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This paper draws on McFadden’s location choice theory and incorporates households’ residential choice decisions as a hierarchical process in a structural travel demand model.  The paper argues that such an approach can effectively tackle the problems of self-selection and multicollinearity.  Contrary to previous findings, empirical results based on OLS and 3SLS reveal that travel demand is highly elastic to certain smart-growth features, if they are measured at different spatial scales.  The results are robust against alternative sequencing of the hierarchical choice process.  An analysis of the quantitative impact of a change in the smart-growth and fuel-tax policies reveals significant returns under both policies.  Finally, a simulation based on California suggests that smart growth policies substantially reduce household travel demand.

Do Smart Growth Strategies Have a Role in Curbing Vehicle Miles Traveled? A Further Assessment Using Household Level Survey Data