ABSTRACT This paper examines the spatial dependency exhibited by the error term variance of hedonic modeling based on German housing price data. To this end, it applies the spatial autoregressive conditional heteroscedasticity (SARCH) model previously discussed in housing literature, which allows for the consideration of spatial dependency when modeling the error variance of hedonic pricing. This model represents a spatialized version of the well-known ARCH-model used in time series analysis. Consistent with previous findings, this paper confirms the existence of spatial conditional heteroscedasticity, i.e. dependency in the error variance. However, this spatial dependency is not a global phenomenon, but can be ascribed to spatial concentrations of apartments with a relatively high variance in a small number of the same neighborhoods. The analysis of spatial heteroscedasticity helps to improve the estimation efficiency and prediction accuracy. In addition, spatial differences can be used to account for idiosyncratic risk when conducting mass appraisal.

中文翻译：

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房价享乐模型中的空间方差聚类分析

摘要本文研究了基于德国住房价格数据的享乐主义模型的误差项方差所表现出的空间依赖性。为此，它应用了住房文献中先前讨论的空间自回归条件异方差（SARCH）模型，该模型在对享乐定价的误差方差建模时考虑了空间依赖性。该模型表示时间序列分析中使用的著名ARCH模型的空间版本。与先前的发现一致，本文证实了空间条件异方差性的存在，即误差方差的依赖性。但是，这种空间依赖性不是全局现象，而是可以归因于少数几个相同社区中相对较高方差的公寓的空间集中度。空间异方差分析有助于提高估计效率和预测精度。此外，在进行大规模评估时，可以使用空间差异来解释特质风险。