Dynamic Housing Search Model Incorporating Income Changes, Housing Prices, and Life-Cycle Events
Publication: Journal of Urban Planning and Development
Volume 141, Issue 4
Abstract
Modeling housing search behavior is a crucial component of land use modeling. Land use modeling from a specific point of view shares close ties with the transport system. As a result, housing search behavior has become an attractive research topic to travel demand modelers and continues to be a topic of interest to urban planners, geographers, and economists. This paper presents a conceptual framework for long-term decisions of household members with a specific focus on residential relocation-related decisions. The reasons for movement and timing of movement are modeled in this paper using two approaches: (1) a competing hazard formulation, and (2) a conditional hazard and discrete choice model. Australian longitudinal data are used to develop the econometric models in which income change, property value, unemployment rate change, and demographic dynamics are available.
Get full access to this article
View all available purchase options and get full access to this article.
References
Ambrose, B. W. (2005). “A hazard rate analysis of leavers and stayers in assisted housing programs.” Stud. Assisted Hous., 8(2), 69–93.
Anastasopoulos, P. C., Islam, M. B., Perperidou, D., and Karlaftis, M. G. (2012). “Hazard-based analysis of travel distance in urban environments: Longitudinal data approach.” J. Urban Plann. Dev., 53–61.
Archer, W. A., Ling, D. C., and Smith, B. C. (2010). “Ownership duration in the residential housing market: The influence of structure, tenure, household and neighbourhood factors.” J. Real Estate Finance Econ., 40(1), 41–61.
Bhat, C. R. (1996). “A generalized multiple durations proportional hazard model with an application to activity behaviour during the evening work-to-home commute.” Transp. Res. Part B: Method., 30(6), 465–480.
Chakravarti, I. M., Laha, R. G., and Roy, J. (1967). Handbook of methods of applied statistics, Vol. I, Wiley, New York.
Cox, D. R. (1959). “The analysis of exponentially distributed life-time with two types of failures.” J. R. Stat. Soc., 21(2), 411–421.
Deng, Y., Gabriel, S. A., and Nothaft, F. E. (2003). “Duration of residence in the rental housing market.” J. Real Estate Finance Econ., 26(2–3), 267–285.
de Uña-Álvarez, J., Arévalo-Tomé, R., and Soledad Otero-Giráldez, M. (2009). “Nonparametric estimation of households’ duration of residence from panel data.” J. Real Estate Finance Econ., 39(1), 58–73.
Dewan, I., Deshpande, J. V., and Kulathinal, S. B. (2004). “On testing dependence between time to failure and cause of failure via conditional probabilities.” Scand. J. Stat., 31(1), 79–91.
Di Salvo, P., and Ermisch, J. (1997). “Analysis of the dynamics of housing tenure choice in Britain.” J. Urban Econ., 42(1), 1–17.
Duarte, F., and Ultramari, C. (2012). “Making public transport and housing match: Accomplishments and failures of Curitba’s BRT.” J. Urban Plann. Dev., 183–194.
Eadie, W. T., Drijard, D., James, F. E., Roos, M., and Sadoulet, B. (1971). Statistical methods in experimental physics, North-Holland, Amsterdam, Netherlands, 269–271.
Fauria, A., and Mathur, S. (2012). “Impact of targeted redevelopment of central business district on housing prices in the surrounding neighborhoods: Evidence from Oakland, California.” J. Urban Plann. Dev., 244–253.
Gronberc, T. J., and Reed, W. R. (1992). “Estimation of duration models using the annual housing survey.” J. Urban Econ., 31(3), 311–324.
Guevara, C. A., and Ben-Akiva, M. (2010). “Addressing endogeneity in discrete choice models: Assessing control-function and latent-variable methods.” Choice modelling: The state-of-the-art and the state-of-practice, S. Hess and A. Daly, eds., Emerald Group Publishing, Bingley, U.K.
Henderson, J. V., and Ioannidis, Y. M. (1989). “Dynamic aspects of consumer decisions in housing markets.” J. Urban Econ., 26(2), 212–230.
Household, Income and Labour Dynamics in Australia (HILDA) Survey. (2011). “Annual report.” Melbourne Institute of Applied Economic and Social Research, 〈http://www.melbourneinstitute.com/downloads/hilda/Annual_Report/areport2011.pdf〉 (Dec. 12, 2012).
Jenkins, S. P. (2004). Survival analysis, unpublished manuscript, Institute for Social and Economic Research, Univ. of Essex, Colchester, U.K.
McFadden, D. (1973). “Conditional logit analysis of qualitative choice behavior.” Frontiers in econometrics, P. Zaremmbka, ed., Academic, New York.
Nooteboom, B., Klos, T., and Jorna, R. (2001). “Adaptive trust and co-operation: An agent-based simulation approach.” Lect. Notes Comput. Sci., 2246, 83–109.
Oakil, A. T. M., Ettema, D., Arentze, T., and Timmermans, H. (2011). “Longitudinal model of longer-term mobility decisions: Framework and first empirical tests.” J. Urban Plann. Dev., 220–229.
Rashidi, T. H., and Mohammadian, A. (2011). “A dynamic hazard-based system of equations of vehicle ownership with endogenous long-term decision factors incorporating group decision making.” Transp. Geog., 19(6), 1072–1080.
Rashidi, T. H., and Mohammadian, A. (2012). “Behavioural housing search choice set formation: A spatial hazard-based screening model.” Int. Reg. Sci. Rev., in press.
Rashidi, T. H., Mohammadian, A., and Auld, J. (2012a). “A behavioural housing search model: A combined hazard-based and multinomial logit approach.” Transp. Res. Part A, 46(7), 1097–1107.
Rashidi, T. H., Mohammadian, A., and Koppelman, F. (2012b). “Modelling interdependencies between vehicle transaction, residential relocation and job change.” Transportation, 38(6), 909–932.
Rouwendal, J. (1999). “Spatial job search and commuting distances.” Reg. Sci. Urban Econ., 29(4), 491–517.
Salvini, P., and Miller, E. J. (2005). “ILUTE: An operational prototype of a comprehensive microsimulation model of urban systems.” Networks Spatial Econ., 5(2), 217–234.
SASv9 [Computer software]. Cary, NC, SAS Institute.
Spiggle, S., and Sewall, M. A. (1987). “A choice sets model of retail selection.” J. Marketing, 51(2), 97–111.
Strassmann, W. P. (2001). “Residential mobility: Contrasting approaches in Europe and the United States.” Hous. Stud., 16(1), 7–20.
Sueyoshi, G. T. (1992). “Semi-parametric proportional hazards estimation of competing risks models with time-varying covariates.” Econometrics, 51(1–2), 25–58.
van Ommeren, J., Rietveld, P., and Nijkamp, P. (1999). “Job moving, residential moving, and commuting: A search perspective.” J. Urban Econ., 46(2), 230–253.
Waddell, P. (1996). “Accessibility and residential location: The interaction of workplace, residential mobility, tenure, and location choice.” Lincoln Land Institute TRED Conf.
Waddell, P., Liming, W., and Xuan, L. (2008). “UrbanSim: An evolving planning support system for evolving communities.” Planning support systems for cities and regions, R. Brail, ed., Lincoln Institute for Land Policy, Cambridge, MA, 103–138.
Xu, Y., Chan, E. H. W., and Yung, E. H. K. (2012). “Neighborhood change in semiurbanized villages: Case study of Shanghai.” J. Urban Plann. Dev., 235–243.
Yannis, G., Kopsacheili, A., and Klimis, P. (2012). “Estimating the adequacy of a metro network.” J. Urban Plann. Dev., 286–292.
Information & Authors
Information
Published In
Journal of Urban Planning and Development
Volume 141 • Issue 4 • December 2015
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Nov 21, 2013
Accepted: Sep 15, 2014
Published online: Oct 17, 2014
Discussion open until: Mar 17, 2015
Published in print: Dec 1, 2015
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.