Re-examining the influence of work and non-work accessibility on residential location choices with a micro-analytic framework

Speaker(s): 
Brian Lee, Doctoral Candidate, University of Washington Interdisciplinary PhD Program in Urban Design & Planning
Event Date: 
Friday, May 1, 2009, 12:00pm to 1:00pm PDT

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The video begins at 2:53.

Abstract: The concept of accessibility has long been theorized as a principal determinant of household residential choice behavior. Research on this influence is extensive but the empirical results have been mixed, with some research suggesting that accessibility is becoming a relatively insignificant influence on housing choices. Further, the measurement of accessibility must contend with complications arising from the increasing prevalence of trip-chains, non-work activities, and multi-worker households, as well as reconcile person-specific travel needs with household residential decisions. This paper contributes to the literature by addressing the gap framed by these issues and presents a novel residential choice model with three main elements of innovation. First, it operationalized a time-space prism (TSP) accessibility measure, which the authors believe to be the first application of its kind in a residential choice model. Second, it represented the choice sets in a building-level framework, the lowest level of spatial disaggregation available for modeling residential choices. Third, it explicitly examined the influence of non-work accessibility at both the local- and person-level. This residential choice model was applied in the central Puget Sound region using a 2006 household activity survey. The model estimation results confirmed that accessibility remain an important influence, with individual-specific work accessibility as the most critical consideration. By using the TSP approach, it was established that non-work accessibility in a trip-chaining context does contribute to the residential choice decision, even after accounting for work accessibility. Empirical tests also revealed a useful aggregation method to incorporate individual-specific accessibility measures into a household-level choice model.