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Aibinu, A A and Odeyinka, H A (2006) Construction Delays and Their Causative Factors in Nigeria. Journal of Construction Engineering and Management, 132(07), 667–77.

Brilakis, I and Soibelman, L (2006) Multimodal Image Retrieval from Construction Databases and Model-Based Systems. Journal of Construction Engineering and Management, 132(07), 777–85.

Caldas, C H, Torrent, D G and Haas, C T (2006) Using Global Positioning System to Improve Materials-Locating Processes on Industrial Projects. Journal of Construction Engineering and Management, 132(07), 741–9.

Galloway, P D (2006) Comparative Study of University Courses on Critical-Path Method Scheduling. Journal of Construction Engineering and Management, 132(07), 712–22.

Galloway, P D (2006) Survey of the Construction Industry Relative to the Use of CPM Scheduling for Construction Projects. Journal of Construction Engineering and Management, 132(07), 697–711.

Ho, S P (2006) Model for Financial Renegotiation in Public-Private Partnership Projects and Its Policy Implications: Game Theoretic View. Journal of Construction Engineering and Management, 132(07), 678–88.

Lædre, O, Austeng, K, Haugen, T I and Klakegg, O J (2006) Procurement Routes in Public Building and Construction Projects. Journal of Construction Engineering and Management, 132(07), 689–96.

Lowe, D J, Emsley, M W and Harding, A (2006) Predicting Construction Cost Using Multiple Regression Techniques. Journal of Construction Engineering and Management, 132(07), 750–8.

  • Type: Journal Article
  • Keywords: Construction costs; Estimation; Regression analysis; Predictions;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)0733-9364(2006)132:7(750)
  • Abstract:
    This paper describes the development of linear regression models to predict the construction cost of buildings, based on 286 sets of data collected in the United Kingdom. Raw cost is rejected as a suitable dependent variable and models are developed for cost∕ m2 , log of cost, and log of cost∕ m2 . Both forward and backward stepwise analyses were performed, giving a total of six models. Forty-one potential independent variables were identified. Five variables appeared in each of the six models: gross internal floor area (GIFA), function, duration, mechanical installations, and piling, suggesting that they are the key linear cost drivers in the data. The best regression model is the log of cost backward model which gives an R2 of 0.661 and a mean absolute percentage error (MAPE) of 19.3%; these results compare favorably with past research which has shown that traditional methods of cost estimation have values of MAPE typically in the order of 25%.

Lucko, G, Anderson-Cook, C M and Vorster, M C (2006) Statistical Considerations for Predicting Residual Value of Heavy Equipment. Journal of Construction Engineering and Management, 132(07), 723–32.

Mullens, M A and Arif, M (2006) Structural Insulated Panels: Impact on the Residential Construction Process. Journal of Construction Engineering and Management, 132(07), 786–94.

Navon, R and Kolton, O (2006) Model for Automated Monitoring of Fall Hazards in Building Construction. Journal of Construction Engineering and Management, 132(07), 733–40.

Reinschmidt, K and Trejo, D (2006) Economic Value of Building Faster. Journal of Construction Engineering and Management, 132(07), 759–66.

Rezgui, Y and Zarli, A (2006) Paving the Way to the Vision of Digital Construction: A Strategic Roadmap. Journal of Construction Engineering and Management, 132(07), 767–76.