Big Data, Accessibility, and Urban House Prices

Steven C. Bourassa* (Corresponding Author), Martin Hoesli, Louis Merlin, John Renne

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
11 Downloads (Pure)


Big data applications are attracting increasing interest on the part of urban researchers. One unexplored question is whether the inclusion of big data accessibility indexes 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 centers. 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.
Original languageEnglish
Pages (from-to)3176-3195
Number of pages20
JournalUrban Studies
Issue number15
Early online date24 Jan 2021
Publication statusPublished - 1 Nov 2021

Bibliographical note

The authors thank Andrew VanValin, Estefania Mayorga, and Jessica Kluttz for helpful research assistance. We also thank the reviewers and editors for helping us to improve the paper.
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Land Economics Foundation.


  • Big data
  • accessibility indexes
  • hedonic models
  • spatial models
  • property valuation


Dive into the research topics of 'Big Data, Accessibility, and Urban House Prices'. Together they form a unique fingerprint.

Cite this