Abstract
Tender price index (TPI) is essential for estimating the likely tender price of a given project. Due to incomplete information on future market conditions, it is difficult to accurately forecast the TPI. Most traditional statistical forecasting models require a certain number of historical data, which may not be completely available in many practical situations. In order to overcome this problem, the grey model is proposed for forecasting TPIs because it only requires a small number of input data. For this study, the data source was based on the TPIs produced by the Government's Architectural Services Department. On the basis of four input data, the grey model forecasted TPIs from 1981Q1 to 2011Q4. The mean absolute percentage errors of forecast TPIs in one quarter and two quarters ahead were 3.62 and 7.04%, respectively. In order to assess the accuracy and reliability of the grey model further, the same research method was used to forecast other three TPIs in Hong Kong. The forecasting results of all four TPIs were found to be very good. It was thus concluded that the grey model could be able to produce accurate TPI forecasts for a one-quarter to two-quarter forecast horizon.
References
Akintoye SA and Skitmore RM (1994). A comparative analysis of three macro price forecasting models. Construction Management and Economics 12 (3): 257–270.
Deng JL (1982). Control problems of grey systems. Systems & Control Letters 1 (5): 288–294.
Deng JL (1989). Multidimensional Grey Planning. Huazhong Polytechnic University Press: Wuhan, China.
Fitzgerald E and Akintoye A (1995). The accuracy and optimal linear correction of UK construction tender price index forecasts. Construction Management and Economics 13 (6): 493–500.
Li X, Ogier J and Cullen J (2006). An economic modelling approach for public sector construction workload planning. Construction Management and Economics 24 (11): 1137–1147.
Lin Y, Chen MY and Liu SF (2004). Theory of grey systems: Capturing uncertainties of grey information. Kybernetes: The International Journal of Systems and Cybernetics 33 (2): 196–218.
Liu S and Lin Y (2006). Grey Information: Theory and Practical Applications. Springer-Verlag: London.
Martin J (2001). The gentle art of forecasting. Chartered Surveyors Monthly 9: 32–33.
Ng ST, Cheung SO, Skitmore RM, Lam KC and Wong LY (2000). Prediction of tender price index directional changes. Construction Management and Economics 18 (7): 843–852.
Ng ST, Cheung SO, Skitmore M and Wong TCY (2004). An integrated regression analysis and time series model for construction tender price index forecasting. Construction Management and Economics 22 (5): 483–493.
Runeson KG (1988). Methodology and method for price-level forecasting. Construction Management and Economic 6 (1): 49–55.
Tysoe BA (1981). Construction Cost Price Indices: Description and Use. E & FN Spon Ltd.: London.
Williams TP (1994). Predicting changes in construction cost indexes using neural networks. Journal of Construction Engineering and Management 120 (2): 306–320.
Wong JMW and Ng ST (2010). Forecasting construction tender price index in Hong Kong using vector error correction model. Construction Management and Economic 28 (12): 1255–1268.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ho, P. Forecasting tender price index under incomplete information. J Oper Res Soc 64, 1248–1257 (2013). https://doi.org/10.1057/jors.2012.168
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1057/jors.2012.168