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.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS380 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
