Report

Predicting Mortgage Pre-payment Risk Introduction Definition Borrower pays off the loan before the contracted term loan length. • Lender loses future part of the income stream associated with the loan. • Inspiration & Previous work Oded Netzer’s talk on text-mining techniques for business applications – loan default prediction. Breakdown of the Problem Target To pin-point among borrower profiles, who all likely to prepay the mortgage. Data UCI Machine Learning Repository : Credit Approval Dataset – 690 observations; 15 attributes. German Credit Dataset – 1000 observations, 20 attributes. Give Me Some Credit - Kaggle credit-scoring competition - very large. Prospective Solutions Techniques for categorization of data Decision Tree - recursively separates observations in branches to construct a tree to improve prediction accuracy, with use of measurements like information gain ratio, Gini index etc. Naïve Bayes Classifier - calculates a set of probabilities by counting the frequency and combinations of values in the dataset. Logistic Regression - measures the relationship between the one dependent binary variable and one or more independent variables. by estimating probabilities using a logistic function.Appropriate when the dependent variable is binary. Evaluation of Results Measure of success How clear the proposed categorization scheme is proposed. • How applicable the scheme is in enabling lenders in managing prepayment risk by providing a useful structure for early mitigation targeting. •