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 eviden...
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| Veröffentlicht in: | The Review of regional studies 2010-01, Vol.40 (3), p.245-255 |
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| creator | Loomis, John B Mueller, Julie M |
| description | 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% 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. |
| doi_str_mv | 10.52324/001c.8175 |
| format | Article |
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| ispartof | The Review of regional studies, 2010-01, Vol.40 (3), p.245-255 |
| issn | 0048-749X 1553-0892 |
| language | eng |
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| source | RePEc; EBSCOhost Education Source; Alma/SFX Local Collection |
| subjects | Bayesian analysis Bayesian Estimation Demographics Dependent variables Econometrics Economic models Estimates Housing prices Landscape ecology Regional studies Spatial Hedonic Models Studies Variables Wildfires |
| title | Bayesians in Space: Using Bayesian Methods to Inform Choice of Spatial Weights Matrix in Hedonic Property Analyses |
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