Welfare Measures to Reflect Home Location Options when Transportation Systems are Modified
Transportation system improvements do not provide simply travel time savings, for a fixed trip table; they affect trip destinations, modes, times of day, and, ultimately, home and business location choices. This paper examines the welfare (or willingness-to-pay) impacts of system changes by bringing residential location choice into a three-layer nested logit model to more holistically anticipate the regional welfare impacts of various system shifts using logsum differences (which quantify changes in consumer surplus). Here, home value is a function of home price, size, and accessibility; and accessibility is a function of travel times and costs, vis-?-vis all mode and destination options. The model is applied to a sample of 60 Austin, Texas, zones to estimate home buyers? welfare impacts across various scenarios, with different transit fares, automobile operating costs, travel times, and home prices.
Results suggest that new locators? choice probabilities for rural and suburban zones are more sensitive to changing regional access, while urban and central business zone choice probabilities are more impacted by home price shifts. Automobile costs play a more important role in residential location choices in these simulations than those of transit, as expected in a typical U.S. setting (where automobile travel dominates). When generalized costs of automobile travel are simulated to rise 20%, 40%, and 60% (throughout the region), estimated welfare impacts (using normalized differences in logit logsum measures) for the typical new home buying household (with $70,000 in annual income and 2.4 household members) are estimated to be quite negative, at -$56,000, -$99,000, and -$132,000, respectively. In contrast, when auto?s generalized costs fall everywhere (by 20%, 40%, and then 60%), welfare impacts are very positive (+$74,000, $172,500, and $320,000, respectively). Such findings are meaningful for policymakers, planners, and others when anticipating the economic impacts of evolving transportation systems, in the face of new investments, rising travel demands, distance-based tolls, self-driving vehicles, and other changes.