A latent auction model of firm establishment location choice in the Greater Toronto Area

We present a maximum bid model for business establishments in the Greater Toronto Area (GTA). The model is based on a latent auction approach, wherein price and utility are jointly modeled in a random utility-maximizing framework. Structural relationships between establishments by their industry classification and type (i.e., headquarters, subsidiary, branch, or single location) are explored through a generalized nesting correlation structure. This structure provides a mechanism for differentiating between branch and single-location establishments, having seemingly similar functions, but differing in their allocation between industry and type nests. Maximum bid models are estimated for the full establishment population in the GTA at the level of individual buildings. The model scope allows us to provide a range of insights into establishment location choice behavior, which was previously infeasible due to data limitations. We find that professional service establishments tend to locate near passenger rail stations (i.e., subway and LRT), while industrial establishments tend to locate near major highway interchanges. We confirm the finding by Kenworthy and Laube (1999) that Canadian cities are less auto-dependent than US cities, albeit from the perspective of establishment location rather than individual vehicle travel data.