Forecasting Private-Sector Construction Works: VAR Model Using Economic Indicators

Michael Sing, David Edwards, Henry Liu, Peter Love

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Accurately modeling and forecasting construction works completed by main contractors is pivotal for policymakers, who require reliable market intelligence to adjust or develop optimal labor and housing policies. Yet, despite its importance, limited research has been conducted to systematically develop approaches to investigating future trends of works completed in the private construction sector. Against this backdrop, this paper provides a study of the annual financial value of construction work in the private residential market. A vector auto-regression (VAR) model developed utilizes economic indicators (used by private financiers when making investment decisions) to estimate the value of annual construction work carried out by main contractors. Using data from the Hong Kong private residential market and constructing an accumulated impulse function, the developed model suggests that construction work completions in private residential markets can be explained by changes in economic indicators such as gross domestic product and the property price index. These economic indicators have been identified as having a large and direct effect on forthcoming construction works. The developed model also provides a high degree of accuracy (producing an adjusted R2 value of 0.72) when simulating or forecasting future changes in the value of construction works over a short-term 5-year forecast. The output of this study contributes to the literature by systematically developing a reliable approach using economic indicators that is useful for forecasting private-sector construction completions. Such knowledge is of paramount importance when estimating the industry’s future workload and supply of residential buildings.
Original languageEnglish
Article number04015037
Pages (from-to)1-9
Number of pages9
JournalJournal of Construction Engineering and Management
Volume141
Issue number11
DOIs
Publication statusPublished - Nov 2015
Externally publishedYes

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