To comply with AB 32 and SB 375, California local and regional governments are working to reduce vehicle miles traveled (VMT). To develop targeted policies with scarce resources, policymakers need guidance as to which policies will be most effective in their jurisdictions. This research uses empirical analysis of travel survey data to quantify how much Californians will change the amount that they drive in response to changes in land use and transport system variables. Our study improves upon past research in three key ways. First, we assemble and use a dataset that consists of merged information from five California-based household travel surveys that were conducted between 2000 and 2009. Second, we develop and employ a novel approach to control for residential self-selection, categorizing neighborhoods into types and using these as the alternatives in a predictive model of neighborhood type choice. Third, we focus on understanding heterogeneity in effects of variables on VMT across two important dimensions--neighborhood type and trip type. We find that the effects of some land use and transport system characteristics do depend on neighborhood type, in ways that are intuitive but had not previously been empirically verified. Results of this research are embedded in the VMT Impact spreadsheet tool, which allows users to easily see the implications of this work for any census tract, city, or region in California.