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Big data, accessibility and urban house prices

Author

Listed:
  • Steven C Bourassa

    (Florida Atlantic University, USA)

  • Martin Hoesli

    (University of Geneva, Switzerland and University of Aberdeen, UK)

  • Louis Merlin

    (Florida Atlantic University, USA)

  • John Renne

    (Florida Atlantic University, USA)

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.

Suggested Citation

  • Steven C Bourassa & Martin Hoesli & Louis Merlin & John Renne, 2021. "Big data, accessibility and urban house prices," Urban Studies, Urban Studies Journal Limited, vol. 58(15), pages 3176-3195, November.
    • Handle: RePEc:sae:urbstu:v:58:y:2021:i:15:p:3176-3195
    DOI: 10.1177/0042098020982508
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    References listed on IDEAS

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    1. Yan, Xiang & Bejleri, Ilir & Zhai, Liang, 2022. "A spatiotemporal analysis of transit accessibility to low-wage jobs in Miami-Dade County," Journal of Transport Geography, Elsevier, vol. 98(C).
    2. Jon Bannister & Anthony O’Sullivan, 2021. "Big Data in the city," Urban Studies, Urban Studies Journal Limited, vol. 58(15), pages 3061-3070, November.

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