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Title: An evaluation of the accuracy of the multiple regression approach in forecasting sectoral construction demand in Singapore
Authors: Bee-Hua, G. 
Keywords: Construction demand
Forecasting accuracy
Model evaluation
Regression techniques
Issue Date: 1999
Citation: Bee-Hua, G. (1999). An evaluation of the accuracy of the multiple regression approach in forecasting sectoral construction demand in Singapore. Construction Management and Economics 17 (2) : 231-241. ScholarBank@NUS Repository.
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 nonlinear regression techniques are applied. The techniques used include multiple linear regression (MLR), multiple log-linear regression (MLGR) and autoregressive nonlinear 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 nonlinear 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 nonlinear forms also expands the scope of regression analysis.
Source Title: Construction Management and Economics
ISSN: 01446193
Appears in Collections:Staff Publications

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