Abstract
Small-sized housing samples and price predictions at nonobserved locations require geostatistical approaches, particularly the kriging estimator. Nevertheless, geostatistics has thus far received little attention in real estate economics. The article’s objective is to empirically compare the prediction accuracy of univariate kriging variants, namely detrended kriging (DK) and universal kriging (UK), and multivariate extensions, including detrended cokriging (DCK) and universal cokriging (UCK). Both latter methods consider structural and neighborhood characteristics as auxiliary variables. While the price surfaces of DK and UK show nearly identical cross-validated accuracies, the cross-validation-based prediction accuracy of DCK and UCK differ in favor of the latter. If real estate agencies are faced with a univariate sample of property prices, either DK or UK can be used, while in the multivariate case, UCK is recommended, although numerically more complex.
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