Using the spatial configuration of the data to improve estimation

Document Type

Article

Publication Date

1-1-2019

Abstract

Using the well-known Harrison and Rubinfeld (1978) hedonic pricing data, this manuscript demonstrates the substantial benefits obtained by modeling the spatial dependence of the errors. Specifically, the estimated errors on the spatial autoregression fell by 44% relative toOLS. The spatial autoregression corrects predicted values by a nonparametric estimate of the error on nearby observations and thus mimics the behavior of appraisers. The spatial autoregression, by formally incorporating the areal configuration of the data to increase predictive accuracy and estimation efficiency, has great potential in real estate empirical work.

Publication Source (Journal or Book title)

Revealed Preference Approaches to Environmental Valuation Volumes I and II

First Page

217

Last Page

224

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