EnergyScore

1. EnergyScore U.S. Department of Energy U.S. Department of Housing and Urban Development April 19, 2010

2. Agenda • Overview • EnergyScore Demo • Platform Backend • Next Steps

3. I. Overview

4. An Information Barrier • Residential Retrofits: Widely accepted that access to information is a barrier to widespread uptake of energy efficiency retrofits. • Real Estate Market: The value of retrofits is usually not reflected in the value of a home due to the lack of reliable, easily communicable information.

5. EnergyScore: A Tool for Homeowners and the Real Estate Market • Calculates and displays a home’s energy performance “score”, based on actual usage and building structure data • Provides customized recommendations for retrofit improvement • Connects the homeowner to contractors and resources to pay for retrofits • Provides feedback loop to stakeholders • Over time, a goal is to develop a reliable score for a home and sub-scores for its individual energy systems that can be reflected in the value of the home

6. EnergyScore Approach • Easy to use functionality and interface for homeowners. The user does NOT need to input any home or energy use info as the tool pre-loads data automatically with the address. • Design of tool uses a methodology that can be replicated in other housing markets and climates. • Launch Regionally • Ensures accuracy and quality control over data. • Enables testing the uptake in the market. • EnergyScore approach complements existing tools and building labeling initiatives (both asset and operational ratings). • Want to collaborate and share our experience and data with others working on residential tools and labeling initiatives (HESPro, etc). • In addition to homeowner uses, tool enables collection of “existing conditions” data on housing markets.

7. Context: EnergyScore Alongside Existing and Developing Tools • [Add graphic representing spectrum of tools]

8. Comparison of Single Family Housing Datasets

9. Querying EPA Yardstick • We queried the Yardstick tool using actual data for 127,359 single family homes in 95 municipalities across Cook County (roughly 10% of total housing stock).

10. II. EnergyScore Demo

11. Site Tree Design

17. Your Energy Score • RS add screenshot

18. Go To Live Demo

19. III. Platform Backend

20. Platform Backend Core Datasets Data Inventory Retrofit Model Core Outputs Obtained via- Building/retrofit analysis- Analysis on pre/post-retrofit usage Obtained via- House-level inventory records- User input on platform- Statistical imputation • Tax Assessor • Data • House structure EUI Score • Savings Table • Potential savings fora given retrofit,segmented by: • House Architecture • Energy Use Quartile • Initial energy characteristic • Utility Data • Electric andnatural gas usage • Home Inventory • House Structure • Systems • Appliances • Household • RetrofitRecommend • Type of retrofit • Range of expected savings • Historical • Retrofit Data • Date, Type, andCost of Retrofit Additional outputs:- Link to additional info, contractors, etc- Written recommendations and savings report- Tool to track implemented retrofits and performance over time- Case Studies- Ask the Expert

21. Filling Out the Data Inventory • Initially, house-level inventory records provide actual data for actual homes • For homes without inventory records, statistical methods using assessor and utility data are used to impute most probable “default values” to fill out the inventory • Users can update data fields via the platform • Over time, more user engagement and collection of “actual data” will make the imputation and the data inventory become more accurate

22. Building the Retrofit Model • Initially, [building analysis] is used to produce a “savings table” yielding a range of potential savings for a given retrofit • Figures are calibrated, and tested against actual usage data and historical retrofit data • Over time, as more data is collected, models can be refined and calibrated to make more accurate estimates and to include additional types of retrofits

23. Bungalow Colonial Frame Cottage Georgian Ranch Newer Luxury Raised Ranch Split level Tudor Townhome Victorian Architectural Style Segmentation a Basis for Analysis Each architectural style has distinct energy use characteristics

24. The Long Term Vision • Accurate, actionable retrofit recommendations with less effort on homeowners’ part • Data collection and model development to enable accurate ratings for homes and sub-ratings by type of energy system that can be incorporated into the value of the home • Part of a spectrum of tools complementing existing tools and building labeling initiatives

25. Current Status • The energy performance of 1.2 MM single family homes in Cook County, IL: • 119 municipalities • Nearly 2 million households • ~ 1,000 square miles • Performance being refined by architectural housing type (Bungalows, Victorians, Tudors, etc). • Begun “beta testing” the accuracy of savings values of 145 Bungalows that have undergone retrofits

26. IV. Next Steps

27. From Product to Business • Data Development • Collect additional retrofit data and individual “audit” data • Model Refinement • Further testing with bungalows database (N=10,000) • Testing the accuracy of the imputation model • Business Planning • Identify and engage business partners • Focus Groups with stakeholder groups to refine design & content: Homeowners; Contractors, Auditors; Real estate professionals (agents, appraisers, mortgage); Potential funders • Rollout

28. “Discussion to Work Together” How much do we want this on a slide? • [Ask: Data Development] • [Ask: “models”? – savings tables, retrofit analysis impact?] • [Ask: partnering] • [Ask: resources] • [Potential Role for DOE/HUD?]

29. Discussion • Could add slide with issues/discussion • E.g. measures/display (slide at end)

30. EnergyScore Contact info:

32. Context: Challenges • Challenges of existing tools in the market • Are still too expensive on a per unit basis • Take too much time and are difficult for homeowners to use without assistance • Have not been able to achieve widespread market acceptance (only hundreds of units) • Accuracy and QA/QC • Data and models are not validated by actual energy consumption

33. Over 80% of Cook County Single Family Homes fall into 5 Assessor Classes

34. Detail if needed. RS to update text if time

35. = median EUI value = 25th percentile EUI value = 75th percentile EUI value Example Bungalow 1428 S. Clinton Berwyn, IL 1,250 sf - built 1927 Conclusion: Energy Scores need context. Local context.

36. Potential: Links to Existing Real Estate Information Resources