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Predicting housing construction period based on a cox proportional hazard model––an empirical study of housing completions in the greater Toronto and Hamilton area

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  • Yu Zhang
  • Eric J Miller

Abstract

The completion progress of residential development projects and the length of construction are frequently discussed in the construction industry, but rarely studied by urban modellers. Nonetheless, a realistic reflection of housing supply processes is important for urban microsimulation and land use modelling. To predict the dwelling units generated over space and time, this paper decomposes the housing supply process into two major components: housing starts and completions, the nature and modelling logic of which are quite different. This paper deals with the latter segment, aiming to answer the question of: how long will it take to complete construction of new dwellings? A Cox Proportional Hazard (CPH) Model is employed to examine the “survival†rate of residential building projects and the probabilistic distribution of construction periods. Narrowing down the scope of research, this study investigates housing completions at the individual project level, and discusses the impact of structure type, surrounding land use, and accessibility on the housing completion rate. The Cities of Toronto, Hamilton, and Brampton in the Greater Toronto and Hamilton Area (GTHA) were selected to conduct the empirical study, with each representing different types of urban form to test model compatibility. The hazard models show good performance in replicating completion rates, and the impact of each factor on hazard ratio indicates that, single detached dwelling units with relatively homogeneous land use have the shortest completion time. This study could provide one component of a comprehensive framework for modelling housing supply, especially in urban microsimulation systems.

Suggested Citation

  • Yu Zhang & Eric J Miller, 2023. "Predicting housing construction period based on a cox proportional hazard model––an empirical study of housing completions in the greater Toronto and Hamilton area," Environment and Planning B, , vol. 50(6), pages 1624-1644, July.
  • Handle: RePEc:sae:envirb:v:50:y:2023:i:6:p:1624-1644
    DOI: 10.1177/23998083221143386
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    References listed on IDEAS

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    1. William H. Greene & David A. Hensher, 2010. "Ordered Choices and Heterogeneity in Attribute Processing," Journal of Transport Economics and Policy, University of Bath, vol. 44(3), pages 331-364, September.
    2. Daniel Chan & Mohan Kumaraswamy, 1999. "Modelling and predicting construction durations in Hong Kong public housing," Construction Management and Economics, Taylor & Francis Journals, vol. 17(3), pages 351-362.
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