SPATIOTEMPORAL MODELING OF RESIDENTIAL SALES DATA

This article focuses on the location, time, and spatio-temporal compon ents associated with suitably aggregated data to improve prediction of individual asset values. Such effects are introduced in the context o f hierarchical models, which we find more natural than attempting to m odel covariance structure. Indeed, our cross-sectional database, a sam ple of 7,936 transactions for 49 subdivisions over a 10-year period in Baton Rouge, Louisiana, precludes covariance modeling. A wide range o f models arises, each fitted using sampling-based methods because like lihood-based fitting may not be possible. Choosing among an array of n onnested models is carried out using a posterior predictive criterion. In addition, one year of data is held out for model validation. A tho rough analysis of the data incorporating all of the aforementioned iss ues is presented.