Abstract

Hedonic models have been used extensively in the economic analysis of multiattribute products in general, with housing as an especially favored focus. Hedonic housing models have two major ends: (1) to explore the relative importance and explanatory power of various housing attributes and (2) to assess the value of specific housing units in the absence of actual market transactions. This paper is of the first type. While complete data are never available, researchers have tended to look for more, rather than fewer, explanatory variables. Taking advantage of a previously computed socioeconomic index (SEI) of neighborhoods, this research proposes a minimal data set of just two predictor variables. Greater Tel Aviv recent housing prices are regressed onto two locational variables: the first, SEI, is used as a proxy for neighborhood quality, and the second represents employment accessibility. Taken together, the two attributes explain nearly three-quarters of the spatial price variation.

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