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

Jan Voltaire Vergara, Maria Y Rodriguez, Jonathan Phillips, Ehren Dohler, Melissa L Villodas, Amy Blank Wilson and Kenneth Joseph
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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

Urban Studies, 2024, vol. 61, issue 5, 838-858

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?

Keywords: displacement/gentrification; housing; machine learning; method; 失所; 绅士化; ä½ æˆ¿; 机器学ä¹; 方法 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:sae:urbstu:v:61:y:2024:i:5:p:838-858

DOI: 10.1177/00420980231189403

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