Journal of Geographical Systems

, Volume 9, Issue 1, pp 77–101 | Cite as

Latent lifestyle preferences and household location decisions

  • Joan L. WalkerEmail author
  • Jieping Li
Original Article


Lifestyle, indicating preferences towards a particular way of living, is a key driver of the decision of where to live. We employ latent class choice models to represent this behavior, where the latent classes are the lifestyles and the choice model is the choice of residential location. Thus, we simultaneously estimate lifestyle groups and how lifestyle impacts location decisions. Empirical results indicate three latent lifestyle segments: suburban dwellers, urban dwellers, and transit-riders. The suggested lifestyle segments have intriguing policy implications. Lifecycle characteristics are used to predict lifestyle preferences, although there remain significant aspects that cannot be explained by observable variables.


Lifestyle Residential location Latent class choice models Mixture models Error components Neighborhood preferences 



The authors gratefully acknowledge useful interactions with Pat Mokhtarian (who provided, among other things, particularly insightful comments on the policy implications), Antonio Páez, Darren Scott, two anonymous reviewers, and participants at a UC Davis seminar and the 52nd North American Regional Science Association International Conference in Las Vegas, Nevada.


  1. Aeroe T (2001) Residential preferences, choice of housing, and lifestyle. PhD dissertation (English summary), Aalborg UniversityGoogle Scholar
  2. Bagley M, Mokhtarian P (1999) The role of lifestyle and attitudinal characteristics in residential neighborhood choice. Transportation and Traffic Theory, pp 735–758Google Scholar
  3. Bagley M, Mokhtarian P (2002) The impact of residential neighborhood type on travel behavior: a structural equations modeling approach. Ann Reg Sci 36:279–297CrossRefGoogle Scholar
  4. Ben-Akiva M, Bowman JL, Gopinath D (1996) Travel demand model system for the information era. Transportation 23:241–266Google Scholar
  5. Ben-Akiva M, Walker J, Bernardino A, Gopinath D, Morikawa T, Polydoropoulou A (2002) Integration of choice and latent variable models. In: Mahmassani H (ed) In perpetual motion: travel behaviour research opportunities and application challenges. Elsevier, Amsterdam, pp 431–470Google Scholar
  6. Bhat C, Guo J (2006) A comprehensive analysis of built environment characteristics on household residential choice and auto ownership levels. In: Presented at the 85th Annual Meeting of the Transportation Research Board, Washington DCGoogle Scholar
  7. Boxall PC, Adamowicz WL (2002) Understanding heterogeneous preferences in random utility models: a latent class approach. Environ Resour Econ 23:421–446CrossRefGoogle Scholar
  8. Cambridge Systematics, Inc. (1996) Data collection in the Portland, Oregon metropolitan area, Travel Model Improvement Program, Track D Data Research Program. Prepared for the U.S. Department of TransportationGoogle Scholar
  9. Cao X, Mokhtarian P (2005) How do individuals adapt their personal travel? Objective and subjective influences on the consideration of travel-related strategies for San Francisco Bay Area commuters. Transp Policy 12:291–302CrossRefGoogle Scholar
  10. Cao X, Handy S, Mokhtarian P (2006) The influence of the built environment and residential self-selection on pedestrian behavior. Transportation 33(1):1–20CrossRefGoogle Scholar
  11. Chiou L, Walker JL (2006) Masking identification of discrete choice models under simulation methods. J Econom (forthcoming)Google Scholar
  12. Chliaoutakis J, Koukouli S, Lajunen T, Tzamalouka G (2005) Lifestyle traits as predictors of driving behavior in urban areas of Greece. Transp Res Part F, 1–16Google Scholar
  13. Dillon WR, Kumar A, de Borrero MS (1993) Capturing individual differences in paired comparisons: an extended BTL Model incorporating descriptor variables. J Mark Res 30(1):42–51CrossRefGoogle Scholar
  14. Ferrell C, Deakin E (2001) Changing California lifestyles: consequences for mobility. The University of California Transportation Center, Berkeley, available from
  15. Gopinath DA (1995) Modeling heterogeneity in discrete choice processes: application to travel demand. PhD dissertation, Massachusetts Institute of TechnologyGoogle Scholar
  16. Greene WH (2003) Econometric analysis, 5th edn. Prentice Hall, Englewood CliffsGoogle Scholar
  17. Greene WH, Hensher DA (2003) A latent class model for discrete choice analysis: contrasts with mixed logit. Transp Res Part B 37:681–698CrossRefGoogle Scholar
  18. Grover R, Srinivasan V (1987) A simultaneous approach to market segmentation and market structuring. J Mark Res XXIV, 139–153CrossRefGoogle Scholar
  19. Guevara CA, Ben-Akiva ME (2006) Endogeneity in residential location choice models. Transp Res Rec (forthcoming)Google Scholar
  20. Handy S, Cao X, Mokhtarian P (2005) Correlation or causality between the built environment and travel behavior? Evidence from Northern California. Transp Res D 10:427–444CrossRefGoogle Scholar
  21. Hojrup T (2003) State, culture and life-modes: the foundations of life-mode analysis. AshgateGoogle Scholar
  22. Kamakura WA, Russell GJ (1989) A probabilistic choice model for market segmentation and elasticity structure. J Mark Res 26(4):379–390CrossRefGoogle Scholar
  23. Kitamura R, Mokhtarian P, Laidet L (1997) A Micro-analysis of land use and travel in five neighborhoods in the San Francisco Bay Area. Transportation 24:125–158CrossRefGoogle Scholar
  24. Kontoleon A, Yabe M (2003) Assessing the impacts of alternative ‘opt-out’ formats in choice experiment studies: consumer preferences for genetically modified content and production information in food. J Agric Policy Res 5:1–43Google Scholar
  25. Krizek K, Waddell P (2003) Analysis of lifestyles choices: neighborhood type, travel patterns, and activity participation. Transp Res Rec 1807:119–128Google Scholar
  26. Lerman SR (1975) A disaggregate behavioral model of urban mobility decisions. PhD dissertation, Massachusetts Institute of TechnologyGoogle Scholar
  27. Lindberg E, Harti T, Garvill J, Gärling T (1992) Residential-location preferences across the life span. J Environ Psychol 12:187–198CrossRefGoogle Scholar
  28. Louviere JJ, Hensher DA, Swait JD (2000) Stated choice methods: analysis and applications. Cambridge University Press, CambridgeGoogle Scholar
  29. Louviere J, Train K, Ben-Akiva M, Bhat C, Brownstone D, Cameron TA, Carson RT, Deshazo JR, Fiebig D, Greene W, Hensher D, Waldman D (2005) Recent progress on endogeneity in choice modeling. Mark Lett 16(3–4):255–265CrossRefGoogle Scholar
  30. Lyons G, Chatterjee K, Beecroft M, Marsden G (2002) Determinants of travel demand: exploring the future of society and lifestyles in the UK. Transp Policy 9:17–27CrossRefGoogle Scholar
  31. Magidson J, Eagle T, Vermunt J (2003) New developments in latent class choice models. In: Sawtooth Software Conference Proceedings, pp 89–112Google Scholar
  32. Milon JW, Scrogin D (2006) Latent preferences and valuation of wetland ecosystem restoration. Ecol Econ 56:162–175CrossRefGoogle Scholar
  33. Moekel R, Spiekermann K, Schurmann C, Wegener M (2003) Microsimulation of land use. Int J Urban Sci 7(1):14–31Google Scholar
  34. Mokhtarian PL, Salomon I (1997) Modeling the desire to telecommute: the importance of attitudinal factors in behavioral models. Transp Res A 31(1):35–50Google Scholar
  35. Ory DT, Mokhtarian PL (2005) When is getting there half the fun? Modeling the liking for travel. Transp Res Part A 39:97–123CrossRefGoogle Scholar
  36. Prevedouros P (1992) Associations of personality characteristics with transport behavior and residence location decisions. Transp Res A 26:381–391CrossRefGoogle Scholar
  37. Salomon I, Ben-Akiva M (1983) The use of the lifestyle concept in travel demand models. Environ Plann A 15:623–638CrossRefGoogle Scholar
  38. Salomon I, Waddell P, Wegener M (2002) Sustainable lifestyles? Microsimulation of household formation, housing choice and travel behavior. In: Black WR, Nijkamp P (eds) Social Change and Sustainable Transport. Indiana University Press, pp 125–131Google Scholar
  39. Scarpa R, Thiene M (2005) Destination choice models for rock climbing in the Northeastern Alps: a latent-class approach based on intensity of preferences. Land Econ 81(3):426–444Google Scholar
  40. Schwanen T, Mokhtarian P (2005) What affects commute mode choice: neighborhood physical structure or preferences toward neighborhoods? J Transp Geogr 13:83–99CrossRefGoogle Scholar
  41. Swait J (1994) A structural equation model of latent segmentation and product choice for cross-sectional preference choice data. J Retail Consum Serv 1(2):77–89CrossRefGoogle Scholar
  42. Swait J, Sweeney JC (2000) Perceived value and its impact on choice behavior in a retail setting. J Retail Consum Serv 7:77–88CrossRefGoogle Scholar
  43. Targa F, Clifton K (2004) Integrating social and psychological processes into the land use-travel behavior research agenda: theories, concepts and empirical study design. In: Prepared for the Seventh International Conference on Travel Survey Methods, Los Suenos, Costa RicaGoogle Scholar
  44. Train KE (2003) Discrete choice models with simulation. Cambridge University Press, CambridgeGoogle Scholar
  45. TRB Special Report 282 (2005) Does the built environment influence physical activity? Examining the evidence. (S Hanson, Chair) Transportation Research Board, Washington DCGoogle Scholar
  46. Waddell P (2000) Towards a behavioral integration of land use and transportation modeling. In: Proceedings of the 9th International Association for Travel Behavior Research Conference, Queensland, AustraliaGoogle Scholar
  47. Walker JL (2001) Extended discrete choice models: integrated framework, flexible error structures, and latent variables. PhD dissertation, Massachusetts Institute of TechnologyGoogle Scholar
  48. Walker J, Ben-Akiva M (2002) Generalized random utility model. Math Soc Sci 43(3):303–343CrossRefGoogle Scholar
  49. Walker J, Ben-Akiva M, Bolduc D (2006) Identification of parameters in normal error components logit mixture models. Working paper available at

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  1. 1.Department of Geography and EnvironmentBoston UniversityBostonUSA

Personalised recommendations