Estimating Census Tract House Price Indexes: A New Spatial Dynamic Factor Approach
提出一种空间动态因子模型，利用稀疏数据构建季度人口普查区级房价指数，比传统方法噪声更小，对政策制定者、房主、银行和投资者有用。
Abstract 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.