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Reexamining the Influence of Work and Nonwork Accessibility on Residential Location Choices with a Microanalytic Framework

Author

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  • Brian H Y Lee

    (School of Engineering, and Transportation Research Center, University of Vermont, Farrell Hall 114, 210 Colchester Avenue, Burlington, VT 05405-0303, USA)

  • Paul Waddell

    (Department of City and Regional Planning, University of California, Berkeley, 228 Wurster Hall, Room 1850, Berkeley, CA 94720-1850, USA)

  • Liming Wang

    (Institute of Urban and Regional Development, University of California, Berkeley, 316 Wurster Hall, Room 1870, Berkeley, CA 94720-1870, USA)

  • Ram M Pendyala

    (Department of Civil and Environmental Engineering, Arizona State University, Room ECG252, Tempe, AZ 85287-5306, USA)

Abstract

The concept of accessibility has long been theorized as a principal determinant of 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, nonwork activities, and multiworker households, and also reconcile person-specific travel needs with household residential decisions. With this paper we contribute to the literature by addressing the gap framed by these issues and present a novel residential choice model with three main elements of innovation. First, we operationalize a time–space prism (TSP) accessibility measure, which we believe to be the first application of its kind in a residential choice model. Second, we represent the choice sets in a building-level framework—the lowest level of spatial disaggregation available for modeling residential choices. Third, we explicitly examine the influence of nonwork accessibility at both the local and the person level. This residential choice model is applied in the central Puget Sound region using a 2006 household activity survey. The model estimation results confirm that accessibility remains an important influence, with individual-specific work accessibility as the most critical consideration. By using the TSP approach we establish that nonwork accessibility in a trip-chaining context does contribute to the residential choice decision, even after accounting for work accessibility. Empirical tests also reveal a useful aggregation method to incorporate individual-specific accessibility measures into a household-level choice model.

Suggested Citation

  • Brian H Y Lee & Paul Waddell & Liming Wang & Ram M Pendyala, 2010. "Reexamining the Influence of Work and Nonwork Accessibility on Residential Location Choices with a Microanalytic Framework," Environment and Planning A, , vol. 42(4), pages 913-930, April.
    • Handle: RePEc:sae:envira:v:42:y:2010:i:4:p:913-930
    DOI: 10.1068/a4291
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    References listed on IDEAS

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    1. Mokhtarian, Patricia L. & Cao, Xinyu, 2008. "Examining the impacts of residential self-selection on travel behavior: A focus on methodologies," Transportation Research Part B: Methodological, Elsevier, vol. 42(3), pages 204-228, March.
    2. Ng, Chen Feng, 2008. "Commuting distances in a household location choice model with amenities," Journal of Urban Economics, Elsevier, vol. 63(1), pages 116-129, January.
    3. Berry Blijie, 2005. "The impact of accessibility on residential choice - empirical results of a discrete choice model," ERSA conference papers ersa05p626, European Regional Science Association.
    4. Genevieve Giuliano & Kenneth A. Small, 1993. "Is the Journey to Work Explained by Urban Structure?," Urban Studies, Urban Studies Journal Limited, vol. 30(9), pages 1485-1500, November.
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    1. Kamruzzaman, Md. & Baker, Douglas & Washington, Simon & Turrell, Gavin, 2013. "Residential dissonance and mode choice," Journal of Transport Geography, Elsevier, vol. 33(C), pages 12-28.
    2. André de Palma & Nathalie Picard & Ignacio Inoa, 2014. "Discrete choice decision-making with multiple decision-makers within the household," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 16, pages 363-382, Edward Elgar Publishing.
    3. Downs, Joni A. & Horner, Mark W., 2012. "Probabilistic potential path trees for visualizing and analyzing vehicle tracking data," Journal of Transport Geography, Elsevier, vol. 23(C), pages 72-80.
    4. Ho, Chinh Q. & Hensher, David A. & Ellison, Richard, 2017. "Endogenous treatment of residential location choices in transport and land use models: Introducing the MetroScan framework," Journal of Transport Geography, Elsevier, vol. 64(C), pages 120-131.
    5. Marc Brechot & Stephan Nüesch & Egon Franck, 2017. "Does sports activity improve health? Representative evidence using local density of sports facilities as an instrument," Applied Economics, Taylor & Francis Journals, vol. 49(48), pages 4871-4884, October.
    6. Ma, Shuhong & Kockelman, Kara M., 2016. "Welfare Measures to Reflect Home Location Options When Transportation Systems Are Modified," Journal of the Transportation Research Forum, Transportation Research Forum, vol. 55(1), April.
    7. Konstadinos G. Goulias & Ram M. Pendyala, 2014. "Choice context," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 5, pages 101-130, Edward Elgar Publishing.

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