Cost forecasting system for prices of construction materials based on spatial and temporal trends - Sarol, Jerom Leonardo D.; Arada, Allyza Miroe A.; Germar, Fernando J.; Aquino, Diocel Harold M. (Quezon City College of Engineering, University of the Philippines Diliman, 2016)
The trends in construction costs have been known to be volatile. Escalation in prices of construction materials due to multiple economic factors have been a common impediment in providing an accurate cost estimate for a construction project over a period of time, hence there is a need for a more reliable method of estimation in the form of an accurate cost prediction. Previous studies have approximated that materials cost comprise a quarter of the total construction cost. Thus, accurate estimation of the price of construction materials over the duration of the project, with price fluctuations accounted for, would be greatly beneficial in estimating the total project cost. In the Philippines, estimating variability in the prices of construction materials depend largely on the Construction Materials Retail Price Index (CMRPI) from the Philippine Statistics Authority. While useful, CMRPI is not directly indicative of the actual price of materials and is also limited to Metro Manila only. This research aims to address such problem by developing a cost forecasting system for construction materials that will be suited for a user selected location (a region in the Philippines) and duration. In this study, the Autoregression and Integrated Moving Average (ARIMA) model was used to forecast the prices of five main construction materials: Portland Cement, Gravel G1, Fine Aggregates, Grade 40 Reinforcing Steel, and A36 Wide Flange Structural Steel. Quarterly material price data from January 2013 to June 2016 for 16 regions in the country were collected from the Department of Public Works and Highways. The time-series plot for each material was analyzed and it was observed that location is an important factor in determining prices of materials. The regional prices of materials were also tested for correlation with gasoline and diesel prices, and it was established that the cement price is not correlated with oil and gasoline price while reinforcing steel price is directly correlated with oil and gasoline price. Then, using the software Gnu Regression, Econometrics, and Time-series Library (GRETL), the Akaike's Information Criterion (AIC), the lowest values of which indicate the best model, was determined for each material for every region. The model that yielded the lowest AIC in the most number of times was ARIMA Model (0,1,1). The results indicate that ARIMA models are better for data sets having unique values as it would yield values within the 95% interval. Using ARIMA models for constant data sets would yield large errors in predictions if not to prediction at all. Thus, it is recommended to employ a different statistic model for constant data sets, and use larger data sets in order to have significant variance in the data. Moreover, it is recommended to apply the predictive models into an actual project to determine the validity of the prediction and the significance of the difference between the predicted and actual values.
Thesis (B.S. Civil Engineering)--University of the Philippines Diliman
Keywords: Building materials -- Costs; Building materials industry -- Forecasting