Modelling residential location choices with implicit availability of alternatives

  • Md Bashirul Haque University of Leeds
  • Charisma Farheen Choudhury University of Leeds
  • Stephane Hess University of Leeds
Keywords: choice set generation, Manski method, constrained multinomial logit model, Greater London

Abstract

Choice set generation is a challenging aspect of disaggregate level residential location choice modelling due to the large number of candidate alternatives in the universal choice set (hundreds to hundreds of thousands). The classical Manski method (Manski, 1977) is infeasible here because of the explosion of the number of possible choice sets with the increase in the number of alternatives. Several alternative approaches have been proposed in recent years to deal with this issue, but these have limitations alongside strengths. For example, the Constrained Multinomial Logit (CMNL) model (Martínez et al., 2009) offers gains in efficiency and improvements in model fit but has weaknesses in terms of replicating the Manski model parameters. The rth-order Constrained Multinomial Logit (rCMNL) model (Paleti, 2015) performs better than the CMNL model in producing results consistent with the Manski model, but the benefits disappear when the number of alternatives in the universal choice set increases. In this study, we propose an improved CMNL model (referred to as Improved Constrained Multinomial Logit Model, ICMNL) with a higher order formulation of the CMNL penalty term that does not depend on the number of alternatives in the choice set. Therefore, it is expected to result in better model fit compared to the CMNL and the rCMNL model in cases with large universal choice sets. The performance of the ICMNL model against the CMNL and the rCMNL model is evaluated in an empirical study of residential location choices of households living in the Greater London Area. Zone level models are estimated for residential ownership and renting decisions where the number of alternatives in the universal choice set is 498 in each case. The performance of the models is examined both on the estimation sample and the holdout sample used for validation. The results of both ownership and renting models indicate that the ICMNL model performs considerably better compared to the CMNL and the rCMNL model for both the estimation and validation samples. The ICMNL model can thus help transport and urban planners in developing better prediction tools.

Author Biographies

Md Bashirul Haque, University of Leeds
PhD Candidate
Charisma Farheen Choudhury, University of Leeds
Associate Professor, Transport Engineering and Emerging Economies, Institute for Transport Studies, University of Leeds https://www.its.leeds.ac.uk/people/c.choudhury
Stephane Hess, University of Leeds
Professor of Choice Modelling

References

Arentze, T., & Timmermans, H. (2005). An analysis of context and constraints-dependent shopping behavior using qualitative decision principles. Urban Studies, 42(3), 435–448.

Bell, M. G. (2007). Mixed routing strategies for hazardous materials: Decision-making under complete uncertainty. International Journal of Sustainable Transportation, 1(2), 133–142.

Ben-Akiva, M., & Lerman, S. (1974). Some estimation results of a simultaneous model of auto ownership and mode choice to work. Transportation, 3, 357–376.

Bhat, C. R. (2015). A comprehensive dwelling unit choice model accommodating psychological constructs within a search strategy for consideration set formation. Transportation Research Part B: Methodological, 79, 161–188.

Bhat, C. R., & Guo, J. (2004). A mixed spatially correlated logit model: Formulation and application to residential choice modelling. Transportation Research Part B: Methodological, 38(2), 147–168.

Bhat, C. R., & Guo, J. Y. (2007). A comprehensive analysis of built environment characteristics on household residential choice and auto ownership levels. Transportation Research Part B: Methodological, 41(5), 506–526.

Bierlaire, M., Hurtubia, R., & Flötteröd, G. (2010). Analysis of implicit choice set generation using a constrained multinomial logit model. Transportation Research Record: Journal of the Transportation Research Board, 2175, 92–97.

Caicedo, F., Lopez-Ospina, H., & Pablo-Malagrida, R. (2016). Environmental repercussions of parking demand management strategies using a constrained logit model. Transportation Research Part D: Transport and Environment, 48, 125–140.

Cascetta, E., & Papola, A. (2001). Random utility models with implicit availability/perception of choice alternatives for the simulation of travel demand. Transportation Research Part C: Emerging Technologies, 9(4), 249–263.

Cascetta, E., & Papola, A. (2009). Dominance among alternatives in random utility models. Transportation Research Part A: Policy and Practice, 43(2), 170–179.

Castro, M., Martínez, F., & Munizaga, M. A. (2013). Estimation of a constrained multinomial logit model. Transportation, 40(3), 563–581.

Farooq, B., & Miller, E. J. (2012). Towards integrated land use and transportation: A dynamic disequilibrium-based microsimulation framework for built space markets. Transportation Research Part A: Policy and Practice, 46(7), 1030–1053.

Guevara, C. A. (2010). Endogeneity and sampling of alternatives in spatial choice models. (Unpublished doctoral dissertation) Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA.

Habib, M., & Miller, E. (2009). Reference-dependent residential location choice model within a relocation context. Transportation Research Record: Journal of the Transportation Research Board, 2133, 92–99.

Haque, M. B., Choudhury, C., & Hess, S. (2018). Investigating the temporal dynamics of long-term and medium-term residential location choices: A case-study of London. Presented at the Transportation Research Board Annual Meeting, Washington, DC.

Kaplan, S., Bekhor, S., & Shiftan, Y. (2011). Development and estimation of a semi-compensatory residential choice model based on explicit choice protocols. The Annals of Regional Science, 47(1), 51–80.

Kwan, M. P., & Hong, X. D. (1998). Network-based constraints-oriented choice set formation using GIS. Geographical Systems, 5, 139–162.

Lee, B. H., & Waddell, P. (2010). Residential mobility and location choice: A nested logit model with sampling of alternatives. Transportation, 37(4), 587–601.

Manski, C. F. (1977). The structure of random utility models. Theory and Decision, 8(3), 22–254.

Martínez, F., Aguila, F., & Hurtubia, R. (2009). The constrained multinomial logit: A semi-compensatory choice model. Transportation Research Part B: Methodological, 43(3), 365–377.

Martínez, F., & Hurtubia, R. (2006). Dynamic model for the simulation of equilibrium status in the land use market. Networks and Spatial Economics, 6(1), 55–73.

Mcfadden, D. (1978). Modelling the choice of residential location. Transportation Research Record, 673, 72–77.

Næss, P. (2009). Residential self‐selection and appropriate control variables in land use: Travel studies. Transport Reviews, 29(3), 293–324.

Paleti, R. (2015). Implicit choice set generation in discrete choice models: Application to household auto ownership decisions. Transportation Research Part B: Methodological, 80, 132–149.

Rashidi, T. H., Auld, J., & Mohammadian, A. (2012). A behavioral housing search model: Two-stage hazard-based and multinomial logit approach to choice-set formation and location selection. Transportation Research Part A: Policy and Practice, 46(7), 1097–1107.

Scott, D. M. (2006). Constrained destination choice set generation: Comparison of GIS-based approaches. Presented at the Transportation Research Board 85th Annual Meeting, Washington, DC.

Swait, J. (2001). A non-compensatory choice model incorporating attribute cutoffs. Transportation Research Part B: Methodological, 35(10), 903–928.

Swait, J., & Ben-Akiva, M. (1987). Incorporating random constraints in discrete models of choice set generation. Transportation Research Part B: Methodological, 21(2), 91–102.

Termansen, M., McClean, C., & Skov-Petersen, H. (2004). Recreational site choice modelling using high-resolution spatial data. Environment and Planning B: Planning and Design, 36, 1085–1099.

Zolfaghari, A. (2013). Methodological and empirical challenges in modelling residential location choices (Doctoral thesis). Center for Transport Studies, Imperial College, London.

Published
2019-07-23
How to Cite
Haque, M. B., Choudhury, C. F., & Hess, S. (2019). Modelling residential location choices with implicit availability of alternatives. Journal of Transport and Land Use, 12(1). https://doi.org/10.5198/jtlu.2019.1450
Section
Articles