Analyzing real estate data problems using the Gibbs sampler

Real estate data are often characterized by data irregularities: missing data, censoring or truncation, measurement error, etc. Practitioners often discard missing-or censored-data cases and ignore measurement error. We argue here that an attractive remedy for these irregularity problems is simulation-based model fitting using the Gibbs sampler. The style of the paper is primarily pedagogic, employing a simple illustration to convey the essential ideas, unobscured by implementation complications. Focusing on the missing-data problem, we show dramatic improvement in inference by retaining rather than deleting cases of partially observed data. We also detail Gibbs-sampler usage for other data problems.

Duke Scholars

Author Alan E. Gelfand Statistical Science