Analyzing Real Estate Data Problems Using the Gibbs Sampler

Real estate data are often characterized by irregularities, e.g., missing data, censoring or truncation, measurement error, etc. Practitioners often discard missing or censored data cases and ignore measurement error concerns. We argue here that we can remedy these irregularity problems through simulation based model tting using the Gibbs sampler. The method is described in the context of these issues , and illustrated with a sample of residential property sales from Baton Rouge, Louisiana. Focusing primarily on the missing data problem, we show dramatic improvement in inference by retaining the partially observed data cases rather than deleting them. We also detail how the other problems can be handled using the Gibbs sampler. While canned software to implement a Gibbs sampler does not exist, for the problems at hand, development is straightforward with substantial reward anticipated for the eeort.