The paper deals with a hedonic model of housing rents focusing on geographical submarkets in the Belgian region of Wallonia. The question is what is the best way to use the delineated submarkets under the condition of a relatively small simple size. While the set of submodels for the submarkets provides an important improvement in the predictive capacity and a considerable reduction in the standard error as well as in the spatial autocorrelation in residuals, this approach has an important drawback unacceptable for the users of the Automated Valuation System “rent calculator”: in the submodels, some crucial structural variables, such as those for living area or building age, are often insignificant. This problem, caused, at least partly, by limited size of subsamples, manifests itself much less in the overall models (OLS and spatial error) with “location-quality values”. The practical advantages of these models are more important than the relative econometric superiority of the geographically weighted and multi-level alternatives.

本文讨论了一个住房租金的享乐模型，重点关注比利时瓦隆地区的地理子市场。问题是在相对较小的简单规模的情况下，使用所划定的子市场的最佳方式是什么。虽然子市场的子模型集在预测能力方面提供了重要改进，并显着降低了标准误差以及残差的空间自相关性，但这种方法具有自动估值系统用户无法接受的重要缺点“租金计算器”：在子模型中，一些关键的结构变量，例如居住面积或建筑年龄，通常是不重要的。这个问题至少部分是由有限的子样本大小引起的，在具有“位置质量值”的整体模型（OLS 和空间误差）中表现得更少。这些模型的实际优势比地理加权和多层次替代方案的相对经济计量优势更重要。