Spatio-temporal modeling of residential sales data


Journal Article

This article focuses on the location, time, and spatio-temporal components associated with suitably aggregated data to improve prediction of individual asset values. Such effects are introduced in the context of hierarchical models, which we find more natural than attempting to model covariance structure. Indeed, our cross-sectional database, a sample of 7,936 transactions for 49 subdivisions over a 10-year period in Baton Rouge, Louisiana, precludes covariance modeling. A wide range of models arises, each fitted using sampling-based methods because likelihood-based fitting may not be possible. Choosing among an array of nonnested models is carried out using a posterior predictive criterion. In addition, one year of data is held out for model validation. A thorough analysis of the data incorporating all of the aforementioned issues is presented. © 1998 Taylor & Francis Group, LLC.

Full Text

Duke Authors

Cited Authors

  • Gelfand, AE; Ghosh, SK; Knight, JR; Sirmans, CF

Published Date

  • January 1, 1998

Published In

Volume / Issue

  • 16 / 3

Start / End Page

  • 312 - 321

Electronic International Standard Serial Number (EISSN)

  • 1537-2707

International Standard Serial Number (ISSN)

  • 0735-0015

Digital Object Identifier (DOI)

  • 10.1080/07350015.1998.10524770

Citation Source

  • Scopus