A New Approach to Improving the Responsiveness to Price Fluctuations of the Range Estimating Model through Autocorrelation Time-Series Analysis

다국어 초록 (Multilingual Abstract)

Dynamic and volatile characteristics are the most significant factors that distinguish the construction industry from other fields.
Many researchers have endeavored to deal with uncertainty caused by the above factors, and significant outcomes have been obtained, owing to the innovative methodologies of data handling. With the help of powerful data processing, enormous information,which is effective in coping with uncertainty, could be gained. During the last three decades, the housing industry in Korea has boomed, resulting in huge data generation. More precise estimation is required at the initiative phase to support decision-making on the possibilities of realization of projects. For more accurate estimation, a Range Estimating Model based on time series analysis is developed and suggested. This paper is organized into three main sections. In the first section, unit prices of residential building projects turn out to be time-dependent, from analysis of the Durbin-Watson ratio. The second section explores i) analysis of stationarity, ii) model development, and iii) model validation and application. In this process, this paper suggests appropriate time series models, such as the ARIMA, and Monte Carlo Simulation using the predicted unit prices. In order to validate the proposed model, priced bills of quantities of 150 housing projects are analyzed, and the results of a t-test on relative accuracies indicates that the proposed model is more accurate than the conventional range estimating technique, using historical cost data, and ignoring price fluctuations.

참고문헌 (Reference)

1 Chatfield, C., "The analysis of time series: An introduction" CHAMPMAN & HALL/CRC 2004

2 Fizgerald, E., "The accuracy and optimal linear correction of UK construction tender price index forecasts" 13 (13): 493-500, 1995

3 Elkjaer, M, "Stochastic budget simulation" 18 (18): 139-147, 2000

4 Yeo, K. T, "Risks, classification of estimates, and contingency management" ASCE 6 (6): 458-470, 1996

5 Barrie, D. S., "Professional construction management" McGraw-Hill Book 198-205, 1992

6 Isidore, L. J., "Probability optimal-cost scheduling" ASCE 127 (127): 431-437, 2001

7 Spooner, J. E., "Probability estimating" ASCE 100 (100): 65-77, 1974

8 Chau, J., "Probabilistic simulation for developing likelihood distribution of engineering project cost" 18 (18): 570-577, 2009

9 Touran, A, "Probabilistic cost estimating with subjective correlations" ASCE 119 (119): 58-71, 1993

10 Vergara, A. J., "Probabilistic approach to estimating and cost control" ASCE 100 (100): 543-552, 1974

11 Lowe, D. J., "Predicting construction cost using multiple regression techniques" ASCE 132 (132): 750-758, 2006

12 Chau, K. W, "Monte carlo simulation of construction costs using subjective data: Response" 15 (15): 109-115, 1996

13 Emblemsvag, J., "Life-cycle costing using activity-based costing and monte carlo methods to manage future costs and risks" John Willey & Sons, Inc. 85-, 2003

14 Emhjellen, K., "Investment cost estimates and investment decisions" 30 (30): 91-96, 2002

15 Stock, J. H., "Introduction to econometrics" Pearson Education, Inc. 2007

16 Sakka, Z. I., "Float consumption impact on cost and schedule in the construction industry" ASCE 133 (133): 124-130, 2007

17 Zhou, M., "Electricity price forecasting with confidence-interval estimation through an extended ARIMA approach" 153 (153): 187-195, 2006

18 Back, E. W., "Defining triangular probability distributions from historical cost data" ASCE 126 (126): 29-37, 2000

19 Decisioneering, "Crystal ball user manual" Decisioneering, Inc 2004

20 Akintoye, S. A., "Construction tender price index: Modelling and forecasting trends" The University of Salford 1991

21 Ng, T. S., "An integrated regression analysis and time series model for construction tender price index forecasting" 22 (22): 483-493, 2004