Spatially Disaggregated Real Estate Indices
用时空马尔可夫随机场模型，从重复销售数据中生成住宅房地产指数，将区域用图连接并考虑时空相关性，以达德县数据验证，并与纯空间模型比较。
A spatial–temporal Markov random-field model is used to produce indices for residential real estate from repeat home sale data. A set of regions is represented by a graph in which neighboring regions are linked. This graph, repeated consecutively a number of times, with each region linked to the same region at adjacent times, defines a spatial–temporal graph that connects regions over space and time in which each node represents a region at a particular time. An index is defined at each node to be the rate of appreciation of log sale price in a region during the preceding time interval. The indices are estimated from data consisting of repeat home sales. The Markov random-field model specifies spatial and temporal correlations between neighboring indices and relations between indices and individual repeat sales. A method is proposed for estimating various parameters in the model and for obtaining real-estate indices. Following this prescription, indices are calculated for the Dade County, Florida, residential real-estate market. Results are compared to those obtained using competing space-only models.