Abstract
In the existing housing literature, there has been no academic consensus on how to combine the spatial dependence and the temporal dependence between housing transactions together. The combination is much dependent on the researcher’s priori knowledge of a referent market. This paper attempts to combine them by utilizing an information entropy-based spatiotemporal approach. The validity of the proposed information entropy-based spatiotemporal approach is tested by spatiotemporal regressions in terms of prices estimation accuracy. The methodology is conducted by using data on dwelling transactions from the San Francisco Bay Area. The empirical results suggest that the proposed information entropy-based modeling technique is a reasonable and efficient way to combine the spatial dependence and the temporal dependence.
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Notes
This problem can be seen in the filtering process in Subsection “The Combination of Building, Regional and Temporal Effects”.
A matrix is row stochastic means the matrix has rows sum up to one.
Since the constant term is a common divisor, when the row is standardized, it can be divided from each element in the row.
The numbers of variables of different factors are identical in this paper.
This paper selects 68 different linear combinations of weight coefficients. The details are explained in the next subsection.
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Zhao, J. Information Entropy-Based Housing Spatiotemporal Dependence. J Real Estate Finan Econ 58, 21–50 (2019). https://doi.org/10.1007/s11146-017-9636-x
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DOI: https://doi.org/10.1007/s11146-017-9636-x