| Title |
Construction Cost Index Forecasting Through Multivariate Time Series Modeling and Leading Indicators |
| Authors |
윤하현(Yoon, Ha-Hyeon) ; 허찬(Heo, Chan) ; 박문서(Park, Moon-Seo) ; 안창범(Ahn, Changbum) |
| DOI |
https://doi.org/10.5659/JAIK.2025.41.5.319 |
| Keywords |
Construction Cost Index; Machine Learning; Multivariate Model; Statistical Validation; Leading Indicators |
| Abstract |
The Construction Cost Index (CCI) is a key measure of price fluctuations in major construction resources, playing a crucial role in cost
estimation and price trend analysis. Accurate CCI forecasting is essential to prevent cost underestimation or overestimation, ensuring the
economic feasibility of construction projects. This study forecasts the CCI using a multivariate time series model, Vector Autoregression
(VAR), to address the limitations of univariate models, especially during economic uncertainty. Through statistical validation, three leading
indicators were identified: construction order amount, business survey index (BSI), and producer price index (PPI) for structural steel. The
proposed model was optimized using the Akaike Information Criterion (AIC), while benchmark models ARIMA, VAR (CPI, PPI), and SVR
were optimized through grid search. Model validation was conducted using data from January 2000 to April 2023, segmented into three
economic phases: stability, heightened uncertainty, and a combined period. Walk-forward cross-validation assessed predictive performance over
short-term forecasts of 3 months, mid-term forecasts of 6 months, and long-term forecasts of 12 months, with evaluation based on averaged
performance metrics over multiple iterations. Results showed that the proposed model achieved the lowest error and highest accuracy in shortand
mid-term forecasts. For long-term forecasts, SVR recorded the lowest error; however, qualitative analysis indicated that the proposed
model more effectively captured overall trends in a balanced manner. By integrating key market indicators, this approach provides a robust
method for CCI forecasting, enhancing cost predictability in the construction industry. |