiz.de/10001767461

Mortgages : Estimating Default Correlation and Forecasting Default Risk

Neumann, Tobias - 2018

appropriate for UK and US mortgages. This model also forecasts mortgage default rates accurately and parsimoniously. The model … generates value-at-risk estimates for future mortgage default rates, which can be used to inform stress-testing and …

Persistent link: https://www.econbiz.de/10012925775

Impact of Mortgage Soft Information in Loan Pricing on Default Prediction using Machine Learning

Luong, Mai; Scheule, Harald (Harry); Wanzare, Nitya - 2022

We analyse 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...

Persistent link: https://www.econbiz.de/10014236050