Forecasting Atlanta Gentrification with Transformers

Abstract

Gentrification is an impactful trend in American cities, yet our ability to measure and predict this process remains weak. This thesis explores the use of machine learning to predict gentrification in Atlanta, Georgia. We use a dataset of land parcels collected by Fulton County Tax Assessors and set out to forecast changes in local land value. Inspired by progress in natural language processing, we apply a machine learning model called a transformer to forecast gentrification. We find our model outperforms typical methods for time-series forecasting of gentrification, and we discuss the implications of our findings for future research.