James Valente () (SSR Realty Advisers, Inc.) ShanShan Wu (Department of Statistics, University of Connecticut) Alan Gelfand (Institute for Statistics and Decision Sciences, Duke University) C.F. Sirmans (Director, Center for Real Estate and Urban Economic Studies, University of Connecticut)
Abstract
This paper provides a new model to explain local variation in apartment rents by introducing the notion of a spatial process. This model differs from those in the literature by explicitly specifying spatial association between pairs of locations as a function of distance between them. Data on apartment rents for the eight markets are used to illustrate the spatial model. Results indicate signi?cant prediction improvement over traditional hedonic rent models that only include indicator variables to capture spatial effects.
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