This paper develops a model of housing decisions which allows for social interactions within residential neighborhoods to impact homeowners' valuation of their own properties. The model is used to structure an empirical investigation with data from the American Housing Survey for 1985 and 1989. It explores in great detail a relatively neglected feature of the data, namely the availability of data of neighborhood clusters for standard metropolitan areas in the United States. This feature of the data allows us to model empirically the effects of social interactions at the immediate residential neighborhood level, with neighborhoods consisting of a dwelling unit and its ten nearest neighbors. Most previous work on neighborhoods has used contextual information associated with the census tract where a unit of observation belongs. It identifies the effect of endogenous social interactions and find that the impact of social interactions is much more important then the dynamic (autoregressive) structure of the model when both variables are present (but both are significant). The findings provide empirical support for the notion, common in the real estate world, of the importance of neighborhood characteristics in property valuations.
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