Commercial Mortgage Default: A Comparison of the Logistic Model with Artificial Neural Networks

摘要

This manuscript explores the use of artifiical neural networks in the mdoeling of foreclosure of commercial mortgages. The study employs a large set of individual loan histories previously used in the literature of proportional hazard models on loan default. Radial basis function networks are trained on the same inputs as those used in the logistic, and performance is assessed in terms of prediction accuracy. Neural networks are shown to be superior to the logistic benchmark in terms of discriminating between "good" and "bad" loans. Sensitivity analysis performed on the everage loan demonstrate the use of neural networks as an analytical tool. Finally the study offers suggestions on further improving prediction of defaulting loans.