2019 Residential New Construction Baseline/Compliance Study (MA19X02-B- RNCBL)

Final April 1, 2020

SUBMITTED TO: The Massachusetts Electric and Gas Program Administrators SUBMITTED BY: NMR Group, Inc.

50 Howard St, Somerville, MA, 02141

Figure 1 Summary of Equipment in Non-Program Homes MA19X02-B-RNCBL - DRAFT

Summary of Shell Measures in Non-Program Homes MA19X02-B-RNCBL - DRAFT

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Table of Contents ACRONYMS ......

SECTION 1 EXECUTIVE SUMMARY ...... 1

1.1 STUDY PURPOSE ...... 1

1.2 RESEARCH QUESTIONS ...... 2

1.3 KEY EFFICIENCY AND COMPLIANCE FINDINGS ...... 2

1.4 KEY BUILDING PRACTICES FINDINGS ...... 5

1.5 RECOMMENDATION ...... 5

1.6 CONSIDERATIONS ...... 6

1.7 RESEARCH METHODOLOGY ...... 7

1.8 KEY LIMITATIONS & SOURCES OF UNCERTAINTY ...... 8

SECTION 2 INTRODUCTION ...... 9

2.1 PURPOSE AND GOALS ...... 9

2.2 BACKGROUND ...... 10

2.3 RESEARCH QUESTIONS ...... 11

2.4 REPORT ORGANIZATION ...... 11

SECTION 3 METHODOLOGY ...... 12

3.1 SAMPLING PLAN ...... 12

3.2 RECRUITING ...... 13

3.3 SAMPLE COMPOSITION ...... 14

3.4 ON-SITE INSPECTION METHODOLOGY ...... 15 3.4.1 On-site Data Collection Inputs ...... 15 3.4.2 On-site Data Collection Procedures ...... 16

3.5 WEIGHTING SCHEME...... 17

3.6 SIGNIFICANCE TESTING ...... 18

SECTION 4 COMPARISONS ...... 19

4.1 PREVIOUS BASELINES ...... 20 4.1.1 Envelope and Duct Tightness in Non-program Homes ...... 21 4.1.2 Shell Measures in Non-program Homes ...... 21 4.1.3 Mechanical Equipment and Water Heating in Non-program Homes ...... 21

4.2 NON-PROGRAM VERSUS PROGRAM HOMES ...... 24

4.3 NEW STRETCH CODE VERSUS PREVIOUS STRETCH CODE ...... 26

4.4 BASE CODE VERSUS STRETCH CODE...... 28

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4.5 CUSTOM VERSUS SPEC-BUILT HOMES ...... 29

SECTION 5 CODE COMPLIANCE ...... 30

5.1 CODE COMPLIANCE METHODOLOGY ...... 30 5.1.1 Estimating Non-Program and Program Home Compliance ...... 31 5.1.2 Estimating Program Penetration Rates ...... 31 5.1.3 Calculating Average Compliance ...... 32

5.2 COMPLIANCE ESTIMATED WITH MA-REC ...... 32 5.2.1 Detailed REM/Rate and Ekotrope MA-REC Results ...... 34 5.2.2 Measure-Level Compliance ...... 35

5.3 COMPLIANCE WITH CODE REQUIREMENTS NOT ESTIMATED WITH MA-REC ...... 36

SECTION 6 BUILDING SHELL ...... 38

6.1 AIR INFILTRATION AND VENTILATION ...... 39

6.2 ABOVE GRADE WALLS ...... 39

6.3 FLAT CEILINGS ...... 40

6.4 VAULTED CEILINGS ...... 40

6.5 FLOORS OVER UNCONDITIONED BASEMENTS ...... 41

6.6 FOUNDATION WALLS ...... 41

6.7 FENESTRATION ...... 42

SECTION 7 MECHANICAL EQUIPMENT ...... 44

7.1 HEATING EQUIPMENT ...... 44

7.2 COOLING ...... 47

7.3 THERMOSTATS ...... 48

7.4 DOMESTIC HOT WATER ...... 48

7.5 DUCT SYSTEMS ...... 50

7.6 RENEWABLES AND ELECTRIC VEHICLES ...... 51

SECTION 8 LIGHTING AND APPLIANCES ...... 53

8.1 LIGHTING ...... 53

8.2 APPLIANCES ...... 55

SECTION 9 FINDINGS AND CONSIDERATIONS ...... 56

9.1 KEY EFFICIENCY AND COMPLIANCE FINDINGS...... 56

9.2 KEY BUILDING PRACTICES FINDINGS ...... 59

9.3 CONSIDERATIONS ...... 59

APPENDIX A ENERGY MODELING AND CONSUMPTION ...... 62

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A.1 HERS INDEX SCORES FROM REM/RATE VERSUS EKOTROPE ...... 62

A.2 ENERGY USE INTENSITY ...... 64

APPENDIX B DETAILED DATA ...... 65

B.1 GENERAL CHARACTERISTICS...... 65

B.2 AIR INFILTRATION AND VENTILATION ...... 65 B.2.1 Air Infiltration ...... 65 B.2.2 Ventilation ...... 66

B.3 BUILDING SHELL CHARACTERISTICS ...... 66 B.3.1 Walls ...... 66 B.3.2 Ceilings ...... 68 B.3.3 Floors ...... 70 B.3.4 Foundation Walls...... 71 B.3.5 Slabs ...... 72 B.3.6 Windows ...... 73

B.4 MECHANICAL EQUIPMENT ...... 74 B.4.1 Heating ...... 74 B.4.2 Cooling ...... 79 B.4.3 Water Heating ...... 82 B.4.4 Thermostats ...... 88

B.5 DUCTS ...... 89 B.5.1 Duct Leakage ...... 89 B.5.2 Duct Insulation ...... 90

B.6 RENEWABLES ...... 90

B.7 ELECTRICAL METERS ...... 91

B.8 LIGHTING ...... 91

B.9 APPLIANCES ...... 92 B.9.1 Refrigerators ...... 92 B.9.2 Freezers ...... 95 B.9.3 Dishwashers ...... 96 B.9.4 Ovens and Ranges ...... 97 B.9.5 Clothes Washers ...... 97 B.9.6 Clothes Dryers ...... 98 B.9.7 Dehumidifiers ...... 99

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APPENDIX C MA-REC DETAILS ...... 101

C.1 METHODOLOGY ...... 101

C.2 REM/RATE VERSUS EKOTROPE ...... 105

C.3 ADDITIONAL REQUIREMENTS ...... 106 C.3.1 Sealing of Sill and Top Plate ...... 108 C.3.2 Insulation baffles ...... 108 C.3.3 Ventilation Dampers ...... 108 C.3.4 No Cavity Ducts ...... 108 C.3.5 Sealed Ducts ...... 109 C.3.6 HVAC Pipe Insulation & Protection ...... 110 C.3.7 DHW Insulation ...... 111 C.3.8 Foundation Wall Insulation ...... 112

C.4 RADON ...... 113

APPENDIX D FINAL UPDATED UDRH INPUTS ...... 115

D.1 BACKGROUND ...... 115

D.2 MEASURE LEVEL FINDINGS ...... 115 D.2.1 Above Grade Walls ...... 115 D.2.2 Frame Floors ...... 116 D.2.3 Ceilings ...... 116 D.2.4 Foundation Walls...... 117 D.2.5 Slabs ...... 117 D.2.6 Windows ...... 119 D.2.7 Skylights ...... 119 D.2.8 Air Infiltration ...... 120 D.2.9 Duct Leakage to the Outside ...... 120 D.2.10 Doors ...... 121 D.2.11 Duct Insulation ...... 121 D.2.12 Heating Efficiencies ...... 122 D.2.13 Cooling ...... 123 D.2.14 Water Heater Energy Factors ...... 123 D.2.15 Other Inputs ...... 124

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Acronyms

AC Air Conditioner ACH50 Air Changes per Hour with a 50-pascal pressure gradient ACS American Community Survey AFUE Annual Fuel Utilization Efficiency ASHP Air-Source Heat Pump BTU British Thermal Unit BTUh British Thermal Units per Hour CAC Central Air Conditioner CCSI Code Compliance Support Initiative CFA Conditioned Floor Area CFL Compact Fluorescent Lamp CFM25 Cubic Feet per Minute with a 25-pascal pressure gradient CLG Cooling COP Coefficient of Performance DHW Domestic Hot Water ECM Electronically Commutated Motor EER Energy Efficiency Ratio EF Energy Factor EkotropeTM A cloud based residential energy modeling software EPS Expanded Polystyrene ERI Energy Rating Index, optional compliance path under IECC ERV Energy Recovery Ventilation EUI Energy Use Intensity EV Electronic Vehicle FGB Fiberglass Batt GSHP Ground Source Heat Pump HERS Home Energy Rating System HPWH Heat Pump Water Heater HRV Heat Recovery Ventilation HSPF Heating Season Performance Factor HTG Heating HVAC Heating Ventilation and Air Conditioning IECC International Energy Conservation Code kWh Kilowatt Hour LAP Lights and Appliances LED Light-Emitting Diode LTO Leakage to Outside MSHP Mini or Multi-Split Heat Pump (commonly referred to as a ductless mini-split) MWh Megawatt Hour NMR NMR Group Inc.

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PAs Program Administrators of Massachusetts Energy Efficiency Programs PTHP Packaged Terminal Heat Pump PV Photovoltaic REM/rateTM Residential Energy Modeling and Rating software by NORESCO RESNET Residential Energy Services Network RNC Residential New Construction R-value A measure of material’s resistance to the flow of heat SEER Seasonal Energy Efficiency Ratio SHGC Solar Heat Gain Coefficient TDL Total Duct Leakage TE Thermal Efficiency U-Factor Measure of the rate of heat transfer of a window or other glazing UC Unconditioned UDRH User-Defined Reference Home UEF Uniform Energy Factor UFFI Urea-formaldehyde XPS Extruded Polystyrene

Section 1 Executive Summary

1.1 Study Purpose This study is part of an evaluation of the Residential New Construction (RNC) program in Massachusetts. The RNC program provides incentives to builders for constructing homes that are more efficient than a baseline home. The program determines a home’s efficiency over a baseline by comparing an energy model of the home to an energy model of a baseline home, called a User Defined Reference Home (UDRH). The characteristics of the UDRH are based on periodic studies of the new construction market in Massachusetts. The last comprehensive update of the UDRH occurred in 2016 and was based on results from a 2015 study. Since then, Massachusetts has adopted both a new base code and a new stretch code.1,2 Additionally, the percent of municipalities that have adopted the stretch code increased from about 42% in 2015 to about 53% by January 1, 2017. This study replicates methods from the 2015 study to update the UDRH. While the new base code represents little change over the previous base code, the new stretch code requires an energy performance that is at least 15% to 21% more efficient than the previous stretch code, depending on the size of the home. This is the first study to examine measure-level efficiencies of homes built under the new codes. This study is also the first to assess code compliance of program and non-program homes under the new codes. The study consisted of 100 on-site energy inspections of single- family, non-program (i.e., not program participant) homes built between 2017 and 2019 in Massachusetts. The inspections occurred June through October of 2019 and included full Home Energy Rating System (HERS) ratings. The study sample includes 51 homes built under the new base code and 49 homes built under the new stretch code.

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1 By default, municipalities are required to enforce the base code. However, in 2009, Massachusetts introduced a stretch code for municipalities to adopt at will. The intention of the stretch code is to provide a code that leads to greater energy efficiency than the base code. 2 Note that although Massachusetts adopted an even newer base code in February of 2020 to reflect the 2018 International Energy Conservation Code, this report refers to the 2015 IECC base code as the “new base code” since that was the new base code at the time of the study.

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1.2 Research Questions The study sought to answer the following research questions:

• Statewide, what are the baseline efficiencies for measures included in the UDRH?

• Which baseline measures have improved since the last study was conducted?

• Statewide, what are the average code compliance levels of homes built under the updated stretch code and base code (2015 IECC) requirements?

• How do current compliance rates compare to those of previous evaluations?

• Which measures could provide the largest savings opportunities for the low-rise RNC program and CSCS moving forward?

• How does measure level efficiency of non-program homes compare to program homes? 1.3 Key Efficiency and Compliance Findings New non-program homes are about 14% more efficient than homes built in 2015. To make this comparison, 100 REM/rate3 energy models from this study were compared to the 146 REM/rate models from the 2015 study. The weighted average HERS index score of homes from this study (59.3) was more efficient (i.e., lower) than the average from the 2015 study (69.0). 69 59 The increase in efficiency is driven by increased envelope tightness, increased duct tightness, and an increased share of 2015 2019 efficient heat pumps used for heating. Program homes are about 7% more efficient than non- 2019 HERS Index Score program homes. To make this comparison, a population of 5,073 program home Ekotrope4 models was compared to 100 Ekotrope models of the non-program homes from this study. The average HERS index score for program homes (51.9) was more 52 56 efficient (i.e., lower) than the average weighted HERS index score of non-program homes from this study (55.7).5 The higher Program Non- efficiency of program homes is driven by increased envelope program tightness, increased duct tightness, and a higher share of efficient lighting among program homes.

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3 REM/rate stands for Residential Energy Modeling and Rating and is software package by NORESCO. 4 Ekotrope is a cloud based residential energy modeling software. 5 Note that when comparing non-program homes to program homes, this study uses Ekotrope models since the program adopted Ekotrope software in 2017. However, when making comparisons to previous studies, this study used REM/Rate generated HERS scores because that was the modeling software used previously.

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The difference in efficiency between program and non-program new homes is decreasing. In 2011, the difference in HERS index scores between non-program and program homes was 16 points. In 2019, that difference was only 4 points. Figure 1 shows the decreasing difference between program and non-program homes over time. This is the result of non-program homes becoming more efficient, while program homes have been relatively static over the last few years. The average HERS index score of program homes in 2015 was 55.6

Figure 1:Difference in HERS Index Scores (Non-Program Minus Program)

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6 The 2015 value is based on REM/Rate models, which vary slightly from Ekotrope models.

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The program comprised 70% of the single-family new construction market in 2019. The program penetration of base code towns increased from 24% in 2015 to 50% in 2019. The Program Penetration program penetration of stretch code towns increased from 70% to 87% over that same time.7 This could be the result of increased code stringencies making program participation less of a lift 41% 70% beyond code. Additionally, the Energy Rating Index (ERI) paths of both base code and stretch code could be leading to the use of 2015 2019 more HERS raters. These raters may then notify clients of opportunities from the program. Also note that, as mentioned above, more municipalities have adopted stretch code since 2015 and stretch code towns tend to have a higher program penetration than base code towns.8

Non-program homes built under the new stretch code are about 11% more efficient than those built under the old stretch code. This increase is slightly less than the 15% to 21% increase in code stringency as measured by the change in HERS index score requirements between stretch codes. The average 64 57 HERS index score of non-program stretch code homes from the 2015 study was 64, while the average HERS index score from 2015 2019 homes in this study was 57.

Including program homes, the average overall code compliance for base code towns has increased from 86% to 94% since 2015. Despite the increase in stretch code stringency, the average for stretch code towns stayed relatively constant, at 96% in 2015 and 98% in 2019. This reflects the limited changes in base code since 2015 and the larger changes in stretch code. The increase in code compliance is largely due to the increase in program penetration. Program homes have higher compliance rates than do non-program homes across all measures. Overall compliance also increased due to increased stretch code adoption since 2015. Stretch code towns have slightly higher average compliance rates than do base code towns. Non- program has remained constant since 2015 at 88% despite an increase in code stringency.9

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7 Based on building permit data, there were an estimated 6,922 new homes built in 2015 and 7,224 new homes built in 2019. 8 The percent of municipalities that had adopted stretch code was 42% by the end of 2015, 46% by the end of 2016, 58% by the end of 2017 and 68% by the end of 2018. 9 The 2015 compliance percentage is based on the combination of 2012 IECC and 2009 Stretch code homes from the 2015 residential new construction and code compliance study.

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1.4 Key Building Practices Findings Efficient builders are changing their practices to address building energy consumption more holistically. One of the clearest examples of this phenomenon is the increased frequency of insulated foundation walls and attic rafters. By choosing to insulate foundation walls instead of basement ceilings and choosing to insulate attic rafters instead of attic floors, builders bring HVAC equipment into conditioned space and thus reduce duct leakage to the outside. The share of homes with insulated basements has increased from 29% to 40% since 2015. The share of homes with sealed attics has increased from 10% to 39% over that same time. Due to these changing practices, the share of homes with all their ducts in unconditioned space dropped from 49% in 2015 to 15% in 2019. Homes increasingly use heat pumps as a primary source of heating, cooling, and water heating. The share of homes with heat pumps as their primary equipment type increased from 2% in 2015 to 14% in 2019 for heating, from 5% to 16% for cooling, and from 11% to 17% for water heating. Heat pumps are more efficient than conventional equipment on a site-level (as opposed to source-level) basis and thus result in lower HERS index scores. Therefore, this increase in heat pump penetration points to greater efficiencies for heating, cooling, and water heating. The use of spray foam as insulation is increasing. The use of spray foam as insulation has increased across all shell measures since 2015. Both open-cell spray foam and closed-cell spray foam comprised a larger share of primary insulation in 2019 than they did in 2015. The changes are especially pronounced in vaulted ceilings (60% in 2019 compared to 14% in 2015), walls (31% compared to 8%), and foundation walls (40% compared to 30%). These spray foams typically replace fiberglass batts, which were the dominant insulation type in all shell measures in 2015. Despite the change in insulation material, the R-value of walls has barely changed since 2015. This could be due to consistent code requirements and limited space in wall cavities. The increased use of spray foams has likely led to tighter building envelopes and an overall reduction in air infiltration. 1.5 Recommendation The RNC program should adopt the UDRH inputs as the baseline values for modeling energy savings for low-rise RNC projects in Massachusetts. The UDRH inputs can be found in Appendix D. This study finds that market transformation effects created by past RNC efforts have created a situation where the savings gap between program homes and non-program

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homes has shrunk considerably in the three years since the previous baseline evaluation. Specifically, the updated UDRH (based on an average HERS score of 55.7) represents a significant improvement in home energy intensity compared with the current UDRH (based on an average HERS score of 69.3). Based on a sample of 2018-2019 program projects, the average home participating in the program (average HERS score of 51.9) was 25% less energy intensive than the current UDRH home, but only 7% less energy intensive than the updated UDRH. This dramatic shift warrants comprehensive consideration by program implementors of how the new baseline will affect participation, claimable savings, and program cost-effectiveness. It will be critical for program implementors to consider whether any changes to program design are necessary to enable the program’s continued pursuit of all cost-effective energy efficiency. 1.6 Considerations The PAs should work with evaluators and other key stakeholders to consider how to measure NTG effectively for the RNC initiative moving forward. The findings and limitations of this study raise questions for any future net-to-gross (NTG) study. This study is study focused solely on new single-family attached and detached homes; however, the effects of the program on single-family homes may differ from those on low-rise multifamily buildings, those participating in the new Passive House pathways, and those participating in the Renovations and Additions pathway. Any future NTG study(s) should consider the differences between these offerings and market segments, as they may be appreciable. Additionally, given this study’s finding of a high penetration of program homes in stretch code towns, a NTG study should examine the possibility that the program has an effect on the adoption rate of the stretch code by municipalities. For the low-rise RNC program, the PAs should consider implementing program changes and then assessing prospective NTG taking those program changes into account. The PAs should be sure to account for the influence of the CSCS initiative in any NTG studies. As this report shows, single-family new construction in Massachusetts has continually grown more efficient. On the surface, high program penetration rates and the narrowing margin between program and non-program homes suggest PAs should expect diminishing impact from the RNC program going forward without changes to the program requirements. That said, the PAs have been executing multiple programs to increase the efficiency of the new construction market including code trainings through the Codes and Standards Compliance and Support (CSCS) initiative for various market actors since 2014. These efforts could be leading to non- participant (non-program) spillover which is reducing the gap between non-program and program homes. In the previous RNC NTG study (TXC48), these efforts were found to have created market effects, primarily in the form of non-participant spillover. The PAs should consider a similar NTG evaluation approach for the single-family new construction market moving forward to ensure that the impacts of all the PAs; efforts in the new construction market are documented and quantified. The PAs should consider what impact increasing program penetration rates will have on program cost-effectiveness. The increase in penetration for the program has likely led to an increase in gross savings due to the sheer volume of projects participating in the program. That said, it is likely that the per-home savings associated with participant projects have decreased as the efficiency of program homes has been relatively static since the previous baseline study. This could reduce the cost-effectiveness of the program. Conversely, since the baseline has gotten

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more efficient, the incremental costs for the program have likely decreased. Therefore, the program could remain cost-effective with less savings per home. The high penetration rates seen in this study could lead to lower net savings for single-family homes as it is possible that free-ridership rates have increased and the potential for program spillover is diminished due to the decreasing non-program market size. If future NTG values decrease it will be important for the PAs to understand how increasing project counts balance with a decreased NTG ratio in the PAs cost-effectiveness screening tool. The PAs should consider conducting a low-rise multifamily baseline evaluation. The PAs have continually monitored progress of the single-family new construction market over time, whereas the multifamily new construction market has rarely been researched. Therefore, the current assumption is that the low-rise multifamily market has similar measure-level efficiencies to the single-family market. It is possible that the multifamily market is substantially different from the single-family market in terms of non-program efficiencies, program penetration rates, and NTG issues such as free-ridership and spillover. Focusing a future study on the low-rise multifamily market would allow evaluators to assess these differences and consider the single- family market and the low-rise multifamily market independently for the first time.

1.7 Research Methodology As mentioned above, the study included 100 energy audits of new, non-program, single-family homes across Massachusetts permitted after January 1, 2017. NMR developed a sample of homes using new electric service request data from Eversource, National Grid, and Unitil for 2017, 2018, and the beginning of 2019. The service request data was cleaned to identify single-family occupied homes. NMR then recruited homeowners through mailings and phone calls. Homeowners were recruited instead of builders to avoid biasing the sample towards efficient builders who could potentially be more willing to participate in an energy-efficiency study. While recruiting homeowners, NMR aimed to audit homes in each county proportional to each county’s non-program construction activity. Non-program construction activity was estimated using permit data from the US census and program penetration rates estimated in 2017. Additionally, NMR targeted 50 stretch code homes and 50 base code homes to facilitate comparisons, as well as 38 custom homes and 62 spec homes to match estimates of construction type proportions from the 2015 study. NMR created full energy models in both REM/Rate and Ekotrope for each home. REM/rate models were created to compare results to previous studies since that was the modeling software used by HERS raters and the program previously. Ekotrope models were made to compare the sample to program homes since the program adopted Ekotrope as its modeling software in 2017.

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1.8 Key Limitations & Sources of Uncertainty One limitation of the study results from the recruitment of homeowners. Since recruitment was dependent on homeowner responses, it is possible that the study is biased towards homeowners who are more interested in energy efficiency and thus more likely to respond to an energy- efficiency study. This would likely have more of an effect in custom homes where homeowners are more involved in construction decisions than in spec homes. Still, we believe that recruiting homeowners has less potential to bias results than recruiting builders. Another limitation of the study results from inspecting already completed homes. Once homes are finished and occupied, certain aspects of the home are difficult to visually inspect on-site. These include insulation in finished walls, insulation under basement slabs, or window U-factors. On-site auditors, who were all certified HERS raters, had to make assumptions on non-visible measures based on other aspects of the home when documentation was not present.

Section 2 Introduction

2.1 PURPOSE AND GOALS This study is part of an evaluation of the Residential New Construction (RNC) program in Massachusetts. The RNC program provides incentives to builders for constructing homes that are more efficient than a baseline home. The program determines a home’s efficiency over a baseline by comparing an energy model of the home to an energy model of a baseline home called a User Defined Reference Home (UDRH). The characteristics of the UDRH are based on periodic studies of the new construction market in Massachusetts. The last comprehensive update of the UDRH occurred in 2016 and was based on results from a 2015 study. Since then, Massachusetts has adopted both a new base code and a new stretch code.10 This baseline study of single-family new construction building practices and code compliance assessed 100 single-family homes built between 2017 and 2019 in Massachusetts. The goals of the study were as follows: • Develop updated specifications for the low-rise RNC program UDRH. • Calculate code compliance rates for homes built under the stretch code and the 2015 International Energy Conservation Code (IECC) with Massachusetts amendments using the Massachusetts Residential Evaluation Contractor (MA-REC) compliance methodology.

• Determine the impacts of new code requirements on new construction building practices in both stretch code and base code homes.

• Develop key inputs and background information to inform future net-to-gross, passive design, and incremental cost evaluations for the RNC program and Code Compliance Support Initiative (CCSI).

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10 By default, municipalities are required to enforce the base code. However, in 2009, Massachusetts introduced a stretch code for municipalities to adopt at will. The intention of the stretch code is to provide a code that leads to greater energy efficiency than the base code.

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The inspections occurred June through October of 2019 and included full energy modeling. The study sample included 51 homes built under the new base code and 49 homes built under the new stretch code.

2.2 BACKGROUND On January 1, 2017, the Commonwealth of Massachusetts adopted both a new base code and a new stretch code. Both are based on the 2015 IECC with amendments. While the new base code differs from the previous base code (2012 IECC) in only minor ways, the new stretch code is substantially more stringent than the previous version of the stretch code, which was based on the 2009 IECC. Stretch code now requires a Home Energy Rating System (HERS) index score of 55 or lower for new construction projects that do not contain renewable energy systems.11 This represents a 15-21% increase in energy efficiency from the previous stretch code, which required a HERS index score of no more than 65 or 70 depending on the size of the home.12 This also represents a 14% increase in efficiency over homes built under the previous stretch code as measured by HERS index scores in the 2015-16 Single-Family Code Compliance/ Baseline Study (i.e., 2015 study).13 While the stringency of the stretch code increased in 2017, this study is the first to examine the impact of the increased stringency on measure-level efficiencies in the residential new construction market. Figure 2 puts this study in context with previous evaluations in Massachusetts. The top portion of the chart shows when each new building code was enacted. The bottom portion of the chart shows the range of permitting dates for homes in each residential baseline evaluation since 2010. This study is the first to examine Massachusetts homes built since 2017.

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11 https://www.mass.gov/files/documents/2018/05/15/Chapter%20115%20amend%202016-08-12-chapters-13-51.pdf 12 See section 401.2 in https://www.mass.gov/files/documents/2018/05/15/Chapter%20115%20amend%202012%20adopt-rescind-and-table- 505-5-2-light-pwr-density.pdf 13 http://ma-eeac.org/wordpress/wp-content/uploads/Single-Family-Code-Compliance-Baseline-Study-Volume-2.pdf

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Figure 2: Residential Evaluation and Code Timeline

2.3 RESEARCH QUESTIONS This study sought to answer the following research questions:

• Statewide, what are the baseline efficiencies for measures included in the UDRH? • Which baseline measures have improved since the last study was conducted? • Statewide, what are the average code compliance levels of homes built under the updated stretch code and base code (2015 IECC) requirements? • How do current compliance rates compare to those of previous evaluations? • Which measures could provide the largest savings opportunities for the low-rise RNC program and CCSI moving forward? • How does measure level efficiency of non-program homes compare to program homes?

2.4 REPORT ORGANIZATION The body of this report is organized as follows:

• Section 3 Methodology: Describes the methodology for sampling, recruiting, conducting, and analyzing on-site inspections. • Section 4 Comparisons: Compares key measures over time and across sample groups. • Section 5 Code Compliance: Describes the results of the code compliance analysis. • Section 6 Building Shell : Describes the results of the building shell analysis. • Section 7 Mechanical Equipment: Describes the results of the mechanical equipment analysis. • Section 8 Lighting and Appliances: Describes the results of the lighting and appliances analysis. • Section 9 Findings and Considerations: Details findings and considerations for future program savings.

Section 3 Methodology

The 100 homes included in this study did not participate in the RNC program (i.e., they were non-program homes). Each home received a full energy inspection that included detailed data collection. HERS index scores were calculated for each home using both REM/rate and Ekotrope. Code compliance was calculated for each home using the methods described in Section 5. 3.1 Sampling Plan Table 1 presents the sampling plan for the energy inspections. The sampling plan was based on estimated non-program building activity by county. NMR used three-year average new single-family permit counts from the U.S. Census Building Permit Survey and RNC program penetration rates to estimate the number of non- program homes by county. 14 The program penetration rates were from a 2018 study which was the most recent program penetration estimate at the time of planning. 15 The plan targeted 50 homes each in stretch and base code towns, for a total of 100 homes. A sample of 50 homes in each code environment allows for comparisons between homes built under the current base and stretch codes, as well as between the current stretch code and previous stretch code. Table 1: Sample Targets by County Population Sample County Three Year Average Share Homes Share Non-program Permits Barnstable 327 9.5% 9 9.0% Berkshire 45 1.3% 1 1.0% Bristol 467 13.6% 14 14.0% Essex 355 10.3% 10 10.0% Franklin 19 0.6% 1 1.0% Hampden 120 3.5% 4 4.0% Hampshire 72 2.1% 2 2.0% Middlesex 629 18.4% 18 18.0% Norfolk 358 10.5% 10 10.0% Plymouth 506 14.8% 15 15.0% Suffolk 18 0.5% 1 1.0% Worcester 511 14.9% 15 15.0% ______

14 https://www.census.gov/construction/bps/ 15 See the forecasted 2019 rates in Table 22 at http://ma-eeac.org/wordpress/wp- content/uploads/TXC_48_RNCAttribution_24AUG2018_Final.pdf

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NMR also targeted 38 custom-built homes and 62 spec-built homes. These sample sizes reflected the percentages of custom and spec homes estimated statewide for the 2015 study. The study sample frame comprised homes permitted on or after January 1, 2017. To identify these homes the PAs provided data on new service electric requests for 2017, 2018, and the beginning 2019. The NMR team cleaned the data to ensure that all homes eligible for selection were single-family, occupied, and not program participants. After cleaning, the sample consisted of only 1,532 unique addresses. NMR supplemented these data with online searches to find 218 homes built and sold in 2017 or later and added that to the sample frame. The sample frame included both detached and attached homes. (Attached homes had to have their own utilities and be separated from the neighboring homes with a single wall extending from the bottom of the basement to the top of the attic rafters.) Based on previous studies, NMR assumed close to 2,000 homes would need to be contacted to complete 100 inspections. Therefore, NMR attempted to recruit every address from the cleaned sample using the method described below. 3.2 Recruiting NMR mailed a letter on letterhead containing the PAs’ logos to each household in the sample. The letters explained the purpose of the study, participation requirements, and offered a $200 incentive for participation. Each letter included a stamped postcard for the homeowner to return if they were interested in participating. The letter also provided a phone number and email that the homeowner could contact if they had any questions or wanted to participate. Once a homeowner expressed interest in participating, NMR confirmed that the home had a qualifying permit date and had not participated in the program. Then NMR reached out by phone or email or both to schedule participation at a time convenient for the homeowner. To reach 100 audits, NMR sent multiple waves of letters and reminder mailings. Letters were sent in three different formats: (1) in a priority USPS envelope, (2) in standard first-class mail, and (3) as a bifold card stock mailing with a detachable post card. NMR also reached out to potential homeowners using phone numbers and emails from the new electric service request data. Qualifying homes identified in online searches received an initial letter and then a reminder letter. The study recruited homeowners instead of builders to avoid biasing the results toward builders who tend to build more energy-efficient homes (on the assumption that such builders would be more likely than others to agree to participate in the study). The study also limited participation to only one home per development to avoid biasing the sample towards a single builder. Overall, NMR reached out to 1,750 unique addresses to complete 100 qualifying energy audits, for a completion rate of 5.7%.

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3.3 Sample Composition Table 2 compares the final sample composition by county to the target from the sample plan. Figure 3 maps the 100 inspected homes. The final sample over-represents homes in Norfolk county and under-represents homes in Plymouth county.

Table 2: Sample Composition by County Percent of Homes County Targeted Completed Barnstable 9 11 Berkshire 1 1 Bristol 14 11 Essex 10 7 Franklin 1 2 Hampden 4 1 Hampshire 2 1 Middlesex 18 20 Norfolk 10 17 Plymouth 15 8 Suffolk 1 1 Worcester 15 20 Total 100 100

Figure 3: Sample Map

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Table 3 shows final sample counts by code and construction type. For both characteristics, the sample is nearly evenly split. Note that the final sample includes more custom homes (48) than were called for in the sample plan (38). While this could bias the study towards custom homes (which tend to be more efficient than spec homes), this bias is accounted for through the weighting scheme described in Section 3.5. Additionally, the larger-than-planned count of custom homes is beneficial since there is greater variance in custom homes than in spec homes. Having a larger share of custom homes than planned offers a better representation of the diverse population of custom homes.

Table 3: Sample Composition: Code and Construction Type Building Code Custom Spec Total Base Code 27 24 51 Stretch Code 21 28 49 Total 48 52 100

Table 4 summarizes the year of construction for each home in the sample. The majority of sample homes were completed in 2018.

Table 4: Sample Composition: Year Built Year Base Code Stretch Code Custom Spec Statewide n 51 49 48 52 100 2019 22% 31% 23% 29% 26% 2018 65% 47% 60% 52% 56% 2017 14% 22% 17% 19% 18% 3.4 On-Site Inspection Methodology

3.4.1 On-site Data Collection Inputs NMR used an electronic data collection form for the energy inspections that enabled auditors to simultaneously gather data for a full HERS rating and to assess code compliance. To aid with data cleaning and quality control, the data collection form included built-in completeness checks to flag incomplete fields and photographic capability for auditors to visually document each measure. Auditors recorded data falling under six primary topics, shown in Table 5.

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Table 5: On-site Data Collection Items

General Information Code Compliance Building Shell

► Home Type ► Plans or drawings ► Walls to unconditioned ► CFA ► Compliance certificate spaces ► Infiltration Volume ► HERS certificate ► Floors over unconditioned ► Stories ► Visual inspection of spaces ► Foundation Type measures with code ► Ceilings ► Bedrooms requirements ► Rim and band joists ► Orientation ► HVAC manufacturer ► Foundation walls ► Water fixture flow rates manuals ► Slabs ► Shelter class ► Windows, doors, skylights ► Meter amperage, smart ► Rim and band joists meter presence Mechanical Equipment Diagnostic Tests Lighting and Appliances

► Heating, cooling, and ► Blower door testing ► Bulb and fixture type water heating equipment ► Duct leakage testing ► Lighting controls ► Mechanical ventilation o Total duct leakage ► Refrigerators and equipment o Leakage to outside freezers ► Duct insulation and ► Ventilation flow-rate ► Dishwashers material testing ► Washers and dryers ► Thermostat type and set ► Dehumidifiers and air point purifiers ► Renewable generation, storage, and EVs

3.4.2 On-site Data Collection Procedures NMR faced the challenge of inspecting completed homes with building envelope components that are not easily accessible or visible. These included the following:

• Wall insulation • Garage and cantilevered frame floor • Window U-factor and SHGC insulation • Vaulted ceiling insulation • Band joist insulation • Exterior foundation wall insulation • Attic top plate sealing • Slab insulation

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NMR relied on the following approaches to gain access to measures for data collection: On-site visual verification of actual component. Actual observations in the field are the first and most important source of data. When direct access to the component was not possible, we looked for non-invasive alternative methods to gather whatever information we could. For example, when trying to determine exterior wall insulation, we might have removed an electrical outlet cover to probe for the presence of insulation and visually confirm the type of insulation directly or with a borescope. On-site visual verification of similar component. Once NMR exhausted opportunities to examine the actual component, we used similar locations to inform our assessment. For example, we might have found that there was visible/accessible above-grade wall insulation in an attic knee wall or a walkout basement that we would then have used to inform our assessment of the enclosed wall cavities. Documentation from the homeowner. In some cases, the homeowner possessed documentation with information on hard-to-access home components. This could include invoices from the insulation contractor, detailed plans, or photos taken during construction. This documentation can provide useful information on insulation types and R-values in inaccessible cavities, window u-factors, and the presence of insulation on exterior foundation walls or under slabs. We asked for this documentation as needed.16 3.5 Weighting Scheme NMR considered three different stratifications on which to base a weighting scheme: (1) construction type (i.e., custom/spec), (2) code environment (i.e., base/stretch), and (3) county. Weighting by county proved impractical. First, county-level sample sizes were small. Second, since building codes vary by municipality, which are geographical regions associated with counties, weighting by both county and code could lead to double counting the effect of building codes. This left just weighting by construction type, code environment, or both. Unweighted results for HERS index scores, air leakage, and duct leakage showed a larger difference between custom and spec homes than between base and stretch code homes for HERS index score and duct leakage – but the opposite for air leakage. For this reason, NMR created a nested weighting scheme by code environment and construction type. The nested weighting scheme accounts for both measure-level differences between code environments and the study’s methodology of sampling only one home from each development. By just sampling one home from each development, the study underrepresents identical homes built by a large builder. Using a larger weight for spec homes adjusts for this discrepancy. NMR weighted only statewide values. Values reported for custom, spec, base code, and stretch code homes are unweighted. Table 6 shows the weighting scheme used for statewide means and standard deviations. The populations are based on the estimated number of non-program homes

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16 When insulation for a particular measure was not visible anywhere in the home, auditors assumed a Grade II installation unless the insulation was a spray foam, in which case auditors assumed a Grade I installation. Auditors were always able to verify insulation materials using probing skewers or through documentation, but at times could not see enough of the insulation to determine the Grade.

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by county shown in Table 1. Permit counts were aggregated at the municipality level to get counts by stretch and base code. To create a nested weight, NMR assumed the proportion of custom and spec homes (38% and 62% respectively) was the same in both base code and stretch code populations.

Table 6: Weighting Scheme Non-program Strata Sample Weight Population Stretch Custom 456 21 0.6334 Stretch Spec 744 28 0.7751 Base Custom 847 27 0.9147 Base Spec 1381 24 1.6790

NMR performed a sensitivity analysis by calculating weighted averages for HERS index scores using a nested weighting scheme, a weighting scheme based only on code, and a weighting scheme based only on construction type. The range of weighted averages across all three weighting schemes varied by less than 1%. 3.6 Significance Testing Significance testing in this report was performed at the 90% confidence level and the terms “significant” and “significantly” are only used to signify a statistically significant difference. The margin of error for statewide results is ±9%. The margin of error for comparisons between custom and spec homes is ±8% and the margin of error for comparisons between base code and stretch code is 9%.

Section 4 Comparisons

This section contains various comparisons for key measures across sample groups and over time. The comparisons are presented in the following order:

• Statewide averages from this study compared to statewide averages from previous studies

• Statewide averages of non-program homes from this study compared to population averages of program homes built during the same time frame

• Averages of non-program stretch code homes from this study compared to average from homes built under the previous stretch code

• Base code averages from this study compared to stretch code averages from this study

• Custom home averages from this study compared to spec home averages from this study Throughout this report, a superscript “a” (e.g., a) signifies that there is a statistically significant difference between the means of base and stretch code homes at the 90% confidence level. A superscript b “b” (e.g., ) signifies that there is a statistically significant difference between the means of custom and spec homes at the 90% confidence level. The report only uses the term “significant” to describe statistical significance.

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4.1 Previous Baselines Table 7 compares weighted measure-level data from the current baseline to the previous four baseline studies (conducted in 2005, 2011,17 2015,18 and 201719). The values from the previous studies are not weighted because the studies either did not use weighting schemes or did not apply weights to their entire sample. There is a gap in the shell measure comparisons in 2017 because the 2017 mini baseline study did not collect information on the building shell. Generally, the data from 2019 show a continued trend of increased measure-level efficiency in new homes. This results in a continually decreasing (i.e., more efficient) HERS index score. Average HERS index score improved 26% since 2011 (Figure 4). This increase in efficiency is driven by increased envelope tightness, duct tightness, shares of heat pumps, and changes in building practices.

Figure 4: Average HERS Index Scores Over Time of Non-program Homes

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17 2005 and 2011 values come from http://ma-eeac.org/wordpress/wp-content/uploads/Massachusetts-2011- Baseline-Study-of-Single-Family-Residential-New-Contruction-Final-Report.pdf 18 http://ma-eeac.org/wordpress/wp-content/uploads/Single-Family-Code-Compliance-Baseline-Study-Volume-2.pdf 19 http://ma-eeac.org/wordpress/wp-content/uploads/RLPNC-17-2-Single-Family-New-Construction-Mini-Baseline- Study.pdf

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4.1.1 Envelope and Duct Tightness in Non-program Homes While homes and ducts have gotten tighter (i.e., more efficient) since 2005, improvement has begun to plateau. Table 7 shows new homes have become 52% tighter since 2005, as measured by ACH50 values. These have fallen from 6.4 in 2005 to 3.1 today.20 Over that same period, duct leakage to outside per 100 ft2 of conditioned floor area has decreased 81%.21 (Duct leakage to outside refers to air that is heated or cooled in ducts that leaks to outside of the house [e.g. to space such as an unfinished attic or unconditioned basement.]) While envelope and duct tightness have improved considerably since 2005, it appears this improvement has begun to plateau. Code requirements remained the same for air leakage and duct leakage from 2015 to 2019, removing incentives to improve during this period. Builders may also have reached practical limits for duct tightness – although, as discussed below, program homes have managed to produce tighter ducts.

4.1.2 Shell Measures in Non-program Homes Vaulted ceilings R-values are the only shell measure that had a substantial increase in efficiency since 2015. The R-value for vaulted ceilings has increased 29% since 2015. This is largely due to greater prevalence of spray foam insulation in 2019 compared to 2015. In 2015, only 14% of new homes with vaulted ceilings used spray foam as their primary insulation. In 2019, that value jumped to 60%. R-values for other shell measures in new homes have remained relatively constant over time. Builders are changing shell practices to bring mechanical equipment into conditioned space. In 2019, builders more frequently chose to insulate attic rafters and basement walls to bring duct systems entirely into conditioned space. The share of new homes with conditioned basements increased from 29% in 2015 to 40% in 2019. The share of new homes with sealed attics increased from 10% in 2015 to 35% in 2019.22

4.1.3 Mechanical Equipment and Water Heating in Non-program Homes The overall efficiencies of heating, cooling, and water heating equipment have increased due to an increase in the prevalence of heat pumps. Due to data limitations from previous studies, Table 7 can only compare efficiencies for specific types of equipment; however, the overall efficiencies of heating, cooling, and water heating in new homes have all increased. The increase is largely the result of an increased share of heat pumps. Heat pumps improve the efficiency of electrically fueled systems and therefore increase the overall efficiency of all systems. Additionally, on a site-level basis (as opposed to a source-level basis) heat pumps are more efficient than conventional systems of any fuel and thus improve HERS index scores.

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20 Air changes per hour at a 50 Pascal pressure gradient between indoors and outdoors, a typical metric of air infiltration. 21 Specifically, it shows the average CFM25/100 ft2 of conditioned floor area. 22 Due to limitations with the 2015 data, this comparison defines homes with sealed attics as homes with vaulted ceilings that were insulated with either open-cell spray foam or closed cell spray foam.

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For heating, Table 7 shows only fossil fuel systems and a plateauing efficiency. However as mentioned above, there was a substantial increase in the share of homes using heat pumps as a primary source of heat. The share of homes that use heat pumps as the primary heating equipment jumped from 3% in 2011 to 14% in 2019 (Figure 5).23 Over that same time, the share of new homes heating with oil decreased to 0%. Since heat pumps consume less energy than fossil fuel systems at a site-level basis, the increased prevalence of heat pumps leads to lower site-level energy consumption for heating in the 2019 sample for all fuel types. Similarly, while the efficiency of all cooling systems has begun to plateau, the share of homes that use heat pumps for cooling has increased from 3% in 2011 to 16% in 2019. Finally, the share of heat pump water heaters (HPWH) has increased (0% in 2011 to 17% in 2019). This has led to an increase in average water heater efficiency from 0.68 in 2011 to 1.30 in 2019.24

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23 Includes ground source heat pumps and air source heat pumps. 24 The average water heater Energy Factor (EF) includes systems rated with Uniform Energy Factors (UEF) converted to EF using RESNET protocols.

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Table 7: Key Measure Comparison Across Baseline Studies in Non-Program Homes

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Figure 5: Share of Homes with Heat Pumps as Primary Mechanical Equipment

4.2 Non-Program versus Program Homes Program homes are about 7% more efficient than non-program homes. Table 8 compares the weighted values for the non-program homes in this study to unweighted program home data. The program home data is based on Ekotrope models from 5,073 program homes. This represents the population of homes from May 2018 to April 2019 that participated in the Residential New Construction program. The average HERS index score of program homes (51.9) is 7% better than the average score of non-program homes (55.7).25 Note that when comparing to program homes, an average HERS index score based on Ekotrope models is used since the program started using Ekotrope in 2017. When comparing to previous studies (e.g., Table 7), an average HERS index score based on REM/rate models is used since that was the software package used previously. The greatest differences between non-program and program homes are in duct leakage to outside (58%), air infiltration (26%), and efficient lighting (11%).

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25 If solar is removed, the non-program average HERS index is 56.4 and the program average is 52.5.

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Table 8: Non-Program vs. Program Homes

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4.3 New Stretch Code versus Previous Stretch Code On January 1, 2017, the Stretch code changed to align with the 2015 IECC code. The previous Stretch Code was based on the 2012 IECC. Homes built under the new stretch code are required to have a maximum HERS index score of 55, while homes built under the previous stretch code are required to have a maximum HERS index score of 65 or 70, depending on home size. Table 9 compares non-program homes built under the previous stretch code to homes built under the new stretch code. The data for the previous stretch code are from 46 homes audited during the 2015 study. Data for the new stretch code are from the 49 stretch code homes audited for this study. Homes built under the new stretch code are more efficient than homes built under the previous stretch code. The average HERS index score for non-program stretch code homes decreased by 11%, from 64 in 2015 to 57 in 2019. This increase in efficiency (i.e., decrease in HERS index) appears to be driven by increases in envelope tightness, ceiling R-values, mechanical equipment efficiencies, and efficient lighting saturation. In addition to the differences shown in Table 9, a larger share of stretch code homes from the 2019 study used heat pumps as their primary heating equipment than did stretch code homes from the 2015 study (22% compared to 7% respectively).

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Table 9: Comparison of New Stretch Code versus Previous Stretch Code

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4.4 Base Code versus Stretch Code Homes built in base code and stretch code towns had the same levels of energy efficiency. There are no statistically significant differences between homes built to each code for the measures displayed in the table. Table 10 compares key efficiency measures for homes in this study built under the base and stretch codes. This is not surprising since both codes require a HERS index score of 55 when demonstrating compliance through energy modeling.26

Table 10: Comparison of Efficiency Measures by Code

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4.5 Custom versus Spec-Built Homes Custom homes are significantly more efficient than spec homes. Table 11 compares key efficiency measures for custom and spec homes. Custom homes had a significantly better HERS index score than spec homes (55.5 versus 61.3). This appears to be driven by greater shell measure efficiencies. Flat ceilings in custom homes had significantly higher R-values than flat ceilings in spec homes (47.0 versus 42.8). While not shown in Table 11, custom homes also had significantly better R-values than spec homes for conditioned walls bordering buffer spaces, such as basements and garages, and significantly better insulation installation Grades (i.e., quality) in floors above unconditioned basements.27

Table 11: Comparison of Efficiency Measures by Custom and Spec

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26 While the codes require a home to have a HERS index score of 55 or less when using an energy rating index path, in base code towns homes can comply prescriptively and thus may have a higher HERS index value than 55. Additionally, varying levels of enforcement can increase the average of HERS index values above 55. 27 See Tables in Appendix B.

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Section 5 Code Compliance

This section describes the average rates of compliance with code of homes built under the updated stretch code and base code (2015 IECC).

5.1 CODE COMPLIANCE METHODOLOGY NMR calculated compliance estimates taking the following steps: 1. Estimating average home-level compliance for non-program and program homes using energy models. 2. Estimating program penetration rates for base and stretch code towns using building permit and program data. 3. Calculating average compliance rates for base and stretch code by weighting non-program and program average compliance by program penetration rates.

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5.1.1 Estimating Non-Program and Program Home Compliance NMR used a methodology for calculating compliance developed for the 2015 Massachusetts Baseline and Code Compliance Study called the Massachusetts Residential Evaluation Contract (MA-REC) approach. The MA-REC methodology focuses on code requirements that directly impact energy consumption and allows for partial compliance with each individual code requirement. The methodology is an alternative to code compliance approaches that include administrative requirements and assess all requirements on a pass-fail basis. In the MA-REC methodology, homes earn points for each code requirement based on how closely they meet the requirement. If a home complies with or surpasses a code requirement, the home earns the full amount of points possible for that requirement. If a home does not meet a requirement, the home earns points proportional to the extent of its compliance with that requirement. The total possible points vary for each requirement, resulting in certain requirements having a greater weight in compliance calculations. The point scheme is tailored for each sample to assign more weight to building components that comprise larger shares of average modeled energy consumption. A home’s overall code compliance is determined by dividing the home’s total points across requirements by the total possible points across all requirements. For a full description of this methodology see Appendix C. Since the MA-REC approach does not account for trade-offs that may take place under Energy Ratio Index (ERI) path for compliance, it is possible that it overstates the level of non-compliance and potential savings associated with homes that use the ERI path for compliance. This should be considered when reviewing the results associated with this methodology.

5.1.2 Estimating Program Penetration Rates NMR estimated separate program penetration rates for base and stretch code towns, since stretch code towns are likely to have higher program penetration than base code towns. For a count of program homes, the program provided all the energy models for single-family homes that participated in the program from May 2018 through April 2019.28 To estimate the population of all homes built over this time, NMR used an average of permit data from the U.S. census at the municipal level for 2017 and 2018.29 This approach assumes a one-year lag between permitting and home completion. Permit data was split into base and stretch code counts by matching municipalities to stretch code adoption status through June 31, 2018. Penetrations were then calculated by dividing the counts of program homes in base and stretch code towns by the counts of permits in stretch and base code towns. Table 12 shows the estimated program penetration rates.

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28 The program switched from REM/Rate to Ekotrope models in May of 2018. The study called for one full year of data from one vendor, which is why the study used Ekotrope and this timeframe. 29 https://www.census.gov/construction/bps/

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Table 12: Program Penetration Rates Base Code Stretch Code Statewide Program Files 1,650 3,423 5,073 Permits 3,282 3,942 7,224 Penetration Rate 50% 87% 70%

5.1.3 Calculating Average Compliance Average compliance was then calculated by weighting the non-program and program average compliance rates by the program penetration in base code and stretch code municipalities, respectively. 푆푡푟푒푡푐ℎ 퐶표푑푒 퐶표푚푝푙𝑖푎푛푐푒 = (푃푟표푔푟푎푚 퐶표푚푝푙𝑖푎푛푐푒 ∗ 푆푡푟푒푡푐ℎ 퐶표푑푒 푃푟표푔푟푎푚 푃푒푛푒푡푟푎푡𝑖표푛) + (푆푡푟푒푡푐ℎ 퐶표푑푒 푁표푛푃푟표푔푟푎푚 퐶표푚푝푙𝑖푎푛푐푒 ∗ (1 − 푆푡푟푒푡푐ℎ 퐶표푑푒 푃푟표푔푟푎푚 푃푒푛푒푡푟푎푡𝑖표푛))

5.2 COMPLIANCE ESTIMATED WITH MA-REC The estimated statewide compliance rate, including program homes, is 96%. Figure 6 shows compliance rates by code.30 Overall, compliance has increased for base code since 2015 but has been relatively consistent for stretch code. The consistency in stretch code compliance despite the increase in stretch code stringency is driven by the increase in program penetration in stretch code towns. Looking only at non-program homes, the compliance in stretch code towns decreased from 93% in 2015 to 89% in 2019. Two background market trends have impacted code compliance: increasing program penetration and increasing adoption of stretch code. Since 2015, program penetration in base code towns has increased from 24% to 50% and program penetration in stretch code towns has increased from 70% to 87%. Since program homes have higher compliance than non-program homes, this increased penetration leads to an increase in code compliance.31 Regarding stretch code adoption, 37% of towns in the 2015 baseline had stretch code in effect. That increased to 60% for the period of construction address in this study.32 Since stretch code has a higher compliance rate, this also has an upwards effect on overall compliance.

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30 Note that base code homes values for 2015 and 2017 are based only on 2012 IECC homes, while the 2019 base code value is based only on 2015 IECC homes. As mentioned above, the 2012 IECC has a very similar stringency to the 2015 IECC. The 2017 study did not look at stretch code homes and neither the 2015 or 2017 study estimated a statewide compliance value. 31 2015 program penetration rates were estimated in the TXC 48 RNC Attribution study: http://ma- eeac.org/wordpress/wp-content/uploads/TXC_48_RNCAttribution_24AUG2018_Final.pdf 32 The percentages of municipalities enforcing stretch code is based on the year’s homes were permitted in each study.

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Figure 6: Compliance Rates by Code

Where possible in the analysis below, NMR has calculated results using both REM/Rate and Ekotrope to facilitate comparison with the previous baseline, which used REM/Rate, as well as program data, which is now collected with Ekotrope. An analysis of differences in the tools and their output is provided in Appendix C. Figure 7 shows estimates of statewide code compliance for non-program and program homes using the MA-REC method. The non-program values are weighted averages and are shown using both REM/Rate version 15.8 and Ekotrope models. The program home analysis is based on a population of 5,073 Ekotrope files from program participants with confirmed HERS ratings submitted to the program from May 2018 through April 2019. 33 Program homes show higher code compliance. The average non-program home is 88% or 90% compliant with code requirements, depending on the modeling software used. The average program home is 99% complaint with code requirements, a statistically significant difference.34 Additionally, the MA-REC analysis does not consider compliance through the Energy Rating Index path. Homes can demonstrate compliance to both the Stretch code and base code by achieving a HERS index score of 55 or less without renewables. Stretch code allows homes to have a higher HERS index score before applying renewables if the home will have onsite renewable generation.35 Based on the REM/rate models from this study, 18% of base code homes and 24% of stretch code homes comply with the code using the ERI path without considering the exception for renewables.

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33 One program site (M3AOJW2B) was omitted from this and later analysis due to improbably perfect insulating windows, and six others were excluded as a result of case-wrapping collisions in Ekotrope identifiers; 47zDeMpY/47zDEMpY, DY6jdWlY/DY6jDwlY, R3kgmGl3/R3kgmgl3. 34 For this comparison, only MA-REC results from Ekotrope models were used. 35 See table C407.6.1.4 in https://www.mass.gov/files/documents/2018/05/15/Chapter%20115%20amend%202016- 08-12-chapters-13-51.pdf

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Figure 7: Average MA-REC Code Compliance for Non-Program and Program Homes

The program comprises 70% of the new construction market. As Table 13 shows, the estimated program penetration has increased in both code environments. This could be the result of increased code stringencies making program participation less of a lift beyond code. The ERI paths of both base code and stretch code could also be leading to more builders using HERS raters and learning from the raters about program opportunities of which they were previously unaware.

Table 13: Program Penetration Rates 2015 2019 Base Code 24% 50% Stretch Code 70% 87% Statewide 41% 70%

5.2.1 Detailed REM/Rate and Ekotrope MA-REC Results Table 14 shows detailed non-program results from the MA-REC analysis using REM/Rate models. Average MA-REC compliance for non-program base code homes has increased (though not significantly) somewhat since the 2017 baseline, from 85% to 87%.36 This is to be expected since the current base code (IECC 2015) is virtually identical to that evaluated in 2016 (IECC 2012). By contrast, stretch code was updated in January 2017 from an IECC 2009-base to a stricter IECC 2015-base. Compliance has decreased under the stricter code from 93% to 89%.

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36 http://ma-eeac.org/wordpress/wp-content/uploads/RLPNC-17-2-Single-Family-New-Construction-Mini-Baseline- Study.pdf

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Table 14: REM/Rate MA-REC Compliance Stretch Base Code Custom Spec Statewide Code n 51 49 48 52 100 Minimum 69% 75% 73% 69% 69% Maximum 100% 100% 100% 100% 100% Mean 87% 89% 88% 87% 88% Sd. 8% 6% 7% 7% 7%

Table 15 shows detailed results from the MA-REC analysis using Ekotrope models. The results are very similar to those of REM/Rate but are slightly higher and have greater variance. Table 15 also shows the program home compliance, since the program provided Ekotrope models.

Table 15: Ekotrope MA-REC Compliance, Non-program versus Program Homes Non-program Homes Program Base Stretch Custom Spec Statewide Homes Code Code n 51 49 48 52 100 5,057 Minimum 55% 72% 65% 55% 55% 80% Maximum 100% 100% 100% 100% 100% 100% Mean 89% 92% 91% 90% 90% c 99% Sd. 12% 8% 9% 11% 11% 2% c Significantly different from program homes at the 90% confidence level. The difference in MA-REC results between REM/Rate and Ekotrope likely arises from differing methods employed to characterize a home built to code in each software. For more discussion on the differences between REM/Rate and Ekotrope in terms of the MA-REC methodology, see Appendix C.

5.2.2 Measure-Level Compliance The measures with the lowest compliance were duct leakage and insulation, frame floor insulation, air leakage, and ceiling insulation. Table 16 shows measure-level compliance for each software package. Ekotrope does not separately report lighting and appliance gains nor duct losses, precluding the assessment of compliance for these requirements.

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Table 16: MA-REC Statewide Measure-level Compliance REM/Rate Ekotrope Non-Program Non-Program Program Mean SD Mean SD Mean SD n 100 100 6,282 Duct leakage and insulation 72.3% 22.6% – – – – Frame floor insulation 81.7% 13.0% 86.7% 19.4% 97.3% 6.9% Air leakage 85.3% 19.2% 87.6% 19.8% 98.9% 6.6% Ceiling insulation 86.7% 19.6% 86.6% 20.1% 98.5% 5.0% Fenestration U-factor 89.2% 7.7% 87.7% 14.8% 98.3% 5.5% Foundation wall insulation 90.8% 18.0% 95.5% 14.3% 98.7% 5.8% Slab insulation 93.3% 17.8% 97.4% 6.5% 98.7% 4.1% Above grade wall insulation 94.5% 5.5% 94.9% 12.3% 99.8% 1.5% Lighting 98.5% 6.7% – – – – Overall 87.5% 7.3% 89.9% 10.8% 98.8% 2.4%

5.3 COMPLIANCE WITH CODE REQUIREMENTS NOT ESTIMATED WITH MA-REC This section examines compliance with the code requirements that are only tangentially related to energy consumption, and thus not addressed by the MA-REC methodology. Figure 8 provides detailed compliance percentages for each of these requirements.

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Figure 8: Additional Requirements Overview

Section 6 Building Shell

This section presents notable findings for key building shell measures in non-program homes, including above-grade walls, flat and vaulted ceilings, frame floors over unconditioned basements, foundation walls in conditioned basements, and fenestration (windows and door glazing). Detailed findings on these measures and others that make up less of the shell area – such as walls to buffer spaces, floors over garages, and floors over ambient conditions – can be found in Appendix B.3. Figure 9 provides a summary of overall average R-values for shell measures observed on-site compared to prescriptive code requirements. Note that homes using an energy rating index path for compliance do not need to comply with individual preceptive requirements if they meet overall performance requirements. The items that follow highlight key findings for shell measures, focusing on trends and comparisons to previous baseline findings and statistically significant differences among the 2019 code and construction type samples.

Figure 9: Statewide R-Value Compared to Prescriptive Requirements

R-49 R-49 50 R-44 R-42 40 R-31 R-30 30

R-20 R-20 20 R-15 R-15

0 Walls Flat Ceilings* Vaulted Ceilings** Frame Floors Cond. Foundation Walls Statewide Prescriptive Code

* Code allows R-38 over all ceiling area where the full height of uncompressed R-38 insulation extends over the wall top plate at the eaves. ** Where the ceiling design does not allow sufficient space for required insulation, code allows for minimum of R-30 as long as the insulation fills the cavity for up to 20% of the ceiling area or 500 ft2, whichever is less.

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6.1 AIR INFILTRATION AND VENTILATION

The average ACH50 value is consistent across codes and construction types and is better than previous baselines. The statewide average ACH50 is 3.1 and does not vary significantly between base and stretch code or between custom and spec homes. The average ACH50 is close to the code requirement of 3.0.37 Fifteen percent of homes have balanced mechanical heat recovery ventilation (HRV) or energy recover ventilation (ERV) systems. Custom homes were significantly more likely than spec homes to have either HRV or ERV systems. This represents an increase from 2015, when just 5% of homes had an HRV or ERV systems.38

6.2 ABOVE GRADE WALLS

Spray foam is much more prevalent than in 2015. Thirty-one percent of homes used spray foam (alone or in combination with other types of insulation) as a primary insulation in conditioned-to-ambient walls, compared to just 8% in 2015.39 There were no significant differences by code or home type in the types of primary insulation found in walls in 2019. The proportion of homes using primarily fiberglass batt insulation fell from 88% in 2015 to 56% in 2019.40 Insulation installation quality has improved. The proportion of homes found to contain Grade I insulation in exterior walls increased considerably, from 11% in 2015 to 38% in 2019.41 Also, the proportion of homes using continuous insulation over a majority of wall area increased from just 1% in 2015 to 11% in 2019.42 Ambient wall R-values have barely increased since 2015. Despite the shift in insulation materials, the average R-value of exterior walls in 2019 rose only slightly to about R-22, compared to R-21 in 2015.43 This could be due to the limited space in wall cavities. Walls in custom homes have significantly higher R-values than those in spec homes. There were no significant differences between codes or home types in exterior wall R-value. However, when expanding the focus to include all walls between conditioned and unconditioned spaces

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37 See Table 36. 38 See Table 39. 39 “Primary” insulation here refers to the insulation type filling a majority of exterior wall area in the home, where multiple insulation types were present. 40 See Table 41. 41 This increase in Grade I installs is due in part to the increased prevalence of spray foam insulation. Without access to the full wall cavity or photos taken pre-drywall, auditors were more comfortable giving spray foam installation a Grade I versus fiberglass batts, which can vary more widely in grade across a home and can be more difficult to install with contact on all sides of the cavity. 42 See Table 43. 43 See Table 40.

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(e.g., walls to garages and unconditioned basements) the average R-value in custom homes (R- 21.4) is significantly higher than in spec homes (R-18.6).

6.3 FLAT CEILINGS Flat ceiling R-values have barely increased since 2015. There have been no major changes in common insulation materials, the prevalence of continuous insulation, and insulation grade for flat ceilings between 2015 and 2019. Flat ceiling R-values increased from R-40 in 2015 to R-44 in 2019.44 Flat ceiling R-values in custom homes were significantly higher than in spec homes. Base and stretch code samples show only minor differences in 2019; however, flat ceilings in custom homes had an average R-value of R-47, versus R-43 in spec homes.

6.4 VAULTED CEILINGS Spray foam has become much more common in vaulted ceilings. After being a primary insulation type in just 14% of homes with vaulted ceilings in 2015, spray foams were a predominant insulation in 60% of homes with vaulted ceilings in 2019. Open-cell spray foam was the single most common insulation type in vaulted ceilings (34% of homes) and closed-cell pray foam was third- most common (16%). 45 Vaulted ceiling R-values in 2019 are much higher than in 2015. Vaulted ceiling assemblies in the 2019 sample have an average R-value of R-42, up from R-33 in the 2015 study. These values approach the average R-values for flat ceilings. By comparison, there was a much wider gap in 2015. There were no significant differences in vaulted ceiling R-values by code or home type in the 2019 sample.46 Sealed attics have become more common as an insulation method. Sealed attics – where insulation is applied at the ceiling rafters rather than at the floor joists in an unfinished attic to pull that attic space into the thermal envelope – were found in 39% of homes in 2019 versus 10% in 2015.47 Spray foam insulation makes this practice easier to accomplish, and open roof cavities allow for thicker applications, often including a continuous layer on the face of the filled cavity. Twenty-three homes had spray foam applied to ceiling rafters with a mix of cavity and continuous. Increasing use of this method plays a role in shrinking the R-value gap between flat and vaulted ceiling types.48

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44 See Table 48. 45 See Table 49. 46 See Table 52. 47 This comparison is based on vaulted ceilings with spray foam insulation from 2015 since data did not distinguish between vaulted ceilings that were finished and those which were merely sealed. The 2019 data is based on attics that were specified as conditioned volume only. 48 Especially when using blown-in or batt insulation, it has traditionally been easier to insulate a flat attic space to a higher R-value, where you can take advantage of the open space above the attic floor joists to place thicker layers of

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6.5 FLOORS OVER UNCONDITIONED BASEMENTS Insulation installation quality has regressed rather than improved. While the share of Grade I installations has increased slightly in 2019 (from 4% in 2015 to 9%), there is a much larger proportion of Grade III installations in 2019. Twenty-eight percent of homes in 2015 had frame floor insulation given a Grade III, compared to 43% in 2019. The increase in spray foam observed in other building shell measures was not observed in framed floors. Fiberglass batts are often installed in open cavities over basements, increasing the odds they lose the fight against gravity and sag away from the floorboards. Efforts to secure the batts and prevent sagging sometimes create compression. The reduction in Grade could be due to efficient builders choosing to insulate foundation walls leaving only inefficient builders as those who comprise the sample of frame floor insulation.49 Grade III insulation is significantly more common in spec homes. The proportion of spec homes with majority Grade III insulation in floors (51%) is almost twice that of custom homes (27%). Insulation type and R-value vary little by code or home type and have changed little since 2015. The average R-value for floors over unconditioned basements has not changed since 2015, and fiberglass batt insulation remains the dominant type found in these floors (84% of homes), regardless of code or home type.50

6.6 FOUNDATION WALLS Conditioning of basement spaces is becoming more common. The proportion of homes with conditioned basement space has risen from about 30% in 2015 to 40% in 2019, but the average R-value of conditioned foundation walls has remained R-15. Spray foam is the most common insulation in foundation walls. Spray foams are the primary insulation type in 40% of homes with conditioned basement space. Fiberglass batts are a primary insulation in 34% of homes, down from 62% in 2015.51 Continuous insulation is nearly as common as cavity insulation. With more spray foam came a shift in insulation methods – continuous insulation was present in over half (52%) of homes with conditioned basements and was nearly as common as cavity insulation (54%). Interior continuous insulation was present in 31% of homes in 2015. Most continuous insulation in 2019 was located

______insulation beyond the cavity depth. In vaulted ceilings in finished spaces, the closure of the cavity limits R-values unless continuous layers of rigid foam or other insulation are placed on the roof above (to date, this has been an uncommon practice). 49 See Table 54. 50 See Table 53 and Table 55. 51 See Table 57.

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on the interior of walls (49% of homes) rather than the exterior (7%). Just 4% of homes had uninsulated foundation walls in 2019 compared to 31% in 2015.

6.7 FENESTRATION Roughly 30% of glazing area in new homes is filled with a specialty gas, such as argon. It can be difficult to confirm the presence of gas fill visually; therefore, auditors would mark as confirmed if they were able to view documentation from the homeowner or builder, or if they saw NFRC rating stickers on-site. As such, this number represents a minimum for the true value. Twenty-six percent of homes were confirmed to contain at least some glazing with a gas fill.52 Almost all glazing has a low-emissivity coating. Just 5% of glazing area in the sample lacked a low-emissivity coating, unchanged from 2015. Figure 10 presents the overall composition of the glazing materials observed during on-sites. Confirmed window U-factors remained consistent with the 2015 baseline. In the 25 homes with verified U-factor data, the average is 0.29; virtually unchanged from 0.30 in 2015.53

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52 See Table 63. 53 See Table 62.

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Figure 10: Composition of Window Glazing Area

Section 7 Mechanical Equipment

The sections below provide key takeaways on the type; fuel type; efficiency; location; and ENERGY STAR® status of mechanical equipment, which includes heating, cooling, and water heating systems. When discussing heating and cooling systems, the report often indicates in text or table headings that findings are specific to primary systems. Where primary systems are discussed, each home is treated as one observation – the primary type of heating or cooling equipment is that which supplies the largest portion of the BTU load, regardless of the number of systems. 54 For comprehensive on-site data findings on mechanical equipment, refer to the tables in Appendix B.

7.1 HEATING EQUIPMENT More homes are using heat pumps as primary heating systems. Heat pumps of all types are primary heating systems in 14% of new homes in 2019, a substantial increase over 2015 (2%) and 2017 (4%). This trend drives the increase in the use of electricity as a primary heating fuel in 2019 (14%) compared to 2015 (2%) and leads to higher heating system efficiencies because heat pumps are more efficient than conventional heating systems on a site-level basis. Table 17 displays the primary heating systems in the 2019 sample by type and fuel. The increase in heat pumps is driven by custom homes. These homes are significantly more likely to have mini- or multi-split heat pumps than spec homes and significantly less likely to use a furnace. 55 Additionally, stretch code homes are significantly less likely to use a furnace than base code homes.

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54 For example, a home with two natural gas furnaces and two CACs, one located in the basement and one in the attic, would be classified as having a natural gas furnace and a CAC as the primary heating and cooling systems. The primary system designation covers both equipment type and fuel. 55 Mini- or multi-split system heat pumps are heat pump systems with an inverter- driven compressor, which traditionally condition space through one or more distribution points (single-zone or multi-zone) – such as a wall-mounted cassette or a ceiling cassette. Ductless mini-split system configurations have evolved over time and now can be either ductless, ducted, or a mixed configuration. For the purposes of this report, the mini- and multi-split heat pump systems (MSHP) include inverter-driven heat pump systems, regardless of configurations; while ASHPs denote traditional heat pump systems, which condition space through a central distribution system.

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Table 17: Primary Heating Equipment by Type and Fuel Type Base Code Stretch Code Custom Spec Statewide n 51 49 48 52 100 Furnace 80% a 59% 58% b 81% 75% Natural Gas 47% a 18% 27% 38% 39% Propane 33% 41% 31% 42% 36% MSHP (Electric) 8% 12% 17% b 4% 8% Boiler (forced hot water) 4% 8% 6% 6% 5% Natural Gas 2% 4% 2% 4% 3% Propane 2% 4% 4% 2% 2% ASHP (Electric) 4% 8% 8% 4% 5% Boiler (hydro-air) 4% 6% 8% 2% 4% Natural Gas 4% 4% 8% 0% 3% Propane 0% 2% 0% 2% 1% Combi appliance 0% 4% 0% 4% 2% (Propane) GSHP-closed loop 0% 2% 2% 0% 1% (Electric)

Heating systems in custom homes are significantly more likely to be fueled by electricity. Due to the larger share of heat pumps in custom homes, significantly more custom homes (27%) use electricity as the primary heating fuel than do spec homes (8%; Table 18). Base code homes use natural gas heat significantly more than stretch code homes. Natural gas is the primary heating fuel in just over half of base code homes, nearly double that of the stretch sample (27%). Statewide, natural gas heating equipment has decreased in frequency by 20% since 2015, replaced with a mix of systems fueled by propane and electricity (Table 18). This could be the result of more stretch code homes being built in areas where gas was not available; however, access to gas was not examined in this study.

Heat pumps appear to be displacing oil fueled systems. Since the 2017 mini baseline, the share of homes heating with oil decreased from 10% to 0%. The increase in heat pumps (i.e., electric heat) could be occurring in new homes where gas is not available and builders decide to use electricity instead of oil. Again, access to gas was not examined in this study.

Table 18: Primary Heating System Fuel Type Fuel Base Code Stretch Code Custom Spec Statewide n 51 49 48 52 100 Natural Gas 53% a 27% 38% 42% 45% Propane 35% 51% 35% 50% 40% Electric 12% 22% 27% b 8% 14%

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ENERGY STAR saturation increased substantially among heating equipment. Sixty-six percent of heating equipment in new homes was ENERGY STAR-qualified in 2019, up from 42% in 2017 and 27% in 2015. 56 Custom homes are significantly more likely to locate heating equipment within conditioned space. Installing heating equipment in conditioned space reduces heat losses. Heating equipment was installed in conditioned space in 45% of custom homes versus 25% of spec homes. Installing heating systems in unconditioned attics was a significantly more common practice in spec homes (26%) compared to custom homes (7%).57 Statewide, the proportion of homes with heating equipment located in conditioned space is mostly unchanged from 2017 (31% versus 30% in the 2017 study). 58 Due to the rise in heat pumps, the average overall heating AFUE of new homes is higher than achievable by fossil fuel systems alone. Table 19 displays the average AFUE for major system types in the 2019 sample. The fossil fuel systems include furnaces and boilers using both hydronic and air distribution. The heat pump systems include both ducted and ductless air source heat pumps as well as a ground source heat pump. To create an all-system average AFUE, heat pump efficiencies were converted to AFUE using RESNET protocols.59,60,61

Table 19: Average Statewide Efficiency for all Fossil Fuel Systems, Electric Heat Pumps, and All Systems Heating Efficiency All Fossil Fuel All Heat Pump All Systems (AFUE) (HSPF) (AFUE) n 132 31 163 Minimum 80.0 8.2 80.0 Maximum 98.0 16.0 470.0 Average 94.0 10.5 128.3 Std. Dev. 4.7 1.4 86.8

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56 See Table 69 57 This is likely due to custom homes more frequently insulating along the attic rafters, typically with spray foam, rather than insulating the attic deck, which encloses the attic within the thermal barrier. 58 See Table 66. 59 Equipment that was rated in HSPF and COP were converted to AFUE using RESNET equations. To convert COP to AFUE, COP values were multiplied by 100. To convert HSPF values to AFUE, HSPF values were multiplied by 0.3413. Fireplaces were excluded from the sample 60 Excludes supplemental electric radiant heat, unit heaters, and fireplaces at four homes. 61 In Appendix D Final Updated UDRH Inputs, average heating efficiencies are calculated differently than the method used here. The UDRH analysis includes source-site conversion factors by fuel. Here, the efficiencies are reported using RESNET protocols since that more closely reflects the method used in energy modeling. However, note that when entering efficiencies into energy models, HSPF and COP values were entered as HSPF or COP values.

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7.2 COOLING As with heating equipment, heat pumps are becoming more prevalent as primary cooling systems. The use of heat pumps (MSHPs and ASHPs) as the primary cooling system in new homes has increased by 8% since the 2017 mini- baseline (Table 20). Mini or multi-split systems are significantly more prevalent as primary systems in custom homes than in spec homes. Not surprisingly since heat pumps often perform both heating and cooling, custom homes are once again significantly more likely to have heat pumps than are spec homes. Table 20 shows the primary cooling systems found in 2019. The only significant difference between groups was in the use of MSHPs in custom homes relative to spec homes. The share of Central ACs (CACs) serving as the primary cooling system has decreased by nearly 10% compared to previous baselines. CACs represented about 90% of primary cooling systems in 2015 and 2017. In 2019 they were the primary cooling system in 80% of homes. The data suggest that CACs are being replaced by heat pumps (Table 20).

Table 20: Primary Cooling Equipment Type per Home Base Type Stretch Code Custom Spec Statewide Code n 51 49 48 52 100 Central Air-split 84% 69% 71% 83% 80% MSHP 8% 12% 17% b 4% 8% ASHP 6% 8% 10% 4% 6% Room Air 2% 6% 0% 8% 4% Conditioner None 0% 2% 0% 2% 1% GSHP-closed loop 0% 2% 2% 0% 1%

The efficiency of cooling systems in new homes has improved modestly since 2015. Table 21 displays the 2019 statewide efficiency (SEER) for all cooling systems, as well as the statewide average efficiency for the three primary cooling systems: Central ACs, MSHPs, and ASHPs. The average SEER across all systems increased from 13.8 in 2015 to 14.9 in 2019. There are no significant differences in equipment efficiency among sample splits.

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Table 21: Statewide Efficiency for Cooling Systems (SEER) Efficiency (SEER) Central AC MSHP ASHP GSHP Combined SEER n 125 23 8 1 157 Minimum 13.0 16.0 14.0 20.7 13.0 Maximum 18.0 33.0 18.5 20.7 33.0 Average 14.1 19.7 16.6 20.7 14.9 Std. Dev. 1.2 2.8 1.9 NA 2.6

As with heating equipment, custom homes are significantly less likely to have equipment located in an unconditioned attic space and are significantly more likely to have an ENERGY STAR-qualified system. No other significant differences were identified among homes in the sample regarding cooling equipment found on-site.62

7.3 THERMOSTATS Ninety percent of thermostats in new homes are both programmable and WiFi- enabled. In 2015, just 9% of thermostats were both programable and WiFi-enabled, and an additional 72% were only programmable. In 2019, 50% of thermostats were smart (i.e., a thermostat capable of setting and adjusting set points based on machine learning) and another 40% were programable and WiFi-enabled. Therefore, a total of 90% of thermostats were WiFi-enabled.63

7.4 DOMESTIC HOT WATER Adoption of gas instantaneous and HPWH systems in new homes continues to erode the share of gas and electric storage tanks. Table 22 displays the water heating equipment observed during on-site inspections, organized by equipment type and fuel. Standalone storage water heaters represented 42% of water heating equipment in 2015 and just 29% in 2019, while instantaneous systems have increased from 19% to 44% of water heaters during this same period. Instantaneous systems were the most common equipment type in the 2019 sample. HPWHs have also increased in prevalence over time, and now represent 17% of water heating systems in new homes (versus 11% in 2015 and 8% in 2017). The unweighted average statewide energy factor (EF) is displayed for each equipment type and by fuel in Table 22.64 There were significantly fewer natural gas storage systems installed in stretch code homes. Generally, there were fewer standalone storage tanks installed in stretch code homes, which were seemingly replaced by HPWH systems.

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62 See Table 88 and Table 90. 63 See Table 123. 64 The unweighted value is used due to small sample sizes when looking at specific system and fuel types.

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Table 22: Water Heater Type, Fuel, and Average Energy Factor Base Stretch Statewide EF Type Custom Spec Statewide Code Code (unweighted) n 54 54 52 56 108 108 Instantaneous 43% 44% 44% 43% 43% 0.94 Natural Gas 30% 20% 21% 29% 27% 0.95 Propane 13% 24% 23% 14% 16% 0.94 Storage, stand 33% 20% 21% 32% 29% 0.74 alone Natural Gas 17% a 2% 8% 11% 12% 0.67 Propane 9% 9% 10% 9% 9% 0.69 Electric 7% 9% 4% 12% 8% 0.89 Heat pump water heater 15% 20% 19% 16% 17% 3.34 (Electric) Indirect 7% 7% 12% 4% 7% 0.71 w/storage tank Natural Gas 6% 6% 10% 2% 5% 0.71 Propane 2% 2% 2% 2% 2% 0.71 Combi appliance 2% 4% 0% 5% 3% 0.90 (Propane) Solar DHW 0% 4% 4% 0% 1% NA

HPWH efficiency is trending steadily upward over time. Table 23 displays the weighted water heater EF for all water heater types found during on-site inspections, except for solar hot water systems (two solar hot water systems were observed at two homes). Not only were more HPWHs installed in 2019 than in past baselines, the average efficiency of the units has also increased steadily. After averaging 2.66 in 2015, new home HPWH EFs increased to 3.14 in 2017 and 3.34 in 2019. This trend in HPWH prevalence and efficiency, coupled with the continued increase in instantaneous systems, fuels an overall increase in site-level EFs across new home baselines. The average unweighted EF went from 1.05 EF in both the 2015 and 2017 baseline studies to 1.30 EF in 2019.65 The stretch code sample having a higher EF than the base code can be attributed to the increased saturation of HPWHs in stretch code homes.

Table 23: Overall DHW Equipment Efficiency (EF) Water heater EF Base Code Stretch Code Custom Spec Statewide n 54 52 50 56 106 Minimum 0.61 0.58 0.61 0.58 0.58 Maximum 3.50 3.63 3.63 3.53 3.63 Average 1.21 1.40 1.35 1.26 1.29 ______

65 Note that water heater efficiencies are presented as EFs rather than the more current Uniform Energy Factor efficiency metric. NMR converted all UEF values into EF values using equations provided by RESNET. This was done for both the ability to compare to previous baseline studies and for modeling water heaters in Ekotrope and REM/Rate, which only allow for EF.

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Std. Dev. 0.89 1.06 1.03 0.94 0.96 *Solar DHW systems are excluded due to not having an EF rating.

Saturation of ENERGY STAR water heating equipment continues to increase. Seventy- seven percent of DHW equipment was ENERGY STAR certified in 2019, up from 71% in 2017 and 64% in 2015. There were no significant differences among the 2019 sample in ENERGY STAR saturation.66

7.5 DUCT SYSTEMS Ducts are installed less frequently in unconditioned space. Builders are increasingly placing ducts in conditioned space in new homes or avoiding ducted HVAC systems, reducing energy losses from duct leakage. They are insulating attics and basements to bring them inside the conditioned envelope (29% in 2015 to 40% in 2019 for sealed attics and 10% in 2015 to 39% in 2019 for basements), or simply using ductless systems (3% in 2015 to 10% in 2019). In 2015, 49% of new homes had all their ducts in unconditioned space. In 2019, that number dropped to 15%.67 . Duct-leakage-to-outside values are plateauing due to static code requirements. Table 24 shows the quantity of air that leaked from ducts to spaces outside of the thermal envelope (e.g., unconditioned basements and unsealed attics) normalized by the conditioned floor area of each home when there is a pressure gradient of 25 pascals between inside and outside. Duct systems that were entirely in conditioned space were assumed to have zero duct leakage to outside. This matches RESNET protocols. The 2019 weighted statewide average of 4.3 is only slightly better than the 2015 value of 4.6 and statistically equal to the 2017 value of 4.2. This reflects the consistency in code requirements over that time. For context, code requires total duct leakage to be less than 4.0 CFM25/100 ft2 CFA. Code does not specify a leakage to outside requirement, but by definition it must be less than the total duct leakage.

Table 24: Duct Leakage to the Outside (CFM25/100 ft2 CFA) Stat Base Code Stretch Code Custom Spec Statewide n 46 41 42 45 87 min 0.0 0.0 0.0 0.0 0.0 max 19.8 31.2 19.8 31.2 31.2 Mean 4.5 3.7 3.6 4.6 4.3 Sd. 4.5 5.7 4.2 5.8 4.6

Duct insulation ranges from about R-5.6 in basements to R-6.6 in attics. Table 25 shows average area weighted R-values for ducts by location. The values are consistent with previous studies.

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66 See Table 105. 67 See Table 126.

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Table 25: Weighted Average Duct R-value by Location Base Code Stretch Custom Spec Statewide Code n R- n R- n R- n R- n R- value value value value value Attic Supply Ducts 22 6.6 22 6.5 14 6.5 30 6.6 44 6.6 Unconditioned 34 5.6 22 5.6 26 5.8 30 5.4 56 5.6 Basement Ducts Attic Return Ducts 22 6.6 20 5.9 12 6.0 30 6.3 42 6.4 Garage Ducts 4 6.8 4 5.1 5 6.0 3 5.7 8 6.1 Enclosed Crawl Space 1 7.0 ------1 7.0 1 7.0 Ducts

7.6 RENEWABLES AND ELECTRIC VEHICLES The share of homes with renewable and electric vehicle technologies is increasing. Table 26 shows the percent of new homes that had solar photovoltaics (PV), electric vehicles (EV), batteries for electricity storage, and solar thermal systems for water heating. The weighted average of 12% of homes with PV is an increase from 9% in the 2015 study. The 2015 study observed no homes with EVs or batteries. Of the five homes with EVs, three had level two chargers and two had level three chargers. No homes had EV chargers that did not also have an EV.

Table 26: Renewables Penetration Base Stretch Custom Spec Statewide Code Code n 51 49 48 52 100 PV 12% 16% 21% 8% 12% EV 6% 4% 10% 0% 4% Battery 4% 0% 4% 0% 2% Solar Thermal 0% 4% 4% 0% 1%

The average size of on-site PV is 7.5 kW. From documentation available on-site or based on solar panel area using the Department of Energy’s PV Watts tool, technicians determined or estimated on-site solar capacity. Table 27 summarizes the capacity values by code and construction type. A 7.5 kW system will typically exceed the average home’s electric consumption annually in Massachusetts.68

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68 This assumes the average monthly kWh consumption for Massachusetts from the EIA here: https://www.eia.gov/electricity/sales_revenue_price/pdf/table5_a.pdf.

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Table 27: PV Capacity (kW) Stat Base Code Stretch Code Custom Spec Statewide n 7 11 14 4 18 min 4.2 3.0 3.0 5.8 3.0 max 11.9 12.5 12.5 9.0 12.5 Mean 8.4 6.8 7.5 7.3 7.5 Sd. 3.2 2.9 3.4 1.5 3.0

Section 8 Lighting and Appliances

This section presents the findings derived from lighting data collected during site visits. Data were collected on bulb and fixture type and location. Bulb data are presented at the socket level and include both hard-wired and plug-load sockets, unless otherwise labeled.

8.1 LIGHTING LED bulbs are the dominant type found in new construction. The prevalence of LED bulbs has increased sharply over previous baselines. Total efficient bulb saturation (LED, CFL, and fluorescent combined) was less than 50% in 2015 and 75% in 2017; in 2019 LED bulbs alone made up 86% of bulbs in new construction sockets. There were no significant differences in LED saturation by code or home type. Figure 11 shows socket saturation levels for all bulb types. Figure 11: Socket-level Bulb Saturation

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LED penetration is now at 100% in new homes. Every home in the sample had at least one LED installed. Three-quarters of homes still had at least one incandescent bulb, 45% of homes used at least one halogen bulb, and just over half of homes used at least one CFL.69 At the home level, average efficient (i.e., LED, CFL or fluorescent) socket saturation was 86%. There were no significant differences by code or home type. Two stretch code homes (one custom, one spec) stood out as outliers with efficient saturation below 20%. Figure 12 displays the distribution of per-home average efficient bulb saturation by code and home type for the 100 sampled homes.70

Figure 12: Per-Home Efficient Socket Saturation

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69 See Table 136. 70 See Table 137.

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8.2 APPLIANCES The share of ENERGY STAR appliances has increased for all appliance types except clothes washers. While the share of ENERGY STAR-labeled appliances has increased since 2015, the ENERGY STAR eligibility requirements have become more stringent for dishwashers, clothes washers, and dehumidifiers. The change in clothes washer eligibility requirements could explain the reduction in the share of ENERGY STAR clothes washers. Table 28 shows the share of ENERGY STAR appliances in the 2015 and 2019 studies.

Table 28: Share of ENERGY STAR Appliances Appliance 2015 Baseline 2019 Baseline Refrigerator 66% 73% Freezer 17% 36% Dishwasher 94% 98% Clothes Washer 85% 81% Clothes Dryer 15% 54% Dehumidifier 86% 92%

The share of electric appliances has increased, while gas and propane alternatives have decreased. Since 2015, the share of electric ovens has increased from 28% to 40% and the share of electric dryers has increased from 78% to 81%.71 This could be due to fewer homes having natural gas and builders choosing to limit the need for propane. As discussed in 4.1.3, the share of homes that use electricity as the primary heating source increased from 2% in 2015 to 14% in this study. However, over the same period, the share of electric ranges decreased from 18% to 16%, indicating that perhaps builders believe that people prefer electric ovens and gas ranges.

______71 See

Table 151: Oven Fuel Table 151 and Table 156.

Section 9 Findings and Considerations

9.1 KEY EFFICIENCY AND COMPLIANCE FINDINGS New non-program homes are about 14% more efficient than homes built in 2015. To make this comparison, 100 REM/rate 72 energy models from this study were compared to the 146 REM/rate models from the 2015 study. The weighted average HERS 69 59 index score of homes from this study (59.3) was more efficient (i.e., lower) than the 2015 2019 average from the 2015 study (69.0). The increase in efficiency is driven by increased envelope tightness, increased duct tightness, and an increased share of efficient heat pumps used for heating. Program homes are about 7% more 2019 HERS Index Score efficient than non-program homes. To make this comparison, a population of 5,073 program home Ekotrope73 models was compared to 100 Ekotrope models of 52 56 the non-program homes from this study. The average HERS index score for Program Non- program homes (51.9) was more efficient program (i.e., lower) than the average weighted HERS index score of non-program homes from this study (55.7).74 The higher efficiency of program homes is driven by increased envelope tightness, increased duct tightness, and a higher share of efficient lighting among program homes.

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72 REM/rate stands for Residential Energy Modeling and Rating and is software package by NORESCO. 73 Ekotrope is a cloud based residential energy modeling software. 74 Note that when comparing non-program homes to program homes, this study uses Ekotrope models since the program adopted Ekotrope software in 2017. However, when making comparisons to previous studies, this study used REM/Rate generated HERS scores because that was the modeling software used previously.

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The difference in efficiency between program and non-program new homes is decreasing. In 2011, the difference in HERS index scores between non-program and program homes was 16 points. In 2019, that difference was only 4 points. Figure 1: shows the decreasing difference between program and non-program homes over time. This is the result of non-program homes becoming more efficient, while program homes have been relatively static over the last few years. The average HERS index score of program homes in 2015 was 55.75

Figure 13: Difference in HERS Index Scores (Non-Program Minus Program)

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75 The 2015 value is based on REM/Rate models, which vary slightly from Ekotrope models.

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The program comprised 70% of the single-family new construction market in 2019. The program penetration of base code towns increased from 24% in 2015 to 50% in 2019. The Program Penetration program penetration of stretch code towns increased from 70% to 87% over that same time. 76 This could be the result of increased code stringencies making program participation less of 41% 70% a lift beyond code. Additionally, the Energy Rating Index (ERI) paths of both base code and stretch code could be leading to the 2015 2019 use of more HERS raters. These raters may then notify clients of opportunities from the program. Also note that, as mentioned above, more municipalities have adopted stretch code since 2015 and stretch code towns tend to have a higher program penetration than base code towns.77

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76 Based on building permit data, there were an estimated 6,922 new homes built in 2015 and 7,224 new homes built in 2019. 77 The percent of municipalities that had adopted stretch code was 42% by the end of 2015, 46% by the end of 2016, 58% by the end of 2017 and 68% by the end of 2018. 78 The 2015 compliance percentage is based on the combination of 2012 IECC and 2009 Stretch code homes from the 2015 residential new construction and code compliance study.

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9.2 KEY BUILDING PRACTICES FINDINGS Efficient builders are changing their practices to address building energy consumption more holistically. One of the clearest examples of this phenomenon is the increased frequency of insulated foundation walls and attic rafters. By choosing to insulate foundation walls instead of basement ceilings and choosing to insulate attic rafters instead of attic floors, builders bring HVAC equipment into conditioned space and thus reduce duct leakage to the outside. The share of homes with insulated basements has increased from 29% to 40% since 2015. The share of homes with sealed attics has increased from 10% to 39% over that same time. Due to these changing practices, the share of homes with all their ducts in unconditioned space dropped from 49% in 2015 to 15% in 2019. Homes increasingly use heat pumps as a primary source of heating, cooling, and water heating. The share of homes with heat pumps as their primary equipment type increased from 2% in 2015 to 14% in 2019 for heating, from 5% to 16% for cooling, and from 11% to 17% for water heating. Heat pumps are more efficient than conventional equipment on a site-level (as opposed to source-level) basis and thus result in lower HERS index scores. Therefore, this increase in heat pump penetration points to greater efficiencies for heating, cooling, and water heating. The use of spray foam as insulation is increasing. The use of spray foam as insulation has increased across all shell measures since 2015. Both open-cell spray foam and closed-cell spray foam comprised a larger share of primary insulation in 2019 than they did in 2015. The changes are especially pronounced in vaulted ceilings (60% in 2019 compared to 14% in 2015), walls (31% compared to 8%), and foundation walls (40% compared to 30%). These spray foams typically replace fiberglass batts, which were the dominant insulation type in all shell measures in 2015. Despite the change in insulation material, the R-value of walls has barely changed since 2015. This could be due to consistent code requirements and limited space in wall cavities. The increased use of spray foams has likely led to tighter building envelopes and an overall reduction in air infiltration.

9.3 CONSIDERATIONS The PAs should consider where cost-effective savings exist and make sure that minimum program requirements both push the market and are cost-effective. The findings from this study show that the efficiency of non-program homes has improved dramatically over the last few

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years while the efficiency of program homes has only modestly improved. This has led to a shrinking efficiency gap between program and non-program homes. The PAs and the implementation contractor for the RNC program are already in the process of updating the UDRH, which will in turn require program homes to be more efficient than they currently are. That said, the PAs should consider increasing the minimum savings requirement for program homes to ensure they exhibit a reasonable efficiency gap over both stretch code and non-program homes while also balancing cost-effectiveness to maintain the current level of savings. For instance, the PAs should also consider that raising the minimum savings requirement too high could potentially exclude potentially cost-effective yet small savings. Alternatively, the PAs could consider trying to funnel more of the market into their new Passive House offerings. The PAs should work with evaluators and other key stakeholders to consider how to measure NTG effectively for the RNC initiative moving forward. The findings and limitations of this study raise questions for any future net-to-gross (NTG) study. This study is study focused solely on new single-family attached and detached homes; however, the effects of the program on single-family homes may differ from those on low-rise multifamily buildings, those participating in the new Passive House pathways, and those participating in the Renovations and Additions pathway. Any future NTG study(s) should consider the differences between these offerings and market segments, as they may be appreciable. Additionally, given this study’s finding of a high penetration of program homes in stretch code towns, a NTG study should examine the possibility that the program has an effect on the adoption rate of the stretch code by municipalities. For the low-rise RNC program, the PAs should consider implementing program changes and then assessing prospective NTG taking those program changes into account. The PAs should be sure to account for the influence of the CSCS initiative in any NTG studies. As this report shows, single-family new construction in Massachusetts has continually grown more efficient. On the surface, high program penetration rates and the narrowing margin between program and non-program homes suggest PAs should expect diminishing impact from the RNC program going forward without changes to the program requirements. That said, the PAs have been executing multiple programs to increase the efficiency of the new construction market including code trainings through the Codes and Standards Compliance and Support (CSCS) initiative for various market actors since 2014. These efforts could be leading to non- participant (non-program) spillover which is reducing the gap between non-program and program homes. In the previous RNC NTG study (TXC48), these efforts were found to have created market effects, primarily in the form of non-participant spillover. The PAs should consider a similar NTG evaluation approach for the single-family new construction market moving forward to ensure that the impacts of all the PAs; efforts in the new construction market are documented and quantified. The PAs should consider what impact increasing program penetration rates will have on program cost-effectiveness. The increase in penetration for the program has likely led to an increase in gross savings due to the sheer volume of projects participating in the program. That said, it is likely that the per-home savings associated with participant projects have decreased as the efficiency of program homes has been relatively static since the previous baseline study. This could reduce the cost-effectiveness of the program. Conversely, since the baseline has gotten more efficient, the incremental costs for the program have likely decreased. Therefore, the program could remain cost-effective with less savings per home.

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The high penetration rates seen in this study could lead to lower net savings for single-family homes as it is possible that free-ridership rates have increased and the potential for program spillover is diminished due to the decreasing non-program market size. If future NTG values decrease it will be important for the PAs to understand how increasing project counts balance with a decreased NTG ratio in the PAs cost-effectiveness screening tool. The PAs should consider conducting a low-rise multifamily baseline evaluation. The PAs have continually monitored progress of the single-family new construction market over time, whereas the multifamily new construction market has rarely been researched. Therefore, the current assumption is that the low-rise multifamily market has similar measure-level efficiencies to the single-family market. It is possible that the multifamily market is substantially different from the single-family market in terms of non-program efficiencies, program penetration rates, and NTG issues such as free-ridership and spillover. Focusing a future study on the low-rise multifamily market would allow evaluators to assess these differences and consider the single- family market and the low-rise multifamily market independently for the first time.

Appendix A Energy Modeling and Consumption

A.1 HERS INDEX SCORES FROM REM/RATE VERSUS EKOTROPE Although multiple software packages have been certified by RESNET as compliant options to calculate HERS index score for several years, REM/Rate has historically been the dominant package employed in Massachusetts. In April of 2018, the residential new construction program adopted Ekotrope as the sole modeling solution for program homes. Despite this, REM/Rate remains popular for rating non-program homes. In fact, seven of the ten sites with accessible builder HERS index scores were generated in REM/Rate, one as recently as March 2019. This study includes results using both REM/Rate and Ekotrope to facilitate comparison between the previous baseline and recent non-program homes.79 Table 29 and Table 30 show the average HERS index scores of the non-program sample as modeled by REM/Rate version 15.8 and Ekotrope, respectively. While the HERS index scores are similar across software packages, HERS index scores from Ekotrope tend to be about 4 points less than ratings from REM/Rate and exhibit a larger variance. The statewide estimated average non-program HERS index score was 59.3 for REM/Rate and 55.7 for Ekotrope. As a point of comparison, stretch code and the ERI path of the base code both require a HERS index score of 55 or lower. Custom homes analyzed with both software packages show a significantly lower (more efficient) average HERS index score than spec homes (Table 29).

Table 29: Average REM/Rate HERS Index Scores Base Code Stretch Code Custom Spec Statewide n 51 49 48 52 100 Minimum 5 -10 2 -10 -10 Mean† 59.6 57.0 55.5 b 61.3 59.3 Maximum 95 90 83 95 95 σ 17.1 18.7 19.0 16.4 17.4

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79 Analysis with both Ekotrope and REM/Rate was possible only for non-program homes, as it requires models for both packages.

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Table 30: Average Ekotrope HERS Index Scores Base Code Stretch Code Custom Spec Statewide n 51 49 48 52 100 Minimum -3 -13 -6 -13 -13 Mean 55.5 53.7 49.8b 58.9 55.7 Maximum 97 129 77 129 129 σ 19.3 22.3 20.4 20.3 20.1

While REM/Rate and Ekotrope calculate different HERS index scores for the same home, as Figure 14 shows there is a strong correlation between REM/Rate and Ekotrope HERS index scores, with an R2 of 0.96 for the linear regression of the two ratings. This excludes four extreme outliers with ratings that differed by more than 20% between the two tools. As we discuss below, NMR explored multiple possible causes for these outliers and for other sites with sizable disparities, including climate, weather, infiltration, mechanical ventilation, and presence of heat pumps. The analysis did not yield any consistent explanation for the discrepancies.

Figure 14: REM/Rate versus Ekotrope HERS Index Scores

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A.2 ENERGY USE INTENSITY The energy use intensity (EUI) values in Table 31 through Table 33 are based on the net energy use of the home, meaning they include credits for any on- site generation. The average estimated EUIs are similar in REM/Rate (35.3) and Ekotrope (34.3). The average program home EUI with credits for on-site generation (32.3) is only slightly lower than the non-program EUI averages.

Table 31: REM/Rate Net EUI (kBTU/ft2) Values Base Code Stretch Code Custom Spec Statewide n 51 49 48 52 100 Min 1.9 -4.1 -0.9 -4.1 -4.1 Max 81.5 63.0 81.5 63.0 81.5 Mean 35.8 33.1 31.9 36.9 35.3 Sd. 13.4 14.4 14.9 12.6 20.3

Table 32: Ekotrope Net EUI (kBTU/ft2) Values Base Code Stretch Code Custom Spec Statewide N 51 49 48 52 100 Min -3.3 -5.0 -3.3 -5.0 -5.0 Max 76.6 74.6 76.6 74.6 76.7 Mean 33.8 34.3 31.1 36.8 34.3 Sd. 14.2 16.1 15.1 14.7 19.6

Table 33:Program Net EUI (kBTU/ft2) With Credits Without Credits n 5,064 5,064 Min -6.9 8.4 Max 63.0 63.0 Mean 32.3 32.5 Sd. 7.5 6.9

Appendix B Detailed Data This section presents detailed findings for all the data collected during on-site visits. As noted in Section LINK TO SECTION HERE, the means and standard deviations in the statewide columns are weighted using the weighting scheme described in Section 3.5. No other values are weighted. A superscript “a” (i.e., a) always signifies that there is a statistically significant difference between the means of base and stretch code homes. A superscript “b” (i.e., b) always signifies that there is a statistically significant difference between the means of custom and spec homes. All tables have a base of homes, not measures, unless noted otherwise.

B.1 GENERAL CHARACTERISTICS

Table 34: CFA Stat Base Code Stretch Code Custom Spec Statewide n 51 49 48 52 100 Min 531.0 1140.0 531.0 896.0 531.0 Max 5548.0 7964.0 6431.0 7964.0 7964.0 Mean 2882.6 3044.5 2983.7 2941.9 2977.6 Sd. 1196.6 1418.5 1215.1 1396.0 1307.2

Table 35: Home Type Home Type Base Code Stretch Code Custom Spec Statewide n 51 49 48 52 100 Detached single-family home 100% a 90% 100% b 90% 96% Attached single-family home 0% 10% 0% 10% 4%

B.2 AIR INFILTRATION AND VENTILATION

B.2.1 Air Infiltration

Table 36: ACH50 stat Base Code Stretch Code Custom Spec Statewide n 51 49 48 52 100 Min 0.7 0.9 0.9 0.7 0.7 Max 8.8 6.5 8.3 8.8 8.8 Mean 3.2 3.0 3.1 3.1 3.1 Sd. 1.8 1.4 1.7 1.5 1.6

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B.2.2 Ventilation

Table 37: Bath Fan (Exhaust Only) Control Type (Base: Bath Fans) Control Base Code Stretch Code Custom Spec Statewide n 164 157 147 174 322 Local Switch 95% a 85% 90% 90% 92% Timer 1% a 10% 6% 5% 4% Continuous 2% 3% 2% 3% 2% Dehumidistat 3% 1% 2% 2% 2% Occupancy Sensor 0% 1% 0% 1% 0%

Table 38: Bath Fan Measured CFM (Base: Bath Fans) Stat Base Code Stretch Code Custom Spec Statewide n 152 154 134 172 306 Min 10.0 10.0 10.0 10.0 10.0 Max 78.0 101.0 101.0 74.0 101.0 Mean 41.5 46.4 45.6 42.7 42.9 Sd. 13.8 16.2 16.6 13.9 15.2

Table 39: HRV, ERV, and WHF Penetration Control Base Code Stretch Code Custom Spec Statewide n 51 49 48 52 100 HRV 8% 12% 17%b 4% 9% ERV 6% 8% 10%b 4% 6% Whole House Fan 2% 0% 0% 2% 2%

B.3 BUILDING SHELL CHARACTERISTICS

B.3.1 Walls

Table 40: Conditioned to Ambient Wall R-value Stat Base Code Stretch Code Custom Spec Statewide n 51 49 48 52 100 Min 17.0 19.0 17.0 19.0 17.0 Max 33.9 42.0 33.9 42.0 42.0 Mean 21.9 21.8 22.2 21.5 21.8 Sd. 3.7 3.7 3.6 3.7 3.5

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Table 41: Conditioned to Ambient Wall Insulation Type Insulation Base Code Stretch Code Custom Spec Statewide n 51 49 48 52 100 FGB 55% 57% 52% 60% 56% OCSF 20% 14% 17% 17% 19% CCSF 4% 8% 8% 4% 5% Rigid foam 8% 0% 6% 2% 4% CCSF and OCSF 4% 4% 2% 6% 4% Cellulose 0% 8% 2% 6% 3% CCSF and FGB 2% 2% 2% 2% 2% FGB and Rigid foam 4% 0% 2% 2% 2% Cellulose and Rigid foam 2% 2% 4% 0% 2% Rock wool 2% 2% 4% 0% 2% FGB and OCSF 0% 2% 0% 2% 1%

Table 42: Percent of Homes Using Continuous Insulation in a Majority of Wall Area Majority Continuous Base Code Stretch Code Custom Spec Statewide n 51 49 48 52 100 Yes 16% 6% 15% 8% 11% No 84% 94% 85% 92% 89%

Table 43: Conditioned to Ambient Wall Insulation Grade Grade Base Code Stretch Code Custom Spec Statewide n 51 49 48 52 100 1 37% 41% 44% 35% 38% 2 55% 55% 48% 62% 56% 3 0% 4% 2% 2% 1% No Cavity Insulation 8% 0% 6% 2% 4%

Table 44: Conditioned Walls R-value Statistics (all walls to unconditioned or buffer spaces) Stat Base Code Stretch Code Custom Spec Statewide n 51 49 48 52 100 Min 0.0 0.0 0.0 0.0 0.0 Max 33.9 30.0 33.9 28.4 33.9 Mean 20.2 19.7 21.4 b 18.6 19.7 Sd. 6.1 5.5 4.7 6.4 6.1

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Table 45: R-values for Walls to Buffer Spaces. (Base: Homes with buffer walls) Stat Base Code Stretch Code Custom Spec Statewide n 43 47 40 50 90 Min 0.0 0.0 0.0 0.0 0.0 Max 25.2 30.0 30.0 24.6 30.0 Mean 17.5 18.4 18.8 17.4 17.7 Sd. 5.1 5.3 4.9 5.3 5.3

B.3.2 Ceilings

B.3.2.1 Flat Ceilings

Table 46: Primary Flat Ceiling Insulation (Base: Homes with flat ceilings) Base Insulation Stretch Code Custom Spec Statewide Code n 38 35 30 43 73 FGB 34% 26% 30% 30% 31% Cellulose 34% 29% 37% 28% 30% Blown FG 13% a 37% 13% 33% 24% Dense-pack FG 5% 0% 3% 2% 3% OCSF 5% 0% 3% 2% 3% None 3% 0% 0% 2% 2% Rock wool 3% 3% 3% 2% 2% CCSF and 0% 3% 3% 0% 1% Cellulose OCSF and CCSF 3% 0% 3% 0% 1% FGB and Cellulose 0% 3% 3% 0% 1%

Table 47: Flat Ceiling Insulation Grade (Base: Homes with flat ceilings) Grade Base Code Stretch Code Custom Spec Statewide n 38 35 30 43 73 1 61% 49% 63% 49% 55% 2 29% 40% 27% 40% 34% 3 8% 11% 10% 9% 9% No Cavity Insulation 3% 0% 0% 2% 2%

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Table 48: Average Flat Ceiling R-value (Base: Homes with flat ceilings) Stat Base Code Stretch Code Custom Spec Statewide n 38 35 30 43 73 Min 0.0 30.0 30.0 0.0 0.0 Max 66.6 69.3 69.3 66.6 69.3 Mean 44.0 45.2 47.0 b 42.8 43.8 Sd. 11.4 9.9 10.0 10.9 11.3

B.3.2.2 Vaulted Ceilings

Table 49: Primary Vaulted Ceiling Insulation (Base: Homes with vaulted ceilings) Insulation Base Code Stretch Code Custom Spec Statewide n 30 31 34 27 61 OCSF 33% 32% 32% 33% 34% FGB 20% 19% 18% 22% 19% CCSF 13% 23% 26% 7% 16% Dense-pack FG 10% 13% 9% 15% 11% OCSF and CCSF 7% 6% 3% 11% 7% Cellulose 7% 6% 6% 7% 6% Rigid foam 7% 0% 6% 0% 3% FGB and CCSF 3% 0% 0% 4% 3%

Table 50: Penetration of Continuous Insulation in Vaulted Ceilings (Base: Homes with vaulted ceilings)80 Continuous Base Code Stretch Code Custom Spec Statewide n 30 31 34 27 61 Cavity Present 60% 68% 59% 70% 64% Continuous Present 50% 39% 50% 37% 44%

Table 51: Vaulted Ceiling Insulation Grade (Base: Homes with vaulted ceilings) Grade Base Code Stretch Code Custom Spec Statewide n 30 31 34 27 61 1 73% 71% 76% 67% 72% 2 20% 19% 18% 22% 20% 3 0% 10% 0% 11% 6% No Cavity Insulation 7% 0% 6% 0% 3%

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80 Percentages can sum to more than 100% since this is a penetration table. Homes may be counted twice if they have two vaulted ceilings and one ceiling has continuous insulation while the other does not.

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Table 52: Average Vaulted Ceiling R-value (Base: Homes with vaulted ceilings) Stat Base Code Stretch Code Custom Spec Statewide n 30 31 34 27 61 Min 19.0 25.4 19.0 29.4 19.0 Max 69.0 73.5 73.5 55.2 73.5 Mean 40.7 43.7 43.8 40.2 41.6 Sd. 9.1 10.3 11.1 7.4 9.2

B.3.3 Floors

Table 53: Primary Insulation in Floors over Unconditioned Basement (Base: Homes with floors over unconditioned basements) Insulation Base Code Stretch Code Custom Spec Statewide n 40 29 30 39 69 FGB 82% 86% 83% 85% 84% Rock wool 8% 7% 13% 3% 6% OCSF 5% 0% 3% 3% 3% None 2% 3% 0% 5% 3% FGB and CCSF 2% 0% 0% 3% 2% Cellulose 0% 3% 0% 3% 1%

Table 54: Cavity Insulation Grade in Floors over Unconditioned Basements (Base: Homes with floors over unconditioned basements) Base Grade Stretch Code Custom Spec Statewide Code n 40 29 30 39 69 1 12% 3% 10% 8% 9% 2 48% 48% 63% b 36% 44% 3 38% 45% 27% b 51% 43% No Cavity 2% 3% 0% 5% 3% Insulation

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Table 55: Average R-value of Floors over Unconditioned Basement (Base: Homes with floors over unconditioned basements) Stat Base Code Stretch Code Custom Spec Statewide n 40 29 30 39 69 Min 0.0 0.0 7.7 0.0 0.0 Max 42.0 38.0 42.0 38.0 42.0 Mean 31.1 29.2 31.8 29.2 30.1 Sd. 6.7 7.5 6.1 7.6 7.1

Table 56: Average R-value of Floors between Conditioned & Unconditioned Space (Base: Homes with floors comprising thermal boundary) Stat Base Code Stretch Code Custom Spec Statewide n 47 44 41 50 91 Min 1.4 0.0 9.3 0.0 0.0 Max 42.0 45.0 45.0 40.5 45.0 Mean 31.0 30.8 32.1 30.0 30.7 Sd. 6.6 6.9 6.2 7.0 6.8

B.3.4 Foundation Walls

Table 57: Primary Insulation in Conditioned Basement Walls (Base: Homes with conditioned basements) Insulation Base Code Stretch Code Custom Spec Statewide n 17 23 25 15 40 CCSF 35% 35% 32% 40% 37% FGB 29% 22% 20% 33% 25% Rigid foam 12% 13% 16% 7% 12% FGB and Rigid foam 6% 0% 0% 7% 5% Cellulose 6% 4% 4% 7% 5% None 6% 4% 8% 0% 4% Rock wool 0% 9% 4% 7% 4% OCSF 0% 9% 8% 0% 4% FGB and Cellulose 6% 0% 4% 0% 3% Foil-faced polyethylene 0% 4% 4% 0% 2%

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Table 58: Conditioned Basement Wall Insulation Grade (Base: Homes with conditioned basements) Grade Base Code Stretch Code Custom Spec Statewide n 17 23 25 15 40 1 29% 22% 28% 20% 26% 2 35% 17% 28% 20% 25% 3 0% 13% 0% 20% 7% No Cavity Insulation 35% 48% 44% 40% 42%

Table 59: Foundation Wall Insulation Arrangement (Base: Homes with conditioned basements) Type Base Code Stretch Code Custom Spec Statewide n 17 23 25 15 40 Cav. Only 41% 52% 48% 47% 44% Interior Cont. Only 35% 35% 28% 47% 38% Cav. And Cont. 12% 0% 4% 7% 7% None 6% 4% 8% 0% 4% Interior Cont. and Ext. 0% 9% 8% 0% 4% Cav. And Ext. 6% 0% 4% 0% 3%

Table 60: Average R-value of Conditioned Basement Wall Insulation (Base: Homes with conditioned basements) Stat Base Code Stretch Code Custom Spec Statewide n 17 23 25 15 40 Min 0.0 0.0 0.0 3.6 0.0 Max 24.0 30.0 30.0 24.0 30.0 Mean 16.2 13.6 15.0 14.2 15.2 Sd. 6.0 6.7 6.7 6.3 6.4

B.3.5 Slabs

Table 61: Proportion of Homes with Insulated Conditioned Slabs (Base: Homes with conditioned slabs) Insulation Base Code Stretch Code Custom Spec Statewide n 23 27 30 20 50 None or Unconfirmed 78% 85% 77% 90% 82% Confirmed 22% 15% 23% 10% 18%

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B.3.6 Windows

Table 62: Average Window U-values (confirmed values only) (Base: Homes with confirmed window U-factors) Stat Base Code Stretch Code Custom Spec Statewide n 12 13 16 9 25 Min 0.23 0.24 0.24 0.23 0.23 Max 0.38 0.36 0.38 0.31 0.38 Mean 0.30 0.28 0.29 0.29 0.29 Sd. 0.04 0.03 0.04 0.03 0.03

Table 63: Glazing Type (Percent of Total Window Area) (Base: Total window area) Glazing Base Code Stretch Code Custom Spec Statewide n 21,098 ft2 19,906 ft2 20,352 ft2 18,652 ft2 39,004 ft2 Double Pane, Lo-E 65.5% 51.5% 46.7% 72.5% 63.9% Double Pane, Lo-E, Argon 23.4% 32.3% 35.0% 19.4% 24.9% Double Pane 3.3% 9.2% 6.7% 5.3% 4.9% Triple Pane, Lo-E, Argon 5.3% 2.7% 5.4% 2.7% 3.9% Triple Pane, Lo-E 2.4% 3.6% 5.6% 0.0% 2.4% Single Pane 0.1% 0.7% 0.7% 0.1% 0.3%

Table 64: Penetration of Gas-Filled Windows in Homes Gas Filled Base Code Stretch Code Custom Spec Statewide n 51 49 48 52 100 Glazing without gas fill present 92% 94% 94% 92% 92% Glazing with gas fill present 24% 35% 35% 23% 26%

Table 65: Penetration of Lo-E Window Coating in Homes Base Stretch LoE Custom Spec Statewide Code Code n 51 49 48 52 100 Glazing without LoE coating 71% 71% 75% 67% 70% present Glazing with LoE coating present 98% 92% 94% 96% 96%

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B.4 MECHANICAL EQUIPMENT

B.4.1 Heating

Table 66: Primary Heating System Location (Base: Primary heating systems) Base Stretch Location Custom Spec Statewide Code Code n 70 58 59 69 128 Unconditioned basement/enclosed 51% 45% 47% 49% 50% crawl Conditioned area 31% 38% 46% b 25% 31% Attic 17% 17% 7% b 26% 19%

Table 67: Primary Heating System Fuel Type Fuel Base Code Stretch Code Custom Spec Statewide n 51 49 48 52 100 Natural Gas 53% a 27% 38% 42% 45% Propane 35% 51% 35% 50% 40% Electric 12% 22% 27% b 8% 14%

Table 68: Primary Heating System by Equipment Type Type Base Code Stretch Code Custom Spec Statewide n 51 49 48 52 100 Furnace 80% a 59% 58% b 81% 75% MSHP 8% 12% 17% b 4% 8% Boiler (forced hot water) 4% 8% 6% 6% 5% ASHP 4% 8% 8% 4% 5% Boiler (hydro-air) 4% 6% 8% 2% 4% Combi appliance 0% 4% 0% 4% 2% GSHP-closed loop 0% 2% 2% 0% 1%

Table 69: Primary Heating System ENERGY STAR Status (Base: Primary heating systems) ENERGY STAR Base Code Stretch Code Custom Spec Statewide n 70 58 59 69 128 ENERGY STAR 67% 69% 76% b 61% 66% Non- ENERGY 33% 31% 24% b 39% 34% STAR

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Table 70: Secondary Heating Equipment Types (Base: Secondary heating systems) Type Base Code Stretch Code Custom Spec Statewide n 6 10 11 5 16 Furnace 33% 30% 18% 60% 38% MSHP 17% 30% 36% 0% 20% Electric radiant surface 17% 0% 0% 20% 12% Fireplace 0% 20% 9% 20% 10% Electric baseboard 17% 0% 9% 0% 6% Unit Heater 17% 0% 9% 0% 6% Boiler (hydro-air) 0% 10% 9% 0% 4% Wood stove 0% 10% 9% 0% 4%

Table 71: ECM Motor in Furnaces (Base: Furnaces) ECM Base Code Stretch Code Custom Spec Statewide n 66 51 48 69 117 ECM installed 59% 53% 62% 52% 55% No ECM 41% 47% 38% 48% 45% installed

Table 72: ENERGY STAR Status for All Furnaces (Base: Furnaces) Base ENERGY STAR Stretch Code Custom Spec Statewide Code n 66 51 48 69 117 Yes 64% 65% 71% 59% 63% No 36% 35% 29% 41% 37%

Table 73: Average Efficiency for All AFUE Systems (Base: All systems with AFUE ratings) Stat Base Code Stretch Code Custom Spec Statewide n 70 62 57 75 132 Min 80.0 80.0 80.0 80.0 80.0 Max 97.5 98.0 98.0 98.0 98.0 Mean 94.3 94.1 95.0 93.6 94.0 Sd. 4.5 4.4 3.1 5.2 4.7

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Table 74: Average Efficiency of All Furnaces (AFUE) (Base: Furnaces) Stat Base Code Stretch Code Custom Spec Statewide n 66 51 48 69 117 Min 80.0 80.0 80.0 80.0 80.0 Max 97.5 98.0 98.0 98.0 98.0 Mean 94.2 94.0 95.1 93.5 93.9 Sd. 4.6 4.8 3.3 5.4 4.9

Table 75: Average Efficiency of Natural Gas Furnaces (AFUE) (Base: Natural gas furnaces) Stat Base Code Stretch Code Custom Spec Statewide n 40 17 24 33 57 Min 80.0 80.0 80.0 80.0 80.0 Max 97.5 98.0 97.5 98.0 98.0 Mean 94.1 93.6 94.4 93.7 93.9 Sd. 4.9 5.3 4.6 5.3 5.1

Table 76: Average Efficiency of Propane Furnaces (AFUE) (Base: Propane furnaces) Stat Base Code Stretch Code Custom Spec Statewide n 26 34 24 36 60 Min 80.0 80.0 95.0 80.0 80.0 Max 97.1 98.0 98.0 97.1 98.0 Mean 94.4 94.2 95.8 93.3 94.0 Sd. 4.3 4.6 0.9 5.5 4.7

Table 77: All Boiler Average Efficiency (AFUE) (Base: Boilers) Stat Base Code Stretch Code Custom Spec Statewide n 4 11 9 6 15 Min 95.0 94.0 94.0 95.0 94.0 Max 95.6 95.0 95.0 95.6 95.6 Mean 95.2 94.7 94.7 95.1 94.9 Sd. 0.3 0.5 0.5 0.2 0.5

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Table 78: Hydronic Boiler Efficiency (AFUE) (Base: Hydronic boilers) Stat Base Code Stretch Code Custom Spec Statewide n 2 4 3 3 6 Min 95.0 94.0 94.0 95.0 94.0 Max 95.6 95.0 95.0 95.6 95.6 Mean 95.3 94.8 94.7 95.2 95.1 Sd. 0.4 0.5 0.6 0.3 0.5

Table 79: Hydro-Air Boiler Average Efficiency (AFUE) (Base: Hydro-air boilers) Stat Base Code Stretch Code Custom Spec Statewide n 2 5 6 1 7 Min 95.0 94.0 94.0 95.0 94.0 Max 95.0 95.0 95.0 95.0 95.0 Mean 95.0 94.6 94.7 95.0 94.8 Sd. 0.0 0.5 0.5 NA 0.5

Table 80: Combined Boiler Space and Water Heating Average Efficiency (AFUE) (Base: Combined boilers) Stat Base Code Stretch Code Custom Spec Statewide n 0 2 0 2 2 Min 0.0 95.0 0.0 95.0 95.0 Max 0.0 95.0 0.0 95.0 95.0 Mean 0.0 95.0 0.0 95.0 95.0 Sd. 0.0 0.0 0.0 0.0 0.0

Table 81: Heat Pump Average Efficiency (HSPF) (Base: Heat pumps) Stat Base Code Stretch Code Custom Spec Statewide n 12 19 25 6 31 Min 9.5 8.2 8.2 9.5 8.2 Max 12.0 16.0 16.0 10.3 16.0 Mean 10.4 10.7 10.7 9.9 10.5 Sd. 0.7 1.9 1.6 0.3 1.4

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Table 82: MSHP Average Efficiency (HSPF) (Base: Mini and multi-split heat pumps) Stat Base Code Stretch Code Custom Spec Statewide n 10 13 19 4 23 Min 9.5 9.6 9.5 9.8 9.5 Max 12.0 14.0 14.0 10.3 14.0 Mean 10.4 10.7 10.7 10.1 10.5 Sd. 0.8 1.2 1.1 0.2 0.9

Table 83: ASHP Average Efficiency (HSPF) (Base: Air source heat pumps)81 Stat Base Code Stretch Code Custom Spec Statewide n 2 5 5 2 7 Min 9.6 8.2 8.2 9.5 8.2 Max 10.5 12.0 12.0 9.6 12.0 Mean 10.1 9.5 9.7 9.6 9.7 Sd. 0.6 1.6 1.6 0.1 1.2

Table 84: GSHP Average Efficiency (HSPF) (Base: Ground source heat pumps) Stat Base Code Stretch Code Custom Spec Statewide n 0 1 1 0 1 Min NA NA NA NA 16.0 Max NA NA NA NA 16.0 Mean NA 16.0 16.0 NA 16.0 Sd. NA NA NA NA NA

Table 85: ENERGY STAR Status – All Equipment Types (Base: Heating systems) ENERGY STAR Base Code Stretch Code Custom Spec Statewide n 82 81 82 81 163 Yes 70% 73% 80% 62% 69% No 30% 27% 20% 38% 31%

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81 Excludes one unit, which was only used for cooling.

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Table 86: Heating Capacity per Square Foot Served (Btu/ft2) Stat Base Code Stretch Code Custom Spec Statewide n 51 49 48 52 100 Min 16.0 14.0 16.0 14.0 14.0 Max 71.6 107.4 71.6 107.4 107.4 Mean 35.0 34.6 34.3 35.3 34.7 Sd. 10.1 16.6 11.1 15.7 13.6

B.4.2 Cooling

Table 87: Primary Cooling System Equipment Type Type Base Code Stretch Code Custom Spec Statewide n 51 49 48 52 100 Central Air-split 84% 69% 71% 83% 80% MSHP 8% 12% 17% b 4% 8% ASHP 6% 8% 10% 4% 6% Room Air Conditioner 2% 6% 0% 8% 4% None 0% 2% 0% 2% 1% GSHP-closed loop 0% 2% 2% 0% 1%

Table 88: Primary Cooling System Location (Base: Primary cooling systems) Base Stretch Location Custom Spec Statewide Code Code n 73 62 64 71 135 Unconditioned basement/enclosed 47% 35% 42% 41% 43% crawl Conditioned area 30% 45% 45% b 30% 33% Attic 23% 19% 12% b 30% 24%

Table 89: Ducted Cooling System Location (Base: Primary cooling systems) Base Stretch Location Custom Spec Statewide Code Code n 73 62 64 71 135 Unconditioned basement/enclosed 47% 40% 47% 41% 45% crawl Conditioned area 29% 40% 41% 29% 31% Attic 24% 19% 12% b 30% 24%

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Table 90: Primary Cooling System ENERGY STAR Status (Base: Primary cooling systems) ENERGY STAR Base Code Stretch Code Custom Spec Statewide n 73 62 64 71 135 No 74% 69% 61% b 82% 74% Yes 26% 31% 39% b 18% 26%

Table 91: All Cooling System Efficiency (SEER) (Base: Cooling systems excluding room air conditioners)82 Stat Base Code Stretch Code Custom Spec Statewide n 82 75 83 74 157 Min 13.0 13.0 13.0 13.0 13.0 Max 22.0 33.0 33.0 20.0 33.0 Mean 14.9 15.3 15.7 14.4 14.9 Sd. 2.2 3.0 3.1 1.7 2.6

Table 92: Central Air Conditioning Average Efficiency (SEER) (Base: Central air conditioners) Stat Base Code Stretch Code Custom Spec Statewide n 69 56 57 68 125 Min 13.0 13.0 13.0 13.0 13.0 Max 17.0 18.0 18.0 17.0 18.0 Mean 14.1 14.2 14.2 14.1 14.1 Sd. 1.1 1.2 1.2 1.1 1.2

Table 93: ASHP Average Efficiency (SEER) (Base: Air source heat pumps) Stat Base Code Stretch Code Custom Spec Statewide n 3 5 6 2 8 Min 14.0 14.0 14.0 17.0 14.0 Max 18.5 18.0 18.5 17.5 18.5 Mean 16.7 16.2 16.1 17.2 16.6 Sd. 2.4 2.0 2.3 0.4 1.9

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82 Includes one GSHP with an EER of 14.6 converted to SEER using the equation SEER = (1.12 - √ (1.2544- (0.08*EER)))/ 0.04.

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Table 94: MSHP Average Efficiency (SEER) (Base: Mini and multi-split heat pumps) Stat Base Code Stretch Code Custom Spec Statewide n 10 13 19 4 23 Min 18.0 16.0 16.0 19.0 16.0 Max 22.0 33.0 33.0 20.0 33.0 Mean 19.8 19.6 19.7 19.5 19.7 Sd. 1.0 4.3 3.6 0.6 2.8

Table 95: Room Air Conditioner Average Efficiency (CEER) (Base: Room air conditioners with CEER rating)83 Stat Base Code Stretch Code Custom Spec Statewide n 2 7 1 8 9 Min 8.1 8.9 11.0 8.1 8.1 Max 11.0 12.0 11.0 12.0 12.0 Mean 9.6 10.8 11.0 10.4 10.3 Sd. 2.0 1.3 NA 1.5 1.6

Table 96: Room Air Conditioner Average Efficiency (EER) (Base: Room air conditioners with EER rating) Stat Base Code Stretch Code Custom Spec Statewide n 0 2 0 2 2 Min 0.0 8.9 0.0 8.9 8.9 Max 0.0 10.8 0.0 10.8 10.8 Mean 0.0 9.9 0.0 9.9 9.8 Sd. 0.0 1.3 0.0 1.3 1.6

Table 97: All Cooling System ENERGY STAR Status (Base: Cooling systems) ENERGY STAR Base Code Stretch Code Custom Spec Statewide n 84 85 84 85 169 Yes 31% 35% 46% b 20% 30% No 69% 65% 54% b 80% 70%

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83 One RAC did not have a nameplate, so while its ENERGY STAR status was verifiable due to an ENERGY STAR logo on the unit, there was no model number to determine an efficiency rating.

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Table 98: Secondary Cooling Equipment Type (Base: Secondary cooling systems) Type Base Code Stretch Code Custom Spec Statewide n 2 3 5 0 5 MSHP 50% 100% 80% – 75% Room Air Conditioner 50% 0% 20% – 25%

Table 99: Cooling Capacity Per Square Foot Served (Btu/ft2) (Base: Homes with cooling systems) Stat Base Code Stretch Code Custom Spec Statewide n 51 48 48 51 99 Min 3.9 7.3 7.3 3.9 3.9 Max 32.0 48.9 48.9 23.4 48.9 Mean 17.3 16.6 18.7 15.3 16.7 Sd. 4.8 7.2 7.3 4.1 5.5

B.4.3 Water Heating

Table 100: Water Heater Type (Base: Water heaters) Type Base Code Stretch Code Custom Spec Statewide n 54 54 52 56 108 Instantaneous 43% 44% 44% 43% 43% Storage, stand alone 33% 20% 21% 32% 29% Heat pump 15% 20% 19% 16% 17% Indirect w/storage tank 7% 7% 12% 4% 7% Combi appliance 2% 4% 0% 5% 3% Solar DHW 0% 4% 4% 0% 1%

Table 101: Water Heater Fuel (Base: Water heaters) Fuel Base Code Stretch Code Custom Spec Statewide n 54 54 52 56 108 Natural Gas 52% a 28% 38% 41% 44% Propane 26% 39% 35% 30% 29% Electric 22% 30% 23% 29% 26% Solar 0% 4% 4% 0% 1%

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Table 102: Water Heater Location (Base: Water heaters) Location Base Stretch Custom Spec Statewide Code Code n 54 54 52 56 108 Unconditioned basement/enclosed 69% 54% 56% 66% 65% crawl Conditioned area 28% 43% 40% 30% 32% Garage or open crawl space 2% 4% 2% 4% 2% Conditioned crawl space 2% 0% 2% 0% 1%

Table 103: Water Heaters by Type and Fuel (Base: Water heaters) Base Stretch Type and Fuel Custom Spec Statewide Code Code n 54 54 52 56 108 Instantaneous, Natural Gas 30% 20% 21% 29% 27% Heat pump, Electric 15% 20% 19% 16% 17% Instantaneous, Propane 13% 24% 23% 14% 16% Storage, stand alone, Natural 17% a 2% 8% 11% 12% Gas Storage, stand alone, Propane 9% 9% 10% 9% 9% Storage, stand alone, Electric 7% 9% 4% 12% 8% Indirect w/storage tank, Natural 6% 6% 10% 2% 5% Gas Combi appliance, Propane 2% 4% 0% 5% 3% Indirect w/storage tank, Propane 2% 2% 2% 2% 2% Solar DHW, Solar 0% 4% 4% 0% 1%

Table 104: Hot Water Storage Tank Sizes (Base: Water heater storage tanks) Tank Size Base Code Stretch Code Custom Spec Statewide n 29 29 29 29 58 40 or Less 13% 7% 3% 17% 13% 41 to 55 37% 50% 41% 45% 40% 56 to 75 17% 7% 21% 3% 11% Greater than 75 33% 36% 34% 34% 36%

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Table 105: DHW ENERGY STAR Status (Base: Water heaters excluding indirect and solar thermal) ENERGY STAR Base Code Stretch Code Custom Spec Statewide n 50 48 44 54 98 Yes 76% 81% 84% 74% 77% No 24% 19% 16% 26% 23%

Table 106: All Water Heater Efficiency (EF) (Base: All water heaters with EF or converted UEF) Stat Base Code Stretch Code Custom Spec Statewide n 54 52 50 56 106 Min 0.61 0.58 0.61 0.58 0.58 Max 3.50 3.63 3.63 3.53 3.63 Mean 1.21 1.40 1.35 1.26 1.29 Sd. 0.89 1.06 1.03 0.94 0.96

Table 107: All Electric Water Heater Efficiency (EF) (Base: All electric water heaters with EF or converted UEF) Stat Base Code Stretch Code Custom Spec Statewide n 12 16 12 16 28 Min 0.84 0.68 0.84 0.68 0.68 Max 3.50 3.63 3.63 3.53 3.63 Mean 2.50 2.61 2.95 2.28 2.54 Sd. 1.18 1.24 0.99 1.29 1.18

Table 108: All Fossil Fuel Water Heater Efficiency (EF) (Base: All fossil fuel water heaters with EF or converted UEF) Stat Base Code Stretch Code Custom Spec Statewide n 42 36 38 40 78 Min 0.61 0.58 0.61 0.58 0.58 Max 0.99 0.97 0.99 0.97 0.99 Mean 0.84 0.87 0.85 0.86 0.85 Sd. 0.13 0.12 0.13 0.13 0.13

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Table 109: Instantaneous Water Heater Efficiency (EF) (Base: Instantaneous water heaters with EF or converted UEF) Base Stretch Stat Custom Spec Statewide Code Code n 23 24 23 24 47 Min 0.82 0.81 0.82 0.81 0.81 Max 0.99 0.97 0.99 0.97 0.99 Mean 0.95 0.94 0.94 0.95 0.95 Sd. 0.03 0.04 0.04 0.03 0.03

Table 110: Heat Pump Water Heater Efficiency (EF) (Base: Heat pump water heaters with EF or converted UEF) Base Stretch Stat Custom Spec Statewide Code Code n 8 11 10 9 19 Min 3.03 3.05 3.03 3.03 3.03 Max 3.50 3.63 3.63 3.53 3.63 Mean 3.29 3.41 3.36 3.36 3.34 Sd. 0.24 0.20 0.23 0.22 0.22

Table 111: Indirect Water Heater Efficiency (EF) (Base: Indirect water heaters with EF or converted UEF) Stat Base Code Stretch Code Custom Spec Statewide n 4 4 6 2 8 Min 0.71 0.71 0.71 0.71 0.71 Max 0.72 0.71 0.71 0.72 0.72 Mean 0.71 0.71 0.71 0.72 0.71 Sd. 0.01 0.00 0.00 0.01 0.00

Table 112: Combi Water Heater Efficiency (EF) (Base: Combined water heaters with EF or converted UEF) Stretch Stat Base Code Custom Spec Statewide Code N 1 2 0 3 3 Min 0.96 0.71 NA 0.71 0.71 Max 0.96 0.95 NA 0.96 0.96 Mean 0.96 0.83 NA 0.87 0.90 Sd. NA 0.17 NA 0.14 0.14

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Table 113: Electric Storage Water Heater Efficiency (EF) (Base: Electric storage water heaters with EF or converted UEF) Stat Base Code Stretch Code Custom Spec Statewide N 4 5 2 7 9 Min 0.84 0.68 0.84 0.68 0.68 Max 0.95 0.95 0.93 0.95 0.95 Mean 0.92 0.84 0.88 0.87 0.89 Sd. 0.05 0.15 0.06 0.13 0.11

Table 114: Gas Storage Water Heater Efficiency (EF) (Base: Gas storage water heaters with EF or converted UEF) Stat Base Code Stretch Code Custom Spec Statewide n 14 6 9 11 20 Min 0.61 0.58 0.61 0.58 0.58 Max 0.73 0.70 0.73 0.71 0.73 Mean 0.68 0.68 0.68 0.68 0.68 Sd. 0.04 0.05 0.04 0.04 0.03

Table 115: Presence of DWH Pipe Wrap (Base: Water heaters) Pipe Wrap Base Code Stretch Code Custom Spec Statewide n 54 54 52 56 108 Not at all 67% 57% 56% 68% 65% Partially 24% 22% 23% 23% 23% Completely 9% 20% 21% 9% 12%

Table 116: Average R-value of DHW Pipe Wrap (Base: Water heaters) Stat Base Code Stretch Code Custom Spec Statewide n 54 54 52 56 108 Min 0.0 0.0 0.0 0.0 0.0 Max 3.0 6.0 6.0 3.0 6.0 Mean 0.9 1.3 1.4 0.9 1.0 Sd. 1.4 1.7 1.7 1.3 1.5

Table 117: Storage Tank Insulated Wrap Presence (Base: Storage tank water heaters) Tank Wrap Base Code Stretch Code Custom Spec Statewide n 26 22 21 27 48 No 100% 100% 100% 100% 100%

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Table 118: Presence of DHW Recirculation systems in Homes Recirculation Base Code Stretch Code Custom Spec Statewide n 51 49 48 52 100 None (standard system) 98% 92% 94% 96% 96% Timer or Uncontrolled 2% 8% 6% 4% 4%

Table 119: Bathroom Sink Average Flow Rate (GPM) (Base: Bathroom sinks with verifiable GPM) Stat Base Code Stretch Code Custom Spec Statewide n 182 151 159 174 333 Min 1.2 1.2 1.2 1.2 1.2 Max 2.2 2.5 2.2 2.5 2.5 Mean 1.3 1.4 1.4 1.4 1.4 Sd. 0.2 0.3 0.2 0.3 0.2

Table 120: Showerhead Average Flow Rate (GPM) (Base: Showerheads with verifiable GPM) Stat Base Code Stretch Code Custom Spec Statewide n 126 118 118 126 244 Min 1.0 1.5 1.0 1.8 1.0 Max 2.8 2.8 2.8 2.8 2.8 Mean 2.2 2.3 2.2 2.3 2.2 Sd. 0.3 0.3 0.3 0.3 0.3

Table 121: Kitchen Sink Average Flow Rate (GPM) (Base: Kitchen sinks with verifiable GPM) Stat Base Code Stretch Code Custom Spec Statewide n 43 52 52 43 95 Min 1.2 1.2 1.2 1.2 1.2 Max 2.2 2.5 2.5 2.2 2.5 Mean 1.7 1.7 1.7 1.7 1.7 Sd. 0.2 0.2 0.2 0.2 0.2

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Table 122: Utility Sink Average Flow Rate (GPM) (Base: Utility sinks with verifiable GPM) Stat Base Code Stretch Code Custom Spec Statewide n 14 11 16 9 25 Min 1.5 1.5 1.5 1.8 1.5 Max 2.2 2.0 2.2 2.0 2.2 Mean 1.8 1.8 1.8 1.8 1.8 Sd. 0.2 0.1 0.2 0.1 0.1

B.4.4 Thermostats

Table 123: Thermostat Configuration Type (Base: Thermostats) Base Stretch Type Custom Spec Statewide Code Code n 100 104 97 107 204 Smart 45% 61% 54% 52% 50% Programmable+WiFi 41% 36% 35% 41% 40% Programmable 10% 0% 6% 4% 6% Manual 3% 4% 4% 3% 3% Baseboard with Built-In 1% 0% 1% 0% 0% Controls

Table 124: Average Summer Set Point (Base: Thermostats with recorded set points) Stat Base Code Stretch Code Custom Spec Statewide n 94 90 93 91 184 Min 66.0 68.0 66.0 68.0 66.0 Max 83.0 80.0 83.0 79.5 83.0 Mean 72.5 73.1 72.9 72.8 72.8 Sd. 3.6 2.5 3.1 3.1 3.1

Table 125: Average Winter Set Point (Base: Thermostats with recorded set points) Stat Base Code Stretch Code Custom Spec Statewide n 91 93 89 95 184 Min 50.0 59.5 50.0 60.0 50.0 Max 72.0 75.0 75.0 72.0 75.0 Mean 67.3 67.4 66.9 67.7 67.5 Sd. 4.1 2.8 4.5 2.2 3.2

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B.5 DUCTS

B.5.1 Duct Leakage

Table 126: Duct Location Location Base Stretch Custom Spec Statewide Code Code n 51 49 48 52 100 All Ducts in Conditioned Space 12% 22% 23% 12% 15% Some Ducts in Conditioned 61% 55% 52% 63% 61% Space All Ducts in Unconditioned 20% 8% 15% 13% 15% Space No Ducts 8% 14% 10% 12% 10%

Table 127: Duct Leakage to the Outside (CFM25/100 ft2 CFA) (Base: Homes with measurable duct systems84) Stat Base Code Stretch Code Custom Spec Statewide n 46 41 42 45 87 Min 0.0 0.0 0.0 0.0 0.0 Max 19.8 31.2 19.8 31.2 31.2 Mean 4.5 3.7 3.6 4.6 4.3 Sd. 4.5 5.7 4.2 5.8 4.6

Table 128: Total Duct Leakage (TDL) (Base: Homes with measurable duct systems) Stat Base Code Stretch Code Custom Spec Statewide n 42 40 39 43 82 Min 3.6 2.8 3.6 2.8 2.8 Max 39.3 36.9 36.9 39.3 39.3 Mean 14.4 11.8 13.5 12.8 13.6 Sd. 8.8 7.6 7.7 8.8 7.9

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84 Ducts entirely in conditioned space were considered to have zero duct leakage to outside.

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B.5.2 Duct Insulation

Table 129: Weighted Average Duct R-value by Location (Base: Duct systems by location) Base Stretch Custom Spec Statewide Code Code R- R- R- R- R- n n n n n value value value value value Attic Supply Ducts 22 6.6 22 6.5 14 6.5 30 6.6 44 6.6 Unconditioned 34 5.6 22 5.6 26 5.8 30 5.4 56 5.6 Basement Ducts Attic Return Ducts 22 6.6 20 5.9 12 6.0 30 6.3 42 6.4 Garage Ducts 4 6.8 4 5.1 5 6.0 3 5.7 8 6.1 Enclosed Crawl Space 1 7.0 ------1 7.0 1 7.0 Ducts

B.6 RENEWABLES

Table 130: Renewables Penetration Base Stretch Type Custom Spec Statewide Code Code n 51 49 48 52 100 Photovoltaics 12% 16% 21% 8% 12% Electric vehicles 6% 4% 10% 0% 4% Battery Storage 4% 0% 4% 0% 2% Solar Thermal 0% 4% 4% 0% 1%

Table 131: Photovoltaic Capacity (kW) (Base: Homes with Solar PV) Stat Base Code Stretch Code Custom Spec Statewide n 7 11 14 4 18 Min 4.2 3.0 3.0 5.8 3.0 Max 11.9 12.5 12.5 9.0 12.5 Mean 8.4 6.8 7.5 7.3 7.5 Sd. 3.2 2.9 3.4 1.5 3.0

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B.7 ELECTRICAL METERS No homes were observed to have three phase power.

Table 132: Electrical Panel Service Orientation Orientation Base Code Stretch Code Custom Spec Statewide n 52 48 48 52 100 Back 0% 4% 0% 4% 2% Bottom 31% 25% 29% 27% 29% Side 2% 0% 2% 0% 1% Top 65% 71% 67% 69% 68%

Table 133: Electrical Service Amperage (Base: Homes with verified amperage) Stat Base Code Stretch Code Custom Spec Statewide n 48 45 43 50 93 Minimum 125 200 200 125 125 Maximum 400 450 450 400 450 Mean 208 228 226 211 214 Sd. 43 72 69 50 87

Table 134: Electrical Service Amperage Verification (Base: Homes with verified amperage) Method Base Code Stretch Code Custom Spec Statewide n 48 45 43 50 93 Homeowner 0% 2% 0% 2% 1% Main breaker 46% 51% 56% 42% 47% Meter capacity 2% 4% 5% 2% 3% Panel capacity 52% 42% 40% 54% 50%

B.8 LIGHTING

Table 135: Bulb Saturation (All Socket Types) (Base: Sockets) Bulb Type Base Code Stretch Code Custom Spec Statewide n 5287 5057 5155 5189 10344 LED 86% 87% 86% 86% 86% Incandescent 7% 7% 7% 7% 7% Halogen 4% 3% 3% b 4% 4% CFL 2% 2% 1% b 2% 2% Fluorescent 1% 1% 2% b 0% 1% Empty Socket 0% 0% 1% b 0% 0%

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Table 136: Efficient Bulb Penetration Bulb Type Base Code Stretch Code Custom Spec Statewide n 51 49 48 52 100 LED 100% 100% 100% 100% 100% CFL 47% 51% 40% 58% 51% Fluorescent 14% 14% 17% 12% 13% Halogen 47% 43% 50% 40% 45% Incandescent 76% 67% 69% 75% 75% Empty Socket 14% 12% 21% 6% 11%

Table 137: Average per-home Efficient Bulb Saturation Stat Base Code Stretch Code Custom Spec Statewide n 51 49 48 52 100 Min 42.0 10.0 10.0 18.0 10.0 Max 100.0 100.0 100.0 100.0 100.0 Mean 85.4 86.5 84.6 87.2 85.6 Sd. 17.6 20.9 20.3 18.2 18.6

B.9 APPLIANCES

B.9.1 Refrigerators

Table 138: Primary Refrigerator ENERGY STAR Status (Base: Primary Refrigerators) ENERGY STAR Base Code Stretch Code Custom Spec Statewide n 51 49 48 52 100 Yes 73% 78% 77% 73% 74% No 27% 22% 23% 27% 26%

Table 139: Primary Refrigerator kWh/Year (Base: Primary Refrigerators) Stat Base Code Stretch Code Custom Spec Statewide n 51 49 48 52 100 Min 436.0 283.0 436.0 283.0 283.0 Max 832.0 784.0 832.0 784.0 832.0 Mean 646.3 653.0 649.2 649.9 646.4 Sd. 79.8 90.4 86.7 83.9 82.3

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Table 140: Primary Refrigerator Volume (ft3) (Base: Primary Refrigerators) Volume Base Code Stretch Code Custom Spec Statewide n 51 49 48 52 100 >25 33% 43% 40% 37% 35% 23-25 22% 22% 21% 23% 22% 20-22 39% 29% 33% 35% 36% 16-19 6% 2% 4% 4% 5% <16 0% 4% 2% 2% 1%

Table 141: Primary Refrigerator Configuration (Base: Primary Refrigerators) Configuration Base Code Stretch Code Custom Spec Statewide n 51 49 48 52 100 Bottom Freezer 84% 76% 81% 79% 81% Side by Side 12% 20% 12% 19% 15% Top Freezer 4% 4% 6% 2% 4%

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Table 142: Secondary Refrigerators Categorical Summary (Base: Secondary Refrigerators) ENERGY Base Stretch Code Custom Spec Statewide STAR Code n 13 24 22 15 37 Yes 23% 38% 32% 33% 29% No 54% 58% 50% 67% 61% Unknown 23% 4% 18% 0% 10% Base Volume Stretch Code Custom Spec Statewide Code n 13 24 22 15 37 >25 8% 0% 5% 0% 3% 23-25 0% 8% 9% 0% 4% 20-22 15% 4% 9% 7% 10% 16-19 31% 54% 45% 47% 44% <10 31% 29% 18% 47% 33% Unknown 15% 4% 14% 0% 7% Stretch Age Base Code Custom Spec Statewide Code n 13 24 22 15 37 2019 8% 8% 5% 13% 9% 2018 23% 12% 18% 13% 18% 2017 0% 21% 18% 7% 10% 2016 0% 4% 0% 7% 2% 2015 0% 4% 5% 0% 2% 2014 8% 12% 9% 13% 9% 2011-2013 0% 4% 5% 0% 2% 2006-2010 15% 12% 9% 20% 16% 2001-2005 8% 8% 5% 13% 9% 1996-2000 0% 4% 0% 7% 2% Unknown 38%a 8% 27% 7% 20% Base Configuration Stretch Code Custom Spec Statewide Code n 13 24 22 15 37 Top Freezer 54% 62% 59% 60% 59% Single Door 38% 21% 18% 40% 31% Bottom Freezer 8% 12% 18% 0% 8% Internal Freezer 0% 4% 5% 0% 2%

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Table 143: Secondary Refrigerators kWh/Year (Base: Secondary Refrigerators with kWh/Year) Stat Base Code Stretch Code Custom Spec Statewide n 13 23 21 15 36 Min 236.0 184.0 236.0 184.0 184.0 Max 747.0 996.0 996.0 669.0 996.0 Mean 486.2 422.3 458.1 427.5 456.9 Sd. 174.3 160.8 177.8 152.7 163.4

B.9.2 Freezers

Table 144: Freezer Categorical Summary (Base: Freezers) Stretch ENERGY STAR Base Code Custom Spec Statewide Code n 5 21 15 11 26 Yes 40% 38% 40% 36% 37% No 60% 62% 60% 64% 63% Base Stretch Volume Custom Spec Statewide Code Code n 5 21 15 11 26 20-22 20% 29% 40% 9% 24% 16-19 80% 33% 47% 36% 46% <10 0% 38% 13% 55% 30% Base Stretch Age Custom Spec Statewide Code Code n 5 21 15 11 26 2019 0% 14% 14% 9% 11% 2018 0% 10% 0% 18% 8% 2017 0% 10% 14% 0% 7% 2016 0% 5% 0% 9% 4% 2015 25% 0% 0% 9% 9% 2011-2013 0% 14% 14% 9% 11% 2006-2010 50% 19% 29% 18% 24% 2001-2005 0% 14% 14% 9% 11% DK 25% 14% 14% 18% 16% Base Configuration Stretch Code Custom Spec Statewide Code n 5 21 15 11 26 Chest 60% 47% 38% 64% 50% Upright 40% 53% 62% 36% 50%

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Table 145: Freezer kWh/Year (Base: Freezers with kWh/Year) Stat Base Code Stretch Code Custom Spec Statewide n 5 20 15 10 25 Min 350.0 172.0 277.0 172.0 172.0 Max 845.0 564.0 845.0 564.0 845.0 Mean 534.6 391.1 472.6 340.6 422.9 Sd. 220.4 124.0 143.4 140.1 156.6

B.9.3 Dishwashers

Table 146: Dishwasher ENERGY STAR Status (Base: Dishwashers) ENERGY STAR Base Code Stretch Code Custom Spec Statewide n 53 50 50 53 103 Yes 98% 98% 98% 98% 98% No 2% 0% 0% 2% 2% Unknown 0% 2% 2% 0% 1%

Table 147: Dishwasher Age Chart (Base: Dishwashers) Age Base Code Stretch Code Custom Spec Statewide n 53 50 50 53 103 2019 8% 12% 10% 10% 9% 2018 63% 56% 54% 65% 64% 2017 21% 28% 30% 19% 21% 2016 2% 2% 2% 2% 2% 2013 2% 0% 0% 2% 2% Unknown 4% 2% 4% 2% 3%

Table 148: Dishwasher kWh/Year (Base: Dishwashers with kWh/Year) Stat Base Code Stretch Code Custom Spec Statewide n 53 49 49 53 102 Min 199.0 230.0 220.0 199.0 199.0 Max 305.0 296.0 296.0 305.0 305.0 Mean 264.1 265.2 266.7 262.7 264.0 Sd. 16.0 11.0 11.1 15.7 15.2

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B.9.4 Ovens and Ranges

Table 149: Oven and Range Types (Base: Ovens and Range Units) Type Base Code Stretch Code Custom Spec Statewide n 71 69 74 66 140 Oven and range 55% 62% 53% 65% 59% Oven only 27% 25% 30% 21% 25% Range only 18% 13% 18% 14% 16%

Table 150: Range Fuel (Base: Ranges) Range Fuel Base Code Stretch Code Custom Spec Statewide n 52 52 52 52 104 Natural Gas 56% a 35% 44% 46% 50% Propane 31% 40% 29% 42% 34% Electric 13% 25% 27% b 12% 16%

Table 151: Oven Fuel (Base: Ovens) Oven Fuel Base Code Stretch Code Custom Spec Statewide n 58 60 61 57 118 Electric 43% 42% 54% b 30% 40% Natural Gas 40% a 23% 31% 32% 34% Propane 17% a 35% 15% b 39% 26%

B.9.5 Clothes Washers

Table 152: Clothes Washer ENERGY STAR Status (Base: Clothes Washers) ENERGY STAR Base Code Stretch Code Custom Spec Statewide n 54 53 53 54 107 Yes 81% 79% 77% 83% 80% No 19% 21% 23% 17% 20%

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Table 153: Clothes Washer kWh/Year (Base: Clothes Washers with kWh/Year) Stat Base Code Stretch Code Custom Spec Statewide n 53 53 52 54 106 Min 60.0 64.0 60.0 75.0 60.0 Max 356.0 552.0 552.0 470.0 552.0 Mean 131.3 157.5 146.2 142.7 141.2 Sd. 61.4 99.7 89.7 77.7 77.8

Table 154: Clothes Washer IMEF (Base: Clothes Washers) Stat Base Code Stretch Code Custom Spec Statewide n 53 53 52 54 106 Min 1.1 0.8 0.8 0.9 0.8 Max 2.9 3.0 2.9 3.0 3.0 Mean 2.5 2.4 2.4 2.4 2.4 Sd. 0.5 0.6 0.6 0.6 0.6

B.9.6 Clothes Dryers

Table 155: Dryer ENERGY STAR Status (Base: Dryers) ENERGY STAR Base Code Stretch Code Custom Spec Statewide n 53 51 51 53 104 Yes 58% 51% 55% 55% 55% No 42% 49% 45% 45% 45%

Table 156: Dryer Fuel (Base: Dryers) Fuel Base Code Stretch Code Custom Spec Statewide n 53 51 51 53 104 Electric 78% 85% 79% 83% 81% Propane 11% 10% 10% 11% 11% Natural gas 11% 6% 12% 6% 9%

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Table 157: Dryer Moisture Sensor Status (Base: Dryers) Moisture Sensing Base Code Stretch Code Custom Spec Statewide n 53 51 51 53 104 Yes 96% 98% 100% 94% 96% No 4% 2% 0% 6% 4%

Table 158: Dryer Energy Factor — CEF (Base: Dryers with CEF or converted EF) Stat Base Code Stretch Code Custom Spec Statewide n 52 48 48 52 100 Min 3.3 3.1 3.3 3.1 3.1 Max 4.5 4.5 4.5 4.5 4.5 Mean 3.8 3.7 3.8 3.7 3.8 Sd. 0.3 0.3 0.2 0.3 0.3

Table 159: Dryer Energy Factor – EF (Base: Dryers with EF or converted CEF) Stat Base Code Stretch Code Custom Spec Statewide n 52 48 48 52 100 Min 2.9 2.7 2.9 2.7 2.7 Max 3.9 3.9 3.9 3.9 3.9 Mean 3.3 3.3 3.3 3.3 3.3 Sd. 0.2 0.2 0.2 0.2 0.2

B.9.7 Dehumidifiers

Table 160: Dehumidifier ENERGY STAR Status (Base: Dehumidifiers) ENERGY STAR Base Code Stretch Code Custom Spec Statewide n 27 27 23 31 54 Yes 85% 100% 87% 97% 92% No 15% 0% 13% 3% 8%

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Table 161: Dehumidifier Age (Base: Dehumidifiers) Age Base Code Stretch Code Custom Spec Statewide n 27 27 23 31 54 2019 19% 19% 9% 26% 22% 2018 19% 11% 17% 13% 16% 2017 19% 22% 26% 16% 19% 2016 11% 11% 13% 10% 10% 2015 4% 11% 9% 6% 7% 2014 11% 7% 9% 10% 9% 2011-2013 7% 4% 9% 3% 5% 2006-2010 7% 11% 4% 13% 10% DK 4% 4% 4% 3% 3%

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Appendix C MA-REC Details

C.1 METHODOLOGY As discussed in Section 5.2, the MA-REC approach uses energy modeling to develop a scoring system that is calibrated to estimated energy consumption. It focuses only on code requirements that directly impact energy consumption and does not account for administrative or non-energy- related code requirements or consider the compliance path used by the builder. In addition, the MA-REC methodology compares homes to the IECC prescriptive requirements that are applicable to each sample. As a result, the MA-REC approach does not account for trade-offs that may take place under ERI paths for compliance. (These paths allow for prescriptive non-compliance with certain measures assuming there are other measures that exceed the prescriptive requirements.) For these reasons, it is possible that the MA-REC approach overstates the level of non- compliance and thus understates the savings associated with homes that use an ERI paths for compliance. The MA-REC approach does not attempt to address these complicating factors, and this should be considered when reviewing the results associated with this methodology. The MA-REC approach uses REM/RateTM or Ekotrope energy consumption estimates to determine the relative importance of various code-related building components. 85 The consumption estimates of individual measures are compared to the overall estimated consumption for a sample of homes in order to develop a detailed point system that is calibrated to overall estimated energy consumption. To account for the fact that codes vary in terms of what measures and levels of efficiency are required, NMR developed unique point systems for each code being considered as part of the study. NMR used the sample of homes built under the 2006 IECC to develop the 2006 IECC point system, the 2009 IECC homes to develop the 2009 IECC point system, etc.86 The point system is based on a ten-point scale where the most important measure (in terms of relative estimated energy consumption for the entire sample of homes) receives a total achievable score of ten points. Other measures are compared to the most important measure to develop a total achievable point value between zero and ten points. The following formulas are examples of how the total possible points for each measure is calculated. The first example assumes above- grade wall insulation was the most important measure in terms of relative consumption:

(푃푇퐶 × 10) 푃표𝑖푛푡푠푃표푠푠푖푏푙푒 = 퐴퐺푇퐶 ______

85 REM/Rate is an energy modeling tool that is used to develop Home Energy Rating Scores (HERS) and to support many residential new construction programs. It was used by the program and raters in the past and is still used by raters today. Ekotrope is a similar 86 Both samples from the 2009 IECC cycle (homes built at the beginning and end of the cycle) were used to develop the 2009 IECC point system.

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Where:

푃푇퐶 = 푃푒푟푐푒푛푡푎푔푒 표푓 푇표푡푎푙 퐶표푛푠푢푚푝푡𝑖표푛 푓표푟 퐴푛푦 푀푒푎푠푢푟푒

퐴퐺푇퐶 = 퐴푏표푣푒 − 퐺푟푎푑푒 푊푎푙푙 푃푒푐푒푛푡푎푔푒 표푓 푇표푡푎푙 퐶표푛푠푢푚푝푡𝑖표푛

The second example shows how this calculation works for lighting using 2009 IECC requirements:

(10% × 10) 푃표𝑖푛푡푠 푃표푠푠𝑖푏푙푒 푓표푟 퐿𝑖푔ℎ푡𝑖푛푔 𝑖푠 5.3 = 18% Where:

푃푇퐶 Lighting = 푃푒푟푐푒푛푡푎푔푒 표푓 푇표푡푎푙 퐶표푛푠푢푚푝푡𝑖표푛 푓표푟 퐿𝑖푔ℎ푡𝑖푛푔 𝑖푠 10%

퐴퐺푇퐶 = 퐴푏표푣푒 − 퐺푟푎푑푒 푊푎푙푙 푃푒푐푒푛푡푎푔푒 표푓 푇표푡푎푙 퐶표푛푠푢푚푝푡𝑖표푛 𝑖푠 18%

Once the point system was developed, NMR used two models to calculate compliance for each home. One was an as-built model (i.e., a model that represents the home as it actually exists) and the other was a code-built model (i.e., a model that represents the same home built to meet prescriptive code requirements). The measure-level percentage change between the code-built models and as-built models was used to assign a point value to each of the measures included in this methodology. If the as-built model meets or exceeded the code for a given measure, that measure was assigned the maximum possible points.87 If the as-built model was less efficient than code, then the measure was assigned partial credit depending on the percentage change of the as-built consumption relative to the code-built consumption. The following formulas were used for these calculations:

(퐶퐵퐶표푛푠 − 퐴퐵퐶표푛푠 ) 푃퐶퐵푎푠푒 = 퐴퐵퐶표푛푠 Where:

푃퐶퐵푎푠푒 = 푃푒푟푐푒푛푡푎푔푒 푑𝑖푓푓푒푟푒푛푐푒 푏푒푡푤푒푒푛 “푐표푑푒 − 푏푢𝑖푙푡” 푎푛푑 “푎푠 − 푏푢𝑖푙푡” 푚표푑푒푙푠

퐴퐵퐶표푛푠 = 퐴푠 − 푏푢𝑖푙푡 푐표푛푠푢푚푝푡𝑖표푛

퐶퐵퐶표푛푠 = 퐶표푑푒 − 푏푢𝑖푙푡 푐표푛푠푢푚푝푡𝑖표푛 Below is an example of how this step in the calculation would work for a home that does not meet the lighting code requirement from the 2009 IECC. In this scenario the as-built model has a higher consumption than the code-built model, since the code-built home is more efficient. (3 MMBtu − 5 MMBtu) 푃푒푟푐푒푛푡푎푔푒 푑𝑖푓푓푒푟푒푛푐푒 푓표푟 퐿𝑖푔ℎ푡𝑖푛푔 (푃퐶 ) 𝑖푠 − 0.4 = 퐵푎푠푒 5 MMBtu Where: ______

87 By providing only the maximum possible points this method does not apply extra credit for exceeding the prescriptive code requirements.

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퐴퐵퐶표푛푠 = 5 푀푀퐵푡푢 푓표푟 퐿𝑖푔ℎ푡𝑖푛푔 퐶표푛푠푢푚푝푡𝑖표푛

퐶퐵퐶표푛푠 = 3 푀푀퐵푡푢 푓표푟 퐿𝑖푔ℎ푡𝑖푛푔 퐶표푛푠푢푚푝푡𝑖표푛

The last step in the calculations was to convert the percentage difference in consumption between the models into an adjusted score for that component. Where:

퐼푓 푃퐶퐵푎푠푒 < 0 푡ℎ푒푛,

푃표𝑖푛푡푠푆푐표푟푒푑 = 1 − |푃퐶퐵푎푠푒| × 푃표𝑖푛푡푠푃표푠푠푖푏푙푒

퐼푓 푃퐶퐵푎푠푒 > 0 푡ℎ푒푛 푃표𝑖푛푡푠푆푐표푟푒푑 = 푃표𝑖푛푡푠푃표푠푠푖푏푙푒

Once again, this step is shown using the same lighting example from above. The first equation from above is used since the code-built model is more efficient than the as-built model. Had the as-built model been more efficient than the code-built model, the home in this example would have received the full 5.3 points for lighting. Points Scored for Lighting is 3.2 = 1—× 5.3 Where:

푃퐶퐵푎푠푒 푓표푟 퐿𝑖푔ℎ푡𝑖푛푔 = −0.4 푃표𝑖푛푡푠 푃표푠푠𝑖푏푙푒 퐿𝑖푔ℎ푡𝑖푛푔 = 5.3

This methodology includes points and compliance calculations for the following building components:

• Roof insulation and installation quality • Above-grade wall insulation and installation quality • Foundation wall insulation and installation quality • Window efficiency • Frame floor insulation and installation quality • Slab insulation and installation quality • Air leakage • Duct leakage and insulation • Lighting efficiency

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The number of points (and thus weights) applied to individual components varied depending on the sample of homes and the code that was under consideration. The total possible points per measure varied between the samples because the relative impact of the measures shifts between different codes and also between different samples of homes; hence, it is critically important that the sample to represent the market. The relative number of possible points across the codes is not a critical comparison because the objective of this methodology is to compare compliance percentages (which are all compared on a 0% to 100% scale) across the codes; the total possible points simply provides an anchor with which to calculate the compliance percentages. This approach is similar to the PNNL scoring system, in which the total possible points varies across different codes due to the number and importance of various code requirements. The PNNL method also re-scales everything on a 0% to 100% scale making compliance scores across codes comparable. Table 162 though Table 164 show the measure-level weights for each code and analysis.

Table 162: REM/Rate MA-REC Measure-Level Weighting Base Code Stretch Code Statewide Fenestration U-factor 25.1% 21.5% 23.8% Above grade wall insulation 20.9% 22.4% 21.5% Air leakage 14.9% 15.0% 14.9% Duct leakage and insulation 12.1% 9.2% 11.0% Ceiling insulation 9.7% 10.9% 10.1% Frame floor insulation 8.3% 8.7% 8.5% Lighting 4.7% 5.1% 4.8% Foundation wall insulation 2.7% 4.2% 3.2% Slab insulation 1.6% 3.0% 2.1%

Table 163: Ekotrope MA-REC Measure-level Weighting Base Code Stretch Code Statewide Program Fenestration U-factor 30.7% 26.7% 29.2% 31.8% Above grade wall insulation 26.9% 26.4% 26.7% 31.7% Air leakage 20.1% 19.6% 19.9% 10.9% Duct leakage and insulation – – – – Ceiling insulation 11.3% 12.0% 11.6% 10.8% Frame floor insulation 5.7% 7.0% 6.2% 5.3% Lighting – – – – Foundation wall insulation 3.0% 4.6% 3.6% 4.0% Slab insulation 2.3% 3.7% 2.8% 5.4%

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Table 164: MA-REC Statewide Measure-level Weighting REM/Rate Ekotrope Non-Program Non-Program Program Fenestration U-factor 23.8% 29.2% 31.8% Above grade wall insulation 21.5% 26.7% 31.7% Air leakage 14.9% 19.9% 10.9% Duct leakage and insulation 11.0% – – Ceiling insulation 10.1% 11.6% 10.8% Frame floor insulation 8.5% 6.2% 5.3% Lighting 4.8% – – Foundation wall insulation 3.2% 3.6% 4.0% Slab insulation 2.1% 2.8% 5.4%

C.2 REM/RATE VERSUS EKOTROPE As discussed in the body of the report, the two different modeling packages estimated differing compliance rates. The difference in MA-REC results between REM/Rate and Ekotrope likely arises from differing methods employed to characterize a home built to code in each software. To assess each code compliance for each home, MA-REC has historically employed a custom REM/Rate user-defined reference home (UDRH) to estimate the energy efficiency of a minimally prescriptive-code compliant geometric clone of a home.88 Ekotrope does not offer an analog of the UDRH but (like REM/Rate) does offer multiple pre-defined reference homes following Section R405 “Simulated Performance Alternative” of the building code to serve this role.89 There are several notable differences between Ekotrope’s reference home approach and the REM/Rate UDRH, in order of decreasing impact:

• Ekotrope ignores the heat recovery efficiency of any mechanical ventilation, while the REM/Rate UDRH does not. Unfortunately, the code is ambiguous about whether heat recovery efficiency should be taken into account: “Energy recovery shall not be assumed for mechanical ventilation” (emphasis added). This difference could contribute to significant differences in infiltration and total compliance of homes with HRVs or ERVs.

• Other than Federal Minimum reference types – which also affect mechanical system efficiency – Ekotrope does not set the code-specified amount of efficient lighting for the home; it instead copies that of the as-built.

• Ekotrope does not separately report lighting and appliance gains nor duct losses, precluding the assessment of compliance for these requirements.

______

88 It was discovered in this analysis that previous MA-REC work overlooked a minimum efficiency requirement for doors, although this had a negligible impact: slightly over-estimating fenestration compliance, and infinitesimally under-estimating the gross technical potential for this measure. Because this only affects the assumed R-values for certain wood doors, it would amount to a less than 1% change in fenestration compliance for 27 homes in this study. 89 State amendments to the reference code require a fenestration U-factor of 0.30, which the MassSave2017Reference home applies to windows; however, it uses 0.35 for doors. The IECC2015Reference was used instead, which applies a U-factor of 0.32 for windows and doors, and the same values were used for the REM/Rate analysis.

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• To make the comparison between the as-built and code compliant home as similar as possible the MA-REC UDRH does not adjust thermostat settings of the reference home. However, R405 specifies a cooling setpoint of 75℉ and a heating setpoint of 72℉.

• A footnote in R405 indicates that instantaneous hot water systems should be replaced with a 40-gallon storage system in the reference home. Depending on the location of the water heater, this could have a small impact on the internal heat gain of the home. Figure 15 shows the distribution of differences between REM/Rate and Ekotrope MA-REC scores for various shell components and the whole home. These histograms suggest that for most homes and measures the two software packages yield similar results; however, there are outliers across all measures. In some cases, REM compliance is 70% higher, and in others, Ekotrope compliance is 60% lower. (A deviation of 0% represents equality.)

Figure 15: Variation in Model Compliance: REM/Rate Minus Ekotrope

C.3 ADDITIONAL REQUIREMENTS Below are some implications of specific mandatory requirements of each code. As shown in Table 165, assuming unknown measures (DK) are compliant, only 12% of homes statewide satisfied the full subset of requirements in this section. Three-fourths (74%) failed up to three requirements, and another 15% failed four to five requirements. Homes most frequently did not comply with the requirement to insulate domestic hot water pipes with R-3 insulation. Conversely, homes most frequently complied with requirements to have dampers, that no building cavities be used as ducts, and at least R-3 insulation on HVAC piping.

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Table 165: Additional Code Requirement Compliance Statistics Compliant Don’t Know Not Applicable Base Stretch Custom Spec State Base Stretch Custom Spec State Base Stretch Custom Spec State Sill 63% 60% 69% 56% 63% 10% 10% 10% 10% 9% 0% 0% 0% 0% 0% Top Plate 29% 33% 35% 27% 31% 58% 54% 54% 58% 56% 0% 0% 0% 0% 0% Baffles 46% 42% 33% 54% 47% 10% 13% 10% 12% 10% 39% 37% 50% 27% 37% Dampers 85% 94% 94% 85% 86% 10% 6% 4% 12% 10% 0% 0% 0% 0% 0% Cavity Duct 81% 92% 83% 88% 86% 6% 0% 6% 0% 3% 8% 8% 8% 8% 7% Sealed Duct 65% 63% 58% 69% 66% 19% 17% 19% 17% 18% 8% 8% 8% 8% 7% HVAC Pipe Ins. 85% 79% 81% 83% 83% 0% 0% 0% 0% 0% 2% 0% 0% 2% 2% HVAC Ins. Prot. 60% 54% 63% 52% 56% 2% 0% 0% 2% 2% 2% 0% 0% 2% 2% DHW Insulation 15% 31% 27% 19% 20% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% DHW Circ. Ctrl 2% 2% 2% 2% 2% 0% 0% 0% 0% 0% 96% 94% 94% 96% 95% Fwall Ins. Prot. 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 98% 94% 94% 98% 97% Pool Cover 4% 6% 8% 2% 4% 0% 0% 0% 0% 0% 90% 94% 88% 96% 93% Pool Switch 6% 6% 10% 2% 6% 2% 2% 2% 2% 2% 90% 94% 88% 96% 93% Pool Timer 2% 4% 6% 0% 2% 2% 2% 2% 2% 2% 90% 94% 88% 96% 93% Wood Fire Door 2% 6% 8% 0% 3% 0% 0% 0% 0% 0% 94% 94% 88% 100% 95% Wood Fire Damper 4% 8% 13% 0% 5% 0% 0% 0% 0% 0% 94% 94% 88% 100% 95% Wood Fire Air 4% 8% 13% 0% 5% 0% 0% 0% 0% 0% 94% 94% 88% 100% 95%

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C.3.1 Sealing of Sill and Top Plate IECC require that sill and top plate framing members be sealed, because doing so reduces infiltration into the home and improves the performance of air-permeable cavity insulation. In many cases it can be difficult to observe whether a sill/rim joist or top plate is sealed, especially if sealant was sparingly applied. Consequently, actual compliance may be higher than was recorded by technicians in the field. More than half of the top plates (56%) were unobservable; of the observable 44%, more than two-thirds (69%, or 31% of all homes) were sealed. Similarly, 62% of observable sills (96% of all homes) were sealed.

C.3.2 Insulation baffles Baffles improve the performance of air-permeable cavity insulation in vented attics. They were present in 74% of applicable installations, with another 17% of homes of unknown status.

C.3.3 Ventilation Dampers Dampers on ventilation inputs/exhausts and clothes dryer exhausts reduce uncontrolled air flow through shell penetrations. These required fixtures were absent from only 4% of homes and unobservable in another 10% of homes, yielding a minimum statewide compliance of 86%.

C.3.4 No Cavity Ducts Figure 16 shows two examples of duct panning, or the practice of using floor or wall cavities as part of duct runs.90 NMR observed this practice in 5% of homes with ducts. The presence or absence of cavity ducts could not be undetermined for 3% of homes.

______

90 IECC 2006 limited the use of shell cavities as duct runs to returns only, “R403.2.3 Building cavities (Mandatory). Building framing cavities shall not be used as supply ducts.” This exception was later removed in IECC 2012, requiring that all ventilation and forced-air conditioning be contained within purpose-built ductwork, “R403.2.3 Building cavities (Mandatory). Building framing cavities shall not be used as ducts or plenums.” These requirements improve air quality and increase HVAC system efficiency by reducing duct leakage and induced shell infiltration from pressure HVAC-driven imbalances.

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Figure 16: Panned Ducts in a Basement Ceiling Cavity

C.3.5 Sealed Ducts Due to the application of insulation or the location of ducts, it was only possible to determine whether ducts were properly sealed in 81% of homes with ductwork. Technicians noted that ducts were not sealed in 9% of homes. Furthermore, technicians found that for 10 of 141 duct systems there was no filter slot cover (see examples in Figure 17). These unrestricted openings contribute to the loss of conditioned air and may introduce unconditioned unfiltered air from basements and attics into the home. The filters in these openings are also frequently difficult to access, as are many custom-made filter-slot covers, dissuading proper filter maintenance, which can further decrease system efficiency.

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Figure 17: Missing Filter-Slot Cover

C.3.6 HVAC Pipe Insulation & Protection Code requires that high-temperature (radiant heating, heat pumps) and low-temperature (air- conditioners and heat pumps) HVAC piping be insulated to maintain overall system efficiency. Four-fifths (85%) of refrigerant lines were consistently coated with foam insulation that met the R- 3 minimum requirement. However, just over half of these (57%) included a protective layer over the foam insulation to protect it from the elements. Figure 18 shows a fully insulated exterior refrigerant line that lacks a weatherproof coating (on the left), compared with an HVAC refrigerant line that is both insulated and protected (on the right). The unprotected insulation will degrade with prolonged exposure to the elements, eventually leaving the refrigerant line under-insulated. Auditors observed that heat pump contractors tended to be more consistent about protecting exterior refrigerant line insulation.

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Figure 18: Unprotected versus Protected Refrigerant Line Insulation

C.3.7 DHW Insulation Figure 19 shows another commonly-observed practice: uninsulated domestic hot water piping. Code has long required that pipes conveying domestic hot water (currently those that are ¾ inch- or-greater) be insulated to at least R-3 regardless of location. Auditors often observed these in in unconditioned basement spaces. Uninsulated PEX or PVC pipes (84%) were somewhat more common than uninsulated copper pipes (68%).

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Figure 19: Uninsulated Domestic Hot Water Pipes

C.3.8 Foundation Wall Insulation In all homes with exterior foundation insulation the insulation was cut-off at grade, despite the fact the above-grade portion of the wall is most in need of the insulation since the soil has some insulating properties of its own.

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C.4 RADON

While not part of compliance with the building energy codes, radon is a consideration in passive homes given their tight envelopes. Therefore, auditors took the opportunity while on-site to record the presence of radon mitigation systems. Figure 20 show the presence or absence of a radon mitigation system for the sampled sites in this study on a map of EPA radon zones furnished by the Department of Public Health.91

Figure 20: Sampled Sites across Radon Potential Zones

The proportion of homes with mitigation systems per radon potential zone is shown in Table 166.

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91 Massachusetts Bureau of Environmental Health. August 14, 2019. https://matracking.ehs.state.ma.us/Environmental-Data/radon/index.html

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Table 166: Homes with Mitigation Systems by Radon Potential Zone Base Code Stretch Code Custom Spec Statewide n 51 49 48 52 100 Zone 1 10% 2% 4% 8% 8% Zone 2 12% 12% 10% 14% 12% Zone 3 6% 20% 21% 6% 11% Total 27% 35% 35% 27% 31%

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Appendix D Final Updated UDRH Inputs The 2019 Residential New Construction Baseline and Code Compliance Study (MA19X02-B- RNCBL) included an update to the User Defined Reference Home (UDRH) for Mass Save’s low- rise residential new construction (RNC) program. This appendix details the new specifications and the rationale for each specification. The updated specifications were selected by a working group of program stakeholders including the Massachusetts Energy Efficiency Evaluation Council (EEAC) consultants, evaluators, Program Administrators (PAs), implementors, and Home Energy Rating System (HERS) raters. NMR facilitated five conference calls with the working group between November, 2019 and January, 2020 to discuss the findings of the 2019 baseline study and to select specifications for each UDRH measure. The new UDRH inputs were finalized in February, 2019. Generally, the working group selected weighted statewide average values from the 2019 baseline study as the new specifications. The weighting scheme was like that used in the body of this baseline study in that it was based on building code (e.g., base or stretch) and construction type (e.g., custom or spec). However, unlike the weighting scheme used elsewhere in the baseline study, which adjusted the sample to reflect the non-program market statewide, the UDRH weighting scheme adjusted the sample to reflect the program market statewide. Before calculating averages, the team removed all measures incentivized by a utility program such as mechanical equipment rebates (through Mass Save’s Residential Rebates) or air sealing and insulation through the Home Energy Services (HES) program.

D.1 BACKGROUND In 2015, the Program Administrators (PAs) sponsored a Residential New Construction Baseline and Code Compliance study in Massachusetts. The study included audits of 50 new non-program homes built under the 2012 IECC base code and 46 non-program homes built under the stretch code. A series of conference calls with stakeholders was convened to discuss the findings of that study and to determine new specifications for the RNC program’s UDRH at the time. That UDRH file was effective through June of 2017 when minor updates were made to HVAC equipment specifications. In 2019, the PAs sponsored this study to update findings from 2015. The sample consisted of 51 homes built under the 2015 IECC base code and 49 homes built under an updated version of the stretch code.

D.2 MEASURE LEVEL FINDINGS

D.2.1 Above Grade Walls

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The previous specification was the average modeled U-factor of conditioned to ambient walls from the 2015 baseline. To determine the new specifications, the working group looked at the area weighted average U-factors for each wall location (e.g., conditioned to ambient, conditioned to garage, conditioned to unconditioned basement, conditioned to attic, and sealed attic wall to ambient) and the average U-factor of all wall locations combined. The working group selected the weighted average U-factor of all conditioned wall locations combined as the new specification after removing one home that received insulation in the HES program. The new specification is, coincidentally, the same as the previous specification.

Table 167: Above Grade Wall U-factor

Previous UDRH Specification New Specification Specification

Above Grade Wall 0.062 0.062 (All Conditioned Locations)

D.2.2 Frame Floors The previous specification is the average modeled U-factor of floors over unconditioned basements from the 2015 baseline. To determine a new specification, the working group looked at the area weighted average U-factors for each floor location (e.g., floor over unconditioned basement and crawl space, floor over garage, floor over ambient) and the average U-factor of all floor locations combined. The working group selected the weighted average U-factor of all floor locations combined after removing the home that participated in the HES program. The working group noted that the new specification is less efficient than the previous specification. NMR found that the reduction in efficiency was due to the 2019 sample of floors having worse insulation Grades than did the 2015 sample. Efficient builders in the 2019 sample appear to have changed practices to insulate basement walls, thus bring floors inside the thermal envelope and making them irrelevant to the UDRH. Therefore, floors in the 2019 sample were more likely to have been insulated by inefficient builders who install insulation at lower Grades.

Table 168: Frame Floor U-factor

Previous UDRH Specification New Specification Specification

Frame Floor 0.047 0.052 (All Conditioned Locations)

D.2.3 Ceilings The previous UDRH had two U-factor specifications for ceilings: one for flat ceilings (0.30) and one for vaulted ceilings or sealed attics. To determine the new specifications, the working group looked at the area weighted average U-factors for each ceiling type individually and combined. The weighted average U-factor in 2019 for flat ceilings was less efficient than that of vaulted

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ceilings and sealed attics. As such, the working group decided to select the average weighted U- factor for all ceiling types combined (i.e., one that was better than the baseline for flat ceilings but worse than the baseline for vaulted ceilings and sealed attics) to encourage more builders to insulate at the rafters and to follow the method used for floors and walls. The weighted average excludes the one home which received incentives for insulation through the HES program.

Table 169: Ceiling U-factor

Previous UDRH Specification New Specification Specification

Ceiling – Flat 0.030 0.033

Ceiling – Vaulted/Sealed Attic 0.038 0.033

D.2.4 Foundation Walls The previous specification for conditioned foundation walls was the average R-value of conditioned foundation walls from the 2015 baseline. The previous specification for unconditioned foundation walls reflected that the building code did not require insulating such walls and that standard practice in 2015 was also to not insulate unconditioned foundation walls. Additionally, the previous UDRH specifies a 10” solid concrete or stone wall for all foundation walls and continuous Grade I insulation for conditioned foundation walls. The new specification for conditioned foundation walls is the weighted average R-value of conditioned foundation walls from the 2019 baseline after removing the one home that received insulation incentives through the HES program. The new specification for unconditioned foundation walls is the weighted average R-value of unconditioned foundation walls from the 2019 study. The new specifications keep the same additional specifications of a 10” solid concrete or stone wall for all foundation walls and continuous Grade I insulation for conditioned foundation walls because this reflects standard industry practice.

Table 170: Foundation Wall R-value

Previous UDRH Specification New Specification Specification

Foundation Walls - Conditioned Basement R-10.4 R-14.3 Foundation Walls - Unconditioned R-0 R-0.6 Basement

D.2.5 Slabs

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D.2.5.1 Unheated Slab Below Grade “Unheated slab below grade” refers to slabs that serve as the floor of a conditioned area below ground level but do not have radiant heat inside the slab. The previous specification reflected the standard practice in 2015 to not insulate unheated below grade slabs in program homes. The working group in 2015 relied on the standard practice of program homes to select the previous specification because it was very difficult to confirm the R- value of slabs in baseline homes since they were already completed at the time of inspection. The new specifications are the weighted average R-values of the slabs for which insulation levels were verifiable by auditors on-site either visually or through documentation. This included slabs which were verified as having no insulation.

Table 171: Unheated Slab Below Grade R-value

Previous UDRH Specification New Specification Specification

Unheated Slab Below Grade—under R-0 R-4.7 insulation Unheated Slab Below Grade—perimeter R-0 R-2.6 insulation

D.2.5.2 Unheated Slab On or Above Grade “Unheated slab on or above grade” refers to slabs that serve as the floor of a conditioned area on or above ground level but do not have radiant heat inside the slab. Again, the previous specifications were based on standard practice as decided by the 2015 working group. The previous working group felt that standard practice was to either not insulate unheated on grade slabs or to use R-10 perimeter only insulation. The previous working group decided to set the specification at R-5 perimeter only insulation. The new specifications are the weighted average R-values of the slabs for which insulation levels were verifiable by auditors on-site either visually or through documentation. This included slabs which were verified as having no insulation.

Table 172: Unheated Slab On or Above Grade R-value

Previous UDRH Specification New Specification Specification

Unheated Slab on or Above Grade—under R-0 R-4.9 insulation Unheated Slab on or Above Grade— R-5 R-2.7 perimeter insulation

D.2.5.3 Heated Slab Any Grade “Heated slab” refers to any slab that has radiant heat installed in the slab.

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The previous specifications for heated slabs were decided by the previous working group based on available information in 2015. In 2015, very few program and non-program homes had heated slabs. The new specifications are the 2015 IECC code requirements for heated slabs because no heated slabs were found in the 2019 baseline sample.

Table 173: Heated Slab Any Grade R-value

Previous UDRH Specification New Specification Specification

Heated Slab—under insulation R-15 R-15

Heated Slab—perimeter insulation R-10 R-15

D.2.6 Windows The previous specifications were based on documented U-factor and SHGC values from visible NFRC (National Fenestration Rating Council) stickers or building department documentation as part of the 2015 study. The new specifications are the weighted average of documented U-factor and SHGC values from NFRC stickers or building plans, HERS certificates, or compliance documentation available on-site. The 2019 study did not include visits to building departments.

Table 174: Window Efficiencies

Previous UDRH Specification New Specification Specification

Windows U-Factor 0.30 0.29

Windows SHGC 0.30 0.35

D.2.7 Skylights The previous specifications were selected because they met ENERGY STAR criteria in 2015 (U- factor ≤ 0.50 and any SHGC). These had also been the previous UDRH values and were selected because skylight efficiencies could only be confirmed at two homes in the 2015 study. The new specifications are identical to the previous specifications because there was no verifiable skylight data in the 2019 baseline and the ENERGY STAR criteria have not changed

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Table 175: Skylight Efficiencies

Previous UDRH Specification New Specification Specification

Skylights—U-Factor 0.48 0.48

Skylights—SHGC 0.30 0.30

D.2.8 Air Infiltration The previous specification was the average Air Changes Per Hour at a 50-pascal pressure gradient between inside and outside (ACH50) from the 2015 baseline. The new specification is the weighted average from the 2019 baseline after removing one home that received air sealing though the HES program. The working group discussed challenges arising from applying this average, which is based entirely on single-family homes, to multifamily units. Since the working group expects RESNET to announce changes to the air leakage requirements for multifamily homes after the publication of this UDRH update, the working group decided to adopt the single- family average out of necessity while allowing the implementation staff to revisit the issue for multifamily homes in the future.

Table 176: Air Infiltation

Previous UDRH Specification New Specification Specification

ACH50 3.57 3.04

D.2.9 Duct Leakage to the Outside The previous specification is the average duct leakage to outside from the 2015 baseline. Duct systems that were entirely in conditioned spaces were considered to have zero duct leakage to outside. Additionally, in 2015, there was one home that was heated with only ductless mini-splits. This home was considered to have zero duct leakage. The new specification is the weighted average from the 2019 baseline following the same method used for the previous specification. In calculating a weighted average, duct systems entirely in conditioned space were considered to have zero duct leakage to outside and the seven homes that used only ductless mini-splits for heating and cooling were considered to have zero duct leakage to outside. NMR noted that the new specification is identical to the previous specification. This could be due to the fact that code requirements did not change for duct leakage to outside between studies.

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Table 177: Duct Leakage to Outside

Previous UDRH Specification New Specification Specification

Duct Leakage to the Outside (CFM25/100 3.8 3.8 ft2 CFA)

D.2.10 Doors The previous specification was based on the REM/Rate default values since auditors in 2015 were not able to document door U-factors. In 2019, auditors again were not able to document door U-factors. HERS raters in the working group explained that doors are hardly considered when trying to make a home more efficient and have a minimal impact. Therefore, the working group decided to make the new door specification the same as the rated home. This removes doors from the savings calculation and simplifies the UDRH.

Table 178: Doors

Previous UDRH Specification New Specification Specification

Doors U-Factor 0.35 Same as rated home

D.2.11 Duct Insulation The previous specifications, except for ducts in conditioned areas converted to unconditioned garages, are the average R-values of ducts from the 2015 baseline with one revision. The previous working group noted that studies had shown bubble wrap insulation to have a lower R- value than the manufacturer stated R-value unless the insulation is installed with an air barrier. The previous working group decided to treat all bubble wrap insulation as R-2 when calculating the averages used for the previous specifications. The previous UDRH specified an R-value for supply ducts in unconditioned attics, ducts in conditioned areas converted to unconditioned garage, and then a single value for all other unconditioned ducts. The new specifications use a weighted average R-value for unconditioned attic supply ducts and then another for all unconditioned ducts. The working group decided to exclude the bubble wrap revision, reasoning that since raters do not de-rate bubble wrap insulation when modeling homes for the program, derating the UDRH would lead to artificial savings.

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Table 179: Duct Insulation R-value

Previous UDRH Specification New Specification Specification

Unconditioned Attic (supply only) R-5.6 R-6.4 All unconditioned duct besides R-4.4 R-5.6 unconditioned attic supply

D.2.12 Heating Efficiencies In the 2015 UDRH update, the working group decided to change the heating efficiency specifications from varying by system type and fuel, to just a single efficiency value based on the average efficiency of all systems. To convert electric efficiencies to AFUE, the ENERGY STAR Portfolio Manager Source-Site conversion factor of 3.14 for electricity was applied to all heating system efficiencies. The equivalent HSPF value was calculated by multiplying the average AFUE by 0.03413 (the REM/Rate standard for converting HSPF to AFUE) and by 3.14 (the ENERGY STAR Portfolio manager Source-Site conversion factor): 93.8 * 0.03413 * 3.14 = 10.05. In 2018, the average HSPF value was updated to reflect a Massachusetts specific source-site conversion factor of 3.01 resulting in an HSPF specification of 9.63. Incentivized equipment was removed from all average calculations. The new specifications include two categories: one for homes where natural gas is available (i.e., “NG available”) and one for homes where natural gas is unavailable (i.e., “NG unavailable”). The NG available specification is the average weighted AFUE of all natural gas systems found in the 2019 study after removing incentivized systems. The NG unavailable specification is the weighted average efficiency of all electric and propane systems found in the 2019 study after removing incentivized systems. There were no oil systems found during the 2019 study. To convert electric efficiencies to AFUE, the working group used the same method that was used for the 2015 UDRH update with one difference: the source-site conversion factor was changed from 3.01 to 2.8 to reflect the updated source-site conversion factor in the new building energy code.92 The working group decided to use two specifications to better reflect the economic choices of a home being constructed within or outside natural gas service territory.

Table 180: Heating System Efficiencies

Previous UDRH Specification New Specification Specification

NG Available (AFUE) 93.8 93.5

NG Unavailable (AFUE / HSPF) 93.8 97.1 AFUE / 9.28 HSPF

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92 See Table C401.2.2 in the state building code, 780 CMR.

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D.2.13 Cooling As done with heating systems, the previous specification used a single SEER resulting from the average of all cooling systems from the 2015 baseline. The EER for Ground Source Heat Pumps is the equivalent EER using the Building America House Simulation Protocols to convert SEER to EER.93 The new specification is the weighted average SEER (and equivalent EER) for all cooling systems with incentivized systems removed. To calculate an average SEER for all systems, the Ground Source Heat Pump EER was converted using the Building America House Simulation Protocols.

Table 181: Cooling System Efficiencies

Previous UDRH Specification New Specification Specification

All systems (SEER / EER) 13.9 14.9 SEER / 12.25 EER

D.2.14 Water Heater Energy Factors The previous specifications for water heater energy factors are based on the average of all fossil fuel water heaters from the 2015 baseline with a few adjustments. There was a single Energy factor for fossil fuel systems and another energy factor for electric systems. The efficiencies of instantaneous systems were multiplied by 92% to account for their reduced efficiencies when used for short draws of hot water. Integrated system efficiencies were calculated as 92% of the boiler AFUE. Once the fossil fuel average of 0.69 was determined, an equivalent specification for electric water heaters (2.08) was calculated by multiplying the fossil fuel average (0.69) by the Massachusetts Source to Site Conversion factor for electricity (3.01). To calculate the previous recovery efficiency specifications, the recorded recovery efficiencies for conventional electric water heaters were divided by the ENERGY STAR’s Portfolio Manager Source-Site Conversion factor of 3.14 for electricity. Recovery efficiencies for heat pump water heaters were excluded from the average recovery efficiency calculation because REMRate does not use recovery efficiencies with heat pump water heaters. The new specifications are split into two categories to match the logic adopted for heating systems: one where natural gas is available and one where natural gas in unavailable. The new specifications are the weighted average efficiencies for each group with incentivized equipment removed and without considering any adjustments for instantaneous systems or source-site

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93 The equation: EER = 1.12 *SEER -0.02*SEER2 can be found at https://www.nrel.gov/docs/fy11osti/49246.pdf.

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conversions.94 The working group decided not to de-rate instantaneous systems as was done in the 2015 UDRH update because raters do not de-rate such systems in practice. De-rating instantaneous systems in the UDRH would therefore generate artificial savings. In June of 2017, the U.S. Department of Energy changed the metric for water heater energy efficiency from EF to Uniform Energy Factor (UEF); however, REM/Rate and Ekotrope only use Energy Factors. To calculate EF averages, UEF values were converted using RESNET protocols as demonstrated in RESNETS’ UEF to EF Converter.95 Additionally, the impacts of two solar thermal water heating systems on two water heaters at two different homes were not considered in the averages. One heat pump water heater and one propane storage water heater were each connected to a solar thermal water heating system. The heat pump and propane water heaters’ nominal efficiencies were used to calculate averages without adjustments for the solar thermal systems. The working group discussed the short-term need for different water heating system specifications for low-rise multifamily buildings, specifically those installing electric resistance water heaters and Heat Pump Water Heaters (HPWHs). There are space limitations associated with installing HPWHs in multifamily buildings that often push program builders to install electric resistance tanks. Given this, and the construction of the UDRH specifications, the EEAC agreed to allow electric resistance and propane water heating in multifamily buildings to assume no savings and no penalty for a period of one year. The EEAC also agreed that HPWHs can be compared to a baseline of an electric resistance water heater for a period of one year as a way to promote the installation of HPWHs. That said, the EEAC believes that this is something that needs to be revisited moving forward.

Table 182: Water Heating System Efficiencies

UDRH Specification Previous Specification New Specification

NG Available (EF / RE) 0.69 / 0.77 0.87 / 0.86 0.69 / 0.77 for fossil fuels NG Unavailable (EF / RE) 1.49 / 0.91 2.08 / 0.98 for electric

D.2.15 Other Inputs

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94 The working group decided to use a simple average of Energy Factors without a source-site conversion because using the method adopted with heating systems would result in an EF of 0.86 for propane and an “equivalent” EF of 2.4 for electric. This would incentivize the use of propane systems which the working group felt went against the intention of encouraging builders to choose the most efficient system given the economic environment of the building. 95 https://www.resnet.us/wp-content/uploads/RESNET-EF-Calculator-2017.xlsx

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Table 183: Other Inputs UDRH Specification Current Specification Climate Location Same as Rated Home Photovoltaics Eliminated Sunspaces Eliminated Thermostats Set point in rated and design home set to 72 and 75 degrees Balanced with RESNET 303.4.1(1) rate, hours, and watt defaults - Mechanical Ventilation same as Rated Home Appliances Same as Rated Home

Lighting Same as Rated Home

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