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Ökmen, & and Öztaş, A (2008) Construction Project Network Evaluation with Correlated Schedule Risk Analysis Model. Journal of Construction Engineering and Management, 134(01), 49–63.
Cui, Q, Johnson, P, Quick, A and Hastak, M (2008) Valuing the Warranty Ceiling Clause on New Mexico Highway 44 Using a Binomial Lattice Model. Journal of Construction Engineering and Management, 134(01), 10–17.
Hassan, M M and Gruber, S (2008) Simulation of Concrete Paving Operations on Interstate-74. Journal of Construction Engineering and Management, 134(01), 2–9.
Khalafallah, A and El-Rayes, K (2008) Minimizing Construction-Related Security Risks during Airport Expansion Projects. Journal of Construction Engineering and Management, 134(01), 40–48.
Liou, F and Huang, C (2008) Automated Approach to Negotiations of BOT Contracts with the Consideration of Project Risk. Journal of Construction Engineering and Management, 134(01), 18–24.
Lu, M and Lam, H (2008) Critical Path Scheduling under Resource Calendar Constraints. Journal of Construction Engineering and Management, 134(01), 25–31.
Seo, J W and Choi, H H (2008) Risk-Based Safety Impact Assessment Methodology for Underground Construction Projects in Korea. Journal of Construction Engineering and Management, 134(01), 72–81.
Stoy, C, Pollalis, S and Schalcher, H (2008) Drivers for Cost Estimating in Early Design: Case Study of Residential Construction. Journal of Construction Engineering and Management, 134(01), 32–39.
- Type: Journal Article
- Keywords: Cost control; Germany; Buildings, residential; Construction management;
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)0733-9364(2008)134:1(32)
- Abstract:
This paper proposes the use of a series of independent variables for an early estimation of the building construction cost of residential buildings. Based on 70 German residential properties, these variables serve as the cost drivers of a project, and the regression model, tested against the 70 properties, has a mean absolute percentage error of 9.6%. When applied to predict the cost of five more properties that were excluded from the 70 in the regression model, the percentage error ranges between–12 and 13%. The identified cost drivers are: compactness of the building, number of elevators, size of the project, expected duration of construction, proportion of openings in external walls, and region.
Xue, X, Shen, Q, Wang, Y and Lu, J (2008) Measuring the Productivity of the Construction Industry in China by Using DEA-Based Malmquist Productivity Indices. Journal of Construction Engineering and Management, 134(01), 64–71.