The energy efficiency retrofit industry for public buildings is relatively well developed worldwide compared to the private building retrofit industry. It is a large market in the United States, with annual revenues for energy service companies in the vicinity of U.S. $4 billion. However, long-term energy efficiency and carbon mitigation targets worldwide will rely heavily on improving the efficiency of the entire building stock. Public buildings represent only about a quarter of the total commercial building stock in the United States, and retrofits have barely begun to touch the private building stock.

One reason for the sluggishness of efficiency in the private building stock is the lack of post-retrofit data on building performance. Although there are many successful examples of retrofits in the private sector, the industry as a whole needs a robust set of data on post-retrofit performance and payback before they will be convinced that the opportunity to reduce operating costs is real, the risks are low, and the ROI is high enough to justify investments in efficiency.

Today, efficiency retrofits are typically based on a predictive model in which building engineers model energy use based on the building’s equipment, envelope, climate, and usage patterns. However, the actual performance of the building can often diverge significantly from the predictive model, and this discrepancy makes private building owners reluctant to invest in energy efficiency. It also makes financial institutions unwilling to provide the necessary financing to support efficiency projects, as the perceived risks (that buildings will not meet their predicted efficiency levels) are too high.

There is also the issue of payback period. Whereas the public sector is willing to accept payback periods of 10-15 years, the private sector rarely accepts paybacks of over 4 years. Additional post-retrofit performance data would ease concerns about the paybacks of certain measures and reduce the perceived risks. Today there is a tendency toward “cream skimming,” or selecting only measures with the fastest paybacks (such as lighting retrofits, energy control management systems, and retrocommissioning). Reliable data on the ROI for all efficiency measures would give investors the confidence they need to invest in efficiency.

The industry is just now starting to take the first few steps toward addressing this major barrier. For example, the Deutsche Bank Americas Foundation, a philanthropic arm of Deutsche Bank, is starting to compile a set of data on several hundred buildings in New York City. Gary Hattem, the president of the foundation, argues that “if underwriters can determine a predictable savings from retrofits, then they can create a financial instrument backed by these savings to sell on the open market.” In other words, data on post-retrofit building performance would reduce the perceived risks and free up capital for efficiency.

If the Deutsche Bank Americas Foundation and other groups can compile post-retrofit performance data in commercial buildings, it would do a lot to push the needle and lower the bar for private building owners to begin investing in energy efficiency.