There is increasing evidence that aggregate housing prices are predictable. Despite this, a random walk in time and independence in space are two maintained hypotheses in most empirical models of housing price measurement. This paper examines the price discovery process in individual dwellings over time and space by relaxing both assumptions, using a unique body of data from the Singapore private condominium market. We develop a model that tests directly the hypotheses that the prices of individual dwellings follow a random walk over time and that the price of an individual dwelling is independent of the price of a neighboring dwelling. The model is general enough to include other widely used models for housing price determination as special cases. The empirical results clearly support mean reversion in housing prices and also diffusion of innovations over space. We find that serial and spatial correlation "matter" in the computation of price indices and the estimation of investor returns. This predictability may suggest that excess returns are possible. We use the monthly price series derived from condominium sales to investigate this issue. When aggregate returns are computed from models that assume a random walk and spatial independence, we find that they are strongly autocorrelated. When returns are calculated from more general models that permit mean reversion, the estimated autocorrelation in investment returns is reduced. Finally, when they are calculated from models permitting mean reversion and spatial autocorrelation, predictability in investment returns is completely absent.
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