Abstracts – Browse Results

Search or browse again.

Click on the titles below to expand the information about each abstract.
Viewing 10 results ...

Adams, D and Hastings, E M (2000) Making urban renewal pay: the Hong Kong experience. Journal of Financial Management of Property and Construction, 5(01), 79–84.

Bollen, R (2000) Applying quantitative techniques to evaluate residential development profitability. Journal of Financial Management of Property and Construction, 5(01), 51–64.

Deakin, M (2000) The development of property asset management: towards a pro-investment form. Journal of Financial Management of Property and Construction, 5(01), 15–31.

Edwards, D J and Holt, G D (2000) Quantifying the cost of plant operators' productivity. Journal of Financial Management of Property and Construction, 5(01), 85–92.

Fortune, C and Birnie, J (2000) Money illusions and the judgements of professional quanitity surveyors. Journal of Financial Management of Property and Construction, 5(01), 41–50.

Kishk, M and Al-Hajj, A (2000) A fuzzy model and algorithm to handle subjectivity in life cycle costing based decision-making. Journal of Financial Management of Property and Construction, 5(01), 93–104.

Ojo, S O, Adeyemi, O Y and Ikpo, I J (2000) Effects of procurement methods on clients objectives of time and cost in the Nigerian construction industry. Journal of Financial Management of Property and Construction, 5(01), 105–8.

Swaffield, L M and Pasquire, C L (2000) Improving early cost advice for mechanical and electrical services by considering functions and client/design team communication. Journal of Financial Management of Property and Construction, 5(01), 3–13.

Tanratanawong, S and Scott, S (2000) A neural network model to forecast national construction output. Journal of Financial Management of Property and Construction, 5(01), 65–77.

  • Type: Journal Article
  • Keywords: construction output; construction demand; neural networks; principal component analysis; regression analysis
  • ISBN/ISSN: 1366-4387
  • URL: http://www.emeraldinsight.com/journals.htm?issn=1366-4387
  • Abstract:
    Construction output forecasts have been routinely made and published to assist construction and construction related firms with their planning processes. They are typically provided by groups of experts, as it is not practical for general construction firms to produce such forecasts for their own uses. The study reported in this paper aims to develop an efficient, practical quantitative method to forecast the national construction demand using economic indicators. Back-propagation neural networks were chosen for this study due to their ability to learn from examples of historical data, to map the relations between various variables without prior knowledge and to generate outputs with relatively high accuracy. Regression models were also developed based on the same data sets. The results were then compared with existing published qualitative forecasts. It was found that the neural network models performed well in forecasting construction output in all three sectors: Housing, Non-housing, and Repair and maintenance. Indeed, in all tests, the neural network models forecasted more accurately than the regression models and were close to the published forecasts. The results show that neural network models can be effectively used as an alternative forecasting tool.

Tse, R Y C (2000) A study of housing and real estate markets in Macau. Journal of Financial Management of Property and Construction, 5(01), 33–40.