41 Pages Posted: 15 Dec 2008
Date Written: December 14, 2008
Economists do not have reliable measures of current house values, let alone housing returns. This ignorance underlies the illiquidity of mortgage-backed securities, which in turn feeds back to deepen the sub-prime crisis. Using a massive new data tape of housing transactions in L.A., we demonstrate systematic patterns in the error associated with using the ubiquitous repeat sales methodology to understand house values. In all periods, the resulting indices under-predict sales prices of less expensive homes, and over-predict prices of more expensive homes. The recent period has produced errors that are not only unprecedentedly large in absolute value, but highly systematic: after a few years in which the indices under-predicted prices, they now significantly over-predict them. We introduce new machine learning techniques from computer science to correct for prediction errors that have geographic origins. The results are striking. Accounting for geography significantly reduces the extent of the prediction error, removes many of the systematic patterns, and results in far less deterioration in model performance in the recent period.
Keywords: House price index, sub prime crisis
JEL Classification: C81
Suggested Citation: Suggested Citation
Caplin, Andrew and Chopra, Sumit and Leahy, John V. and LeCun, Yann and Thampy, Trivikraman, Machine Learning and the Spatial Structure of House Prices and Housing Returns (December 14, 2008). Available at SSRN: https://ssrn.com/abstract=1316046 or http://dx.doi.org/10.2139/ssrn.1316046