An Autoregressive Approach to House Price Modeling

A statistical model for predicting individual house prices is proposed utilizing only 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 latter piece is applied only to homes sold repeatedly while the former two components are applied to all of the data. In addition, the autoregressive component incorporates heteroscedasticity in the errors. To evaluate the proposed model, single-family home sales for twenty U.S. 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. It is also shown that the time effect in the proposed model can be converted into a house price index. Finally, the special case of Los Angeles, CA is discussed as an example of history repeating itself in regards to the current housing market meltdown