Bayesians in space: Using Bayesian methods to inform choice of spatial weights matrix in hedonic property analyses

The choice of weights is a non-nested problem in most applied spatial econometric models. Despite numerous recent advances in spatial econometrics, the choice of spatial weights remains exogenously determined by the researcher in empirical applications. Bayesian techniques provide statistical evidence regarding the simultaneous choice of model specification and spatial weights matrices by using posterior probabilities. This paper demonstrates the Bayesian estimation approach in a spatial hedonic property model estimating the impacts of repeated wildfires on house prices in Southern California. We find that improper choice of spatial model and weights can result in up to 5 percent difference in estimated coefficients and in our case study up to a $15 Million difference in total benefits of reducing wildfires in Los Angeles County.