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Baker, S, Ponniah, D and Smith, S (1999) Risk response techniques employed currently for major projects. Construction Management and Economics, 17(02), 205-13.
Baldwin, A N, Austin, S A, Hassan, T M and Thorpe, A (1999) Modelling information flow during the conceptual and schematic stages of building design. Construction Management and Economics, 17(02), 155-67.
Chan, A P C (1999) Modelling building durations in Hong Kong. Construction Management and Economics, 17(02), 189-96.
Goh, B-H (1999) An evaluation of the accuracy of the multiple regression approach in forecasting sectoral construction demand in Singapore. Construction Management and Economics, 17(02), 231-41.
- Type: Journal Article
- Keywords: construction demand; forecasting accuracy; non-linear; model evaluation; regression technique
- ISBN/ISSN: 0144-6193
- URL: https://doi.org/10.1080/014461999371736
- Abstract:
In the current state of research in construction demand modelling and forecasting there is a predominant use of the multiple regression approach, particularly the linear technique. Because of the popularity, it may be useful at this stage to gain an insight into the accuracy of the approach by comparing the forecasting performance of different forms of regression analysis. It is only through such formal means that the relative accuracy of different regression techniques can be assessed. In a case study of modelling Singapore’ s residential, industrial and commercial construction demand, both linear and non-linear regression techniques are applied. The techniques used include multiple linear regression (MLR), multiple log-linear regression (MLGR) and auto regressive non-linear regression (ANLR). Quarterly time-series data over the period 1975-1994 are used. The objective is to evaluate the reliability of these techniques in modelling sectoral demand based on ex-post forecasting accuracy. Relative measures of forecasting accuracy dealing with percentage errors are used. It is found that the MLGR outperforms the other two methods in two of the three sectors examined by achieving the lowest mean absolute percentage error. The general conclusion is that non-linear techniques are more accurate in representing the complex relationship between demand for construction and its various associated indicators. In addition to improved accuracy, the use of non-linear forms also expands the scope of regression analysis.
Green, S D (1999) The missing arguments of lean construction. Construction Management and Economics, 17(02), 133--7.
Gyi, D E, Gibb, A G F and Haslam, R A (1999) The quality of accident and health data in the construction industry: interviews with senior managers. Construction Management and Economics, 17(02), 197-204.
Li, H and Love, P E D (1999) Combining rule-based expert systems and artificial neural networks for mark-up estimation. Construction Management and Economics, 17(02), 169-76.
Loosemore, M (1999) Bargaining tactics in construction disputes. Construction Management and Economics, 17(02), 177-88.
Proverbs, D G, Holt, G D and Olomolaiye, P O (1999) European construction contractors: a productivity appraisal of in situ concrete operations. Construction Management and Economics, 17(02), 221-30.
Ray, R S, Hornibrook, J, Skitmore, M R and Zarkada-Fraser, A (1999) Ethics in tendering: a survey of Australian opinion and practice. Construction Management and Economics, 17(02), 139-53.
Sozen, Z and Kucuk, M A (1999) Secondary subcontracting in the Turkish construction industry. Construction Management and Economics, 17(02), 215-20.
Tan, W (1999) Construction cost and building height. Construction Management and Economics, 17(02), 129-32.