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This paper introduces an explicit geographic perspective for modeling the housing price determination process. The traditional econometric models that utilize the hedonic price regression are accepted but are extended to incorporate spatial neighborhood dynamics. The expansion method is used as the general modeling framework. The question posed by this research is: Do housing attributes produce different pricing differentials depending on location? Consequently, the models that are generated are characterized by spatial variation with respect to the influence of housing attributes on housing prices. In addition to its conceptual concern, this paper also deals with spatial dependence, an issue that has not been addressed in the previous empirical investigations. The empirical segment of this research uses data for the Columbus, Ohio MSA. The results suggest that the models constructed using the expansion method more accurately mirror the workings of the residential real estate markets.
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Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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