A Neural Network Analysis of Mortgage Choice

G. Grudnitski, A. Quang Do and J. D. Shilling

Intelligent Systems in Accounting, Finance and Management, 1995, vol. 4, issue 2, 127-135

Abstract: There exists today an unanswered question as to whether, and the degree to which, borrower characteristics impact the choice between fixed and adjustable rate mortgages. In this paper, we apply a neural network analysis to supply evidence that answers this question. We find evidence that the characteristics of a borrower's net worth, marital status and education level and whether a co‐borrower is involved contribute in a significant way to the neural network's ability to determine mortgage choice. Further, we show how, because of the facility of neural networks in modeling intrasample differences, they achieve material and statistically significant accuracy gains over qualitative choice models in predicting whether a borrower will choose a fixed or adjustable rate mortgage.

Date: 1995
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