Latent Segmentation of Urban Space through Residential Location Choice

Understanding the preferences of households in their location decisions is key for residential demand forecast and urban policy making. Accounting for preference heterogeneity across agents is useful for the modelling process but not enough to completely describe location choice behavior. Due to place-specific conditions, the same agent may have different preferences depending on the sector of the city considered as potential location, a phenomena known as spatial heterogeneity. Segmenting the city by defining zones where agents are supposed to behave similarly has been a common modelling solution, assigning different zonal preference parameters in the estimation process. This has been usually done with two-step methods, where spatial segmentation is done independently of the location choice process, something that could bias estimation results. We propose and test a one-step model for simultaneous estimation of location preference parameters and spatial segmentation, therefore accounting for heterogeneity across agents and space. The model is based on Ellickson's bid-auction approach for location choice and latent class models. We test our model with a case study in Santiago, Chile and compare it with other models for spatial segmentation. In terms of predictive power, our approach outperforms a model with no zones, a model with zones defined exogenously, and a clustering-based two-step model. This novel approach allows for a better conceptual ground for urban predictive models with spatial segmentation.