BIG DATA ACCESSIBILITY MEASURES AND URBAN LAND VALUATION

Steven Bourassa, Martin Hoesli, Louis Merlin and John Renne

AfRES from African Real Estate Society (AfRES)

Abstract: Big data applications are attracting increasing interest on the part of urban researchers. One such application is the use of accessibility indexes based on travel data aggregated from personal devices, such as cell phones, in hedonic price models. This paper evaluates the benefits of using big data employment accessibility indexes in the context of urban property valuation. The study compares big data indexes with traditional measures of accessibility based on straight-line distances to key locations and indexes derived from regional travel demand models used by local transportation planning agencies. Controls for geographic submarkets is also used as a means for measuring the value of location. Using residential property transactions from the Miami, Florida, metropolitan area, the study concludes that the traditional straight-line distances and especially the geographic submarkets perform better than the big data and travel demand model measures.

Keywords: accessibility indexes; Big data; Hedonic Models; Property Valuation; travel demand models (search for similar items in EconPapers)
JEL-codes: R3 (search for similar items in EconPapers)
Date: 2019-09-01
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Persistent link: https://EconPapers.repec.org/RePEc:afr:wpaper:2019-060

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