건설공사비지수 예측을 위한 선행지표 기반 다변량 시계열 분석 및 모델 성능 비교 = Construction Cost Index Forecasting Through Multivariate Time Series Modeling and Leading Indicators

국문 초록 (Abstract)

건설공사비지수(CCI)는 건설 자원 가격 변동을 측정하는 지수로, 정확한 예측이 필수적이다. 본 연구는 VAR 모델을 활용해 CCI를 예측하며, 건축 수주액, BSI, PPI-형강을 선행지표로 선정했다. AIC 기반 최적화 후 ARIMA, VAR(CPI, PPI), SVR과 비교 검증하였으며, 2000~2023년 데이터로 Walk-Forward 교차 검증을 수행했다. 분석 결과, 제안된 모델은 단기·중기 예측에서 가장 낮은 오차를 기록했으며, 장기 예측에서는 SVR이 우수했으나 전체 추세 반영에서는 제안된 모델이 더 균형적이었다.

다국어 초록 (Multilingual Abstract)

The Construction Cost Index (CCI) is a key measure of price fluctuations in major construction resources, playing a crucial role in costestimation and price trend analysis. Accurate CCI forecasting is essential to prevent cost underestimation or overestimation, ensuring theeconomic 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 leadingindicators were identified: construction order amount, business survey index (BSI), and producer price index (PPI) for structural steel. Theproposed model was optimized using the Akaike Information Criterion (AIC), while benchmark models ARIMA, VAR (CPI, PPI), and SVRwere optimized through grid search. Model validation was conducted using data from January 2000 to April 2023, segmented into threeeconomic phases: stability, heightened uncertainty, and a combined period. Walk-forward cross-validation assessed predictive performance overshort-term forecasts of 3 months, mid-term forecasts of 6 months, and long-term forecasts of 12 months, with evaluation based on averagedperformance metrics over multiple iterations. Results showed that the proposed model achieved the lowest error and highest accuracy in shortandmid-term forecasts. For long-term forecasts, SVR recorded the lowest error; however, qualitative analysis indicated that the proposedmodel more effectively captured overall trends in a balanced manner. By integrating key market indicators, this approach provides a robustmethod for CCI forecasting, enhancing cost predictability in the construction industry.