Modelling and predicting construction durations in Hong Kong public housing

Wai Ming Chan, Mohan M. Kumaraswamy

Research output: Journal article publicationJournal articleAcademic researchpeer-review

36 Citations (Scopus)

Abstract

Construction time performance is provoking world-wide concern and discussion within the industry. This paper reports the results of a survey in the fourth stage of an investigation seeking to identify a set of significant variables influencing construction durations of projects in Hong Kong, the stage addressing the formulation of standard norms for overall construction durations of public housing projects by modelling the primary work packages in the building process, namely piling, pile caps/raft, superstructure, E and M services, finishes and their respective sequential start-start lag times, on the basis of the identified groups of critical factors. Data were collected from a sample of 56 standard 'Harmony' type domestic blocks of the Hong Kong Housing Authority; (the 'Harmony' series of block design having become popular for average quality public housing blocks in the 1990s, ranging from 30 to 40 storeys and containing about 16 residential units on each floor). These data were analysed through a series of multiple linear regression exercises that helped to establish the time prediction model. This model was then tested and validated using information from a further nine projects from the Housing Authority. Both the usefulness and shortcomings of the model are briefly presented and discussed. It is concluded that the model is applicable to the public housing industry in Hong Kong, and that the methodology used may be applied to develop similarly useful models in other subsectors, and in other countries.
Original languageEnglish
Pages (from-to)351-362
Number of pages12
JournalConstruction Management and Economics
Volume17
Issue number3
DOIs
Publication statusPublished - 1 Jan 1999
Externally publishedYes

Keywords

  • Construction durations
  • Hong Kong public housing
  • Modelling
  • Multiple linear regression
  • Predicting

ASJC Scopus subject areas

  • Management Information Systems
  • Building and Construction
  • Industrial and Manufacturing Engineering

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