Recent developments in the field of machine learning offer new ways of modelling complex socio-spatial processes, allowing us to make predictions about how and where they might manifest in
the future. Drawing on earlier empirical and theoretical attempts to understand gentrification and
urban change, this paper shows it is possible to analyse existing patterns and processes of neighbourhood
change using Machine Learning to identify areas likely to experience change in the future.