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Nowcasting Gentrification: Using Yelp Data to Quantify Neighborhood Change

Author

Listed:
  • Edward L. Glaeser
  • Hyunjin Kim
  • Michael Luca

Abstract

Data from digital platforms have the potential to improve our understanding of gentrification, both by predicting gentrification and by characterizing the local economy of gentrifying neighborhoods. To explore, we identify gentrifying neighborhoods using government data, and then use Yelp data to analyze local business activity. We find that gentrifying neighborhoods tend to have growing numbers of local groceries, cafes, restaurants, and bars, with little evidence of crowd-out of other types of businesses. Moreover, local economic activity, as measured by Yelp data, is a leading indicator for housing price changes and can help to predict which neighborhoods are gentrifying.

Suggested Citation

  • Edward L. Glaeser & Hyunjin Kim & Michael Luca, 2018. "Nowcasting Gentrification: Using Yelp Data to Quantify Neighborhood Change," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 77-82, May.
    • Handle: RePEc:aea:apandp:v:108:y:2018:p:77-82
    Note: DOI: 10.1257/pandp.20181034
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    Citations

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    Cited by:

    1. Susan Athey & Michael Luca, 2019. "Economists (and Economics) in Tech Companies," Journal of Economic Perspectives, American Economic Association, vol. 33(1), pages 209-230, Winter.
    2. Ahlfeldt, Gabriel M. & Barr, Jason, 2022. "Viewing urban spatial history from tall buildings," Regional Science and Urban Economics, Elsevier, vol. 94(C).
    3. repec:osf:socarx:8jbvg_v1 is not listed on IDEAS
    4. Morgan Ubeda, 2020. "Local Amenities, Commuting Costs and Income Disparities Within Cities," Working Papers halshs-03082448, HAL.
    5. Millar, Melanie I. & White, Roger M., 2024. "Do residential property assessed clean energy (PACE) financing programs affect local house price growth?," Journal of Environmental Economics and Management, Elsevier, vol. 124(C).
    6. 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.
    7. Jan Voltaire Vergara & Maria Y Rodriguez & Jonathan Phillips & Ehren Dohler & Melissa L Villodas & Amy Blank Wilson & Kenneth Joseph, 2024. "An evaluation framework for predictive models of neighbourhood change with applications to predicting residential sales in Buffalo, NY," Urban Studies, Urban Studies Journal Limited, vol. 61(5), pages 838-858, April.
    8. Olson, Alex & Calderon-Figueroa, Fernando & Bidian, Olimpia & Silver, Daniel & Sanner, Scott, 2020. "Reading the city through its neighbourhoods: Deep text embeddings of Yelp reviews as a basis for determining similarity and change," SocArXiv 8jbvg, Center for Open Science.
    9. Imryoung Jeong & Hyunjoo Yang, 2021. "Using maps to predict economic activity," Papers 2112.13850, arXiv.org, revised Apr 2022.
    10. Mohammed Alyakoob & Mohammad S. Rahman, 2022. "Shared Prosperity (or Lack Thereof) in the Sharing Economy," Information Systems Research, INFORMS, vol. 33(2), pages 638-658, June.
    11. Wang, Yixuan, 2024. "Urban Redevelopment and Gentrification: Evidence from the Atlanta BeltLine," 2024 Annual Meeting, July 28-30, New Orleans, LA 343550, Agricultural and Applied Economics Association.
    12. Christopher Rick & Jeehee Han & Spencer Shanholtz & Amy Ellen Schwartz, 2022. "Examining the Link Between Gentrification, Children’s Egocentric Food Environment, and Obesity," Center for Policy Research Working Papers 245, Center for Policy Research, Maxwell School, Syracuse University.
    13. 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.
    14. 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.
    15. Stephan D. Whitaker, 2023. "Understanding Migration Trends to Prepare for the Post-Pandemic Future," Cleveland Fed Regional Policy Report, Federal Reserve Bank of Cleveland, issue 20230801, pages 1-32, August.
    16. Fe, Hao & Sanfelice, Viviane, 2022. "How bad is crime for business? Evidence from consumer behavior," Journal of Urban Economics, Elsevier, vol. 129(C).
    17. Glaeser, Edward L. & Luca, Michael & Moszkowski, Erica, 2023. "Gentrification and retail churn: Theory and evidence," Regional Science and Urban Economics, Elsevier, vol. 100(C).
    18. devin michelle bunten & Benjamin Preis & Shifrah Aron-Dine, 2024. "Re-measuring gentrification," Urban Studies, Urban Studies Journal Limited, vol. 61(1), pages 20-39, January.
    19. 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).
    20. Breithaupt, Patrick & Kesler, Reinhold & Niebel, Thomas & Rammer, Christian, 2020. "Intangible capital indicators based on web scraping of social media," ZEW Discussion Papers 20-046, ZEW - Leibniz Centre for European Economic Research.
    21. Antwan Jones & Prentiss Dantzler, 2021. "Neighbourhood perceptions and residential mobility," Urban Studies, Urban Studies Journal Limited, vol. 58(9), pages 1792-1810, July.
    22. Makoto Sakuma & Kazushi Matsuo & Morito Tsutsumi & Toyokazu Imazeki, 2024. "Measuring office attendance during the COVID-19 pandemic with mobility data to quantify local trends and characteristics," Asia-Pacific Journal of Regional Science, Springer, vol. 8(1), pages 185-237, March.
    23. 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:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets
    • R32 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Other Spatial Production and Pricing Analysis
    • R58 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis - - - Regional Development Planning and Policy

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    1. Nowcasting Gentrification: Using Yelp Data to Quantify Neighborhood Change (AEA Papers & Proceedings 2018) in ReplicationWiki

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