Sustainability in cities, and its accurate and exhaustive assessment, represent a major keystone of environmental sciences and policy making in urban planning. This study aims to provide methods for a reproducible, descriptive, predictive and prescriptive analysis of urban residential choices and mobility, which are key components of an urban system’s sustainability. Using the DPSIR framework for building agent evolution rules, we design an agent-based model of the canton of Geneva, Switzerland. The model leverages real geographical data for the canton of Geneva and its public transportation network. The resulting simulations show the dynamics of the relocation choices of commuters, in terms of the function of their travel time by public transportation to their workplace. Results show that areas around the city centre are generally preferred, but high rent prices and housing availability may prevent most residents from relocating to these areas. Other preferred housing locations are distributed around major tram and train lines and where rent prices are generally lower. The model and its associated tools are capable of spatialising aggregated statistical datasets, inferring spatial correlations, and providing qualitative and quantitative analysis of relocation dynamics. Such achievements are made possible thanks to the efficient visualisation of our results. The agent-based modelling methodology represents an adequate solution for understanding complex phenomena related to sustainability in urban systems, which can be used as guidance for policy making.