Modeling the probability of mortgage default via logistic regression and survival analysis
The goal of this thesis is to model and predict the probability of default (PD) for a mortgage portfolio. In order to achieve this goal, logistic regression and survival analysis methods are applied to a large dataset of mortgage portfolios recorded by one of the national banks. While logistic regression has been commonly used for modeling PD in the banking industry, survival analysis has not been explored extensively in the area. Here, survival analysis is offered as a competitive alternative to logistic regression.^ The results of the final modeling for both methods show very similar fit in terms of the ROC with the survival model having slightly better performance than logistic regression in the training dataset and almost the same performance in the testing dataset. In term of prediction of defaulted and non-defaulted mortgage portfolios, the logistic regression model outperforms survival analysis in the training dataset, while survival model outperforms logistic regression in the testing dataset.^ Overall, the results support that the survival analysis approach is competitive with the logistic regression approach traditionally used in the banking industry. In addition, the survival methodology offers a number of advantages useful for both credit risk management and capital management.^
Business Administration, Banking
"Modeling the probability of mortgage default via logistic regression and survival analysis"
Dissertations and Master's Theses (Campus Access).