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Spatial autoregressive hedonic models utilize house prices lagged in space and time to produce local house price indices, for example, the spatial and temporal autoregressive (STAR) model might be used this way. This paper complements these models with a semiparametric approach, the Local Regression Model (LRM). The greater flexibility of the LRM may allow it to identify space–time asymmetries missed by other models. The LRM is fitted to 49,511 sales from 1972Q1 to 1991Q2 in Fairfax County, Virginia. The local price indices display plausible and significant variations over space and time. The LRM price indices in selected neighborhoods are shown to differ significantly from those in some other neighborhoods. A new method for estimating standard errors addresses an overlooked problem common to all local price indices: how to evaluate the amount of noise in the estimates. Out‐of‐sample prediction errors demonstrate that LRM adds significant information to the hedonic model.
John M. Clapp (Tue,) studied this question.