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An evaluation framework for predictive models of neighbourhood change with applications to predicting residential sales in Buffalo, NY

Author

Listed:
  • Jan Voltaire Vergara

    (University at Buffalo, USA)

  • Maria Y Rodriguez

    (University at Buffalo, USA)

  • Jonathan Phillips

    (University of Minnesota Duluth, USA)

  • Ehren Dohler

    (University of North Carolina, USA)

  • Melissa L Villodas

    (George Mason University, USA)

  • Amy Blank Wilson

    (University of North Carolina, USA)

  • Kenneth Joseph

    (University at Buffalo, USA)

Abstract

New data and technologies, in particular machine learning, may make it possible to forecast neighbourhood change. Doing so may help, for example, to prevent the negative impacts of gentrification on marginalised communities. However, predictive models of neighbourhood change face four challenges: accuracy (are they right?), granularity (are they right at spatial or temporal scales that actually matter for a policy response?), bias (are they equitable?) and expert validity (do models and their predictions make sense to domain experts?). The present work provides a framework to evaluate the performance of predictive models of neighbourhood change along these four dimensions. We illustrate the application of our evaluation framework via a case study of Buffalo, NY, where we consider the following prediction task: given historical data, can we predict the percentage of residential buildings that will be sold or foreclosed on in a given area over a fixed amount of time into the future?

Suggested Citation

  • 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.
    • Handle: RePEc:sae:urbstu:v:61:y:2024:i:5:p:838-858
    DOI: 10.1177/00420980231189403
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    References listed on IDEAS

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    1. Guerrieri, Veronica & Hartley, Daniel & Hurst, Erik, 2013. "Endogenous gentrification and housing price dynamics," Journal of Public Economics, Elsevier, vol. 100(C), pages 45-60.
    2. Sue Easton & Loretta Lees & Phil Hubbard & Nicholas Tate, 2020. "Measuring and mapping displacement: The problem of quantification in the battle against gentrification," Urban Studies, Urban Studies Journal Limited, vol. 57(2), pages 286-306, February.
    3. Ding, Lei & Hwang, Jackelyn & Divringi, Eileen, 2016. "Gentrification and residential mobility in Philadelphia," Regional Science and Urban Economics, Elsevier, vol. 61(C), pages 38-51.
    4. Zhong-guo Zhou, 1997. "Forecasting Sales and Price for Existing Single-Family Homes: A VAR Model with Error Correction," Journal of Real Estate Research, American Real Estate Society, vol. 14(2), pages 155-168.
    5. Karen Chapple & Ate Poorthuis & Matthew Zook & Eva Phillips, 2022. "Monitoring streets through tweets: Using user-generated geographic information to predict gentrification and displacement," Environment and Planning B, , vol. 49(2), pages 704-721, February.
    6. Glaeser, Edward L. & Nathanson, Charles G., 2017. "An extrapolative model of house price dynamics," Journal of Financial Economics, Elsevier, vol. 126(1), pages 147-170.
    7. Jonathan Reades & Jordan De Souza & Phil Hubbard, 2019. "Understanding urban gentrification through machine learning," Urban Studies, Urban Studies Journal Limited, vol. 56(5), pages 922-942, April.
    8. Alexander Bogin & William Doerner & William Larson, 2019. "Local House Price Dynamics: New Indices and Stylized Facts," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 47(2), pages 365-398, June.
    9. Alexander N. Bogin & William M. Doerner & William D. Larson, 2016. "Missing the Mark: House Price Index Accuracy and Mortgage Credit Modeling," FHFA Staff Working Papers 16-04, Federal Housing Finance Agency.
    10. Nikodem Szumilo, 2021. "Prices of Peers: Identifying Endogenous Price Effects in the Housing Market," The Economic Journal, Royal Economic Society, vol. 131(639), pages 3041-3070.
    11. Alvin Murphy, 2018. "A Dynamic Model of Housing Supply," American Economic Journal: Economic Policy, American Economic Association, vol. 10(4), pages 243-267, November.
    12. Patrick Bayer & Robert McMillan & Alvin Murphy & Christopher Timmins, 2016. "A Dynamic Model of Demand for Houses and Neighborhoods," Econometrica, Econometric Society, vol. 84, pages 893-942, May.
    13. Benjamin Preis & Aarthi Janakiraman & Alex Bob & Justin Steil, 2021. "Mapping gentrification and displacement pressure: An exploration of four distinct methodologies," Urban Studies, Urban Studies Journal Limited, vol. 58(2), pages 405-424, February.
    14. Sonya Williams & George Galster & Nandita Verma, 2013. "Home Foreclosures as Early Warning Indicator of Neighborhood Decline," Journal of the American Planning Association, Taylor & Francis Journals, vol. 79(3), pages 201-210, July.
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