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Purpose Building energy benchmarking is required for adopting an energy certification scheme, promoting energy efficiency and reducing energy consumption. It demonstrates the current level of energy consumption, the value of potential energy improvement and the prospects for additional savings. This paper aims to create a new data envelopment analysis (DEA) model that overcomes the limitations of existing models for building energy benchmarking. Design/methodology/approach Data preparation: the findings of the literature search and subject matter experts’ inputs are used to construct the DEA model. Particularly, it is ensured that the included variables would not violate the fundamental assumption of DEA modeling, DEA convexity axiom. New DEA formulation: controllable and non-controllable variables, e.g. weather conditions, are differentiated in the new formulation. A new approach is used to identify outliers to avoid skewing the efficiency scores for the rest of the buildings under consideration. Efficiency analysis: three distinct efficiencies are computed and analyzed in benchmarking building energy: overall, pure technical, and scale efficiency. Findings The proposed DEA approach is successfully applied to a data set provided by a utility management and energy services company that is active in the multifamily housing industry. Building characteristics and energy consumption of 124 multifamily properties in 15 different states in the USA are found in the data set. Buildings in this data set are benchmarked using the new DEA energy benchmarking formulation. Building energy benchmarking is also conducted in a time series manner showing how a particular building performs across the period of 12 months compared with its peers. Originality/value The proposed research contributes to the body of knowledge in building energy benchmarking through developing a new outlier detection method to mitigate the impact of super-efficient and super-inefficient buildings on skewing the efficiency scores of the other buildings; avoiding ratio variables in the DEA formulation to adhere to the convexity assumption that existing DEA methods do not follow; and distinguishing between controllable and non-controllable variables in the DEA formulation. This research contributes to the state of practice through providing a new energy benchmarking tool for facility managers and building owners that strive to relatively rank the energy-efficiency of their properties and identify low-performing properties as investment targets to enhance energy efficiency.

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Mechanical, Electrical, and Plumbing (MEP) systems coordination is a process during the pre-construction phase through which the proposed location and route of each system components are specified. The MEP coordination process has significantly changed due to utilization of Building Information Modeling (BIM). There is a gap in knowledge regarding productivity measurement of MEP coordination team. Moreover, there is a need for research to enhance our understanding about important factors affecting productivity of MEP coordination. The main objectives of this research are: (i) to document approaches for conducting MEP coordination using BIM, (ii) to identify metrics for measuring productivity of MEP coordination; and (iii) to identify factors affecting MEP coordination productivity. A questionnaire survey was conducted to achieve these objectives. The survey show that the most frequently used metric for conducting MEP coordination using BIM is square feet of coordinated area per total coordination hour. Moreover, experience level of MEP coordination team members is the top factor that significantly affects MEP coordination productivity. The findings of this study indicate that construction industry lacks a systematic procedure to record information to track, measure, and compare MEP coordination productivity across different coordination projects.