Spatio-Temporal Modeling of Residential Sales Data

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 the authors find more natural than attempting to model covariance structure. Indeed, their cross-sectional database, a sample of 7,936 transactions for forty-nine subdivisions over a ten-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. Coauthors are Sujit K. Ghosh, John R. Knight, and C. F. Sirmans.