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March 2011 An autoregressive approach to house price modeling
Chaitra H. Nagaraja, Lawrence D. Brown, Linda H. Zhao
Ann. Appl. Stat. 5(1): 124-149 (March 2011). DOI: 10.1214/10-AOAS380

Abstract

A statistical model for predicting individual house prices and constructing a house price index is proposed utilizing information regarding sale price, time of sale and location (ZIP code). This model is composed of a fixed time effect and a random ZIP (postal) code effect combined with an autoregressive component. The former two components are applied to all home sales, while the latter is applied only to homes sold repeatedly. The time effect can be converted into a house price index. To evaluate the proposed model and the resulting index, single-family home sales for twenty US metropolitan areas from July 1985 through September 2004 are analyzed. The model is shown to have better predictive abilities than the benchmark S&P/Case–Shiller model, which is a repeat sales model, and a conventional mixed effects model. Finally, Los Angeles, CA, is used to illustrate a historical housing market downturn.

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Chaitra H. Nagaraja. Lawrence D. Brown. Linda H. Zhao. "An autoregressive approach to house price modeling." Ann. Appl. Stat. 5 (1) 124 - 149, March 2011. https://doi.org/10.1214/10-AOAS380

Information

Published: March 2011
First available in Project Euclid: 21 March 2011

zbMATH: 1220.62109
MathSciNet: MR2810392
Digital Object Identifier: 10.1214/10-AOAS380

Keywords: Housing index , repeat sales , time series

Rights: Copyright © 2011 Institute of Mathematical Statistics

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Vol.5 • No. 1 • March 2011
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