Big data, accessibility and urban house prices

Steven Bourassa, Martin Hoesli, Louis Merlin and John Renne
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Louis Merlin: Florida Atlantic University, USA
John Renne: Florida Atlantic University, USA

Urban Studies, 2021, vol. 58, issue 15, 3176-3195

Abstract: Big data applications are attracting increasing interest among urban researchers. One unexplored question is whether the inclusion of big data accessibility indices improves the accuracy of hedonic price models used for residential property valuation. This paper compares a big data index with an index derived from a regional travel demand model developed by local transportation planning agencies and traditional measures of accessibility defined as distances to employment centres. Controls for submarkets and a combined spatial autoregressive and spatial error model are also assessed as tools for capturing the value of location. Using single-family residential transactions from the Miami, Florida, metropolitan area, the study’s main conclusion is that the big data accessibility measure does not add meaningful explanatory or predictive power. In contrast, the spatial autoregressive and error model outperforms the other options considered.

Keywords: accessibility indices; big data; hedonic models; property valuation; spatial models; å ¯è¾¾æ€§æŒ‡æ•°; å¤§æ•°æ ®; äº«ä¹ æ¨¡åž‹; 房地产估值; 空间模型 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:urbstu:v:58:y:2021:i:15:p:3176-3195

DOI: 10.1177/0042098020982508

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