Predicting subjective well-being based on the physical appeal of residential locations using a computer vision model

Abstract

Over recent years, subjective well-being (SWB) has become a primary goal in urban planning, with research showing that the built environment can significantly influence residents’ well-being. This study focuses on the role of the subjective nature of aesthetic quality, which traditional segmentation-based computer vision approaches often fail to capture. To address this, we evaluate the Computer Vision-enriched Discrete Choice Model (CV-DCM) developed by Van Cranenburgh and Garrido-Valenzuela (2025), which uses a vision transformer and classifier to extract holistic visual features from Google Street View images and estimate continuous utility scores that reflect perceived visual quality, trained on stated trade-offs that people make between visual environments. We link these scores to life satisfaction and hedonic well-being measures from the Netherlands Mobility Panel (SWLS, 2020–2022; MHI-5, 2020) and analyze their relationships using Structural Equation Modeling (SEM), controlling for socio-demographic and built environment variables. Results show that PC5-level utility aligns more closely with life satisfaction than PC6, indicating that broader neighborhood context matters more than immediate street conditions. When non-linear age effects are modeled, a small but significant direct path from utility to life satisfaction emerges, whereas no significant association is found for hedonic well-being. Overall, the current explanatory power for SWB is modest and appears mainly driven by who lives where. Nevertheless, a perception-based computer vision model provides a scalable way that can quantify subjective visual quality, which could gain relevance when improved model fit is achieved by reducing variance in data collection or retraining the model on SWB-specific objectives.