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Empirical Evidence and Scenario Testing

A Spatial Analysis of Housing and Transportation Affordability in Los Angeles County

PI: Salon
Funder: UCTC

Increases in fuel prices, combined with the deep downturn in the economy, have raised concerns about the burdens of transportation costs on low-income families. We investigate this issue, focusing specifically on neighborhood variation in the percentage of household incomes spent on housing and transportation. We hypothesize that the phrase "drive 'til you qualify" (for a mortgage) has some truth; poor and moderate- income households living in suburban areas—particularly inner-ring suburbs—will pay less for housing, but more for transportation than households living in wealthy suburban neighborhoods or in central-city neighborhoods well served by public transit.

We are working to test this hypothesis using individual data on vehicle miles traveled, vehicle type and fuel efficiency, and housing costs. Specifically, we are combining property-level housing data, vehicle-specific fuel use information, and block-group demographic data for households in Los Angeles County. We will be examining spatial variation in housing-transport costs relative to income. We then will analyze how neighborhood affordability has evolved since 2000, a period in which gas prices rose significantly. Finally, we will assess whether there is a relationship between neighborhood income levels and changes in vehicle miles traveled and the fuel economy of neighborhood vehicles.

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Quantifying the effect of local government actions on VMT

PI: Salon
Funder: California Air Resources Board

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 land use 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 land use type and trip type. We find considerable differences in the VMT effect of policy-sensitive variables across land use types, and we find some variation across trip types. Results of this research will be 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.

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