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Apartment Rent Prediction Using Spatial Modeling

Author

Listed:
  • James Valente
  • ShanShan Wu
  • Alan Gelfand
  • C. F. Sirmans

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 significant prediction improvement over traditional hedonic rent models that only include indicator variables to capture spatial effects.

Suggested Citation

  • James Valente & ShanShan Wu & Alan Gelfand & C. F. Sirmans, 2005. "Apartment Rent Prediction Using Spatial Modeling," Journal of Real Estate Research, Taylor & Francis Journals, vol. 27(1), pages 105-136, January.
    • Handle: RePEc:taf:rjerxx:v:27:y:2005:i:1:p:105-136
    DOI: 10.1080/10835547.2005.12091148
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    Cited by:

    1. Löchl, Michael & Axhausen, Kay W., 2010. "Modelling hedonic residential rents for land use and transport simulation while considering spatial effects," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 3(2), pages 39-63.
    2. Eilers, Lea, 2016. "Spatial Dependence in Apartment Offering Prices in Hamburg," VfS Annual Conference 2016 (Augsburg): Demographic Change 145639, Verein für Socialpolitik / German Economic Association.

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