000			01842nas a2200253Ia 4500
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022			_a0042-0980
100			_aVergara, Jan Voltaire _9119786
245		3	_aAn evaluation framework for predictive models of neighbourhood change with applications to predicting residential sales in Buffalo, NY
260			_bUrban Studies
260			_c2024
300			_a838-858
520			_aNew 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?
650			_a Displacement/Gentrification _9119787
650			_a Machine Learning _971338
650			_a Method _951936
650			_a Neighbourhood _916535
650			_aHousing _9284
700			_a Dohler, Ehren _9119788
700			_a Phillips, Jonathan _9119789
700			_a Rodriguez, Maria Y _9119790
700			_a Villodas, Melissa L _9119791
856			_uhttps://doi.org/10.1177/00420980231189403
999			_c133549 _d133549