Revealing implicit associations between urban form and socioeconomic indices

This study provides empirical evidence that parcel-level socioeconomic indices can be reliably inferred from urban form using deep learning. Building on a representation that links cadastral parcels with digital surface models, we train EfficientNet variants to predict return on investment (ROI)—used here as a representative parcel-level economic indicator—directly from volumetric form. Across more than 24,000 parcels in a North American city, the models exhibit stable convergence and consistent generalization, capturing systematic variations across residential, commercial, and industrial morphologies. Although errors increase in dense commercial cores—where financial volatility and vertical complexity are highest—predictions remain bounded and preserve spatial gradients of ROI. The learned embeddings further reveal coherent manifolds structured by building massing, footprint geometry, and block configuration, indicating that the model extracts underlying spatial principles that correlate with economic outcomes. These findings demonstrate that urban form encodes measurable signals of economic performance and establish morphological learning as a viable pathway for integrating financial, environmental, and socio-cultural indices into form-based urban analysis and design.