Advertisement

Estimating Construction Demand in Singapore: Potential of Neural Networks

  • Freddie Tan
  • George Ofori
Chapter
  • 84 Downloads
Part of the Applied Econometrics Association Series book series (AEAS)

Abstract

In Singapore, the construction sector’s share in GDP has steadily climbed from 5.4 per cent in 1989 to 7.1 per cent in 1995. Its output has been growing at 11.9 per cent per annum since 1989. A growing outward trend has been the ‘regionalisation’ of the local construction industry with the result that there has been an increase in construction firms competing for projects abroad. Contracts won abroad will bring in export earnings to the economy that can offset, in part, the leakage due to imports of foreign services and building materials. With the maturing of Singapore’s economy, we shall see increasing refurbishment and restoration work in the future. As construction output is a derived demand, it also reflects the importance of inter-sectoral linkages and associative growth. This notion is supported by the high percentage which capital formation in construction contributes to the Gross Fixed Capital Formation (GFCF) in Singapore.

Keywords

Gross Domestic Product Hide Node Construction Industry Money Supply General Regression Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Bergstrom, A.R. (1967), The Construction and Use of Economic Models, London, English Universities Press.Google Scholar
  2. Bon, R. (1989), ‘Direct and Indirect Resource Utilisation by the Construction Sector: The Case of the USA since World War II’, Habitat International, 12(1), 49–74.CrossRefGoogle Scholar
  3. DiPasquale, D. and W.C. Wheaton (1995), Urban Economics and Real Estate Markets, Englewood Cliffs, NJ, Prentice Hall.Google Scholar
  4. Garson, G.D. (1991), ‘Interpreting Neural-Network Connection Weights’, AI Expert, 6, 47–51.Google Scholar
  5. Goh, B.H. (1996), ‘Residential Construction Demand Forecasting Using Economic Indicators: A Comparative Study of Artificial Neural Networks and Multiple Regression’, Construction Management & Economics, 14(1), 25–34.CrossRefGoogle Scholar
  6. Hebb, D.O. (1949), The Organisation of Behavior, New York, Wiley.Google Scholar
  7. Hecht-Nielsen, R. (1989), ‘Theory of the Backpropagation Neural Network’, Proceedings of the International Joint Conference on Neural Networks, 1, 593–611, New York, IEEE Press. Hillebrandt, P. (1984), Analysis of the British Construction Industry, London, Macmillan.Google Scholar
  8. Hirshleifer, J. (1958), ‘On the Optimal Investment Decision’, Journal of Political Economy, 66(4), 329–52.Google Scholar
  9. Hodgkin, A.L. and A.F. Huxley (1952), ‘A Quantitative Description of Membrane Current and its Application to Conduction and Excitation in Nerve’, Journal of Physiology, 117, 500–44.Google Scholar
  10. Hopfield, J.J. (1982), ‘Neural Networks and Physical Systems with Emergent Collective Computational Abilities’, Proceedings of the National Academy of Sciences, 79, 2554–58.Google Scholar
  11. Jacobs, R.A., M.I. Jordan, S.J. Nowlan and G.E. Hinton (1991), ‘Adaptive Mixtures of Local Experts’, Neural Computation, 3, 79–87.Google Scholar
  12. Koh, A.M.M. (1987), An Econometric Model for Forecasting Industrial Space Demand in Singapore, Unpublished PhD dissertation, University of Georgia, USA.Google Scholar
  13. Matyas, J. (1965), ‘Random Optimization’, Automation and Remote Control, 26, 246–53.Google Scholar
  14. Minsky, M. and S. Papert (1969), Perceptrons: An Introduction to Computational Geometry, Cambridge, MA, MIT Press.Google Scholar
  15. Ofori, G. (1988), ‘Construction Industry and Economic Growth in Singapore’, Construction Management and Economics, 6, 57–70.Google Scholar
  16. Ofori, G. (1993), Managing Construction Industry Development: Lessons from Singapores Experience, Singapore, Singapore University Press, NUS.Google Scholar
  17. Rosenblatt, F. (1958), ‘The Perceptron: A Probabilistic Model for Information Storage and Organisation in the Brain’, Psychological Review, 65(6), 386–408.Google Scholar
  18. Samad, T. (1988), ‘Backpropagation is Significantly Faster if the Expected Value of the Source Unit is Used for Update’, International Neural Network Society Conference Abstracts. Google Scholar
  19. Tan, W. (1989), Subsector Fluctuations in Construction, Occasional Paper No. 1, Construction Management and Economics Research Unit, School of Building and Estate Management, National University of Singapore, Singapore.Google Scholar
  20. Tang, J.C.S., P. Karasudhi and P. Tachopiyagoon (1990), ‘Thai Construction Industry: Demand and Projection’, Construction Management & Economics, 8, 249–57.Google Scholar
  21. Turin, D.A. (1973), The Construction Industry: its Economic Significance and its Role in Development, London, University College London.Google Scholar
  22. Widrow, B. and M.E. Hoff (1960), ‘Adaptive Switching Circuits’, IRE WESCON Convention Record, New York, 96–104.Google Scholar

Copyright information

© Applied Econometrics Association 2003

Authors and Affiliations

  • Freddie Tan
  • George Ofori

There are no affiliations available

Personalised recommendations