IDEAS home Printed from https://ideas.repec.org/a/sae/urbstu/v61y2024i5p838-858.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/00420980231189403
    Download Restriction: no
    File URL: https://libkey.io/10.1177/00420980231189403?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:urbstu:v:61:y:2024:i:5:p:838-858. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: http://www.gla.ac.uk/departments/urbanstudiesjournal .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.