Hedonic Predicted House Price Indices Using Time-Varying Hedonic Models with Spatial Autocorrelation

Hedonic housing price indices are computed from estimated hedonic pricing models. The commonly used time dummy hedonic model and the rolling window hedonic model fail to account for changing consumer preferences over hedonic characteristics and typically these models do not account for the presence of spatial correlation in prices reflecting the role of locational characteristics. This paper develops a class of models with time-varying hedonic coefficients and spatially correlated errors, provides an assessment of the predictive performance of these compared to the commonly used hedonic models, and constructs and compares corresponding price index series. Alternative weighting systems, plutocratic versus democratic, are considered for the class of hedonic imputed price indices. Accounting for seasonality in house sales data, monthly chained indices and annual chained indices based on averages of year-on-year monthly indexes are presented. The empirical results are based on property sales data for Brisbane, Australia over the period 1985 to 2005. On the basis of root mean square prediction error criterion the time-varying parameter with spatial errors is found to be the best performing model and the rolling-window model to be the worst performing model.