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Measuring Gentrification: Using Yelp Data to Quantify Neighborhood Change

Author

Listed:
  • Edward L. Glaeser
  • Hyunjin Kim
  • Michael Luca
Abstract
We demonstrate that data from digital platforms such as Yelp have the potential to improve our understanding of gentrification, both by providing data in close to real time (i.e. nowcasting and forecasting) and by providing additional context about how the local economy is changing. Combining Yelp and Census data, we find that gentrification, as measured by changes in the educational, age, and racial composition within a ZIP code, is strongly associated with increases in the numbers of grocery stores, cafes, restaurants, and bars, with little evidence of crowd-out of other categories of businesses. We also find that changes in the local business landscape is a leading indicator of housing price changes, and that the entry of Starbucks (and coffee shops more generally) into a neighborhood predicts gentrification. Each additional Starbucks that enters a zip code is associated with a 0.5% increase in housing prices.

Suggested Citation

  • Edward L. Glaeser & Hyunjin Kim & Michael Luca, 2018. "Measuring Gentrification: Using Yelp Data to Quantify Neighborhood Change," NBER Working Papers 24952, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:24952
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    Citations

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    Cited by:

    1. Morgan Ubeda, 2020. "Local Amenities, Commuting Costs and Income Disparities Within Cities," Working Papers halshs-03082448, HAL.
    2. Kristian Behrens & Brahim Boualam & Julien Martin & Florian Mayneris, 2024. "Gentrification and Pioneer Businesses," The Review of Economics and Statistics, MIT Press, vol. 106(1), pages 119-132, January.
    3. Blanco, Hector & Neri, Lorenzo, 2023. "Knocking It Down and Mixing It Up: The Impact of Public Housing Regenerations," IZA Discussion Papers 15855, Institute of Labor Economics (IZA).
    4. Ahlfeldt, Gabriel M. & Barr, Jason, 2022. "Viewing urban spatial history from tall buildings," Regional Science and Urban Economics, Elsevier, vol. 94(C).
    5. Yunmi Park & Minju Kim & Kijin Seong, 2021. "Happy neighborhoods: Investigating neighborhood conditions and sentiments of a shrinking city with Twitter data," Growth and Change, Wiley Blackwell, vol. 52(1), pages 539-566, March.
    6. Kristian Behrens & Julien Martin & Florian Mayneris, 2021. "Analyse de la gentrification urbaine dans l'agglomération de Montréal et regard particulier sur les secteurs traversés par la ligne rose," CIRANO Project Reports 2020rp-36, CIRANO.
    7. Fe, Hao & Sanfelice, Viviane, 2022. "How bad is crime for business? Evidence from consumer behavior," Journal of Urban Economics, Elsevier, vol. 129(C).
    8. Glaeser, Edward L. & Luca, Michael & Moszkowski, Erica, 2023. "Gentrification and retail churn: Theory and evidence," Regional Science and Urban Economics, Elsevier, vol. 100(C).
    9. Manuel Hermosilla & Jian Ni & Haizhong Wang & Jin Zhang, 2023. "Leveraging the E-commerce footprint for the surveillance of healthcare utilization," Health Care Management Science, Springer, vol. 26(4), pages 604-625, December.

    More about this item

    JEL classification:

    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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