PREDICTING URBAN LAND PRICES: A COMPARISON OF FOUR APPROACHES
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This paper investigates forecasting accuracy of four different hedonic approaches, when vacant urban land prices are predicted in local markets. The investigated hedonic approaches are: 1) ordinary least squares estimation, 2) robust MM-estimation, 3) structural time series estimation and 4) robust local regression. Post-sample predictive testing indicated that more accurate predictions are obtained if the unorthodox methods of this paper are used instead of the conventional least squares estimation. In particular, the predictive unbiassness can significantly be improved when using the unconventional hedonic methods of the study. The paper also studied the structure of urban land prices. The most important attribute variables in explaining land prices were permitted building volume, house price index, northing and easting. The influence of parcel size variable and different indicator variables on land prices were much weaker.
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