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Real
Estate Problems, Spatial Modeling, and Bayesian Inference
Alan E. Gelfand and C. F. Sirmans
The goal of this presentation is to consider several real estate problems which can be usefully addressed using spatial process models and to detail the implementation of and benefits of fitting such models within a Bayesian framework. First, we review the formulation of general classes of hierarchical spatial models introducing spatial random effects modeled through spatial processes. We then briefly discuss simulation based strategies for the fitting of such models.
We then consider three illustrative problems: spatially varying coefficient
models to allow for heterogeneous capitalization rates across a region;
multivariate modeling of price and income surfaces using coregionalization;
and detecting gradients in real estate markets using directional derivative
processes.