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This paper aims to deal with the construction of seasonal price indices for the housing market, based on Rosen's hedonic equations and using spatial econometric autoregression (SAR) techniques.

More precisely, the hedonic equations are estimated using disaggregated data, and the extracted indices are averaged over zip code areas. Then the seasonality, which is considered deterministic, is extracted after eliminating the spatial effects. The data set used consists of 8,685 valuations of dwellings, detached dwellings and detached houses that took place in Attica on behalf of a commercial bank during the period 2000‐2009.

The paper concludes that evidence exists to support the hypothesis that property prices are affected by seasonal and spatial effects beyond structural effects and the effects of the general economic situation. Property valuations are strongly connected with deterministic exogenous variables, such as the size, age and location of the property, the general economic situation, and to a lesser effect the spatial system and the season during which the valuation took place. The estimated spatial effect is positive and quite large in value, indicating a landscape consisting of large homogeneous sub‐areas, while the results demonstrate a seasonal upturn during the first semester and downturn towards the end of the year.

This paper provides a framework for incorporating spatial and seasonal effects in property price index construction.