Geographically and temporally granular housing price indexes are difficult to construct. Data sparseness, in particular, is a limiting factor in their construction. A novel application of a spatial dynamic factor model allows for the construction of census tract level indexes on a quarterly basis while accommodating sparse data. Specifically, we augment the repeat sales model with a spatial dynamic factor model where loadings on latent trends are allowed to follow a spatial random walk thus capturing useful information from similar neighboring markets. The resulting indexes display less noise than similarly constructed non-spatial indexes and replicate indexes from the traditional repeat sales model in tracts where sufficient numbers of repeat sales pairs are available. The granularity and frequency of our indexes is highly useful for policymakers, homeowners, banks and investors.

地理和时间上的细粒度房价指数很难构建。尤其是数据稀疏性是其构建的限制因素。空间动态因子模型的新颖应用允许按季度构建人口普查区域级别指数，同时适应稀疏数据。具体来说，我们用空间动态因子模型增强重复销售模型，其中允许潜在趋势的负载遵循空间随机游走，从而从类似的邻近市场捕获有用的信息。与类似构造的非空间索引相比，生成的索引显示出更少的噪音，并且在有足够数量的重复销售对的区域中复制来自传统重复销售模型的索引。我们指数的粒度和频率对于政策制定者非常有用，