Real estate properties are naturally location-fixed. When space related factors are not fully incorporated in a standard pricing equation, spatial autocorrelation is likely to exist. This results in inefficiencies in estimations and raises the need for more complex spatial models. This paper analyzes the determinants of spatial dependence and evaluates the performance of the hedonic regression equation when the determinants of spatial dependence are controlled for. Using a novel dataset for a metropolitan housing market, we document the spatial clustering of housing characteristics such as area, total number of floors and the building age. We find support for the hypotheses that the construction process, shared social services and high-rise residential complexes cause spatial correlation. Our findings show that spatial correlation is significantly reduced when the factors of spatial dependence and district level data is controlled for in the standard hedonic regression.

房地产属性自然是固定的。当与空间相关的因素未完全纳入标准定价公式中时，可能存在空间自相关。这导致估计效率低下，并且需要更复杂的空间模型。本文分析了空间依赖性的决定因素，并评估了控制空间依赖性的决定因素时享乐回归方程的性能。使用针对大城市房屋市场的新颖数据集，我们记录了房屋特征的空间聚类，例如面积，楼层总数和建筑年龄。我们发现支持以下假设：建设过程，共享的社会服务和高层住宅区导致空间相关。