Date of this Version
Annals of Applied Statistics
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.
housing index, time series, repeat sales
Nagaraja, C. H., Brown, L. D., & Zhao, L. (2011). An Autoregressive Approach to House Price Modeling. Annals of Applied Statistics, 5 (1), 124-149. http://dx.doi.org/10.1214/10-AOAS380
Date Posted: 27 November 2017
This document has been peer reviewed.