Abstract
This study evaluates the ability of a range of popular aggregate house price indexes to predict house prices out-of- sample at the transaction level for a small geographic area. The analysis particularly addresses the utility of spatial econometric methods. The results suggest that spatial econometric methods, which more explicitly consider the spatial aspects of observed house prices, provide better predictive accuracy as compared to more traditional estimation techniques, such as the repeat sales index, a hybrid repeat sales-hedonic price index, and hedonic price models estimated through least squares. The conclusions are drawn from a sample of 38,984 single-family residential real estate transactions for the city of Milwaukee, Wisconsin over the years 2002-2008.
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