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Grey Forecasting of Construction Demand in Hong Kong over the Next Ten Years

handle: 11541.2/111335 , 1959.13/1331333
AbstractThe Hong Kong construction industry since 1997 has experienced a difficult time in regard to construction output until recently. The future of the local construction industry remains uncertain. Forecasting of future development would be attractive to parties in the industry. Based on past data of the local construction industry, a grey forecasting model is developed to predict the annual growth of construction demand for Hong Kong over the next ten years. With the implementation of ten large infrastructure projects and further collaboration with the Pearl River Delta region, there will be many potential opportunities for the local industry, especially in the transportation sector. The data and the forecast are useful reference for clients, contractors and other industry participants to formulate their short-term and long-term strategic planning.
- University of Newcastle Australia Australia
- University of South Australia Australia
- Bond University Australia
- Hong Kong Polytechnic University China (People's Republic of)
- University of Newcastle Australia Australia
690, grey model, forecasting, construction demand, Hong Kong, Grey model, Construction Engineering
690, grey model, forecasting, construction demand, Hong Kong, Grey model, Construction Engineering
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).12 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
