IDEAS home Printed from https://ideas.repec.org/a/jre/issued/v27n12005p105-136.html
   My bibliography  Save this article

Apartment Rent Prediction Using Spatial Modeling

Author

Listed:
  • 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.

Suggested Citation

  • James Valente & ShanShan Wu & Alan Gelfand & C.F. Sirmans, 2005. "Apartment Rent Prediction Using Spatial Modeling," Journal of Real Estate Research, American Real Estate Society, vol. 27(1), pages 105-136.
  • Handle: RePEc:jre:issued:v:27:n:1:2005:p:105-136
    as

    Download full text from publisher

    File URL: http://pages.jh.edu/jrer/papers/pdf/past/vol27n01/05.105_136.pdf
    File Function: Full text
    Download Restriction: no

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Marcus Allen & Ronald Rutherford & Thomas Thomson, 2009. "Residential Asking Rents and Time on the Market," The Journal of Real Estate Finance and Economics, Springer, vol. 38(4), pages 351-365, May.
    2. 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.
    3. Beth Wilson & James Frew, 2007. "Apartment Rents and Locations in Portland, Oregon: 1992 – 2002," Journal of Real Estate Research, American Real Estate Society, vol. 29(2), pages 201-218.
    4. Eilers, Lea, 2016. "Spatial Dependence in Apartment Offering Prices in Hamburg," Annual Conference 2016 (Augsburg): Demographic Change 145639, Verein für Socialpolitik / German Economic Association.
    5. Füss, Roland & Koller, Jan A., 2016. "The role of spatial and temporal structure for residential rent predictions," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1352-1368.
    6. Morito Tsutsumi & Hajime Seya, 2009. "Hedonic approaches based on spatial econometrics and spatial statistics: application to evaluation of project benefits," Journal of Geographical Systems, Springer, vol. 11(4), pages 357-380, December.
    7. Bing Zhu & Roland Füss & Nico Rottke, 2011. "The Predictive Power of Anisotropic Spatial Correlation Modeling in Housing Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 42(4), pages 542-565, May.
    8. Morito Tsutsumi & Hajime Seya, 2008. "Measuring the impact of large-scale transportation projects on land price using spatial statistical models," Papers in Regional Science, Wiley Blackwell, vol. 87(3), pages 385-401, August.
    9. Olivier Parent & Rainer Hofe, 2013. "Understanding the impact of trails on residential property values in the presence of spatial dependence," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 51(2), pages 355-375, October.

    More about this item

    JEL classification:

    • L85 - Industrial Organization - - Industry Studies: Services - - - Real Estate Services

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jre:issued:v:27:n:1:2005:p:105-136. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (JRER Graduate Assistant/Webmaster). General contact details of provider: http://www.aresnet.org/ .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.