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Explorations in specifying construction price forecast loss functions

Author

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  • Martin Skitmore
  • Franco K. T. Cheung

Abstract

Typical measures of goodness of construction price forecasts are the mean and standard deviation, coefficient of variation and root mean square of the deviations between forecasted and actual values. This can only be valid, however, if the pain, or loss, incurred as a result of such deviations is directly proportional to the square of their value. Two approaches are used to test this. The first of these analyses 10 sets of data collected from around the world, while the second explores the use of a postal questionnaire survey to elicit construction industry client disutilities. The results of the first analysis mitigate against any general view that projects tend to be overestimated but do clearly suggest asymmetric under/overestimates for the measures used. The second analysis results in an approximated loss function although in ordinal terms only. This also suggests that the functional form varies between building types, with Commercial and Residential being the most asymmetric and Schools and Industrial being less asymmetric. The work to date indicates that, for construction price forecasting, the loss functions involved are asymmetric, with the degree of asymmetry increasing according to the level of commercial financial viability at stake.

Suggested Citation

  • Martin Skitmore & Franco K. T. Cheung, 2007. "Explorations in specifying construction price forecast loss functions," Construction Management and Economics, Taylor & Francis Journals, vol. 25(5), pages 449-465.
  • Handle: RePEc:taf:conmgt:v:25:y:2007:i:5:p:449-465
    DOI: 10.1080/01446190600794571
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    References listed on IDEAS

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    1. Elliott, Graham & Komunjer, Ivana & Timmermann, Allan G, 2003. "Estimating Loss Function Parameters," CEPR Discussion Papers 3821, C.E.P.R. Discussion Papers.
    2. Dale J. Poirier, 1995. "Intermediate Statistics and Econometrics: A Comparative Approach," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262161494, March.
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