Modeling and forecasting construction labor demand: Multivariate analysis

James M W Wong, Ping Chuen Chan, Yat Hung Chiang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

25 Citations (Scopus)


This paper presents the development of advanced labor demand forecasting models at project level. A total of 11 manpower demand forecasting models were developed for the total project labor and ten essential trades. Data were collected from a sample of 54 construction projects. These data were analyzed through a series of multiple linear regression analyses that help establish the estimation models. The results indicate that project labor demand depends not only on a single factor, but a cluster of variables related to the project characteristics, including construction cost, project complexity attributes, physical site condition, and project type. The derived regression models were tested and validated using four out-of-sample projects and various diagnostic tests. It is concluded that the models are robust and reliable, which merit for contractors and HKSAR government to predict the labor required for a new construction project and facilitate human resources planning and budgeting, and that the methodology used may be applied to develop equally useful models in other subsectors, and in other countries.
Original languageEnglish
Pages (from-to)664-672
Number of pages9
JournalJournal of Construction Engineering and Management
Issue number9
Publication statusPublished - 25 Aug 2008


  • Forecasting
  • Hong Kong
  • Labor
  • Project management
  • Regression models

ASJC Scopus subject areas

  • Building and Construction
  • Civil and Structural Engineering
  • Industrial and Manufacturing Engineering


Dive into the research topics of 'Modeling and forecasting construction labor demand: Multivariate analysis'. Together they form a unique fingerprint.

Cite this