Rethinking the Discrete‐Continuous Choice Problem by Uncovering Asymmetry in High‐Resolution Urban Modeling

A primary bottleneck in integrated urban simulation is the Discrete‐Continuous Choice problem within Land Use Supply modeling, specifically, the challenge of simultaneously determining building type and floorspace capacity. This study develops a high‐resolution, parcel‐level, multi‐stage econometric framework in which the bidirectional interactions between these decisions can be estimated rather than assumed. Using a large‐scale dataset assembled for the Greater Toronto‐Hamilton Area from 2015 to 2023, the framework incorporates real‐world zoning constraints to simulate market behavior. This granular approach also uncovers the behavioral heterogeneity across retail, office, industrial, and mixed‐use developers through their unique spatial preferences and patterns of inter‐sectoral competition. The results reveal an asymmetric relationship between the two decisions. Expected floorspace significantly affects the choice of development type, but only when specified with alternative‐specific parameters. In contrast, the reverse effect is not statistically significant in the floorspace models. Although this asymmetry may be specific to the sectors, region, and period examined, it suggests that bidirectional dependence should not be assumed by default. When the Discrete‐to‐Continuous relationship is weak or not statistically supported, a simpler sequential specification may offer lower modeling complexity. The proposed framework provides a direct way to examine which structure is more appropriate for a given application.