Bayesian Model Averaging In The Context Of Spatial Hedonic Pricing
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Bayesian Model Averaging in the Context of Spatial Hedonic Pricing
Specification uncertainty arises in spatial hedonic pricing models because economic theory provides no guide in choosing the spatial weighting matrix and explanatory variables. Our objective in this paper is to investigate whether we can resolve uncertainty in the application of a spatial hedonic pricing model. We employ Bayesian Model Averaging in combination with Markov Chain, Monte Carlo Model Composition. The proposed methodology provides inclusion probabilities for explanatory variables and weighting matrices. These probabilities provide a clear indication of which explanatory variables and weighting matrices are most relevant, but they are case specific.
Bayesian model averaging in the context of spatial hedonic pricing
Another problem is that there is little in the way of theory to guide the choice of the covariates to be included in the hedonic pricing model. [...] However, we first use the method of LeSage and Fischer (2007) to sort out which of the nearest-neighbors' weighting matrix to consider - one of the matrices with one to ten nearest neighbors; we select the binary weighting matrix with the number of nearest neighbors that had the highest model probability of being included. [...] The GIS-based hedonic pricing model uses the per hectare market value of land as the dependent variable; the covariates include size of the farmland parcel, type of farm, topographical features of the land, a fragmentation index, distance to Victoria, an ALR dummy variable and the number of hectares excluded from the ALR each year. [...] The fragmentation index is specified as the percentage of the perimeter bordering other farmland parcels multiplied by the size of the total farm block of all the farmland that is adjacent to the parcel. [...] This index is designed to capture the importance of both the proximity to other farms and the total size of the farm block of which the parcel is a part.