Forecasting Mortgage Demand: An Application of Traditional Methods, Machine Learning, and Neural Networks

111 Pages Posted: 27 Aug 2020

See all articles by James Ming Chen

James Ming Chen

Michigan State University - College of Law

Amy Baker

affiliation not provided to SSRN

Date Written: July 21, 2020

Abstract

Demand forecasting relies heavily on traditional methods with well known limitations. Improved accuracy in predicting demand for mortgages, whether for purposes of purchase or refinance, is critical to profitability in home lending. To overcome obstacles to prediction using nonlinear relationships between variables and to long-term accuracy, we apply time-invariant machine-learning methods such as random forests. We also perform time-series analysis with a wide variety of deep learning architectures, including convolutional and recurrent neural networks. Time-series analysis through deep learning produces the most accurate results. Even shortcomings in forecast accuracy can reveal tacit changes in relationships among household-level and macroeconomic predictors of mortgage demand.

Keywords: Demand Forecasting, Deep Learning, Mortgage Demand, Purchase Demand, Refinance Demand, LSTM, RNN, Production Capacity, Uncertain Demand, Decision Science

Suggested Citation

Chen, James Ming and Baker, Amy, Forecasting Mortgage Demand: An Application of Traditional Methods, Machine Learning, and Neural Networks (July 21, 2020). Available at SSRN: https://ssrn.com/abstract=3656924 or http://dx.doi.org/10.2139/ssrn.3656924

James Ming Chen (Contact Author)

Michigan State University - College of Law ( email )

318 Law College Building
East Lansing, MI 48824-1300
United States

Amy Baker

affiliation not provided to SSRN

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