Throughout the U.S. energy services company (ESCO) industry's history, public and institutional sector customers have provided the greatest opportunities for ESCOs to develop projects. Generally speaking, these facilities are large, possess aging infrastructure, and have limited capital budgets for improvements. The convergence of these factors with strong enabling policy support makes performance contracting an attractive and viable option for these customers. Yet despite these shared characteristics and drivers, there is surprising variety of experience among public/institutional customers and projects. This collaborative study examines the public/institutional markets in detail by comparing the overarching models and project performance in the federal government and the ''MUSH'' markets municipal agencies (state/local government), universities/colleges, K-12 schools, and hospitals that have traditionally played host to much of the ESCO industry's activity. Results are drawn from a database of 1634 completed projects held in partnership by the National Association of Energy Services Companies and Lawrence Berkeley National Laboratory (the NAESCO/LBNL database), including 129 federal Super Energy Savings Performance Contracts (ESPC) provided by the Federal Energy Management Program (FEMP) (Strajnic and Nealon 2003). Project data results are supplemented by interviews with ESCOs.

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A statistical model was developed to relate residential building shell leakage to building characteristics such as building height, floor area, floor leakage, duct leakage, and year built or the age of the house. Statistical regression techniques were used to determine which of the potential building characteristics best described the data. Seven preliminary regressions were performed to investigate the influence of each variable. The results of the eighth and last multivariable linear regression form the predictive model. The major factors that influence the tightness of a residential building are participation in an energy efficiency program (40% tighter than ordinary homes), having low-income occupants (145% leakier than ordinary) and the age of a house (1% increase in Normalized Leakage per year). This predictive model may be applied to data within the range of the data that was used to develop the model.