Apartment Rent Prediction Using Spatial Modeling

James Valente (), ShanShan Wu, Alan Gelfand and C.F. Sirmans
Additional contact information
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

Journal of Real Estate Research, 2005, vol. 27, issue 1, pages 105-136

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.

JEL-codes: L85 (search for similar items in EconPapers)
Date: 2005
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