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Apipattanavis, S, Sabol, K, Molenaar, K R, Rajagopalan, B, Xi, Y, Blackard, B and Patil, S (2010) Integrated Framework for Quantifying and Predicting Weather-Related Highway Construction Delays. Journal of Construction Engineering and Management, 136(11), 1160–8.

Ashuri, B and Lu, J (2010) Time Series Analysis of ENR Construction Cost Index. Journal of Construction Engineering and Management, 136(11), 1227–37.

  • Type: Journal Article
  • Keywords: Construction costs; Time series analysis; Auto-regressive models; Auto-regressive moving-average models; Construction costs; Time series analysis; Autoregressive models; Autoregressive moving-average models; Autoregressive integrated moving average (ARIMA
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000231
  • Abstract:
    Every month, Engineering News-Record (ENR) publishes the construction cost index (CCI), which is a weighted aggregate index of the 20-city average prices of construction activities. Although CCI increases over the long term, it is subject to considerable short-term variations, which make it problematic for cost estimators to prepare accurate bids for contractors or engineering estimates for owner organizations. The ability to predict construction cost trends can result in more-accurate bids and avoid under- or overestimation. This paper summarizes and compares the applicability and predictability of various univariate time series approach for in-sample and out-of-sample forecastings of CCI. It is shown that the seasonal autoregressive integrated moving-average model is the most-accurate time series approach for in-sample forecasting of CCI, while the Holt-Winters exponential smoothing model is the most-accurate time series approach for out-of-sample forecasting of CCI. It is also shown that several time series models provide more-accurate out-of-sample forecasts than the ENR’s subject matter experts’ CCI forecast. Cost estimators can benefit from CCI forecasting by incorporating predicted price variations in their estimates and preparing more-accurate bids for contractors and budgets for owners. Owners and contractors can use CCI forecasting in reducing construction costs by better-timed project execution.

Bhargava, A, Anastasopoulos, P C, Labi, S, Sinha, K C and Mannering, F L (2010) Three-Stage Least-Squares Analysis of Time and Cost Overruns in Construction Contracts. Journal of Construction Engineering and Management, 136(11), 1207–18.

Blackman, I Q and Picken, D H (2010) Height and Construction Costs of Residential High-Rise Buildings in Shanghai. Journal of Construction Engineering and Management, 136(11), 1169–80.

Chen, J, Su, M and Huang, D (2010) Application of a SOM-Based Optimization Algorithm in Minimizing Construction Time for Secant Pile Wall. Journal of Construction Engineering and Management, 136(11), 1189–95.

Lingard, H, Francis, V and Turner, M (2010) Work-Family Conflict in Construction: Case for a Finer-Grained Analysis. Journal of Construction Engineering and Management, 136(11), 1196–206.

Marsh, K and Fayek, A R (2010) SuretyAssist: Fuzzy Expert System to Assist Surety Underwriters in Evaluating Construction Contractors for Bonding. Journal of Construction Engineering and Management, 136(11), 1219–26.

Shepherd, S and Woskie, S R (2010) Case Study to Identify Barriers and Incentives to Implementing an Engineering Control for Concrete Grinding Dust. Journal of Construction Engineering and Management, 136(11), 1238–48.

Thal, A E, Cook, J J and White, E D (2010) Estimation of Cost Contingency for Air Force Construction Projects. Journal of Construction Engineering and Management, 136(11), 1181–8.