Application of Hazard Based Model for Housing Location Based on Travel Distance to Work

Document Type: Research Paper


1 Imam Khomeini International University, Ghazvin, Iran

2 University of Illinois at Chicago, Illinois, Chicago, USA


Residential location choice modeling is one of the areas in transportation planning that attempts to examine households location search behavior incorporating their trade-offs between housing quality, prices or rents, distance to work and other key factors. This brings up the need to come up with methods to logically allocate credible choice alternatives for individuals.This article attempts to provide a detailed study of this practice to develop a modeling framework that can replicate the choice process. In order to show the potential of the method, a decision criterion—maximum distance to work—is considered the potential attribute that the household evaluates for feasible housing alternatives. It is postulated that alternatives will only be included in the choice set if the maximum work distancesatisfies the household thresholds. This research explores the application of proportional hazard models in the housing search process. Some of the specifications of hazard-based models that are typically used on temporal data are examined on work distance. A log-logistic function is used for hazard base-line. The study has used the household travel behavior survey conducted by Chicago Metropolitan Agency for Planning (CMAP). Furthermore, several extensive land use and transportation related data sources are incorporated to complement the scope of the modeling results.


-Auld, J. and Mohammadian, A. (2011)”Planning constrained destination choice in the ADAPTS activity-based model”, Paper presented at the 90th Annual Transportation Research Board Meeting, Washington DC.

-Ben-Akiva, M. E. and Bowman, J. L. (1998) “Integration of an activity-based model system and a residential location model”, Urban Stud. 35, pp. 1131-1153.

-Ben-Akiva, M. E. and Lerman, S.R. (1985) “Discrete choice analysis: theory and application to travel demand”, The MIT Press, Cambridge.

-Ben-Akiva, M. and Watanatada, T. (1981)” Application of a continuous spatial choice logit model, Structural analysis of discrete data with econometric applications”. MIT Press, Cambridge, Mass.

-Boots, B. N. and Getis, A. (1988) “Point pattern analysis”, Newbury Park, CA: Sage.

-Cox, D. R. (1959)” The analysis of exponentially distributed life-time with two types of failures”,  Journal of Royal Statistical Society 21B: pp. 411–421.

-Cox, D. R. (1972)” Regression models and life-tables” Journal of Royal Statistical Society 34B: pp.187-220.

-Cox, D. R. and Oakes, D. (1984) “Analysis of survival data”, London, UK: Chapman and Hall.

-Clark, W.A.V. and Withers, S. D.(1999) “Changing jobs and changing houses: mobility outcomes of employment transitions”. J. Regional Sci. 39, pp. 653-673

-Carruthers, J. I., Lewis, S. Knaap, G. J. and R. N. Renner. (2009) “Coming undone: a spatial
hazard analysis of urban form in American metropolitan areas.’’ Papers in Regional Science 89:pp.65–88.

-Diggle, P. (1983) “Statistical analysis of spatial point patterns”, New York, NY: Academic Press.

-Fotheringham, A. (1988) “Consumer store choice and choice set definition”, Marketing Science, 7, pp. 299-310.

-Guevara, C.A. and Ben-Akiva, M. E. (2013) “Sampling of alternatives in multivariate extreme value (MEV) models”, Transportation Research Part B: Methodological, pp. 31-52.

-Guo, J.Y. and Bhat, C. R. (2007)” Operationalizing the concept of neighborhood: Application to residential location choice analysis”, J. Transp. Geogr. 15, pp.31-45.

-Habib, M. A. and Miller, E. J. (2008) “Modeling residential mobility and spatial search behaviorestimation of continuoustime hazard and discrete-time panel logit models for residential mobility”, In: Transportation Research Board 87th Annual Meeting, Washington, DC.

-Han, A., and J. A. Hausman. (1990) “Flexible parametric estimation of duration andcompeting risk models.” Journal of Applied Econometrics 5:pp.1–28.

-Langerudi, M. F., M. Javanmardi, A. Mohammadian, A.and Sriraj, P. S.(2014) “Choice set imputation: two-step weighted stratified and hazard-based approach”, Transportation Research Record, Journal of Transportation Research Board, No. 2429,1. pp. 79-89.

-Langerudi, M. F., Javanmardi, M., Mohammadian, A., Sriraj, P. S. and Amini, B. (2013) “Choice set formation of housing location: A comparative analysis”, in West Virginia University, 46th annual meeting.

-Lee, B. H.Y., Waddell, P. A., Wang, L. and Pendyala, R. M. (2010)” Operationalizing time-space prism accessibility in a building-level residential choice model: empirical results from the Puget Sound region”. Environmental .Planning , .A 42.

-Kim, S. (1992) “Search, hedonic prices and housing demand”, The Review of Economics and Statistics 74: pp.503–508.

-Lerman, S. R. (1984) “Recent advances in disaggregate demand modeling”,  In Transportation Planning Models, edited by M. Florian. Amsterdam, The Netherlands: North-Holland.

-McFadden, D. (1974) “Conditional logit analysis on the temporal stability of disaggregate travel demand models”, Transportation Research Part B 16: pp.263–278.

-McFadden, D. (1978)” Modelling the choice of residential location.” In: Karlqvist, A., Lundqvist, L., Snickars, F.,Weibull, J. (eds.), Spatial Interaction Theory and Planning Models, pp. 75-96.

-Odland, J. and Ellis, M. (1992) “Variations in the spatial pattern of settlement locations: an analysis based on proportional hazards models”, Geographical Analysis, 24: pp.97–109.

-Pinjari, A. R., Pendyala, R. M., Bhat, C. R. and Waddell, P. A. (2008) “Modeling the choice continuum: Integrated model of residential location automobile ownership bicycle ownership and commute tour mode choice decisions”, Transportation Research Board 87th Annual Meeting, Washington, DC
-Rashidi, T. H., Mohamamdian, A. and Koppel man, F. S.  (2011) “Modeling interdependencies between vehicle transaction, residential relocation and job change”, Transportation 38: pp. 909–932.

-Thill, J. C., and Horowitz, J. L. (1991) “Estimating a destination-choice model from a choice-based sample with limited information”, Geographical Analysis 23: pp.298–315.

-Van Ommeren, J., P. Rietveld, and P. Nijkamp. (1997)” Commuting: In Search of Jobs and Residences.”  Journal of Urban Economics 42: pp.402–21.

-Waldorf, B. S. (2003). “Spatial point patterns in a longitudinal framework.”International
Regional Science Review 26: pp.269–88.

-Zheng, J.,Guo, J. (2008)” Destination Choice Model Incorporating Choice Set Formation”,In: Transportation Research Board 87th Annual Meeting, Washington, DC

-Zolfaghari A., Sivakumar A., PolakJ. (2011),” Choice set formation in residential location choice modelling “, In: International Choice Modelling Conference