A utility-based spatial analysis of residential street-level conditions a case study of Rotterdam

Abstract

This study sheds light on how utility derived from street-level conditions is spatially distributed, from a residential location choice perspective, at a city-wide scale. Unlike previous studies that analyse perceptions of urban environments from street-level imagery, this work maps preferences—that is, the utility residents derive from observable street-level conditions. To this end, we first develop a residential location discrete choice model that builds on two premises: (1) street-level images effectively capture street-level conditions, and (2) state-of-the-art segmentation models can extract salient information from these images and convert them into structured (i.e. tabular) data. We then apply the model to over 200 thousand geo-tagged street-level images of Rotterdam (the Netherlands) to map how utility derived from street-level conditions varies across the city. Results show strong local variation, with conditions changing rapidly even within neighbourhoods, and reveal that high real-estate prices in the city centre cannot primarily be attributed to attractive street-level conditions. As a secondary methodological contribution, the paper integrates foundation segmentation models into discrete choice analysis. Unlike conventional segmentation approaches limited to predefined object classes, our pipeline leverages prompt-based detection (GroundingDINO + SAM) to identify novel and more granular categories (e.g. transformer houses, shrubs vs. trees) overlooked in standard datasets. This integration enables a richer, fine-grained quantification of street-level conditions and demonstrates how visual information can be systematically embedded into residential location choice models. As such, this paper's findings and methodological contribution pave the way for further studies to explore integrating street-level conditions in urban planning.