Time-of-Sale Energy Labeling of Homes: A Concept

July 01, 2010
July/August 2010
A version of this article appears in the July/August 2010 issue of Home Energy Magazine.
Click here to read more articles about HERS
In 2003, the European Commission passed the Energy Performance of Buildings Directive (EPBD)—perhaps the first mandatory implementation of time-of-sale energy labeling of buildings. To date, the United States has not followed suit. However, recent local and state initiatives have sought to implement similar policies, and now the U.S. government is beginning to explore the possibility of doing so. The technical subject matter is complex, and numerous public policy and market challenges have yet to be resolved in the United States, in the European Union, and internationally.

Philip Fairey is the deputy director of the Florida Solar Energy Center and president of the RESNET board of directors. (Image credit: FSEC)
One fact is salient in the debate: Buildings consume more than 40% of primary energy resources and are responsible for more than 38% of greenhouse gas emissions in the United States. Thus buildings represent the largest single sector of primary energy use and greenhouse gas emissions nationwide. Numerous studies indicate that increased building energy efficiency offers the single largest opportunity for cost-effective reductions in energy use and greenhouse gas emissions. It is precisely for this reason that public policymakers worldwide are actively considering mandatory building energy labeling as a marketplace tool that can substantially influence homebuyers’ decisions about energy efficiency.

While the European Union implemented public policy on building energy efficiency from a regulatory perspective, the United States has chosen to approach it from a voluntary, market-based perspective. Federal examples include EPA’s Energy Star program, DOE’s Builders Challenge program, and federal income tax incentives for highly efficient homes. These federal programs have relied heavily on voluntary energy labeling within the structure of the program itself. This labeling has taken the form of a home energy rating system (HERS) rating that produces a HERS index of relative home energy use. Both EPA and DOE have relied on this HERS index to establish the qualifying efficiency levels of their beyond-code programs. In 2006, the IRS adopted the calculation procedures used in this rating system to determine a builder’s qualification for federal tax credits. The Energy Star program has now reached more than a million new homes through this market-based approach, and there is growing evidence that home buyers and local, state, and national efficiency programs are now seeking even greater efficiencies. In the process, the HERS index—a numerical value on a scale where 100 represents the 2004 national minimum code standard and 0 represents a home that uses no net purchased energy over the course of the year—has become a marketable product, with more than 4,000 certified energy rating practitioners in the field in all 50 states.

In 2008, DOE launched its Builders Challenge program at the International Builders Show in Orlando, Florida. The Builders Challenge program adopted the HERS index as the basis for a home energy efficiency label called the EnergySmart Home Scale (E-Scale). DOE created this label by using market research and focus groups to establish a look and feel that consumers could easily relate to and understand.

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Figure 1. DOE EnergySmart Home Scale, illustrating and explaining the attributes of the label. (Image credit: www1.eere.energy.gov)
Figure 1 illustrates the E-Scale and is reproduced from the DOE Builders Challenge Web site. This home energy label provides a numerical score that shows the relative efficiency of the home compared to other homes, including projected annual energy use and estimated monthly cost to operate the home. For comparison, it also shows the average performance of a home built to comply with the 2004 International Energy Conservation Code (IECC) and the estimated average energy performance of the existing housing stock. The DOE Web site provides a further explanation of the E-Scale score. In this case, the score indicates that the home saves about 36% in energy use compared with the 2004 IECC reference point (100).

Public Policy for the Future

To date, home energy labeling in the United States has been strictly voluntary. It has focused predominantly on new homes and enhanced energy efficiency programs that reach beyond codes. Additional effort is required to expand public policy on energy efficiency and to make building energy labeling mandatory. One concern is cost. Currently, a complete home energy rating costs approximately $400–$500. While this cost pales in comparison with the life cycle energy cost savings that are normally achieved by these beyond-code programs, most stakeholders agree that it would be impractical to require every one of the 110 million homes in the United States to have a verified home energy rating—especially since ratings per se do not necessarily result in energy improvements; they only provide an estimate of relative energy efficiency. Consequently, many stakeholders oppose making complete home energy ratings mandatory, including stakeholders who are in the business of retrofitting homes to be more energy efficient.

If public policy is to embrace mandatory building energy labeling, we must find a less expensive way to determine the relative energy efficiency of the existing building stock. At the same time, considerable technical and consensus-building effort has been invested in the development, standardization, and commercialization of the voluntary home energy rating system in place today. To throw away all that effort seems senseless if it can inform public policy for the future.

Examining the cost structure of home energy ratings reveals that the predominant expense lies in the requirements for field verification and testing. Generation of the numeric score is a minor portion of the effort. The effort required to generate this score is the effort necessary to describe the physical attributes of the building to a software calculation tool. Hypothetically, if there is some innovative way to enter an approximation of these physical building characteristics into the software tool with little or no effort, the software tools can generate a numeric score at a very low cost.

However, this approximation would necessarily be imprecise. Would an approximation be appropriate for some purposes but not necessarily appropriate for others? Perhaps it would. It is possible that we need different levels of accuracy for different purposes. This concept is often referred to as granularity. A roughly accurate approximation is termed coarse granularity; a close approximation is fine granularity. Labeling the general efficiency of a home at the time of sale may only require course granularity to move the marketplace such that more buyers will purchase efficient homes, even if the label is only approximate. At the same time, a much finer granularity may be required to convince the financial community to invest in capital improvements to real properties. The present HERS already depicts this finer level of granularity. The question, then, is how we can best overlay the coarse granularity on the fine granularity, so that the two systems are consistent, or even congruent.

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Figure 2. Normal distribution of existing home energy efficiencies around a mean of 130 on the E-Scale—the average energy performance of existing housing stock. (Image credit: Philip Fairey)
First, assume that DOE had its E-Scale correct. Figure 2 shows a normal distribution of housing stock efficiencies around a score of 130 on the E-Scale—the score that represents the average energy performance of existing housing stock. Note that a very large majority of homes have scores considerably above 100, which represents the 2004 national minimum code requirement. We also see that qualification for the DOE Builders Challenge program (score of 70 or less) appears to be beyond the second standard deviation of the distribution, indicating that less than 1% of existing housing stock would probably fall in this range.

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Figure 3. Moving the mean from 130 to 100 moves a substantial portion of the housing stock from relatively poor efficiency to significantly greater efficiency. (Image credit: Source: Philip Fairey)
Second, set an ambitious goal. Decide where we would like to move the average efficiency of the existing housing stock. For the sake of discussion, assume that we would like to move the mean from 130 to 100, the 2004 national minimum code requirement. It is clear from Figure 3 that this would move a substantial portion of the housing stock from relatively poor efficiency to significantly greater efficiency. The likely result would be substantial energy savings on a national basis. Next, distribute the possible scores into some set of reasonable “bins” that represent a coarse granularity of efficiencies. One option for distributing these scores is to use letter grades similar to school grades or grades used on European building energy certificates. The proposal presented here is to use six divisions with three bins on each side of 100.

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Figure 4. One option for distributing these scores is to use letter grades similar to school grades or grades used on European building energy certificates. (Image credit: Philip Fairey)
Figure 4 illustrates the proposed set of bins. The logic for establishing the bins is reasonably straightforward. Make the grade of C correspond roughly with minimum code compliance, and make the A grade a stretch goal that is difficult to achieve. An evaluation of E‑Scale scores will show that the minimum requirements of the 2009 IECC will fall near 90 on the E-Scale or about 10 points better than the 2004 IECC. Even for new homes, an E-Scale score of 60 is difficult but possible to achieve, so make that the requirement for an A. In Figure 4, the area under the C portion of the normal distribution curve represents approximately 30% of the distribution. The area under the B portion represents about 15% of the distribution, and the area under the A portion represents about 5%. The D, E, and F grades are established in the same way and represent the same percentages of the normal distribution as those on the opposite side of the mean value—that is, 30%, 15%, and 5%, respectively.

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Figure 5. Scores greater than 140 receive no stars. (Image credit: Philip Fairey )
There are alternatives to letter grades. For example, the 5‑star rating system has been in wide use for hotel and restaurant ratings for many years, and it is now even more widely used for ratings posted on the Internet. As a consequence, the 5‑star rating system is familiar to virtually all consumers. To adapt the same set of E-Scale bins to the 5-star rating system would require only that scores greater than 140 receive no stars. Figure 5 shows a distribution of bins using the 5-star system.

Now we have two distinct sets of granularity, with a coarse set of six bins overlaid on a much finer set of numerical scores. The next goal is to keep the cost of energy labels as low as possible. We avoided this issue earlier by simply assuming that there was some innovative way to enter the approximate physical characteristics of a home into the software tool that calculates the E-Scale score.

However, what if we do not need all of the data the software tool requires to get a reasonable approximation of an E-Scale score? What if we were willing to settle for letter grades or stars that were assigned within some coarse range of accuracy—say ±10%? For such a case, what would be the minimum physical information required? Based on the algorithms used to determine E-Scale scores, we have a reasonable idea of what matters most and what matters least. For example, there are two parameters that govern many aspects of home energy use in all software tools. These parameters are the conditioned floor area and the number of bedrooms, data that are readily available for almost any home.

While these two parameters are necessary, they are not sufficient to reach a reasonable level of accuracy. For reasonable accuracy, we also need information about
  • climate location;
  • number of stories;
  • foundation type and insulation level;
  • estimated wall area, type, and insulation level;
  • estimated ceiling area, type, and insulation level;
  • estimated window area, type, distribution, and shading;
  • estimated heating, cooling, and water-heating system efficiency;
  • estimated envelope leakage; and
  • estimated heating-and-cooling distribution system efficiency.

The question, then, is how to derive reasonable estimates for these parameters. One potential answer is to use the year of construction, informed by a database of local or regional construction practices at the time (modified for likely upgrades over time for older homes). A set of standard geometric assumptions can also be derived for determining ceiling, roof, and wall areas from floor area, foundation type, and number of stories. Window area can be roughly estimated from plans or simple site inspection and distributed to walls based on characteristic assumptions about window placement in typical homes. With some set of characteristic assumptions such as these, the only required specific information becomes
  • climate location (zip code);
  • year of construction;
  • conditioned floor area;
  • number of bedrooms;
  • number of stories;
  • foundation type—slab-on-grade, crawlspace, basement
  • wall type—frame or mass;
  • estimated window area—small, medium, or large; and
  • heating, cooling, and hot water system—type and vintage.

All other parameters could be generated automatically by software tools, based on a standardized set of characteristic assumptions informed by a database of local or regional construction characteristics based on vintage. Using such a system, a coarse estimate of the E-Scale score could be made, and based on this estimate, a letter grade, star rating, or other symbolic rating could be assigned with minimal effort. This efficiency indicator would be coarse by design. Nonetheless, it would send the correct message to consumers: It is better to own a 5‑star home than a 1-star home.

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Figure 6. Data from one level of the hierarchy feeds the next level of the hierarchy. (Image credit: DOE)
Once entered into a software tool, these approximations could easily be updated and improved to provide increasing levels of detail about the physical attributes of the home—as inspected, as measured and tested, or as informed by model calibration against a valid set of utility billing data. DOE has proposed such a hierarchy for its list of Integrated Rating Tools (see Figure 6).

The proposed DOE strategy calls for data from one level of the hierarchy to feed the next level of the hierarchy in a way that allows for increasing precision. At the top of the hierarchy, only approximate data are required. At the bottom of the hierarchy, the most comprehensive and detailed data are required. Within the hierarchy, the level of detail varies substantially, but the indication of relative home energy efficiency remains consistent throughout.

Within this strategy, different indicators of home energy efficiency could be used to differentiate different levels of detail. For example, letter grades or stars might be used for the top tiers of the hierarchy, and the numerical E-Scale scores might be used for the bottom tiers. In this way, the indicators would remain consistent with one another but could serve entirely different purposes in the marketplace, where stars might be sufficient for real estate labels but numerical E-Scale scores might be required to qualify for financial incentives.

Summing Up

Both private industry and the federal government have invested heavily in the development and commercialization of uniform, voluntary rating systems now in use across the United States. These existing rating systems could be enhanced to include market functions that serve different purposes, require different levels of granularity, and have different costs. While we need to find innovative means to implement least-cost labeling of homes, there is no reason why we cannot do this within the existing framework. This paper proposes one means of establishing different levels of granularity within the existing framework consistent with DOE findings on residential rating systems.

Home Energy is a benefit of membership for RESNET Certified Raters. Membership in RESNET keeps your organization abreast of the latest developments in residential energy efficiency.

For more information:

European Directive on the Energy Performance of Buildings
The Builders Challenge program

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