●

A national laboratory of the U.S. Department of Energy 

Office of Energy Efficiency & Renewable Energy 

National Renewable Energy Laboratory 

Innovation for Our Energy Future 

A New Market Paradigm Technical Report 

NREL/TP-550-38304-01 for Zero-Energy Homes: December 2006 

The Comparative San Diego 

Case Study 

Volume 1 of 2 

B.C. Farhar 

National Renewable Energy Laboratory 

T.C. Coburn 

Abilene Christian University 

NREL is operated by Midwest Research Institute Battelle Contract No. DE-AC36-99-GO10337 

Link to Volume 2: Appendixes
A New Market Paradigm 

for Zero-Energy Homes: 

The Comparative San Diego 

Case Study 

Volume 1 of 2

B.C. Farhar 

National Renewable Energy Laboratory 

T.C. Coburn 

Abilene Christian University

Prepared under Task No. ZB03.3003 

Technical Report

NREL/TP-550-38304-01

December 2006 

National Renewable Energy Laboratory

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Office of Energy Efficiency and Renewable Energy

by Midwest Research Institute • Battelle 

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Preface

Preface 

This study was built from the ground up. Its foundation is the pilot work in the field with 

SheaHomes staff, the advisory committee, and the homeowners. This study is not an engineering 

and economic analysis. It is a statistical, empirical analysis of the homes at the Scripps Highlands 

developments in San Diego that involved observation and qualitative interviews with the 

homeowners, SheaHomes’ professional staff, and the staff of the utility company (San Diego Gas 

& Electric). 

Early findings have been published previously in three papers presented at the American Council 

for an Energy Efficient Economy Summer Study in 2002 and 2004. Not all the findings from the 

preliminary qualitative pilot work are included here. 

The Home Builder 

SheaHomes was the innovator in this project. The company took the risk of using new 

technologies in its homes. Its staff was incredibly generous in sharing information with us about 

which homes were planned as PV standard and which were PV eligible. We spent time at the 

sales center taking virtual tours of the homes, observing, and reading informational materials. 

The builder also shared with us a contractor’s study of the media coverage and videos of 

coverage on the evening news in the San Diego area. 

SheaHomes also provided a list of people who had visited the Scripps Highlands sales center but 

who did not buy homes. We did a separate analysis (called the “lost lookers” study) to follow up 

on their experiences. 

The Homeowners 

The homeowners were also very generous and patient with our surveys and interviews, even 

though they had been subjected to a good many of both before we had a chance to talk to them. 

We asked SheaHomes owners to tell us their stories. We asked them: “Please tell us the story of 

how you came to buy your home,” with many probes and follow-ups. We also asked them to tell 

us about living in their new homes. If they did not bring up their homes’ energy features (which 

they often did not), we brought up the subject and asked them to show us these features. This was 

important because we could then determine what they knew about them and if the energy features 

mattered to the homeowners. 

We visited 25 highly energy-efficient homes with solar water heating, both with and without 

solar PV, and looked at the solar water heating panels, the temperature of the water coming into 

the water heaters, the PV panels, the air-conditioning units, the refrigerators, dishwashers, 

windows, and thermostats. We also looked at utility bills and spreadsheets that some 

homeowners maintained to track their energy use. Some homeowners showed us compact 

fluorescent lights (CFLs) that they had installed in their kitchens after move-in. 

vii 
The Utility 

We met with staff at San Diego Gas & Electric Company in San Diego to discuss the study, the 

utility analysis, and the requirements for accomplishing the transfer of data with homeowner 

permission. The staff described their experiences in net metering and interconnectivity 

agreements with residential customers. 

The approach of building the quantitative study, which is the subject of this report, on earlier 

qualitative field work is a highly reliable method. The relevance of survey questions, their 

exhausting the range of potential responses, and the mutual exclusiveness of the responses help 

to assure an interpretable set of findings. The cooperation of SheaHomes, the homeowners, and 

all of the organizations who helped us was invaluable. We hope the results warrant the trust and 

confidence they have invested in us. 

Any errors in this report are the responsibility of the authors. 

viii 
Acknowledgments

Lew Pratsch of the Building Technologies Program, U.S. Department of Energy, Washington, 

D.C., was the Program Manager for the study. 

The National Renewable Energy Laboratory (NREL) was advised in this study by a study 

advisory group. The following individuals served on the study advisory group and contributed 

their insights to this project: 

• Scott Anders, then at the San Diego Regional Energy Office; now at the Energy Policy 

Initiative Center, University of San Diego 

• Tom Bohner, Sun Systems, Inc. 

• Jonathan Done, then at SheaHomes, San Diego 

• Rob Hammon, ConSol, Inc. 

• Russ Hewett, National Renewable Energy Laboratory 

• Les Nelson, California Solar Energy Industries Association 

• Marc Roper, then at AstroPower, Inc.; now at Schott Solar 

• Teri Shusterman, SheaHomes, San Diego 

Ryan Green, formerly on the SheaHomes-San Diego staff and now at Centex Homes, San Diego, 

also provided valuable insights to the study. 

Extensive assistance to the study was provided by other SheaHomes’ staff in San Diego, 

including, among others, Diane Rivera, former Director of Marketing; Pattie Walker, former 

Director of Sales; and Pam Beaird, Sales Agent. 

Bob Keithly of San Diego Gas & Electric Company (SDG&E) coordinated the company’s 

protocol review for the utility release forms and utility consumption and cost data for customers 

agreeing to have their utility records released to NREL for inclusion in the study’s analysis. 

SDG&E also provided utility records to NREL in electronic form for the accounts that had 

formally released their data. 

The late Nancy Collins of Q4

 Associates, Oakland, California provided invaluable consulting 

assistance during the qualitative phase of the study, and was also principal investigator of the 

“Lost Lookers” study. 

In addition, the Building Industries Institute of California contributed $4,000 to the Colorado 

Energy Science Center (CESC) to provide $10 financial incentives for homeowners in the 

universe of study to encourage them to respond to the mail questionnaires. With this assistance, 

and with the help of Pat Keegan (CESC director), it was possible to achieve the response rates 

accomplished. 

ix 
The participation of the many homeowners who took their time to respond to the questionnaires 

they were sent is acknowledged and is very much appreciated. 

The report was peer reviewed by the study advisory group who provided numerous helpful 

suggestions and comments. All errors are the responsibility of the authors. 

Don Hagengruber, NREL’s Chief Counsel, provided extensive support and oversight in the 

Human Research Review process. The Human Research Committee of the University of 

Colorado-Boulder served as the Institutional Review Board (IRB) and reviewed and approved the 

protocols used in this research. 

Ron Judkoff, Director, Center for Buildings and Thermal Systems, NREL, provided unflagging 

support throughout this effort. Tim Merrigan, NREL, was the NREL Program Manager for this 

study. Jay Burch and Ron Judkoff, Center for Buildings and Thermal Systems, NREL, provided 

technical assistance to the statistical analysis comparing electricity and gas consumption and 

utility costs between the high performance and comparison homes. Megan Murphy, doctoral 

candidate, Department of Sociology, University of Colorado, Boulder, Colorado, provided 

extensive research assistance to Dr. Farhar. A. J. Sterling, student at Abilene Christian 

University, provided research assistance to Professor Coburn. 

Co-editors of the report provided yeoman efforts; they were Stefanie Woodward and Nancy 

Wells. Kay Vernon provided superior word-processing expertise. Diane Littau assisted 

manuscript preparation in a variety of ways. 

The authors would also like to acknowledge 

• Susan Sczepanski, NREL, for excellent graphics for the study’s questionnaires 

• Fay Hoover and the NREL Copy Center 

• Alexander’s Data Services, Inc., for data entry and cleaning. 

x 
Executive Summary 

Introduction and Background 

“It’s good for society.” 

In April 2001, SheaHomes began to offer high-performance - This and other quotes are from homes at Scripps Highlands in San Diego, California. This comments made by owners in the was the first such offering in the United States by a SheaHomes Scripps Highlands production builder. The 306 homes, sold by November communities. 

2003, were highly energy efficient; 293 had solar water 

heating systems; and 120 had photovoltaic (PV) systems. 

The National Renewable Energy Laboratory (NREL) used a diffusion-of-innovations theoretical 

perspective to follow this development over time. The study focused on the builder experience, 

market response to high-performance homes, increases in home values over time, and the 

consumption and cost of electricity and gas in the high-performance and adjacent comparison 

homes. 

We began our work by meeting with a project advisory group to define key research questions. 

During the first, qualitative phase of the study, we conducted numerous interviews of executives 

and staff of SheaHomes, organizations partnering with the builder, and other interested parties. 

Field work was conducted at the SheaHomes community with early buyers and lost lookers. 

Researchers collected background information on the home sales processes. Qualitative 

interviews focused on the homeowners’ reasons for purchase and their perceptions of the energy 

features of their new homes. In this early phase, a total of 43 respondents in 25 households were 

interviewed; the information obtained was used to formulate questions for a more extensive 

survey of all homeowners. 

We also selected a comparison community of 103 homes built by a different builder of similar 

vintage, size, and price adjacent to San Angelo and Tiempo. Although they were built to Title 24 

building codes, thus providing more energy efficiency than conventional building codes in other 

states, the comparison homes were offered with no special energy or solar features standard. 

The quantitative phase consisted of a comprehensive mail survey and detailed statistical analysis 

of the responses from SheaHomes and comparison homeowners. Questionnaires were mailed 

early in 2004 to all homebuyers. The overall survey response rate was 63% (65% from the 

SheaHomes communities and 56% from the comparison community). The survey addressed 

perceptions and preferences of the new homebuyers and the roles, if any, that energy played in 

their home purchase decisions. The survey also examined homebuyer satisfaction, willingness to 

pay for solar PV, preferences about energy policies, experiences with the homes, aesthetics of 

solar PV, satisfaction with utilities, and demographics, including environmentalism and 

innovativeness. 

Respondents were asked to sign release forms for SDG&E to provide data on electricity and 

natural gas consumption and costs. The utility company provided the data to NREL, which 

performed analyses to determine if statistically significant differences in energy consumption and 

xi 
energy costs can be attributed to the energy efficiency and solar features of the high-performance 

homes. These analyses controlled for an annual usage cycle, climate, square footage, number of 

occupants, and other variables. This unique research opportunity gave us the chance to put 

conventional wisdom about ZEH markets to the test; the detailed findings from our study are 

contained in this comprehensive 800-page report. 

Home Sales Prices 

High-performance homes are competitive on the market. Based on actual sales data, per square 

foot, they sold for 9.2% less than comparison homes of the same vintage, on average. This 

difference, though small, is statistically significant. When house size is controlled for, the 

difference remains. Thus, even when controlling for the fact that housing prices per square foot 

decrease with house size, we find that the SheaHomes were competitively priced. 

Uptake of Optional PV Systems 

“It’s best to integrate the solar electric 

Ultimately, 120 of the 306 SheaHomes were system into the entire home purchase rather 

sold with some sort of PV system. Hence, 39% than having it offered as an option in a 

were sold with PV systems and 61% were not. piecemeal way. It should all be rolled into 

However, only 260 homes were PV-eligible; the overall price.” 

hence, 46% of these were sold with PV 

systems. Clearly, the uptake on optional PV 

equipment was not as strong as it might have 

been. A total of only 12% of all PV-eligible homes were sold with PV systems optionally. Most 

of the PV systems sold came standard. 

However, we believe the lackluster sales of 

optional PV systems was the result of sales “We feel the builders know what they are 

staff failure to offer the optional PV systems doing, so if they offer the solar as part of

to buyers of PV-eligible homes. In fact, our the package, there must be a reason.”

data show that a majority (56%) of those who 

could have purchased optional PV systems 

were not told about the option. Thus, 

the uptake rate is not 12%, but 44% of those actually offered the PV systems. Homebuyers relied 

heavily on sales staff for information about PV systems, and staff were more concerned about 

closing home sales and less focused on sales of PV systems that might complicate the deal. Staff 

received no extra compensation for sales of PV systems. 

Who Are These Homebuyers? 

The buyers of high-performance homes and the buyers of new conventional homes share the 

same characteristics. SheaHomes and comparison homebuyers brought virtually identical 

attributes to their home purchase decisions, such as demographics, environmental attitudes, and 

early adopter characteristics. 

xii 
As expected, residents of both communities mostly represent upper-middle class married couples 

with children, or mature couples. They are relatively affluent with well-paying occupations. 

Fifteen percent more of the SheaHomes owners (19%) than of the comparison owners (4%) enjoy 

an annual income of more than $200,000. Yet, because SheaHomes’ sales prices were 

competitive, higher income would not have influenced their decisions to buy there. No 

differences between SheaHomes and comparison homebuyers are found in results on measures of 

early adopter characteristics or environmentalism. 

Three-quarters of the buyers visited both the SheaHomes and comparison communities when 

they were shopping for new homes. However, neither group was well informed about home 

energy features, although buyers of SheaHomes knew a bit more at the time of purchase. A 

majority of the comparison buyers were unaware that they featured energy efficiency and solar 

energy, even though they may have visited SheaHomes. 

Variables on which the types of homeowners differ were by and large those affected by their 

experiences in living in their new homes (survey data were collected after owners had lived in 

their new homes for at least six months). For example, six in ten of SheaHomes owners agree 

that solar water heating systems are cost effective, and half of SheaHomes owners agree that 

solar PV systems are cost effective. The corresponding percentages of comparison homebuyers 

are 40% and 36%, respectively. 

Despite some difficulties with interconnectivity issues, owners of SheaHomes with PV systems 

have more positive attitudes toward SDG&E than other homeowners. These differences are 

significant. A majority of PV homeowners are pleased with SDG&E’s billing processes. 

Similarly, almost one-third of PV owners believed that electricity rates had come down since 

they moved in, compared with 18% of SheaHomes owners without PV systems. 

It is not the qualities the homebuyers brought to the 

home purchase decision, but rather the experience of “We isolate things to see what the PV ownership that changes attitudes and perceptions. electricity hogs are. We’re already It also seems to change energy behavior. Living in more energy conscious because of the highly energy efficient homes with solar water heating feedback device.” 

and PV systems promotes increased familiarity with 

and interest in those systems, which ultimately leads 

to heightened awareness of household energy 

practices. The behavioral interaction of consumers 

with PV technology based on the digital display of kWh production and consumption—and to 

some extent the electric meter—provides feedback that seems to affect homeowner energy 

behavior. Feedback may be significant in bringing about behavioral changes that optimize energy 

and cost savings. To a limited degree, the PV owners also seem more sensitive than others to 

savings from solar water heating systems, even though these have no feedback devices. 

xiii 
Aesthetics and Resale Value 

Neither qualitative nor quantitative data 

identified aesthetics as barriers to purchase 

of homes with solar panels. However, 

because we primarily studied homeowners 

who bought such homes, we cannot 

conclude that no one objects to the 

aesthetics of solar panels. It seems fair to 

conclude that the new homebuying market is large enough that it does not matter if some people 

object; in fact, home sales at Scripps Highlands were brisk. 

Regarding aesthetics— 

“Huh?” 

“Satellite dishes are more offensive.” 

Similarly, based on our data, any concerns about solar panels diminishing resale value appear 

unwarranted. In the first 3.5 years, 13% of the comparison homes were resold compared with 5% 

of the SheaHomes, suggesting a more rapid turnover of comparison homes. SheaHomes 

experienced a mean dollar gain of 55.4% for a mean ownership length of 22.5 months. 

Comparison homes experienced a mean dollar gain of 44.7% for a mean ownership length of 

28.1 months. The mean dollar gain per month owned was $14,500 for SheaHomes and $9,300 

for comparison homes. 

Home Purchase Decisions 

The most important reasons for purchase for both categories of buyers were the home’s location 

in a safe and secure quality neighborhood, the overall home value, and the investment potential. 

The relative rankings of reasons for purchase were the same for both categories of homebuyers. 

Concerns about the San Diego 2001 electricity crisis did not influence home purchase decisions. 

Energy was not an important factor in the purchase decisions of most of the study’s new 

homebuyers. The reputation of the builder was more important to SheaHomes than to 

comparison buyers. Buyers who were more concerned about their residential energy consumption 

were more likely to buy SheaHomes than comparison homes. Every home feature mentioned in 

our study had a higher average importance rating for those who did not purchase PV homes than 

for those who did, suggesting that home characteristics other than energy features were more 

important to those not purchasing homes with PV systems. 

The findings on willingness to pay (WTP) more for PV systems suggest that $5,000 may be a 

threshold for 1.2 PV systems. More than one-third of non-PV-purchasing homebuyers indicate a 

WTP at least $5,000 more for PV systems that could replace 50% to 70% of their electricity 

needs. This level of savings would require a larger PV system. SheaHomes buyers who upgraded 

from 1.2 to 2.4 PV systems paid an additional $4,000; those who purchased optional 1.2 PV 

systems paid $6,000 (later raised to $7,000). Those who purchased optional 2.4 PV systems paid 

$10,000 (later raised to $11,000). Reasons for not purchasing PV systems tend to center around 

the expense. Subsidies and amortization would be required to permit installation of larger 2.4 to 

3+ PV systems that would be needed to reduce electricity costs by 60% to 70%. 

xiv 
Satisfaction 

Most buyers are satisfied with their new 

homes, but SheaHomes buyers, and especially 

buyers of homes with PV systems, are more 

satisfied than are comparison buyers. A 

significantly higher percentage of SheaHomes owners than owners of comparison homes (77% 

versus 67%) indicate they would buy the same houses again. Although this would not be the only 

factor affecting satisfaction, the comparison homeowners report significantly higher monthly 

utility bills than do the SheaHomes owners. Both sets of homeowners find their homes 

comfortable, but comparison buyers pay higher utility costs to maintain their comfort levels. 

Owners of SheaHomes believe their homes are energy efficient. 

“When people come to visit, the first thing 

we do is show them the solar equipment.” 

By owners’ estimates, living in PV homes has resulted in significantly lower utility bills than 

those reported by the rest of the homebuyers. Two-thirds of PV owners have bragged to others 

about their utility bills, compared to one-quarter of owners without PV. A majority of PV owners 

indicate their expectations for utility bills have been met, compared with less than one-third of 

other SheaHomes owners. 

Three dimensions of advantages of PV ownership result 

from factor analysis. The first of these is “altruistic” 

benefits (such as helping to reduce global warming, 

helping the local economy, benefitting future 

generations, and helping to improve local air quality). The second is the financial advantage 

(such as reduced electricity bills, free electricity once the system is paid for, selling electricity 

back to SDG&E, and increasing the home’s resale value). Finally, personal satisfaction includes 

increased self-sufficiency, being technologically innovative, and feeling good about owning the 

home. 

“We brag about our windows.” 

Policy Preferences 

SheaHomes and comparison owners agreed on 

energy efficiency and solar energy policy 

“Solar electricity should be available and 

affordable on all housing.” 

preferences. For example, 92% of both sets of 

homeowners agree or strongly agree that 

“builders should build very energy-efficient homes if they cost less per month to own and 

operate.” Eighty-five percent of SheaHomes and 81% of comparison buyers agree or strongly 

agree that “the federal government should support research on highly energy-efficient homes that 

produce all the energy they use.” Interestingly, majorities of both sets of homeowners agree that 

solar water heating and solar PV systems are desirable innovations for new homes. 

xv 
Are Energy and Costs Saved? 

SheaHomes advertised that its homes, incorporating “the latest in solar electric home power 

generation, solar water heating, and energy-efficiency technology,” would enable homeowners to 

reduce their utility bills by 30% to 50% over conventionally built homes. The original 

SheaHomes concept has been validated by our utility analysis. Among the homes studied, 

SheaHomes consume less electricity and gas, on average, than adjacent comparison homes. 

Similarly, SheaHomes households incur lower utility costs, on average, than comparison 

households. For example, the combined average monthly total utility bill for homes with 2.4 PV 

systems is 54% lower than for comparison homes, a result that is statistically significant. 

A New Market Paradigm 

The value of our study does not lie in describing the 

motivations of recent new homebuyers, but rather in 

suggesting a conceptually fresh alternative paradigm 

for the building and marketing of new ZEHs. When this paradigm is used, builders, new 

homebuyers, and utility companies will benefit. When appropriately applied to business practice 

and public policy, this new paradigm will help builders create the sustainable communities so 

necessary for our well-being and that of future generations. 

“All builders should be doing it.” 

Conventional wisdom on the markets for ZEHs, relying on a diffusion-of-innovations tradition, 

holds that ZEHs will appeal only to niche early-adopter markets. It posits that ZEHs cost more to 

build and therefore are more expensive to buy than conventional homes. It would follow that 

production builders should offer them optionally to buyers with unique motivations, such as 

environmentalism. In this view, ZEH aesthetics (in particular, the solar PV panels) are often 

considered barriers to most mainstream homebuyers, and as impediments to resale, negatively 

affecting home values. Conventional wisdom also assumes that mainstream homebuyers are 

motivated by economic payback on an incremental financial investment for zero-energy features 

for which they have opted. Homebuyers’ satisfaction, then, is considered contingent on the 

perceived payback of energy features. 

Our results suggest a new market paradigm for ZEHs that appears to stand conventional wisdom 

on its head. This paradigm, though it originates from the San Diego case study, may be useful 

elsewhere in California and in the rest of the country, and, indeed, internationally. The table 

below captures some of the notions that we have termed conventional wisdom and summarize 

the new market paradigm along these same dimensions. 

The results of this case study suggest that the markets for new housing are essentially equivalent 

to the markets for ZEHs standard, assuming a policy frame that provides subsidies and builder 

pricing similar to those in effect when San Angelo and Tiempo were built and sold. However, 

this does not mean that the diffusion-of-innovations approach is irrelevant to the widespread 

acceptance of ZEHs. Rather, the early adopters are the builders, utility companies, and policymakers whose adoption of ZEHs will make these homes available standard to many willing 

homebuyers. For us to benefit from ZEHs, the innovative building practices for which 

xvi 
SheaHomes has led the way and the ZEH-supportive policies for which California is becoming 

increasingly famous are the innovations that must diffuse. 

Market Paradigms for Zero-Energy Homes 

Attributes Conventional Wisdom New Market Paradigm 

Sales Prices 

ZEHs cost more to build than 

other homes and are more 

expensive on the market. 

Quality upscale high-performance homes with 

market appeal can be built by production builders 

and sold competitively and profitably, especially 

where subsidies are in place. 

Uptake 

In new developments, builders 

should offer ZEHs optionally, 

and only a few will be sold. 

Builders should offer ZEHs standard; most 

buyers will buy them. In addition, the pace of 

sales may well be accelerated over that of 

conventional homes. 

Homebuyers 

Only innovators and early 

adopters will buy ZEHs (a very 

small percentage of the market). 

High-performance homebuyers are ordinary 

buyers of new production homes in their price 

ranges; they have no special demographic 

attributes; their environmentalism and earlyadopter characteristics are no different from those 

of other buyers; some may, in fact, be “unwitting 

adopters.” 

Aesthetics 

Aesthetics are major barriers to 

ZEH purchase for most buyers 

and negatively affect resale 

value. 

Enough buyers are unconcerned about aesthetics 

that they purchase homes with solar panels, at 

least in a seller’s market, at an accelerated pace. 

Resale homes with solar panels have higher value 

than comparison homes. 

Home 

Purchase 

Decisions 

Other than early adopters, 

buyers of ZEHs would be 

motivated by economic payback 

for an incremental financial 

investment for which they have 

opted. 

ZEH buyers, for whom energy features are only 

“icing on the cake,” may be unaware of any 

potential additional financial investment if the 

costs of energy systems are built into the homes’ 

sales prices and into their mortgages. In fact, 

some buyers are “unwitting adopters.” However, 

buyers are aware of their substantial benefits 

from reduced utility bills. In this model, financial 

incentives (e.g., rebates) go to the builder, 

although buyers may receive income tax credits 

or renewable energy credits. 

Satisfaction 

Homebuyer satisfaction is 

contingent on perceived 

payback of energy features. 

Owners of high-performance homes with PV 

systems perceive three major kinds of benefits: 

(1) altruistic, (2) financial, and (3) personal 

satisfaction. These owners appear to become 

increasingly satisfied over time as they receive 

feedback from their systems, modify their 

behavior, and observe (and brag about) their 

utility bills. 

xvii 
Recommendations and Concluding 

Remarks “We wanted to get the house because the 

system was already there. We didn’t have 

A recommendation from our findings is that to decide about it. We’re glad it’s here. 

We’re lucky to have the PV.” builders should offer ZEHs standard (rather than 

optional). Highly efficient, and with solar water 

heating, these homes should have at least 2.4 PV 

systems and should include digital feedback displays showing consumption and production of 

electricity. Transaction costs are too high when homes and solar energy systems are sold 

separately, and homebuyers have difficulty determining the value of solar features as home 

options when juxtaposed with other options. Our research suggests that from a marketing 

perspective using this standard-package approach when offering homes with specific energy 

packages is simply more effective. 

In conclusion, this study is replete with findings that support the rapid development of highperformance homes with PV systems, near-ZEHs, and ZEHs. Once offered standard, the costs of 

these homes to the builder appear to be manageable, the product provides differentiation on the 

market, and ordinary homebuyers want to buy these homes. Once they live in them, homeowners 

become even more enthusiastic. Policies that support the deployment of ZEHs, such as netmetering legislation, simplified interconnectivity agreements, building codes and standards, 

utility rebates, and subsidies for solar water heating and PV systems, will be rewarded by rapid 

diffusion of an idea whose time has come. 

Through its pioneering work in building the nation’s first high-performance home development 

at the highly desirable Scripps Highlands location from 2001 through 2003, SheaHomes has 

provided a tremendous service to its homebuyers, San Diego, the California and U.S. housing 

industry, and energy professionals everywhere. The upscale homes it built are very energy 

efficient with solar water heating systems. Because SheaHomes offered one-third of its homes 

with solar photovoltaic (PV) systems standard, and left solar PV adoption for the rest up to the 

homebuyers, a rare opportunity for insight into the behavior of the ZEH market emerged. 

xviii 
Glossary

Term Definition

Analysis of variance (ANOVA) A one-way ANOVA is a parametric statistical test that performs a 

comparison of means among more than two groups (see t-test). 

Chi square (P2

) 

A nonparametric test of statistical significance for bivariate tabular 

analysis that relies on differences between observed and expected 

frequencies. 

CNA homeowners Owners of PV-eligible SheaHomes who were offered solar PV 

systems but who chose not to purchase them. 

Coefficient of variation 

Stated in percent, an index ranging in size from 0 to 100 that 

indicates the variability in a specific set of responses above and 

beyond the average response value, computed as 100*(Standard 

deviation/mean). See Appendix F. 

Comparison homes 

Homes in the comparison community adjacent to San Angelo and 

Tiempo developments by SheaHomes, which were not offered as 

energy efficient or with solar energy features 

Ineligible/early homes 

Homes without PV systems because their owners had no opportunity 

to purchase PV systems. The first 13 homes were “early” and, 

though highly energy efficient, had no solar water heating systems. 

Main homes SheaHomes in San Angelo and Tiempo without solar PV systems 

even though they were PV-eligible. 

Mean The arithmetic average, or sum of all scores divided by the number 

of scores; a measure of central tendency. 

n The number of cases included in an analysis. 

Non-PV owners Owners of all SheaHomes without solar PV systems. 

n.s. Not significant; that is, results of statistical analyses are not 

significant at the p=.05 level. 

p-score, p-value, or p The probability that the results are due to chance. 

PV homes SheaHomes in San Angelo and Tiempo with solar PV systems 

SEE homes SheaHomes that are highly energy-efficient and have solar water 

heating systems 

xix 
Spearman rank correlation 

coefficient 

A non-parametric (distribution-free) rank statistic proposed by 

Spearman in 1904 as a measure of the strength of the associations 

between two variables. The Spearman rank correlation coefficient 

can be used to give and is a measure of monotone association that is 

used when the distribution of the data make Pearson's correlation 

coefficient undesirable or misleading. 

Statistically significant; 

significantly 

Analytical results have a p-value # .05. This is a technical term-ofart, not a lay term. 

Standard deviation (s.d.) 

The square root of the variance; at least 75% of the values of any 

population are within two standard deviations from the mean; a 

measure of statistical dispersion. 

t-test 

A parametric statistical test that computes the difference between the 

means of two groups by dividing the variance in each group by the 

number of cases in the group and dividing the variances by the 

standard error of the difference. 

xx 
Contents 

Page 

Chapter 1. Introduction and Background .................................. 1

Background of the SheaHomes Development ............................... 1

A Field Experiment .................................................... 4

The Comparison Community ............................................ 7

Study Background ..................................................... 7

Overview of the Report ................................................. 10

Chapter 2. Guiding Ideas ................................................ 12

Rates of Adoption ..................................................... 12

Stages in the Adoption of Innovations ..................................... 13

Characteristics of Innovation Adopters .................................... 15

High-Performance Homes as an Innovation ................................. 17

Innovation Attributes .................................................. 18

Innovation Attributes and High-Performance Homes .......................... 19

Prior Research on PV Adoption .......................................... 20

Hypothesis and Research Questions ....................................... 20

Advisory Group Guidance .............................................. 21

A Limited Continuance Decision ......................................... 22

Summary ............................................................ 22

Chapter 3. Study Methods and Data Resources ............................. 27

Overview ............................................................ 27

Types and Sources of Data .............................................. 28

Contextual Data ...................................................... 28

Household and Homeowner Data ......................................... 30

Utility Data .......................................................... 34

Data Analysis ........................................................ 36

Limitations of the Data ................................................. 38

Chapter 4. The SheaHomes Experience .................................... 39

Are High Performance Homes Competitive? ................................. 39

Sales of Homes ........................................................ 42

Management’s View ................................................... 42

Percentages of Homes with Solar PV Systems ................................ 44

Benefits to the Builder .................................................. 47

Costs to the Builder .................................................... 50

SheaHomes Position .................................................... 53

Summary ............................................................ 53

Chapter 5. Increases in Property Values ................................... 55

Introduction .......................................................... 55

Findings ............................................................. 55

xxi 
Contents 

(Continued) 

Page 

Chapter 6. Respondent Characteristics .................................... 59

Introduction ........................................................ 59

Findings ........................................................ 59

Summary ............................................................ 61

Chapter 7. New Home Search and Purchase Decision ........................ 62

Introduction .......................................................... 62

New Home Search and Purchase Decision ................................... 62

Comparison Buyer Awareness of SheaHomes ................................ 63

Reasons for Purchase ................................................... 64

Most Important Reasons for Purchase for SheaHomes and Comparison Homebuyers . 65

Importance of New Home Features in Purchase Decisions ...................... 67

Summary ............................................................. 69

Chapter 8. Solar PV Purchase Decisions ................................... 71

Introduction .......................................................... 71

Number of PV Homes among SheaHomes Respondents ........................ 71

Factors Affecting the Decision to Purchase PV Homes ......................... 74

Importance of Home Features in the PV Home Purchase Decision ................ 80

Barriers to Purchasing Homes with Solar PV Systems ......................... 83

Barriers to Purchasing Optional Upgrades to 2.4-kW PV Systems ................ 86

Adding PV Systems after Home Purchase ................................... 86

Stated Willingness to Pay More for Solar PV on the Part of Owners of SheaHomes

without PV Systems and Comparison Homebuyers ........................ 88

Summary ............................................................. 89

Chapter 9. Knowledge and Information .................................... 91

How Informed Buyers Were about Energy Efficiency and Solar Energy When

How Informed Buyers Are about Energy Efficiency and Solar Energy after Living

Introduction .......................................................... 91

How Buyers First Heard about the SheaHomes and Comparison Communities ...... 91

Satisfaction with Information Provided by Builder Staff ........................ 93

Satisfaction with Staff Response to Problems after Move-in ..................... 93

Satisfaction with Staff Information on Energy Features among SheaHomes Buyers . . 94

Looking for New Homes ............................................. 94

in New High-Performance Homes ...................................... 95

Most Important Information Source on PV System ............................ 95

Channels of Information on the Solar PV System ............................. 96

Informed Decision-Making about PV Ownership and Upgrades? ................. 97

What Has Been Learned about Owning a PV System? ......................... 98

Summary ............................................................. 98

xxii 
Contents 

(Continued)

 Page 

Chapter 10. Homebuyer Satisfaction with the Purchased Home ................ 100

Introduction .......................................................... 100

Would Homebuyers Buy Their Homes Again? ............................... 100

Perceived Comfort of New Homes ......................................... 101

Perceived Energy Efficiency of New Homes ................................. 102

Satisfaction with Key Aspects of the New Homes ............................. 102

Features Liked Best .................................................... 105

Features Liked Least .................................................... 106

Bragging about Energy Features of the Homes ............................... 108

Role of Energy Efficiency and Solar Features in Future Home Purchase Decisions . . . 109

Satisfaction with Utility Bill Savings ....................................... 110

Summary ............................................................. 112

Chapter 11. Experiences with PV Systems .................................. 114

Introduction .......................................................... 114

Demographic Differences between PV Owners and Non-PV Owners ............. 114

Perceived Benefits of Owning a PV System ................................. 114

How Happy PV Owners Are with Their Systems ............................. 115

Importance of Solar PV Attributes ......................................... 116

Uses of the Digital Displays .............................................. 117

Operational Aspects of PV Systems ........................................ 119

Summary ............................................................. 121

Chapter 12. Attitudes toward San Diego Gas & Electric Company ............. 123

Introduction .......................................................... 123

Attitudes toward the Utility by Household Category ........................... 124

Attitudes toward the Utility by PV System Ownership ......................... 125

Summary ............................................................. 126

Chapter 13. Attitudes toward Solar Features in New Housing ................. 128

Introduction .......................................................... 128

Are Solar Features Desirable Innovations? .................................. 128

Are Solar Features Cost Effective? ........................................ 130

Chapter 14. Policy Preferences Relative to High-Performance Homes .......... 133

Introduction .......................................................... 133

Should Solar Features Be Standard or Optional in New High-Performance Homes? . . 133

Should Utility Companies Provide Rebates for Energy-Efficient Appliances? ....... 135

Should Home Builders Offer ZEHs? ....................................... 135

Should State and Federal Governments Foster ZEHs? ......................... 135

Summary ............................................................. 136

xxiii 
1

1

2

2

3

3

4

4

5

5

Contents 

(Continued) 

Page 

Chapter 15. Self-Reported Resource Conservation Behaviors .................. 138

Introduction .......................................................... 138

Analysis of Ownership of SheaHomes and Comparison Homes ................. 138

Analysis by PV Ownership .............................................. 138

Analysis by Gender ..................................................... 139

Chapter 16. Environmentalism and Early Adopter Characteristics ............. 142

Introduction .......................................................... 142

Environmentalism ...................................................... 142

Early Adopter Characteristics ............................................. 144

Summary ............................................................ 145

Chapter 17. Data Reduction .............................................. 148

Introduction .......................................................... 148

Part One: Items Asked of All Respondents .................................. 150

Part Two: Items Asked of Main Respondents Only ............................ 165

Part Three: Items Asked Only of PV Owners ................................ 168

Chapter 18. Analysis of Factors by PV Adopter Categories ................... 178

Introduction ......................................................... 178

The Development of Adopter Categories .................................... 178

Witting and Unwitting Adopters .......................................... 181

Comparisons of Adopter Categories by Demographic Variables ................. 183

Differences in Satisfaction Factors by Adopter Categories ...................... 185

Differences in Other Factors by Adopter Categories ........................... 187

Summary ............................................................. 190

Chapter 19. Analysis of Factors by Demographic Variables ................... 19

Introduction .......................................................... 19

Summary of the Differences in Factor Scores by Gender ....................... 19

Summary of the Differences in Factor Scores by Age .......................... 19

Summary of the Differences in Factor Scores by Marital Status .................. 19

Summary of the Differences in Factor Scores by Household Composition .......... 19

Summary of the Differences in Factor Scores by Education Attainment ............ 19

Summary of the Differences in Factor Scores by Occupation .................... 19

Summary of the Differences in Factor Scores by Annual Household Income ........ 19

Summary ............................................................. 19

xxiv 
Contents 

(Continued) 

Page 

Chapter 20. Comparative Analysis of Utility Consumption and Cost ............ 197

Introduction .......................................................... 197

Measures of Utility Consumption and Cost .................................. 200

Other Terminology ..................................................... 202

Data Processing ....................................................... 202

Analysis of Utility Consumption .......................................... 204

Analysis of Utility Cost ................................................. 237

Summary and Discussion ................................................ 255

Chapter 21. Modeling of Utility Consumption and Cost ...................... 259

Introduction .......................................................... 259

Modeling Approach .................................................... 260

Preliminary Considerations .............................................. 262

Development of Models ................................................. 274

Summary and Discussion ............................................... 302

Chapter 22. Perceived and Actual Utility Bills .............................. 307

Introduction .......................................................... 307

Methodology for Computing Actual Average Monthly Utility Bills ............... 308

Prior Utility Bills and Sizes of Homes ...................................... 308

Reported Utility Bills ................................................... 308

Relationship between Perceived and Actual Utility Bills ....................... 311

Summary ............................................................. 312

Chapter 23. Findings, Conclusions, and Discussion .......................... 314

Introduction .......................................................... 314

Who Are These Homebuyers? ............................................ 314

Environmentalism ...................................................... 315

The Purchase Decision Process ........................................... 315

Satisfaction ........................................................... 316

PV Ownership ......................................................... 317

Conclusions from the Utility Analysis ...................................... 320

Conclusions from the Modeling Work ...................................... 326

The Business Aspects of High-Performance Homes ........................... 326

The Increase in Property Values of High-Performance Homes over Time .......... 331

Answers to the Advisory Group’s Questions ................................. 331

Summary Remarks ..................................................... 335

xxv 
Contents 

(Concluded) 

Page 

Chapter 24. Recommendations and Summary Remarks ...................... 336

Part One: Recommendations for the Residential Real Estate Marketplace .......... 337

Part Two: Information Dissemination and Environmental Concerns .............. 342

Part Three: Recommendations for Government Agencies ....................... 344

Part Four: Research Recommendations ..................................... 346

Part Five: Audiences for the Report ........................................ 348

Part Six: Final Summary Remarks ......................................... 352

Bibliography ........................................................... 353

Epilogue .............................................................. 358

Introduction .......................................................... 358

New Production Solar Home Developments in California ...................... 358

Performance of Near-ZEHs: The Premier Gardens Case ........................ 360

Percentages of Energy Cost Savings at the SheaHomes Scripps Highlands

Relative to Comparison Homes ........................................ 361

A Comparative Analysis ................................................. 364

Concluding Remarks ................................................... 367

xxvi 
Appendixes

(Volume 2 of This Report)

 Page 

Note: The letters “I” and “O” are not used in these appendix designations. 

Chapter 1 

Appendix A. New Developments in San Diego .............................. A-1

Chapter 2 

Appendix B. Questionnaire for SheaHomes PV Respondents: Household Questionnaire 

on Purchasing and Owning a High-Performance Home with a 

Solar PV System ........................................... B-1

Appendix C. Questionnaire for SheaHomes Main Respondents: Household Questionnaire 

on Purchasing and Owning a High-Performance Home ............. C-1

Appendix D. Questionnaire for SheaHomes Early Respondents: Household Questionnaire 

on Purchasing and Owning a Highly Energy-Efficient Home ......... D-1

Appendix E. Questionnaire for Comparison Respondents: Household Questionnaire 

on Purchasing and Owning a Recently Built Home ................ E-1

Chapter 6 

Appendix F. Base n’s, Means, Standard Deviations, and Coefficients of Variation 

for Scaled Responses ........................................ F-1

Appendix G. Respondent Characteristics: Demographics, Values and Lifestyles, 

and Other Variables ......................................... G-1

Chapter 17 

Appendix H. Detailed Factor Analysis Results ............................... H-1

Appendix H1. Factors by Demographics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H1-1

Chapter 20 

Appendix J. Scatter Diagrams of Average Monthly Electricity Consumption by

Average Monthly Electricity Cost for All Homes in the Study ........ J-1

Appendix K. Scatter Diagrams of Average Monthly Gas Consumption by Average 

Monthly Gas Cost for All Homes in the Study .................... K-1

xxvii 
Appendixes 

(continued)

Page

Appendix L. Line Graphs for Month-to-Month Consumption of Electricity for 

Individual Homes in the Study .............................. L-1

Appendix M. Line Graphs for Month-to-Month Consumption of Gas for 

Appendix S. Descriptive Statistics on Total and Average Electricity Consumption and Total 

and Average Gas Consumption with and without Selected Equipment, by

Appendix U. Descriptive Statistics on Average Monthly Electricity Cost, Average Monthly

Gas Cost, and Average Monthly Utility Bills with and without Selected 

Individual Homes in the Study ....................................... M-1

Appendix N. Descriptive Statistics, 12-Month Utility Consumption, and Cost Data ......... N-1

Appendix P. Histograms of 12-Month Utility Data for All Homes Combined ............. P-1

Appendix Q. Histograms of 12-Month Utility Data for SheaHomes and Comparison Homes . Q-1

Appendix R. Histograms of 12-Month Utility Data for PV and SEE Homes ............... R-1

Categories of Homes (12-Month Data) ................................ S-1

Appendix T. Histograms of 12-Month Utility Data for Comparison and SEE Homes ....... T-1

Equipment, by Categories of Homes (12-Month Data) ..................... U-1

Chapter 21 

Appendix V. Scatter Diagrams of Average Monthly Electricity Cost Consumption by Average

Monthly Cost for All Homes in the Study ........................... V-1

Appendix W. Electricity and Gas Consumption for 1.2-kW and 2.4-kW Solar PV Systems in the

SheaHomes Communities ........................................... W-1

xxviii 
List of Tables 

Page 

Chapter 2 

Table 1 Innovation Attributes of ZEHs Related to Potential Builders and New 

Homebuyer Perceptions ........................................... 23

Chapter 3 

Table 2 Types and Sources of Study Data ..................................... 29

Table 3 Numbers and Percentages of the Four Categories of Homeowners among All

Homeowners and among Survey Respondents ........................... 32

Table 4 Comparison of Homeowners in SheaHomes Communities with and without 

PV Systems: All Homeowners versus Survey Respondents ................. 33

Table 5 Comparison of Length of Utility Service for Homes in the SheaHomes and 

Comparison Communities ........................................... 35

Chapter 4 

Table 6 Sales Prices and Sizes of SheaHomes and Comparison Homes .............. 40

Table 6a ANOVA on Sales Prices and Sizes of PV Homes, Other SheaHomes, and 

Comparison Homes ................................................ 40

Table 6b Analysis of Sales Prices Controlling for Square Footage ................... 41

Table 7 Numbers and Percentages of Homes with and without PV Systems ........... 45

Table 8 Number, Percentages, and Decision Status of PV Systems on SheaHomes ..... 45

Table 8a Net Costs to SheaHomes of Offering Solar Features at Scripps Highlands ..... 49

Chapter 5 

Table 9 Comparisons of Gains in Property Values and Length of Ownership for 

Homes in the SheaHomes and Comparison Developments (as of 2/7/05) ...... 57

Table 10 Comparisons of Gains in Property Values and Length of Ownership

for SheaHomes with and without PV Systems (as of 2/7/05) ................ 58

xxix 
List of Tables 

(Continued) 

Page 

Chapter 6 

Table 11 Percentage Distribution of Annual Household Income for SheaHomes 

and Comparison Homeowners . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 

Table 12 This table number is not used in the report 

Table 13 Percentages of Ownership of PV Systems by Age, among Owners of 

SheaHomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 

Chapter 7 

Table 14 Average Importance Ratings of Reasons for Home Purchase, Ordered 

by Mean Scores for SheaHomes Respondents ........................... 66 

Table 15 Three Most Important Categories of Reasons for Home Purchase, with 

Percentages of Responses for Buyers of SheaHomes and Comparison Homes . . 67 

Table 16 Importance of New Home Features Ordered by Mean Scores for SheaHomes 

Respondents ...................................................... 69 

Chapter 8 

Table 17 Percentages of PV Respondents with Standard and Optional PV Systems ...... 72 

Table 18 Importance Ratings of Home Purchase Reasons for SheaHomes Buyers with 

and without PV Systems (Ordered by Mean Scores for PV Purchasers) ....... 77 

Table 19 Three Most Important Categories of Reasons for Home Purchase, with 

Percentages of Responses for PV and Non-PV Owners .................... 79 

Table 20 Importance Ratings of Home Purchase Reasons for SheaHomes Buyers with 

and without PV Systems (Ordered by Mean Scores for PV Purchasers) ....... 81 

Table 21 Importance of New Home Features to Buyers in SheaHomes Communities 

with and without PV Systems (Ordered by Mean Scores for Purchasers 

of Homes with PV Systems) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 

Table 22 Importance of New Home Features to PV Owners and Main CNAs 

(Ordered by Mean Scores for Purchasers of Homes with PV Systems) ........ 85 

Table 23 Percentages of Main Respondents Identifying Reasons for Not Opting 

for a PV System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 

xxx 
List of Tables 

(Continued) 

Page 

Table 24 Percentages of PV Owners Citing Barriers to Purchase of Larger (2.4-kW) 

Solar PV System .................................................. 88

Table 25 Stated Willingness to Pay for PV Systems, Percentages of Main, Ineligible/

Early, and Comparison Homebuyers ................................... 89

Chapter 9 

Table 26 How Buyers First Heard about the Homes They Purchased ................. 92

Table 27 How SheaHomes Buyers First Heard about the Homes They Purchased, 

by PV Ownership .................................................. 93

Table 28 Percentages of PV and Non-PV Owners Indicating Knowledge Levels about 

Energy Efficiency and Solar Energy before and after Buying Their Homes .... 96

Table 29 Single Most Important Information Source on PV System .................. 96

Chapter 10 

Table 30 Percentage Comparison by SheaHomes and Comparison Homeowners 

with Regard to Repeat Purchase of the Same Home ....................... 101

Table 31 Percentage Comparison for the Two Respondent Groups with Regard to Repeat 

Purchase of the Same Home ......................................... 101

Table 32 Satisfaction with Key Aspects of the Home by SheaHomes and Comparison

Homeowners ..................................................... 103

Table 33 Satisfaction with Key Aspects of the Home by PV System Ownership ........ 104

Table 34 Home Features Liked Best (open-ended) ............................... 105

Table 35 Home Features Liked Least (open-ended) ............................... 107

Table 36 Bragging about Energy Features by PV Ownership ....................... 108

Table 37 Percentage Comparison of Responses from PV Owners and Non-PV Owners 

by Their Satisfaction with Savings on Their Utility Bills ................... 110

Table 38 Percentage Comparison of Responses from PV and Non-PV Homeowners 

in the SheaHomes Communities by the Perceived Impact of Solar Water 

Heating Systems on Utility Bills ...................................... 111

xxxi 
List of Tables 

(Continued) 

Page 

Chapter 11 

Table 39 Benefits of PV Ownership Perceived by PV Owners ...................... 115

Table 40 Average Importance Ratings of Attributes of Solar PV Systems by PV Owners

Ordered by Average Mean Score ...................................... 117

Table 41 Frequency of PV Owners Receiving Feedback from Their Digital Displays .... 118

Table 42 Percentages of PV Owners Indicating Uses of the Solar PV Digital Display .... 118

Table 43 Percentages of PV Owners Regarding Operational Aspects of Their 

PV Systems ...................................................... 119

Chapter 12 

Table 44 Attitudes toward SDG&E by SheaHomes and Comparison Homeowners ..... 124

Table 45 Attitudes toward SDG&E by PV Ownership ............................ 126

Chapter 13 

Table 46 Average Agreement Ratings on Desirability of Solar Features as Innovations 

for New Housing by Categories of Homebuyers .......................... 129

Table 47 Average Agreement Ratings on Desirability of Solar Features as Innovations 

for New Housing by PV Ownership ................................... 130

Table 48 Average Agreement Ratings on Cost Effectiveness of Solar Features by

Categories of Homebuyers ........................................... 130

Table 49 Average Agreement Ratings on Cost Effectiveness of Solar Features by

PV Ownership .................................................... 132

Chapter 14 

Table 50 Preferences for Standard or Optional Offerings of Solar PV Systems on New

Homes by SheaHomes and Comparison Homebuyers ...................... 134

Table 51 Preferences for Standard or Optional Offerings of Solar Water Heating Systems 

on New Homes by SheaHomes and Comparison Homebuyers ............... 134

xxxii 
List of Tables 

(Continued) 

Page 

Table 52 Percentages of Respondents Agreeing or Strongly Agreeing with Statements on 

Builder Decisions Concerning Efficiency, Solar Features, and Home Energy

Labeling by Four Categories of Homeowners ............................ 136

Table 53 Percentages of Respondents Agreeing or Strongly Agreeing with Policy

Statements on Government Incentives and Subsidies for Solar Features by

Four Categories of Homeowners ...................................... 137

Chapter 15 

Table 54 Self-Reported Energy Behaviors by SheaHomes and Comparison Respondents . 140

Table 55 Self-Reported Energy Behaviors among SheaHomes Owners by Ownership 

of PV Systems .................................................... 141

Chapter 16 

Table 56 Environmentalism by Builder and by PV Ownership ...................... 146

Table 57 Early Adopter Characteristics and Environmental Attitudes by SheaHomes 

versus Comparison Buyers and by PV Ownership ........................ 147

Chapter 18 

Table 58 Distribution of Types of Solar PV Adopters and Non-Adopters among All 

Categories of Homebuyers (SheaHomes and Comparison Homes) ........... 180

Table 64* Level of Educational Attainment of Witting and Unwitting PV Adopters ...... 182

Table 59 Adopter Categories by Age .......................................... 184

Table 60 Adopter Categories by Gender ....................................... 184

Table 61 Adopter Categories by Occupation .................................... 185

Table 62 ANOVA on Satisfaction Factors by Adopter Categories ................... 186

Table 63 ANOVA on Other Factors by Adopter Categories ........................ 188

* Table 64 is intentionally out of order. 

xxxiii 
List of Tables 

(Continued) 

Page 

Chapter 19 

Tables 65 through 71 are not used in the body of the text. They are available in Appendix H (Volume 2) 

Chapter 20 

Table 72 Categories of Homes Used in the 12-Month Utility Analysis ................ 198

Table 73 Energy Efficiency and Renewable Energy Features of Categories of Homes

in the Utility Consumption and Cost Analysis ........................... 199

Table 74 Fuels Used for End-Use Applications in All Homes Encompassed by the Study . 200

Table 75 Comparison of Mean Utility Consumption Measures for SheaHomes and

Comparison Homes ................................................ 209

Table 76 Descriptive Statistics on Square Footages for Various Categories of Homes .... 210

Table 77 Comparative Percentages of SheaHomes and Comparison Homes Occupied 

by Adults Only and by Adults plus Children ............................. 211

Table 78 Comparative Percentages of SheaHomes and Comparison Homes Occupied 

by Three or Fewer Individuals and by Four or More Individuals ............. 216

Table 79 Comparative Percentages of SheaHomes and Comparison Homes with a 

Pool and/or Hot Tub or with Neither ................................... 217

Table 80 Statistical Comparisons of the Percentage of Ownership of Various Other 

Energy-Related Equipment among SheaHomes and Comparison Homes ....... 220

Table 81 Comparison of Mean Utility Consumption Measures for PV and SEE Homes . . 222

Table 82 Comparison of Mean Utility Consumption Measures for PV Homes with 

1.2-kW and 2.4-kW Systems ......................................... 223

Table 83 Comparison of Mean Utility Consumption Measures for PV Homes with 

1.2-kW Systems and SEE Homes ..................................... 223

Table 84 Comparison of Mean Utility Consumption Measures for Comparison Homes 

and SEE Homes ................................................... 225

xxxiv 
Table 85 

List of Tables 

(Continued) 

95% Confidence Intervals on the Mean Values of Selected Utility Consumption 

Measures for Three Home Categories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 

Page 

227 

Table 86 95% Confidence Intervals on the Mean Values of Selected Utility Consumption 

Measures for Three Groups of Homes .................................. 227 

Table 87 Correlation between Home Size (Square Footage) and Four Utility Consumption 

Measures for Base-Case PV, SEE, and Comparison Homes . . . . . . . . . . . . . . . . . 230 

Table 88 Comparison of Mean Average Monthly Combined Utility Bill for SheaHomes 

and Comparison Homes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240 

Table 89 Comparison of Mean Average Monthly Combined Utility Bill for PV Homes 

and SEE Homes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 

Table 90 Comparison of Mean Average Monthly Combined Utility Bill for PV Homes 

with 1.2-kW and 2.4-kW PV Systems .................................. 242 

Table 91 Comparison of Mean Average Monthly Combined Utility Bill for PV Homes 

with 1.2-kW PV Systems and SEE Homes .............................. 243 

Table 92 Comparison of Mean Average Monthly Combined Utility Bill for Comparison 

and SEE Homes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244 

Table 93 95% Confidence Intervals on the Mean Value of Selected Utility Cost 

Measures for PV, SEE, and Comparison Homes . . . . . . . . . . . . . . . . . . . . . . . . . . 245 

Table 94 95% Confidence Intervals on the Mean Values of Selected Utility Cost 

Measures for PV (with 1.2-kW Systems), SEE, and Comparison Homes ...... 246 

Chapter 21 

Table 95 Correlation between Average Monthly Electricity and Gas Consumption, with 

and without Adjustment for Square Footage, for Various Categories of Homes . 265 

Table 96 Features, Characteristics, and Items Considered as Prospective 

Explanatory Variables, by Category . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 

Table 97 Variables that Are Most Highly Correlated with Average Monthly 

Electricity and Gas Consumption (with and without an Adjustment for Square 

Footage) for PV Homes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275 

xxxv 
List of Tables 

(Continued) 

Page 

Table 98 Variables that Are Most Highly Correlated with Average Monthly 

Electricity and Gas Consumption (with and without the Square Footage 

Adjustment) for 1.2 PV Homes ....................................... 276 

Table 99 Variables that Are Most Highly Correlated with Average Monthly 

Electricity and Gas Consumption (with and without the Square Footage 

Adjustment) for SEE Homes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 

Table 100 Variables that Are Most Highly Correlated with Average Monthly 

Electricity and Gas Consumption (with and without the Square Footage 

Adjustment) for Comparison Homes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 278 

Table 101 Multiple Regression Models of Average Monthly Electricity Consumption for 

Three Categories of Homes (without Adjustment for Square Footage) ........ 284 

Table 102 Multiple Regression Models of Average Monthly Electricity Consumption 

per Square Foot for Three Categories of Homes . . . . . . . . . . . . . . . . . . . . . . . . . . 285 

Table 103 Multiple Regression Models of Average Monthly Gas Consumption for Three 

Categories of Homes (without Adjustment for Square Footage) ............. 286 

Table 104 Multiple Regression Models of Average Monthly Gas Consumption per 

Square Foot for Three Categories of Homes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 

Table 105 Stepwise Multiple Regression Modeling of Average Monthly Electricity 

Consumption for PV Homes (without Adjustment for Square Footage) ....... 288 

Table 106 Multiple Regression Models of Average Monthly Electricity Cost, Including 

Taxes and Miscellaneous Charges, for Three Categories of Homes . . . . . . . . . . . 291 

Table 107 Multiple Regression Models of Average Electricity Cost, Excluding 

Taxes and Miscellaneous Charges, for Three Categories of Homes . . . . . . . . . . . 292 

Table 108 Multiple Regression Models of Average Monthly Electricity Cost per Square 

Foot, Including Taxes and Miscellaneous Charges, for Three Categories 

of Homes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293 

Table 109 Multiple Regression Models of Average Monthly Electricity Cost per Square 

Foot, Excluding Taxes and Miscellaneous Charges, for Three Categories 

of Homes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294 

Table 110 Multiple Regression Models of Average Monthly Gas Bill, Including Taxes 

and Miscellaneous Charges, for Three Categories of Homes . . . . . . . . . . . . . . . . 295 

xxxvi 
List of Tables 

(Continued) 

Page 

Table 111 Multiple Regression Models of Average Gas Utility Bill, Excluding Taxes 

and Miscellaneous Charges, for Three Categories of Homes . . . . . . . . . . . . . . . . 296 

Table 112 Multiple Regression Models of Average Monthly Gas Bill per Square Foot, 

Including Taxes and Miscellaneous Charges, for Three Categories of Homes . . . 297 

Table 113 Multiple Regression Models of Average Monthly Gas Bill per Square Foot, 

Excluding Taxes and Miscellaneous Charges, for Three Categories of Homes . . 298 

Table 114 Multiple Regression Models of Average Monthly Combined Utility Bill, 

Including Taxes and Miscellaneous Charges, for Three Categories of Homes . . . 299 

Table 115 Multiple Regression Models of Average Monthly Combined Utility Bill, 

Excluding Taxes and Miscellaneous Charges, for Three Categories of Homes . . 300 

Table 116 Multiple Regression Models of Average Monthly Combined Utility Bill per 

Square Foot, Including Taxes and Miscellaneous Charges, for Three Categories 

of Homes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301 

Table 117 Multiple Regression Models of Average Monthly Combined Utility Bill per 

Square Foot, Excluding Taxes and Miscellaneous Charges, for Three 

Categories of Homes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302 

Chapter 22 

Table 118 Estimates of Monthly Utility Bills Reported by Respondents by Household 

Category . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309 

Table 119 Comparison of the Percentage Distribution of Average Monthly Utility Bills 

Reported by Homeowners in the SheaHomes and Comparison Communities ... 310 

Table 120 Comparison of the Percentage Distributions of Average Monthly Utility Bills 

Reported by the Homeowners in the SheaHomes and Comparison Communities 

(PV Owners Omitted) ........................................... 311 

Table 121 Comparison of the Percentage Distributions of Average Monthly Utility Bills 

Reported by Homeowners in the SheaHomes Communities with and without 

PV Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311 

Table 122 Comparison of Reported Combined Monthly Utility Bills with Actual Monthly 

Utility Bills by Categories of Households ............................... 312 

xxxvii 
List of Tables 

(Concluded) 

Page 

Epilogue 

Table 123 Solar Home Developments in California in 2005 ......................... 359 

Table 124 Mean Monthly Gas Costs for PV, SEE, and Comparison Homes . . . . . . . . . . . . . 361 

Table 125 Percentage Differences in Electricity and Gas Costs of SEE Homes and 

Comparison Homes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363 

Table 126 Percentage Differences in Electricity and Gas Costs of SheaHomes with 

PV Systems and Comparison Homes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364 

Table 127 Percentage Differences in Electricity and Gas Costs of SheaHomes with 

PV Systems and SEE Homes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365 

Table 128 Percentage Differences in Electricity and Gas Costs of SheaHomes with 

1.2-kW PV Systems and SheaHomes with 2.4-kW Systems ................. 366 

Table 129 Percentage Differences in Electricity and Gas Costs of Comparison Homes 

and SheaHomes with 2.4-kW PV Systems .............................. 368 

Table 130 Average Monthly Combined Utility Bills Comparing PV, SEE, and 

Comparison Homes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369 

xxxviii 
List of Figures 

Page 

Chapter 1 

Figure 1 A Home in the SheaHomes Community with a 1.2-kW Solar PV System ....... 2

Figure 2 Technical Attributes of a High-Performance Home ........................ 3

Figure 3 Site Map for the San Angelo Neighborhood .............................. 5

Figure 4 Site Map for the Tiempo Neighborhood ................................. 6

Figure 4a A Comparison Home ............................................... 8

Chapter 2 

Figure 5 The Bell-Shaped Frequency Curve and the S-Shaped Cumulative Curve for an 

Adopter Distribution .............................................. 13

Figure 6 Examples of Diffusion Curves ........................................ 14

Figure 7 Model of the Innovation-Adoption Decision Process ...................... 15

Chapter 20 

Figure 8 Side-by-Side Box Plots of Total 12-Month Electricity Consumption for Three 

Categories of Homes: PV Homes, SEE Homes, and Comparison Homes ...... 206

Figure 9 Side-by-Side Box Plots of Total 12-Month Gas Consumption for Three 

Categories of Homes: PV Homes, SEE Homes, and Comparison Homes ...... 207

Figure 10 Scatter Diagram of Average Total 12-Month Electricity Consumption ($) 

versus Average Square Footage for PV Homes ........................... 212

Figure 11 Scatter Diagram of Average Total 12-Month Electricity Consumption ($) versus 

Average Square Footage for PV Homes with 1.2-kW Systems .............. 213

Figure 12 Scatter Diagram of Average Total 12-Month Electricity Consumption ($) versus 

Average Square Footage for SEE Homes ............................... 214

Figure 13 Scatter Diagram of Average Total 12-Month Electricity Consumption ($) versus 

Average Home Size (ft2

) for Comparison Homes ......................... 215

xxxix 
List of Figures

(continued) 

Page 

Figure 14 Side-by-Side Percentage Bar Chart Showing the Comparative Distributions of 

Household Size for SheaHomes and Comparison Homes . . . . . . . . . . . . . . . . . . . 218 

Figure 15 Side-by-Side Bar Chart Depicting the Percentages of SheaHomes and 

Comparison Homes with Hot Tubs and/or Pools . . . . . . . . . . . . . . . . . . . . . . . . . 219 

Figure 16 Comparison of Mean Average Electricity Consumption for Four Categories of 

Homes: No Energy-Intensive Equipment (Base-Case Homes), Standalone 

Freezer, Two Refrigerators, and Standalone Freezer and Two Refrigerators . . . . 231 

Figure 17 Comparison of Mean Average Electricity Consumption for Four Categories 

of Homes: Pool, Pool and Standalone Freezer, Pool and Two Refrigerators, 

and Hot Tub . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232

Figure 18 Comparison of Mean Average Electricity Consumption for Four Categories 

of Homes: Hot Tub and Two Refrigerators; Pool and Hot Tub; Pool, 

Hot Tub, and Standalone Freezer; and Pool, Hot Tub, and Two Refrigerators . . 233 

Figure 19 Comparison of Mean Average Gas Consumption (therms) for Four Categories 

of Homes: No Energy-Intensive Equipment (Base-Case Homes), Standalone 

Freezer, Two Refrigerators, and Standalone Freezer and Two Refrigerators . . . . 234 

Figure 20 Comparison of Mean Average Gas Consumption (therms) for Four Categories 

of Homes: Pool, Pool and Standalone Freezer, Pool and Two Refrigerators, 

and Hot Tub . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235

Figure 21 Comparison of Mean Average Gas Consumption (therms) for Four Categories 

of Homes: Hot Tub and Two Refrigerators; Pool and Hot Tub; Pool, Hot Tub, 

and Standalone Freezer; and Pool, Hot Tub, and Two Refrigerators . . . . . . . . . . 236

Figure 22 Comparison of Mean Average Monthly Combined Utility Bill, Including Taxes 

and Miscellaneous Charges, for Four Categories of Homes with No EnergyIntensive Equipment; Standalone Freezer, Two Refrigerators, and Standalone 

Freezer and Two Refrigerators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249

Figure 23 Comparison of Mean Average Monthly Combined Utility Bill, Including Taxes 

and Miscellaneous Charges, for Four Categories of Homes: Pool, Pool and 

Standalone Freezer, Pool and Two Refrigerators, and Hot Tub . . . . . . . . . . . . . . 250

Figure 24 Comparison of Mean Average Monthly Combined Utility Bill, Including Taxes 

and Miscellaneous Charges, for Four Categories of Homes: Hot Tub and Two 

Refrigerators; Pool and Hot Tub; Pool, Hot Tub, and Standalone Freezer; and 

Pool, Hot Tub, and Two Refrigerators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251

xl 
List of Figures

(continued) 

Page 

Figure 25 Comparison of Mean Average Monthly Combined Utility Bill, Excluding Taxes 

and Miscellaneous Charges, for Four Categories of Homes: No EnergyIntensive Equipment, Standalone Freezer, Two Refrigerators, and Standalone 

Freezer and Two Refrigerators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252

Figure 26 Comparison of Mean Average Monthly Combined Utility Bill, Excluding Taxes 

and Miscellaneous Charges, for Four Categories of Homes: Pool, Pool and 

Standalone Freezer, Pool and Two Refrigerators, and Hot Tub . . . . . . . . . . . . . . 253

Figure 27 Comparison of Mean Average Monthly Combined Utility Bill, Excluding Taxes 

and Miscellaneous Charges, for Four Categories of Homes: Hot Tub and 

Two Refrigerators; Pool and Hot Tub; Pool, Hot Tub, and Standalone Freezer; 

and Pool, Hot Tub, and Two Refrigerators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254

Chapter 21 

Figure 28 Scatter Diagram of Average Monthly Electricity Consumption (kWh) versus 

Average Monthly Electricity Cost ($), Excluding Taxes and Miscellaneous 

Charges, for PV Homes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264

Figure 29 Scatter Diagram Depicting the Relationship between Average Monthly Electricity 

Consumption and Average Monthly Gas Consumption for PV Homes . . . . . . . . 266 

Figure 30 Scatter Diagram Depicting the Relationship between Average Monthly Electricity 

Consumption and Average Monthly Gas Consumption for PV Homes with 

1.2-kW Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267

Figure 31 Scatter Diagram Depicting the Relationship between Average Monthly Electricity 

Consumption and Average Monthly Gas Consumption for SEE Homes . . . . . . . 268 

Figure 32 Scatter Diagram Depicting the Relationship between Average Monthly Electricity 

Consumption and Average Monthly Gas Consumption for Comparison Homes . 269 

Figure 33 Scatter Diagram Depicting the Relationship between Average Monthly Electricity 

Consumption and Average Monthly Gas Consumption for SheaHomes . . . . . . . 270 

Figure 34 Scatter Diagram Depicting the Relationship between Average Monthly Electricity 

Consumption and Average Monthly Gas Consumption for All Homes Combined. 271 

xli 
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xlii 
Chapter 1

Introduction and Background

Background of the SheaHomes Development 

A zero energy home (ZEH) combines state-of-the-art, energy-efficient construction and 

appliances with commercially available renewable energy systems to reward its owner with 

significantly reduced energy consumption. A ZEH, like any other home, is connected to the 

utility grid, but overall it produces as much energy as it consumes. With net metering, the home’s 

electric meter runs backward when the home is producing more power than it is using. With its 

reduced energy needs and solar energy systems, a ZEH can, over the course of a year, produce as 

much energy as it uses. ZEHs are thought to have a number of advantages, including improved 

comfort, protection against electricity price spikes, reduced peak load on the utility grid, and 

environmental sustainability. 

The SheaHomes Scripps Highlands development near San Diego, California, was the first 

offering of its kind in the United States. The homes provide homebuyers with the potential to 

reduce their utility bills from 30% to 50% over a conventionally built home. SheaHomes decided 

to pursue the idea by partnering with ConSol, Inc., to design homes more energy efficient than 5­

star ENERGY STAR® Homes (homes that exceeded California’s Title 24 standards for energy 

efficiency), with Sun Systems, Inc. to provide solar water preheating for the homes, and with 

AstroPower, Inc., to provide solar photovoltaic (PV) systems for on-site electricity to the homes 

tied to the utility grid. SheaHomes decided to call its homes “high-performance” homes to reflect 

their solar energy and energy-efficiency features. Although technically the homes are not true 

ZEHs because they do not provide, in the net, all of their own energy, they are the first examples 

of high-performance homes offered by a large-production home builder in the United States. 

Today’s near-ZEHs being built in California cut overall utility bills by at least 50%. For the 

nation, by 2020 ZEHs are expected to be commonly available. 

SheaHomes began closing on homes in its San Angelo and Tiempo developments at Scripps 

Highlands in April 2001 and, 31 months later, in November 2003, all 306 homes had been sold. 

The homes ranged in price from $400,000 to $840,000. The Scripps Highlands area, situated on a 

mesa north of San Diego close to Interstate 15, is considered highly desirable. The view from the 

area includes rolling hills, valleys, and the Pacific Ocean 15 miles to the west. New homebuyers 

in this area were exempt from certain property taxes that new homebuyers elsewhere in the San 

Diego area had to pay. 

Working with its partners, SheaHomes designed its San Angelo and Tiempo developments in 

such a way that all of its homes were highly energy efficient, and all but the first 13 homes 

featured standard solar water preheating that resembled a skylight to bring heated water to the hot 

water tank in the garage. Natural gas then brought the preheated water to the desired temperature. 

Figure 1 is a photograph of a home in the SheaHomes communities with a 1.2-kW DC PV 

system. Figure 2 exhibits some of the technical features of the high-performance homes; these 

included the following: 

1
Figure 1: A Home in the SheaHomes Community with a 1.2-kW Solar PV System 

2
Figure 2: Technical Attributes of a High-Performance Home 

3
• Solar radiant barriers 

• Windows with spectrally selective glass 

• Tight, sealed ducts and pipes 

• Thermal expansion valves 

• Inspection and diagnostic blower door tests. 

Figure 3 shows the site map for the SheaHomes San Angelo community, and Figure 4 shows the 

site map for the SheaHomes Tiempo community. 

The SheaHomes communities at Scripps Highlands featured five to six bedrooms, three-car 

garages, granite tile counter tops standard, and spacious layouts. Most homes had two floors, but 

a single-story option was also available. Four different floor plans were offered. In addition to 

being built to Title 24 energy codes, the homes were more highly energy efficient, had solar 

water heating systems standard, and in one-third of the homes, a solar electric system standard, as 

noted. 

A Field Experiment 

A member of the SheaHomes staff, Ryan Green, was instrumental in the inception and initiation 

of the ZEH concept at San Angelo and Tiempo. Green had attended a seminar on ZEHs at the 

National Renewable Energy Laboratory (NREL) in October 2000, where he learned about the 

concept and potential of ZEHs. Enthused about his new knowledge, Green returned to San Diego 

and became a champion of the ZEH concept at SheaHomes as the company planned its San 

Angelo and Tiempo developments at Scripps Highlands. In four months time, SheaHomes issued 

its press release announcing its new high-performance homes. The builder took a subdivision 

already under construction in January 2001 and shifted to the high-performance home concept.

 Ryan Green wanted to use San Angelo and Tiempo as a field experiment to discover if the ZEH 

concept that he had embraced so enthusiastically was marketable. Therefore, instead of offering 

solar PV systems standard on all the PV-eligible homes, he decided to make standard solar PV 

systems available on only a portion of them. For homes that came with solar PV systems, Green 

decided to include 1.2-kW systems standard and offer optional upgrades of 1.2-kW, resulting in 

2.4-kW DC systems, for $4,000. For the remaining PV-eligible homes, solar PV systems (either 

1.2-kW for $6,000 or 2.4-kW for $10,000) would be offered as optional upgrades. 

In essence, buyers of homes with PV systems standard had their PV costs rolled into the prices of 

the homes and did not have to make separate purchase decisions about solar PV, except to 

determine if they wanted to upgrade their systems. Buyers of homes for which PV systems were 

not available (PV-ineligible homes) did not have to choose at all. On the other hand, buyers of 

PV-eligible homes that came without solar PV had to decide (1) about systems they barely 

understood or didn’t understand at all and (2) whether to pay an extra $6,000 to $10,000 for 

them.1

 In the context of hundreds of decisions homebuyers had to make at the time of home 

1

By comparison, buyers could upgrade to solid granite countertops in their sizable kitchens for $10,000. 

4
Figure 3: Site Map for the San Angelo Neighborhood 

5
Figure 4: Site Map for the Tiempo Neighborhood 

6
purchase on all manner of options, such as paint colors, flooring, rooms, window sills, and 

counter tops—all of which are well-understood choices—the purchase of PV was a difficult 

decision to make. 

Green had not understood at the time that the field experiment he had designed stacked the deck 

in such a way as to make it difficult for buyers to opt for PV systems. He also had not fully 

comprehended the implications of the fact that sales staff—who are there to sell new homes— 

would also have to sell optional PV systems one buyer at a time and would receive no financial 

reward for doing so. Nonetheless, SheaHomes proceeded with its innovative project to build and 

market high-performance homes in San Diego. 

The Comparison Community 

The 103 homes in the comparison community used in this study were constructed by a different 

builder. The first home closed on May 22, 2001, and the last closed on November 10, 2003, a 

period of 30 months. Although they were built to Title 24 building codes, thus providing more 

energy efficiency than other state building codes, the comparison homes were offered with no 

special energy or solar features. Also, like San Angelo and Tiempo homes, the comparison 

homes had no Mello-Roos tax requirements.2 Figure 4a shows a home in the comparison 

community adjacent to the SheaHomes communities. 

The comparison homes featured three floor plans with five or six bedrooms, and two- or threecar garages. One of their flyers mentioned their energy-efficient construction, which featured 

dual glaze windows and increased insulation. Options available included low-e windows, 

increased insulation to R19 or R30, roller shades, setback thermostats, and high-efficiency 

furnaces. Their appliances were guaranteed to meet or exceed state and federal energy standards. 

Bathrooms came with water-saving faucets and showerheads. 

Study Background 

This quantitative study is based on NREL’s earlier qualitative investigation of new homebuyers 

in two adjoining residential communities—San Angelo and Tiempo—developed by SheaHomes, 

Inc., in the Scripps Highlands area of San Diego (Farhar, Coburn, and Collins 2002). The earlier 

qualitative study centered on the homeowners’ reasons for purchase and their perceptions of the 

energy features of their new homes. 

2

Mello-Roos taxes are a form of property taxation for new home developments passed by legislation; these taxes 

provide for the development of new infrastructure such as roads and schools. Certain land holdings, including the 

Scripps Highlands parcels owned by SheaHomes and the comparison builder, were exempted from Mello-Roos taxes 

at the time the bill was passed. 

7 
Figure 4a. A Comparison Home 

8
The study also includes responses from the original buyers of new homes in a community that is 

geographically close to the SheaHomes yet offered by a different builder. These are considered 

the comparison responses. Although the comparison homes were required to be built to 

California statewide Title 24 building code—which increased their energy efficiency beyond that 

found in most new construction outside of the state—they were otherwise conventional homes. 

Further, the comparison homes’ energy-efficiency features were not emphasized by the sales staff 

or by brochures and other advertising.3 The homes were also located at Scripps Highlands and 

were priced similarly to the SheaHomes. 

The inclusion of a comparison group is important for four reasons. First, it permits comparative 

analysis of differences between utility performance attributes of high-performance homes and 

conventional housing in the identical market and climatic regimes. Second, it permits comparison 

of the knowledge, attitudes, and practices of the buyers of SheaHomes and conventional homes. 

Third, it controls for the influence that the California electricity crisis of 2000–2001 might have 

had on home purchase decisions in the San Diego area where the crisis was particularly acute. 

SheaHomes had already planned to build its San Angelo and Tiempo developments before the 

electricity crisis occurred; therefore, the offering of high-performance homes subsequent to this 

crisis happened serendipitously. Fourth, it controls for the upward spiraling of real estate values 

in the San Diego area. 

The research has four specific objectives: 

• To provide credible findings that will increase understanding of the customer response to 

ZEHs, as compared with customer response to similar conventional housing 

• To compare the electricity and gas consumption of the SheaHomes with that in the 

comparison community. 

• To compare the utility costs of the two communities. 

• To develop a research protocol (the methodology used in this study) that can be adapted and 

applied by others to assess local-area markets for new ZEHs. 

A key question of the research is how attractive high-performance homes, near-ZEHs (cutting at 

least 50% of utility bills) and ZEHs are to new homebuyers and whether they represent a 

marketing advantage for builders. Although the homes built by SheaHomes at Scripps Highlands 

are not, strictly speaking, ZEHs, it was estimated when they were planned that they would save 

38% of heating, cooling, and water-heating energy beyond the strict California Title 24 

guideline.4

 All of the homes are highly efficient, above the level of a 5-star ENERGY STAR 

3

This is known from researcher visits to model homes and conversations with the SheaHomes and comparison homes 

sales staffs. 

4

The 38% energy savings is Title 24 savings, which include heating, cooling, and water heating. The whole-house 

energy use includes the additional end-uses of appliances, lighting, and plug loads (such as computers and instant-on 

television sets). Gas cost savings because of the solar water heating and electricity cost savings because of the solar 

9
Home. They were termed ComfortWise Homes by ConSol, Inc. Most (293) of the 306 homes 

have solar water preheating systems. In addition, 120 of the homes have solar PV systems 

interconnected with the utility grid. SheaHomes sales agents referred to these as “solar PV” 

systems. 

The study’s findings are intended to inform state, federal, and utility policy-makers and incentive 

program designers concerning the production and purchase of ZEHs, as well as to inform 

builders in California and elsewhere about the market perception of desirability and importance 

of energy efficiency and solar features in new homes. No quantitative studies currently address 

consumer response to ZEHs; therefore, this investigation begins to fill a significant gap in this 

field of knowledge. 

Overview of the Report 

The report presents comprehensive findings from the study. The report essentially has five sets of 

chapters. 

• The first set of chapters (Chapter 1 to Chapter 3) introduces the study’s purposes and research 

approaches. 

• The second set of chapters (Chapter 4 and Chapter 5) describes the business and financial 

aspects of the project. This includes the SheaHomes experience as an innovative largeproduction builder in terms of the company’s competitiveness in offering the highperformance homes, sales of the homes, and the benefits and costs of the project to the 

company. Chapter 5 describes how these homes have increased in resale value compared with 

the overall San Diego market. Lessons learned from the SheaHomes experiment are then 

discussed. Appendix A (New Developments in San Diego) describes a new initiative in San 

Diego to foster the building of new sustainable homes. 

• The third set of chapters (Chapter 6 to Chapter 19) presents the comparative univariate 

analysis of the homeowner survey findings, describes a data-reduction analysis, describes the 

development of PV home adopter categories relevant to this study, and presents an analysis 

of their factor scores. In addition, this section discusses respondent characteristics; reasons 

for the home purchase decision; knowledge and information about the new homes and their 

energy features, including the role of the sales staff and how informed buyers are about 

energy-efficiency and solar features; the decision to purchase a new home with a PV system; 

willingness to pay for PV systems; homebuyers’ satisfaction with their new homes; 

experience with PV systems; attitudes toward San Diego Gas & Electric (SDG&E)—the 

local utility company; attitudes toward solar features in new housing; self-reported resource 

conservation behaviors; policy preferences relative to high-performance homes; and 

environmentalism and early adopter characteristics. 

electric system would be in addition (Hammon 2006). 

10 
• The fourth set of chapters (Chapter 20 to Chapter 22) deals with a comparative analysis of the 

utility cost and consumption data, modeling of the study variables that affect electricity and 

gas consumption in different categories of homes, and a comparison of perceived and actual 

utility bills. 

• The final set of chapters (Chapter 23 and Chapter 24) discusses the study’s conclusions and 

recommendations for builders, policy-makers, and researchers. An epilogue reviews the 

recent literature on electricity cost savings in near-ZEHs in California built since the 

SheaHomes development and compares percentages of electricity cost savings in the current 

study to the published findings. 

This report includes numerous appendixes, including the four survey data collection instruments; 

details on respondent characteristics; base n’s, means, standard deviations, and coefficients of 

variation of each scaled survey variable; complete factor analysis results; utility cost and 

consumption charts by each category of home; histograms of the 12-month utility data; and line 

graphs of electricity and gas consumption by each home in the study with utility data. 

11
Chapter 2

Guiding Ideas

Diffusion-of-innovations theory has guided 

much research in public acceptance of solar Chapter Highlights 

energy and energy efficiency innovations C The concept of diffusion of innovations guided (Rogers and Shoemaker 1971; Rogers 1995). the research. The theory is based on numerous studies of 

national, regional, and statewide populations C The innovation being studied is new highthat involve a myriad of innovations. performance homes built by a large-production 

Diffusion-of-innovation theory is widely builder and tied to a utility grid. 

accepted in the energy analysis community. C Sub-innovations in the study are solar PV 

systems and solar water-preheating systems. Rates of Adoption 

C Innovation attributes that affect the rate of their 

Generally, diffusion theory is applied to adoption are perceived relative advantage, 

compatibility, complexity, trialability, populations of potential users or customers of observability, and perceived risk. an innovation. These can be individual 

customers, organizations and companies, or C Builders must adopt and build high-performance 

entire communities. Generally, innovations homes, near-ZEHs, and ZEHs before 

diffuse more quickly if individuals can homeowners can buy them. Thus, builders, not 

homebuyers, are the early adopters of ZEHs. decide, more slowly if organizations must 

decide, and even more slowly if communities C The attributes of high-performance homes, near- have to decide (Farhar and Coburn 2000, pp. ZEHs, and ZEHs may, on balance, be perceived 

4–5). Obviously such decisions increase in as disadvantageous by large-production builders. 

complexity when more people are involved C The attributes of these homes, when offered and, thus, take longer. standard, may on balance be perceived by buyers 

of new homes as quite advantageous. A large body of empirical research has 

shown that the adoption of an innovation 

usually follows a normal bell curve. If the 

cumulative number of adopters is plotted, the result is an S-shaped curve. Figure 5 shows the 

bell-shaped curve for an adopter distribution and an S-shaped curve for the data on a cumulative 

basis (Rogers 1995). 

The product diffusion process is classically launched by “innovators” and “early adopters.” 

Gradually, the number of adopters builds until saturation is reached. The length of time this 

process takes varies by the complexity of the innovation and its perceived relative advantage, 

among other factors. For example, the Internet began in 1969 as ARPANET with only a handful 

of users. By 1981, it became BITNET, used by 14 universities. After the University of California 

at Berkeley joined BITNET in 1982, critical mass was achieved, and the number of nodes 

doubled every 6 months. As of mid-1993, there were 15 million Internet connections, a number 

that was doubling annually at that time. 

12
Figure 5. The Bell-Shaped Frequency Curve and the S-Shaped

Cumulative Curve for an Adopter Distribution

Source: Adapted from Rogers (1995) 

Innovations take varying lengths of time for adoption. Innovations that can be adopted by 

individuals, such as the birth control pill, can reach “saturation” within 5 years. Those that require 

organizational and community change, such as kindergarten, can take as long as 50 years to reach 

saturation. Figure 6 shows rates of adoption of three innovations over time. 

Stages in the Adoption of Innovations 

To help explain the potential market for any innovative product or practice, we should understand 

the innovation-adoption decision process. Figure 7 shows the accepted model of the decision 

process that underpinned the research in this report. 

Social conditions and the characteristics of decision makers affect the dynamic innovationadoption process, which moves through stages. 

1. The knowledge stage refers to individuals, households, and organizations (called market 

“actors”) who have heard about the innovation. They might be interested because of prior 

experience, professional interest, business interest, interest in technology, social pressure, and 

social values. At the end of this stage, an actor may be eager to know more, be disinterested, 

be opposed, or be somewhere between. 

13
Figure 6. Examples of Diffusion Curves 

Source: Adapted from Rogers and Shoemaker (1971) 

2. The persuasion stage refers to the aware actor’s exposure to more information about the 

innovation, how it works, how much it costs, who is using it and with what results, who is for 

and who is against it, and how it might fit in the actor’s own situation. By the end of this 

stage, an actor has formed a stronger favorable or unfavorable attitude—a position—toward 

the innovation, both in terms of its general use and its specific relevance to the actor. Actors 

may be generally favorable to the new idea but unfavorable to their own involvement with it. 

3. If favorable to becoming involved, the actor moves to the next stage of the process: the 

decision stage. During this stage, the actor decides to become involved with the innovation 

and makes plans to adopt it within the foreseeable future. The actor’s “behavioral intention” is 

to adopt the innovation. If no major obstacles intervene, the actor will probably pass to the 

next stage. 

4. In the implementation stage, the actor purchases or otherwise implements the innovation. This 

stage is not yet considered full adoption because experience with the innovation may cause the 

actor to reject it. Once the implementation stage has been reached, the last stage inevitably 

follows. 

5. In the confirmation stage, the actor lives with the positive and negative consequences of 

implementation. After a time, the actor decides whether the choice is satisfactory. If problems 

arise during this stage, the actor tries to resolve them. 

The end result of the process is a continuance or discontinuance of the adoption decision made in 

the decision stage. 

14
Figure 7. Model of the Innovation-Adoption Decision Process 

Source: Diffusion of Innovations, 4th Edition by Everett M. Rogers (p. 163). Copyright © 1995 by 

Everett M. Rogers. Copyright © 1962, 1971, 1983 by The Free Press. Reproduced with the permission of 

The Free Press, a division of Simon & Schuster, Inc. 

Characteristics of Innovation Adopters 

As noted earlier, individuals, organizations, and communities adopt innovations. The 

characteristics of organizational and individual innovation adopters are as follows. 

Organizational Innovation Adopters 

Rogers (1995) reviewed numerous studies of the organizational diffusion of innovations. After 

several decades of studies on individual adoption decisions, the attention of researchers turned 

toward studies in which the units of analysis were organizations. Rogers states: “Important 

innovations spread among the firms in an industry in a diffusion process that is similar to the way 

that an innovation diffuses among individuals in a community or some other system.” (p. 377). An 

informal network links the companies together.1 

The role and importance of innovation “champions” within organizations is well documented. 

Rogers (1995) states: “The involvement of an innovation champion contributes to the success of 

1

Later, researchers focused on the innovation process within organizations, examining variables related to 

more- or less-innovative organizations. These related to size and structural characteristics such as centralization, 

complexity, formalization, interconnectedness, and slack. This body of research findings is less germane to this study 

than is the rest of diffusion research. 

15 
an innovation in an organization” (p. 398). Indeed, without a champion, the new idea would, in 

fact, die. Champions tend to be more charismatic than others, higher risk takers, and more 

frequent initiators of action. For successful innovations that are costly, visible, or that represent 

radical new directions for organizations, champions are generally powerful individuals holding 

high offices within the organizations.2 

Individual Innovation Adopters 

The population is distributed along a bell curve, with time to adoption as the y axis. The first 2.5% 

of the population to adopt are “innovators.” Next, a group of approximately 13.5% is defined as 

“early adopters”—those who benefit from the experience of innovators and maximize their 

advantages in adopting the innovation while minimizing their risks. Early adopters are also 

frequently “opinion leaders” who catalyze shifts in the innovation’s penetration from the select 

few to the “early majority” (34%). Gradually, the “late majority” (34%) adopts the innovation, for 

not doing so would leave them in a worse position relative to everyone else. Finally, the 

“laggards” (16%) get around to adopting. When most people have adopted an innovation, the 

market is said to be “saturated” (Rogers 1995). 

Innovators tend to be venturesome and members of social groups of like-minded individuals. 

They tend to control substantial resources, have complex technological knowledge, and tolerate 

uncertainty in outcomes. 

Early adopters are well integrated into local communities and tend to be people to whom others 

look for advice before adopting an innovation. 

The early majority—the most numerous adopter category—are more deliberate than the first two 

groups, taking longer to adopt new ideas. 

The late majority are skeptical of new ideas and cautious about adopting them. They tend not to 

adopt until others have done so. 

Laggards are the last in the social system to adopt an innovation; they are more local than 

cosmopolitan in orientation and may be less well integrated in social networks. Their resources 

are relatively limited, and their caution is often financially necessary (Rogers 1995, pp. 263–267). 

Some demographic characteristics of earlier adopters compared with later adopters are as follows 

(Rogers 1995, p. 269): 

C Earlier adopters tend to have higher levels of formal education 

C They tend to have higher socioeconomic status 

C They have a great degree of upward social mobility 

C They control larger units (such as companies) 

C They are no different in age from others. 

2

The innovation champion at the San Diego division of SheaHomes was a development planner; he was not 

in a top management position. 

16 
Importantly, adopter characteristics are relative to the innovation under consideration. For 

example, some members of a population may be innovators relative to high-technology 

electronics and laggards relative to sports equipment. Nevertheless, these patterns of adopter 

characteristics have been found to hold across many kinds of innovations. 

In summary, then, among other aspects, diffusion theory classifies individuals in terms of their 

likelihood to be adopters of innovation. Approximately 16% of the population are innovators and 

early adopters, who are posited to have higher levels of formal education, higher socioeconomic 

status, upward social mobility, and broader spans of social control. They are not predicted to be 

different in age than others. Innovativeness tends to be linked with wealth, yet wealth does not 

explain innovative behavior. 

High-Performance Homes as an Innovation 

The high-performance homes built by SheaHomes in San Diego, California, are innovative for 

several reasons. Although the concepts of whole-building energy efficiency have been 

implemented by builders in various parts of the United States (particularly under the ENERGY 

STAR homes and Building America programs), the resulting homes did not necessarily contain 

solar water heating or solar electric systems. On the other hand, many custom homes are energy 

efficient and contain either solar water heating or solar electric systems, or both, but they have 

generally been built one-at-a-time, and most are not tied to an electricity grid. 

The SheaHomes high-performance home project is innovative because it offers homes that 

approach the ZEH concept built by a large-production builder and tied to a utility grid. Thus, the 

innovation of focus in this study is the high-performance home as a precursor of near-ZEHs and 

ZEHs built by large-production builders. These homes contain four key elements of ZEHS: (1) 

they are highly energy efficient; (2) their water is preheated by the sun; (3) they produce 

electricity; and (4) they can supply electricity to, store electricity on, and take electricity from a 

utility grid. The significance of such an offering by a large-production builder is that it potentially 

makes the offer of these types of homes routine rather than unique specialty commodities offered 

only by custom builders.3 The successful routinization of ZEHs will ensure higher quality homes 

that are widely available, less expensive than conventional homes to operate (all other things 

being equal), and environmentally friendlier than conventional homes. 

Although much of the focus of this study is on the performance and market acceptance of highperformance homes compared with conventional homes, a subinnovation is solar PV systems tied 

to the utility grid with net-metered electricity. Consequently, some of the study focuses on the 

response to the solar PV subinnovation. Certain homebuyers may have desired to adopt the solar 

PV system more than the high-performance home as a whole. However, separating the adoption of 

solar PV systems from the adoption of high-performance homes is difficult.4 

3

Routinization occurs when the innovations are incorporated into regular activities of organizations, and

they are no longer thought of as new ideas (Rogers 1995, p. 399).

4

A second sub-innovation is the presence of solar water preheating systems in the high-performance homes. 

The presence of these systems in and of themselves did not seem to drive home purchase decisions. Nevertheless, 

they were part of the package of energy features that were important to some homebuyers. 

17 
Innovation Attributes 

According to an extensive review of the empirical literature on diffusion of innovations (Rogers 

1995), from 49% to 87% of the variance in rate of adoption can be explained by five attributes: (1) 

relative advantage, (2) compatibility, (3) complexity, (4) trialability, and (5) observability. 

Relative Advantage 

The perceived relative advantage of an innovation is positively related to its rate of adoption. 

Relative advantage is “the degree to which an innovation is perceived as being better than the idea 

it supersedes” (Rogers 1995, p. 212). Often, the relative advantage of an innovation is expressed 

in terms of economic and prestige advantages. When prices decrease rapidly, or when a great deal 

of value is added, or both, a rapid rate of adoption is encouraged. 

Compatibility 

The perceived compatibility of an innovation is positively related to its rate of adoption. 

Compatibility is “the degree to which an innovation is perceived as consistent with the existing 

values, past experiences, and needs of potential adopters” (Rogers 1995, p. 224). Innovations may 

be compatible or incompatible with sociocultural values and beliefs, with other ideas, and with the 

adopter’s needs. If PV water pumping in a village disrupts the patterns of sociability among 

women, even though it reduces their manual labor, it could be rejected. 

Complexity 

The perceived complexity of an innovation is negatively related to its rate of adoption. 

Complexity is “the degree to which an innovation is perceived as relatively difficult to understand 

and use” (Rogers 1995, p. 242). The first adopters of home computers loved technological 

gadgets; many were engineers with extensive mainframe experience. But most people had 

difficulty using personal computers and had to join computer clubs, take courses, obtain help from 

friends, or find other means to cope with the difficulties their computers posed. This slowed the 

rate of adoption. Eventually, personal computers became more user friendly and, by 1994, about 

30% of households owned one. 

Trialability 

The more the innovation can be tried out, the faster its rate of adoption. Trialability is “the degree 

to which an innovation may be experimented with on a limited basis” (Rogers 1995, p. 243). If 

new ideas can be tried out without too much risk, uncertainty can be dispelled. The perceived 

trialability of an innovation is positively related to its rate of adoption. Early adopters are more 

concerned with trialability than are later ones. 

Observability 

The perceived observability of an innovation is positively related to its rate of adoption. 

Observability is “the degree to which the results of an innovation are visible to others” (Rogers 

1995, p. 244). The effects of some ideas are readily observable; the effects of others are difficult 

18
to discern. The observability may include how visible adoption is to others, thereby conferring 

status on the adopter, or showing that the innovation indeed “works.” Observability may also 

include the ability to actually see the effects of the innovation. 

Perceived Risk 

In addition to Rogers’ five innovation attributes, the perceived risk of adopting innovations has 

been found to be negatively associated with favorability to an innovation (Farhar et al. 1978; 

Farhar, Pilgrim, and Unseld 1982). 

Other Factors 

Assuming that innovations may be perceived as having characteristics desirable for adoption, 

other factors come into play that can expedite or impede decisions to adopt them. These include 

the following: 

C The innovation has to be available through regular organizational channels. 

C The adopters have to understand enough about the innovation to make a decision. 

C The adoption decision has to have salience—it has to be important enough to be at or near the 

top of an individual’s or a household’s action list. 

C The adopters need a support system, preferably the organization from which the innovation 

was purchased, and access to friends or others who understand the innovation. 

C The adopters need the financial wherewithal to purchase the innovation, or financing 

arrangements to make purchase possible. 

Innovation Attributes and High-Performance Homes 

As discussed above, five attributes of innovations are thought to influence their rate of adoption. 

We can examine each in turn for their potential relevance in the acceptance of high-performance 

homes, near-ZEHs, and ZEHs. In these cases, there are two levels of adopters: (1) builders must 

adopt these kinds of homes before (2) homebuyers have the opportunity to do so. 

The diffusion of innovations in the form of “best building practice” applies to the voluntary 

spread of innovative practices through networks of builder companies. Innovative builders, such 

as the San Diego Division of SheaHomes, set the initial example—in this case, the Scripps 

Highlands project. Other builders hear about the innovation and, depending on the SheaHomes 

experience, a few might be interested in emulating it. If diffusion theory is correct, however, most 

will not be interested. A very small percentage of large-production builders—#2.5%—will be 

interested in building innovative high-performance homes or near-ZEHs. The builder decisions 

are likely to be based on the perceived “attributes of innovations” discussed in the following 

sections. 

19
Table 1 summarizes the innovation attributes of ZEHs5

 as potentially perceived by builders and by 

homebuyers. The information in Table 1 highlights the different interests of builders and 

homebuyers in deciding about ZEHs in the context of innovation attributes. These are perceptions, 

not necessarily facts, and they are important because what people believe to be real is real in its 

consequences. 

As described in Table 1, from the builder standpoint, the potentially perceived disadvantages of 

adopting ZEHs appear to significantly outweigh the potentially perceived advantages. Just the 

opposite is the case for homebuyers, for whom the potential advantages are numerous and 

substantial, compared with the disadvantages. If this characterization of differential perceptions is 

accurate (and all other things being equal), diffusion theory would predict that, on the basis of 

innovation attributes, builders will resist the adoption of ZEHs, whereas homebuyers will rapidly 

adopt them when they are made available. Of course, for new ZEHs to become widely available, 

builders will have to adopt the ZEH concept. 

Prior Research on PV Adoption 

The relationship between diffusion theory and interest in adoption of grid-tied photovoltaic (GPV) 

systems on the part of Colorado homeowners was described in Farhar and Coburn (2000). A 

majority (68%) of respondents in that study favored GPV being made more widely available to 

Colorado residents. However, homeowners knew little about GPV, as would be expected; there 

was more favorability than familiarity. Perceived benefits of GPV made it seem advantageous 

compared with conventional energy sources, such as coal. The highest scoring benefits included 

long-term environmental benefits (including conserving natural resources), homeowner financial 

benefits, and the prestige and pioneering advantages associated with adopting GPV. The potential 

barriers to GPV included initial system cost and maintenance, technical performance and 

reliability of PV systems and providers, and situational aspects, such as codes or covenants that 

prohibit GPV adoption, what friends and neighbors might say, or the amount of space needed at 

one’s home. Clearly, the perception of a majority of respondents was that the advantages 

outweighed the disadvantages, even though the example prices mentioned in the study ranged as 

high as $28,000. Interestingly, the demographic characteristics ordinarily associated with early 

adoption of innovations (such as higher socioeconomic status) did not differentiate the 

homeowners most likely to adopt GPV in the near term. 

Hypotheses and Research Questions 

These findings refer to the retrofit market for PV; that is, the potential interest of Colorado 

homeowners in retrofitting their homes with solar electric systems tied to the utility grid. The 

current study focuses on the new home market. Also, unlike the Colorado respondents, all of the 

San Diego respondents have the demographic characteristics associated with early adopters. Thus, 

we anticipated that distinguishing early adopters from later adopters within the San Diego study 

would be difficult. We hypothesized that, in the unlikely case that such differences could be 

5

ZEH is the generic term used here to refer to high-performance homes and near-ZEHs, as well as true 

ZEHs. 

20 
measured, early adopter characteristics would more likely be found among the SheaHomes buyers 

than buyers of conventional housing. 

Environmental concern has often been linked to interest in renewable energy (e.g., Buhrmann and 

Farhar 1998; Farhar 1993; Farhar and Coburn 2000). This study hypothesized that environmental 

concern would be linked with purchase of PV systems and possibly of ZEHs. 

Advisory Group Guidance 

The study’s advisory group (see Acknowledgments section for the list of members) recommended 

that the research answer the following questions: 

• How much did buyers know about the energy features of the homes? How well do the 

consumers understand them? What messages are the sales staff communicating about the 

energy features? 

• What is the role of the home builder “image” and reputation in the sales of the ZEHs? 

• Do ZEHs have more market value than conventional homes and resale homes? Did energy 

features bring out people who were originally shopping for resale homes as well as new 

homes? 

• What is the additional value to the customer of these systems? What price could be added to 

the price of a ZEH over a conventional home? 

• To what extent are energy performance features important in drawing people to look at the 

homes? To buy the homes? 

• Should solar features be standard or optional? Are optional upgrades a good idea? 

• How are ZEH purchasers different from purchasers of conventional homes in motivation, 

attitudes, and demographics? 

• Among energy features, which are the most important to homebuyers—efficiency features, 

solar water heating systems, or solar electric systems? Which feature has the most appeal? Or 

does an integrated ZEH house with all features have the most draw? 

• Is aesthetics a barrier? Is it positive, negative, or neutral? How important were they in the 

purchase decision? Does it matter if solar equipment is on the front or back of the house? 

• How important is the feedback device (showing the amount of electricity the house is using 

and the amount the PV system is producing)? 

• How satisfied are customers with their home purchases? 

The advisory committee believed that answers to these questions would be valuable to builders, 

policy-makers, utility companies, trade and professional organizations, and the energy-efficiency 

21
and solar-energy communities, as well as to marketers, researchers and energy analysts, and 

homebuyers. 

The questions were completely focused on the buyers of SheaHomes’ high-performance homes. The 

advisory committee asked no questions about the SheaHomes experience in pursuing the building of highperformance homes or about whether SheaHomes perceived the experience as, on balance, positive or 

negative. Thus, the major focus of this investigation was on individual homebuyers; however, qualitative 

information was also inevitably gathered on the SheaHomes experience as an inherent part of the study. 

A Limited Continuance Decision 

Although this investigation did not set out to study the organizational characteristics that led to 

SheaHomes’ adopting the idea of building high-performance homes, qualitative information about the 

builder’s experience was inevitably gathered and is reported in Chapter 4. Even before the Scripps 

Highlands project was complete, the San Diego Division of SheaHomes had decided to pursue its 

adoption of high-performance homes only in the Ladera Ranch and Bella Rosa Developments. The 

management at that time stated that they wanted to study market acceptance further before committing to 

building more large-scale new home developments of high-performance homes or near-ZEHs. 

Summary 

The innovation being studied is new high-performance homes built by a large-production builder and tied 

to a utility grid. Sub-innovations are solar PV systems and solar water-preheating systems. Innovation 

attributes that affect the rate of their adoption are perceived relative advantage, compatibility, complexity, 

trialability, and observability. Builders must adopt and build high-performance homes, near-ZEHs, and 

ZEHs before homeowners can do so. The attributes of high-performance homes, near-ZEHs, and ZEHs 

may, on balance, be perceived as disadvantageous by large-production builders. The attributes of these 

homes, when offered standard, may on balance be perceived as quite advantageous by buyers of new 

homes. SheaHomes-San Diego was awaiting the results of this study. 

22
Table 1. Innovation Attributes of ZEHs Related to Potential Builders and New Homebuyer Percepti

Innovation Attribute Potential Builder Perceptions Potential New HomRelative advantage is “the degree to which an innovation is perceived as being better than the idea it supersedes” (Rogers 1995, p. 212), usually in economic and prestige advantages. The perceived relative advantage of an innovation is positively related to its rate of adoption. In the decision process, advantages are weighed against disadvantages, and against doing nothing. 

Advantages • Receiving rebates for PV systems (in 

California, SheaHomes received 50% rebates for PV systems) 

• Free advertising for ZEHs through media 

coverage of ZEH developments (in the case of SheaHomes this amounted to more than $1 million worth in less than a year) 

• Free market research funded by utility 

companies or federal or state governments 

• Free technical assistance from national 

laboratories and state and local programs 

• Testimonials from satisfied customers • Favorable treatment on the part of local and 

state officials (for example, the speed with which building permits are granted) 

• Sizable profits • Enhanced builder reputations for producing 

quality homes 

• Reduced number of callbacks • Faster rate of ZEH home sales. 

Advantages • Up-front costs amort

improved monthly ca

• Significant savings o• Modest savings on ga• Selling excess electri

grid and using the grelectricity 

• Greater value at resal

homes 

• “Bragging rights” an

for owning ZEHs 

• Helping to protect th

now and for future ge

• Supporting the local • Learning about how • Learning about “ener

and adapting energy-maximize the benefit

*These attributes are potential perceptions, not necessarily established social facts. 

23
Table 1. Innovation Attributes of ZEHs Related to Potential Builders and New Homebuyer Perceptions,* 

Innovation Attribute Potential Builder Perceptions Potential New Hom

Relative advantage, continued Disadvantages 

• Reduces competitive edge • Costs of changing building practices; climbing 

the learning curve 

• Requires finding reliable suppliers of unfamiliar 

equipment 

• Uncertainty about demand/market • Complicates relationship with the utility 

company 

• Upsets collegial relationships with other 

builders 

• Results in a greater number of transactions with 

state and local government officials 

• Requires extensive education of homebuyers • Involves a great number of call backs • Slows down the rate of sales • Results in an influx of curious visitors with no 

intentions of buying, but who take staff time. 

Disadvantages • For some, up-front co• For some, aesthetics (

appears to be a noniss

• Complex interconnec

the utility company. 

Compatibility is “the degree to which an innovation is perceived as consistent with the existing values, past experiences, and needs of potential adopters” (Rogers, p. 224). The perceived compatibility of an innovation is positively related to its rate of adoption. 

Builders may perceive ZEHs as incompatible with standard building practices and accepted industry norms. They may view policies to foster ZEHs as unacceptable attempts to control builders, which have traditionally been unacceptable to them. 

Because California has eelectricity shortages that disruption and hardship, costs, widespread antinustrong concern for the enhomeowners may view Zwith cultural norms. Subprobably vary by state ancountry. 

*These attributes are potential perceptions, not necessarily established social facts. 

24
Table 1. Innovation Attributes of ZEHs Related to Potential Builders and New Homebuyer Perceptions,* 

Innovation Attribute Potential Builder Perceptions Potential New HomComplexity is “the degree to which an innovation is perceived as relatively difficult to understand and use” (Rogers 1995, p. 242). Perceived complexity is negatively related to an innovation’s rate of adoption. 

The components that make up a ZEH (energy-efficiency measures, solar water heating, and PV equipment) may be seen as difficult and time-consuming for a builder to schedule into a large housing development. The purchase of these components requires that new business relationships be established with previously unknown suppliers. 

Because ZEHs “just sit electricity” (in the wordand save energy and heawithout much interventipart, they are simple for Complexity is not a barOn the other hand, a depurchase a PV system iis definitely an impedimwhich PV is offered as 

Trialability is “the degree to which an innovation may be experimented with on a limited basis” (Rogers 1995, p. 243). If new ideas can be tried out, uncertainty about them can be overcome. 

Builders may know very little about advances in efficiency, water heating, and solar electricity. They may be unfamiliar with the “whole building” concepts that underlie ZEHs. Because few ZEHs have been built, few builders have been able to “try them out.” Building a custom ZEH as an experiment is an expensive proposition. 

Purchasing a ZEH is an homebuyers. They can tliving in them. For techinexperienced homebuyprobably a barrier to pu

Observability is “the degree to which the results of an innovation are visible to others” (Rogers 1995, p. 244). The perceived observability of an innovation is positively related to its adoption. 

Builders may want to know if the markets and profits are there for ZEHs. They may want to know if they will get an edge on their competitors if they build them. This is difficult because the information on profits is proprietary. Observability can be improved by research and by more builders building ZEHs. 

If ZEH homeowners brathey will gain the attentmedia. If researchers cabills, the observability o

*These attributes are potential perceptions, not necessarily established social facts. 

25
Table 1. Innovation Attributes of ZEHs Related to Potential Builders and New Homebuyer Perceptions,* 

Innovation Attribute Potential Builder Perceptions Potential New Hom

Concern about risk is the perceived financial or other harm that the adopter of an innovation could incur. Perceived risk is negatively associated with adoption. 

Builders may be concerned that building an entire ZEH development could be quite expensive and that the homes might not sell. They could also be concerned about unanticipated costs or scheduling delays that could detract from the bottom line. Concern about financial risk could be a barrier for builders. 

Homebuyers will want solar water heating anprotect them from uneand repair costs. 

*These attributes are potential perceptions, not necessarily established social facts. 

26
Chapter 3

Study Methods and Data Resources

Overview 

This study is an empirical and statistical Chapter Highlights investigation of the differences between buyers 

of new high-performance homes and buyers of • This study comprises an empirical and 

new conventional homes. It includes contextual, statistical investigation of the differences 

qualitative, and quantitative data, but it does not between buyers of new high-performance and 

comparison homes. include engineering data or engineering 

analyses. The study encompasses two distinct • The study encompasses two phases of work— phases of work—a qualitative phase and a qualitative and quantitative phases—during 

which different types of data were collected. 

types and sources of data were investigated. 

quantitative phase—during which different 

• The study includes contextual data, household 

and homeowner data, and utility data. The homes covered by the study were 

constructed and offered for sale by two different • The new homebuyer surveys had a 63% overall 

response rate (65% from SheaHomes 

respondents and 56% from comparison 

builders. The principal target population 

consisted of 306 homebuyers in SheaHomes’ respondents). San Angelo and Tiempo developments at 

Scripps Highland in northern San Diego • A broad-based approach to statistical analysis 

County. All homes in these developments are was taken, including descriptive statistics, 

factor analyses, t-tests, analyses of variance, and 

regression analysis. 

highly energy efficient, and most of them are 

high-performance homes with solar features. 

The comparison group was 103 buyers of 

conventional homes constructed by a different 

builder in an adjacent community. 

The comparison community was chosen because of its proximity to the San Angelo and Tiempo 

developments and the similarity of price range and housing type. The weather and climate of the 

two communities is identical. The homes in the comparison community do not have any special 

energy-efficiency and solar features, unlike the homes in San Angelo and Tiempo. 

The first, or qualitative, phase of the study included meetings with the project advisory 

committee, as well as numerous interviews of staff members and representatives of SheaHomes, 

organizations and companies partnering with SheaHomes, and the builder of the comparison 

homes. Other interested parties within the ZEH community were also interviewed. Field 

researchers visited homes in the SheaHomes community that were already occupied. They also 

spent time at the SheaHomes sales centers to observe the manner in which the sales staff 

interacted with the potential homebuyers, especially relative to the homes’ energy-efficiency and 

solar features. Researchers visited the comparison home sales office and show homes, posing as 

interested home shoppers to learn whether the sales staff said anything about energy efficiency in 

the comparison homes (they didn’t). Although the SheaHomes sales staff was aware of the 

27
researchers’ role, the comparison home staff was not. In total, field researchers visited five 

developments before they selected one as the comparison community to San Angelo and Tiempo. 

These meetings, observations, and interviews facilitated the collection of contextual data and 

information necessary to appropriately focus future data-collection efforts. Also during this 

phase, information about “lost lookers”1 was obtained through a telephone survey of visitors to 

the SheaHomes’ Scripps Highlands Sales Center. Results of these interviews are reported in 

Collins (2003). Finally, a significant component of the first phase of the study consisted of 

qualitative interviews of early homebuyers in SheaHomes’ Scripps Highlands development. 

Results of these interviews are reported in Farhar, Coburn, and Collins (2002). Along with the 

contextual data, the findings from the interviews of early homebuyers served as a foundation for 

the study’s second phase. 

The second, or quantitative, phase consisted of a comprehensive mail survey, along with a 

detailed statistical analysis of the survey responses. This survey encompassed all homebuyers in 

SheaHomes’ San Angelo and Tiempo developments, as well as all homebuyers in a nearby 

development of similar but conventional homes. As suggested above, the questionnaires used in 

the survey were based, in part, on the results of the qualitative interviews of early homebuyers in 

the SheaHomes communities. Preliminary findings from the quantitative phase of the study are 

reported in Coburn, Farhar, and Murphy (2004). 

The second phase also included collection and analysis of utility billing and consumption data 

among respondents in SheaHomes and comparison homes who gave permission for San Diego 

Gas and Electric Company to share their utility records with NREL. 

Types and Sources of Data 

Three categories of data were collected and analyzed during this study: contextual data, 

household data (including homeowner survey data), and utility data. Table 2 further defines these 

categories, identifies the sources of the information, and describes the data-collection techniques 

employed. 

Contextual Data 

Many of the conclusions about the builder experience in this report are based on contextual data. 

In addition, these data represent important background information that is relevant to the analysis 

of homeowners’ responses to survey questions. During the course of the study, interviews were 

conducted between 2002 and 2004 with Ryan Green and Dale Holbrook, SheaHomes, San Diego, 

and with various other members of the SheaHomes staff at the time, including Teri Shusterman, 

Diane Rivera, Pam Beaird, and Pattie Walker. Representatives of companies partnering with 

SheaHomes were also interviewed, including Kirk Stokes of Altair Energy, a Colorado PV 

systems and services provider; Scott Anders, then of the San Diego Regional Energy Center; Rob 

1

A “lost looker” is someone who visited the sales center but did not purchase a home. 

28 
Hammon of ConSol, Inc., in Stockton, California; Marc Roper, then of AstroPower, Inc., in 

California; and Tom Bohner, Sun Systems, Inc., in Phoenix, Arizona. 

Table 2. Types and Sources of Study Data 

Category of Data Phase 1 - Qualitative Data Phase 2 - Quantitative Data 

Contextual data • Interviews with builder staff 

• Observation at sales centers 

• Studies sponsored by 

SheaHomes 

• Other artifacts (e.g., news 

clippings, magazine articles, and 

TV news clips) 

• Home sales and 

characteristics (builder 

data) 

• Public records on sales 

Household and 

homeowner data 

• Focused interviewing of 

homebuyers 

• Focused interviewing of lookers 

for new homes 

Survey of homeowners’ 

attitudes, opinions, and 

practices (both SheaHomes 

and comparison home 

owners) 

Utility data Interviews with utility staff about 

their experience with grid-tied PV 

on residences, dates of meter 

reading of study households, utility 

rates, and other relevant points 

• Actual therm usage by 

households in SheaHomes 

and comparison 

communities 

• Actual kWh usage by 

households in SheaHomes 

and comparison 

communities 

• Actual utility cost/billing 

data for all households 

who gave permission 

SheaHomes also provided other documentation, including reports from its contractors on market 

response and media coverage, videotapes of television broadcasts, newspaper clippings, sales 

materials, site maps, and related materials. 

Other contextual data were obtained from the public records on home sales and from SheaHomes 

records on the energy features of homes in the Scripps Highlands development. 

Public Records on Home Sales 

Data on the closing dates, sales price, and square footage of the new homes purchased were 

obtained from the San Diego Assessor’s Office. 

29
SheaHomes Records on Energy Features of Homes 

SheaHomes provided data from its records on the energy features included at each new address in 

the San Angelo and Tiempo neighborhoods. 

Household and Homeowner Data 

Data on households and homeowners were obtained through focused interviewing of selected 

homebuyers, focused interviewing of selected individuals looking for new homes, and the formal 

mail survey of all homebuyers in the study universe. The mail survey was the most 

comprehensive source of household and homeowner data. 

Universe of Study for the Homeowner Survey 

As previously suggested, the universe for the study consisted of two different groups of homes 

and households: all households in the 306-home SheaHomes San Angelo and Tiempo 

developments at Scripps Highlands in San Diego and all 103 households in the adjacent 

comparison community of conventional homes constructed by the different builder. 

Survey Operations 

On January 22, 2004, questionnaires were mailed to 271 homebuyers in the 306-home Scripps 

Highland community (high-performance homes) and 98 homebuyers2

 in an adjacent 103-home 

community (conventional homes) who had lived in their homes for at least 6 months. Data 

collection continued for 12 weeks. The 6-month restriction on time of residence was imposed to 

ensure that all respondents had experienced living in their homes for at least two seasons of the 

year. On May 8, 2004, questionnaires were mailed to the remaining 35 SheaHomes buyers who 

had purchased their homes late in 2003 and who would have lived in them for at least 6 months 

by that time. This phase of the data concluded on July 15, 2004. 

Over the time period in which the survey was conducted, the following five mailings were sent in 

stages in an effort to maximize the survey response rate: an initial postcard alerting potential 

respondents that they were part of a study, a questionnaire package containing a crisp new $10 

bill3

 and a utility release form, a reminder postcard, a reminder letter, and a final package 

containing a second copy of the questionnaire. 

Questionnaire Development and Construction 

Based on results from the qualitative phase of the study, four different respondent groups were 

identified and separate questionnaires were developed for each group. The four questionnaires 

2

Only 98 of the 103 comparison community households could be identified. 

3

Funding to cover this expense was provided by the California Building Industries Institute to the Colorado Energy 

Science Center, which oversaw the preparation of the questionnaire mailings and delivered the sealed mailings to the 

NREL Mail Room. 

30 
contained a number of similar, and even identical, items; but they also contained some items 

unique to the respective respondent groups for which they were designed. Although the 

administration of four separate questionnaires required more organization and administrative 

control, it actually helped streamline data collection while providing comparable information 

from the different respondent groups. 

The four homeowner respondent groups are defined as follows: 

• SheaHomes homeowners with PV systems. The questionnaire for this group was termed the 

“PV” questionnaire; “PV” is also the moniker assigned to the associated homes, homeowners, 

and survey respondents. 

• SheaHomes owners of homes without PV systems even though they were PV-eligible. The 

questionnaire for this group was termed the “main” questionnaire; “main” is also the moniker 

assigned to the associated homes, homeowners, and survey respondents. 

• SheaHomes owners of homes without PV systems because they had no opportunity to 

purchase them. The questionnaire for this group was termed the “ineligible/early” 

questionnaire; “ineligible/early” is also the moniker assigned to the associated homes, 

homeowners, and survey respondents. 

The first 13 homes constructed by SheaHomes at Scripps Highlands (called “early” homes) 

were highly energy-efficient homes. However, unlike all other SheaHomes, these had no solar 

water preheating or solar PV systems because the builder had not yet integrated these features 

into the homes. AstroPower, Inc., designated another 33 sites as unsuitable for PV systems 

because of orientation of the home or because the roof line of the home was unsuitable. These 

homes, which were marked black on the site map are termed “ineligible” because they were 

ineligible for PV systems. These 46 (33+13) homes did not have PV systems and are 

collectively termed “ineligible/early.” 

• Comparison homeowners. The questionnaire for this group was termed the “comparison” 

questionnaire; “comparison” is also the moniker assigned to the associated homes, 

homeowners, and survey respondents. Homes in the comparison community, which is adjacent 

to Scripps Highlands, were not advertised as energy efficient nor as offering any solar energy 

features. 

As suggested above, the four questionnaires included general questions permitting comparisons 

across each of the groups, as well as items specific to each group. For example, only owners of 

homes with PV systems were asked questions about their experience with PV systems. 

Response Rate 

A total of 271 questionnaires were successfully delivered to SheaHomes homebuyers in the two 

questionnaire mailings, and 96 were successfully delivered to comparison community 

31
homebuyers.4 A total of 231 completed usable questionnaires were received at NREL. Of these, 

177 were received from homeowners in the SheaHomes communities (72 PV, 85 main, 20 

ineligible/early), and 54 were received from homeowners in the comparison community. The 

overall survey response rate was 63% (65% from the SheaHomes communities and 56% from the 

comparison community). 

The respondent pool is deemed to be representative of the entire study universe. Table 3 shows 

the numbers and associated percentages of the four categories of homeowners in the universe and 

in the respondent pool. A P2 test suggests there is no significant difference in the corresponding 

percentage distributions (P2=1.60; p=.66). 

Table 3. Numbers and Percentages of the Four Categories of Homeowners among All

Homeowners and among Survey Respondents

Homeowner Category 

All Homeowners Survey Respondents 

n % n % 

PV 120 29% 72 31% 

Main 140 34% 85 37% 

Ineligible/early 46 11% 20 9% 

Comparison 103 25% 54 23%

 Total 409 99%* 177 100% 

*Does not add to 100% because of rounding 

Table 4 shows a more restricted comparison of all homeowners in SheaHomes communities to 

those responding to the survey with regard to PV ownership. This is important because, if 

homeowners with PV systems were more likely to complete the questionnaire than homeowners 

without PV systems, the responses from homeowners in the SheaHomes communities could be 

more representative of, or more heavily slanted toward, PV owners than non-owners. The 

information in Table 4 suggests that homeowners with PV systems were not more apt to 

complete questionnaires than were other homeowners in the SheaHomes communities. In fact, a 

P2 test suggests that there is no significant difference in the corresponding percentage 

distributions (P2=.10; p=.75). 

4

Some of the original home buyers had moved on and were not included. 

32 
Table 4. Comparison of Homeowners in SheaHomes Communities with and without PV Systems:

All Homeowners versus Survey Respondents

Homeowner Category 

All Homeowners in 

SheaHomes Communities 

SheaHomes Survey 

Respondents 

n % n % 

Homeowners with PV 120 39% 72 41% 

Homeowners without PV 186 61% 105 59%

 Total 306 100% 177 100% 

Reasons for Non-Response 

As part of the information provided in the survey mailings, all prospective respondents were 

supplied with a study contact name, telephone number, mailing address, and e-mail address. The 

return address on the 9" x12" manila envelope for the National Renewable Energy Laboratory in 

Colorado may have been unfamiliar to the San Diego respondents. As a result, a few respondents 

telephoned after receiving follow-up mailings to say that they had thrown out the original 

mailing, not realizing what it was. (The $10 financial incentive to complete the questionnaire was 

also thereby lost.) Thus, it seems reasonable to assume that some of the addressees threw out the 

envelope without opening it. A few other respondents called to say that they were too busy to 

complete the questionnaire and asked to be taken off the mailing list. One or two callers 

indicated they had an illness in the family. It would be reasonable to assume that these callers 

represented some other potential respondents who were too busy or had an illness. A certain 

number of questionnaires were not successfully delivered by the U.S. Postal Service either 

because the homeowners had moved or because they refused the mailing. A few respondents may 

have opened the envelope, pocketed the $10, and thrown away the questionnaire. There is no 

evidence to suggest how many fell into each of these categories. However, none of the reasons 

for non-response of which we are aware suggested a bias for or against the study or the 

questionnaire itself. 

We learned from the SheaHomes sales staff that the San Angelo and Tiempo homebuyers had 

been incessantly pursued by the media seeking interviews; private companies that collect 

information for homebuilders had also sent them questionnaires. Reportedly, one or two 

homeowners had told the SheaHomes staff that they were going to refuse any further requests for 

interviews. Therefore, it seems reasonable that a certain degree of respondent fatigue had set in 

by the time the NREL questionnaires arrived and this, too, could have affected the response rate. 

Nevertheless, this fatigue was not directly related to the study or the data-collection instruments. 

Some respondents phoned to ask questions about their PV systems, indicating a need for more 

information and education for PV homeowners. Eventually (and after data collection had ended), 

to assist the PV owners the research staff prepared a letter listing PV information sources (such 

as SheaHomes and AstroPower, Inc.) and their contact information to assist the PV owners. 

Thus, it seems reasonable to conclude that study non-response did not bias the study’s findings in 

any particular direction. 

33
Survey Data Coding and Reconciliation 

The protocols for data handling were approved by the University of Colorado Human Research 

Committee. These protocols follow established federal regulations and guidelines for the 

protection of human subjects.5

 All returned questionnaires were received at NREL in the sealed 

return envelopes provided to the prospective respondents. Outer envelopes were opened and 

separated from the unmarked sealed white inner envelopes. The outer envelopes were used to 

check respondents off the list for follow-up mailings and were filed in a locked file. The inner 

envelopes were opened and marked with a unique three-digit respondent identification number. 

The name of the type of questionnaire (PV, main, ineligible/early, or comparison) was written in 

the upper right-hand corner. The questionnaires were then hand-delivered to Alexander’s Data 

Services, Inc., the firm responsible for data entry. 

In cooperation with Alexander’s, NREL developed the codebook, which was reviewed, revised, 

and approved by the senior author. Questionnaires were checked and data were entered by two 

independent coders and checked by computer for consistency. Data were stored in Excel files and 

delivered to NREL. NREL staff cleaned the data of all anomalies and inconsistencies. 

At NREL, staff imported the data into an SPSS-11 file. The codebook was maintained at NREL 

in a Word file. All corrections and recodings were recorded in the codebook, and each new 

Master File was dated. Electronic records were checked against hardcopy questionnaires to 

ensure accuracy. 

Duplicates of the master files were maintained at NREL and at Abilene Christian University6

 in 

Abilene, Texas. All hardcopy and electronic files identifying respondents were stored at NREL in 

locked file cabinets in a locked storage room. 

Utility Data 

As previously noted, each homeowner was asked to sign and return a utility release form, in 

addition to completing a questionnaire, for San Diego Gas & Electric (SDG&E) to release to 

NREL their monthly electricity and natural gas consumption and costs. Utility release forms7 

with valid signatures8 were received from 132 homeowners, and the original forms were 

forwarded to SDG&E. These 132 homeowners represent 57% of all those responding to the 

survey questionnaires (56% of the SheaHomes survey respondents and 61% of the comparison 

community survey respondents). With respect to the total number of surveys mailed, the response 

5

Protocol number 0603.04 Human Research Committee, Office of Research Integrity, Office of the Vice Chancellor 

for Research, University of Colorado at Boulder. 

6

Abilene Christian University is the organizational affiliation of this report’s second author. 

7

The utility data release forms were prepared by the NREL legal staff and approved by the SDG&E legal staff. 

8

Four of the utility release forms were signed by an occupant of the household other than the person in whose name 

the utility account was listed; therefore, data could not be obtained for those households. 

34 
rate for utility release forms is only 36%, considerably lower than response rate (63%) for the 

survey questionnaires themselves. 

Upon receipt of the utility release forms, SDG&E provided NREL with actual monthly billing 

and consumption data from the first date of service for each household represented in Excel 

format. Data were obtained for all households through the June 2004 billing cycle. A typical 

monthly utility bill for one of these households from initial occupancy contains the following 

items: days of service in the billing month, amount of electricity used (in kWh), cost and tax for 

electricity used, and miscellaneous costs associated with electricity usage, plus amount of gas 

used (in therms) and cost and tax for gas usage. Other information associated with the dates of 

service is also included. 

Because respondents moved into their homes and initiated service on different dates, the number 

of months of data for each household varies. Table 5 shows the earliest and latest first dates of 

utility service, as well as the mean number of months of service, for households in the 

SheaHomes and comparison communities. The earliest first date of service among the 132 

households was June 2001; the latest date of service was June 2004. From this information it can 

be determined that the overall time period encompassed by the utility data provided by SDG&E 

is approximately the same for households in the SheaHomes and comparison communities, but 

that the mean length of utility service is significantly shorter for households in the SheaHomes 

communities (23.8 months) than for those in the comparison community (27.5 months) (t= 

–2.578; p=.012). This finding suggests that, although SheaHomes and the builder of the 

comparison homes started developing their communities about the same time, SheaHomes was 

building many more homes and approached build-out at a later date. 

Table 5. Comparison of Length of Utility Service for Homes 

in the SheaHomes and Comparison Communities 

Development 

Earliest First Date 

of Utility Service 

Latest First Date 

of Utility Service 

Mean Months 

of Service* 

SheaHomes June 2001 November 2003 23.80 

Comparison homes June 2001 September 2003 27.45

 All homes June 2001 November 2003 25.52 

*As of June 2004 

As earlier discussed, geographically, the two housing developments are immediately adjacent to 

each other located on the top of a mesa in northern San Diego County. Hence, they experience 

identical climatological regimes. Nonetheless, climate can only be considered constant and 

controlled between the two developments during time frames that encompass the same months of 

utility service. This constraint impacts the selection of data for analysis of the utility data (see 

Chapter 19, Comparative Analysis of Utility Consumption and Cost), because the number of 

households and months of service is effectively restricted. 

35
Data Analysis 

A master data file containing household survey responses and associated utility data was 

developed. All respondent households were identified with three-digit identification numbers. 

The data were analyzed with statistical routines available on Version 11 of the Statistical 

Package for the Social Sciences (SPSS-11), Version 8 of the Statistical Analysis System (SAS 

v8), and Microsoft Excel (Office 2000). 

Various graphical displays, such as histograms, box plots, and time-series line graphs were 

invaluable to the early formulation of analytical trends and further screening and cleaning of the 

data. Pearson product moment correlation and Spearman rank correlation were used to establish 

the basic linear relationships between pairs of items and variables. 

A broad-based approach to statistical analysis of the data was taken. Considering each individual 

question as a stand-alone response item, descriptive statistics were computed, including 

percentages, means and/or medians, standard deviations and/or ranges, and coefficients of 

variation. Tables containing these descriptive statistics are contained in Appendix E (Base n’s, 

Means, Standard Deviations, and Coefficients of Variation for Scaled Responses). 

The results of the statistical tests were evaluated using p=.05 (95% confidence level) as the 

criterion of the statistical significance. Thus if the p-score (probability) is #.05, the results of the 

statistical tests are considered statistically significant, whereas if the p-score is >.05, the results 

are generally considered not significant (n.s.). A p-score of .05 means that there are only five 

chances in 100 that the results are due to chance. When results are almost significant (e.g., .055), 

we point out that they “near” statistical significance. We use the p-score of .05 because it is the 

standard accepted in professional statistical and social sciences research. 

Conventional Z-tests, t-tests, and analyses of variance were used to determine the significance of 

observed differences in proportions and mean responses on key study variables and other items 

given by different respondent groups or household categories. Such tests provided important 

comparative results to facilitate an overall interpretation of the data. Comparisons of percentages 

and mean responses between SheaHomes and comparison community homeowners were 

essential to the study. Other analyses focused on the differences between the PV owners and nonPV owners (both those who did and did not have an opportunity to purchase PV). Such 

comparisons are detailed throughout the main body of this report. 

Homeowners were classified into various categories to permit useful analyses and interpretations 

of the data, once the data set was better understood. Such classifications are described in the 

chapters in which findings from their analyses are presented and discussed. 

In addition, numerous cross-tabulations were performed comparing response patterns on various 

items for different respondent groups or home and household categories (e.g., demographic 

characteristics), or response patterns on pairs of items for one specific respondent group. The 

36
Chi-square (P2) tests of significance were used to guide the interpretation of these crosstabulations. 

After a thorough univariate assessment of the data was concluded, numerous multivariate 

statistical analyses were performed. The first step in the multivariate analysis involved the use of 

factor analysis to help reduce the data to a more manageable and interpretive form. Although it is 

highly informative and descriptive, the univariate analyses of such a large number of survey 

items proved somewhat difficult to synthesize. Therefore, factor analysis was used to collapse 

this large body of information into a smaller collection of “factors” that could be used to more 

succinctly characterize the distribution of responses. One important aspect of factor analysis is 

that it takes into account the linear relationships among items that are sometimes difficult to 

identify when considering items one at a time from a univariate perspective. The procedures 

available in SPSS-11 were used to conduct the factor analysis, including the application of a 

conventional varimax rotation to refine the computations and make the results more interpretable 

(see Kinnear and Gray 1997; Dillon and Goldstein 1984). The results of the factor analysis are 

detailed in Chapter 17 on Data Reduction. 

After the factors were calculated, individual factor scores were computed for each survey 

respondent (i.e., based on their individual responses to survey items, each respondent was 

assigned a series of new values in the database, one per factor). These scores were subsequently 

standardized using a conventional Z-score transformation resulting in uniform scaling of the 

factor scores. The scores representing the factors were then further analyzed using conventional 

univariate procedures (see details in Chapter 18, Analysis of Factors by PV Adopter Categories). 

Similar analytical techniques were applied to the utility data, but the objectives were somewhat 

different. In addition, analysis of the utility data had to account for differences in the number of 

months of usage per household and the number of days per billing cycle. Consequently, some of 

the procedures incorporated weighting to produce accurate computations. To standardize the time 

frame for utility service and to cover all the seasons of one year, data for homes with a service 

period shorter than 12 months were eliminated from all computations. In addition, box plots, line 

graphs, and other visual displays were used to identify outliers in the utility costs and 

consumption amounts. Further, three Early homes were excluded on the grounds they were not 

strictly comparable to other homes in SheaHomes communities because they had no solar water 

preheating systems which could have affected their energy consumption. The results of these 

techniques are presented in the chapters and appendixes on the analysis of utility data. 

Subsequent to these database refinements, descriptive statistics on utility cost and usage were 

computed. T-tests and analyses of variance were used to determine the significance of observed 

differences in the mean values for different groups or types of homes. Finally, multiple 

regression analysis was used to construct models of utility cost and consumption based on the 

most important home and respondent characteristics. 

37
Limitations of the Data 

The data reported derive from a case study of new homes in the San Diego climate regime. As 

such, although they are suggestive for high-performance home projects in California and 

elsewhere, they cannot be generalized to any other location. Respondents to the questionnaires 

are heads of household and original buyers of a newly constructed home in either the 

SheaHomes’ San Angelo or Tiempo communities or in the comparison community. The 

questionnaires were voluntarily completed by either the male or female head-of-household at the 

householders’ discretion. The voluntary responses, therefore, reflect the perceptions of that one 

respondent on behalf of the household. The other non-responding head-of-household could have 

responded somewhat differently. However, the study’s assumption is that, because the homepurchase decision and the experience of living in the new home are both highly significant and 

are likely to be thoroughly discussed by the purchasers, the respondent who opted to “speak for” 

the household probably reflected, in general, the shared views of the household heads. However, 

where gender analyses are discussed in this report, the assumption is made that differences 

between male and female respondents are based on gender—rather than household—differences. 

Queries about purchase decision factors rely on the respondents’ memories of a decision made 

some 6 to 18 months earlier. Still, although these are retrospective data, the decision in question 

is one of the most important and financially significant decisions most families make in their 

lifetimes. For this reason, the data are probably more reliable than most retrospective data would 

ordinarily be. 

In some of the study’s analyses, as data were categorized, the number of cases became quite 

small, either because of the increased number of cells or because non-response to even one item 

meant that 10 or 12 items from that household could not be included in an analysis. Given the 

limitation that the universe of study itself numbered approximately 400 homes and that not all 

homeowners responded, it is unavoidable that certain analytical cells contain smaller numbers 

than would be desirable. Nevertheless, because the concepts being analyzed are important, the 

statistical tests were completed with the caveat that the number of individuals responding is 

small. The findings from such analyses with a small number of individuals responding are 

instructive and suggestive of directions for future research. 

As is usual in studies of human knowledge, attitudes, preferences, opinions, and behavior, not all 

of the variance in response is accounted for. There are clearly variables beyond those measured in 

this study that affect responses. On the whole, however, the amount of variance explained 

generally ranges from 55% to 90%. In addition, there were items or factors which we were 

unable to observe, measure, or record, such as orientation of the house; information about roof 

lines; amount of roof space; number of bedrooms, bathrooms, and living areas, total number of 

rooms in the house, and exact ages of household occupants. 

Despite these limitations, this investigation is the most comprehensive study of its kind. No other 

study is available that covers market response to high-performance homes, as well as largeproduction builder experience with them, their resale value, and their actual electricity and gas 

consumption and costs. Also, this study has adhered to a rigorous research design. 

38
Chapter 4

The SheaHomes Experience

An important question for builders is whether 

high-performance homes, near-ZEHs, or ZEHs 

can be built and sold competitively in the new 

housing market. This chapter compares data on 

the sales prices of SheaHomes and comparison 

homes and compares data on the SheaHomes 

experiment between standard and optional solar 

PV systems. The chapter also describes the 

benefits and costs to SheaHomes of completing 

this innovative project. 

Are High-Performance Homes 

Competitive? 

Prices of the homes in the SheaHomes communities ranged from $437,900 to $840,938. 

The mean price was $601,984. Homes in the 

comparison community were somewhat more 

expensive, ranging from $473,990 to $875,000. 

The mean price was $615,029. 

To address the issue of whether SheaHomes 

could offer high-performance homes and still 

price them competitively, we compared the 

mean sales price, the mean square footage, and 

the price per square foot between the 

SheaHomes and the comparison homes. The 

data, summarized in Table 6, support the 

conclusion that SheaHomes was able to deliver 

highly energy-efficient homes equipped with 

solar water heating systems, a percentage of 

which also included PV systems, at competitive 

prices. In fact, despite their quality and 

amenities, the homes built by SheaHomes sold 

for less per square foot than those built by their 

competitors. 

Chapter Highlights 

C High-performance homes are competitive in the 

market. Per square foot, they sell for 9.2% less 

than comparison homes on average. This 

difference, though small, is statistically 

significant. 

C When controlled for by house size, smaller 

SheaHomes sold for less on average than 

comparison homes. The differences are 

statistically significant. Larger SheaHomes also 

sold for less than comparison homes, but the 

differences are not statistically significant. 

C Thirty-nine percent of the 306 SheaHomes highperformance homes (n=120) were sold with solar 

PV systems. 

C Thirty-one percent of the 306 high-performance 

homes (n=96) came with 1.2 solar PV systems 

standard. 

C Only an estimated 44% of the buyers of PVeligible homes were offered the option of 

purchasing solar PV systems. 

C Of the estimated 72 buyers of SheaHomes who 

were offered solar PV systems on an optional 

basis, 44% (32) chose to purchase them. 

C SheaHomes enjoyed significant benefits by 

offering innovative high-performance homes, 

including financial incentives, partnerships, 

valuable media exposure, an enhanced 

reputation, and greater exposure to the homebuying market. 

39
Table 6. Sales Prices and Sizes of SheaHomes and Comparison Homes 

Variable 

SheaHomes 

(n=306) 

Comparison 

Homes 

(n=103) 

% 

Difference Significance 

Mean sales price $601,984 

SD*: $74,222 

$615,029 

SD.: $88,149 2.12 n.s. 

Range, sales 

price 

Low: $437,900 

High: $840,938 

Low: $473,990 

High: $875,000 

Mean square 

footage 3,091 ft2 2,860 ft2 7.47 t=5.961; 

p#.000 

Mean price 

per ft2 

$195.96 

SD.: $24.81 

$215.89 

SD: $30.85 9.23 t=6.569; p#.000 

Range, sales 

price per ft2 

Low: $147.61 

High: $266.22 

Low: $156.90 

High: $303.25 

*SD= standard deviation 

Table 6a. ANOVA on Sales Prices and Sizes of PV Homes,

 Other SheaHomes, and Comparison Homes 

Variable 

SheaHomes with 

PV Systems 

(n=120) 

SheaHomes without 

PV Systems 

(n=120) 

Comparison Homes 

(n=103) 

Mean sales price 

(F=3.646; p=.027) $614,465 $593, 932 $615,029 

Mean square footage 

(F=18.118; p=.000) 3,112 ft2 3,078 ft2 2,860 ft2 

Mean price per ft2 

(F=22.487; p=.000) $198.45 $194.36 $215.89 

To further refine this analysis, the differences in closing prices for PV homes and homes without 

PV were compared with those of comparison homes. Table 6a summarizes the results. The 

analysis of variance summarized in Table 6a shows significant differences as follows. Although 

the mean closing price of PV homes is lower than that of comparison homes, the difference is not 

significant (p=.957). However, SheaHomes without PV systems cost significantly less, on 

average, than did comparison homes (p=.027), and also less than PV homes (p=.024). In terms of 

square-footage, the analysis shows that SheaHomes with and without PV systems are each 

significantly larger, on average, than are comparison homes. Thus, the mean price per ft2

 for PV 

homes ($198.45) is significantly lower than that for comparison homes ($215.89)(p=.000). 

Similarly, the mean price per ft2

 for SheaHomes without PV systems is lower ($194.36) than the 

mean price per ft2

 for comparison homes ($215.89). 

40
 

 

 

 

Because sales price per square foot can decrease with increased size of home, an additional 

analysis of sales price controlled for square footage. Table 6b presents the results of this analysis. 

Table 6b. Analysis of Sales Prices Controlling for Square Footage 

Home Size Categories SheaHomes 

(n=306) 

Comparison Homes 

(n=101)* 

# 2,600 ft2 

(t= –2.133; p=.037)** 

Mean: $536,246 

SD: 61,791 

(n=35) 

Mean: $567,471 

SD: 58,717 

(n=33) 

2,601 – 3,002 ft2 

(t= –1.970; p=.052)*** 

Mean: $590,373 

SD: 63,844 

(n=53) 

Mean: $621,289 

SD: 87,542 

(n=40) 

3,003 – 3,300 ft2 

(t= –.999; p=.319)§ 

Mean: $607,166 

SD: 72,653 

(n=140) 

Mean:: $627,653 

SD: 78,159 

(n=14) 

3,301 – 3,376 ft2 

(t= –1.733; p=.103)§ 

Mean: $630,071 

SD: 70,346 

(n=78) 

Mean: $679,755 

SD: 103,055 

(n=14)

 *Of the 103 comparison homes, square footage was available for 101.

 **Statistically significant difference

 ***Result nears statistical significance at the 95% confidence level

 §n.s. 

As the results show, the smallest SheaHomes sold for 5.5% less than comparison homes of the 

same size, a result that is statistically significant at the 5% significance level. The next smallest 

SheaHomes cost 5% less than the next smallest comparison homes, a result that is significant at 

the 5.2% significance level. Although the medium-size and largest SheaHomes sold for less than 

comparison homes in the same size ranges (3.2% and 7.3% less, respectively), these differences 

are not statistically significant. These findings show that SheaHomes, despite their energy 

efficiency and solar features, cost less per square foot than adjacent conventional homes when 

controlling for square footage. 

Thus, SheaHomes’ ability to offer the homes at competitive prices was not hampered by the 

inclusion of energy-efficiency features, solar water heating, and solar PV systems. Nor would 

other factors, such as lower land prices, have affected the closing prices because SheaHomes 

would have brought the sales prices up to market value. The comparison builder offered no 

standard features that would have made their homes more expensive. In fact, energy-efficient 

windows and increased insulation were optional features in the comparison homes. Therefore, 

the conclusion that SheaHomes sold high performance homes competitively seems warranted by 

the findings. 

41
Costs of the Solar Features 

Each solar water heating system included standard on 293 of the SheaHomes cost the builder 

approximately $1,800 installed. Each 1.2 PV system cost SheaHomes approximately $7,900 

installed ($6,500 system cost plus $1,400 installation) (Roper 2006). Each 2.4 PV system cost the 

builder approximately $14,200 installed ($11,800 system cost plus $2,400 installation) (Roper 

2006). Closely reflecting these estimates, the company submitted to the CEC a cost of $7,951 for 

the 1.2 PV systems and $14,631 for the 2.4 PV systems and received a 50% rebate for PV 

systems (Nelson 2006). The costs per system and in the net are discussed in the section in this 

chapter on “Benefits to the Builder.” 

Sales of Homes 

The demand for housing in San Diego was high in 2001 and there was a waiting list and a lottery 

system for the first purchases of SheaHomes at Scripps Highlands. After a construction trailer 

was placed at the site, the SheaHomes office immediately began to receive numerous calls about 

the development. All of the initial homes built were sold without prospective buyers having the 

benefit of model homes. Only the land itself, floor plans, and sketches of house elevations were 

available for potential buyers to see. This represents a "sellers" market for both SheaHomes and 

the comparison builder. 

Management’s View 

In July 2003, Dale Holbrook, then vice-president of the San Diego Division of SheaHomes and 

the decision-maker on high-performance homes, outlined in a background interview the 

management perspective on the Scripps Highlands project and this study. He indicated that onethird of the homes were “pre-plots” (which meant that they came with solar PV systems 

standard). From his perspective, solar water heating was “a given,” but PV was the “big deal” 

because it added the largest cost. He commented that, despite rebates, the solar PV was not 

profitable for the company. 

Holbrook observed that SheaHomes received “notoriety” because of the high-performance homes 

project. Although the project sold out a full year earlier than originally planned, he attributed the 

accelerated rate of sales to the desirable location. He said that SheaHomes had satisfied a “niche” 

demand for people interested in energy savings and environmental issues, and that was “probably 

the extent of it.” San Angelo and Tiempo were built on the last available land in Scripps Ranch, 

ahighly desirable community, and homes could be purchased with no Mello Roos taxes (unlike 

most other new developments in the San Diego area). 

42
He said he wanted to know answers to questions, as follows: 

• How many people made their home purchase decision on the basis of PV? 

• Which buyers were influenced by the fact that PV was available? 

• How strong an influence on the purchase decision was the availability of PV systems? 

• What benefits from the PV systems can be documented? What is the performance of PV 

systems over time in terms of utility costs? 

• Will homebuyers, the next time they buy a house, look for PV and energy efficiency? 

• Will homebuyers pay the extra $10,000 for a PV system or will they purchase a competitor’s 

less costly home? 

• How educated do homebuyers feel about making a decision on PV?1 

• Did builder reputation play a role in the homebuying decision? 

• After living in their homes, are the homebuyers still satisfied with their decisions? 

Holbrook hypothesized that people were becoming accustomed to paying higher utility bills, and 

that "no one was still talking about it" (in July 2003), so that if SheaHomes came out with a 

product “today, it might not make much of a splash.” If there were another spike in utility prices 

or concern about supply, he said, then perhaps it would create more market interest. 

He said: “We’re not offering solar [in our future developments] because so few people opted for 

it [emphasis added]. We don’t see an advantage economically, so we haven’t done it.” Holbrook 

stated: “The results of the [NREL] study will have a great impact on our decision-making in the 

future.” 

SheaHomes did not actively promote the solar PV systems, according to several staff members. 

We observed that there were no brochures on solar electric systems in the SheaHomes sales 

center, although there were displays on one wall. The virtual tour on the sales center computer 

said nothing about high-performance homes, energy efficiency, or solar features. Sales staff were 

not trained about the homes’ energy features. Staff considered solar PV “an extracurricular 

activity” in a highly competitive home-building environment and defined the learning curve as 

“enormous.” Pam Beaird commented, “Solar is a big ‘Wow!’ when I am selling [preplotted]2 

homes. But it is harder to add the $6,000 for the optional systems.” From the sales agent’s 

perspective, options are harder to sell, and “we get paid whether the home is solar or not. If it 

helps sell homes, it’s something we talk about.” Others told us that, at the sales center, most of 

the people looking at solar PV equipment were the people who were not there to buy homes. 

One staff member said that the building industry saw solar PV in a negative light—it’s unknown, 

expensive, the return-on-investment is not proven, and the prevailing attitude of builders is 

1

Holbrook noted that “Pam Beaird was evangelical in her sales of the solar PV,” and that she was articulate about its 

benefits. 

2

“Preplotted” homes featured 1.2-kW PV systems standard. 

43 
“show me” that it works. This staffer said that customers and SheaHomes staff were uneducated 

about solar PV, and that “the customers see little value in it.” 

SheaHomes did not provide any financial analysis on the solar PV investment to its customers. 

Yet a staff member said that the utility bills were “amazingly low.” However, the company 

included a disclosure in its contracts so that their customers would not think their PV systems 

would produce more than they would. 

Percentages of Homes with Solar PV Systems 

The percentage of SheaHomes with solar PV systems is an important finding of this study. Table 

4 in Chapter 3 (Study Methods and Data Resources) shows that, ultimately, 120 homes were sold 

with some sort of PV system. Hence, 39% (120 ÷ 306) of the SheaHomes were sold with PV 

systems and 61% (186 ÷ 306) were not. Forty-six of the homes (46 ÷ 360, or 15%) were actually 

PV-ineligible (see the limitations noted in Chapter 3, page 5). Hence, of the 260 (or 306 – 46) 

PV-eligible homes, 46% (120 ÷ 260) were sold with PV systems of some sort, and 54% 

(140 ÷ 260) were not. 

A key question for Ryan Green and SheaHomes was whether more PV systems would be sold if 

they were offered standard or as optional features. After the entire development was built out and 

the homes were sold, it became possible to give an accurate answer to this question. 

Ninety-six of the homes were sold with 1.2-kW PV systems standard (which represents 80% of 

the total 120 sold with PV systems, or 37% of all homes that were PV-eligible). Those given the 

option of upgrading their systems to 2.4-kW (24 panels) could pay an additional $4,000, and 

eight homebuyers did so. The remaining 164 buyers of PV-eligible homes (or 260 – 96) could 

potentially have had the option of purchasing either a 1.2-kW system for $6,000 or a 2.4-kW 

system for $10,000.3

 Of these individuals, 16 purchased 1.2-kW systems and another eight 

purchased 2.4-kW systems. Counting those homebuyers that upgraded their standard 1.2-kW 

systems, a total of 32 homebuyers made some sort of optional PV purchase. These 32 

homebuyers represent 27% of all the homes sold with PV systems or 12% of all PV-eligible 

homes. Tables 7 and 8 provide more details about these results. 

Some important summary observations can be made. First, slightly fewer than half of the PVeligible homes were actually sold with some type of PV system. Clearly the uptake on optional 

PV equipment was not as strong as it might have been. Second, a large percentage (80%) of the 

PV systems that were sold came standard. This finding suggests that offering PV systems as an 

optional feature is not an optimal marketing strategy. Third, the smaller 1.2-kW systems offered 

by SheaHomes were dominant among all those sold, a finding that impacts the analysis and 

modeling of actual utility data discussed in Chapters 20 (Comparative Analysis of Utility 

3

These prices increased to $7,000 and $11,000, respectively, late in the construction process. 

44 
Consumption and Cost) and 21 (Modeling of Utility Consumption and Cost). In fact, only 16 

(13%, or 16 ÷ 120) of the homes have the larger 2.4-kW systems. 

Table 7. Numbers and Percentages of Homes with and without PV Systems 

Type of House 

% of 

All Shea 

Homes n 

% of PVEligible 

Homes 

With optional PV systems (wholly or in part) 10% 32 12% 

With standard PV systems only 29% 88 34%

 Total PV homes 39% 120 46% 

PV systems not elected4 46% 140 54%

 Total PV-eligible homes 85% 260 100% 

PV-ineligible homes 15% 46

 Total homes 100% 306 

Table 8. Number, Percentages, and Decision Status of PV Systems on SheaHomes 

Sizes and Decision Status of PV Systems on SheaHomes n % 

% 

(n=260) 

1. 1.2-kW PV systems as a standard feature 88 73 34 

2. Optional 1.2-kW PV systems 16 13 6 

3. Optional 2.4-kW PV systems 8 7 3 

4. Standard 1.2-kW systems with optional upgrades 

to 2.4-kW systems 8 7 3

 Total homes with PV systems 120 100 46 

Homes with optional systems only (Categories 2 and 3) 24 20% 9% 

Homes involving optional systems (Categories 2, 3, and 4) 32 27% 12% 

Homes involving standard systems (Categories 1 and 4) 96 80% 37% 

PV systems that were 1.2-kW systems 104 87% 40% 

PV systems that were 2.4-kW systems 16 13% 6% 

4

Many of these buyers of PV-eligible homes were apparently not offered PV systems, as discussed in the text. 

45 
Since SheaHomes only sold 96 of the 260 PV-eligible homes with PV systems standard, the 

remaining 164 homes could have had PV systems. We conjecture that they would all have been 

sold, and essentially within the same time frame. Certainly, none of them remain unsold today. 

Our study suggests several reasons why homebuyers might purchase homes that include PV 

systems standard, but might not, on their own, purchase the same PV system as an optional 

feature. However, we believe the lackluster sales of optional PV systems was the result of 

ineffective marketing or even failure to offer the optional PV systems to buyers of PV-eligible 

homes. As will be discussed in Chapter 8 (The Solar PV Purchase Decision), the option to 

purchase solar PV systems was apparently not made to all buyers of PV-eligible homes. In fact, 

only 44% of the buyers of PV-eligible homes (36 out of 82 survey respondents in this category) 

recall being offered such a system at the time of purchase.5 If we extrapolate this percentage to all 

SheaHomes buyers (that is, to the 164 buyers of PV-eligible homes who could have purchased an 

optional PV system), we estimate that only 72 of them (44%) were actually offered the option, 

leaving 92 (56%) having not been given the option. 

These distinctions are important in understanding the actual rate of uptake of optional PV 

systems. Using the 72 homebuyers that we estimate above as having been offered optional PV 

systems, then the 24 who actually exercised the option (16 who opted for 1.2-kW systems and 8 

who opted for 2.4-kW systems) would represent an uptake of 33% (24 ÷ 72) rather than the 12% 

reported above. If we include the additional eight homebuyers who optionally upgraded their 

standard 1.2-kW systems to 2.4-kW systems, then the uptake percentage is even higher 

(44%, or 32 ÷ 72).6 

At the outset of our study, in obtaining background information from the SheaHomes staff, we 

understood that all buyers of PV-eligible homes were going to be offered the option to purchase 

PV systems. The lead sales agent (Pam Beaird) appeared to be enthusiastic about the PV concept, 

and indeed, she had purchased a PV home herself. Yet, even though a few respondents could 

have forgotten or ignored the offer, it seems highly unlikely that more than half of them would 

have forgotten it. 

In trying to understand what might have happened, we examined the closing dates of all the PVeligible home sales to determine whether the SheaHomes management had discontinued offering 

PV systems before building out the San Angelo and Tiempo communities, but we did not find 

any evidence to support this conjecture. Indeed, approximately half of the buyers of PV-eligible 

homes that closed prior to August 2001—near the beginning of the project—responded in the 

homeowner survey that they were not offered optional PV systems. As knowledgeable staff 

members described the situation in 2001, the sales staff was more concerned about finalizing 

actual home sales and less focused on sales of PV systems that might complicate the deal. 

5

These survey data are discussed in more detail in Chapter 8 (Solar PV Purchase Decision). 

6

We do not have data on the rate of uptake for other options offered by SheaHomes. 

46 
The PV option was apparently not aggressively marketed by sales staff. Further, Ryan Green, the 

original high-performance home champion, left the SheaHomes staff before October 2001 and no 

one stepped up to take his place. In any case, the fact that homebuyers were not actually offered 

the PV option substantially muddies the effort to quantify the uptake of the PV technology as 

accurately as had been planned at the outset of this study. Nonetheless, based on the foregoing 

analysis, we believe the uptake for optional PV systems to be at least 40% when buyers are 

actually given the option. 

We return briefly to the discussion of why homebuyers might purchase a home that includes a 

PV system standard, but might not, on their own, purchase the same PV system as an optional 

feature. Qualitative data (Farhar, Coburn, and Collins 2002) and preliminary quantitative data 

(Coburn, Farhar, and Murphy 2004) show that homebuyers found it easier to purchase PV 

systems when they did not have to make separate decisions about it. Homebuyers reported that 

they found it difficult to make the trade-off between PV systems, on the one hand, and amenities 

that improved the aesthetics of their homes (e.g., granite counter tops, special window sills, or 

upgraded flooring), on the other. To some homebuyers, the latter seemed to be options in a 

totally different class (e.g., a choice about energy-saving equipment versus a choice about 

improving their homes’ aesthetics). Finally, some of the data suggest that homebuyers who chose 

not to purchase PV systems thought they were too expensive.7 

Benefits to the Builder 

SheaHomes accomplished a complex technical and institutional achievement in completing the 

San Angelo and Tiempo developments. Besides selling out the 306-home development in 31 

months, the builder also sold almost half with PV systems. The San Diego office of SheaHomes, 

in carrying out its innovative project, experienced both benefits and costs. Five major benefits 

appeared to accrue to SheaHomes because of its involvement in the construction of highperformance homes: (1) financial incentives and net costs related to solar features, (2) 

partnerships with organizations interested in solar energy and energy efficiency, (3) media 

coverage, (4) enhanced reputation through becoming an innovator with ZEH technology, and (5) 

greater exposure to the home-buying market. 

7

Although PV systems might be seen as expensive in an absolute sense, this perspective clearly results from 

homebuyers being unaware of the usual unsubsidized costs of PV systems, which are generally at least double the 

cost of those offered at Scripps Highlands. Survey data on this question are discussed in Chapter 8 (Solar PV 

Purchase Decision). 

47 
Financial Incentives and Net Costs 

SheaHomes enjoyed economic advantages for building high-performance homes. The company 

received from the CEC a 50% subsidy on the cost of the PV systems—the first time a residential 

builder in California had received the subsidy from the state. The company also received a $750 

rebate from SDG&E for the installation of each solar water preheating system. The $750 solar 

water heating rebates were provided through a CEC program called the "Solar Energy and 

Distributed Generation Grant Program," authorized by SB 1345 (Anders 2006). In addition, 

California provided a 15% tax credit for PV housing (Anders 2006).8 

Taking the financial incentives into consideration, the cost to SheaHomes of providing solar 

water heating standard was approximately $950 per system. The cost of providing each 1.2 PV 

system standard was approximately $3,380. The original price to homebuyers for an optional 1.2 

PV systems was $6,000.9 

Table 8a summarizes the types and numbers of solar energy systems and the net costs and profits 

for each type. In summary, offering the solar features for 93 of its 306 homes under the scenario 

that SheaHomes was working at the time, the total net cost to the builder for all the homes was 

approximately $550,000, averaging approximately $1,872 per home. These figures are only 

estimates because they do not account for several other variables, as follows: 

• A price increase to $7,000 for optional 1.2 PV systems and to $11,000 for optional 2.4 PV 

systems later in the project 

• The cost of energy-efficiency measures throughout the project 

• The benefit of any downsizing of equipment that was done 

• The manner in which these costs were factored into the homes’ market prices. 

Still, if we estimate that SheaHomes was able to complete its San Angelo and Tiempo highperformance communities for a net cost of approximately $2,000 per home, it seems reasonable 

to estimate that the builder recovered the costs. However, this does not take into account other 

costs, such as those mentioned above and later in this chapter. 

8

This tax credit is no longer available.

9

These calculations assume a 50% rebate plus a 15% tax credit on the full system cost.

48 
Table 8a. Net Costs to SheaHomes of Offering Solar Features at Scripps Highlands 

System Type Number of Units 

Net Cost 

per Unit Totals 

Solar water heating 293 $950 $278,350 

1.2 PV standard 96 $3,380 $324,480 

1.2 PV optional 

(assuming $6,000 

price)

 16 ($2,620) ($41,920) 

2.4 PV optional 

(assuming $10,000 

price)

 8 ($3,782) ($30,256) 

1.2 PV standard with 

1.2 PV optional 

upgrade (2.4 PV 

system) (charged 

$4,000 for the upgrade)

 8 $2,218 $17, 744 

Subtotal net costs: $620,574 

Subtotal net profits: (72,176) 

Total net cost: $548, 398 

Partnerships 

SheaHomes increased its involvement with a number of business, nonprofit, and governmental 

organizations as a direct result of its decision to build high-performance homes. For example, the 

builder has worked with the U.S. Department of Energy’s (DOE) Zero-Energy Homes initiative; 

NREL; the CEC; AstroPower, Inc.10; Sun Systems, Inc.; ConSol, Inc.; SDG&E; and the San 

Diego Regional Energy Office. Such partnerships give SheaHomes a business edge in innovative 

home building techniques and energy technologies. They provide a network of resources to 

which SheaHomes can turn for information about best construction practices and market 

research. These partnerships help to establish SheaHomes as a leader in the field of energy 

features in new housing. It is difficult to put a dollar value on such resources, but these types of 

contacts are generally understood to be “coin of the realm.” 

10AstroPower, Inc. has since been sold to General Electric Company. 

49 
Media Exposure 

By its own contractor’s estimate, SheaHomes received, without cost, print and broadcast media 

coverage on the San Angelo and Tiempo developments that would have cost $1 million in paid 

advertising. The coverage included San Diego television evening news, local newspapers, and 

trade journals. 

Enhanced Reputation 

Because nearly half of all buyers at SheaHomes’ Scripps Highlands development purchased PV 

systems, it seems reasonable to say that SheaHomes was successful in marketing innovative 

ZEHs with efficiency and solar features. In doing this, SheaHomes in San Diego established its 

reputation as a visionary company willing to offer quality products that provide value to its 

customers and that benefit the broader San Diego community. Though it was difficult for the 

company, SheaHomes has no doubt benefitted through the increased goodwill of the community 

by attempting and carrying out this project. These qualitative benefits are likely to be longer 

range than the immediate impacts on the bottom line and will unfold for years to come, not only 

in the local area, but everywhere in the nation that the company builds and people hear about the 

San Diego project.11 

Greater Exposure to the Once and Future Home-Buying Market 

Owing in part to media coverage and the high demand for housing in the San Diego market, 

SheaHomes experienced a large volume of visitors to its visitor center (there were no model 

homes in the early stages of the development). Many were curious about the development’s solar 

features, although they were not seriously shopping for new homes (Collins 2003). Conceivably, 

a small percentage of these visitors might shop for new homes in the future; they will have been 

exposed to SheaHomes and to the ZEH concept. 

Costs to the Builder 

The ZEH experience was something of a double-edged sword for SheaHomes. Several of the 

benefits also involved costs. For example, although SheaHomes climbed a steep learning curve 

regarding the incorporation of energy features into its new housing products, there was a cost in 

terms of staff time. Some lessons were hard won. It was difficult for SheaHomes to anticipate the 

problems it would encounter in these uncharted waters. 

11Recent informal conversations on this study indicate that people want to know who the builder was so they could 

spread the word in a positive way. 

50 
Costs of Building High-Performance Homes 

Although significant costs were involved in the building of high-performance homes, especially 

in the installation and interconnection of PV systems, the financial incentives available helped to 

offset at least some of the costs. SheaHomes management at the time believed that, despite 

rebates for solar water heating systems and PV systems, the high-performance homes did not 

result in an economic advantage. PV systems seemed expensive to SheaHomes and the 

management believed that relatively few buyers opted for them, apparently not necessarily 

realizing that the PV was not offered to most potential buyers of PV-eligible homes. Although 

our data do not bear this out, the management at the time believed some of the market interest 

may have occurred because of the electricity price spikes in San Diego in 2001,. Buyers become 

accustomed to higher electricity rates, the company believed. Although SheaHomes indicated 

that the ZEHs did not sell any faster because of the energy features, the Scripps Highlands 

development sold out a year faster than planned. The management attributed this to the 

desirability of the Scripps Highlands location, rather than to the energy attributes of its homes. 

Climbing the Learning Curve 

SheaHomes had no staff experienced in ZEHs except Ryan Green. The learning curve associated 

with producing the ZEHs was characterized as “enormous,” including all the new language, 

acronyms, companies, products, and governmental agencies. The company had no previous 

experience that would help it to anticipate and avoid problems. SheaHomes found climbing this 

curve to be a painful experience. 

Transaction Costs of Selling and Scheduling Installation of Optional PV Systems 

Because SheaHomes buyers had the option of upgrading PV systems that came standard or 

adding 1.2-kW or 2.4-kW PV systems to their homes, sales staff had to explain solar PV systems 

and the different ways the homebuyers could purchase them. In addition to this time-consuming 

chore, the SheaHomes staff had to schedule the installation of the two sizes of PV systems on a 

house-by-house basis. The fact that this was burdensome was illustrated by an anecdote from a 

SheaHomes buyer. He said: “When we were purchasing our system, it came with 12 panels. We 

were offered an additional 12 panels for $7,000. We jumped on it! However, when they built the 

house, we discovered that they had only put on 12 panels. We contacted SheaHomes who said, 

‘How about we just give you back the $7,000?’ They really tried to get us to pass up our 12-panel 

addition. However, we insisted that they put in the additional 12 panels. They had to change out 

the inverter when they put in the additional 12 panels and put in a new top-of-the-line inverter 

that enables us to monitor our electricity use on the computer. We can put it in an Excel file. 

What matters to me is that I want to monitor my electricity use once a month starting on the day 

that SDG&E reads my meter.” 

51
Obtaining the Rebate for PV Systems 

The CEC was offering a rebate of 50% of the installed cost of PV systems to residential owners 

and businesses installing them, as well as to builders including them in new homes. The CEC’s 

application procedures for obtaining the rebate were designed to deal with one system at a time. 

Because the SheaHomes project was so innovative, SheaHomes had to work with the CEC had to 

invent a procedure to collect the rebate for the group of PV systems it had purchased to install at 

San Angelo and Tiempo. 

Interconnectivity issues 

Net-metering legislation requires California utilities to allow net metering to zero for homes with 

PV systems tied to the utility grid. This means that the electric meters at homes with PV systems 

run forward and backward. SDG&E was still developing its own procedures for interconnectivity 

agreements with residential electricity producers when SheaHomes began building its Scripps 

Highlands project. The utility was sending to the homebuyers for their signatures highly complex 

legal documents dealing with interconnection to the utility grid appropriate for corporations. 

Neither SheaHomes staff nor homebuyers had any prior experience in dealing with 

interconnectivity agreements. New homebuyers did not understand these SDG&E 

interconnectivity agreements and turned to the SheaHomes staff for help. The staff, therefore, 

spent a good deal of time interfacing with SDG&E and homebuyers on interconnectivity issues. 

SDG&E eventually simplified its forms for residential electricity producers, who are billed 

separately for electricity and gas and have the option of being billed for electricity monthly or 

annually.12 

Tax Implications 

Various state and federal income tax credits were thought to be applicable to the SheaHomes. 

The San Diego Regional Energy Office worked on an analysis to define the tax benefits of ZEHs 

and shared its findings with the SheaHomes staff. Staff spent time trying to understand the 

income tax implications and how to explain them to the homebuyers so that they might benefit 

from them. 

Education 

SheaHomes provides orientation at “Shea University” to all of its new homebuyers. The class 

was usually held in the garage of one of the homes on Saturday mornings and covered many of 

the features of the new homes and the procedures for completing purchase, moving in, and 

dealing with problems. The staff responsible for orientation did not always include information 

on the energy features of the new homes, including efficiency, water heating, and PV systems. 

12PV owners receive a monthly statement of their electricity use and costs regardless of whether they pay monthly or 

annually (which was at their option). 

52 
Thus, SheaHomes buyers were not sufficiently briefed on the benefits of their homes in terms of 

energy performance. 

Visitors 

Many general public visitors who were curious about the solar features, but who were not serious 

buyers, took up staff time with their questions. In addition, SheaHomes staff handled many other 

professional visitors working on media stories, research, government programs, nonprofit 

educational programs, and the like, including researchers for this study. 

Complaints 

A few homebuyers complained about hot water systems, but the complaints did not actually 

relate to the solar hot water systems. For example, one complaint was that it took too long for the 

hot water to reach the bathroom faucets located farthest from the hot water tank. This had 

nothing to do with solar water heating; nevertheless, in a few instances, it was perceived as such. 

One or two potential buyers asked to have standard PV panels taken off the roof, although there 

is no evidence that any home sales were lost because of PV panels. 

Competitiveness Issues 

Because of its innovative position relative to energy features, SheaHomes suffered from a certain 

amount of animosity (perhaps sparked by concern that they could be forced into dealing with 

ZEH technology to remain competitive) on the part of the San Diego builder community. 

SheaHomes Position 

The management has changed at the San Diego office of SheaHomes since the data for this study 

were collected. The new management has not been interviewed in connection with the study. 

SheaHomes was one of 10 builders who participated in the Ladera Ranch project of 500 highperformance homes. The builder also built the Bella Rosa affordable housing project in San 

Diego. However, otherwise its other developments since Scripps Highlands have not included 

high performance homes. 

Summary 

A central question of the Ryan Green experiment was whether optional solar PV systems would 

sell homes more quickly and whether standard PV systems would impede sales. Although the 

innovative high-performance homes generated a good deal of media and home-shopper interest, 

it was easier for people to buy homes with standard PV systems than to make decisions about 

optional PV systems. It was harder for SheaHomes sales staff to sell both homes and solar PV 

systems and conversely easier for them to sell homes with PV systems standard. 

53
The data show that SheaHomes did not offer optional PV systems to the majority of buyers of all 

PV-eligible homes. Despite this, the estimated uptake of optional PV systems among those 

offered PV systems was 44%. Thus, if more buyers had been made aware of the PV option, more 

homes would almost certainly have been built with them. 

On balance, the San Diego Division of SheaHomes enjoyed substantial benefits from its Scripps 

Highlands project, and the company subsequently offered high-performance homes in the Ladera 

Ranch and the Bella Rosa affordable housing developments. 

54
Chapter 5

Increases in Property Values

Introduction 

Chapter Highlights 

One research question in this study relates to 

whether high-performance homes hold their C SheaHomes and comparison homes increased 

value over time or, indeed, if they provide markedly in value (as of 2/7/05). 

financial advantages to their owners at the C SheaHomes had been held an average 22.5 time of resale. During the years between months and comparison homes 28.1 months 2002 to 2004, housing prices in the San before resale. 

Diego area were increasing rapidly. For 

example, an Internet search in March 2005 C Resale prices for 29 resold homes show that the 

increase in value for SheaHomes averaged showed that, between October 2003 and 55.4% and for the comparison homes 44.7%. April 2004, average housing prices in San 

Diego County increased 14.4%. C The mean gain in property value per month was 

$14,492 for SheaHomes and $9,301 for 

Farhar, Coburn, and Mu comparison homes. rphy (2004) reported 

that the resale property value of both 

categories of homes had increased by January 

2004 (based on a small sample of 10 SheaHomes and six comparison homes). Property values 

had increased more for the SheaHomes than the comparison homes. 

Findings 

Resale data were checked again on February 7, 2005. Table 9 shows the original and resale prices 

for the two developments as of that date. The same pattern of results continued to hold. Twentynine homes had been resold by that date—15 (approximately 5%) of the SheaHomes and 13 

(approximately 13%) of the comparison homes. 

The SheaHomes and the comparison homes have increased markedly in value since they were 

originally purchased but, based on the selling prices of this group of 29 resold homes, the 

SheaHomes have increased in value more. The increase in value for the SheaHomes averaged 

$306,510 (55.4%) whereas the increase in value for the comparison homes averaged $262,968 

(44.7%). Thus, the resold SheaHomes have increased in value 14% more than the comparison 

homes on average.1 

The data in Table 9 show that the homes in the two communities were held, on average, a 

comparable length of time before resale, although homes in the comparison community were 

held somewhat longer (a mean of 22.5 months by the SheaHomes owners and 28.1 months by the 

comparison owners). More strikingly, the data show that the mean gain in property value per 

month owned was $14,492 for the SheaHomes and $9,301 for the comparison homes, a gain 36% 

1

These calculations do not take into account the fact that SheaHomes originally sold for $10/ft2 less than the 

comparison homes. 

55 
higher for SheaHomes than comparison homes. The mean gain in property value per square foot 

per month owned was $4.97 for the SheaHomes and $3.23 for the comparison community 

homes. The mean percentage gain in property value per square foot was .019 for the SheaHomes 

and .016 for the comparison homes. 

The greatest single gain in value was $446,410 for a home in the SheaHomes communities with a 

PV system owned for 26.9 months (a 79% increase in value). In comparison, the single largest 

gain for a home in the comparison community was $378,769 for a home owned for 40.2 months 

(a 61% increase). 

Data are not available in this study for many factors that can affect property values. However, the 

study does include information about the energy features of the resold SheaHomes. The average 

gains in value for SheaHomes with PV systems are higher than for those without PV systems (see 

Table 10). The average dollar gain per month of SheaHomes with PV installations was $16,302, 

whereas the average dollar gain for SheaHomes without PV installations was $13,834; PV homes 

appreciated 15% more per month. The average gain per month per square foot for PV homes was 

$5.71, and for homes without PV systems it was $4.70. The SheaHomes with PV systems 

appreciated 6% more overall than did SheaHomes without PV systems. 

Of the 103 comparison homes, 13, or 12.6% (counting the home sold twice) were resold by 

2/7/05. Of the 306 SheaHomes, 15, or 4.9% were resold by 2/7/05. This more rapid turnover of 

comparison homes compared with that of SheaHomes was unexpected. There is no reason to 

believe that the kinds of life changes that might cause homeowners to put their homes on the 

market—including changes in employment or financial situations, marital status, or 

health—would occur more frequently in one home development rather than the other. Thus, it 

may be reasonable to speculate that the turnover rate constitutes more evidence that the 

comparison homeowners are somewhat less satisfied with their homes (as other data in this study 

indicate) than the SheaHomes owners. 

Based on this analysis of the property values of resold homes at Scripps Highlands, it seems fair 

to conclude that, at a minimum, high-performance homes not only hold their value but increase 

their value at a faster rate than do conventional homes. 

56
Table 9. Comparisons of Gains in Property Values and Length of Ownership

 for Homes in the SheaHomes and Comparison Developments (as of 2/7/05) 

Variable 

Homes in SheaHomes 

Communities (n=15) 

Homes in the Comparison 

Community (n=12)* 

Original price Range: $482,900–$701,184 

Mean: $556,344 

Range: $538,522–$711,887 

Mean: $598,028 

Resale price Range: $680,000–$1,100,000 

Mean: $862,853 

Range: $760,000–$995,900 

Mean: $862,590 

Home size (in ft2

) Range: 2,222–3,678 

Mean: 2,961.8 

Range: 2,486–3,502 

Mean: 2,975.2 

Mean length of ownership before 

resale 22.5 mos. 28.1 mos. 

Length of ownership (range) 9.9–43.9 mos. 17–40.2 mos. 

Mean $ gain in property value $306,510** $262,968 

Mean % gain in property value 55.4% 44.7% 

Range of percentage $ gain 

in property value 

High = 80.5% 

(ineligible, owned 24.6 mos.) 

Low = 30.5% 

(main, owned 14. 9 mos.) 

High = 69% 

(owned 39.7 mos.) 

Low = 21.5% 

(owned 22.2 mos.) 

Range of $ gain 

High = $446,410 

(PV, owned 26.9 mos.) 

Low = $190,354 

(main, owned 14.9 mos.) 

High = $378,769 

(owned 40.2 mos.) 

Low = $153,113 

(owned 22.2 mos.) 

Mean $ gain per mo. owned $14,492 $9,301 

Mean $ gain in property value 

per ft2 $104.70 $92.99 

Mean $ gain in property value 

per ft2

 per mo. 

$4.97 $3.23 

Mean % gain in property value 

per ft2 .019 .016 

*An additional home was resold twice by 2/7/05 but was excluded for purposes of this analysis 

**The mean gain for SheaHomes was 16.6% more than for homes in the comparison community 

57
Table 10. Comparisons of Gains in Property Values and Length of Ownership for SheaHomes with 

and without PV Systems (as of 2/7/05) 

Attribute 

Homes with PV Systems 

(n=4) 

Homes without PV 

Systems (n=11) 

Original price Range: $505,700–$636,730 

Mean: $564,329 

Range: $482,900–$624,646 

Mean: $553,440 

Resale price Range: $739,000–$1,010,000 

Mean: $884,950 

Range: $769,500–$1,100,000 

Mean: $854,818 

Home size (in ft2

) Range: 2,584–3,165 

Mean: 2,868.3 

Range: 2,222–3,678 

Mean: 2,995.8 

Mean length of ownership before 

resale 20.3 mos. 23.2 mos. 

Length of ownership (range) 13.6–26.9 mos. 9.9–43.9 mos. 

Mean $ gain in property value $320,621* $301,378 

Mean % gain in property value 56.8% 54.5% 

Range of percentage $ gain 

in property value 

High = 79.2% 

(owned 26.9 mos.) 

Low = 46.1% 

(owned 23 mos.) 

High = 80.5% (ineligible home, 

owned 24.6 mos.) 

Low = 30.5% (main home, 

owned 14.9 mos.) 

Range of $ gain 

High = $446,410 

(owned 26.9 mos.) 

Low = $233,300 

(owned 17.9 mos.) 

High = $425,100 (ineligible 

home, owned 43.9 mos.) 

Low = $190,354 (main home, 

owned 14.9 mos.) 

Mean $ gain per mo. owned $16,302 $13,834 

Mean $ gain in property value 

per ft2 $111.77 $102.13 

Mean $ gain in property value 

per ft2

 per mo. 

$5.71 $4.70 

Mean % gain in property value 

per ft2 .02 .019 

*The mean gain for homes with PV systems was 6.4% more than for homes without PV systems 

58
Chapter 6

Respondent Characteristics

Introduction 

Chapter Highlights 

As described earlier, respondents in the survey C Respondents are male and female heads-of- are heads of households and original buyers of household; more than half (56%) are male. 

new homes in either the SheaHomes or 

comparison communities. Only 10% of the C They are original owners of the homes; 90% 

previously owned a home, whereas 10% were respondents were purchasing a home for the first buying a home for the first time. time. Questionnaire respondents were asked to 

select one head of household for answering C A plurality of respondents (44%) are younger 

than 40 years of age; 75% are 25–50 years of 

age. 

demographic questions about themselves and 

their households. For all but one of the 

demographic variables there is no statistically C Most respondents are married; two-thirds have significant difference between respondents who families. 

are homeowners in the SheaHomes communities 

and those who are homeowners in the C Respondents are highly educated with 

professional, business, and scientific occupations comparison community. Appendix F consonant with their educational attainment. (Respondent Characteristics, Demographics, 

Values and Lifestyles, and Other Variables) C A significantly higher percentage of SheaHomes 

contains tables detailing responses to all owners than comparison owners have annual 

incomes that exceed $200,000. Otherwise the 

SheaHomes and comparison homeowners are not 

questions pertaining to respondent 

characteristics, including demographics, significantly different in any demographic lifestyles, and values by SheaHomes and measure. comparison respondents. 

Findings 

SheaHomes and comparison community respondents are similar on all but one of the demographic 

variables that were measured as follows: 

• Respondents (heads of households) are predominantly male (male, 56%; female,

44%).

• The largest percentage (44%) of respondents are younger than 39 years old (30% are 

40 to 49 years of age; 26% are 50 years of age or older). 

• Ninety-five percent of the respondents are married. 

• In terms of household composition, 68% have two adults living with children and

32% are adult-only households.

• Reported household size ranges from one to 11 people, with a mean of 3.53 occupants 

(77% of all respondent households are composed of two to four residents). 

59
• The respondents are a highly educated group: 97% have completed at least some 

college; 80% have at least a bachelor’s degree; 20% have a master’s degree; and 16% 

have doctoral degrees. 

• Reported occupations include business owners, investments/financial, health care,

scientists and engineers, managers, and professionals.

• Thirty-two percent of all respondents indicate that they plan to stay in their homes 

permanently, whereas 44% do not know how long they will live in their new homes. 

The remainder of the data suggests a bimodal pattern with peaks at 5 years (7%) and 

10 years (6%). 

• Most respondents (86%) moved to the Scripps Highlands neighborhood from 

elsewhere in the San Diego area. This means that most buyers had been exposed to 

the publicity concerning the electricity price spikes in San Diego and the California 

energy crisis during 2000–2001, before buying their new homes. 

As expected, all respondents report relatively high household income levels. However, 

SheaHomes buyers have significantly higher incomes than do those in the comparison 

community. Table 11 shows that an almost equal percentage of buyers in each group (22% of 

SheaHomes buyers and 20% of comparison homebuyers) have incomes less than $100,000. A 

higher percentage of comparison homebuyers report annual incomes in the $100,000 to $200,000 

range (76%) than do SheaHomes buyers (59%). A higher percentage of SheaHomes buyers report 

annual incomes of at least $200,000 (19%) compared with comparison homebuyers (4%). These 

differences are statistically significant (P2=6.092; p=.048). 

Table 11. Percentage Distribution of Annual Household Income 

for SheaHomes and Comparison Homeowners* 

Annual Income ($) 

Percentage of 

SheaHomes 

Owners 

(n=155) 

Percentage of 

Comparison Home 

Owners 

(n=45) 

#$99,999 22 20 

$100,000–$199,999 59 76 

$$200,000 19 4

 Totals 100 100 

*Statistically significant difference, p=.048 

The findings on demographic variables offer little support for the “early adopter” hypothesis. The 

findings for education and occupation do not support the expected pattern for early adopters 

because there are no significant differences between the homebuyer groups. The only difference 

60
in the predicted direction is income. These findings provide only limited evidence that the buyers 

of SheaHomes are more likely to be early adopter types than the buyers of the comparison homes. 

In fact, they may simply be a bit more able to afford upgrades than the buyers of the comparison 

homes. 

Among the 72 respondents owning PV systems, the distribution of system size is as follows: 

• A 1.2-kW system, whether standard or optional: 64 respondents 

• A 2.4-kW system, always at least partially optional: 8 respondents. 

Among SheaHomes owners, PV ownership was analyzed relative to the study’s demographic 

variables.1 Among these variables, two significant differences are found. First, the percentage of 

respondents that is male is significantly higher among respondents representing PV homes than 

those representing non-PV homes (P2=6.994; p=.03). This is probably because PV technology is 

somewhat more likely to be seen as within the purview of men than women. The second 

significant difference is among older and younger respondents. The percentage of respondents 50 

years old or older is significantly higher among respondents representing PV homes than among 

those representing non-PV homes (P2=6.994; p=.03). Table 13 summarizes the data by age. 

Table 13.2 Percentages of Ownership of PV Systems 

by Age, among Owners of SheaHomes* 

Type of owner 

#39 years old 

(n=73) 

40–49 years old 

(n=55) 

$50 years old 

(n=42) 

Totals 

(n=170) 

PV owners 40 53 26 41 

Non-PV owners** 60 47 74 59

 Totals 100 100 100 100 

*Statistically significant at p=.03

** “Non-PV owners” are owners of SheaHomes that do not have solar PV systems, including main

and early homeowners.

Summary 

As expected, residents of both the SheaHomes and comparison communities demographically 

constitute upper-middle-class married couples with children or adult couples. They are, as a 

group, highly educated with well-paying occupations, and they are relatively affluent. A 

significantly higher percentage of SheaHomes owners than comparison homeowners enjoys an 

annual income of more than $200,000. 

1

For the head of household completing the questionnaire: sex, age, marital status, household composition, household 

size, income. 

2

Table 12 is not used in this document. 

61 
Chapter 7

New Home Search and Purchase Decision

Introduction 

Key study questions focused on differences 

between SheaHomes and comparison buyers. In 

particular, we looked at why they selected the 

homes they did and how significant the highperformance home features (e.g., energy 

efficiency, solar water preheating, and solar PV 

systems) were in influencing the home purchase 

decision. These questions are covered in this 

chapter. 

This chapter discusses new home search and 

purchase decisions and compares SheaHomes 

and comparison homeowners on factors in the 

new home search process, purchase decision 

factors, and the importance of new home 

features in home purchase decisions. 

New Home Search and Purchase 

Decision Process 

Ninety-nine percent of the respondents were 

original owners of the homes, with no 

statistically significant differences between 

SheaHomes and comparison buyers.1

 Ten 

percent of respondents indicate that these were 

their first homes. Approximately one-quarter of 

respondents (20% of SheaHomes buyers and 

29% of comparison homebuyers) purchased 

new homes that had already been under 

contract, but had fallen out of escrow.2 

During their searches for a home, 60% of the 

new homebuyers had also visited resale 

housing. Seventy-two percent of the 

Chapter Highlights 

C Study respondents are the original owners of the 

SheaHomes and comparison homes. 

C Approximately 25% of respondents purchased 

homes that had fallen out of escrow. 

C Sixty percent of buyers had also visited resale 

housing during their new home search process. 

C Three-quarters of the buyers visited both the 

SheaHomes and comparison developments when 

they were shopping for a new home; however, 

they were not well informed about highperformance home features. 

C By and large, concerns about the California (and 

San Diego) 2001 electricity crisis did not appear 

to influence home-purchase decisions. 

C Energy was not a very important factor in the 

purchase decisions of most of the study’s new 

homebuyers. 

C Buyers who were more concerned about their 

residential energy consumption were more likely 

to buy SheaHomes than comparison homes. 

C The reputation of the builder was significantly 

more important to SheaHomes than to comparison 

buyers. 

C The home’s location and the financial aspects of 

the home purchases were the most important 

reasons for purchase for both categories of buyers. 

However, energy was in the top three reasons for 

6% of SheaHomes buyers but none of the 

comparison buyers. 

1

Three respondents purchased their homes from previous owners (two SheaHomes buyers and one comparison 

homebuyer.) 

2

This meant that the options and amenities of the homes had been selected by the prior intended purchasers rather 

than by the final buyers. As we shall see in Chapter 18 (Analysis of Factor Scores by PV Adopter Categories), this 

could have important consequences relative to the markets for ZEHs and PV ownership in particular. 

62 
comparison homebuyers visited resale homes during their home searches, whereas 57% of the 

SheaHomes buyers did so, a difference that nears statistical significance (P2=.835; p=.057). This 

finding suggests that the SheaHomes buyers may have been persuaded to purchase by the 

qualities of the SheaHomes more quickly during their new-home searches than the comparison 

buyers, who may have needed to search for a longer time. 

Almost three-quarters (73%) of the new homebuyers indicate that, when they were searching for 

new homes, they visited both the SheaHomes and the comparison home developments. This 

suggests that homebuyers appear to have had choices within their price ranges and preferred 

location and that they selected homes with features they preferred. However, other evidence 

suggests that the homebuyers were not specifically well informed about the features of the highperformance homes. This is discussed in Chapter 9 (Knowledge and Information). 

Most home purchase decisions (93%) were made by two people rather than one person (7%). All 

of the comparison home purchase decisions were joint decisions, whereas 91% of the 

SheaHomes purchase decisions were joint decisions (P2 =5.308; p=.021). This finding suggests 

that at the higher end, most home purchase decisions will not be made by a single individual 

because few single people live in these types of neighborhoods, which are designed primarily for 

couples, families with children, and extended families. 

All respondents were asked: “On a scale of 1 to 10, how concerned were you about the electricity 

costs in San Diego at the time you purchased your home?” SheaHomes respondents exhibited 

more concern (mean=6.31) than did comparison home respondents (mean=5.69), but the 

differences are not significant (t=1.465; p=.144). A P2 test yields results similar to the t-test 

results. Although PV owners were more concerned (mean=6.54) than non-PV owners 

(mean=5.99), the difference is not significant (t= –1.420; p=.157). Thus, concern about the 

California electricity crisis, and in particular the crisis in San Diego, did not appear to influence 

the home selections of most of these new homebuyers. However, during the qualitative 

interviews, a few buyers had commented on the high electricity bills of their prior homes during 

the crisis, so the higher incomes of these buyers probably insulated most of them from serious 

financial impacts of the San Diego electricity crisis. 

Comparison Buyer Awareness of SheaHomes 

A majority of the comparison homebuyers were unaware of the SheaHomes high-performance 

homes when they were in the search process. Comparison respondents were asked: “At the time 

you were looking for a new home, were you aware that, in your price range and location, highly 

energy-efficient homes were available that featured solar electric (photovoltaic or PV) systems as 

well as solar water heating?” 

A majority of the comparison community buyers (53%) indicated they were not aware of the 

SheaHomes offerings when they were shopping for their new homes; 43% indicate they were 

aware. Those aware were then asked: “Why did you choose not to buy one of these homes?” and 

the first three reasons were coded, yielding a total of 35 responses. These responses can be 

summarized as follows: 

63
• Never saw SheaHomes (33% of reasons mentioned) 

• Preferred the comparison house layout (31%) 

• Preferred a better value in the comparison home (12%) 

• Preferred the comparison lot (11%) 

• Other (9%).3 

Thus, the majority of comparison homebuyers were unaware of the SheaHomes highperformance home offerings. Of the plurality who were aware of the San Angelo and Tiempo 

homes, most preferred the comparison layouts and lots and perceived a better value in the 

comparison homes than in the SheaHomes. Only one person mentioned a desire to avoid solar 

features as a reason not to purchase a home in the SheaHomes development. 

Reasons for Purchase 

Questions about the importance of purchase decision factors were constructed in such a way that 

energy features would attain the level of importance they actually had within the context of the 

many variables known to affect housing purchase decisions. The four types of questionnaires 

asked respondents to rate the importance of a set of their homes’ characteristics in their purchase 

decisions with the following question: “How important were each of the following features in 

your decisions to purchase your new home?” Respondents were asked to rate characteristics on a 

1-to-5 scale (where 1=Not at all important and 5=Very important). SheaHomes respondents were 

asked to rate the importance of 23 characteristics, including four items specifically dealing with 

energy features. A single item pertaining to energy was asked of the comparison respondents. 

They were asked to rate how important “Energy use of the home” was to their home purchase 

decisions. 

The two reasons eliciting significantly different responses from the SheaHomes and the 

comparison homebuyers did not pertain to energy features. These were “Desirability of the area” 

and “Reputation of the builder.” 

• A higher percentage of respondents from the comparison community rated “Desirability of the 

area” as an important reason for purchase (mean=4.58) than did those of the SheaHomes 

communities (mean=4.33; t= –2.308; p=.022) 

• A higher percentage of SheaHomes respondents rated “Reputation of the builder” as an 

important reason for purchase (mean=3.96) than did comparison respondents (mean=3.57; 

t=2.610; p=.011). 

Energy was not a very important factor in the purchase decisions for most of these new 

homebuyers. Average ratings of importance for all but two of the 23 listed reasons for purchase 

were the same for the SheaHomes and the comparison homebuyers. The reasons for purchase 

most frequently rated as most important by both categories of homebuyers are “Safe area/secure 

feeling,” “Quality of neighborhood/community,” “Overall home value (size and quality of home 

3

Other responses include one respondent who said the comparison home construction quality was better and one who 

wanted to avoid solar features. The total of the percentages does not add to 100 because of rounding. 

64 
for the price),” and “No Mello-Roos taxes.” Other frequently mentioned reasons rated as 

important include “Investment potential,” “Desirability of area,” “Freeway access,” and “Exterior 

designs.” Table 14 summarizes the average rated importance of reasons for purchase and presents 

the percentages of SheaHomes respondents rating each reason as very important (a rating of 5 on 

the response scale) and of those rating each reason as at least important (a rating of 4 or 5 on the 

scale). 

At the time they purchased their homes, SheaHomes buyers appear to have been more concerned 

about the homes’ energy usage than were the comparison buyers. This finding suggests that 

homebuyers who are already concerned about residential energy use constitute a stronger market 

for ZEHs than those who are not. In addition, “Availability of very energy-efficient homes” (the 

item asked of SheaHomes respondents) is rated by them more frequently as a more important 

reason (mean=3.87) than is “Energy use of the home” rated by the comparison respondents 

(mean=3.54).4 

Taken on the whole, the information contained in Table 14 provides additional perspective on the 

importance ratings assigned to 20 new-home purchase reasons5

 by homebuyers in the 

SheaHomes and comparison communities. Although respondents were not asked to specifically 

rank these various reasons, the order of the mean importance ratings, when listed high to low 

(ignoring the number of respondents associated with each value), is approximately the same for 

both homebuyer groups. In fact, the correlation between the two ordered listings is quite high and 

statistically significant (Spearman’s Rho=.9323; p.0). This would suggest that both homeowner 

groups tend to assign the same level of importance (in terms of the magnitude of the mean 

importance rating) to each of the 20 purchase reasons, providing some overall indication of their 

relative importance. 

Most Important Reasons for Purchase for SheaHomes and Comparison 

Homebuyers 

In addition to these findings, the importance of reasons for purchase was measured in another 

way. All respondents were asked to indicate the top three most important reasons for purchase. 

The 24 listed reasons for purchase were categorized into five types (shown on the questionnaires, 

see Appendixes A, B, C, D): (1) location, (2) financial, (3) builder, (4) community, and (5) 

energy. After they had responded, respondents were asked to go back over the list and indicate 

the three most important reasons for purchase. These responses were coded by category; results 

are summarized in Table 15 with percentages calculated on the number of responses in the top 

three choices. Location and financial reasons are far and away the most important factors in the 

home purchase decision. Although 23% of the SheaHomes buyers rate energy as an 

4

This difference cannot be tested for statistical significance because the item wording used for the two sets of 

respondents was not identical, although the intention of the questions was similar. 

5

Three of the new home purchase reasons listed in Table 14 are excluded from consideration here because they were 

rated by respondents from the SheaHomes communities only. On the other hand, a single item associated with home 

energy is included even though the item was not identically worded on the questionnaires completed by SheaHomes 

and comparison community respondents; the items are similar in meaning. 

65 
Table 14. Average Importance Ratings of Reasons for

 Home Purchase, Ordered by Mean Scores for SheaHomes Respondents*

Feature 

Mean Score, 

SheaHomes 

Respondents 

Mean Score, 

Comparison 

Respondents 

SheaHomes/ 

Comparison Homes

 % Responding Very 

Important (5) 

SheaHomes/ 

Comparison Homes 

% Responding 

Important or Very 

Important (4,5) 

Safe area/secure feeling 4.67 4.74 70/80 97/96 

Quality of neighborhood/community 4.58 4.52 61/63 97/93 

Overall home value 4.52 4.52 58/59 93/93 

No Mello-Roos taxes 4.49 4.48 64/67 87/85 

Investment potential 4.35 4.35 50/52 90/83 

**Desirability of area (p=.022) 4.33 4.58 45/64 90/94 

Freeway access 4.07 4.04 38/35 74/76 

Exterior designs 4.01 4.19 26/32 78/91 

Quality of schools 3.99 4.24 52/52 70/83 

Access to services, shopping, and 

entertainment 3.97 3.96 27/28 74/72 

Closeness to work 3.96 3.98 38/33 71/72 

**Reputation of builder (p=.011) 3.96 3.57 29/23 73/49*** 

SheaHomes: Availability of very energyefficient home 

Comparison homes: Energy use of the home 

3.87 3.54 

SheaHomes: 23 

Comparison: 17 

SheaHomes: 69 

Comparison: 56 

Helpfulness and knowledge of sales staff 3.81 3.61 25/26 64/54 

Prior knowledge of area 3.8 3.57 29/30 63/54 

Great view 3.71 3.66 33/36 58/55 

Feeling of community spirit 3.68 3.60 22/25 60/54 

The package of energy features taken 

together 3.56 -- 14 58 

Availability of solar water heating 

(SheaHomes only) 3.49 -- 17 52 

Discount or incentive 3.43 3.27 21/18 50/47 

Availability of PV system

 (Main and PV only) 3.34 -- 12 49 

Closeness to friends/family members 3.29 3.13 20/19 44/40 

Closeness to parks/playgrounds 3.08 3.09 12/13 34/28 

*The reasons are listed in order from high to low mean scores for the SheaHomes respondent group. The comparison group’s mean scores vary

in order somewhat from the SheaHomes mean scores.

**Statistically significant difference in mean scores between SheaHomes and comparison respondent groups.

***P2=12.439; p=.006.

66
Table 15. Three Most Important Categories of Reasons for Home Purchase,

with Percentages of Responses for Buyers of SheaHomes and Comparison Homes*

Category of 

Reasons for Purchase 

SheaHomes 

Responses 

(n=471) 

Comparison 

Responses 

(n=149) 

Location 48% 60% 

Financial 31% 28% 

Community 10% 9% 

Energy 6% 0% 

Builder 4% –** 

Other –** 2%

 Totals 99%*** 99%*** 

*Responses for the three reasons are combined. SheaHomes respondents=159; Comparison home

respondents=50 PV

**<.05%

***Percentages do not add to 100 because of rounding.

important decision category, none of the comparison homebuyers do so (P2=16.505; p=.006).6 

Most of the SheaHomes buyers rating energy as important count it as their third most important 

reason for purchase, rather than as the first or second reason. This constitutes additional evidence 

that SheaHomes buyers were somewhat more concerned about energy issues than were the 

comparison homebuyers at the time they purchased their homes. 

A P2 analysis of the SheaHomes and comparison homes responses in Table 15, but omitting the 

“Other” category, results in a statistically significant difference. A higher percentage of 

SheaHomes buyers select “Energy” among their three most important reasons than do comparison 

buyers (P2=16.4596; p=.0025). 

Importance of New Home Features in Purchase Decisions 

In addition to the importance of reasons for purchase relating to the area, location, builder, energy, 

and other factors, respondents were queried about the importance of a list of features of the home 

itself when they made their home purchase decisions. Based on earlier qualitative interviews and 

prior research, 15 features known to be important to homebuyers were identified. Consequently, 

all four questionnaires asked respondents to rate this set of home features on a scale of 1 to 5 

(1=Not at all important and 5=Very important) with the following question: “How important were 

each of the following home features when you made your purchase?” 

6

Strictly speaking, the P2 test may not be valid because there were empty cells where comparison respondents failed 

to include energy in their most important reasons for purchase. Nevertheless, the test shows the marked difference 

between the two groups of respondents on this variable. 

67 
Table 16 presents the average ratings, with features listed in descending order according to values 

of the mean scores associated with SheaHomes respondents. Table 16 also presents the 

comparative percentages of SheaHomes and comparison community respondents who rated each 

feature as very important, as well as the corresponding percentages of the two respondent groups 

who rated each feature as important or very important. 

As is evident in Table 16, the features with the highest average importance ratings for both 

SheaHomes and comparison buyers are “Floor plan and layout,” “Quality of construction,” 

“Spaciousness and openness,” “Size and square footage,” and “Quality or sense of light.” The 

lowest mean importance rating is associated with “Single-story option,” again for homebuyers in 

both communities. 

SheaHomes and comparison community homebuyers assign essentially the same mean importance 

ratings to all but three of the features: (a) quality of construction, (b) availability of a three-car 

garage, and (c) granite counter tops as a standard feature. 

• SheaHomes respondents assign higher ratings, on average, to “Quality of construction” 

(mean=4.5) than do the comparison community respondents (mean=4.3). This difference nears 

statistical significance (t=1.903; p=.058). 

• SheaHomes respondents assign higher ratings, on average, to “Availability of a three-car 

garage” (mean=4.02) than do the comparison community respondents (mean=3.65). This 

difference is statistically significant (t=2.074; p=.041). 

• SheaHomes respondents assign higher ratings, on average, to “Granite counter tops as a 

standard feature” (mean=3.75) than do the comparison community respondents (mean=3.04).7 

Again, this difference is statistically significant (t=3.502; p=.001). 

All the listed home features are important to both SheaHomes and comparison homebuyers. The 

differences suggest that SheaHomes buyers were particularly concerned about the quality of their 

new homes, a characteristic emphasized by SheaHomes, but not by the builder of the comparison 

homes. Granite counter tops appear to be a feature associated with higher quality and, possibly, 

more expensive homes, which could help account for its greater appeal to the SheaHomes buyers. 

As in Table 14, the information contained in Table 16 provides additional perspective on the 

importance ratings assigned to the 15 new home features by homebuyers in the SheaHomes and 

comparison communities. Respondents were not asked to specifically rank these various features. 

The order of the mean importance ratings, when listed high to low (ignoring the number of 

respondents associated with each value), is approximately the same for both homebuyer groups. In 

fact, the correlation between the two ordered listings is quite high and statistically significant 

(Spearman’s Rho=.9401; p<.0004). This suggests that both homeowner groups tend to assign the 

same level of importance (in terms of the magnitude of the mean importance ratings) to each of 

the 15 features, providing some overall indication of the relative importance of these features. 

7

Granite tile counter tops were a standard feature in the SheaHomes, but solid granite counter tops were an optional 

upgrade that cost on the order of an additional $10,000. 

68 
Table 16. Importance of New Home Features

Ordered by Mean Scores for SheaHomes Respondents

Feature Mean Score, 

SheaHomes 

Mean Score, 

Comparison 

SheaHomes/ 

Comparison 

% Very 

Important 

Shea Homes/ 

Comparison % 

Important or 

Very Important 

Floor plan/layout 4.50 4.64 60/66 93/98 

Quality of constructiona 4.50 4.30 58/46 94/85 

Spaciousness/openness 4.37 4.40 48/46 91/94 

Size/square footage 4.36 4.30 44/35 92/95 

Quality or sense of light 4.25 4.23 41/36 85/89 

Number of bedrooms 4.14 4.25 32/35 84/90 

Architectural design 4.10 4.17 33/42 80/79 

Quiet area 4.09 4.18 37/40 77/84 

Many amenities included as 

standard features 4.06 3.84 32/28 75/65 

Three-car garageb 4.02 3.65 38/29 74/56 

Large closets/pantries 3.95 3.73 29/23 71/60 

Lot size/yard 3.83 4.09 31/38 66/79 

Availability of many options 3.79 3.81 22/25 64/74 

Granite counter tops as 

standard featuresc 3.75 3.04 26/18 62/38 

Single-story option 2.52 2.17 14/6 26/17 

a. t=1.903; p=.058 (nears significance); P2 n.s. 

b. t=2.074; p=.041; P2 n.s. 

c. t=3.502; p=.001; P2=23.048; p<.000. 

Table 16 shows that SheaHomes respondents (4.06) give a somewhat higher mean importance 

rating to the item “Many amenities included as standard features” than do comparison respondents 

(3.84), which provides support to the notion that even though SheaHomes cost less per square 

foot, buyers perceived them as including many desirable standard features. This was somewhat 

more important to them than comparison buyers, who less frequently perceived this as important 

relative to their home purchases. This finding suggests that SheaHomes came with more amenities 

than did the comparison homes, despite their lower cost per square foot. 

Summary 

It appears that, in terms of average ratings, the most important reasons that SheaHomes buyers 

decided to purchase homes at Scripps Highlands were the desirable location (including the quality 

69
of the neighborhood and the safety of the area), the financial aspects (including the overall home 

value, the lack of Mello-Roos taxes, and the potential for a sound financial investment); the reputation 

of the SheaHomes builder in offering quality homes; and the availability of energy features. The 

features that appeal most to SheaHomes buyers, in terms of average ratings, include the floor plans, 

the quality of construction, the size and openness of the homes, the spaces for bedrooms and closets, 

and—significantly more so than to the comparison community buyers—the availability of a three-car 

garage and the inclusion of granite counter tops standard. These seem to be features that buyers 

associate with the quality of homes. 

The most important purchase reason for the comparison buyers appear to have been the location of 

the home, including the safety, quality, and desirability of the area. Investment potential also appears 

to have been important. A third reason frequently mentioned as important is the quality of the schools. 

The features that appeal most to the comparison buyers in terms of average importance ratings are the 

floor plans, the size and openness of the homes, the number of bedrooms, the size of the yards, the 

design, and the availability of many options. 

Because SheaHomes prides itself on the quality of its homes’ construction and positions its homes 

in the market based on quality, it is interesting that the average importance ratings assigned to 

construction quality as a purchase feature is significantly higher for the SheaHomes buyers than for 

the comparison homebuyers. The SheaHomes marketing message about quality may well be reaching 

the homebuying market. 

70
Chapter 8 

Solar PV Purchase Decisions 

Introduction 

PV owners received their own questionnaire 

containing a number of questions unique to 

owning a PV system, as well as duplicating 

questions asked of other categories of 

homeowners. In this chapter, responses of PV 

owners are analyzed relative to their decisions to 

purchase homes with solar PV systems. As was 

discussed in Chapter 4 (The SheaHomes 

Experience), most PV homeowners bought their 

PV systems standard. In general, they decided 

whether they liked the houses and purchased 

them more on those grounds than because they 

had PV systems. A minority of the PV owners 

opted to purchase PV systems; for these 

homebuyers, purchasing PV systems was a 

conscious choice. 

In this chapter, home features important to PV 

owners and non-PV owners are compared and 

reasons for not choosing to purchase or to 

upgrade a solar PV system are explored. In 

addition, responses of main owners who did and 

did not have a choice to purchase PV systems 

are compared. A few homeowners have added 

solar PV since they purchased their homes; 

information is provided on the frequency of this. 

In addition, this chapter reports on stated 

willingness to pay for solar PV by non-PV 

owners in both the SheaHomes and comparison 

communities. 

Number of PV Homes among 

SheaHomes Respondents 

Of the 177 SheaHomes respondents, 72 (41%) 

own homes with PV systems. These respondents 

have either standard PV systems (1.2-kW), 

optional PV systems (1.2-kW or 2.4-kW), or a 

combination (1.2-kW with an additional 1.2-kW 

Chapter Highlights 

C There are 72 PV-owner respondents in the study; 

the majority own 1.2-kW PV systems; 12% own 

2.4-kW PV systems. 

C Among respondents who were buyers of PVeligible homes, only 44% were offered PV 

systems. Forty-six percent of respondents offered 

PV systems opted to purchase them. 

C Energy items were not the most important reason 

for purchase for either the PV or main owners. 

The purchase reason with the highest mean 

importance rating for both categories is “Safe 

area/secure feeling.” 

C The average importance ratings of PV owners are 

lower than those of main owners for all home 

purchase reasons except for the “Availability of 

PV system” and “The package of energy features 

taken together.” 

C Every home feature considered has a higher 

average importance rating for the main 

purchasers than for the PV owners, suggesting 

that home characteristics other than energy 

features were more important to main 

homebuyers than to buyers of PV homes. 

C The overall rankings of the reasons for purchase 

are the same, regardless of homebuyer category. 

C The most frequently mentioned barriers to 

purchase of PV homes involve financial 

considerations such as initial costs and length of 

payback. 

C The findings on willingness to pay (WTP) more 

for PV systems suggest that $5,000 may be a 

price cut-off point for 1.2-kW PV systems. More 

than one-third of non-PV-purchasing homebuyers 

indicate a WTP of at least $5,000 more for a PV 

system that could replace 50% to 70% of their 

electricity requirements. However, a 1.2-kW 

system will not save that much electricity. 

71
optional upgrade).1

 Table 17 summarizes the system sizes and whether they were standard PV 

systems that came with the home. Among homes represented by these respondents, 72% bought 

solar PV systems standard. At least three of these were homes that had fallen out of escrow. A 

total of 28% of these respondents opted to purchase PV systems for their homes. Note that these 

figures are different for those reported in the section on “Sales of Homes.” As described in this 

chapter, homes were analyzed only for SheaHomes with solar PV systems owned by study 

respondents. 

As previously mentioned, demand for housing in San Diego was intense when SheaHomes first 

put up its construction trailer for the Scripps Highlands developments. The sales agents were 

operating in a seller’s market and did not have to explain to prospective buyers the elements of 

the energy features of the SheaHomes to entice buyers. 

Table 17. Percentages of PV Respondents

 with Standard and Optional PV Systems 

Type of System 

Percentage 

(n=72) 

1.2-kW standard PV system (n=52) 72% 

1.2-kW optional PV system (n=12) 17% 

2.4-kW optional PV system (n=4) 6% 

2.4-PV system (standard 1.2-kW system plus 

optional 1.2-kW system) (n=4) 6% 

Total 101** 

*Note: These percentages differ slightly from those reported in Table 8 (in Chapter 4) 

because these are only for study respondents and those are for all SheaHomes. 

**Percentage does not add to 100 because of rounding. 

As discussed in Chapter 4 (The SheaHomes Experience), not all buyers of PV-eligible homes 

were offered PV systems. Of the 177 SheaHomes respondents, 20 homebuyers (11.3%) (20 ÷ 

177) had no opportunity to purchase PV systems because they were buyers of PV-ineligible or 

early homes. Eighty-five (48%) (85 ÷ 177) bought PV-eligible homes. The remaining 72 

homebuyers (41%) (72 ÷ 177) purchased homes with PV systems. 

Main respondents were asked: “Were you offered the option of purchasing a solar PV system at 

the time you bought your new home?” Eighty-two of the 85 main respondents answered, with the 

following results: 

1

These numbers and percentages reflect only the respondent pool of homes, whereas those discussed in Chapter 4 

(The SheaHomes Experience) reflect all 306 homes at San Angelo and Tiempo. 

72 
• Yes, we were offered a PV system - 36 respondents (44%) 

• No, we don’t remember being offered a PV system - 45 respondents (55%) 

• We were told a PV system would not be appropriate - 1 respondent (1%). 

As discussed in Chapter 4, even though the builder reported that all main homebuyers (n=82) 

were to be offered PV systems, this apparently did not occur. Among the main respondents, 56% 

(46 ÷ 82) indicate that they never received an offer to purchase homes with PV systems or to 

upgrade PV systems; only 44% (36 ÷ 82) indicate they did receive such an offer. 

This finding suggests at least two possibilities: 

(1) The sales staff did not inform all of the potential purchasers of PV systems about the 

possibility of purchasing a PV system. 

(2) Homebuyers did not recall being told about PV at the time of purchase. Perhaps the concept 

was unfamiliar to them, and they paid little heed to it.2 

To speculate, both of these likely occurred during the hurly-burly of the early stages of the 

SheaHomes sales process. Nevertheless, it is highly unlikely that 56% would forget they were 

offered PV systems. In any event, if the homebuyers did not realize they had the option to 

purchase solar PV systems, they cannot comprise a group for analytical purposes that consciously 

chose whether to purchase PV systems. 

Of the 82 main respondents, only 36 (44% [36 ÷ 82]) said they were offered PV systems. If we 

assume that the 52 PV owners were actually offered the opportunity to upgrade from 1.2-kW to 

2.4-kW systems,3

 then a total of only 88 (52 + 36) of the buyers of PV standard and PV-eligible 

homes were given the opportunity to purchase or upgrade PV systems. 

This implies that only 56% of the 157 (177-20 PV-ineligible=157) respondents who, theoretically 

“should have” been offered PV systems were actually offered them. It further shows that 46% 

(72 ÷ 157) of study respondents offered PV systems opted to purchase them. This percentage is 

slightly higher than the 44% uptake of PV systems among all SheaHomes buyers who were 

offered them as described in Chapter 4 (The SheaHomes Experience). 

Because only 36 main respondents had the opportunity to purchase PV systems and subsequently 

chose not to purchase one, a separate analytical category for these buyers was defined. This 

distinction is conceptually important because main buyers could not be considered decisionmakers about PV (and thus early adopters of the innovation) if they never had the opportunity to 

decide. The main buyers who knew about the option to purchase PV and who elected not to are 

2

These are retrospective data: the respondents were asked to think back to a purchase decision process that occurred 

at least six months before they completed the questionnaire. On the other hand, as previously noted, because any 

home-purchase decision is highly significant, recall about it is likely to be accurate. 

3

We did not collect data directly on this question. 

73 
termed main buyers who “chose not to adopt” (CNA) solar PV systems. Below, the responses of 

main CNAs, as well as of all main owners, are compared with those of PV owners to discern 

whether measurable differences exist between these two groups in their reasons for purchase. 

Along these same lines, ineligible and early buyers were asked: “Which of the following 

statements best describes the situation regarding the availability of a solar PV system at your 

house?” Seventeen ineligible/early respondents answered each question by indicating whether the 

statement applied to them. 

• “We were told a solar PV system could not be installed on our house for technical reasons.” 

Seventy-seven percent indicate that this statement applied to them; 23% indicate that it did 

not. 

• “We bought a house before solar water heating and solar PV systems were available.” Thirty 

percent indicate that this statement applied to them; 70% indicate that it did not. 

• “We purchased a house that fell out of escrow and the construction schedule prevented us 

from exercising the solar PV system option.” None of the respondents indicate that this 

statement applied to them. 

Each of the first two statements is likely to be accurate. The third one is, in fact, not accurate, and 

none of the ineligible/early respondents indicated that this statement applied to them., suggesting 

a certain level of awareness at purchase time. 

Factors Affecting the Decision to Purchase PV Homes 

In analyses of factors affecting PV purchase decisions, only the purchase decisions of those who 

actually had the opportunity to purchase PV systems should be analyzed to determine the 

importance of the availability of PV systems in the home-purchase decision. The analysis of the 

purchase decisions presented here compares the following responses: 

• The 72 respondents owning homes with PV systems (termed “PV owners”) 

• The 85 respondents who theoretically had the opportunity to purchase PV but chose not to 

(termed “main” buyers). 

Ineligible and early home owners are excluded from this analysis because they definitely had no 

option to purchase PV systems. 

As described earlier, questions were asked of respondents about the importance of several 

purchase decision factors. As noted in Chapter 7 (New Home Search and Purchase Decision), 

this allowed energy features to attain the level of importance they actually had within the context 

of the many variables known to affect a housing purchase decision. During earlier qualitative 

interviews, approximately one-third of the PV owner respondents indicated that the availability 

of a PV system was one of the top three reasons they had purchased their homes, and it was of 

74
interest to determine whether this same opinion persisted beyond those homeowners involved in 

the qualitative investigation. 

PV Owners Compared with Main Owners 

Table 18 presents the average ratings on 23 different items from SheaHomes owners, some of 

whom have PV systems and some of whom do not, with the average item ratings listed in 

descending order according to the values associated with PV-owner respondents. For several of 

the 23 individual items, there are statistically significant differences in the mean responses for the 

two groups, and the percentage distributions of the item responses for the two groups are also 

significantly different. Table 18 also presents the comparative percentages of PV owners and 

main owners who rated each feature as “Very important,” as well as the corresponding 

percentages of the two respondent groups who rated each feature as “Important” or “Very 

important.” 

As indicated in Table 18, the average importance ratings of PV owners are lower than those of 

main owners for all home purchase reasons except for some of those involving the energy 

package and the solar PV system. In fact, two of the energy items elicited a significantly different 

response for PV owners than for main owners: 

• A higher percentage of respondents (69%) who purchased homes with PV systems rated “The 

package of energy features” as an important or very important reason for purchase than did 

respondents who purchased main homes (53%). In addition, the mean response was 

significantly higher for respondents who purchased homes with PV systems (3.75 and 3.34, 

respectively, with p=.043). 

• A higher percentage of respondents (76%) who purchased homes with PV systems rated 

“Availability of a PV system” as an important or very important reason for purchase than did 

respondents who purchased main homes (68%). Again, the mean response was significantly 

higher for respondents who purchased homes with PV systems (3.60 versus 3.11, respectively, 

with p=.004). Obviously, in this case, such a difference would not be unexpected. 

These findings suggest that, in terms of average ratings, those who purchased PV homes found 

the availability of energy features, and particularly the PV system itself, significantly more 

important than main owners. 

On the other hand, there was no significant difference in response between PV owners and main 

owners for either of the following items: “Availability of a very energy-efficient home” and 

“Availability of solar water heating.” This finding suggests that it was the availability of a solar 

electric system itself that appealed the most to those purchasing homes with PV systems. Such 

features appear to have been less important to buyers of main homes. 

Also as shown in Table 18, other significant differences exist between PV owners and main 

owners with respect to the average importance ratings of reasons for purchase. For example, 

access to the I-15 freeway received a higher average rating (4.26) by purchasers of main homes 

75
than purchasers of homes with PV systems (3.86). Purchasers of main homes also gave 

significantly higher ratings to the following items: “Access to services, shopping and 

entertainment,” “Great view,” and “Feeling of community spirit.” Clearly, then, in context, the 

solar energy features seem to have mattered somewhat more to those who purchased homes with 

PV systems than to those purchasing the main homes. 

From a more macroscopic inspection of Table 18, it can be observed that energy items were not 

the most important reasons for purchase for either the PV owners or the main owners (in the 

sense that, for both groups, other items received higher average ratings). Although respondents 

were not asked to specifically rank the various reasons, the order of the mean importance ratings 

assigned to the individual items, when listed high to low (ignoring the number of respondents 

associated with each value), is approximately the same for both homebuyer groups. In fact, the 

correlation between the two ordered listings is quite high (Spearman’s Rho = .899; p.0). This 

would suggest that both PV and main owners tend to assign the same levels of importance (in 

terms of the magnitude of the mean importance rating) to this specific set of purchase reasons, 

providing some overall indication of the relative importance among them. As is evident in Table 

18, the highest mean importance rating is associated with “Safe area/secure feeling” for 

homebuyers in both the PV and main buyer groups, and the lowest mean importance rating is 

associated with “Closeness to parks/playgrounds,” again for homebuyers in both groups. 

Most Important Reasons for Purchase for PV and Non-PV Homebuyers 

In addition to these findings, the importance of reasons for purchase was measured in another 

way. All respondents were asked to indicate the top three most important reasons for purchase. 

The 24 listed reasons for purchase were categorized into five types (shown on the questionnaires; 

see Appendixes B, C, D, E): (1) location, (2) financial, (3) builder, (4) community, and (5) 

energy. After they had responded, respondents were asked to go back over the list and indicate 

the three most important reasons for purchase. These responses were coded by category; results 

are summarized in Table 19 with percentages calculated on the number of responses in the top 

three choices. Location and financial reasons are far and away the most important factors in the 

home purchase decision. Six percent of the PV owners and non-PV owner responses include 

“energy” among the three most important categories of reasons for purchasing their new homes. 

A P2 analysis of the PV owners’ and non-PV owners’ responses are in Table 19, but omitting the 

“Other” category shows no statistically significant differences between these two groups 

(P2=3.90; p=.419) on the top three most reasons for purchase. 

PV Owners Compared with Main Owners Who Chose Not to Purchase PV 

Systems 

As discussed earlier, the main buyers who had the opportunity to purchase PV and who chose not 

to purchase systems are termed “main CNAs.” Table 20 summarizes the results of t-tests and P2 

76
tests measuring differences in responses regarding 23 reasons for home purchase between PV 

owners and main CNAs. In contrast to the findings presented in Table 21 that compared the 

responses of PV owners with all main owners resulting in several significant differences, these 

findings show only two significant differences between PV owners and the main CNAs. 

Table 18. Importance Ratings of Home Purchase Reasons 

for SheaHomes Buyers with and without PV Systems 

(Ordered by Mean Scores for PV Purchasers)* 

Feature 

Mean 

Score, PV 

Owners 

(n=72) 

Mean Score, 

Main 

Purchasers 

(n=85) 

PV/Main 

Purchasers

 % Responding 

Very Important 

(5) 

PV/Main 

Purchasers 

% Responding 

Important or 

Very Important 

(4,5) 

Safe area/secure feeling 4.62 4.72 63/77 99/96 

Overall home value 4.48 4.53 58/58 92/95 

Quality of neighborhood/ 

communitya 4.46 4.70 52/71 97/99

No Mello-Roos taxes 4.42 4.59 62/68 86/93 

Desirability of area 4.27 4.40 39/51 81/93 

Investment potential 4.24 4.40 47/51 86/90 

Quality of schools 3.96 4.00 44/59 71/71 

Exterior designs 3.94 4.11 25/29 75/82 

Closeness to work 3.92 4.01 35/40 69/73 

Reputation of builder 3.92 4.00 24/35 70/73 

Availability of very energyefficient home 3.89 3.91 21/27 76/68 

Freeway accessb 3.86 4.26 28/49 66/81

Access to services, 

shopping, and 

entertainmentc 

3.83 4.11 17/35 72/75

Helpfulness and knowledge 

of sales staff 3.79 3.87 24/28 63/67 

The package of energy 

features taken togetherd 3.75 3.43 17/11 69/53 

(Table 18 continued on next page) 

77 
Table 18. Importance Ratings of Home Purchase Reasons 

for SheaHomes Buyers with and without PV Systems 

(Ordered by Mean Scores for PV Purchasers) 

(concluded) 

Feature 

Mean 

Score, PV 

Owners 

(n=72) 

Mean Score, 

Main 

Purchasers 

(n=85) 

PV/Main 

Purchasers

 % Responding 

Very Important 

(5) 

PV/Main 

Purchasers 

% Responding 

Important or 

Very Important 

(4, 5) 

Prior knowledge of area 3.69 3.90 24/32 61/65 

Availability of PV systeme 3.60 3.11 16/9 63/36

Availability of solar water 

heating 3.54 3.52 3/21 61/52 

Great viewf 3.46 3.89 23/44 52/62 

Feeling of community spiritg 3.44 3.87 14/29 47/70

Discount or incentive 3.40 3.51 17/27 51/53 

Closeness to friends/family 

members 3.31 3.21 19/18 43/44 

Closeness to 

parks/playgrounds 3.12 3.04 12/11 33/35 

*See text for distinction between PV purchasers and main purchasers. The reasons are listed in order from high to 

low mean scores for the PV purchasers group. The main purchaser group’s mean scores vary in order somewhat from 

the PV owners mean scores. 

a. t=2.562; p=.012; P2=6.610; p=.037. 

b. t=2.700; p=.008; P2=9.629; p=.047. 

c. t=2.157; p=.033; P2=8.163; p=.043. 

d. t=!2.041; p=.043; P2 test n.s. 

e. t=!2.894; p=.004; P2=10.893; p=.028. 

f. t=2.207; p=.029; P2=9.350; p=.053 (near significance). 

g. t=!2.684; p=.008; P2 =11.862; p=.018. 

PV owners rate the importance of the “Package of energy features taken together” in their homepurchase decisions significantly higher on average (mean=3.75) than do the main CNAs 

(mean=3.33) (t=2.126; p=.036). PV owners also rate the importance of the “Availability of a PV 

system” (mean=3.60) significantly higher, on average, than do the main CNAs (mean =2.97) 

(t=3.044; p=.003). The two groups do not differ significantly in the mean importance ratings they 

assigned to “Availability of a very energy-efficient home” or “Availability of solar water 

heating.” 

78
As is the case with the findings reported in Table 18, Table 20 shows the mean ratings of the 

main CNAs are higher, if only slightly so, for every home purchase reason except for the energyrelated ones. This finding seems to indicate that the energy features were more important to the 

purchasers of PV homes than they were to the main CNAs, although CNAs gave a slightly higher 

rating of 3.56 to the availability of solar water heating than did PV owners (3.54). The lowest 

mean importance rating the main CNAs assigned to any of the reasons for home purchase is 2.97 

for the “Availability of PV system.” This is also the lowest mean rating in the entire table by 

either group. 

Table 19. Three Most Important Categories of Reasons for Home Purchase, 

with Percentages of Responses for PV and Non-PV Owners* 

Category of 

Reasons for Purchase 

PV Owners 

Responses 

(n=196) 

Non-PV Owners 

Responses 

(n=143) 

Location 43% 52% 

Financial 35% 29% 

Community 10% 7% 

Energy 6% 6% 

Builder 6% 6% 

Other –** –**

 Totals 100 100 

*Responses for the three reasons are combined. PV owner respondents=66; Non-PV owner respondents=93. 

**<.05%.

***Percentages do not add to 100 because of rounding.

Despite these observations, the ordering of home features on the basis of mean ratings is 

approximately the same for both homebuyer groups. Although respondents were not asked to 

specifically rank the 23 reasons associated with a home purchase, the order of the mean 

importance ratings, when listed high to low (ignoring the number of respondents associated with 

each value), is approximately the same for both homebuyer groups. In fact, the correlation 

between the two ordered listings is quite high and statistically significant (Spearman’s 

Rho=.9286; p.0). This would suggest that both homeowner groups tend to assign the same level 

of importance (in terms of the magnitude of the mean importance rating) to each of the 23 

purchase reasons, providing some overall indication of the relative importance of them. As is 

evident in Table 20, the highest mean importance rating is again associated with “Safe 

area/secure feeling” for homebuyers in both the SheaHomes and comparison communities, and 

the lowest mean importance rating is associated with “Closeness to parks/playgrounds,” for PV 

homebuyers and “Availability of PV system” for comparison homebuyers. A similar analysis 

with essentially identical results was conducted on the information presented in Table 18. 

79
Importance of Home Features in the PV Home Purchase Decision 

Also as discussed earlier, respondents were queried about the importance of 15 different home 

features when they made their home purchases. For SheaHomes respondents, these data were 

analyzed with regard to PV ownership. Responses to this question were compared by PV owners 

versus main owners and by PV owners versus main CNAs. 

PV Owners Compared with Main Owners 

Every home feature listed has a higher average importance rating for the main purchasers than for 

the PV owners. In combination with the findings on reasons for purchase, this suggests that home 

characteristics other than energy features were more important to main homebuyers than to the 

buyers of high-performance homes with PV systems at the time of the home purchases. 

Table 21 presents the average ratings, with features listed in descending order according to the 

values of the mean scores associated with PV-owner respondents. Table 21 also presents the 

comparative percentages of PV owners and main owners that rated each feature as very 

important, as well as the corresponding percentages of the two respondent groups that rated each 

feature as important or very important. 

As indicated in Table 21, main owners assigned higher average importance ratings to all 15 of 

the home features than did PV owners. This difference is statistically significant for eight of the 

home features: “Floor plan/layout,” “Size/square footage,” “Quality or sense of light,” “Quiet 

area,” “Number of bedrooms,” “Architectural design,” “Large closets/pantries,” and “Availability 

of many options.” In addition, a ninth feature—“Many amenities included as standard features” 

—was assigned an average importance rating that is nearly significantly different between the 

two categories of homeowners. There are corresponding differences in the percentage 

distributions of responses for the two home groups for these same items. The main owners are 

apparently more enthusiastic, on whole, about these features of their new homes than are the PV 

owners. 

Although respondents were not asked to specifically rank the various home features, the order of 

the mean importance ratings, when listed high to low (ignoring the number of respondents 

associated with each value), is somewhat different for the PV and main owner groups. In fact, the 

correlation between the two ordered listings is only moderately positive (Spearman’s Rho=.58; 

p=.0294). This would suggest that the two homeowner groups tend to assign different relative 

levels of importance (in terms of the magnitude of the mean importance rating) to the 15 features. 

In fact, differences in the orderings are readily apparent. PV owners assign the highest average 

importance ratings to “Quality of construction,” “Spaciousness and openness,” and “Floor 

plan/layout,” in that order, whereas main owners assign the highest average importance ratings to 

“Floor plan/layout,” “ Quality of construction,” and “Size/square footage,” in that order. In 

addition, the average importance rating given by PV owners to “Three-car garage” is sixth 

highest, whereas the average rating given to this feature by main owners is eleventh highest. In 

combination with the findings on reasons for purchase, these additional findings about the 

importance of home features suggest that home characteristics other than energy features are 

more important to purchasers of homes without PV systems. 

80
PV Owners Compared with Main Owners Who Chose Not to Purchase PV 

Systems 

The findings underscore the differences between those consciously choosing not to purchase 

homes with PV systems from those choosing PV homes. The new home features were, on 

average, less important to the PV owners than to the main CNAs at the time of purchase. 

Table 22 summarizes the results of t-tests and P2 analyses measuring differences in responses 

about the importance of the 15 home features between PV owners and main CNAs. 

Table 20. Importance Ratings of Home Purchase Reasons 

for SheaHomes Buyers with and without PV Systems 

(Ordered by Mean Scores for PV Purchasers)a 

Feature 

Mean Score, 

PV Owners 

(n=72) 

Mean Score, 

Main 

CNAs 

(n=36) 

PV/Main 

CNAs

 % Responding 

Very 

Important (5) 

PV/Main 

CNAs 

% Responding 

Important or 

Very Important 

(4, 5) 

Safe area/secure feeling 4.62 4.79 63/79 99/100 

Overall home value 4.48 4.53 58/59 92/97 

Quality of neighborhood/ 

communityb 4.46 4.68 52/68 97/100 

No Mello-Roos taxes 4.42 4.62 62/71 86/91 

Desirability of area 4.27 4.44 39/47 81/97 

Investment potential 4.24 4.53 47/62 86/91 

Quality of schools 3.96 4.21 44/65 71/77 

Exterior designs 3.94 4.24 25/32 75/91 

Closeness to work 3.92 4.15 35/39 69/83 

Reputation of builder 3.92 4.06 24/36 70/79 

Availability of very energyefficient home 3.89 3.88 21/24 76/68 

Freeway access 3.86 4.24 28/50 66/79 

Access to services, shopping, 

and entertainment 3.83 4.09 17/35 72/77 

Helpfulness and knowledge 

of sales staff 3.79 3.97 24/35 63/66 

(Table 20 continued on next page) 

81 
Table 20. Importance Ratings of Home Purchase Reasons 

for SheaHomes Buyers with and without PV Systems 

(Ordered by Mean Scores for PV Purchasers)a 

(concluded) 

Feature 

Mean Score, 

PV Owners 

(n=72) 

Mean Score, 

Main 

CNAs 

(n=36) 

PV/Main 

CNAs

 % Responding 

Very 

Important (5) 

PV/Main 

CNAs 

% Responding 

Important or 

Very Important 

(4, 5) 

The package of energy 

features taken togetherc 3.75 3.33 17/9 69/46 

Prior knowledge of area 3.69 3.85 24/32 61/68 

Availability of PV systemd 3.60 2.97 16/6 63/24

Availability of solar water 

heating 3.54 3.56 13/21 61/47 

Great view 3.46 3.76 23/38 52/56 

Feeling of community spirit 3.44 3.82 14/29 47/65 

Discount or incentive 3.40 3.45 17/29 51/45 

Closeness to friends/family 

members 3.31 3.16 19/16 43/41 

Closeness to 

parks/playgrounds 3.12 3.12 12/12 33/30 

a. See text for distinction between PV purchasers and main CNAs. The reasons are listed in order from high to low 

mean scores for the PV purchasers group. The main CNA mean scores vary in order somewhat from the PV owners 

mean scores. 

b. t= –1.949; p=.055 (nears significance) 

c. t=2.126; p=.036; P2 test n.s. 

d. t=3.044; p=.003; P2=14,420; p=.006 

Main CNAs give a higher mean rating to all 15 of the new home features than do the PV owners, 

suggesting that these features were more important to them than they were to the PV owners at 

the time they were deciding on which homes to buy. Seven of these differences in mean ratings 

are statistically significant. (Recall that there are 10 significant differences in Table 21, which 

compares PV owners to all main owners.) On average, main CNAs assign higher importance 

ratings to “Floor plan/layout” (mean=4.78) than do PV owners (mean=4.25). They also assign 

higher importance ratings, on average, to “Quality or sense of light” (mean=4.46) than do PV 

owners (mean=4.10), as well as to “Architectural design” (CNA mean=4.36 compared with PV 

owners mean=3.90), “Many amenities included as standard as standard features” (CNA 

mean=4.25 compared with PV owners mean=3.82), “Large closets/pantries” (CNA mean=4.31 

compared with PV owners mean=3.73), “Availability of many options” (CNA mean=4.17 

82
compared with PV owners mean=3.59), and “Granite counter tops as a standard feature” (CNA 

mean=4.03 compared with PV owners mean=3.59). 

The feature that was most frequently rated as not important (1 or 2 on the 5-point importance 

scale) was the single-story option. Buyers who considered this option as important might have 

bought elsewhere because the number of single-story homes available was small. 

Despite these observations, the ordering of home features on the basis of mean rating is 

approximately the same for both homebuyer groups. Although respondents were not asked to 

specifically rank these 15 features, the order of the mean importance ratings, when listed high to 

low (ignoring the number of respondents associated with each value), is not much different 

overall between the PV and CNA owner groups. In fact, the correlation between the two ordered 

listings is quite high and statistically significant (Spearman’s Rho=.8565; p=.0014). This would 

suggest that both homeowner groups tend to assign the same level of importance (in terms of the 

magnitude of the mean importance rating) to each of the 15 features, providing some overall 

indication of the relative importance of these features. A similar analysis with essentially 

identical results was conducted on the information presented in Table 21. 

Barriers to Purchasing Homes with Solar PV Systems 

As already discussed above, buyers of homes classified as main respondents who were aware that 

they had an option to purchase homes with solar PV systems, but chose not to. A key study 

question was to determine why this is the case. It is particularly relevant because the cost of the 

PV systems offered by SheaHomes at Scripps Highlands was $6,000 for a 1.2-kW system and 

$10,000 for a 2.4-kW system—far lower than normal market prices. These low prices were made 

possible by the subsidies provided by the California Energy Commission (CEC) for the 

installation of grid-tied residential and commercial PV systems at the time Scripps Highlands 

was built. Although, in this case, these subsidies went to the builder rather than to the 

homebuyers (a situation of which the buyers may have been unaware), their effect was shown in 

the prices that SheaHomes charged for these systems. 

Main respondents who were aware of the PV option were asked: “To what extent do you agree 

with each of the following potential reasons for your decision not to purchase a solar PV 

system?” A list of 13 reasons was presented; these reasons were based on the literature and 

qualitative research. The response categories comprised a 5-point Likert scale in which 

1=Strongly disagree and 5=Strongly agree. Table 23 summarizes the responses to this question in 

order of percentages of agreement with statements. 

The major barriers appear to be that potential PV purchasers believed at the time of home 

purchase that the system was too expensive (84%) and that payback would be too long (59%). 

They appear to have been reasonably certain of their views on this. Pluralities indicate that they 

were concerned about maintenance issues (49% agreeing and 26% indicating they were unsure) 

and were unsure about the reliability of the system (36% agreeing and 39% indicating they were 

unsure). 

83
Table 21. Importance of New Home Features to Buyers 

in SheaHomes Communities with and without PV Systems

(Ordered by Mean Scores for Purchasers of Homes with PV Systems)*

Feature 

Mean Score, 

PV Owners 

(n=72) 

Mean Score, 

Main 

Purchasers 

(n=85) 

PV/Main 

Purchasers 

% Very 

Important 

PV/Main 

Purchasers 

% Important or 

Very Important 

Quality of construction 4.41 4.60 52/65 92/95 

Spaciousness/openness 4.27 4.48 39/57 90/92 

Floor plan/layouta 4.25 4.67 45/69 86/99

Size/square footageb 4.17 4.47 32/51 86/96

Quality or sense of lightc 4.10 4.42 31/52 82/90

Three-car garage 3.99 4.17 31/46 72/80 

Quiet aread 3.93 4.26 25/48 73/82

Number of bedroomse 3.90 4.26 18/39 75/89

Architectural designf 3.90 4.28 23/41 73/87

Many amenities includes as 

standard featuresg 3.82 4.25 21/43 63/83

Large closets/pantriesh 3.73 4.19 19/41 64/80

Lot size/yard 3.73 3.82 28/31 62/66 

Availability of many optionsi 3.59 3.92 13/28 66/72 

Granite counter tops as 

standard featurej 3.59 3.95 17/35 56/70 

Single-story option 2.34 2.73 10/19 19/33 

*See text for distinction between PV purchasers and main purchasers. 

a. t= !3.649; p<.000; P2=13.969; p=.007 

b. t= !2.946; p=.004; P2=10.463; p=.034 

c. t= !2.816; p=.006; P2 =8.611; p=.035 

d. t= !2.401; p=.018; P2 =8.044; p=.045 

e. t= !3.121; p<.002; P2=11.255; p=.010 

f. t= !3.109; p=.002; P2 =10.277; p=.016 

g. t= !3.458; p=.001; P2=11.491; p=.009 

h. t=3.437; p=.001; P2=11.745; p=.019 

i. t= !2.487; p=.024; P2 test n.s. 

j. t=2.284; p=.024; P2 test n.s. 

84
Table 22. Importance of New Home Features to

PV Owners and Main CNAs

(Ordered by Mean Scores for Purchasers of Homes with PV Systems)*

Feature 

Mean Score, 

PV Owners 

(n=72) 

Mean Score, 

Main 

CNAs 

(n=36) 

PV/Main 

CNAs % 

Very 

Important 

PV/Main 

CNAs 

% Important or 

Very Important 

PV/CNA 

% Not at 

All 

Important 

or Not 

Important 

Quality of construction 4.41 4.57 52/63 92/93 8/6 

Spaciousness/openness 4.27 4.47 39/57 90/92 10/8 

Floor plan/layouta 4.25 4.78 45/78 86/100 4/0

Size/square footage 4.17 4.36 32/44 86/92 14/8 

Quality or sense of lightb 4.10 4.46 31/51 82/94 18/6

Three-car garage 3.99 4.22 31/47 72/83 4/6 

Quiet area 3.93 4.20 25/40 73/80 27/20

Number of bedrooms 3.90 4.17 18/33 75/86 25/14 

Architectural designc 3.90 4.36 23/47 73/89 27/11

Many amenities included 

as standard featuresd 3.82 4.25 21/42 63/83 37/17

Large closets/pantriese 3.73 4.31 19/47 64/86 7/3

Lot size/yard 3.73 3.69 28/22 62/61 13/11 

Availability of many 

optionsf 3.59 4.17 13/33 66/83 8/0 

Granite counter tops as a 

standard featureg 3.59 4.03 17/47 56/69 11/8 

Single-story option 2.34 2.80 10/20 19/37 69/49 

*See text for distinction between PV owners and main CNAs. 

a. t= –4.240; p<.001; P2=12.023; p=.017 

b. t= –2.432; p=.017; P2 n.s. 

c. t= –2.910; p=.004; P2 =8.507; p=.037 

d. t= –2.725; p=.008; P2 =7.112; p=.068 (nears significance) 

e. t= –3.302; p=.001; P2=11.604; p=.021 

f. t= –3.489; p=.001; P2 =11.281; p=.024 

g. t= –2.063; p=.042; P2=12.714; p=.013 

85
Majorities of these main respondents disagree that “Energy is not that important” (79%), that the 

system “would negatively affect resale value” (70%), and that they “Did not like how the system 

looks” (57%). In general, main respondents who did not opt for PV systems indicate they were 

unsure whether homeowner insurance premiums would increase, property taxes would increase, 

or that the system could become outdated technologically. Sixty percent agree or are unsure 

whether they knew enough to evaluate the PV system option. 

After living in their homes for six months or more, a majority of main respondents are less sure 

than they were at the time of home purchase that they made the right choice in not adopting the 

PV systems, or they now wish they had purchased PV systems. The questionnaire for main 

respondents asked: “Do you now wish you had chosen to purchase a solar PV system at the time 

you purchased your home?” Fifteen percent indicate that they wish they had purchased PV 

systems at the time they purchased their homes. Five percent say they wish they had purchased 

2.4-kW systems for $10,000, and 11% say they wish they had purchased 1.2-kW systems for 

$6,000. Forty-four percent are unsure, whereas 40% say they do not wish they had purchased PV 

systems, thereby underscoring their original decision. We speculate that these buyers might have 

been comparing notes with their neighbors on utility bills. Of course, if these buyers had opted 

for PV systems, it would have affected the rate of uptake of PV systems. 

Barriers to Purchasing Optional Upgrades to 2.4-kW PV Systems 

The uptake rate was also affected by perceived barriers. Respondents who purchased homes that 

came with standard 1.2-kW solar PV systems were asked: “Why did you decide at the time not to 

purchase or upgrade to a 2.4-kW solar PV system (24 panels)?” Six response categories were 

provided, and 50 of the relevant 52 homebuyers responded. Table 24 summarizes the responses. 

The issue of expense is mentioned most frequently (by 36% of those responding), as is lack of 

knowledge about the level of system performance to expect (by 34%). An issue related to 

expense is that 28% say they wanted to select other options for the houses, and upgrading the PV 

systems was a trade-off they did not want to make. Approximately one-fifth of those responding 

say that they would have wanted to upgrade, but they missed making the decision in time to meet 

the tight construction schedule that SheaHomes was following. A few say they were unsure 

where the larger systems would be placed on their roofs. Sixteen percent of the respondents were 

unaware that the 2.4-kW option was available. Table 24 summarizes the responses to this 

question. 

Adding PV Systems after Home Purchase 

Owners of homes with solar PV systems were asked: “Have you added on to your solar PV 

system since you moved into your house?” Main homeowners and ineligible/early homeowners 

were asked: “Have you added a solar PV system since you moved into your house?” Of the 115 

responding to the first question, two respondents—one PV owner and one owner of a main 

home—indicate that they have added a PV system to their homes. The PV owner indicates that 

86
12 panels of PV totaling 1.44 kW were added.4

 The main owner indicates that five panels were 

added; the owner does not specify the number of kW. It is probable, given the system’s size, that 

these panels are for a solar thermal pool-heating system and were erroneously identified as solar 

PV systems. 

Sixteen percent of the respondents, although they have not added on to their PV systems, state: 

“No, but we have thought about it.” Eighty-two percent indicate: “No, we haven’t considered it.” 

Table 23. Percentages of Main Respondents Identifying 

Reasons for Not Opting for PV Systems*

Statements 

% Agree 

or 

Strongly 

Agree 

% 

Unsure 

or Neutral 

% 

Disagree 

or Strongly 

Disagree 

Totals 

% 

It was too expensive 84 16 – 100 

Payback would be too long 59 32 8 99** 

Concerned about maintenance issues 49 26 26 101** 

Unsure about the reliability of system 36 39 25 100 

Wanted other options instead 29 55 16 100 

Didn’t know enough to evaluate system 22 38 41 101** 

Thought property taxes would increase 20 46 34 100 

Could become outdated technologically 16 43 41 100 

Didn’t know where it would go on the roof 15 28 58 101** 

Did not like how the system looks 14 29 57 100 

Thought homeowners insurance premium 

would increase 6 53 41 100 

Thought it would negatively affect resale value 3 28 70 101** 

Energy is not that important 3 18 79 100 

*n’s vary for each item from 34 to 38 responses. Items are listed in order of percentages of agreement with each

statement.

**Percentages do not add to 100 because of rounding.

4

The owner may have meant either that the added panels produce 1.2 kW or that the total system produced 2.4 kW. 

87
Table 24. Percentages of PV Owners Citing Barriers 

to Purchase of Larger (2.4-kW) Solar PV System (n=50)

Barriers 

Percentage of Buyers 

of Homes with Standard 

1.2-kW PV Systems 

Thought it was too expensive to upgrade 36 

Didn’t know what level of performance to expect 34 

Wanted other options for the house instead of solar PV system upgrade 28 

Missed the cut-off date in the construction schedule 22 

Wasn’t sure where it would go on the roof 16 

Didn’t know a 2.4-kW system was available 16 

Stated Willingness to Pay More for Solar PV on the Part of Owners of SheaHomes 

without PV Systems and Comparison Homebuyers 

All respondents in the study except owners of homes with PV systems were asked: 

“If it had been available as an option when you bought your home, what is the 

most you would have been willing to pay for a solar PV system that could replace 

50% to 70% of your electricity requirements (depending on the way your 

household uses electricity)?” 

Response categories ranged from “Nothing more” to “More than $11,000.” Results are shown in 

Table 25, categorized by homebuyer category. A little more than one-third of each homebuyer 

type states a willingness to pay between $5,000 and $11,000 for PV systems. This is within the 

range of the costs of the PV options charged by SheaHomes. However, more than half of the 

comparison buyers (56%) and 42% of the ineligible/early buyers indicate they would have been 

willing to pay less than $5,000; the PV systems would have been too expensive for them. In 

addition, one in five of the ineligible/early buyers state they would have been willing to pay 

nothing more, an even higher percentage of 11% of main buyers and 10% of comparison buyers. 

Interestingly, more than one-third of the main CNAs appear to indicate they would be willing to 

pay the cost of solar PV systems (35% respond that they would pay at least $5,000 for a system). 

This suggests a kind of cut-off point in price at around $5,000 for a 1.2-kW PV system. 

In all, then, more than one-third (ranging from 34% to 37%) of the homebuyers analyzed indicate 

they were willing to pay at least $5,000—approximately $1,000 less than the amount that the 

solar PV systems cost at the time the homes were purchased. 

88
Table 25. Stated Willingness to Pay for PV Systems,

Percentages of Main, Ineligible/Early, and Comparison Homebuyers

Stated Amount Willing to Pay 

Main % 

(n=38) 

Ineligible/Early % 

(n=19) 

Comparison % 

(n=50) 

Total % 

(n=107) 

#$4,999 55 42 56 53 

$5,000–$6,999 24 32 14 21 

$7,000–$8,999 8 0 6 6

 $9,000–$10,999 0 5 14 8 

$$11,000 3 0 0 1 

Nothing more 11 21 10 12

 Totals 101* 100 100 101* 

*Does not add to 100 because of rounding. 

The comparison and ineligible/early respondents were asked an open-ended question regarding 

the amount they stated as willing to pay (or not pay) for solar PV systems. Volunteered responses 

were coded. Nine ineligible/early respondents discuss reasons having to do with inadequate or 

prolonged payback from PV system purchases. Thirty-nine comparison respondents mention the 

following ideas: 

• Payback (56% mentioning) 

• PV should have been standard equipment (26%) 

• Unsure whether PV works well (13%) 

• Better for environment and utilities (10%). 

The economics of the PV system purchase—including original cost and payback—is as 

important a barrier for the comparison and ineligible/early respondents as it is for the main 

respondents. These findings suggest that these respondents would not have purchased solar PV 

systems, even if the opportunity had existed, unless they had been standard equipment. 

Summary 

A lack of knowledge of the PV option and sufficient information seems to have impeded 

homebuyers from purchasing PV systems. 

A majority of 56% of the respondents who could have purchased PV homes were not given the 

opportunity. Comparison homebuyers were unaware of the energy features SheaHomes offered 

them. 

A separate analytical category of main respondents who had the opportunity to purchase PV 

systems (n=36) was identified and termed main CNAs (for those who “chose not to adopt”). The 

comparison of responses of PV owners with main CNAs showed that PV owners rate the 

89
importance of the availability of the package of energy features and of PV systems as more 

important, on average, than do the CNAs, except for solar water heating. 

When comparing important reasons for purchase between PV and main owners (as a group), in 

terms of average ratings, those who purchased PV homes identified the availability of energy 

features and, particularly, the PV system itself, as important reasons for purchase. 

The most frequently mentioned barriers to purchase of PV homes involve financial 

considerations: the initial expense was considered too high and the perceived payback period was 

perceived as too lengthy. 

More than one-third of each category of homebuyer, including comparison homebuyers, 

indicated a stated willingness to pay (WTP) at least $5,000 more for a solar PV system that could 

replace 50% to 70% of the respondents’ electricity requirements. However, the economics of the 

PV system purchase remains an important barrier. The findings suggest that $5,000 may be a cutoff point in price for a PV system because majorities of main CNAs and comparison buyers 

indicate WTP at or below $5,000. Yet if the prices were rolled into the cost of new homes and 

the PV systems came standard, these decisions would not need to be directly weighed. 

90
Chapter 9

Knowledge and Information

Introduction 

The new-home market was sizzling in San 

Diego when the SheaHomes and comparison 

home developments were planned and built. 

In 2001, any new development stimulated 

significant interest, and this was particularly 

the case with a new development offering 

homes with innovative solar features located 

in one of the most desirable locations in 

northern San Diego. 

The research sought to identify the ways in 

which high-performance homeowners and 

comparison homebuyers learned about the 

homes they ultimately bought and how 

satisfied they were with the information they 

received, especially on innovative energy 

features. It also measured how satisfied 

homeowners were with the response of the 

two different builder staffs to problems 

immediately after move-in. Another area of 

inquiry was the level of knowledge that highperformance homeowners had before they 

bought their homes and after living in them. 

An additional area of focus was the 

information sources used by PV owners, their 

level of knowledge about PV when they 

made PV purchase decisions, and PV 

attributes they learned about since living with 

their systems. 

Chapter Highlights 

C Majorities of SheaHomes and comparison buyers 

first learned about the homes they purchased by 

driving past the property. 

C Seventy-eight percent of SheaHomes and 67% of 

comparison buyers indicate they were fairly to very 

satisfied with the information provided by the 

builders’ sales staffs. 

C SheaHomes buyers are significantly more satisfied 

with the builder’s response to problems after movein than are comparison buyers. 

C SheaHomes buyers were, at the time of home 

purchase, significantly more informed than 

comparison buyers about energy efficiency and 

solar features. 

C PV owners, after having lived in their homes for at 

least 6 months, are significantly more informed 

about energy efficiency and solar features than are 

non-PV owners. 

C The single most important source of information on 

PV systems for new homebuyers is the builder sales 

staff. 

C Majorities of PV owners indicate that, since living 

with their PV systems, they have learned about 

utility bill savings, financial incentives, and how PV 

systems work. 

How Buyers First Heard about the SheaHomes and Comparison Communities 

SheaHomes buyers—in particular PV homebuyers—relied more heavily on word of mouth 

through personal connections than did other buyers. The qualitative interviews with SheaHomes 

buyers showed that several homeowners had prior efficiency features and solar energy systems or 

knew people who had. Personal knowledge about these technologies appeared to contribute to 

their awareness of and interest in the SheaHomes communities. 

91
SheaHomes respondents were asked, “How did you first hear about SheaHomes’ Scripps 

Highlands development?” and comparison respondents were asked, “How did you first hear 

about [the comparison home] development?” Table 26 shows the pattern of responses. 

Buyers in both developments heard about the homes in similar ways. More of the SheaHomes 

buyers (62%) than the comparison buyers (52%) saw physical evidence that a new development 

was going up at Scripps Highlands (a construction trailer and a billboard). Comparison buyers 

were more likely to have heard about their new homes from real estate agents or brokers (17%) 

Table 26. How Buyers First Heard about the Homes They Purchased* 

Information Source 

% SheaHomes 

Buyers 

(n=175) 

% Comparison 

Buyers 

(n=52) 

Drove by and saw flags, signs, or construction trailer 62 52 

Heard about it from friends/relatives/acquaintances 21 20 

Real estate agent or broker (volunteered) 3 17 

Saw an ad in the newspaper 6 4 

Saw it on the Internet 2 7 

Had previous experience with the builder 3 – 

Other 3 –

 Totals 100 100 

*Although a P2 test resulted in a significant difference (P2=12.183; p=.032), the test is not valid because there are 

two empty cells in the matrix 

than were SheaHomes buyers, whereas 3% of SheaHomes buyers—and no comparison 

buyers—had previous experience with the builder offering the new homes. 

The pattern of findings is similar between the buyers of homes with PV systems and homes 

without them (see Table 27). PV owners are slightly more likely to have heard about the 

developments by word-of-mouth than are non-PV owners and comparison buyers. PV owners are 

also slightly more likely to have read about the developments in the newspaper than are the other 

categories of buyers. To conjecture, these buyers might have been attracted by the rather 

extensive media coverage attending the launch of the SheaHomes San Angelo and Tiempo 

developments. 

The demand for housing in San Diego was so high that even the appearance of a construction 

trailer caused an influx of customer contacts. As mentioned in Chapter 4 (The SheaHomes 

Experience), SheaHomes received so many customer inquiries that they decided to hold a lottery 

for prospective buyers; those winning the lottery had the first opportunities to select their 

building lots, floor plans, and other options. 

92
Table 27. How SheaHomes Buyers First Heard about the Homes

They Purchased, by PV Ownership*

Information Source 

% PV Owners 

(n=71) 

% Non-PV Owners 

(n=104) 

Drove by and saw flags, signs, or construction trailer 55 66 

Heard about it from friends/relatives/acquaintances 25 18 

Saw an ad in the newspaper 10 4 

Other 6 7 

Had previous experience with the builder 3 3 

Saw it on the Internet 1 2

 Totals 100 100 

*These results are not significantly different 

Satisfaction with Information Provided by Builder Staff 

Buyers in both communities indicate a fairly high level of satisfaction with the information that 

staff provided to them to support their home purchase decisions. 

All respondents were asked: “How satisfied were you with the performance of the [SheaHomes] 

[comparison home] staff in providing you with accurate and adequate information to assist you in 

your home purchase decision?” Again, respondents were asked to respond on a 10-point scale, 

where 1=Not at all satisfied and 10=Very satisfied. 

A majority of both SheaHomes and comparison buyers give a rating of 7 or higher on the 10-

point scale (78% of SheaHomes and 69% of comparison home buyers). Forty-four percent of the 

SheaHomes buyers indicate that they are very satisfied (a rating of 9 or 10 on the scale), whereas 

only 32% of the comparison buyers give ratings that high. When a t-test is run, the mean score 

for the SheaHomes respondents is 7.77 and for the comparison respondents is 7.26; this 

difference in mean scores is not statistically significant. 

Satisfaction with Staff Response to Problems after Move-in 

The homebuyers were also asked to rate the responsiveness of the builder staff once they had 

moved into their new homes. The query was: “How satisfied were you with the performance of 

the (SheaHomes) (comparison home) staff in responding to problems and concerns after you 

moved in?” A 10-point response scale, from 1=Not at all satisfied to 10=Very satisfied, was 

provided. Findings show that although a majority of buyers in both developments give a positive 

rating, SheaHomes buyers more frequently give a positive rating of 7 to 10 on the scale (71%) 

than do the comparison respondents (56%). On the other hand, double the percentage of the 

comparison buyers (30%) give a negative rating of 1 to 4 on the 10-point scale, indicating that 

they are not very or not at all satisfied, compared with 15% of the SheaHomes buyers rating staff 

93
response that low. The mean scores are 7.34 for the SheaHomes buyers and 6.3 for the 

comparison buyers, a statistically significant difference (t=2.572; p=.011). 

Satisfaction with Staff Information on Energy Features among 

SheaHomes Buyers 

SheaHomes buyers were asked to rate how well the staff provided information on the energy 

features of the homes. The question was: “How satisfied were you with the performance of the 

SheaHomes staff in providing information on the energy efficiency and solar energy aspects of 

your home?” Again, the 1-to-10 response scale was provided, where 1=Not at all satisfied and 

10=Very satisfied. A majority of each SheaHomes buyer type gives a rating from 7 to 10 on the 

scale (PV=67%, main=73%, and ineligible/early=60%). The mean satisfaction ratings are 7.34 

for PV buyers, 7.31 for main buyers, and 6.55 for ineligible/early buyers. There is a slight 

indication that some of the ineligible/early and PV buyers may have wanted more information 

than they received on the energy efficiency and solar features of their new homes. 

How Informed Buyers Were about Energy Efficiency and Solar Energy When 

Looking for New Homes 

Although SheaHomes owners were more informed on energy features than comparison owners, 

PV purchasers were not more well-informed than non-PV purchasers. All respondents were 

asked: “When you were looking for your new home, how well informed would you say you were 

about energy efficiency and solar energy features?” Responses were requested on a 10-point 

scale, with 1=Not at all informed and 10=Very informed. Responses to this question were 

analyzed by the homebuyer categories. A t-test was used to compare how informed the 

SheaHomes and comparison buyers say they were at the time they were shopping for their 

homes. 

On the 1-to-10 scale, the mean rating for the SheaHomes respondents is 5.73 and for the 

comparison respondents is 4.81 (t=2.44; p=.015). Thus, the average rating assigned by 

SheaHomes buyers is significantly higher than the average rating assigned by comparison home 

buyers, suggesting that SheaHomes buyers were more informed about energy efficiency and solar 

features at the time they were shopping for new homes. 

The responses to this question were also analyzed by PV system ownership among the 

SheaHomes owners. No significant differences are found in the mean responses of these two 

groups. PV owners’ mean score on how well informed they were at time of purchase is 5.77, 

compared with a mean score of 5.69 for non-PV purchasers.1 

1

The non-PV purchasers discussed in this chapter are all SheaHomes owners who did not purchase a PV system, 

whether they knew about them or not, and whether they were available to them or not. These analyses remain to be 

done for main CNAs. 

94 
How Informed Buyers Are about Energy Efficiency and Solar Energy after 

Living in New High-Performance Homes 

Living in a PV home apparently enhances knowledge and awareness of energy efficiency and 

solar energy more than living in a high-performance home without PV. To measure whether living 

in high-performance homes for at least 6 months had increased their awareness of energy 

efficiency and solar features, SheaHomes respondents were asked: “How well informed would 

you say you are now?”2

 Responses were requested on a 1-to-10 scale where 1=Not at all informed 

and 10=Very informed. The responses to this question were analyzed by PV system ownership 

among the SheaHomes owners. 

When PV owners and non-PV owners were asked about how informed they are now, significant 

differences emerge. Those who lived in PV homes for at least 6 months more frequently rate 

themselves as more informed about energy efficiency and solar features than do those living 

without PV systems. PV owners respond with a mean score of 7.63, whereas the mean score for 

non-PV owners is 7.13. This difference nears statistical significance (t=1.714; p=.088). When the 

responses are subjected to P2 analysis, the difference between PV and non-PV owners is highly 

significant (p=.014). Table 28 presents the findings, which show that the percentage of PV owners 

indicating that they are informed to very informed is more than double (at 77%) the percentage at 

the time of purchase (37%). The percentage of more-informed non-PV owners is higher as well 

(64% compared with 37% at time of purchase), indicating that—although PV owners believe they 

have learned a good deal about energy features by living in their high-performance homes—nonPV owners have also become more informed about energy efficiency and solar energy since living 

in their SheaHomes. Nevertheless, a significantly higher percentage of PV owners have become 

more well informed than non-PV owners on energy efficiency, solar water heating, and solar PV 

systems since living in their homes. 

Most Important Information Source on PV Systems 

PV owners were asked: “Who was the single most important information source of information 

on your solar PV system?” Table 29 summarizes the findings. 

Far and away, the SheaHomes sales staff was the most important source of information on PV 

systems for buyers of SheaHomes than any other source and, indeed, than all other sources put 

together. The sales staff role was apparently pivotal in the PV purchase decision, as was noticed 

by direct observation, as well as documented by these survey findings. In fact, the sales staff 

controlled whether the PV system was even offered to the homebuyers. 

2

This question was not asked of comparison buyers because they did not have the experience of living in a highperformance home. 

95 
Table 28. Percentages of PV and Non-PV Owners Indicating Knowledge Levels about Energy

Efficiency and Solar Energy before and after Buying Their Homes

Response 

% PV Owners 

(n=71) 

% Non-PV Owners 

(n=104) 

Then Now* Then Now* 

Not at all informed/not very 

informed (1-4) 28 10 28 4 

Moderately informed (5-6) 35 13 36 32 

Informed/very informed (7-10) 37 77 37 64

 Totals 100 100 101** 100 

*Statistically significant difference (P2=12.515; p=.014). 

**Does not add to 100 because of rounding 

Table 29. Single Most Important Information Source on PV Systems 

Most Important Information Source on PV System 

% PV Owners 

(n=69) 

SheaHomes sales staff 61 

AstroPower 16 

Shea University 9 

Friends, acquaintances, colleagues, personal network 7 

Other 4 

San Diego Gas and Electric Company 3

 Total 100 

Channels of Information on the Solar PV System 

PV owners had difficulty getting information about their systems. A fact sheet on solar PV 

prepared by AstroPower, Inc., was available at the SheaHomes Sales Center in a plastic holder 

mounted on the wall. AstroPower, Inc., also prepared an operations manual for PV owners, 

although qualitative research suggested that not all PV owners received one. SheaHomes and 

AstroPower, Inc., maintain Web sites that homebuyers could use to gather information on solar 

PV systems. A portion of the PV owners, but not all, received a video about the AstroPower solar 

PV system. 

PV owner respondents were asked: “Have you used any of the following to learn about the way 

your solar PV system operates?” A list of four possible responses was provided. The 52 

respondents to this question most frequently check off the fact sheet about the solar PV system 

96
(58%), followed by the operating manual for the PV system (54%), the Internet (27%), and the 

video on operations and maintenance (23%). 

These findings and others suggest that it was not easy for homeowners to get information about 

their PV systems. Respondents in this study contacted researchers at NREL to request more 

information about their systems and how they operated. This occurred frequently enough that 

eventually the research staff sent a letter to the PV owners listing the names and contact 

information of organizations (such as SheaHomes customer service and AstroPower, Inc.) that PV 

owners could contact for reliable PV system information. 

Qualitative field work also made it clear that no information on owning and operating a PV 

system was made available at “Shea University” classes. These classes, held in neighborhood 

garages on Saturday mornings, were designed to orient new SheaHomes buyers to their new 

homes and how they operated. Based on researcher observation and questioning, the instructors at 

Shea University were not trained in the energy features of the high-performance homes; therefore, 

they did not mention them in the Saturday morning orientation sessions. 

Informed Decision-Making about PV Ownership and Upgrades? 

PV home purchasers were, on average, not more informed than non-PV home purchasers about 

solar PV systems. One hundred sixty-four buyers of SheaHomes had the opportunity to make 

decisions about PV systems.3 If homes came with 1.2-kW systems standard, the buyers 

supposedly had the option of upgrading to a 2.4-kW system. If the homes were PV-eligible, the 

buyers supposedly had the option of installing a 1.2 or a 2.4 PV system. The study explored how 

well informed the SheaHomes buyers felt they were in making the PV purchase decisions. 

The question asked was: “How well informed did you feel you were about the solar PV system at 

the time of purchase? Did you know enough to make an informed decision about adding one to 

your home or upgrading the one that came with your home?” The 1-to-10 response scale was 

used, with 1=Not at all informed and 10=Very informed. Interestingly, there is no significant 

difference among the various categories of SheaHomes buyers in their assessment of how 

informed they were about energy features when shopping for their homes. 

Among these categories of homes, the mean score for PV owners is 5.9 and for main CNAs is 

6.66, not significantly different. A slightly, but not significantly, higher percentage (63%) of main 

CNAs rate their knowledge levels about PV as 7 to 10 on the 10-point scale. Only 43% of PV 

owners assign a rating this high. 

3

As discussed in detail in Chapter 4 (The SheaHomes Experience), of 306 homes, 46 were PV-ineligible; 96 came 

with 1.2-kW systems, the buyers of which could choose to upgrade to 2.4-kW systems; and 76 were main homes the 

buyers of which were offered PV systems and could elect to install 1.2-kW or 2.4-kW systems. 

97 
To speculate, it seems that at least a percentage of main owners4

 felt more confident in deciding 

not to purchase PV than certain PV owners felt about purchasing a home with a system. In fact, 

qualitative data show that a few of the PV owners were “unwitting adopters”—that is, they bought 

their homes unaware they came with PV systems. 

For responses from main owners only, a t-test was run on the mean rating for level of knowledge 

on energy efficiency and solar energy between main CNAs and those who do not recall being 

offered a PV system on their homes. No significant differences are found between these two 

groups (mean scores=5.69 and 5.77, respectively) at the time of the home purchase decision. 

What Has Been Learned about Owning PV Systems? 

Although most PV owners have learned a good deal about their utility savings, tax credits, and 

how their systems work, they still do not understand the payback period. PV owners were asked: 

“Since living in your home, have you learned about the following aspects of solar PV?” A 

checklist of six potential characteristics that owners might have learned about was provided. 

Responses from 64 PV owners are as follows: 

• Savings on the utility bill (69%) 

• Tax credits or rebates to help offset the cost (61%) 

• How the system works (61%) 

• Net metering and interconnecting with the utility grid (48%) 

• Amount or percentage of your electricity use the solar PV system produces (48%) 

• Payback period for solar PV system purchase (17%). 

Summary 

SheaHomes and comparison homebuyers learned about their homes in similar ways, although a 

higher percentage of SheaHomes buyers than comparison buyers saw physical signs of the new 

development, and a higher percentage of comparison buyers than SheaHomes buyers heard about 

their new homes from real estate agents or brokers. A higher percentage of SheaHomes buyers 

(and especially of PV owners) than comparison homebuyers relied more heavily on word-ofmouth and personal contacts about the new homes and about solar technologies. 

Large majorities of both categories of homebuyers give positive ratings to the builder staff on 

providing them with adequate information for their home purchase decisions. SheaHomes buyers 

give significantly higher ratings than comparison buyers on the responsiveness of staff to 

problems with the houses after move-in. A majority of SheaHomes buyers tend to be quite 

satisfied with the information on the energy features of their homes provided by the SheaHomes 

staff. 

4

This analysis remains to be done for CNAs. 

98 
The SheaHomes buyers, on average, give significantly higher ratings than do comparison buyers 

to their knowledge levels on energy efficiency and solar features when they were shopping for 

their new homes. However, when average ratings of PV and non-PV owners were compared on 

this point, no significant differences emerge. However, after having lived in their homes for at 

least six months, a significantly higher percentage of PV owners than non-PV owners indicate 

they are informed or very informed about energy efficiency and solar energy features. Thus, living 

in a PV home is found to increase knowledge about these features. 

When they were buying their homes, a majority of PV homebuyers relied on the SheaHomes sales 

staff as the single most important source of information on PV systems. Although some 

information on PV systems was available through a fact sheet, an operating manual on PV 

systems, Web sites, and a video on operations and maintenance, it was not easy for PV owners to 

locate and obtain information on their PV systems. The PV system manufacturer did not provide 

follow-through to make certain that PV systems had been installed, were working properly, and 

that their owners knew how to use and maintain them. Also, although SheaHomes oriented their 

new homeowners, the company did not follow through in educating new PV owners about their 

systems. 

Majorities of PV owners indicate that, since living with their PV systems, they have learned about 

savings on their utility bills, tax credits, or rebates to help offset PV system costs, and how the PV 

system works. Pluralities of nearly half indicate that they have learned about net metering and 

interconnectivity issues and about the performance of their PV systems in producing electricity. 

Only 17% of PV owners indicate that they understand the payback period for PV system 

purchases. 

99
Chapter 10

Homebuyer Satisfaction with the Purchased Home

Introduction 

There is every reason to believe that, because of 

cognitive dissonance,1

 homebuyers could be 

inclined to say they are satisfied with their new 

home purchases. Studies of buyer satisfaction are 

known to be fraught with difficulties in 

measurement and interpretation. In this instance, 

homebuyers had lived in their homes for at least 

6 months, so the “honeymoon” could have worn 

off. The measures of satisfaction used in this 

study tended to be straightforward. 

Findings are discussed on whether homebuyers 

would purchase their homes all over again, the 

perceived comfort and perceived energy 

efficiency of the new homes, satisfaction with 

key aspects of the homes (such as quality of 

construction), and features liked best and least 

once buyers had lived in their homes for a time. 

Would Homebuyers Buy Their Homes 

Again? 

For example, the homeowners were asked to 

what extent they agreed with the statement: “We 

would buy our same house again if we had it to 

do over.” The intent of this question was to distill 

the essence of all the pluses and minuses of home 

ownership. SheaHomes owners significantly 

more frequently than comparison owners indicate 

they would buy their homes again. 

Table 30 summarizes the responses. Although a 

high percentage of both groups tend to agree or 

strongly agree that they would buy their homes 

Chapter Highlights 

C Owners of SheaHomes (77%) are significantly 

more likely to agree that they would buy the 

same house over again than are owners of 

comparison homes (67%). 

C Both SheaHomes and comparison homebuyers 

find their homes to be comfortable. 

C Owners of SheaHomes significantly more often 

rate their homes, on average, as more energy 

efficient than do owners of comparison homes. 

C Both SheaHomes and comparison owners are 

satisfied with their homes’ investment potential, 

location, size, and layout, but SheaHomes 

respondents are more satisfied with lot size, 

builder reputation, storage space, and quality of 

construction. 

C Non-PV owners give significantly higher mean 

satisfaction ratings to location, size of home, lot 

size, layout, and storage space than do PV 

owners. This bears out a pattern of findings that 

these characteristics were significantly more 

important to non-PV buyers than to PV buyers in 

the home purchase decision. PV owners give 

significantly higher mean satisfaction ratings to 

energy features than do non-PV owners. 

C Two-thirds of PV owners have bragged to others 

about their utility bills compared with 26% of 

non-PV owners—a statistically significant 

difference. 

C PV owners are significantly more satisfied with 

their utility bills than are non-PV owners. 

again, the percentage is significantly higher for SheaHomes (77%) than for comparison homebuyers 

(76%) (P2=6.061; p=.048). Fifteen percent of the comparison homebuyers disagreed that they would 

buy their homes again, compared with 5% of SheaHomes buyers. 

1

The basic principle of cognitive dissonance is that buyers are psychologically inclined to remain favorable to their 

major purchase decisions once they are made to reduce the emotional discomfort resulting from the thought of 

having made the wrong decision. However, this concept fails to account for the phenomenon of regret. 

100 
Table 30. Percentage Comparison by SheaHomes and Comparison

Homeowners with Regard to Repeat Purchase of the Same Home

Responses 

Respondents from 

SheaHomes 

Communities (n=174) 

Respondents from 

Comparison Community 

(n=52) 

Total 

(n=226) 

Agree or strongly agree 77% 67% 75% 

Unsure/neutral 18% 17% 18% 

Disagree or strongly disagree 5% 15% 7%

 Totals 100% 99%* 100% 

*Does not add to 100% because of rounding 

The responses to this question were also analyzed by comparing the responses of SheaHomes 

owners with and without PV systems on their homes, but the responses are not significantly 

different. As the data in Table 31 show, very high percentages of both categories agree or 

strongly agree that they would purchase their homes again: 83% of PV owners and 73% of nonPV owners.

2 

Table 31. Percentage Comparison for the Two Respondent Groups with

Regard to Repeat Purchase of the Same Home

Response 

SheaHomes Respondents 

Owning PV Homes 

(n=71) 

SheaHomes 

Respondents Not 

Owning PV Homes 

(n=103) 

Total 

(n=174) 

Agree or strongly agree 83% 73% 77% 

Unsure/neutral 13% 21% 18% 

Disagree or strongly disagree 4% 6% 5%

 Totals 100% 100% 100% 

Perceived Comfort of New Homes 

Homeowners tend to rate their homes as comfortable, regardless of category. In addition, 

respondents were asked to rate the overall comfort of their homes on a 1 to 10 scale, with 1 being 

“Not at all comfortable” and 10 being “Very comfortable.” The mean score for SheaHomes 

respondents is 8.31 and for the comparison respondents is 8.40. The difference is not statistically 

significant. This suggests that both groups believe their homes to be comfortable. Similarly, the 

mean score for PV owners is 8.24 and for non-PV owners is 8.36, a difference that is not 

statistically significant. Both PV and non-PV owners rate their homes as quite comfortable. 

2

These analyses remain to be done for main CNAs. Non-PV owners include all categories of SheaHomes 

households except PV owners. 

101 
Perceived Energy Efficiency of New Homes 

Perceived energy efficiency of the new homes varies by homeowner category. Respondents were 

also asked to rate the overall energy efficiency of their new homes on a 1 to 10 scale, with 1 

being “Not at all energy efficient” and 10 being “Very energy efficient.” The mean score for 

SheaHomes respondents is 7.35 and for the comparison respondents is 6.31, a statistically 

significant difference (t=3.787, p#.000). The mean score for PV owners is 7.48 and for non-PV 

owners is 7.26, a difference that is not significant. The mean score for owners of home with PV 

systems is 7.48 and for non-PV owners is 7.26, a difference that also is not significant. 

A significantly higher percentage of SheaHomes owners rate their homes as energy efficient 

(77%) than comparison homeowners (50%) (P2=19.763; p=.001). In addition, a significantly 

higher percentage of PV owners rate their homes as energy efficient (84%) than do non-PV 

owners (65%) (P2=17.644; p=.001). 

Satisfaction with Key Aspects of the New Homes 

Overall, based on the following data, the owners of SheaHomes are more satisfied with their 

homes than the comparison owners are with theirs. Respondents in both communities were asked 

to rate nine key aspects of their homes: “Please rate your satisfaction with each of the following 

now that you have lived in your home for a while,” with 1 being “Not at all satisfied” and 5 being 

“Very satisfied.” The aspects listed were location, investment potential, reputation of builder, 

size/square footage, layout/floor plan, storage space, lot size/yard, quality of construction, and 

number of thermostats. Table 32 summarizes the responses to this question by categories of 

homeowners. 

In rating these aspects, the SheaHomes owners tend to give significantly higher average ratings 

than the comparison homeowners do on four of the nine features. Their average ratings of 

another four aspects are also more favorable than those of comparison community owners, but 

not significantly so. With one dimension of the homes—“Layout/floor plan”—the two groups of 

homeowners are equally satisfied, on average. 

Investment potential is the highest rated aspect among SheaHomes respondents, whereas location 

is the aspect rated most highly by comparison respondents. “Lot size/yard,” “Builder reputation,” 

“Storage space,” and “Quality of construction” are aspects with which owners of SheaHomes are 

significantly more satisfied than are their comparison counterparts. 

The satisfaction ratings were also analyzed by PV ownership. In addition to the key aspects listed 

in Table 32, owners of SheaHomes with PV systems, main homeowners, and ineligible/early 

homeowners were asked about the package of energy features. Owners of homes with PV 

systems were also asked about their satisfaction with net metering. Table 33 summarizes the 

findings. The non-PV owners (main, ineligible, and early homeowners) tend to be more satisfied 

than the PV owners on nine of the 10 features asked about; their average ratings are significantly 

higher than PV owners for four of these nine features. On a fifth of these nine features, their 

102
higher average rating nears statistical significance. On only one feature—the package of energy 

features of the home—do PV owners give a higher average rating (mean=4.06) than do the nonPV owners (3.83), yet this difference is not statistically significant. 

Table 32. Satisfaction with Key Aspects of the Home by

SheaHomes and Comparison Homeowners**

Aspect of Home 

Mean Score, 

SheaHomes 

Respondents 

Mean Score, 

Comparison 

Respondents 

% Very Satisfied 

SheaHomes 

Respondents 

% Very Satisfied 

Comparison 

Respondents 

Investment potential 4.67 4.55 71 66 

Location 4.62 4.74 66 76 

Size/square footage 4.38 4.23 51 43 

Layout/floor plan 4.09 4.11 36 36 

*Lot size/yard 

(t=2.479; p=.015) 

(P2=10.296; p=.036) 

4.08 3.70 34 23 

*Builder reputation 

(t=4.627; p#.000) 

(P2=22.008; p#.000) 

3.90 3.13 31 9 

*Storage space 

(t=2.472; p=.014) 

(P2=9.316; p=.054) 

3.84 3.47 27 15 

*Quality of 

construction 

(t=5.009; p#,000) 

(P2=23.645; p#.000) 

3.73 2.94 19 2 

Number of thermostats 3.51 3.17 21 11 

*Statistically significant difference 

**Ordered by the mean scores of the SheaHomes respondents 

It is difficult to interpret why the non-PV owners give significantly higher mean satisfaction 

ratings to so many more home features than do PV owners. However, the findings bear out a 

pattern that these characteristics and features were significantly more important to non-PV buyers 

than PV buyers in the home purchase decision. Thus, they may contribute more to non-PV 

owners satisfaction than they do to PV owner satisfaction with their homes. Answers to these 

conjectures must await further research. However, it is fair to say that PV owners rate their 

satisfaction with their package of energy features, on average, higher than do the non-PV owners; 

77% of them, compared with 67% of the non-PV owners, indicate they are satisfied or very 

satisfied with the package of energy features on their homes. Again, this is consistent with PV 

owners rating the package of energy features and the PV systems as more important in their 

purchase decision than did the non-PV owners. 

103
Table 33. Satisfaction with Key Aspects of the Home by PV System Ownership*** 

Aspect of Home 

Mean Score, 

PV Owners 

(n=70) 

Mean Score, 

Non-PV 

Owners 

(n=103) 

% Satisfied/ 

Very 

Satisfied 

PV Owners 

% Satisfied/ 

Very Satisfied 

Non-PV Owners 

Investment potential 4.63 4.70 31/66 22/74 

*Location 

(t= !2.146; p=.034) (P2 

nears statistical significance; 

P2 =7.518; p=.057) 

4.50 4.70 39/56 24/74 

**Size/square footage 

(t= !3.098; p=.002) 

(P2 =9.52; p=.009) 

4.19 4.51 44/37 31/60 

Package of energy features 4.06 3.83 44/33 44/23 

**Lot size/yard 

(t= !2.165; p=.032) 

(P2 n.s.) 

3.91 4.18 47/24 42/40 

**Layout/floor plan 

(t= !3.575; p=.000) 

P2 =14.017; p=.007) 

3.81 4.28 48/22 40/46 

PV owners only: 

Net metering 3.76 – 32/27 – 

Builder reputation 3.73 4.02 41/24 41/36 

Quality of construction 3.61 3.81 46/15 52/21 

**Storage space 

(t= !3.084; p=.002) 

(P2=13.083; p=.011) 

3.59 4.02 43/13 37/36 

Number of thermostats 3.46 3.54 32/20 36/22 

*P2 test nears statistically significance; t-test is statistically significant 

**Statistically significant difference 

***Ordered by the mean scores of the PV owners 

104
Features Liked Best 

Each of the four types of questionnaires asked respondents: “What three or four things do you 

like best about your new home?” with an open-ended item. The written responses were coded 

into seven categories; a total of 530 comments were received. The comments focused on (1) 

layout of the house, (2) location, (3) energy efficiency and solar energy features, (4) outdoor 

aesthetics, (5) quality of construction, (6) a safe and secure neighborhood, and (7) financial 

investment. Table 34 summarizes the distribution of the three responses mentioned first in order 

of frequency of mention, comparing the responses of the SheaHomes respondents with those of 

the comparison respondents and, within the SheaHomes buyers, the PV owners with the non-PV 

owners. 

Table 34. Home Features Liked Best (open-ended) 

Features Liked Best 

Percentage of First Three Comments Mentioned 

SheaHomes 

Owners % 

(n=390) 

Comparison 

Owners % 

(n=140) 

SheaHomes 

PV Owners 

% (n=157) 

SheaHomes 

Non-PV 

Owners % 

(n=225) 

Layout and features (kitchen 

layout, size, one-story, 5th bedroom, 

hook-ups, ceiling, doors, light) 

44 41 39 48 

Location (view, proximity to 

important places, neighborhood, 

close to freeway, schools, area, 

weather, community) 

32 41 32 31 

Energy efficiency/solar features 

(low utility bill, windows) 8 0 15 5 

Outdoor aesthetics (exterior 

design, landscaping, modern, yard 

space)

 4 6 4 4 

Construction quality (appliances, 

kitchen features, engineering, new, 

low maintenance)

 5 1 3 7 

Safe/secure feeling (comfort, quiet, 

privacy, neighbors, clean) 3 5 3 3 

Financial aspects (value; no Mello 

Roos; investment) 3 6 4 1 

 Totals 99* 100 100 99* 

*Does not add to 100 because of rounding 

105
The distribution of responses is very similar between the SheaHomes and comparison 

homebuyers, with a few differences. The home layout is most frequently cited as a reason for 

liking the home (44% of the top three reasons given by SheaHomes respondents and 41% by the 

comparison respondents). The comparison responses (41%) more frequently concern the 

desirable location as a source of satisfaction than the SheaHomes responses (32%). However, the 

SheaHomes responses included mention of liking energy efficiency and solar features (8%), 

whereas none of the comparison responses reference energy features. Quality of construction is a 

more frequently cited source of satisfaction (5%) in the SheaHomes responses than in the 

comparison responses. 

Among PV and non-PV responses (all SheaHomes owners), 48% of the non-PV responses 

mentioned an aspect of the home’s layout, compared with 39% of the PV responses. As might be 

expected, the positive responses of PV owners more frequently dealt with energy features (15%) 

than did the positive responses of non-PV owners (5%). 

Features Liked Least 

Following the question about the three or four things respondents like best about their new 

homes, each of the four types of questionnaires asked an open-ended question: “Is there anything 

you are unhappy about? What do you like least?” A total of 299 comments were received, 

markedly fewer than the number of comments on features liked best. The volunteered responses 

were coded into eight categories. The comments focused on (1) quality of construction, (2) floor 

plan/layout, (3) outdoor aesthetics, (4) neighborhood issues, (5) customer service, (6) comfort, 

(7) energy efficiency and solar energy features, and (8) other complaints. Table 35 summarizes 

the distribution of the three responses listed first in order of frequency of mention, comparing the 

responses of the SheaHomes buyers with those of the comparison buyers and, within the 

SheaHomes buyers, the owners of PV systems with the non-PV system owners. 

The pattern of response was similar by SheaHomes and comparison homeowners regarding 

complaints about construction quality, comfort, and other issues. Differences in response 

occurred among five of the complaint types, about half more concerning to SheaHomes owners 

and the other half more concerning to comparison owners. 

More frequently mentioned among SheaHomes owners (19% of comments) are complaints that 

rooms and closets are too small, compared with 10% of comparison owner comments. Fifteen 

percent of SheaHomes comments, compared with 10% of comparison comments, mention 

complaints about lack of views, driveway layouts, and small backyards. More than one in five of 

comparison comments (22%) complain about noise, traffic, and unfriendly neighbors, compared 

with 9% of SheaHomes comments. Comparison comments also complain about poor customer 

service on the part of their builder (13%), compared with 5% of SheaHomes comments. 

106
Table 35. Home Features Liked Least (open-ended) 

Features Liked Least 

Percentage of First Three Comments Mentioned 

Shea Homes 

Respondents

 % 

(n=219) 

Comparison 

Respondents

 % 

(n=80) 

Shea Homes 

PV Owners 

% 

(n=95) 

Shea Homes 

Non-PV 

Owners 

% (n=117) 

Construction quality (bad products, 

quality of plumbing, windows, low 

flow cheap toilet, cheap equipment, 

bathtubs, drawers broke, hot water 

takes too long reaching faucets, 

wanted vaulted ceilings, walls rough) 

33 35 29 39 

Floor plan/layout (closets, room 

sizes, garage too small) 19 10 23 13 

Outdoor aesthetics (no view, sandy 

ground, garage layout/driveway 

layout, houses too close, can’t 

expand, backyard too small) 

15 10 18 13 

Neighborhood issues (noise, traffic, 

unfriendly neighbors) 9 22 8 9 

Customer service 5 13 7 3 

Comfort (too cool only one 

thermostat, location of thermostats, 

rooms cold in winter) 

9 4 7 11 

Energy efficiency/solar features 

(not enough insulation, house did not 

come with energy-efficient 

appliances, light fixtures didn’t come 

with CFLs, we don’t have solar 

packages, solar unit didn’t work for 6 

months, want to monitor each panel, 

high electric bill) 

9 4 5 9 

Other (CFLs, lots of options, wind at 

night, schools, costs, taxes) 2 3 1 3

 Totals 101* 101* 98* 100 

*Does not add to 100 because of rounding 

107
Interestingly, 9% of SheaHomes complaints concern energy features, including complaints that 

not enough was done with energy efficiency, compared with 4% of comparison comments. 

Comments show that some homeowners wanted, but did not receive, compact flourescent lights 

(CFLs) in their lighting fixtures, solar packages, sufficient insulation, and energy-efficient 

appliances. Other comments involved complaints about high electricity bills (which came from 

both groups) and a non-working PV system (obviously a SheaHomes owner comment). 

When comments are contrasted between PV and non-PV owners within the SheaHomes 

communities, 39% of the non-PV owner complaints deal with construction quality, compared 

with 29% of the PV owner comments. Nearly a quarter of PV owner complaints deal with floor 

plan or layout issues, compared with 13% of non-PV owner complaints. More non-PV owner 

complaints (11%) address comfort issues than do PV owner complaints (7%). Finally, more nonPV owner comments (9%) concern energy issues than do PV owner complaints (5%). These 

differences, however, are not statistically significant. 

Bragging about Energy Features of the Homes 

During the qualitative interviews, owners of SheaHomes had mentioned that they bragged to 

friends and family about various energy features in their new homes. Therefore, as another 

measure of satisfaction with the energy features of their new homes, the following survey 

question was included: “Have you ever bragged about your home’s energy features to friends and 

acquaintances or shown them to visitors to your home?” Results are summarized in Table 36. 

Table 36. Bragging about Energy Features by PV Ownership 

Bragged about ... 

Percentages of 

PV Owners 

(n=68) 

Percentages of 

Non-PV Owners 

(n=94) 

Totals 

% 

Solar PV system (PV owners only) 84% – – 

Solar water heating 71% 64% 67% 

*Lower utility bills 

(P2=29.218; p#.000) 69% 26% 45% 

Home’s comfort 62% 62% 62% 

Digital readout for solar PV 

(PV owners only) 56% – – 

*Low-e glass in windows 

(P2=4.317; p=.038) 33% 50% 43% 

*Statistically significant difference 

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The data indicate that a significantly higher percentage (69%) of owners of homes with solar PV 

systems have bragged about their utility bills. Only 26% of owners of homes without solar PV 

systems have done so. One PV homeowner commented: “I pay less than my friends. I have a 

bigger home by at least 1,000 square feet and a large family of six. They have a family of four.” 

On the other hand, a significantly higher percentage (50%) of owners of homes without solar PV 

systems report that they have bragged about their spectrally selective windows, suggesting that 

non-PV owners are concerned about energy efficiency. Only 33% of owners of homes with solar 

PV have done so. With regard to other related characteristics, the difference is less clear cut. 

There appears to be no significant difference in the percentages of PV and non-PV homeowners 

who say they have bragged about solar water heating or the overall comfort of the home. 

Based on reported “bragging rights,” homeowners with solar PV systems seem to be enthusiastic 

about them. Of those responding, 84% report having bragged about their PV systems and more 

than half (56%) say they have bragged about the digital readout that shows their kWh 

consumption and production in real time. 

Role of Energy Efficiency and Solar Features in Future Home Purchase Decisions 

Another way that satisfaction with the new home purchase was measured was to ask about the 

role that energy features would play in a hypothetical future home purchase decision. All 

respondents were asked: “To what extent do you agree with each of the following statements?” 

and presented with a 5-point Likert scale from “Strongly disagree” to “Strongly agree” for each 

statement: 

• If we buy another new home, it will be a very energy-efficient home. 

• If we buy another new home, it will have solar water heating. 

• If we buy another new home, it will have solar PV. 

SheaHomes owners (85%) agree or strongly agree significantly more frequently than do 

comparison owners (60%) that they would purchase an energy-efficient home in the future 

(P2=14.966; p=.001). Similarly, two-thirds of SheaHomes owners agree or strongly agree that 

they would purchase solar water heating in a new home compared with 22% of the comparison 

owners (P2= 34.532; p#.000). In addition, 54% of the SheaHomes owners agree or strongly 

agree that they would buy solar PV in a new home, compared with 28% of comparison 

respondents (P2=12.108; p=.002). 

Responses of PV system owners were compared with those of non-PV owners on these 

questions, resulting in no significant differences, except that PV owners (63%) are significantly 

more likely than non-PV owners (41%) to state that they would buy solar PV in a new home. 

109
Satisfaction with Utility Bill Savings 

SheaHomes respondents were asked about the extent to which they agreed with the following 

statement: “The savings on our utility bills have met or exceeded our expectations.” Thirty-seven 

percent respond agree or strongly agree, 42% are unsure, and 21% respond disagree or strongly 

disagree. These results do not represent a ringing endorsement for the perceived efficacy of the 

homes from the standpoint of energy savings, particularly in light of the findings described 

above, and may reflect (1) a lack of knowledge about potential energy savings on the part of new 

homebuyers, (2) ineffective communication about potential savings on the part of sales staff, or 

(3) even possibly a disconnect about the intent or implications of the question since many of the 

homebuyers likely “moved up” to larger houses in which they might have expected higher overall 

energy costs than they experienced in their previous homes. In any case, there is little reason to 

believe owners knew what to expect in terms of utility bill performance. 

More PV owners are convinced that their solar energy systems (both PV and solar water heating) 

are saving them money on their utility bills than are non-PV owners. On this point, it is 

informative to consider the differences in answers from respondents whose homes include PV 

and those whose homes do not. Table 37 summarizes the results. Among the SheaHomes 

respondents who are also PV owners, 52% agree or strongly agree with the statement: “The 

savings on our utility bills have met or exceeded our expectations,” 32% are neutral or unsure, 

and 16% disagree or strongly disagree. Among the SheaHomes respondents whose homes do not 

include PV, 28% respond that they agree or strongly agree with the statement, 47% are unsure, 

and 25% respond that they disagree or strongly disagree. There is a statistically significant 

difference in the two distributions of percentages (P2

=10.324; p=.006), suggesting that the PV 

owners experience greater satisfaction that their expectations for energy savings were met or 

even exceeded. Further analysis underscores this finding. A t-test shows that PV owners gave a 

3.39 average positive rating on the 5-point satisfaction scale relative to satisfaction with utility 

cost savings, compared with a 2.97 average rating in the neutral range given by non-PV owners. 

This difference in mean satisfaction ratings is significant (t=2.791; p=.006). 

Table 37. Percentage Comparison of Responses from PV Owners and Non-PV Owners 

by Their Satisfaction with Savings on Their Utility Bills 

Response 

All SheaHomes 

Respondents 

(n=166) 

SheaHomes 

Respondents 

with PV (n=69) 

SheaHomes 

Respondents 

without PV (n=97) 

Agree/strongly agree 38% 52% 28% 

Unsure 41% 32% 47% 

Disagree/strongly disagree 21% 16% 25% 

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In addition, PV owners were asked the extent to which they agreed with the statement “Our 

electricity bills are lower than they would have been without our solar PV system.” Sixty percent 

of the PV owners agree or strongly agree that their electricity bills are lower than they would 

have been without their PV systems; 17% are unsure; and 23% disagree. 

Almost all SheaHomes respondents have homes with solar water heating systems (all but the first 

13 homes, as previously noted). Because these homes use natural gas for water heating, a key 

study question was whether SheaHomes owners perceive a difference in their natural gas bills 

that they attribute to their solar water heating systems. 

Because PV owners are billed separately for electricity and natural gas, it was possible to ask 

them directly about the extent to which they agreed or disagreed with the following statement: 

“Our natural gas bills are lower than they would have been without our solar water heating 

system.” SheaHomes respondents who are non-PV owners were asked about the extent to which 

they agreed or disagreed with the following statement: “Our utility bills are lower than they 

would have been without our solar water heating system.” The results are summarized in Table 

38. 

Table 38. Percentage Comparison of Responses from PV and Non-PV Homeowners in the

SheaHomes Communities by the Perceived Impact

of Solar Water Heating Systems on Utility Bills

Item 

SheaHomes Owners 

with PV Systems (n=70) 

SheaHomes Owners without 

PV Systems (n=97) 

Our natural gas bills are lower 

than they would have been 

without our solar water heating 

system 

49%: Strongly agree/agree 

30%: Unsure 

21%: Strongly disagree/

 disagree 

N/A 

Our utility bills are lower than 

they would have been without 

our solar water heating system N/A 

51%: Strongly agree/agree 

32%: Unsure 

17%: Strongly disagree/ 

disagree 

Of the PV owners, a plurality (49%) agree or strongly agree that their natural gas bills are lower 

than they would have been without the solar water heating systems, whereas 21% disagree or 

strongly disagree, and 30% say they are unsure. Of the respondents who do not have PV 

installations on their homes, a majority (51%) agree or strongly agree that their utility bills are 

lower than they would have been without the solar water heating systems. About 17% disagree or 

strongly disagree, whereas 32% are unsure. These results suggest that most homeowners in the 

SheaHomes communities with solar water heating systems believe they contribute to lower 

energy costs, although nearly one-third are unsure and nearly one in five disagree. 

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Summary 

The most striking pattern of findings on the various measures of homeowner satisfaction used in 

the study is that, where statistically significant differences occur between SheaHomes and 

comparison homebuyers, SheaHomes buyers are consistently more favorable toward their 

experience than are comparison homebuyers. 

Owners of SheaHomes (77%) are significantly more likely to agree that they would buy the same 

house if they had it to do over again than are owners of comparison homes (67%). Although both 

categories of homebuyers rate their homes as comfortable, SheaHomes buyers (77%) are 

significantly more likely to rate their homes as energy efficient than are comparison buyers 

(50%). PV owners significantly more frequently rate their homes as energy efficient (84%) than 

do non-PV owners (65%). 

Both SheaHomes and comparison respondents are satisfied with their homes’ investment 

potential, location, size and layout; however, SheaHomes respondents are significantly more 

satisfied than comparison respondents with their homes’ lot sizes, builder reputation, quality of 

construction, and storage space. Non-PV owners are more satisfied than PV owners on these 

same characteristics, which were more important to them than to PV owners at the time of 

purchase. Conversely, PV owners are more satisfied than non-PV owners with their homes’ 

energy characteristics; again, these were more important to them at the time of purchase decision. 

Favorite home features among all categories of respondents are layout, certain features (such as 

size, extra bedroom, and the one-story option), and location. Least favored are problems 

involving quality of construction issues, storage space, and outdoor aesthetics—these seem to be 

similar among all categories of respondents. 

PV owners are particularly likely to enjoy their “bragging rights” with respect to utility 

bills—more so than do non-PV owners. Eighty-four percent of PV owners report that they have 

bragged to others about their PV systems, and 56% say they have bragged about their digital 

readouts showing their kWh consumption and production in real time. Seventy-one percent of 

PV owners report that they have bragged about their solar water heating systems compared with 

64% of non-PV owners. 

Another way satisfaction was measured was to ask whether energy efficiency and solar features 

would be important to the homeowners in future home purchase decisions. Owners of 

SheaHomes are significantly more likely than owners of comparison homes to agree that they 

would buy a home with energy efficiency and solar energy features in the future. 

Relative to utility bill savings, owners of SheaHomes do not seem to know what to expect from 

their homes’ performance. Findings seem to reflect a lack of knowledge about potential energy 

savings or that respondents may not have known what to expect because they were moving to 

larger homes. In any event, only 37% indicate utility bills have met their expectations, 42% are 

unsure, and 21% indicate utility bills have not met their expectations. 

112
On the other hand, a majority of PV owners (52%) indicate that their expectations for utility bills 

have been met, compared with 28% of non-PV owners. Clearly, the presence of PV makes a 

significant difference in perception of utility bill savings.3 Sixty percent of PV owners agree that 

their electricity bills are lower than they would have been without their PV systems, and 49% 

agree that their natural gas bills are lower than they would have been without their solar water 

heating systems. Among non-PV owners, 51% agree that their utility bills are lower than they 

would have been without their solar water heating systems. 

3

Perceived and actual utility bills are discussed in Chapter 22 (Perceived and Actual Utility Bills). 

113 
Chapter 11

Experiences with PV Systems

Introduction 

PV owners are experiencing an innovative Chapter Highlights 

technology in their homes. Ways of 

measuring their reactions to this experience C Most of the PV owners are, in general, satisfied 

with their PV systems, giving them a mean rating of include asking about the benefits they 7.66 on a 10-point scale. perceive in owning homes with PV systems, 

inquiring how happy they are with their C Perceived benefits of owning a PV system are 

systems and why, and the attributes owners reduced electricity bills, increased resale value, 

environmental benefits, and feeling good about it. believe are important. The role of feedback 

through the use of the digital display on C The most important attribute of PV systems is the electricity production and consumption was amount of electricity they produce; also important 

also investigated. Finally, owner knowledge are ease of maintenance and length of warranty. 

of how their systems interact with the utility C A majority of PV owners consult their digital grid was also tested. displays on electricity production and consumption 

at least once a week; 15% do so daily. Demographic Differences between PV 

Owners and Non-PV Owners C PV owners are not highly knowledgeable about the 

operational aspects of their systems and are unsure 

Among SheaHomes owners, responding if they pay their electricity bills monthly or 

annually. heads of households of PV owners are 

significantly more likely to be male than are C PV owners need more information and education 

heads of non-PV households. Two-thirds of about their PV systems. 

heads of PV households are male whereas 

50% of non-PV households are male 

(P2=5.9; p=.015). This finding suggests that, 

among PV households, the male head of household was more likely to complete the questionnaire 

than was the case among the non-PV households. To speculate, this is probably because PV 

ownership is viewed as a technical topic about which men might be expected to know more than 

women. 

Perceived Benefits of Owning a PV System 

PV owner respondents were asked: “Listed below are some statements concerning the benefits of 

having a solar PV system on your high-performance home. Please indicate the extent to which 

you agree with each statement. Our solar PV system: . . .” Fifteen potential benefits of PV 

ownership were presented. Respondents were asked to respond on a Likert scale from 1=Strongly 

disagree to 5=Strongly agree. Findings are summarized in Table 39. The list of benefits was 

derived from qualitative interviews and prior research (cf., for example, Farhar and Coburn 

2000). 

114
A majority of the PV owner respondents agree with all but one of the statements about potential 

benefits. Most frequently agreed upon (by more than 80%) are that the PV system “Reduces 

electricity bills,” “Increases our home’s resale value,” and “Allows us to help the environment.” 

Table 39. Benefits of PV Ownership Perceived by PV Owners (n=71) 

Perceived Benefits 

% Agree/ 

Strongly 

Agree 

% Neutral/ 

Unsure 

% Disagree/ 

Strongly 

Disagree 

Reduces electricity bills 83.1 12.7 4.2 

Increases our home’s resale value 83.1 15.5 1.4 

Allows us to help the environment 80.3 16.9 2.8 

Helps conserve natural resources 78.9 18.3 1.4 

Allows us to increase our awareness of our 

household’s energy use 73.9 20.3 5.8 

Helps to benefit future generations 73.2 23.9 2.8 

Makes us feel good to have it 67.6 25.4 7.0 

Helps improve air quality in our area 66.2 29.6 4.2 

Allows us to sell excess electricity back to the 

utility company 60.6 25.4 14.1 

Provides us an opportunity to be technologically 

innovative 59.2 33.8 7.0 

Increases our self-sufficiency 58.6 34.3 7.1 

Helps San Diego’s economy 58.0 33.3 8.7 

Helps reduce global warming 56.3 31.0 12.7 

Protects us against rising electricity costs 50.7 35.2 14.1 

Provides free electricity once our system is paid for 49.3 31.9 18.8 

How Happy PV Owners Are with Their Systems 

PV homeowners were asked: “How happy are you with your solar PV system?” Responses were 

requested on a 1-to-10 scale, where 1=Not at all happy and 10=Very happy. The mean response 

from 68 PV homeowners is 7.66; thus, on average, PV homeowners appear to be reasonably 

happy with their systems. More than three-quarters of PV homeowners indicate that they are 

happy with their systems; approximately 8% indicate that they are unhappy with them, and 15% 

indicate they are in the neutral range about the PV systems. 

115
The PV respondents were asked two open-ended questions: “What have been the best things 

about having a solar PV system?” and “What drawbacks, if any, have there been to having a solar 

PV system?” Volunteered responses were coded. 

PV owners gave 58 reasons for liking their systems. The most frequently mentioned of these can 

be classified as financial in nature. The positive aspects of PV ownership mentioned are as 

follows: 

• Savings on utility bills – 74% of the reasons given 

• Feedback on electricity consumption – 7% of the reasons 

• Protects environment – 7% 

• Self-sufficiency – 7% 

• Investment – 3% 

• Ease of maintenance – 3% 

• Enjoy technology – 2%. 

More than half (55%) of the PV owners say there are no drawbacks to owning a PV system. The 

remaining PV owners mentioned 19 drawbacks. The most frequently mentioned drawbacks 

appear to pertain to wishing the system were producing more electricity than is apparent to the 

owners. The drawbacks given are as follows: 

• Performance – 32% of the drawbacks mentioned 

• Unhappy with cloudy weather/unpredictability – 21% of the drawbacks 

• Difficulty telling whether it is working properly – 16% 

• Aesthetics – 5% 

• No battery backup – 5%. 

Importance of Solar PV Attributes 

PV owners were asked: “How important to you are each of the following aspects of your solar 

PV system? They were presented with a list of nine attributes that had been mentioned in 

qualitative interviews. Respondents were asked to rate the attributes on a 1-to-5 scale where 

1=Not at all important and 5=Very important. This question was followed by one which asked: 

“Now, please go back over the list and indicate below the number of the most important aspect.” 

Table 40 summarizes the findings. 

Clearly, the attribute perceived to be most important is the amount of electricity the PV system 

produces. Other more important attributes are identified as “Ease of maintenance,” “Length of 

warranty,” “Attractiveness/unobtrusiveness of the system,” and “Digital display showing 

electricity production and consumption.” When asked to identify the single most important 

attribute, more than half of the PV owners (56%) cite the amount of electricity produced. The 

attribute identified as the most important in second place is the digital display, cited by 11%. 

Other attributes are cited as the single most important by fewer than 8% each, except for 

“Upgrade capability,” which is not selected as the single most important attribute by anyone. 

116
This pattern of findings shows a set of diverse interests in the PV system attributes; however, the 

amount of electricity produced and a way to tell how much is produced (the digital display) seem 

to emerge as most important. Convenience and low risk of the systems are also important to PV 

owners. 

Table 40. Average Importance Ratings of Attributes of Solar PV Systems 

by PV Owners (n=70) Ordered by Average Mean Score 

Attributes of Solar PV Systems Mean Rating 

Percentage 

Rating This as 

a “Very 

Important” 

Attribute (5) 

Percentage 

Rating This as 

the Most 

Important 

Attribute 

Amount of electricity produced 4.24 50 55.6 

Ease of maintenance 4.07 34 7.9 

Length of warranty 4.04 30 1.6 

Attractiveness/unobtrusiveness of the system 3.91 31 4.8 

Digital display showing electricity production 

and consumption 3.88 25 11.1 

Upgrade capability 3.64 21 0.0 

Net metering 3.59 19 7.9 

Finance through home mortgage 3.49 21 7.9 

Owning the solar PV system outright 3.43 14 3.2 

Uses of the Digital Displays 

As just discussed, the digital display showing kWh production and consumption in real time is 

perceived as a key component of the PV system by a good many PV owners. In fact, 70% rate the 

display with a 4 or 5 on the 1-to-5 scale where 1=Not at all important and 5=Very important. PV 

owners were then asked: “About how often do you look at the solar electric digital display that 

tells you how much electricity your system is producing and how much your home is 

consuming?” Table 41 summarizes the results. 

More than half of PV owner respondents (58%) indicate that they look at their display at least 

once a week; 15% say they look at it once a day. Forty percent report that they look at the display 

approximately once a month or less often; 2% say they never look at it. 

PV owners were then asked: “In which of the following ways have you used your solar PV digital 

display? We use it to. . .” A list of seven potential uses of the display was provided. Table 42 

summarizes the findings. 

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Table 41. Frequency of PV Owners Receiving

Feedback from Their Digital Displays (n=67)

Frequency of Use of Digital Displays % 

Once a day 15 

At least once a week 43 

Approximately once a month 24 

Between once a month and once every six months 16 

Never 2

 Total 100 

Table 42. Percentages of PV Owners Indicating

Uses of the Solar PV Digital Display (n=67)

Uses 

% 

Indicating Use 

Optimally schedule electricity-consuming chores 92.5 

Record our cumulative electricity production and consumption 82.1 

Determine whether anything has inadvertently been left on before we leave the 

house 76.1 

Help change energy-consuming behavior 70.1 

Obtain feedback on the electricity use of specific lights and appliances 53.7 

Become more sensitive to our household’s electricity consumption patterns 49.3 

Determine whether our system is functioning 40.3 

As Table 42 shows, the PV digital display is used to reduce the amount of electricity consumed 

in the home overall and to increase the proportion of electricity consumed by the home at times 

when the PV system is producing more electricity, such as on sunny afternoons. The PV system’s 

digital display can be set to show the electricity consumption and production in real time or 

cumulatively. It has long been noted in the literature (cf., for example, Kempton and Layne 1988; 

Farhar and Fitzpatrick 1989) that feedback is an important component of energy-conserving 

behavior. In contrast to a monthly utility bill, the PV digital display provides real-time feedback 

on electricity consumption that interested homeowners can use immediately to modify their 

electricity-consuming behavior. 

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Operational Aspects of PV Systems 

The research tested the extent to which PV owners are familiar with factual information about 

their PV systems and how it is interconnected with the utility grid. To do this, the questionnaire 

asked: “Regarding the operational aspects of your solar PV system, please tell us whether each of 

the following situations exists.” A list of eight situations was provided. Table 43 summarizes the 

findings, which are discussed briefly for each of the statements. 

Table 43. Percentages of PV Owners Regarding Operational Aspects of Their PV Systems* 

Situation % Yes % No 

% Don’t 

Know 

Our solar PV systems automatically shuts down when 

there is a utility power outage. (True) 14 11 76** 

SDG&E credits us at the retail rate for the amount of 

electricity we put into the grid. (True, up to a point) 25 11 65** 

We have one electric meter. (True) 81 6 13 

SDG&E bills us only for the net electricity we actually 

consume. (True) 68 2 30 

The utility grid’s demand for electricity is at its peak on 

hot sunny afternoons when our system produces the most 

electricity. (True) 

69 2 29 

We signed an interconnectivity agreement with SDG&E 

when we purchased our home. (True) 66 6 28 

We pay our electricity bill once a year rather than once a 

month. (Optional) 14 3 83 

We will receive a check from SDG&E at the end of the 

year if we produce enough electricity. (False) 16 39 45 

*Items are presented in the order in which they were asked in the questionnaire 

**Total does not add to 100 because of rounding 

These findings seem to reflect the lack of knowledge and information discussed in Chapter 9, and 

the lack of information provided by the builder staff. 

PV System Shutdown during Power Outages 

The situation was stated as: “Our solar PV system automatically shuts down when there is a 

utility power outage.” This statement is actually true. The PV systems at Scripps Highlands have 

no battery backups. The reason for this decision, according to a SheaHomes staffer, was that the 

battery backup would have to go in the garage, and the garages were not deep enough to 

accommodate a battery backup system. Seventy-six percent of the PV owners do not know if 

their PV system would shut down during a power outage. Fourteen percent correctly respond that 

119
it would. Eleven percent incorrectly believe that their PV system will keep running when the 

utility grid goes down. 

Utility Rate of Payment for Electricity Put Back into the Grid 

The situation was stated as: “SDG&E credits us at the retail rate for the amount of electricity we 

put into the grid.” This statement is also true, up to a point. Under California law, a PV system is 

net-metered to zero, meaning that a home that produces more electricity than it uses will not 

receive a check from the utility company. In fact, that homeowner will have to pay a nominal 

monthly charge to be hooked up to the utility grid. However, during the hours when a PV system 

is producing more electricity than the home is using, the electricity meter does, in fact, run 

backwards. This means that, in effect, the utility company is paying the homeowner the retail rate 

for the electricity that home is providing. Sixty-five percent of PV owners do not know whether 

they are, in effect, being paid the retail rate for PV electricity they provide to the grid. Onequarter correctly believe that they are, whereas 11% do not believe they are being paid for their 

excess electricity at retail rate. 

Number of Electric Meters 

The situation was stated as: “We have one electric meter.” Because utility companies can only 

stay in business if they both buy electricity at wholesale rates and sell it at retail rates, they may 

be interested in having two electricity meters at homes with PV systems. One meter would 

measure the excess electricity produced by the home, which the utility would purchase at a 

wholesale rate. The other meter would measure the electricity the utility provides to the home, 

which the utility would sell to the homeowner at retail rate. However, there are problems with 

this approach; for one, it is expensive to install two electric meters. For another, the California 

net-metering legislation does not provide for two meters. Most PV owners at Scripps Highlands 

(81%) know that they have only one electric meter; 12% say they do not know; and 6% 

incorrectly believe they have more than one electric meter. 

Utility Billing for Net-Metered Electricity 

The situation was stated as: “SDG&E bills us only for the net electricity we actually consume.” 

This statement is correct. The PV homes are net metered, allowing for the meter to run backward 

when the home produces more electricity than is it consumes. The utility only bills homeowners 

for the amount of electricity read on the meter monthly. Approximately two-thirds (68%) of the 

PV owners correctly believe that SDG&E bills them only for the net electricity they consume. 

Almost one-third (30%) do not know; and approximately 2% incorrectly believe that the 

statement is not true. 

The Relationship between Utility Peak Demand and PV System Supply 

The situation was stated as: “The utility grid’s demand for electricity is at its peak on hot sunny 

afternoons when our system produces the most electricity.” This statement is true; SDG&E is a 

summer peaking utility for electricity, especially because of air-conditioning, and PV systems 

120
produce the most power on hot sunny afternoons when air-conditioning demand is at its highest. 

More than two-thirds of the PV owner respondents (69%) know that this is accurate; 29% say 

they do not know, and almost 2% incorrectly believe this is not the case. 

Interconnectivity Agreement 

The situation was stated as: “We signed an interconnectivity agreement with SDG&E when we 

purchased our home.” SDG&E staff indicate that it is impossible to have an operational PV 

system without the homeowner signing an interconnectivity agreement with the utility company. 

Almost two-thirds of PV owners (66%) state that this is correct; 28% do not know; and 6% 

believe that they do not have an interconnectivity agreement with the utility company. If they are 

correct, their PV system would not be producing electricity. However, according to SDG&E, 

every home with a PV system has an interconnectivity agreement to receive electricity service. 

Frequency of Utility Bill Payment 

The situation was stated as: “We pay our electricity bill once a year rather than once a month.” 

SDG&E bills PV homeowners separately for gas and electricity, and it offers an option to PV 

homeowners to pay their electricity bills monthly or annually. The utility company provides a 

monthly statement of electricity usage so that homeowners will be aware of their electricity 

consumption even if they have opted to pay annually. The purpose of the question was to learn 

the percentage of PV owners choosing to pay their electricity bills once a year. The answer is 

14%, whereas 83% say they pay monthly, and 3% answer that they do not know. 

Receive Check from Utility Company at the End of the Year 

The situation was stated as: “We will receive a check from SDG&E at the end of the year if we 

produce enough electricity.” As discussed earlier, the California legislation only allows net 

metering to zero, such that the utility is not required to pay for any excess electricity it receives 

beyond that consumed by the home. Thirty-nine percent of PV owners realize that they will not 

be receiving yearly checks from the utility company. A plurality of 45% are not sure whether they 

will receive checks. Sixteen percent of PV owners will be or have already been disappointed to 

learn that they will not be receiving such checks. 

Summary 

The largest perceived benefit of owning PV systems is saving on utility bills. Also frequently 

mentioned are increases in resale value, helping the environment and future generations, and 

increasing awareness of household energy use. A majority of PV owners indicate there are no 

disadvantages to owning PV systems. 

The most important attribute of PV systems perceived by their owners is the amount of electricity 

produced. Also mentioned as important, but by far fewer respondents, are ease of maintenance 

and length of warranty. 

121
PV owners tend to look at their digital displays (which tell them the amount of electricity being 

produced and consumed in real time) at least once a week (the modal response); 15% look at 

their displays at least once a day. Most PV owners use their displays to schedule electricityconsuming chores, record electricity production and consumption, determine whether anything 

has been left on in the house before leaving, change energy-consuming behavior, and obtain 

feedback on which lights and appliances are using the most electricity. 

PV owners are not highly knowledgeable about operational aspects of their systems. Majorities 

correctly identify that they have one meter, that they are billed only for the net electricity they 

consume, that their electricity production meets utility peak load, and that they signed an 

interconnectivity agreement with the utility company. However, most do not know if they pay 

their electricity bills monthly or annually. Most incorrectly believe or do not know if the utility 

will send them a check at the end of the year. Most do not know if their solar PV system shuts 

down where there is a power outage (it does). Most also do not understand if they are being 

credited with the retail rate when their meter runs backward (they are). It seems clear that PV 

owners could use a good deal more information about their PV systems and how they operate. 

122
Chapter 12

Attitudes toward San Diego Gas & Electric Company

Introduction 

Chapter Highlights 

The utility company plays a key role in any C Attitudes toward electricity rates, power outages, high-performance home development. The and the utility billing process are quite similar utility company provides the interconnection between owners of SheaHomes and comparison 

between PV systems and the utility grid and is homes and are in the neutral range. Problems with 

responsible for billing homeowners with PV power outages and electricity rates were not a 

factor in home purchase decisions. systems for electricity. Therefore, the utility 

company must help educate homeowners about C PV owners are significantly more pleased with the net metering and interconnectivity agreements. utility billing process than are non-PV owners. 

In addition, the utility company may offer 

rebates to buyers of energy-efficient homes and C PV owners are significantly more likely to believe 

that electricity rates have come down since they appliances. moved in than are non-PV owners. 

The study included several questions that C PV owners appear to be unsure or neutral about 

elicited opinions regarding the utility company the way in which SDG&E treated them with regard 

serving both SheaHomes and the comparison to net metering and interconnectivity agreements. 

community—San Diego Gas & Electric C In general, PV owners exhibit attitudes far more Company (or SDG&E). Results from these are favorable to SDG&E than do either the other 

relevant to answering the following study SheaHomes owners or the comparison 

questions. homeowners. 

• Were power outages perceived as a problem 

by new homebuyers in the San Diego area? If they were, this could be a reason for homebuyers 

being interested in purchasing high-performance homes. 

• Did homebuyers believe that their electricity rates had decreased since they purchased their 

homes? (Utility rates had actually increased, but this is a question of perception.) 

• PV owners receive separate bills for their electricity and gas services. They also have the 

option of being billed annually for electricity services; if they choose this option, they receive 

a monthly statement of account, but they do not have to pay it. Did this difference in billing 

make any difference in attitudes toward SDG&E compared with other homeowners receiving 

regular utility bills with gas and electricity combined? 

123
PV owners had other special interactions with SDG&E that led to additional questions. 

• Purchasers of SheaHomes with solar PV systems had to deal with the utility company to 

interconnect their systems. Were those interactions with SDG&E perceived as helpful or 

problematic by the PV homeowners? 

• The PV systems are tied to the utility grid, requiring homeowner interaction with the utility 

company about net metering. How successfully did SDG&E communicate with owners of PV 

systems concerning billing and other questions related to net metering? 

This chapter presents the findings on these study questions. 

Attitudes toward the Utility by Household Category 

No significant differences in attitudes toward electricity rates, power outages, and the utility 

billing process are found. All respondents were asked whether their electricity rates have 

decreased since they moved into their homes. Responses were solicited on a 5-point Likert scale 

from 1 = Strongly disagree to 5 = Strongly agree. The mean response for the SheaHomes 

respondents is 2.6 on the 5-point scale, and for the comparison respondents it is 2.32, showing a 

general tendency toward disagreement that rates had come down. Although a majority of 59% of 

the comparison owners disagree that electricity rates had declined and a plurality of 46% of 

SheaHomes respondents also disagree with the statement, the difference between the two groups 

is not significant. Table 44 summarizes the findings for this and the next two items. 

Table 44. Attitudes toward SDG&E by SheaHomes and Comparison Homeowners* 

Item 

SheaHomes 

Owners 

Mean 

Comparison 

Home 

Owners 

Mean 

SheaHomes 

Owners

 % 

Comparison 

Homeowners 

% 

We are pleased with the billing 

process for utilities (PV owners: 

We are pleased with the billing 

process for electricity.) 

3.11 3.12 

A: 34** 

U: 44 

D: 22 

A: 27 

U: 60 

D: 13 

Power outages are a problem in 

the San Diego area. 2.63 2.70 

A: 18 

U: 31 

D: 51 

A: 25 

U: 30 

D: 45 

Electricity rates have come down 

since we moved in. 2.60 2.32 

A: 22 

U: 32 

D: 46 

A: 17 

U: 24 

D: 59 

*Statistical test results: t-tests for differences in means are not significant; P2 test results are not significant 

**A=Agree or strongly agree; U=Neutral or unsure; D=Disagree or strongly disagree 

124
No significant differences in patterns of response are found for the other two items. A majority of 

SheaHomes respondents (51%) disagree with the statement “Power outages are a problem in the 

San Diego area,” as does a plurality of comparison respondents (45%). Sixty percent of 

comparison respondents are unsure or neutral regarding the statement “We are pleased with the 

billing process for utilities,” whereas 44% of SheaHomes respondents are unsure or neutral. Onethird of the SheaHomes owners agree with the statement, compared with 27% of the comparison 

homeowners. 

Therefore, it appears that perceived problems with power outages and electricity rates were not a 

factor in home purchases. Utility billing processes are not particularly popular; however, possibly 

no billing process whatsoever could be popular. 

Attitudes toward the Utility by PV System Ownership 

A very different picture emerges when these items are analyzed with respect to PV ownership. 

Table 45 summarizes the findings. A significantly higher percentage of PV owners (53%) agree 

or strongly agree that they are pleased with the billing process for electricity than non-PV owners 

(22%). To underscore this contrast in perception, an P2 test was run on this item comparing 

responses from PV owners with comparison homeowners. Fifty-three percent of PV owners are 

pleased with the billing process compared with 27% of comparison homeowners (P2=9.778l; 

p=.008). 

A significantly higher percentage of owners of SheaHomes without PV systems (54%) disagree 

or strongly disagree than homeowners with PV systems (34%) with the statement “Electricity 

rates have come down since we moved in,” suggesting that PV owners have a markedly different 

view of electricity pricing than do non-PV owners. Even though the question refers to electricity 

rates paid by everyone, PV owners seem to believe electricity rates have come down because they 

were using their lower electricity bills as a point of reference. Fewer non-PV owners, not having 

this experience with their utility bills, apparently see electricity rates as lower. An P2 test was run 

on this item comparing responses from PV owners with comparison homeowners. Fifty-nine 

percent of comparison homeowners disagree or strongly disagree that electricity rates have come 

down compared with 34% of PV owners (P2=7.343; p=.025). 

There is no significant difference between the percentage of homeowners with and without PV 

systems who perceive power outages as a problem in the San Diego area. 

PV owners were asked two questions about their experiences with SDG&E relative to purchasing 

homes with solar PV systems. The responses reflect an unsureness or neutrality with respect to 

the way that SDG&E treated them in this innovative situation. The first statement was “SDG&E 

sent clear, concise, and beneficial information about net metering.” The mean response is 3.1 on 

the 5-point scale, a response that is in the neutral range. A plurality of 40% are unsure; 36% 

agree with the statement, and 24% disagree. 

125
Table 45. Attitudes toward SDG&E by PV Ownership* 

Item 

PV 

Owners 

Mean 

Non-PV 

Owners 

Mean 

t -test 

Results 

PV 

Owners 

% 

Non-PV 

Owners

 % 

P2 

Results 

We are pleased with the 

billing process for utilities 

(PV owners: We are pleased 

with the billing process for 

electricity.) 

3.43 2.90 t=3.717 

p#.000 

A: 53 

U: 32 

D: 15 

A: 22 

U: 51 

D: 27 

P2 = 

17.579 

p#.000 

Power outages are a problem 

in the San Diego area. 2.68 2.60 n.s. 

A: 18 

U: 31 

D: 51 

A: 18 

U: 30 

D: 51 

n.s. 

Electricity rates have come 

down since we moved in. 2.87 2.42 t=2.674 p=.008 

A: 28 

U: 38 

D: 34 

A: 18 

U: 28 

D: 54 

P2 = 

6.790 

p=.034 

(PV owners only): SDG&E 

sent clear, concise, and 

beneficial information about 

net metering. 

3.10 – – 

A: 36 

U: 40 

D: 24 

– – 

(PV owners only): SDG&E 

was helpful about connecting 

our solar PV systems with 

the utility grid 

3.13 – – 

A: 31 

U: 54 

D: 15 

– – 

*n.s.=not significant; A=Agree or strongly agree; U=Neutral or unsure; D=Disagree or strongly disagree 

The second statement was “SDG&E was helpful about connecting our solar PV system with the 

utility grid.” The mean response is 3.15, again in the neutral range; a majority of 54% of the PV 

owners are neutral or unsure about the statement, 31% agree, and 15% disagree. 

Clearly, the PV homeowners’ experiences with SDG&E in initiating their net-metering service 

was not overly positive. On the other hand, PV owners exhibit attitudes far more favorable to 

SDG&E than do either the other SheaHomes owners or the comparison homeowners. 

Summary 

When the responses of homeowners with PV systems are compared with other homeowners, 

significant differences are found. A significantly higher percentage of homeowners with PV 

systems agree that electricity rates have declined. Likewise, a significantly higher percentage of 

homeowners with PV systems are pleased with the utility billing process than those without PV 

systems. No difference is found by PV ownership on perception of power outages in the San 

Diego area. On the other hand, owners of homes with PV systems are relatively neutral about 

126
their experiences with SDG&E in terms of the interconnectivity agreement and the information 

provided to them about net metering. 

127
Chapter 13

Attitudes toward Solar Features in New Housing

Introduction 

General attitudes toward solar water heating Chapter Highlights and solar electric systems are important to 

measure because some buyers of new homes C General attitudes toward solar water heating and are still confused about the difference between solar PV systems were measured and analyzed 

the 1970s solar hot water systems and today’s by homeowner categories. 

solar thermal and solar electric systems. To the C Majorities of SheaHomes owners and of degree that “horror stories” about the solar comparison homeowners agree that solar water panels in the 1970s affect general attitudes heating and solar PV systems are desirable about solar features in current markets, the innovations for new homes. 

demand for ZEHs could be negatively affected. C Fifty-nine percent of SheaHomes owners and In addition, many new homebuyers believe that 40% of comparison homeowners agree that solar solar features in housing are too expensive for water-heating systems are cost effective. what they receive in return.1 

C Forty-nine percent of SheaHomes owners and 

36% of comparison homeowners agree that solar 

PV systems are cost effective. 

The questionnaires used Likert items to ask 

homebuyers whether they agreed that solar 

water-heating and solar PV systems are C Living with a PV system fosters a positive desirable innovations for new homes. They also attitude toward the cost effectiveness of solar 

asked whether they considered these features to water heating (63% agree) and solar PV systems 

(62% agree). 

agreement or disagreement were sought were 

preceded by the question: “To what extent do 

you agree with the following statements about 

solar PV and solar water-heating systems?” 

be cost effective. The statements for which 

Are Solar Features Desirable Innovations? 

The two statements about the desirability of the innovation were “Solar PV systems are a 

desirable innovation of new homes” and “Solar water-heating systems are a desirable innovation 

for new homes.” Responses were requested on a 1-to-5 scale where 1=Strongly disagree and 

5=Strongly agree. Table 46 summarizes the findings by SheaHomes and comparison 

homeowners for these two questions. 

The results in Table 46 show positive average agreement ratings with the statement that solar PV 

and solar water heating are desirable innovations for new homes on the part of both SheaHomes 

and comparison homebuyers. SheaHomes buyers respond to the statement about solar water 

heating with a mean rating of 4.14 on the 5-point scale. The positive mean rating for solar water 

1 These perceptions became evident, for example, during the qualitative interviews for this study. 

128 
heating on the part of SheaHomes buyers is significantly higher than the average mean rating of 

3.73 given by the comparison homebuyers. To speculate, this higher rating by the SheaHomes 

owners could be the result of positive attitudes toward solar energy in general—or solar waterheating systems in particular—that they had before the new home purchase and may even have 

been causal in the selection of the new home. Alternatively, the higher mean rating about the 

desirability of solar water heating could be a result of living with solar water-heating systems, 

which were included standard on all SheaHomes except the early homes. 

Table 46. Average Agreement Ratings on Desirability of Solar Features 

as Innovations for New Housing by Categories of Homebuyers 

Statement 

SheaHomes 

Owners 

Mean 

(n=170) 

Comparison 

Homeowners 

Mean 

(n=53) 

SheaHomes

 Owners % 

Responding 

Agree or 

Strongly Agree 

Comparison 

Homeowners % 

Responding 

Agree or 

Strongly Agree 

Solar water-heating 

systems are a desirable 

innovation for new 

homes.* 

4.14 3.73 82 54 

Solar PV systems are a 

desirable innovation for 

new homes.** 

4.04 3.72 77 55 

*Statistically significant difference in means: t=3.361; p=.001; statistically significant difference in percentages 

agreeing: P2=17.043; p=.000 

**Statistically significant difference in means: t=2.599; p=.011; statistically significant difference in percentages 

agreeing: P2=11.204; p=.004 

Similarly, SheaHomes owners give a significantly higher mean rating (4.04) to the desirability of 

solar PV systems than do comparison homeowners (3.72). This difference is statistically 

significant as well and is particularly interesting because, as previously noted, not all SheaHomes 

owners have homes with PV systems. Again, a reasonable speculation is that positive attitudes 

toward solar features could have preceded and played a role in the selection of the home 

purchased, or living with solar features could have positively influenced or reinforced positive 

attitudes toward them. 

These variables were also analyzed by PV ownership (results summarized in Table 47). The 

pattern of responses shows that PV owners give significantly higher mean ratings to the 

statements about solar features as desirable innovations than do non-PV owners. In addition, PV 

owners give a mean positive rating of 4.35 to the statements that pertain to solar water heating, 

compared with a mean rating of 4.00 given by the owners of SheaHomes without PV systems. 

PV owners respond with a mean rating of 4.32 for the statements that pertain to solar PV 

systems, compared with the mean rating of 3.87 by the owners of SheaHomes without PV 

systems. These results again suggest both interpretations already mentioned in connection with 

129
the differences between the SheaHomes and comparison homeowners; however, the findings 

even more strongly support the idea that living with a PV system fosters positive attitudes toward 

the desirability of including such systems in new housing. 

Table 47. Average Agreement Ratings on Desirability of Solar Features 

as Innovations for New Housing by PV Ownership 

Statement 

PV 

Owners 

Mean 

(n=68) 

Non-PV 

Owners 

Mean 

(n=101) 

PV

 Owners % 

Responding 

Agree or 

Strongly Agree 

Non-PV 

Owners % 

Responding 

Agree or 

Strongly Agree 

Solar water-heating 

systems are a desirable 

innovation for new 

homes.* 

4.35 4.00 88 77 

Solar PV systems are a 

desirable innovation for 

new homes.** 

4.32 3.87 88 69 

*Statistically significant difference in means: t=3.069; p=.003; no statistically significant difference by percentages

agreeing.

**Statistically significant difference in means: t=3.967; p=.000; statistically significant difference in percentages

agreeing: P2=8.382; p=.015.

Are Solar Features Cost Effective? 

The two statements about the cost effectiveness of the solar features were “Solar PV systems are 

cost effective” and “Solar water heating systems are cost effective.” Responses were requested 

on a 1-to-5 scale where 1=Strongly disagree and 5=Strongly agree. Table 48 summarizes the 

findings for SheaHomes and comparison homeowners on these two questions. 

Table 48. Average Agreement Ratings on Cost Effectiveness 

of Solar Features by Categories of Homebuyers 

Statement 

SheaHomes 

Owners 

Mean 

(n=170) 

Comparison 

Homeowners 

Mean 

(n=53) 

SheaHomes

 Owners % 

Responding 

Agree or 

Strongly Agree 

Comparison 

Homeowners % 

Responding 

Agree or 

Strongly Agree 

Solar water-heating 

systems are cost 

effective.* 

3.70 3.49 59 40 

Solar PV systems are cost 

effective. 3.54 3.36 49 36 

*Statistically significant difference in percentages agreeing: P2=6.199; p=.045 

130 
The results in Table 49 show average agreement ratings in the unsure to positive range with the 

statement that solar PV and solar water heating systems are cost effective. SheaHomes buyers 

respond to the statement on solar water heating systems with a mean rating of 3.70 on the 5-point 

scale. The mean rating for solar water heating on the part of SheaHomes buyers is not 

significantly higher than the mean rating of 3.49 given the comparison homebuyers. However, 

the percentage of SheaHomes owners who agree with the statement that solar water heating is 

cost effective (59%) is significantly higher than the percentage of comparison owners who so 

agree (40%). This higher percentage of SheaHomes owners, almost all of whom own solar water 

heating systems, tends to support the notion that, by and large, they are positive toward their 

experience with this technology. To speculate, the SheaHomes owners could have had favorable 

attitudes toward solar energy or solar water heating before their home purchases, and such 

attitudes may even have been causal in their new home selection. 

Although the owners of SheaHomes give a higher mean rating (3.54) to the cost effectiveness of 

solar PV systems than do comparison homeowners (3.36), the difference is not significant. Also, 

both mean ratings tend to be in the neutral to mildly positive range. The findings suggest a higher 

degree of uncertainly about the cost effectiveness of solar PV systems on the part of the owners 

of SheaHomes than about the desirability of the innovation. This, in turn, suggests that the 

desirability of solar PV is not solely dependent on its cost effectiveness. The percentage of 

SheaHomes owners who indicate that solar PV systems are cost effective (49%) is 10 points 

lower than the percentage who indicate that solar water-heating systems are cost effective. It is, 

however, 10 points higher than the 36% of comparison homeowners who agree that solar PV 

systems are cost effective. 

These variables were also analyzed with respect to PV ownership (results summarized in Table 

49). PV owners give a mean rating of 3.78 to the statement on the cost effectiveness of solar 

water heating, compared with a mean rating of 3.64 given by the owners of SheaHomes without 

PV systems, a difference that is not statistically significant. Although 63% of PV owners agree 

with the statement about the cost effectiveness of solar water heating, compared with 56% of 

non-PV owners, the difference is not significant. The pattern of responses shows that PV owners 

give significantly higher mean ratings to the statements that solar PV systems are cost effective 

than do non-PV owners. 

PV owners respond with a mean rating of 3.76 for the statement about the cost effectiveness of 

solar PV systems, compared with the mean rating of 3.39 given by the owners of SheaHomes 

without PV systems. In addition, 62% of PV owners agree with the statement about cost 

effectiveness, compared with 40% of the non-PV owners—again, a significant difference. 

These results show favorable attitudes toward the cost effectiveness of solar features in new 

housing on the part of PV owners; non-PV owners are less certain. Again, this finding suggests 

that living with a PV system fosters a positive attitude toward the cost effectiveness of including 

solar features in new housing. 

131
Table 49. Average Agreement Ratings on Cost Effectiveness 

of Solar Features by PV Ownership 

Statement 

PV Owners 

Mean 

(n=68) 

Non-PV 

Owners 

Mean 

(n=101) 

PV Owners % 

Responding 

Agree or 

Strongly Agree 

Non-PV Owners 

% Responding 

Agree or 

Strongly Agree 

Solar water heating systems 

are cost effective. 3.78 3.64 63 56 

Solar PV systems are cost 

effective.* 3.76 3.39 62 40 

*Statistically significant difference in means: t=3.076; p=.002; statistically significant in percentages agreeing: 

P2=7.596; p=.022. 

132
Chapter 14 

Policy Preferences Relative to High Performance Homes 

Introduction 

Chapter Highlights The U.S. Department of Energy is interested 

in understanding the preferences of C A majority of owners of SheaHomes prefer that homebuyers concerning the ways in which solar water heating systems be offered standard 

on new homes; 42% of comparison buyers agree. 

federal levels should support the development 

builders and government at both the state and 

C A plurality of 46% of owners of SheaHomes and offering of ZEHs. Several items were 

prefer that solar PV systems be offered standard included in the study to explore these on new homes; 34% of comparison homeowners preferences. agree. 

C A plurality of comparison homebuyers prefer 

that PV systems be offered both standard and 

The chapter includes consideration of whether 

homeowners believe that solar features, optionally. especially PV systems, should be offered on 

new homes. It also reports findings on C 96% of PV owners and 86% of non-PV owners 

questions about preferred home builder agree that ZEHs should be the standard new 

actions in offering ZEHs and on government home sold, if they are cost effective; 90% of both 

SheaHomes and comparison homeowners agree. actions to foster ZEHs. 

C Strong majorities of respondents support federal Should Solar Features Be Standard tax credits for buyers of energy-efficient homes 

or Optional in New High- and federal research on ZEHs. 

Performance Homes? C The CEC rebates for solar water heating are 

strongly supported. Because SheaHomes had designed its 

program to include a percentage with PV 

systems standard and the balance with PV 

systems optional, the question arose as to 

which approach homeowners prefer. 

Respondents were asked: “In your opinion, which of the following would be the best way to offer 

solar PV systems to buyers of new homes?” Table 50 summarizes the findings by categories of 

homeowners. A plurality of SheaHomes buyers prefer solar PV systems to be offered standard 

(46%), whereas a plurality of comparison home buyers prefer that both choices be available 

(42%). However, these differences are not statistically significant. 

Similarly, respondents were asked, “In your opinion, which of the following would be the best 

way to offer solar water heating systems to buyers of new homes?” Table 51 summarizes the 

responses. A majority of SheaHomes buyers (59%) prefer that solar water heating be offered as a 

standard feature, compared with 42% of the comparison buyers. These differences are not 

statistically significant. 

133
Table 50. Preferences for Standard or Optional Offerings of Solar PV Systems on

New Homes by SheaHomes and Comparison Homebuyers

Statement 

SheaHomes % 

Agreeing or 

Strongly Agreeing 

(n=168) 

Comparison Homes % 

Agreeing or 

Strongly Agreeing 

(n=53) 

Solar PV systems should be offered as a standard 

feature with the cost included in the price of the 

home. 

46 34 

Solar PV systems should be offered as an optional 

feature with the cost added to the price of the 

home. 

15 15 

Both choices should be available. 35 42 

Don’t know 4 9

 Totals 100 100 

Table 51. Preferences for Standard or Optional Offerings of Solar Water Heating Systems on

New Homes by SheaHomes and Comparison Homebuyers

Statement 

SheaHomes % 

Agreeing or 

Strongly Agreeing 

(n=168) 

Comparison Homes % 

Agreeing or 

Strongly Agreeing 

(n=53) 

Solar water heating systems should be offered as a 

standard features with the cost included in the 

price of the home. 

59 42 

Solar water heating systems should be offered as 

an optional feature with the cost added to the price 

of the home. 

11 21 

Both choices should be available. 26 33 

Don’t know 4 4

 Totals 100 100 

When responses are analyzed by PV ownership, the pattern of findings is approximately the 

same. Pluralities of 46% of each group prefer that PV be offered standard. Approximately 60% 

of both PV and non-PV owners prefer that solar water heating be offered standard. No significant 

differences in response are found between the PV and non-PV owners. 

134
The reasons given by those preferring solar energy systems to be offered standard are (1) that 

buyers are unaware of the benefits, (2) it helps the energy situation and the environment, and (3) 

it saves buyers money and time. The reasons given by those who prefer that solar features be 

offered only optionally are that it provides freedom of choice and it helps the environment. 

Should Utility Companies Provide Rebates for Energy-Efficient Appliances? 

One ZEH policy question asked respondents for their opinions about whether utility companies 

should provide rebates for energy-efficiency appliances. Responses were solicited on a 1-5 scale 

where 1=Strongly oppose and 5=Strongly favor. When responses are analyzed by categories of 

homebuyers, no statistically significant difference between them is found. The mean response for 

SheaHomes owners is 4.35 and for comparison homeowners is 4.37. Ninety-one percent of 

SheaHomes buyers and 89% of comparison buyers agree that utility companies should give 

rebates for energy-efficient appliances, values that are statistically equivalent. The analysis with 

regard to PV ownership also reveals no statistically significant differences between PV and nonPV owners (most of whom also agree that utility companies should give such rebates). 

Should Home Builders Offer ZEHs? 

Large majorities of respondents indicate agreement with the statements that show preferences for 

the use of energy-efficiency and solar features in home building as a standard practice if cost 

effective. T-tests were run on all of these items by categories of homebuyers, with no significant 

results. Table 52 shows the percentages of SheaHomes and comparison homeowners, and of PV 

and non-PV owners, agreeing with each of the statements. As the data in Table 52 indicate, no 

significant differences in response are found between the SheaHomes and comparison 

homeowners based on P2 tests. Between owners of homes with and without PV systems, only 

one difference in a P2 analysis approaches statistical significance: PV owners more frequently 

agree (96%) than do non-PV owners (86%) that a zero-energy type of home should be the 

standard home offered by builders, if they are cost effective. 

Should State and Federal Governments Foster ZEHs? 

Items regarding policy statements about government actions supporting ZEHs solicit opinions on 

research, incentives (e.g., tax credits and rebates), and subsidies for solar features in housing. 

Strong majorities of at least 80% of respondents in all four categories indicate that they agree or 

strongly agree with statements favoring federal income tax credits for energy-efficient homes. 

(See Table 53.) Similar results show support for federal funding for research on ZEHs and statefunded rebates for the purchase of PV systems and solar water heating systems. Approximately 

three-quarters of respondents indicate that they favor subsidies for solar features in affordable 

housing developments. 

135
Table 52. Percentages of Respondents Agreeing or Strongly Agreeing with Statements 

on Builder Decisions Concerning Efficiency, Solar Features, and Home Energy Labeling 

by Four Categories of Homeowners 

Statement 

SheaHomes 

% Agreeing 

(n=170) 

Comparison 

% Agreeing 

(n=52) 

PV Owners 

% Agreeing

 (n=68) 

Non-PV 

Owners 

% Agreeing 

(n=102) 

New homes that are technically 

rated as very efficient should be 

given an ENERGY STAR label. 

86 87 88 84 

Builders should build very energyefficient homes if they cost less per 

month to own and operate than 

conventional homes. 

92 92 93 92 

Builders should build homes that cut 

energy bills if their appearance and 

comfort are superior to that of 

conventional housing. 

88 89 85 90 

A complete energy package (with 

efficiency and solar features) should 

be standard equipment in new 

homes today if cost effective.* 

90 90 96* 86* 

*The difference in response between PV and non-PV owners nears statistical significance (P2=5.376; p=.068) 

Summary 

Among SheaHomes buyers, the weight of the responses is in favor of providing solar PV and 

solar water heating as standard features on new homes. Comparison buyers are somewhat less 

committed to this idea, although the differences in response are not statistically significant. 

Significant pluralities of both categories of homeowners would like to see both standard and 

optional solar water heating and PV systems offered on new homes. 

Virtually all of the homebuyers agree that utility companies should provide rebates for energyefficient appliances. Most of them also agree that builders should offer ZEHs routinely if they are 

cost effective. 

136
Table 53. Percentages of Respondents Agreeing or Strongly Agreeing with Policy Statements on

Government Incentives and Subsidies for Solar Features by Four Categories of Homeowners

Statement 

SheaHomes 

% Agreeing 

or Strongly 

Agreeing 

(n=170) 

Comparison 

% Agreeing 

or Strongly 

Agreeing 

(n=52) 

PV Owners 

% Agreeing 

or Strongly 

Agreeing

 (n=68) 

Non-PV Owners 

% Agreeing or 

Strongly 

Agreeing 

(n=102) 

Federal income tax credits 

should be given to buyers of 

energy-efficient homes. 

89 83 93 87 

The federal government 

should support research on 

highly energy-efficient homes 

that produce all the energy 

they use. 

85 81 91 80 

The California Energy 

Commission should give a 

rebate for purchasing a solar 

PV system. 

85 83 91 81 

The California Energy 

Commission should give a 

rebate for purchasing a solar 

water heater. 

84 85 91 79 

Affordable housing 

developments should receive 

subsidies for installing solar 

PV systems. 

73 75 79 69 

Affordable housing 

developments should receive 

subsidies for installing solar 

water heaters. 

71 75 81* 65* 

*The difference in response between PV and non-PV owners on the last item is statistically significant (P2=7.294; 

p=.026) 

137
Chapter 15 

Self-Reported Resource Conservation Behaviors 

Introduction 

Chapter Highlights The study addressed the question of whether 

living in high-performance homes is associated C Most homebuyers in the SheaHomes and 

with a higher incidence of resource-conserving comparison communities agree that they practice 

behaviors as reported by respondents. energy-conserving behaviors, recycle, and try to 

Respondents were asked about their agreement conserve water. 

with 12 statements on energy-conservation C Minorities of both categories of homebuyers 

behaviors such as turning off lights and adjusting indicate that they drive fuel-efficient vehicles. 

thermostats, recycling, water conservation, 

ownership of fuel-efficient vehicles, and general 

thriftiness. 

C No significant difference on self-reported 

conservation behaviors are found between 

SheaHomes and comparison homeowners. 

Analysis by Ownership of SheaHomes C The findings suggest that PV owners may engage 

and Comparison Homes slightly more in environmental behaviors, such 

as recycling, than do non-PV owners. 

Respondents to all questionnaires were asked: 

“Please indicate the extent to which you agree or 

disagree with the following statements.” 

C Male respondents (75%) are significantly more 

likely than female respondents (59%) to report 

that they turn off their computers when not in 

Responses to 12 statements concerning resource use. 

conservation behaviors were sought on a 5-point 

Likert scale from “Strongly disagree” to 

“Strongly agree.” As summarized in Table 54, 

most respondents agree or strongly agree with most of the statements presented. 

Results from this item on self-reported conservation behaviors were analyzed by SheaHomes and 

comparison groups. Table 54 summarizes the results. It was hypothesized that SheaHomes 

buyers would be more inclined to engage in energy-conserving behaviors because of owning 

high-performance homes; however, no significant differences in reported energy-related 

behaviors are found by homeowners in the two communities. In fact, although the differences are 

not significant, there is a slight tendency for comparison homeowners to report more “energy 

conscious” behaviors than do SheaHomes owners, providing a very small bit of evidence for the 

existence of a “take-back effect” among SheaHomes owners. However, this evidence is so slight 

that it cannot be concluded such an effect exists. 

Analysis by PV Ownership 

The self-reported energy behavior results for SheaHomes owners were also analyzed by PV 

system ownership. Results are summarized in Table 55. Again, it was hypothesized that PV 

system owners would be more inclined to engage in energy conservation behaviors, either 

because they own PV systems with digital displays and would be more attuned to their energy 

usage, or because they already had a predilection to conserve energy before they purchased their 

138
homes, or both. This had been suggested by previous qualitative interviews with SheaHomes 

owners. PV owners are more likely to agree that they modify their thermostat settings when 

away, and during winter and summer. Yet a statistically significant difference in responses by PV 

ownership is found for only one of the 12 listed behaviors when t-tests and P2 analyses are run. 

On the other hand, the mean scores are higher among PV owners for eight of the 12 listed items. 

Among SheaHomes owners, a significantly higher percentage of PV owners than non-PV owners 

agree that they recycle regularly (t= –2.332; p=.021; P2=8.916; p=.012). In fact, although the 

results are not significant, non-PV owners (95%) are more likely than PV owners (88%) to agree 

that they turn off lights when not in use, and that they turn off computers when not in use (74% 

of non-PV owners compared with 62% of PV owners). Thus, the hypothesized positive effect of 

PV ownership on energy conservation behavior over the short-term (from 6 to at least 12 months) 

is not supported by these data. The data do suggest that PV owners may engage in more 

environmental behavior, such as recycling, than do non-PV owners. 

Analysis by Gender 

The self-reported energy behaviors were also crosstabulated by gender of the head-of-household 

respondent. Only one significant difference is found among them: a higher percentage of male 

heads-of-household completing the questionnaire (75%) than female heads-of-household 

completing the questionnaire (59%) report that they turn off their computers when not in use. To 

speculate, this result may be a function of more frequent computer usage among the males than 

the females in the two groups. For example, as a relevant anecdote, during the qualitative phase 

of the study, one home was identified that housed a business involving significant computer 

usage that was run exclusively by men. 

139
Table 54. Self-Reported Energy Behaviors by SheaHomes and Comparison Respondents 

Behaviors 

SheaHomes 

Owners 

Mean Score 

(n=175) 

Comparison

Home Owners 

Mean Score 

(n=52) 

% Agree or

Strongly Agree

(both groups) 

We turn off the lights in our house when we are not

using them.* 4.41 4.60 

SheaHomes: 93% 

Comparison: 98%

Total: 94% 

When we are away, we modify our thermostat

settings to save energy and utility costs. 4.27 4.52 

SheaHomes: 84% 

Comparison: 90%

Total: 86% 

We regularly recycle paper, plastic, or cans. 4.20 4.23 

SheaHomes: 89% 

Comparison: 85%

Total: 88% 

We are more energy conscious than we used to be. 4.01 4.12 

SheaHomes: 80% 

Comparison: 81%

Total: 80% 

In the winter months, we regularly set our

thermostat to 70° F or lower. 3.98 3.98 

SheaHomes: 75% 

Comparison: 73%

Total: 74% 

We work to conserve water. 3.93 3.98 

SheaHomes: 74% 

Comparison: 77%

Total: 75% 

We turn off computers when we leave the house. 3.83 3.75 

SheaHomes: 69% 

Comparison: 67%

Total: 69% 

In the summer months, we regularly set our

thermostat to 75° or higher. 3.60 3.83 

SheaHomes: 61% 

Comparison: 65%

Total: 62% 

We tend to be thrifty. 3.48 3.58 

SheaHomes: 56% 

Comparison: 62%

Total: 57% 

In our new home, we use air conditioning less than

we did in our previous home. 3.43 3.46 

SheaHomes: 50% 

Comparison: 54%

Total: 51% 

We drive fuel-efficient vehicles. 3.07 3.16 

SheaHomes: 38% 

Comparison: 42%

Total: 39% 

We practice xeriscaping. 2.80 2.76 

SheaHomes: 13% 

Comparison: 17%

Total: 13% 

*Statistically significant difference found using a t-test but not found using a P2 test, (t= –2.036; p=.044) 

(P2 n.s.) 

140 
Table 55. Self-Reported Energy Behaviors among SheaHomes Owners 

by Ownership of PV Systems 

Behaviors 

PV Owners 

Mean Score 

(n=175) 

Non-PV 

Owners 

Mean Score 

(n=52) 

% Agree or 

Strongly Agree 

(both groups) 

We turn off the lights in our house when we are not 

using them. 4.30 4.48 

PV Owner: 88% 

Non-PV Owner: 95% 

Total: 93% 

When we are away, we modify our thermostat 

settings to save energy and utility costs. 4.40 4.19 

PV Owner: 90% 

Non-PV Owner: 81% 

Total: 84% 

We regularly recycle paper, plastic, or cans.* 4.39 4.08 

PV Owner: 97% 

Non-PV Owner: 83% 

Total: 89% 

We are more energy conscious than we used to be. 4.12 3.94 

PV Owner: 81% 

Non-PV Owner: 79% 

Total: 80% 

In the winter months, we regularly set our thermostat 

to 70°F or lower. 4.01 3.96 

PV Owner: 80% 

Non-PV Owner: 71% 

Total: 75% 

We work to conserve water. 3.87 3.97 

PV Owner: 73% 

Non-PV Owner: 75% 

Total: 74% 

We turn off computers when we leave the house. 3.60 3.97 

PV Owner: 62% 

Non-PV Owner: 74% 

Total: 69% 

In the summer months, we regularly set our 

thermostat to 75° or higher. 3.72 3.51 

PV Owner: 68% 

Non-PV Owner: 57% 

Total: 61% 

We tend to be thrifty. 3.57 3.42 

PV Owner: 57% 

Non-PV Owner: 55% 

Total: 56% 

In our new home, we use air conditioning less than 

we did in our previous home. 3.46 3.41 

PV Owner: 49% 

Non-PV Owner: 51% 

Total: 50% 

We drive fuel-efficient vehicles. 3.03 3.10 

PV Owner: 35% 

Non-PV Owner: 39% 

Total: 38% 

We practice xeriscaping. 2.84 2.77 

PV Owner: 12% 

Non-PV Owner: 13% 

Total: 13% 

*Statistically significant difference resulted using both a t-test and a P2 test; t= –2.332, p=.21; P2=8.916; p=.012 

141 
Chapter 16

Environmentalism and Early Adopter Characteristics

Introduction 

Chapter Highlights Two hypotheses were tested in this part of the 

project. These are (1) that buyers of SheaHomes C Only one significant difference in seven would be more environmentally concerned than environmentalism measures was found between 

buyers of comparison homes, and (2) that SheaHomes and comparison community buyers. 

SheaHomes buyers are more likely to link 

household energy consumption with 

buyers of SheaHomes would be more likely to 

exhibit early adopter characteristics. Both of environmental problems. these hypotheses are of interest to builders 

seeking to position their ZEHs in the home- C No significant differences in early adopter 

buying market. For example, if builders characteristics are found between SheaHomes 

advertise ZEHs as "green," can they expect to and comparison community buyers. 

attract potential home buyers who are C Among SheaHomes owners, no significant environmentally concerned? Are buyers of differences are found between PV owners and high-performance homes more interested in non-PV owners on either environmentalism 

technological innovations? measures or early adopter characteristics. 

In a word, no significant differences in 

environmentalism are found between the 

SheaHomes and comparison community buyers. Similarly, no significant differences are found in 

early adopter characteristics between the SheaHomes buyers and the comparison buyers. Finally, 

no significant differences are found between PV owners and non-PV owners within the 

SheaHomes community. 

Environmentalism 

Generally, the marketing of products that are considered “green” because they help protect the 

environment is oriented toward the environmentally concerned segment of the public. Highperformance homes could be considered as loosely falling in the category of “green” buildings 

because they are efficient, have solar water heating, and produce solar electricity, although they 

do not use recycled materials and other hallmarks of "green" buildings. Yet opinion supportive of 

the environment is quite widespread in the U.S. public (Dunlap et al. 2000; Dunlap and Van 

Liere 1978), although it has declined in recent years (Greenberg 2005; Jl 2004). The potential 

buyers of upscale SheaHomes and comparison homes might not be very different from each 

other. Still, research has shown that those interested in high-performance housing articulate 

environmental concern as one of their reasons. Those interested in the research wanted to know 

whether the SheaHomes buyers differ from the comparison community buyers in 

environmentalism. 

Therefore, the first hypothesis is that the buyers of the high-performance SheaHomes would be 

more environmentally concerned than the buyers of the comparison homes. This idea derives 

from prior research suggesting that environmental concern is a key reason for adults to seek out 

142
and purchase products such as homes and cars offering greater environmental protection than 

other products. 

Environmentalism was measured in two ways. The first was using items from the New 

Environmental Paradigm (NEP) that have been tested extensively in empirical research on adult 

populations (Van Liere 2003; Van Liere personal communication). 

These five items—included in all four questionnaires—asked respondents, using a 5-point Likert 

scale, whether they strongly disagree (a score of 1) to strongly agree (a score of 5) with each 

statement. The five statements were as follows: 

1. We have a serious responsibility to preserve the environment for future generations. 

2. Many of the supposed threats to the environment have been greatly exaggerated. 

3. Current federal regulations provide adequate protection for the environment. 

4. Individuals need to take personal responsibility for protecting the environment. 

5. Household energy consumption is not a major contributor to environmental problems. 

For four of the five NEP items, no significant differences are found between SheaHomes and 

comparison home buyers. Although the frequency of responses is consistently in the expected 

direction, the hypothesis that SheaHomes buyers would be more environmentally concerned is 

not supported by the data. A significant difference is found for the fifth item, “Household energy 

consumption is not a major contributor to environmental problems” (P2=11.165; p=.004). 

However, this difference is not found to be statistically significant when a t-test for differences in 

mean response is run. The mean score given by SheaHomes owners is 2.70 and by the 

comparison owners is 2.55. To speculate, the wording of this item in the NEP scale, inverting the 

“agree” and “disagree” response that would be needed to indicate environmental concern might 

have yielded this result. On the other hand, SheaHomes buyers could have been somewhat more 

concerned about energy issues than comparison buyers. 

The NEP items were also tested for differences in response by PV versus non-PV ownership 

among the SheaHomes owners. Table 56 summarizes the findings for both the SheaHomes 

versus the comparison buyers and the PV versus the non-PV owners. PV owners respond more 

frequently than non-PV owners in the expected direction; however, no statistically significant 

results are found. For example, 38% of PV owners, compared with 28% of non-PV owners, 

disagree that threats to the environment have been exaggerated. Fifty-seven percent of PV 

owners compared with 45% of non-PV owners disagree with the thought that household energy 

consumption is not a major contributor to environmental problems. Yet these differences in 

patterns of response do not achieve statistical significance. 

The second way environmentalism was measured was to include two environmental attitude 

items asking respondents to agree or disagree on a Likert scale from 1 to 5, as described 

previously. 

1. I tend to buy environmentally friendly products, even if they cost more. 

2. I am willing to accept modifications to my lifestyle if it helps the environment. 

143
These items were included with the items measuring early adopter characteristics, discussed 

below, and were included on all four questionnaires. Presented in Table 57, the results from these 

two items show that a plurality of both SheaHomes and comparison buyers agree that they tend 

to buy environmentally friendly products even if they cost more (between 40% and 50%). 

Approximately two-thirds of both categories of homebuyers agree they are willing to accept 

modifications to their lifestyles to help the environment. 

Early Adopter Characteristics 

As discussed in Chapter 2, diffusion-of-innovation theory would suggest that, across the entire 

adult population in the United States, those most apt to adopt innovations first (innovators and 

early adopters) have certain fairly well-understood characteristics. Innovators (the leading edge 

innovation adopters comprising approximately 2.5% of the population) tend to be venturesome, 

control substantial resources, have complex technological knowledge, and tolerate uncertainty in 

outcomes. Early adopters are frequently “opinion leaders” who catalyze shifts in the innovation’s 

penetration from the select few to the “early majority.” Early adopters (the next 13.5% of 

adopters) are well integrated in local communities and tend to be people to whom others look to 

for advice before adopting an innovation.1 

The second hypothesis in this study was that the SheaHomes buyers would exhibit more 

characteristics of innovators and early adopters than the comparison buyers. This idea also 

derives from diffusion-of-innovation theory, and is based on the concept that certain potential 

buyers could be interested in high-performance homes as technologically innovative, or as 

offering environmental benefits, financial advantages, or pacesetting benefits (including status 

and prestige of being among the first to own the innovation). The “early adopters” concept is 

generally accepted in the energy analysis community. 

Four items were used to test innovativeness and two were used to further test the environmental 

attitudes of the respondents. Respondents were asked to agree or disagree on a Likert scale, as 

previously described. In all four questionnaires these four items were presented as a set. The four 

early-adopter items were: 

1. I like to be as independent as possible so I don’t have to rely on others to meet my needs. 

2. I like to experiment with new ways of doing things. 

3. I am seen as a leader in my work life, social life, or volunteer activities. 

4. I am intrigued with new technology. 

Table 57 summarizes the findings for responses by SheaHomes versus comparison owners and 

by PV versus non-PV owners. No consistent patterns of differences are found that distinguish 

owners of SheaHomes from owners of comparison homes. Further, the items do not differentiate 

between owners of homes with and without solar PV systems. 

1

Other demographic characteristics of early adopters are discussed in the section on the study’s key dependent 

variables. See also Chapter 2, Guiding Ideas. 

144 
Summary 

Although environmentalism and early adopter characteristics are often associated with purchase 

of innovative “green” products, and high-performance homes could be seen as both innovative 

and protective of the environment, apparently these motivations do not distinguish the purchasers 

of high-performance homes from other new homebuyers. Indeed, support for environmental 

protection is so widespread in the population that we would be unlikely to discern differences 

among the categories of homebuyers at Scripps Highlands. The one difference found among the 

environmental variables is that a significantly higher percentage of SheaHomes purchasers link 

household energy consumption with environmental problems than are comparison home 

purchasers. In addition, as we have discussed, other home features are far more influential in 

home purchase decisions than are energy and environmental characteristics. 

145
Table 56. Environmentalism by Builder and by PV Ownership 

Item 

Shea 

Homes 

Mean 

Score 

Comparison 

Mean 

Score 

Shea 

Homes 

% 

Comparison 

% 

PV 

Owner 

Mean 

Score 

Non-PV 

Owner 

Mean 

Score 

PV 

We have a serious responsibility to preserve the environment for future generations. 

4.40 4.47 

A: 94 

U: 6 D: 0 

A: 92 

U: 8 D: 0 

4.37 4.42

 A

 U D

Individuals need to take personal responsibility for protecting the environment. 

4.36 4.41 

A: 94 

U: 6 D: 0 

A: 94 

U: 6 D: 0 

4.37 4.35 

A

UD

Many of the supposed threats to the environment have been greatly exaggerated.§ 

2.98 3.18 

A: 34 U: 34 D: 32 

A: 24 U: 43 D: 33 

3.12 2.88 

AUD

Current federal regulations provide adequate protection for the environment.§ 

2.99 3.26

 A: 29** 

U: 41 D: 29 

A: 18 U: 46 D: 36 

3.13 2.90 

AUD

*Household energy consumption is not a major contributor to environmental problems.§ 

3.30 3.45 

A: 22 U: 28 D: 50 

A: 8 

U: 51 D: 41 

3.38 3.24 

AUD

*Statistically significant difference between SheaHomes and comparison home respondents (

P2=11.165; p=.004; t-test results n.s.) 

** Does not add to 100 because of rounding A=Agree or strongly agree U=Unsure or neutral D=Disagree or strongly disagree 

§Scoring was inverted for these items; the higher the mean score, the more positive toward the environment. The percentages for the “agree” and “dis

reported in their original form; that is, they are not inverted. In this case, “agree” means a less environmental response, and “disagree” means a more e

146
Table 57. Early Adopter Characteristics and Environmental Attitudes by SheaHomes versus Comparison Buyers a

Item 

Shea 

Homes 

Mean 

Score 

Comparison Mean 

Score 

Shea 

Homes 

% 

Comparison 

% 

PV 

Owners 

Mean 

Score 

Non-PV 

Owners 

Mean 

Score 

PV 

I like to be as independent as possible so I don’t have to rely on others to meet my needs. 

4.19 4.31 A: 84** U: 13 

D: 4 

A: 88 U: 8 D: 4 

4.28 4.13 A: 

I am intrigued with new technology. 4.09 4.08 

A: 81 U: 16 D: 3 

A: 80 U: 18 D: 2 

4.16 4.04 

AU

D

I like to experiment with new ways of doing things. 3.92 3.75 

A: 74 U: 24 

D: 2 

A: 63 U: 35 D: 2 

4.01 3.85 

AU

D

I am seen as a leader in my work life, social life, or volunteer activities. 

3.82 3.96 

A: 67 U: 29 

D: 4

 A: 77** 

U: 22 D: 2 

3.85 3.79 

AU

D

I am willing to accept modification to my lifestyle if it helps the environment. 

3.74 3.76 

A: 66 U: 29 

D: 5 

A: 67 U: 31 D: 2 

3.75 3.75 

AU

D

I tend to buy environmentally friendly products, even if they cost more. 

3.46 3.29 

A: 47 U: 42 D: 11 

A: 41 U: 43 D: 16 

3.44 3.47 

AU

D

*No significant differences. ** Does not add to 100 because of rounding A=Agree or strongly agree U=Unsure or neutral D=Disagree or strongly disagree 

147
Chapter 17

Data Reduction

Introduction 

Many surveys provide vast amounts of 

information that cannot be usefully interpreted 

without some preliminary synthesis. Various 

statistical techniques are available for 

accomplishing the synthesis. Factor analysis is 

one such statistical technique by which several 

variables can be reduced to a smaller number of 

“factors” or “dimensions” by considering and 

identifying which of the original variables are 

similar to each other (Morrison 1976). In the 

context of survey research, factor analysis can 

be used to distill the total number of questions, 

items, or response variables that constitute a 

survey instrument down to a smaller set of 

factors that more succinctly reflect the answers 

of all the respondents. 

Each factor produced with factor analysis 

represents a weighted combination of some of 

the original variables. The numerical weights 

that are used to combine the variables to form 

factors are referred to as “factor loadings.” 

Factor loadings may vary from !1.0 to +1.0. A 

positive factor loading for a contributing 

variable indicates it is positively associated with 

the overall factor. Similarly, a negative factor 

loading for a contributing variable indicates it is 

negatively associated with the overall factor. 

Chapter Highlights 

C All scaled items in the questionnaires were factor 

analyzed, resulting in 49 factors, 29 of which 

applied to all respondents, thus permitting 

differentiation among them. 

C Reasons for purchase factors are builder 

reputation; safe, secure area; familiarity with area; 

convenience of access; and investment potential. 

C Factors for importance of home features are 

overall design; size of home and number of 

bedrooms, and single-story option. 

C Three factors for the perceived benefits of solar 

PV ownership are altruistic, financial, and 

personal satisfaction dimensions. 

C Five dimensions of barriers to PV purchase are 

unsureness about efficacy, effect on resale value, 

up-front cost, uncertainty about placement of 

system on roof, and indifference. 

C Satisfaction factors include the home’s quality, 

investment potential, intent to purchase energy 

efficiency and solar features in any new home 

purchased, and estimated monthly utility bill. 

C Factor analysis resulted in anti-environmentalism 

and pro-environmentalism factors. 

The most important factors are determined to be those that collectively explain a high percentage 

of the data variability. These are often the factors for which the highest factor loadings are 

determined. A high positive or negative factor loading from any variable or item indicates that 

the item helps to “define” the factor. In the survey context, the key definers are variables that are 

truly able to differentiate respondents into categories. 

Once the most important factors have been determined, they can be used to compute a factor 

response, or “factor score,” in much the same way that each original variable yields a response, 

value, or score. These factor scores can then be substituted for the values of the original variables 

in further analyses. 

148
Factor analysis was used to investigate many of the items and variables contained in the 

questionnaires for the present study.1

 A particular kind of factor analysis that involved varimax 

rotation (Dillon and Goldstein 1984) was used. The results of all the factor analyses from the 

study and all the items used in them are contained in Appendix H (Detailed Factor Analysis 

Results), and discussion of the factors and their defining items is provided in this chapter. 

Having determined the most important factors that represent the study variables, factor scores for 

each were computed for every respondent, and the scores were subsequently standardized (by 

using the statistical Z-transformation). All standardized factor scores essentially range from !3.0 

to + 3.0. These scores were ultimately used to help differentiate variations within market 

segments of the SheaHomes and comparison community respondents. All factor analyses were 

run on the entire set of respondents (including responses from SheaHomes and comparison 

community homeowners), unless the questions had been limited to a particular group, such as PV 

owners or main owners. 

This chapter is divided into three parts. The first part presents results of the factor analyses 

conducted on items asked of all respondents; that is, responses from the main, ineligible/early, 

PV, and comparison questionnaires. The second part presents results of the factor analysis 

conducted on an item asked only of main respondents. The third part presents results of the factor 

analyses conducted on responses to items asked only of PV respondents. 

1

Appendix F presents, for the study’s individual questions, base n’s, means, standard deviations, and 

coefficients of variation for scaled responses. 

149 
PART ONE: ITEMS ASKED OF ALL RESPONDENTS 

Introduction 

This part details findings from factor analyses that deal with answers from all respondents on sets 

of items in the questionnaire, including reasons for home purchases, home features, attitudes 

toward solar features, equipment owned, self-reported conservation behaviors, energy policy 

preferences, environmental attitudes, early adopter characteristics, and homeowner satisfaction 

with their new homes. 

Factor Analysis of Reasons for Purchase 

Factor analysis of 20 of the 24 potential reasons for purchasing a particular home resulted in six 

dimensions. These 20 items are discussed in Chapter 7 (New Home Search and Purchase 

Decision), and were asked of all respondents.2 

These six dimensions are as follows: 

1. Company reputation and performance 

2. Safe and secure area 

3. Familiarity with area 

4. Convenience of access 

5. Investment potential 

6. Other advantages of the location. 

Company Reputation and Performance 

The first dimension reflects respondent perception of and experience with the company that 

offered the home. The reputability of the builder, the helpfulness of the sales staff, and—to a 

lesser extent—the energy use aspects of the home combine to form this factor. The items that 

define this dimension and their factor loadings are shown below. 

Factor One: Company Reputation and Performance 

Eigenvalue3

=3.148 and % of variance explained=15.738 

Key Definers Factor Loadings 

Helpfulness of staff .821 

Builder reputation .811 

Availability of very energy-efficient home .696 

2

The other four items were asked only of subsets of respondents. 

3

Eigenvalues are a special set of scalars associated with a linear system of equations that are sometimes also 

known as characteristic roots, proper values, or latent roots (Marcus and Mini 1988). 

150 
Safe and Secure Area 

The second dimension reflects concerns that homebuyers with families exhibit, including if the 

neighborhood is safe and secure, if the schools are good, and, in general, if the home is in a highquality neighborhood. Three-quarters of the buyers have children; therefore, this factor clearly 

represents their concerns. The items that define this dimension and their factor loadings are 

shown below. 

Factor Two: Safe and Secure Area 

Eigenvalue=2.034 and % of variance explained=10.168 

Key Definers Factor Loadings 

Safe and secure feeling .819 

Quality of schools .627 

Quality of neighborhood/community .609 

Familiarity with the Area 

The third dimension reflects previous knowledge of the area and the people who live there, as 

well as common areas for family and friends to gather. Although related to the idea of a safe, 

secure, and high-quality neighborhood, familiarity with places and people forms a separate 

dimension. The items that define this dimension and their factor loadings are shown below. 

Factor Three: Familiarity with Area 

Eigenvalue=1.998 and % of variance explained=9.991 

Key Definers Factor Loadings 

Close to friends .797 

Close to parks/playgrounds .759 

Knows the area .577 

Convenience of Access 

The fourth dimension reflects the convenience of access to and from the neighborhood in terms 

of closeness to work (for example, the I-15 freeway that runs north and south and can be taken to 

downtown San Diego) and the proximity of shopping centers and other services. The items that 

define this dimension and their factor loadings are shown below. 

151
Factor Four: Convenience of Access 

Eigenvalue=1.961 and % of variance explained=9.805 

Key Definers Factor Loadings 

Close to work .735 

Near freeway .703 

Near services .673 

Investment Potential 

At the time they purchased their homes, respondents considered the investment potential of the 

property. The fifth dimension reflects this as a distinct area of concern. The items that define this 

dimension and their factor loadings are shown below. 

Factor Five: Investment Potential 

Eigenvalue=1.439 and % of variance explained=7.193 

Key Definers Factor Loadings 

Investment potential .694 

Desirable area .613 

Other Advantages of the Location 

The sixth dimension reflects other advantages of the property considered by homeowners at the 

time of purchase. The fact that these homes were exempt from Mello-Roos taxes—which could 

have amounted to as much as $500 to $700 a month—was a positive consideration. In addition, 

because the homes were built on a mesa some 15 miles east of the Pacific Ocean, they had 

pleasing views of the surrounding hills and, in some cases, of the ocean. The items that define this 

dimension and their factor loadings are shown below. 

Factor Six: Other Advantages of the Location 

Eigenvalue=1.409 and % of variance explained=7.045 

Key Definers Factor Loadings 

No Mello-Roos taxes .586 

Great view .581 

Together, these six dimensions (factors) account for 59.940% of the variance in responses to the 

20 items. These findings convey the sense that homeowners in the upper-middle price range are 

differentiated in looking at their experience with the product and the sales staff; whether the 

homes are located in a safe, secure quality area; their familiarity with the area and its people; the 

convenience of the homes relative to their places of business, freeway access, and services; 

152 
whether the home will increase in value; and other distinct advantages the homes might have, 

such as lower taxes than other new homes and views. These dimensions together help to describe 

the key attributes and experiences that a variety of new homebuyers desire. 

Factor Analysis of Home Features 

The four questionnaires replicated 15 items that pertain to the importance of the features of the 

house in the home-purchase decision, and these were similarly factor analyzed. The items form 

three major dimensions: 

1. Overall design 

2. Size of home and number of bedrooms 

3. Single-story option. 

Overall Design 

The first dimension reflects aspects of the house’s overall design, including quality of light, sense 

of openness, spaciousness, layout, and architectural design. The items that define this dimension 

and their factor loadings are shown below. 

Factor One: Overall Design 

Eigenvalue=3.726 and % of variance=24.840 

Key Definers Factor Loadings 

Quality or sense of light .766 

Spaciousness/openness .741 

Floor plan/layout .699 

Architectural design .690 

Size of Home and Number of Bedrooms 

The second dimension reflects the homebuyers’ interest in the number of bedrooms and the 

overall square footage of the home. SheaHomes offered a room that could optionally be a sixth 

bedroom or a study, playroom, or other space. This option was commented on favorably by 

several homeowners in the qualitative interviews before the survey. The items that define this 

dimension and their factor loadings are shown below. 

Factor Two: Size of Home and Number of Bedrooms 

Eigenvalue=2.253 and % of variance=15.022 

Key Definers Factor Loadings 

Number of bedrooms .764 

Size/square footage .693 

153
Single-Story Option 

The third dimension concerned the availability of a single-story option, offered only by 

SheaHomes. This option was critical to a subset of the homebuyers, according to information 

obtained in the qualitative interviews. Because SheaHomes had limited the number of singlestory homes, some homebuyers reported they had reluctantly purchased two-story homes because 

they loved the area, builder, and other aspects of the homes, but that single-story homes were not 

available. Most homebuyers had children and were happy with the two-story option. The item 

that defines this dimension and its factor loading are shown below. 

Factor Three: Single-Story Option 

Eigenvalue=2.094 and % of variance=13.960 

Key Definer Factor Loading 

Single-story option .739 

Together, these three dimensions (factors) account for 53.822% of the variance in response to the 

15 items. The findings convey the sense that architectural design and, perhaps surprisingly, the 

quality of light, are key aesthetic aspects that appeal to different homebuyers. The size of the 

homes and the number of bedrooms (as well as the versatility of the bedrooms for other uses) is 

also a distinct consideration. For some buyers, a single-story option is important. The availability 

of this option may have contributed to age diversity in the SheaHomes neighborhoods. 

Factor Analysis of Attitudes toward Solar Features 

Four items queried respondents on how desirable and cost effective solar water heating and solar 

PV systems are in new homes, as discussed in Chapter 13 (Attitudes toward Solar Features in 

New Housing). Interestingly, the factors do not form around solar technologies, but instead form 

around the desirability of the innovations and their cost effectiveness. The factor analysis of these 

four variables resulted in two dimensions: 

1. Desirable innovation 

2. Cost effectiveness of solar features. 

Desirable Innovation 

The first dimension reflects the sense that solar PV and solar water heating systems are desirable 

innovations for new homes. The items that define this dimension and their factor loadings are 

shown below. 

Factor One: Desirability of Solar Features as Innovations 

Eigenvalue=1.804 and % of variance explained=45.092 

Key Definers Factor Loadings 

Solar PV systems are a desirable innovation for new homes .930 

Solar water heating systems are a desirable innovation for new homes .922 

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Cost Effectiveness of Solar Features 

The second dimension reflects the sense that solar PV and solar water heating systems are cost 

effective. The items that define this dimension and their factor loadings are shown below. 

Factor Two: Cost Effectiveness of Solar Features 

Eigenvalue=1.737 and % of variance explained=43.430 

Key Definers Factor Loadings 

Solar PV systems are cost effective .927 

Solar water heating systems are cost effective .892 

Together, these two dimensions (factors) account for 88.522% of the variance in response to the 

four items. The difference in perceived desirability and cost effectiveness of the solar features 

distinguishes among respondents. 

Factor Analysis of Equipment Owned 

Nine questions asked respondents if they owned the following equipment, which was potentially 

relevant to their energy use:4

 two refrigerators, ceiling fans, standalone freezers, dimmer switches 

for lights, CFLs, hot tubs, hot water flow regulators, pools, and dual-zone heating/airconditioning systems. The factor analysis of these nine variables resulted in four dimensions: 

1. Equipment for fun: pool and hot tub combination 

2. Smaller efficiency measures 

3. Convenience measures 

4. Measures for comfort. 

Equipment for Fun: Pool and Hot Tub Combination 

The first dimension reflects a tendency to own both a pool and hot tub. The items that define this 

dimension and their factor loadings are shown below. 

Factor One: Equipment for Fun: Pool and Hot Tub Combination 

Eigenvalue=1.448 and % of variance explained=16.092 

Key Definers Factor Loadings 

Pool .797 

Hot tub .753 

4

See Appendix RC (Respondent Characteristics). 

155 
Smaller Efficiency Measures 

The second dimension includes smaller efficiency measures such as dimmer switches and ceiling 

fans. The items that define this dimension and their factor loadings are shown below. 

Factor Two: Smaller Efficiency Measures 

Eigenvalue=1.399 and % of variance explained=15.549 

Key Definers Factor Loadings 

Dimmer switches for lights .778 

Ceiling fans .738 

Convenience Measures 

The third dimension seems to reflect a convenience factor. The hot water flow regulator keeps 

hot water on tap in the bathrooms distant from the hot water tank, which prevents several 

minutes of running the tap waiting for hot water to arrive from the other end of the house. Having 

a second refrigerator allows for extra storage of food and beverages for parties and large families. 

The items that define this dimension and their factor loadings are shown below. 

Factor Three: Convenience Measures 

Eigenvalue=1.316 and % of variance explained=14.617 

Key Definers Factor Loadings 

Hot water flow regulator .749 

Two refrigerators .679 

Dual-zone Heating and Air-Conditioning 

The fourth dimension seems to reflect a subset of respondents who owned features at opposite 

ends of a spectrum (note the negative value on CFLs). These respondents own dual-zone heating 

and air-conditioning systems particularly in two-story homes. 

Factor Four: Dual-zone Heating and Air-Conditioning 

Eigenvalue=1.164 and % of variance explained=12.939 

Key Definers Factor Loadings 

Dual-zone heating/air-conditioning system .702 

Compact fluorescent light bulbs (CFLs) –.690* 

*Note negative value. 

Together, these four dimensions (factors) account for 59.197% of the variance in response to the 

nine questions on equipment ownership. Only one expresses a preference for conservation and 

156 
that on only a limited scale. Comfort and convenience are more strongly supported among these 

factors that differentiate among homeowners. 

Factor Analysis of Self-Reported Resource Conservation Behaviors 

Respondents were presented with 12 statements on energy conservation behaviors (Table 54 in 

Chapter 15). Ten of these were included in a factor analysis that optimized the best, most 

interpretable solution.5

 The questions were designed to elicit information on resource 

conservation and environmental behaviors. This factor analysis resulted in three dimensions: 

1. Adjusting thermostats 

2. Resource conscious (energy and water) 

3. Conserve water and electricity. 

Adjusting Thermostats 

The first dimension reflects energy-conserving behavior by adjusting thermostats in winter, 

summer, and when away. The items that define this dimension and their factor loadings are 

shown below. 

Factor One: Adjusting Thermostats 

Eigenvalue=2.196 and % of variance explained=21.963 

Key Definers Factor Loadings 

In winter, set thermostat at 70°F or lower .902 

In summer, set thermostat at 75°F or higher .844 

When away, modify thermostat settings .756 

Resource Consciousness 

The second dimension seems to reflect energy and water resource consciousness. Interestingly, 

the practice of xeriscaping does not factor with conserving water, which is in the next dimension, 

but instead factors with using less air-conditioning, driving a fuel-efficient car, and being more 

energy conscious. The items that define this dimension and their factor loadings are shown 

below. 

5

The statements on being thrifty and regularly recycling were omitted from this analysis to optimize the 

solution. 

157 
Factor Two: Resource Consciousness 

Eigenvalue=2.032 and % of variance explained=20.324 

Key Definers Factor Loadings 

Practice xeriscaping .767 

Use air conditioning less than in previous home .751 

Drive fuel-efficient vehicles .670 

More energy conscious than we used to be .559 

Conserve Water and Electricity 

The third dimension reflects both energy and water conservation. The items that define this 

dimension and their factor loadings are shown below. 

Factor Three: Conserve Water and Electricity 

Eigenvalue=1.958 and % of variance explained=19.579 

Key Definers Factor Loadings 

Conserve water .808 

Turn off lights .774 

Turn off computers .756 

Together, these three dimensions account for 61.866% of the variance in response to the 10 items 

on resource conservation behaviors, as reported by respondents. Adjusting thermostats appears to 

be an energy-efficiency dimension that differentiates among respondents, as does resource 

consciousness. 

Factor Analysis of Energy Policy Preferences 

Eleven statements elicited respondents’ preferences among policies relative to high-performance 

homes. The factor analysis of these items resulted in three major dimensions: 

1. Financial incentives 

2. Policies relative to builders and ZEHs 

3. Subsidizing affordable housing. 

Financial Incentives 

The first dimension reflects responses that show preferences for financial incentives for energy 

efficiency and solar PV and water heating systems, including rebates and tax credits from various 

158
levels of government and utility companies. This reflects a preference for “money back” policies 

for energy efficiency and solar features. 

Factor One: Financial Incentives 

Eigenvalue=3.084 and % of variance explained=28.036 

Key Definers Factor Loadings 

California Energy Commission should give rebates for PV systems. .847 

California Energy Commission should give rebates for solar water heating. .817 

Federal income tax credits should be provided to buyers of energy-efficient 

homes. .804 

Utility companies should give rebates for energy-efficient appliances. .750 

Policies Relative to Builders and ZEHs 

The second dimension seems to revolve around the policies of builders and government policies 

that would affect builders. This dimension reflects a preference for private-industry approaches. 

Factor Two: Policies Relative to Builders and ZEHs 

Eigenvalue=3.081 and % of variance explained=28.011 

Key Definers Factor Loadings 

Builders should build energy-efficient homes if they cost less. .869 

Builders should build energy-efficient homes if they are comfortable and look 

better. .810 

Energy packages should be standard if cost effective. .704 

Federal government should support research on ZEHs. .622 

Homes technically rated efficient should be given an ENERGY STAR label. .608 

Subsidizing Affordable Housing 

The third dimension reflects a subset of responses that support subsidies for solar features so that 

they can be used in affordable housing. The items that define this dimension and their factor 

loadings are shown below. 

159
Factor Three: Pro-Subsidies Position 

Eigenvalue=2.222 and % of variance explained=20.199 

Key Definers Factor Loadings 

Affordable housing subsidies for PV systems .897 

Affordable housing subsidies for solar water heating systems .896 

Together, these three dimensions account for 76.246% of the variance in response to the 11 items 

on energy policy preferences. Interestingly, the three dimensions that differentiate among 

respondents reflect a “money back” position, a “private industry” position, and a position that 

supports solar features in affordable housing. 

Factor Analysis of the New Environmental Paradigm 

Five items from the New Environmental Paradigm (NEP) scale, recommended by Van Liere 

(2003), were included to measure differentiation among buyers of high-performance homes on 

these attitudes. These items are discussed in Chapter 16 (Environmentalism and Early Adopter 

Characteristics, Table 56 in Chapter 16). The factor analysis of these items resulted in two 

dimensions: 

1. Anti-environmentalism 

2. Environmentalism. 

Anti-Environmentalism 

The first dimension reflects anti-environmentalist sentiments relative to environmental protection 

and to housing’s role in environmental problems. The items that define this dimension and their 

factor loadings are shown below. 

Factor One: Anti-Environmentalism 

Eigenvalue=2.038 and % of variance explained=40.757 

Key Definers Factor Loadings 

Current federal regulations provide adequate protection for the environment. .844 

Many of the supposed threats to the environment have been greatly exaggerated. .836 

Household energy consumption is not a major contributor to environmental 

problems. .775 

Environmentalism 

The second dimension reflects a positive attitude toward environmental protection. The items 

that define this dimension and their factor loadings are shown below. 

160 
Factor Two: Environmentalism 

Eigenvalue=1.561 and % of variance explained=31.221 

Key Definers Factor Loadings 

Individuals need to take responsibility for protecting the environment. .888 

We have a serious responsibility to preserve the environment for future 

generations. .863 

Together, these two dimensions account for 71.978% of the variance in response to the five items 

from the NEP scale. This result suggests that both pro- and anti-environmental respondents were 

included in the study. 

Factor Analysis of Early Adopter Characteristics 

Six items derived from earlier studies of early adopter characteristics and environmental 

protection (Farhar and Coburn 2000) were included in the study to measure differences on early 

adopter characteristics (four items) and environmental attitudes (two items) among categories of 

homebuyers, resulting in two dimensions: 

1. Early adopter characteristics 

2. Environmental attitudes. 

Early Adopter Characteristics 

The first dimension includes the variables that are intended to measure early adopter 

characteristics. The items that define this dimension and their factor loadings are shown below. 

Factor One: Early Adopter Characteristics 

Eigenvalue=1.757 and % of variance explained=29.289 

Key Definers Factor Loadings 

Seen as leader in work, social, or volunteer .775 

Like to be as independent as possible .722 

Like to experiment with new ways of doing things .607 

Environmental Attitudes 

The second dimension includes the two items that are intended to measure environmental 

attitudes different from the NEP items. The items that define this dimension and their factor 

loadings are shown below. 

161
Factor Two: Environmental Attitudes 

Eigenvalue=1.673 and % of variance explained=27.881 

Key Definers Factor Loadings 

Buy environmentally friendly even if it costs more .867 

Willing to modify lifestyle to help the environment .835 

Together, these two dimensions account for 57.171% of the variance in response to the six items 

on early adopter characteristics and environmental attitudes. Each factor represents an 

interpretable part of the six-item scale. 

Factor Analysis of Satisfaction 

Sixteen variables, asked of all respondents and intended as operationalized measures of 

homebuyer satisfaction, were factor analyzed. The variables are discussed in Chapter 10 

(Homebuyer Satisfaction with the Purchased Home). They include questions about the perceived 

comfort and energy efficiency of the home; ratings of satisfaction on key aspects, such as location, 

investment potential, builder reputation, layout of the home, storage, lot size, construction quality, 

and number of thermostats; the respondent’s estimated monthly utility bill for the home; and 

whether a respondent would, in the future, purchase a home that is highly energy efficient, has 

solar water heating, and comes with a solar PV system. 

These items form four major dimensions: 

1. Quality of construction, builder reputation, and comfort 

2. Investment potential of home 

3. Behavioral intention with respect to energy features in future home purchases 

4. Estimated monthly utility bill. 

The cumulative percentage of variance in response explained by these five dimensions is 67.643. 

Quality of Construction, Builder Reputation, and Comfort 

The first dimension reflects satisfaction with the quality and performance of the home. Items that 

define this dimension have to do with the perceived quality of construction, the reputation of the 

builder, and the home’s comfort. These defining items and their factor loadings are shown below. 

162
Factor One: Quality of Construction, Builder Reputation, and Comfort 

Eigenvalue=4.725 and % of variance explained=20.703 

Key Definers Factor Loadings 

Satisfaction with quality of construction .831 

Satisfaction with builder reputation .743 

Perceived comfort of home overall .630 

Certain buyers are more concerned about the quality of their homes. This dimension expresses the 

ways in which quality is “operationalized” in these buyers’ thinking—a home’s quality is 

enhanced by better construction, an excellent builder reputation, large size (i.e., 3,000 ft2 or more), 

and comfort. 

Investment Potential of Home 

The second dimension reflects satisfaction with the home’s investment potential. Factoring with 

the investment potential item is satisfaction with the home’s location and size. 

Factor Two: Investment Potential of Home 

Eigenvalue=2.620 and % of variance explained=16.377 

Key Definers Factor Loadings 

Satisfaction with home’s location .758 

Satisfaction with investment potential of home .726 

Satisfaction with size/square footage .724 

This dimension appears to represent variables that, together, differentiate among homeowners on 

an aspect of satisfaction pertaining to the homes value because of where it is located, how large it 

is, and the expectation that it will have good resale value. 

Behavioral Intention with Respect to Energy Features in Future Home Purchases 

The third dimension reflects satisfaction as measured by statements about potential future 

decisions about energy efficiency and solar features in any new home the respondent might 

purchase. Expressions of intent to purchase these features are taken to mean that respondents are 

satisfied with their current experience with these features or wish they had them, if they do not 

currently own them. 

163
Factor Three: Behavioral Intention with Respect to 

Energy Efficiency and Solar Features in Future House Purchases

Eigenvalue=2.356 and % of variance explained=14.726 

Key Definers Factor Loadings 

Will buy solar water heating in new home .879 

Will buy solar PV in new home .874 

Will buy energy efficiency in new home .827 

The solar features especially define this dimension on behavioral intention. Whether homeowners 

lived in homes with solar features or interacted with neighbors who lived in such homes, this 

dimension reflects their interest in ZEH features. 

Estimated Monthly Utility Bill 

The questionnaires asked all respondents to estimate their monthly utility bills. These responses 

were recoded from the actual dollar amount reported by dividing the dollar amounts into five 

categories, with each category containing 20% of the responses. The categories are (1) #$80 per 

month, (2) $80.01–$110, (3) $110.01–$150, (4) $150.01–$210, and (5) >$210. The resulting 

scale, from 1 to 5, matched the scales of the other items used in the factor analysis. This item, 

thought to measure a part of satisfaction with the home’s perceived performance, resulted in a 

fourth dimension of satisfaction. 

Factor Four: Reported Monthly Utility Bill 

Eigenvalue=1.152 and % of variance explained=7.201 

Key Definer Factor Loading 

Estimated monthly utility bill .865 

The assumption is that the lower the estimated utility bills, the more satisfied homeowners would 

be. 

Together, the four dimensions (factors) for satisfaction with the homes account for 67.643% of the 

variance in response. This tells us that these four dimensions—the home’s perceived quality, its 

investment potential, whether the owners would purchase energy-efficiency and solar features in a 

new home they might buy, and their estimated monthly utility bills—represent important and 

distinct dimensions of homeowner satisfaction. 

164
PART TWO: ITEMS ASKED OF MAIN RESPONDENTS ONLY

A multi-part question, asked only of main respondents, explored their reasons for deciding not to 

purchase a solar PV system. This part of the chapter presents the findings for these questions. 

These respondents had the option (at least theoretically) to purchase a 1.2-kW system for $6,000 

or a 2.4-kW PV system for $10,000.6 

Factor Analysis of Barriers to Solar PV Systems 

Thirteen variables that could have been perceived as barriers to PV purchase were factor analyzed. 

These are described in Chapter 8, Solar PV Purchase Decision. They include statements about the 

added expense of the PV system, reliability, maintenance, trade-offs with other options being 

offered, lack of knowledge about how it would work, aesthetics, property taxes, homeowners 

insurance premiums, resale value, and indifference. 

The factor analysis resulted in five dimensions: 

1. Unsure about efficacy of solar PV technology 

2. Solar PV might negatively affect resale value 

3. Tangible, immediate financial barriers 

4. Uncertainty about PV system placement on the roof 

5. Indifference to PV. 

Unsure about Efficacy of Solar PV Technology 

The first dimension reflects some main respondents’ concerns about the performance of solar PV 

systems—whether they would soon become outdated, whether they would require costly and 

difficult maintenance, and whether they would produce electricity reliably. The items that define 

this dimension and their factor loadings are shown below. 

Factor One: Unsure about Efficacy of Solar PV Technology 

Eigenvalue=2.644 and % of variance explained=20.335 

Key Definers Factor Loadings 

Could become outdated technologically .841 

Concerned about maintenance issues .788 

Unsure about the reliability of system .783 

This dimension expressed concerns similar to those included in the first factor on perceived 

barriers to solar PV adoption among Colorado homeowners who might consider a PV retrofit 

(Farhar and Coburn 2000, p. 53). 

6

As discussed earlier, some main respondents do not recall being offered a PV system. 

165 
Solar PV Might Negatively Affect Home Value 

The second dimension appears to reflect concerns of some main respondents that solar PV 

systems might negatively affect the home’s resale value. Aesthetic considerations could play a 

part in these concerns because the second highest loading variable refers to how the systems look. 

Interestingly, the factor loadings are negative on (1) the length of payback and (2) the idea that the 

PV system was too expensive, which indicates that this factor is not about the up-front expense 

and operational performance of the system. The items that define this dimension and their factor 

loadings are shown below. 

Factor Two: Solar PV Could Negatively Affect Home Value 

Eigenvalue=2.158 and % of variance explained=16.601 

Key Definers Factor Loadings 

Thought it would negatively affect resale value .784 

Did not like how the system looks .598 

Payback would be too long –.822* 

*Note negative values. 

Tangible, Immediate Financial Barriers 

The third dimension reflects concerns on the part of some main respondents that a PV system 

might result in unanticipated extra costs, such as increased homeowner insurance premiums and 

property taxes based on increased home valuation. These concerns suggest that the SheaHomes 

sales staff might not have explained the broader aspects of PV ownership to prospective 

homebuyers. The items that define this dimension and their factor loadings are shown below. 

Factor Three: Immediate Financial Barriers 

Eigenvalue=2.143 and % of variance explained=16.485 

Key Definers Factor Loadings 

Thought homeowner’s insurance premium would increase .927 

Thought property taxes would increase .919 

Uncertainty about PV System Placement on the Roof 

The fourth dimension reflects the uncertainty that some main respondents experienced in trying to 

decide about a PV purchase. They knew they could afford to purchase the system (note the 

negative factor loading on the statement that the system was too expensive), yet they could not 

visualize how the system would fit on their roofs. The items that define this dimension and their 

factor loadings are shown below. 

166
Factor Four: Uncertainty about PV System Placement on the Roof

Eigenvalue=1.773 and % of Variance Explained=13.637

Key Definers Factor Loadings 

Didn’t know where it would go on the roof .860 

It was too expensive –.679* 

*Note negative value 

Relative Indifference to PV 

The fifth dimension reflects a sense of relative indifference to solar PV systems on the part of 

some main respondents. Other features were apparently more important to these buyers. They 

were concerned that the presence of solar PV systems could negatively affect the resale value of 

their homes. It appears that the SheaHomes sales staff did not convince these buyers that solar PV 

was a good deal. The items that define this dimension and their factor loadings are shown below. 

Factor Five: Indifference to PV 

Eigenvalue=1.364 and % of variance explained=10.496 

Key Definers Factor Loadings 

Thought it would negatively affect resale value .848 

Wanted other options .523 

Didn’t know enough to evaluate .507 

Together, these five dimensions (factors) account for 77.554% of the variance in response to the 

13 items. The findings convey the sense that an extra educational effort about PV and its benefits, 

as well as conveying to potential buyers the bargain prices for which the PV systems were being 

offered, would have convinced more of the main buyers who were actually offered PV systems to 

purchase them. AstroPower, Inc., was not represented on site by personnel, and there was a 

paucity of literature about the PV systems at the sales office. 

167
PART THREE: ITEMS ASKED ONLY OF PV OWNERS 

Introduction 

Certain questions were asked only of owners of homes with PV systems.7

 These included 

questions on (1) perceived benefits of PV ownership, (2) PV system attributes, (3) information 

channels on PV used, (4) uses of the digital display, (5) aspects learned about PV systems since 

living with them, and (6) operational aspects of PV systems. 

Factor Analysis of Perceived Benefits of Solar PV Ownership 

Fifteen statements on potential benefits of owning a PV system elicited respondents’ agreement or 

disagreement (see Table 39 in Chapter 11, Experience with Solar PV Systems). The factor analysis 

of these items resulted in three dimensions: 

1. Altruistic benefits 

2. Financial benefits 

3. Personal satisfaction. 

Altruistic Benefits 

The first dimension reflects responses for what are termed the “altruistic” benefits of PV ownership. 

These include helping to reduce global warming, helping the local economy, benefitting future 

generations, and helping to improve air quality in the area. This factor closely resembles the first 

factor of perceived benefits of PV ownership, termed “environmental benefits,” in the study of 

Colorado homeowners (Farhar and Coburn 2000, p. 51). The items that define this dimension and 

their factor loadings are shown below. 

Factor One: Altruistic Benefits 

Eigenvalue=4.120 and % of variance explained=27.467 

Key Definers Factor Loadings 

Helps reduce global warming .877 

Helps San Diego’s economy .849 

Benefits future generations .824 

Helps improve air quality in the area .724 

7

These are discussed in Chapter 11 (Experience with Solar PV Systems). 

168 
Financial Benefits 

The second dimension reflects responses to items that focus on the financial advantages of PV 

ownership. These include reduced electricity bills, free electricity once the system is paid for, and 

selling excess electricity back to the utility. This factor is quite similar to the second factor of 

perceived benefits of PV ownership, termed “financial advantages,” in the Colorado homeowner 

study (Farhar and Coburn 2000, pp. 51–52). The items that define this dimension and their factor 

loadings are shown below. 

Factor Two: Financial Benefits 

Eigenvalue=3.613 and % of variance explained=51.552 

Key Definers Factor Loadings 

Reduces electricity bills .792 

Provides free electricity once system is paid for .750 

Sell excess electricity back to utility .692 

Increases home’s resale value .604 

Personal Satisfaction 

The third dimension reflects responses that appear to focus on the personal satisfaction that can be 

derived from owning and living with a PV system. These include a sense of self-sufficiency, being 

technologically innovative, increasing the home’s resale value, and feeling good about the entire 

experience. The items that define this dimension and their factor loadings are shown below. 

Factor Three: Personal Satisfaction 

Eigenvalue=2.409 and % of variance explained=16.062 

Key Definers Factor Loadings 

Increases self-sufficiency .832 

Technologically innovative .713 

Feels good to have it .556 

Together, these three dimensions account for 67.615% of the variance in response to the 15 items 

on the perceived benefits of PV ownership. Two of these dimensions—altruistic benefits 

(including benefits to the environment) and financial benefits are quite similar to factors that 

resulted from a study of Colorado homeowners when they were asked about the benefits of 

retrofitting a PV system. This suggests that these dimensions differentiate not only among new 

homebuyers, but also among homeowners who might contemplate adding PV systems to their 

homes. 

169
Factor Analysis of Solar PV System Attributes 

The importance of nine attributes of PV systems was measured (Table 40 in Chapter 11, 

Experiences with PV systems). These variables include amount of electricity produced, ease of 

maintenance, warranty, aesthetics, digital display, upgrade capability, net metering, financing 

through a home mortgage, and owning the solar PV system outright. The factor analysis of these 

items resulted in two dimensions: 

1. Solar PV system performance 

2. Investment qualities of PV systems. 

Solar PV System Performance 

The first dimension reflects responses on the importance of PV system performance and 

reliability, as well as feedback on the amount of electricity produced so that PV owners will know 

the system is performing well. The items that define this dimension and their factor loadings are 

shown below. 

Factor One: Solar PV System Performance 

Eigenvalue=2.966 and % of variance=32.960 

Key Definers Factor Loadings 

Amount of electricity produced .811 

Digital display showing electricity production and consumption .800 

Length of warranty .797 

Ease of maintenance .757 

Investment Qualities of PV Systems 

The second dimension appears to reflect the importance of financial and investment aspects of the 

PV system. System attractiveness and unobtrusiveness are perceived to be related to the resale 

value of the home. Financing through the mortgage ameliorates any difficulty of paying up-front 

costs. The inference in upgrade capability is that, if a system is performing well, a retrofit of 

additional panels would result in more electricity production, which would result in even lower 

electricity bills. As described earlier, net metering represents the ability to sell one’s excess 

electricity back to the utility company and, in effect, to use the utility grid for storage and avoid 

the cost and space requirements for battery backups. The items that define this dimension and 

their factor loadings are shown below. 

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Factor Two: Investment Qualities 

Eigenvalue=2.432 and % of variance explained=27.027 

Key Definers Factor Loadings 

Attractiveness/unobtrusiveness of the system .753 

Finance through home mortgage .740 

Upgrade capability .709 

Net metering .631 

Together, these two dimensions account for 59.987% of the variance in response to the nine items 

that measure the importance of PV attributes. These two dimensions are similar to two of the 

factors identified in the Colorado homeowners study (Farhar and Coburn 2000, pp. 54–55), which 

suggests that solar PV system performance and the financial arrangements are two critical 

dimensions that differentiate among new homebuyers and among homeowners in the PV retrofit 

market. 

Factor Analysis of Information Channels Used 

PV owners were asked which channels they had used to gather information about their PV 

systems. The questionnaire mentioned four such channels: a video on operations and maintenance, 

a fact sheet about solar PV systems, an operating manual, and Web sites (see Chapter 9, 

Knowledge and Information). The factor analysis of these four items resulted in three dimensions: 

1. Operating manual for the solar PV system 

2. Web sites 

3. Video. 

Because the four items resulted in three factors that differentiate among homebuyers, we posit that 

different homebuyers must have used different information channels. 

Operating Manual for the Solar PV Systems 

The first dimension primarily reflects the operating manual for PV systems that AstroPower, Inc., 

provided. This item and its factor loading are shown below. 

Factor One: Operating Manual for Solar PV System 

Eigenvalue=1.164 and % of variance explained=29.111 

Key Definer Factor Loading 

Operating manual for PV systems .868 

171
Web Sites 

The second dimension primarily reflects Web sites. This item and its factor loading are shown 

below. 

Factor Two: Web Sites 

Eigenvalue=1.152 and % of variance explained=28.802 

Key Definers Factor Loading 

Web sites .920 

Video 

The third dimension primarily reflects a video on operations and maintenance. This item and its 

factor loading are shown below. 

Factor Three: Video 

Eigenvalue=1.135 and % of variance explained=28.375 

Key Definer Factor Loading 

Video on operations and maintenance .920 

The fact sheet was not a defining item in any of the factors. Together, these three dimensions 

account for 86.288% of the variance in response to the four items on information channels used. 

The fact that these four items form three dimensions that differentiate among homeowners 

suggests that there were few information channels and that most homeowners, if they had 

information at all, had it from only one or two sources. This further underscores the relative lack 

of information on PV systems, even to PV owners, discussed earlier. 

Factor Analysis of Uses of Digital Display 

Seven statements about the potential uses of the PV system’s digital display (see Table 42 in 

Chapter 11, Experiences with PV Systems) were factor analyzed. These items dealt with the 

effects of the display—which provides feedback on electricity production and consumption in a 

home in real time—on energy-related behaviors. The factor analysis resulted in two dimensions: 

1. Use of the display for feedback on system performance and electricity use 

2. Optimizing electricity cost savings. 

172
Use of the Display for Feedback on System Performance and Electricity Use 

The first dimension reflects responses that pertain to using feedback about electricity use to 

manage energy-related behaviors and monitor electricity production and use. The items that define 

this dimension and their factor loadings are shown below. 

Factor One: Feedback on System Performance and Electricity Use 

Eigenvalue=2.339 and % of variance=33.411 

Key Definers Factor Loadings 

Help change energy-consuming behavior .788 

Obtain feedback on electricity use .744 

Become more sensitive to household electricity consumption .621 

Determine if the system is functioning .592 

Determine if anything is left turned on in the house .579 

Optimizing Electricity Cost Savings 

The second dimension reflects responses relative to record keeping on electricity production and 

consumption and scheduling of electricity-consuming activities to optimize the financial benefits 

of the PV system. This suggests that, when possible, a household might try to use appliances on 

sunny afternoons when their electricity production is highest and might avoid other uses at that 

time or when their electricity production is lower. In the qualitative study, interviewees expressed 

surprise that their kitchen “can” lights used so much electricity and indicated that they are careful 

with their use of kitchen lights. One homeowner had replaced his can lights with CFLs. The items 

that define this dimension and their factor loadings are shown below. 

Factor Two: Optimizing Electricity Cost Savings 

Eigenvalue=1.552 and % of variance explained=22.170 

Key Definers Factor Loadings 

Record cumulative electricity production and consumption .878 

Optimally schedule electricity-consuming chores .780 

Together, these two dimensions account for 55.581% of the variance in response to the seven 

items about uses of the PV system’s digital display. Considering that most systems are small 1.2­

kW systems that provide only a portion of a home’s electricity, it seems remarkable that some PV 

owners are using their displays as much as they are to glean feedback about their electricity use 

and to adapt their behavior to optimize their PV systems’ benefits to their households. Ownership 

of even small PV systems providing only a portion of household electricity seems to draw 

attention to electricity use. 

173
Factor Analysis of Aspects Learned Since Living with Solar PV Systems 

A factor analysis of seven items on aspects of PV systems about which PV homeowners might 

have gleaned knowledge since living in their homes (see Chapter 9, Knowledge and Information) 

resulted in two dimensions: 

1. Monetary benefits 

2. Technical aspects. 

Monetary Benefits 

The first dimension reflects responses about the advantageous monetary or financial benefits of 

PV ownership. The items that define this dimension and their factor loadings are shown below. 

Factor One: Monetary Benefits 

Eigenvalue=1.736 and % of variance explained=24.795 

Key Definers Factor Loadings 

Learned about amount or percentage of electricity use that the solar PV system 

produces .776 

Learned about tax credits or rebates to help offset the cost .699 

Learned about payback period for PV system purchase .689 

Technical Aspects 

The second dimension reflects responses that seem to pertain to the technical aspects of the PV 

system, such as net metering, interconnectivity, and how the system works. The items that define 

this dimension and their factor loadings are shown below. 

Factor Two: Technical Aspects 

Eigenvalue=1.645 and % of variance squared=23.500 

Key Definers Factor Loadings 

Learned about net metering and interconnecting with the grid .880 

Learned about how the system works .754 

Together, these two dimensions account for 54.667% of the variance in response to the seven 

items on learning about PV systems that differentiate among PV system owners. 

174
Factor Analysis of Operational Aspects of Solar PV Systems 

As discussed in Chapter 11 (Experience with PV Systems), eight statements were provided to the 

PV owners to determine how well informed they were about some operational aspects of solar PV 

systems. Most of the statements presented were true but others were false. The inaccurate 

statements were based on misperceptions discovered by listening to interviewees who were PV 

owners during the qualitative phase of the research.8 The factor analysis of these eight items 

resulted in three dimensions: 

1. Relationship with the utility company 

2. Electricity bill payments 

3. One electric meter. 

Relationship with the Utility Company 

The first dimension reflects aspects of the PV owners’ relationship with SDG&E. These 

statements are all accurate. The items that define this dimension and their factor loadings are 

shown below. 

Factor One: Relationship with the Utility Company 

Eigenvalue=1.750 and % of variance explained=21.871 

Key Definers Factor Loadings 

Have interconnectivity agreement with utility .685 

Utility demand at peak on sunny afternoons .663 

Utility bills PV owners only for the electricity consumed .603 

PV owner is paid retail rate for electricity put into the grid .518 

Electricity Bill Payments 

The second dimension pertains to the electricity bill payments. The PV owners had the option of 

paying their electricity bills annually (although they receive monthly statements that inform them 

about how much electricity they are using) or monthly in the traditional way. In addition, some 

respondents believe they will receive checks from the utility company at the end of each year. 

This perception is indeed inaccurate because California law provides net metering only to zero, 

which means any excess electricity produced by a home beyond the amount consumed by that 

home is provided gratis to the utility company. The negative value of the factor loading for this 

8

This item does not reflect an exhaustive list of misperceptions, but only the more commonly heard ones. 

For example, not asked about on this list was the efficiency of the PV system, which is, factually, less than its 

kilowatt rating, according to an AstroPower, Inc., spokesman. 

175 
item shows that respondents correctly disagreed with the item as posed in the questionnaire. The 

items and their factor loadings are shown below. 

Factor Two: Electricity Bill Payments 

Eigenvalue=1.460 and % of variance explained=18.249 

Key Definers Factor Loadings 

Pay electricity bill annually .783 

Receive check from utility annually for extra electricity –.684* 

*Note the negative value. 

One Electric Meter 

The third dimension involves statements that tend to be perceived accurately. The statements 

pertain to having only one electric meter (this is correct) and the utility paying the retail rate for 

electricity put into the grid (this is, in essence, correct because the electric meter runs backward 

when the home is feeding electricity into the grid). However, the PV systems produce more excess 

electricity on sunny afternoons before SDG&E’s demand reaches its peak because of airconditioning loads. The items and their factor loadings are shown below. 

Factor Three: One Electric Meter 

Eigenvalue=1.239 and % of variance explained=15.492 

Key Definers Factor Loadings 

Have one electric meter .790 

Is paid retail rate for electricity put onto the grid .534 

*Note negative value. 

Together, the three dimensions account for 55.770% of the variance in response to the eight items 

on the operational aspects of PV systems. For the most part, the homeowner responses appear to 

be accurate. Interestingly, these dimensions of knowledge about PV systems differentiate among 

PV owners. 

Summary 

The reduction of the data to 49 factors permitted more efficient analysis of the breadth of the 

survey findings. Fifteen factors applied only to PV owners, and five factors applied only to main 

owners. The remaining 29 factors applied to all study respondents. 

As suggested in this chapter, the key definers on these factors are those that differentiate among 

respondents. Among reasons for home purchase, for example, “overall home value” and 

“desirability of the area” are highly rated, yet their influence does not contribute to defining any of 

176
the reasons-for-purchase factors. This is because these are reasons that are important to most 

respondents and they do not distinguish among respondents. Conversely, reasons that are rated 

highly by far fewer respondents, such as “helpfulness and knowledge of sales staff” define factors 

and differentiate among respondents. 

177
Chapter 18

Analysis of Factors by PV Adopter Categories

Introduction 

The results of the factor analyses described 

in the preceding chapter were used to 

discern differences among various 

categories of homebuyers. As reported 

earlier, ownership of PV homes is a more 

important distinguishing characteristic 

among homebuyers than the ownership of 

homes built by SheaHomes or of nearby 

conventional homes. The importance of PV 

ownership holds sway throughout this 

study’s analyses, including analyses of 

resale value, survey data, and utility data. 

In this chapter, the development of 

empirically based adopter categories 

relative to PV ownership is described. The 

adopter categories are then analyzed by 

demographic variables, homeowner 

satisfaction factors, and other factors. The 

empirical support for a concept of witting 

and unwitting adopters is presented. 

Conclusions from this analysis of factor 

scores relative to adopter categories are 

discussed. 

The Development of Adopter 

Categories 

The presence of solar PV systems, in 

Chapter Highlights 

C Three categories relative to PV adoption were 

developed from the responses of SheaHomes and 

comparison respondents: PV adopters (n=72), nonadopters (n=59), and unaware buyers (n=96). 

C PV adopters are more frequently male than female, a 

difference that nears significance. 

C The percentage of respondents who are scientists and 

engineers is highest among PV adopters and lowest 

among buyers in the unaware buyers category. 

C PV adopters are more satisfied with the energy 

performance of their homes than are non-adopters. 

C Witting and unwitting adopters of PV systems were 

identified and their responses analyzed. Unwitting PV 

adopters have significantly lower mean scores relative 

to how informed they were in making the PV purchase 

decision, yet a significantly higher percentage of them 

have doctoral degrees than do witting adopters. 

C The significant differences found by PV adoption are 

likely to be the result of the experience of PV 

ownership than of qualities brought to the PV 

purchase decision. 

addition to efficiency features and solar water heating, transformed SheaHomes into highperformance homes.1

 To be classified as adopters of high-performance homes, homebuyers had 

to decide to purchase homes that included solar PV systems. If the homebuyers were unaware of 

the availability or presence of solar PV systems when they purchased homes, they could not be 

considered adopters. In this study, 56 SheaHomes PV owners were aware of solar PV on their 

homes and purchased them. These are classified as PV adopters. 

1

Labeled “ComfortWise” Homes by ConSol, Inc. 

178 
Moreover, certain houses with solar PV systems fell out of escrow after the original buyers opted 

out of their contracts. These homes were resold and some of these buyers did not realize that they 

had purchased homes with solar PV systems. This phenomenon resulted in buyers that we termed 

“unwitting adopters.” This type of buyer was discovered during the qualitative interviewing 

process when an interviewee who owned a home with a solar PV system did not learn about the 

system until the time of the interview. The owners became aware of the PV systems they owned 

when the interviewer asked them to show and talk about the energy features of their homes. 

It was known which houses fell out of escrow because all respondents were asked: “Did you 

purchase a new home that had already been under contract, but had fallen out of escrow?” The 

owners of homes with solar PV systems who had purchased these homes after they had fallen out 

of escrow were classified as unwitting adopters. Although some of these buyers could have been 

aware that the homes they were buying had solar PV systems, they had not made the original 

decision to include PV systems on their homes, which had been made by the earlier buyers had 

made that decision. Based on the qualitative research preceding the survey, some SheaHomes 

buyers clearly were unaware they were purchasing homes with solar PV systems. 

The main questionnaire asked directly whether homeowners were offered the option of 

purchasing solar PV systems at the time they bought their new homes. As reported in Chapter 8 

(PV Purchase Decision), more than half of the main respondents do not recall having been 

offered solar PV systems, whereas only 44% recall being offered one and turning it down. The 

group of 36 respondents who knew about the availability of solar PV systems and turned them 

down are termed “Non-adopters.” The remaining 46 main respondents who did not know about 

the availability of solar PV systems, and who could therefore not make a decision about adopting 

them, are counted in an unaware buyers category relative to solar PV adoption—they did not 

know of the availability of solar PV and they did not adopt it. 

Finally, comparison homebuyers did not have the option to purchase solar PV systems at the 

comparison community. However, as reported in Chapter 9 (Knowledge and Information), the 

comparison respondents were asked whether they were aware that, in their price range and 

location, highly energy-efficient homes were available featuring solar PV systems as well as solar 

water heating. Twenty-three comparison respondents indicated they were aware of this offering 

at the time they were shopping for new homes. These 23 were classified as “non-adopters”—that 

is, they knew about the availability of solar PV systems but did not adopt them. Thirty-one 

comparison respondents indicate they were unaware of these systems; these are therefore counted 

in the unaware buyers category relative to solar PV adoption (i.e., they did not know of the 

availability of solar PV systems and did not adopt them). 

To summarize, then, Table 58 defines four adopter categories based homeowners’ knowledge of 

the availability of solar PV systems and their purchase decisions. These four categories of 

homebuyers are as follows: 

179
• PV adopters (aware of PV systems who decided to purchase them) 

• Non-adopters (aware of PV systems who decided not to purchase them) 

• Unwitting PV adopters (unaware of PV, yet who purchased homes with PV systems) 

• Unaware buyers (those who were unaware of PV, or who did not have a choice, and who 

purchased non-PV homes) 

Table 58. Distribution of Types of Solar PV Adopters and Non-Adopters among 

All Categories of Homebuyers (SheaHomes and Comparison Homes)* 

PV Ownership 

Category 

Knew of Availability 

of Solar PV Systems/ 

Had a Choice 

Did Not Know of 

Availability of Solar PV 

Systems/Had No Choice Totals 

Own solar PV system PV adopters (n=56) Unwitting adopters 

(n=16) n=72 

Do not own solar 

PV system 

Non-adopters 

(n=59)** 

Unaware buyers 

(n=96)*** n=155

 Totals n=115 n=112 n=227 

*Missing data for four respondents.

**Main=36; comparison=23.

***Ineligible/early=20; main=49; comparison=31. Ineligible and early respondents did not have a choice to purchase

homes with solar PV systems, as described earlier, and thus are classified in this cell.

A series of analysis of variance (ANOVA) tests that used the four adopter categories to 

distinguish among differences in Z-scores2

 shows that the unwitting adopter category does not 

differentiate among witting and unwitting PV homeowner responses sufficiently to warrant 

keeping them as a separate analytical category. That is, no statistically significant differences 

were found between the unwitting and witting PV adopters when analyzed by the study’s 

factors.3 For this reason, the analysis of differences among adopter categories was pursued using 

the following three adopter categories: 

• PV adopters (n=72) (those owning PV systems, regardless if they witting or unwitting 

adopters) 

• Non-adopters (n=59) (those aware of PV systems who chose not to adopt them) 

• Unaware buyers (n=96) (those not owning PV systems who were unaware PV systems were 

available or who had no choice in the matter).4 

2

Throughout this chapter, “Z-scores” refer to normalized factor scores. 

3

A univariate analysis of witting versus unwitting PV adopters resulted in a few significant differences; 

these are discussed toward the end of this chapter. 

4

As suggested above, these buyers are distinguished from unwitting adopters by the fact that they did not 

know about PV systems and do not own them. 

180 
These adopter categories (n=227) include respondents from both the SheaHomes and the 

comparison communities.5 

Witting and Unwitting Adopters 

As noted earlier in this chapter, no significant differences between witting and unwitting adopters 

were found. The measurement of unwitting adopters was somewhat imprecise in that respondents 

were not directly queried as to whether they were aware at the time of purchase that the homes 

they were purchasing had PV systems. However, as noted earlier, at least one homebuyer 

interviewed in the qualitative phase of the study who had purchased a home with a solar PV 

system was completely unaware of the system. 

Consequently, a surrogate indicator for unwitting adopters was established with the survey data. 

As noted, buyers of PV homes were classified as unwitting adopters if their homes were 

purchased out of escrow. It was possible to make this determination because a question to this 

effect was directly asked of all respondents. The small numbers involved (n=15) could account 

for a lack of differentiation between the witting and unwitting PV adopters mentioned at the 

beginning of this chapter. 

However, at least one piece of empirical evidence suggests that this distinction is on the right 

track. PV owners were asked: “How well informed did you feel you were about the solar PV 

system at the time of purchase? Did you know enough to make an informed decision about 

adding one to your home or upgrading the one that came with your home?” Responses were 

sought on a 1 to 10 scale, where 1=Not at all informed and 10=Very informed. The difference in 

the mean responses given by witting and unwitting PV adopters on this item is statistically 

significant. The mean score for witting PV adopters is 6.40, whereas for “unwitting” PV 

adopters, it is significantly lower at 4.25 (t=3.221; p=.002). 

The existence of unwitting adopters is an interesting phenomenon because it departs radically 

from the notion that homebuyers willing to buy high-performance homes will exhibit, in ways 

perhaps theoretically predictable, characteristics of “innovators” or “early adopters” (see Chapter 

2, Guiding Ideas). If, in fact, the homebuyers had no idea they were adopting an innovation—in 

this case, high-performance homes with PV systems—they could hardly be considered “early 

adopters” of the innovation. Thus, it was decided to pursue a descriptive analysis by these two 

categories of PV adopters with respect to the key study variables to determine whether anything 

further could be learned about them. Of all the variables encompassed by the study, significant 

differences were found with respect to only four of them, as follows: 

5

Outlier cases in the utility analysis (see Chapter 19, Comparative Analysis of Utility Consumption and 

Cost) were also examined to determine whether they were also outliers (that is, on the tails of the distribution) 

relative to Z-scores (normalized factor scores) for all respondents. The utility outlier cases were determined not to be 

outliers here. 

181 
• Educational level of head of household 

• Purchase decision variable: availability of discount or incentive 

• Purchase feature variable: quiet area 

• Purchase feature variable: granite counter tops. 

Educational Level of Head of Household 

Table 64 suggests there is a statistically significant relationship between the highest level of 

educational attainment and type of PV adoption. The percentage of witting PV adopters with a 

bachelor’s degree (48%) is much higher than for unwitting adopters (7%), whereas the 

percentage of unwitting adopters with a master’s degree (33%) is quite a bit higher than for the 

witting adopters (19%). Similarly, a higher percentage of unwitting PV adopters have graduate 

work beyond the master’s degree or a doctoral degree (27%) than do the witting adopters (19%), 

whereas a lower percentage of unwitting PV adopters have less than a bachelor’s degree (33%) 

than do witting adopters (14%) (P2=19.053; p=.023). 

Table 64.6 Level of Educational Attainment of Witting and Unwitting PV Adopters 

Level of Educational 

Attainment 

Percentage of 

Witting PV Adopters 

(n=52) 

Percentage of 

Unwitting PV 

Adopters (n=15) 

Totals 

(n=67) 

Post-master’s or doctoral degree 19 27 21 

Post-baccalaureate or master’s degree 19 33 22 

Bachelor’s degree 48 7 39 

Less than bachelor’s degree 14 33 18 

Occupation may be a contributing or associated variable. As discussed earlier, the percentage of 

PV adopters that are scientists and engineers is higher than for the rest of the homebuyers. An 

analysis of occupation by witting and unwitting PV adopters shows that 46% of the witting and 

33% of the unwitting adopters are scientists or engineers. Because the bachelor’s degree is the 

qualifying degree for engineers, this may help account, at least in part, for the significantly higher 

percentage of baccalaureate degrees among the witting adopter group. Perhaps the advanced 

degrees held by the unwitting adopters are in other fields and specializations than science or 

engineering.7

Importance of a Discount or Other Incentive 

Among the 24 purchase decision variables measured in the study, a significant difference was 

found between witting and unwitting adopters on only one—the importance of a discount or 

6

Table 64 is intentionally out of order. Tables 59 through 63 follow. 

7

Respondents were not asked for the fields in which they received their degrees. 

182 
other incentive in the home purchase decision when the buyers were deciding on their homes. 

The mean score for witting adopters (3.22 on a 1-to-5 scale, where 1=Not at all important and 

5=Very important) is significantly higher than the mean score for unwitting adopters (4.00) (t= 

–2.384; p=.02). In general, several of the purchase decision factors were less important to PV 

adopters than to other types of buyers, and this could be another example of this pattern. 

“Quiet Area” and “Granite Counter Tops”

Respondents were asked how important each of several home features were when they made 

their home purchases, using the same 1-to-5 response scale described above. Of 14 home features 

listed, significant differences are found for only two. Witting adopters give significantly higher 

responses on average to “Quiet area” (4.05) than do unwitting adopters (t=2.422; p=.018). 

“Granite counter tops as a standard feature” receives a higher mean score from unwitting 

adopters (4.00) than from witting adopters (t= –2.325; p=.026). These findings do not appear to 

evidence a clear pattern differentiating the home purchase decisions of the two types of adopters. 

Summary of Analysis with Respect to Witting and Unwitting Adopters 

Few significant differences emerge between the two types of adopters. This situation perhaps 

exists because, although they may have approached the purchases of their homes slightly 

differently, both types of PV adopters have experienced living in PV homes. That experience has 

almost certainly guided most of their questionnaire responses. The bits of evidence indicating 

that unwitting adopters have different types of education than witting adopters and that witting 

adopters knew more about solar PV systems before home purchase than did their unwitting 

counterparts serve to underscore the notion that these types of adopters are somewhat different 

from each other. To speculate, the highly educated unwitting adopters may have been too busy to 

pay much attention to the details about the energy features of their homes.8

 More research on the 

existence and characteristics of unwitting adopters may be worthwhile because many future 

buyers of ZEHs may very well be unwitting adopters. 

Comparisons of Adopter Categories by Demographic Variables 

The study’s respondent characteristics (see Appendix G—Respondent Characteristics, 

Demographics, Values and Lifestyles, and Other Variables) were analyzed with respect to the 

three adopter categories. There was a significant difference among the three categories for only 

one of the 10 relevant variables (age), although for two others the difference was nearly 

significant (gender and occupation). 

Table 59 shows the percentage distributions of responding heads of households in the three 

adopter categories with respect to age. A much higher percentage of responding heads of PV 

households are 40 to 49 years old than in the other two categories. In the unaware buyers 

category, a much higher percentage of respondents is 50 years old or older. Further, a higher 

8

For example, one is a medical specialist with a busy practice. 

183 
percentage of responding heads of non-adopter households are in the youngest age group (39 

years old or younger). Overall, the age distributions for the three adopter categories are 

significantly different (P2 = 12.918; p=.012). These results suggest that non-adopters are 

somewhat younger than the other two groups, PV adopters are more middle-aged, and unaware 

buyers are somewhat older. 

Table 59. Adopter Categories by Age 

Age 

category 

Percentage of 

PV Adopters 

(n=69) 

Percentage of 

Non-Adopters 

(n=59) 

Percentage of 

Unaware Buyers 

(n=91) 

Percentage 

of Total 

(n=219) 

#39 years old 42 53 40 44 

40 - 49 years old 42 25 24 30 

$50 years old 16 22 36 26 

Table 60 shows the percentage distributions of responding heads of households in the three 

adopter categories with respect to gender. The questionnaire could be completed by either male 

or female heads of household at their discretion. Of the responding heads of households in the 

PV adopter category, a higher percentage of the PV adopter category is male than in the other 

two categories. On the other hand, in the non-adopter category a higher percentage are female 

than in the other two categories. Overall, the difference in gender distributions for the three 

adopter categories nears significance (P2 = 5.837; p=.054). These results may be a function of the 

fact that PV, as a technological innovation, is perceived as more within the purview of male 

rather than female heads of household. If that is the case, the task of completing the questionnaire 

in the PV households may have fallen more readily to males than to females. It may also reflect 

the notion that men are somewhat more interested in PV systems than women, and thus gender 

differences could have played a somewhat more important role in adopting homes with PV 

systems. 

Table 60. Adopter Categories by Gender 

Gender 

Category 

Percentage of 

PV Adopters 

(n=68) 

Percentage of 

Non-Adopters 

(n=57) 

Percentage of 

Unaware Buyers 

(n=89) 

Percentage

 of Total 

(n=214) 

Male 68 47 53 56 

Female 32 53 47 44 

Table 61 contains the percentage distributions of responding heads of household in the three 

adopter categories with respect to occupation. The percentage of responding heads of household 

who are scientists and engineers is highest (43%) among the PV adopter category and lowest 

(24%) in the unaware buyers category. Overall, the difference in occupation distributions for the 

three adopter categories is nearly significant (P2

 = 5.870; p=.053). Further, as might be expected, 

there is a nearly significant relationship (P2

 = 3.765; p=.052) between gender and occupation 

184
among the respondents represented by the three adopter categories. About two-thirds of the 

scientists and engineers are male, whereas 52% of respondents in other occupations are male. 

The percentages of heads of households in the three adopter categories are not significantly 

different with respect to any of the other respondent characteristics, including: 

• Educational level 

• Income 

• Marital status 

• Household composition (adults only, adults with children) 

• Number of occupants in the household 

• Member of builder staff 

• Planned length of stay in home. 

Table 61. Adopter Categories by Occupation 

Occupation 

Category 

Percentage of 

PV Adopters 

(n=63) 

Percentage of 

Non-Adopters 

(n=52) 

Percentage of 

Unaware Buyers 

(n=76) 

Percentage 

of Total 

(n=191) 

Scientists and engineers 43 31 24 32 

All others 57 69 76 68 

Differences in Satisfaction Factors by Adopter Categories 

As described in Chapter 17 (Data Reduction), four satisfaction factors were identified. These four 

factors were analyzed with respect to the three adopter categories to determine whether there 

were significant differences in homeowner satisfaction, particularly between PV adopters and 

those who did not purchase homes with PV systems. The results of this ANOVA are presented in 

Table 62. 

Quality of Construction and Builder Reputation 

The first satisfaction dimension deals with the perceived quality of construction and reputation of 

the builder as well as satisfaction with size and square footage of the home and the home’s 

comfort. As Table 62 shows, no significant differences in mean factor scores are found among 

the homeowners in the three adopter categories on this satisfaction factor. Therefore, it seems 

reasonable to conclude that, although the mean scores vary somewhat, all the homeowners 

express similar levels of satisfaction with their homes on this dimension. 

Investment Potential of Home 

The second satisfaction dimension deals with the investment potential of the home, its location, 

and its size and square footage. As Table 62 shows, the ANOVA results in a significant 

difference among the three categories of homeowners on this dimension. The significant 

differences in mean factor scores occur between PV adopters and the other two categories. The 

185
mean factor score is significantly lower for PV adopters than for non-adopter buyers (p=.025). 

The mean factor score for PV adopters is also significantly lower than for unaware buyers 

(p=.003). There is no difference in mean factor scores between non-adopter and unaware buyers. 

Interestingly, although the analysis of resale values of PV homes shows them gaining value at a 

faster rate than other homes, apparently PV owners had not, at the time they completed their 

questionnaires, yet perceived the market edge that their homes enjoy. 

Table 62. ANOVA on Satisfaction Factors by Adopter Categories 

Satisfaction 

Factors9 

Mean Z-scores 

PV Adopters 

(n=52) 

Mean Z-Scores 

Non-Adopters 

(n=47) 

Mean Z-scores 

Unaware Buyers 

(n=74) 

Quality of construction and builder 

reputation 

(F=1.164; p=.315)

 .146 .020 – .128 

Investment potential of home 

(F=4.911; p=.008) – .366 .079 .172 

Behavioral intention with respect to 

solar features and energy efficiency 

in future home purchases 

(F=3.419; p=.035)

 .288 – .188 –.098 

Estimated monthly utility bill 

(F=13.955; p<.0001) – .575 .316 .193 

Behavioral Intention with Respect to Energy Features in Future Home Purchases 

The third satisfaction dimension deals with behavioral intention measures relative to energy 

features in future home purchase; that is, how likely homeowners are to say that, in the future, 

they will buy new homes with solar water heating, energy efficiency, and solar PV. Again, with 

respect to this dimension, the PV adopters’ mean factor score varies significantly from those of 

the other two groups (see Table 62). The mean factor score for PV adopters is significantly 

higher than the mean factor score for non-adopter buyers (p=.018), and is significantly higher 

than the mean factor score for the unaware buyers (p=.032). This suggests that PV adopters are 

more likely to select highly energy-efficient homes with solar water heating and solar electric 

systems if and when they are in the market for a new home. 

Estimated Monthly Utility Bill 

As described in Chapter 17 (Data Reduction), the respondents’ estimated monthly utility bills 

were recoded into categories: (1) #$80 per month, (2) $80.01–$110, (3) $110.01–$150, (4) 

$150.01–$210, and (5) >$210. The resulting scale, from 1 to 5, matched the scales of the other 

items used in the factor analysis. This item resulted in a fourth dimension of homebuyer 

9

For a more detailed description of all factors discussed in this chapter, see Chapter 17 (Data Reduction). 

186
satisfaction, the idea being that the lower the estimated monthly utility bill, the higher the 

satisfaction with the home’s performance. As Table 62 shows, the ANOVA indicates a 

significant difference among these categories of homeowners on this dimension. With respect to 

the estimated monthly utility bills dimension, the PV adopters’ mean factor score varies 

significantly from the scores of the other two groups. This means that PV adopters are 

significantly more likely to indicate a lower estimated monthly utility bill than are either nonadopter buyers (p=.000) or unaware buyers (p=.000). 

Summary of the Analysis of Satisfaction Factors Analyzed with Respect to 

Adopter Categories 

The value of the multivariate analysis is that it draws together findings from many survey 

questions into factor scores or dimensions that meaningfully reflect the data, making it more 

efficient to interpret them. Because of the careful empirical construction of the adopter categories 

that took into account whether homebuyers actually made choices about purchasing highperformance homes with solar PV systems, the results of the multivariate analysis are even more 

powerful than the univariate results presented in Chapter 10 (Homeowner Satisfaction). These 

findings clearly support a hypothesis that PV adopters (that is, those who knowingly selected 

homes with solar PV systems) are more satisfied with the energy performance of their homes (as 

rated by their estimates of monthly utility bills) than are non-adopters or unaware buyers. 

Additionally, homeowners in each of the adopter categories are approximately equally satisfied 

with the quality of their homes, although the PV adopters have a higher mean score on this 

dimension. Least satisfied with the investment potential of their homes are the PV adopters, 

indicating their lack of experience with the actual resale market values of PV homes in the area at 

the time they completed their questionnaires. In this analysis, only PV adopters exhibit scores 

significantly different from other buyers; non-adopter and unaware buyers exhibit factor scores 

that, although not identical, are not significantly different from each other. 

Differences in Other Factors by Adopter Categories 

As described in Chapter 17 (Data Reduction), scaled study variables other than the satisfaction 

variables were also factor analyzed. These included all the variables on the purchase decision, 

home features, barriers to PV purchase,10 attitudes toward solar features, equipment ownership, 

self-reported conservation behaviors, and policy preferences. An ANOVA was performed on the 

factor scores for these 25 dimensions, with significant differences found among the means on 

only four. These results are presented in Table 63. 

Overall Design 

The first dimension deals with the importance in the home purchase decision of aspects of the 

home’s design, emphasizing the quality of light in the home, the home’s spaciousness, and its 

10For main respondents only. 

187
sense of openness. As Table 63 shows, significant differences are found among the adopter 

categories’ mean scores on this dimension. The mean factor score for the PV adopters is 

significantly lower than that of non-adopter buyers (p=.003), suggesting that this factor is less 

important in the home purchase decisions by the PV adopters than in those of the non-adopters 

and unaware buyers (p=.043). The mean factor scores for the non-adopters and unaware buyers 

are not significantly different on this dimension. 

Table 63. ANOVA on Other Factors by Adopter Categories 

Satisfaction 

Factor11 

Mean Z-Scores 

PV Adopters 

Mean Z-Scores 

Non-Adopters 

Mean Z-Scores 

Unaware Buyers 

Importance of overall design (quality 

of light, spaciousness, openness) in 

home purchase decision 

(F=4.564; p=.010) 

– .275 

(n=69) 

.268 

(n=51) 

.056 

(n=80) 

Importance of size and number of 

bedrooms in home purchase decision 

(F=3.461; p=.033) 

– .255 

(n=69) 

.027 

(n=51) 

.168 

(n=80) 

Solar features (both PV and SWH) 

are “desirable innovations” 

(F=13.946; p=.000) 

.410 

(n=68) 

.133 

(n=57) 

– .371 

(n=92) 

Adjust thermostats (self-reported 

conservation behaviors) 

(F=3.166; p=.044) 

.160 

(n=60)

 .128 

(n=48) 

– .221 

(n=79) 

Size and Number of Bedrooms 

The second dimension deals with the importance of size and number of bedrooms at the time of 

the home purchase decision. As Table 63 shows, significant differences are found among the 

adopter categories’ mean scores on this dimension. Although the mean score for the PV adopters 

is not significantly different from that of the non-adopter buyers, it is significantly lower than that 

of the unaware buyers (p=.01), indicating that this variable was rated as less important in the 

home purchase decisions of the PV adopters than those of the unaware buyers. This result is 

consistent with findings reported in Chapter 10, in which energy features were more important in 

the home purchase decisions of PV owners than were other home features. Again, the mean 

factor scores on this dimension for the non-adopters and unaware buyers are not significantly 

different. 

11For a more detailed description of all factors discussed in this chapter, see Chapter 17 (Data Reduction). 

188 
Solar Features as Desirable Innovations 

The third dimension deals with attitudes toward solar features (including solar PV and solar 

water heating) as desirable innovations for housing. As Table 63 shows, significant differences 

are found among the adopter categories’ mean factor scores on this dimension. Although the 

mean factor score for the PV adopters is not significantly different from that of the non-adopters, 

it is significantly higher than that of unaware buyers (p=.000), suggesting that the PV adopters 

are more favorable toward solar features than are the unaware buyers. This is interesting because 

the PV adopters had lived in their homes a minimum of six months before completing the 

questionnaire, and some had lived in them for up to 1-1/2 years; therefore, they had some time to 

observe the performance of their homes. The non-adopter buyers’ mean score is also significantly 

higher than that of the unaware buyers (p=.002), suggesting that they, too, favor the use of solar 

features in new housing. Although the PV adopters have a higher mean score than do nonadopters, the difference in their scores is not statistically significant. 

Conservation Behaviors: Adjusting Thermostats 

The fourth dimension deals with one dimension of self-reported conservation behaviors— 

adjusting thermostats. As Table 63 shows, significant differences are found among the adopter 

categories’ mean factor scores on this dimension. Although the mean factor score for the PV 

adopters is not significantly different from that of the non-adopters, it is significantly higher than 

that of unaware buyers (p=.025), suggesting that PV adopters are more likely to report that they 

adjust their thermostats to promote energy efficiency than are the unaware buyers. The nonadopters’ mean score is higher than that of the unaware buyers in a result that nears statistical 

significance (p=.055), suggesting that they, too, are more likely to adjust thermostats than are the 

unaware buyers. Although PV adopters have a higher mean score than do non-adopter buyers on 

this dimension, the difference is not statistically significant. 

Summary of the Analysis of Other Factors with Respect to Adopter Categories 

The 25 factors identified and described in Chapter 17 (Data Reduction), which were based on 

questions asked of all respondents, deal with purchase decision variables, home features, 

attitudes toward solar features, equipment ownership, self-reported conservation behaviors, 

housing and energy policy preferences, environmentalism, and early adopter characteristics. Only 

four of them differentiate among the three adopter categories. The four results show the 

following: 

• Specific home features (overall design and size and number of bedrooms) were less important 

to the PV adopters than to the non-adopters and unaware buyers at the time of home 

purchase. 

• The PV adopters are more likely to perceive PV and solar water heating as “desirable 

innovations” than are the rest of the homebuyers. 

189
• The PV adopters are more likely to adjust thermostats for energy efficiency than are the rest 

of the buyers. 

Summary 

By and large, those who knowingly selected homes with solar PV systems are very much like all 

other homebuyers in the SheaHomes and comparison communities. The few differences detected 

through detailed data analysis are likely the result of their having lived in PV homes before 

completing the questionnaire, not of qualities they brought to the home purchase decision. The 

findings suggest that those consciously opting for homes with PV systems tend more frequently 

than non-PV households to be male heads of household in their 40s with training as scientists 

and engineers. Living in PV homes has resulted in significantly lower utility bills, by their own 

estimates, than those reported by the rest of the homebuyers.12 The PV experience has also 

apparently resulted in an even more positive attitude toward the desirability of energy efficiency 

and solar features in new housing, and an intention to buy such housing in the future should the 

PV homeowners move. 

Although the analysis suggests that PV owners are somewhat less satisfied than other buyers with 

the investment potential of their homes, objective analysis of home resale values (see Chapter 6) 

shows that PV homes more than hold their own in the resale market. The PV adopters will very 

likely become aware of this advantage over time, if they have not already done so. 

These findings reasonably support a conclusion that, once homebuyers experience living in highperformance homes with PV systems, they become more favorable toward these homes. Thus, 

buyers of high-performance homes with PV systems are, by and large, like the buyers of other 

nearby homes of similar qualities and in a similar price range. The experience of PV ownership 

changes their attitudes and perceptions. 

These PV homebuyers are not “early adopters” of an innovation: they do not have higher 

education, occupation, or income levels than others; they do not display more early adopter 

characteristics, such as opinion leadership, than others; and they are not more environmentally 

oriented than others. The diffusion-of-innovation concepts are not supported by the findings 

representing this case study of high-income homeowners. 

12See Chapter 10 (Homeowner Satisfaction). Analysis of respondents’ utility bills, reported in Chapters 19 

(Comparative Analysis of Utility Consumption and Cost), Chapter 20 (Modeling of Utility Consumption), and in the 

Epilogue to this report buttresses these homeowner perceptions. 

190 
Chapter 19

Analysis of Factors by Demographic Variables

Introduction 

The factors described in Chapter 17 (Data 

Reduction) were analyzed by key demographic variables. All respondents in the 

study were included in these analyses. 

Factors that result from questions asked only 

of main, comparison, or PV respondents 

include responses only from those categories 

of homebuyers. 

In addition, the 49 factors were analyzed by 

ownership of SheaHomes compared with 

comparison homes, and by PV ownership. 

No significant differences were found in 

these sets of analyses. However, when all 

respondents are included, significant 

differences are found by demographic 

characteristics. 

Mean standardized factor scores (Z-scores) 

for seven demographic variables were 

analyzed relative to the 49 factors described 

in Chapter 17, as follows: 

• Gender 

• Age 

• Marital status 

• Household composition (adults with 

children or adults only) 

• Educational attainment 

• Occupation (science and engineering 

occupations or all other occupations) 

• Annual household income. 

This chapter reports on the significant 

Chapter Highlights 

C The 49 factors defined in Chapter 17 were analyzed 

by ownership of SheaHomes versus comparison 

homes, and by PV ownership; no significant 

differences are found. 

C The factor scores for all study respondents were 

analyzed by seven key demographic characteristics, 

resulting in 44 significant differences. 

C The variables used were gender, age, marital status, 

household composition, educational attainment, 

occupation, and income. 

C Women are significantly more interested in safe and 

secure housing, whereas men seem more interested 

in solar features and their attributes. 

C Respondents aged 50+ are more sensitive to personal 

action to protect the environment than are those 39 

or younger. 

C The unmarried are more likely to exhibit early 

adopter characteristics than the married ones. 

C At the time of home purchase, households with 

children are significantly more concerned with safety 

and security, familiarity with the area, convenience 

of access, and size of home than are adults-only 

households. 

C High-school graduates and those with some college 

are significantly more interested in the single-story 

options than are other educational groups. 

C Scientists and engineers are significantly more 

interested in the technical aspects of PV systems than 

those employed in non-scientific occupations. 

differences resulting from these analyses. Details of the analysis are presented in Appendix H-1 

(Factors by Demographics). 

191
Summary of the Differences in Factor Scores by Gender 

All 49 factors were analyzed by gender; three of the analyses resulted in significant differences. 

These three factors relate to home purchase decisions, attitudes toward solar features in new 

housing, and solar PV system attributes. Table H1-1 in Appendix H1 (Factors by Demographics) 

summarizes the findings from these analyses. 

Gender is not a distinguishing demographic variable for most of the findings identified in 

Chapter 17. However, gender does emerge as a significant variable when the importance of 

neighborhood safety, security, and quality are considered; females are more concerned about 

these matters than males are. On the other hand, males are more interested in solar PV and water 

heating systems as new home innovations and in the efficacy and reliability of PV systems than 

are females. These differences suggest that marketing high-performance new homes should be 

approached somewhat differently between the genders. 

Summary of the Differences in Factor Scores by Age 

The responses by age were recoded into three categories: (1) #39 years old, (2) 40–49 years old, 

and (3) $50 years old. All 49 factors were analyzed by these three age categories; nine of the 

analyses resulted in significant differences by age. These nine factors relate to home purchase 

decisions, home features, attitudes toward solar features in new housing, equipment owned, 

environmentalism and environmental attitudes, and PV information channels used. Table H1-2 in 

Appendix H1 (Factors by Demographics) summarizes the findings from these analyses. 

Age emerges as a significant differentiating variable for the mean scores of nine factors. The 

buyers 50 or older are more concerned about builder reputation and performance in their home 

purchase decisions than are the middle and younger age groups. They are also less concerned 

about familiarity of the area than are the buyers who are 39 years old or younger. This may be 

because the younger buyers are more likely to have young families. The 50+ age category is more 

favorable toward the single-story option, which seems logical because the younger buyers would 

be looking for larger homes with many bedrooms for their families, and older folks don’t want to 

climb stairs! 

Those 40–49 years of age more strongly perceive solar features as cost-effective for new homes, 

whereas the 50+ age group less strongly views them this way. Those 40–49 years of age are also 

more strongly inclined to own a pool and hot tub combination than the youngest age group. On 

the other hand, those 39 years of age or younger are more strongly inclined than those 50+ to 

own smaller efficiency measures. Interestingly and unexpectedly, the 50+ group holds the 

strongest environmental attitudes of the three age groups in terms of being willing to buy 

environmental products and change lifestyles to help the environment. The 50+ group also tends 

to reject ideas that federal environmental regulations are adequate, that environmental threats 

have been exaggerated, and that household energy consumption is not a major contributor to 

environmental problems. 

192
Summary of the Differences in Factor Scores by Marital Status 

The responses on marital status were recoded in two categories: (1) married/in a committed 

relationship and (2) not married. All 49 factors were analyzed by marital status; seven of the 

analyses resulted in significant differences and the result of one additional analysis nears 

significance. These factors relate to home purchase decisions, home features, equipment owned, 

early adopter characteristics, barriers to PV purchase, and aspects learned since living with PV 

system. Table H1-3 in Appendix H1 (Factors by Demographics) summarizes the findings from 

these analyses. 

in these analyses, marital status is a significant variable for seven factors, and a nearly significant 

variable in an eighth. More married than unmarried homebuyers are concerned about the 

convenience of access that the Scripps Highlands location provides them. Unmarried 

householders are significantly more interested in exemption from Mello-Roos taxes and in the 

views than are married ones. Married buyers seem to be more interested in the size of the home 

and the number of bedrooms than are unmarried buyers, presumably because many of the 

married couples are seeking housing for themselves and their children and need more rooms than 

unmarried people, who, in this respondent set, do not have children living with them. Married 

homeowners are more likely than unmarried ones to own pools and hot tubs. This may be an 

affordability issue, or it may suggest that people with spouses (and probably children) have a 

greater interest in owning these items as part of their family lifestyles. 

Unmarried respondents less frequently agree with statements on early adopter characteristics in 

the dimensions of being opinion leaders, independent, and innovative. However, they are more 

inclined to express that they perceive greater tangible, immediate financial barriers to owning PV 

systems. Those married respondents who do own PV systems are more strongly inclined than 

their unmarried counterparts to have learned about various aspects of those systems after living 

with them. The results on the marital status variable no doubt overlap the results on the 

household composition variable, discussed next. 

Summary of the Differences in Factor Scores by Household Composition 

The responses by household composition were recoded into two categories: (1) households with 

adults and children and (2) households with adults only. All 49 factors were analyzed by these 

two categories; nine of the analyses resulted in significant differences. These nine factors relate 

to reasons for purchase, home features, equipment owned, barriers to PV purchase, conservation 

behaviors, policy preferences, and use of digital display. Table H1-4 in Appendix H1 (Factors by 

Demographics) summarizes the findings from these analyses. 

There are nine statistically significant differences in the mean factor scores by household 

composition. Four of these differences relate to reasons for purchase. The aspects of a safe and 

secure area and familiarity with the area distinguishes households with children (higher factor 

scores) than from those without. Convenience of access in terms of closeness to work, shopping, 

and other services distinguishes homebuyers with children (higher scores) from adults-only 

households (lower scores). Other advantages (such as exemption from Mello-Roos taxes and a 

193
great view) differentiate homebuyers with children (higher scores) from those without (lower 

scores). 

As would be expected, the size of the home and the number of bedrooms differentiates 

households with children (higher scores) than adults-only households (lower scores). This result 

overlaps that found for married and unmarried occupants, presumably because many unmarried 

people either do not have children or their children live elsewhere. 

Relative to policy preferences, favorability toward financial incentives such as the CEC rebates 

for PV systems, federal tax credits, and utility rebates differentiates family households (higher 

scores) from adults-only households (lower scores). Adults-only households appear to find it 

easier to adjust their thermostats than do families. This may reflect a tendency for parents living 

with to spend more time at home than those without children. It may also reflect a higher level of 

distraction in households with children. 

Summary of the Differences in Factor Scores by Educational Attainment 

The questionnaire responses on educational attainment were recoded into five categories: (1) 

high school graduate or below, (2) some college, (3) bachelor’s degree or bachelor’s degree plus, 

(4) master’s degree or master’s plus, and (5) doctoral degree. All 49 factors were analyzed by 

these five categories of educational attainment; seven of these analyses resulted in significant 

differences by this variable. These seven factors relate to reasons for home purchase, importance 

of home features, attitude toward solar features in new housing, conservation behaviors, and 

early adopter characteristics. Table H1-5 in Appendix H1 (Factors by Demographics) 

summarizes the findings from these analyses. 

Seven dimensions show statistically significant differences by educational attainment. Four of 

them revolve around reasons for purchase and home features. The others involve perceptions of 

solar features in new housing as desirable, adjusting thermostats, and early adopter 

characteristics. 

Summary of the Differences in Factor Scores by Occupation 

These responses on occupation were recoded into two categories: (1) science and engineering 

occupations and (2) all other occupations. All 49 factors were analyzed by these two occupational 

categories, resulting in four significant differences by occupation. These four factors relate to 

satisfaction with home purchase, appliances and equipment owned, solar PV system attributes, 

and aspects learned about since living with solar PV systems. Table H1-6 in Appendix H1 

(Factors by Demographics) summarizes the findings from these analyses. 

Most of the study’s factors do not differ significantly by occupation. However, when occupation 

is bifurcated between those working in science and engineering and all other occupations, and 

factor analysis is performed, four factors are identified that differ significantly by occupation. 

The respondents in the science and engineering occupations score significantly higher on 

satisfaction with their estimated utility bills than do those in other occupations. Those in all other 

194
occupations score significantly higher in ownership of dual-zone heating and air-conditioning 

systems and CFLs than those in the science and engineering professions. Among PV owners, 

scientists and engineers score the importance of the technical aspects of their PV systems more 

highly and more frequently indicate that they have learned about the technical aspects since 

living in their PV homes than do those in other occupations. 

Summary of the Differences in Factor Scores by Annual Household Income 

These responses by income were recoded into three categories: (1) #$99,000, (2) $100,000 to 

$199,000, and (3) $$200,000. All 49 factors were analyzed by these three income categories; 

four of the analyses resulted in significant differences by income. These four factors relate to 

reasons for purchase, home features, equipment owned, and PV information channels employed. 

Table H1-7 in Appendix H1 (Factors by Demographics) summarizes the findings from these 

analyses. 

Most of the study’s factors do not differ significantly by income; however, four dimensions 

resulted in significant differences when analyzed by income. A safe and secure area is, on 

average, rated as more important to home purchase by those with household incomes of 

$100,000 to $199,000 than households with incomes of $$200,000. The importance of quality of 

light and spaciousness differentiates between those making $100,000 to $199,000 (higher scores) 

from those in the lowest income bracket (lower scores). Ownership of efficiency equipment such 

as ceiling fans and dimmer switches differentiates between households earning $$200,000 and 

those in the lowest income bracket. 

Summary 

In all, 343 t-tests and analyses of variance (ANOVAs) were run (49 factors by seven key 

demographic characteristics), resulting in 44 statistically significant results (13%). This lends 

additional support to the idea already discussed that, by and large, the purchasers of homes in the 

Scripps Highlands developments, whether SheaHomes or comparison, are relatively 

homogeneous demographically. Still, these significant results suggest potentially important 

differences among various demographic categories that could affect marketing strategies used by 

builders and others interested in selling high-performance homes, near-ZEHs, and ZEHs. 

Because the differences on gender, household composition, marital status, and particularly on 

education do not appear to form an interpretable pattern, it seems reasonable to conclude that 

other demographic variables may be confounding the results, and further research and analysis 

involving control by third variables will be needed to elucidate subtle patterns of differences 

among demographic characteristics of upscale new home buyers. One potential conclusion, 

however, is that the findings provide little support for diffusion theory, suggesting that buyers of 

new high-performance homes are not different on key demographic variables from buyers of 

conventional new homes in the same price ranges. 

195
Nevertheless, these demographic differences suggest ways in which marketing messages might 

be targeted toward the different interests of new upscale home shoppers. Clearly, people with 

children have needs that are somewhat different from those without them, which is already 

known by the housing industry. It may be less obvious that men in their 40s, especially those 

with science and engineering training, appear to be more interested in the technical aspects of 

ZEHs and, in particular, of PV systems. It may not be well understood that the oldest new 

homebuyers are more interested in protecting the environment through personal action than the 

are younger buyers. 

196
Chapter 20

Comparative Analysis of Utility Consumption and Cost

Introduction 

This chapter presents a comparative 

analysis of actual utility consumption and 

cost in homes encompassed by the study. 

The analysis consists of an empirical 

statistical examination that uses various 

measures of cost and consumption rather 

than an engineering investigation. 

As discussed in Chapter 3, Study Methods 

and Data Resources, to obtain actual utility 

consumption and cost data for study homes, 

respondents were asked to sign a utility 

release form that permitted SDG&E to 

provide their utility data to NREL. Of the 

231 questionnaire respondents, 132 (57%) 

returned the utility release forms with valid 

signatures. NREL provided the utility 

release forms to SDG&E, which then 

provided NREL with the monthly 

electricity and gas consumption and cost 

data in Excel format for each household 

that had given its permission.1

 The utility 

data cover the period from the time the 

house was occupied until June 30, 2004. 

NREL has utility data for 132 homes. Table 

72 summarizes the categories and numbers 

of homes for which we have utility data. 

Because these homes are subsets of the 

homes described earlier in the report, the 

terminology used to describe them is 

somewhat different than that used before. In 

particular, we use the acronym SEE here to 

refer to highly energy-efficient homes with 

solar water heating but without solar PV 

systems. We also here refer to PV homes as 

Chapter Highlights 

C A statistical and empirical comparative analysis of 12 

months of data on utility consumption and cost by 109 

SheaHomes and comparison homes was completed. 

C Based on the analysis, SheaHomes households 

consume less electricity and gas on average than do 

comparison households, despite the fact that 

SheaHomes are larger than comparison homes. The 

two categories of households are essentially the same 

in terms of household composition and number of 

occupants. 

C Utility consumption was not found to increase with 

square footage of homes. 

C A significantly higher percentage of comparison 

households than SheaHomes households have energyintensive appliances and amenities, which complicates 

the analysis. 

C PV homes have significantly lower average monthly 

electricity and gas consumption than other 

SheaHomes or comparison homes irrespective of PV 

system size, when energy-intensive equipment is 

omitted from the analysis. 

C PV homes with 2.4-kW systems have significantly 

lower electricity consumption than do PV homes with 

1.2-kW systems. 

C Mean average gas consumption is significantly lower 

in high-performance homes without PV than in 

comparison homes, yet these two categories do not 

vary significantly on overall utility bills. Thus, most 

of the utility consumption and cost savings in 

SheaHomes appear to result from the presence of PV 

systems. 

high-performance homes built by SheaHomes with solar water heating and solar PV systems. 

1

We were unable to obtain the specific rate structures for these homes from SDG&E. 

197 
Table 72. Categories of Homes Used in the 12-Month Utility Analysis 

Categories of Households Description 

SheaHomes with solar PV 

systems (termed PV homes) 

(n=44; 33%) 

SheaHomes high-performance homes with 1.2-kW or 2.4-kW 

solar PV systems to produce electricity and solar water heating, 

the owners of which permitted access to their utility data 

SheaHomes energy-efficient 

homes (termed SEE homes) 

(n=55; 41.7%) 

SheaHomes high-performance homes with solar water heating, 

but without solar PV systems (including main and ineligible 

homes), the owners of which permitted access to their utility data 

SheaHomes (n=99; 75%) Both categories of SheaHomes, including PV homes and SEE 

homes, the owners of which permitted access to their utility data 

Comparison homes (n=33; 25%) 

Comparison homes near the SheaHomes developments that were 

built to California’s Title 24 energy standards, the owners of 

which permitted access to their utility data 

An important overall objective of the research is to determine if there are statistically significant 

differences in total or average monthly utility consumption and cost (electricity plus gas, 

including any associated taxes and miscellaneous charges) between the SheaHomes and the 

comparison homes. Another important objective is to determine whether solar PV systems have a 

significant impact on total or average monthly utility consumption and cost. Secondarily, we 

identified factors (such as household composition or occupant behaviors that affect energy 

consumption) that may lead to significantly higher or lower average monthly energy bills. 

Several conventional statistical approaches, including t-tests, analysis of variance (ANOVA), 

Chi-square (P2

) analysis, and correlation analysis, were used to investigate the possible effects.2 

Ultimately, it would be desirable to quantify and state the impact of such factors on utility 

consumption and cost so that both builders and consumers could be more adequately informed. 

Average monthly utility cost can be viewed from several perspectives. Generally, homeowners 

themselves view their bills as the total of the combined electricity and natural gas expenditures, 

including associated taxes and add-on fees. Homeowner perceptions of their utility bills are 

discussed in Chapter 22 (Perceived and Actual Utility Bills). However, analysts’ perspectives 

would call for examining the effects of energy features (such as efficiency measures and PV) on 

specific components of the utility record. With regard to PV, separating expenditures for 

electricity and natural gas is instructive because only the electricity portion of the monthly bills 

could be offset by PV systems. Hence, the analysis described here considers the consumption and 

dollar amounts for electricity and gas, as well as combined utility costs. 

The effects of climate are automatically controlled throughout the analysis. The boundaries of the 

SheaHomes communities and the comparison community are literally within a few blocks of 

each other; both developments are on a mesa in northern San Diego County. Hence, there are 

2

Results were obtained using the Statistical Package for the Social Sciences (SPSS 11.0), the Statistical 

Analysis System (SAS 8), and Microsoft Excel. 

198 
effectively no weather or climate differences. In addition, the utility data for SheaHomes and 

comparison homes encompass the same 12-month period, which eliminates any possibility of 

variation caused by seasonality or timing of the data collection process. Because the computation 

of degree heating days would be identical, further adjustments to the data, such as those that 

might be accomplished through the application of PRISM3

, are unnecessary. 

Many variables are known to affect residential utility consumption, such as the orientation of the 

house (Christensen and Barker 2001). However, data could not be collected on all such variables 

(this is a limitation of the analysis). Further, this is not a standard engineering analysis of energy 

usage in high-performance homes,4

 but instead relies on careful statistical analysis of actual 

utility records provided by the utility company in electronic format. 

It is also instructive as context to understand the efficiency and renewables features of the 

different home categories and the fuels used for end-use applications at residences in the 

SheaHomes and comparison homes. Tables 73 and 74 summarize these data. As can be seen, 

SEE homes have all of the features that PV homes have except for PV systems. All homes were 

built to the Title 24 building code in effect at the time of construction. Residences in the area 

routinely use natural gas for space heating, water heating, pool and spa heating, clothes drying, 

and cooking. They use electricity for air conditioning, lighting, appliances, and plug loads. 

Table 73. Energy Efficiency and Renewable Energy Features of

Categories of Homes in the Utility Consumption and Cost Analysis 

Features 

Solar PV 

Homes SEE Homes 

Comparison 

Homes 

Meets Title 24 code X X X 

ComfortWise efficiency rating X X !

Solar radiant barriers X X !

Thermal windows X X !

Solar water heating X X !

Solar PV systems X ! !

Tightly wrapped ducts X X !

3

PRISM is an acronym for the Princeton Scorekeeping Method. PRISM is a software package developed at 

Princeton University’s Center for Energy and Environmental Studies that calculates a weather-corrected index of 

energy consumption. 

4

Such an analysis, for at least one of the SheaHomes and which involved instrumentation of the home, is 

being conducted by the National Association of Home Builders (NAHB) Research Center. 

199 
Table 74. Fuels Used for End-Use Applications in

All Homes Encompassed by the Study

End-Use Application Electricity Natural Gas 

Space heating ! X 

Water heating* ! X 

Air conditioning X !

Pool/spa heating** ! X 

Clothes drying*** ! X 

Cooking ! X 

Lighting X !

Appliances X !

Plug loads (TV, computers, etc.) X !

*In SheaHomes, water heating is also provided by solar water pre-heating systems.

**A few pools are heated by solar water heating systems.

***Based on focused interviews with 43 homeowners in 25 households during the study’s

qualitative phase. Homeowners selected their own appliances, which may or may not have been

highly energy-efficient.

Measures of Utility Consumption and Cost 

Utility consumption and cost can be defined and measured in many ways. Several measures of 

both consumption and cost are used here to convey energy use and expenditures among the 

various categories of homes. However, because the present analysis constitutes an empirical 

statistical investigation rather than an engineering study, such measures are limited to 

conventional descriptive statistics (e.g., averages and totals of consumption and cost over 

specified periods of time) rather than more formal estimates of engineering parameters. 

Utility consumption is taken to mean either the total or average household consumption of 

electricity or gas over a specified period of time. Six measures of utility consumption are 

presented and discussed here: 

1. Total electricity consumption 

2. Average monthly electricity consumption 

3. Average monthly electricity consumption per square foot 

4. Total gas consumption 

5. Average monthly gas consumption 

6. Average monthly gas consumption per square foot. 

200
Total consumption represents all electricity or gas used by a household over a specified period of 

time. For example, total consumption per year represents household electricity or gas use over a 

12-month period and encompasses the year’s seasonal cycles. Average monthly consumption 

represents the average household use of electricity or gas each month. Twelve-month averages 

provide a measure of consumption across the year’s seasonal variations. Average monthly 

consumption per square foot facilitates comparisons between homes with more or less square 

footage. 

Electricity and gas consumption are measured in different units (kWh and therms). These units 

must be kept separate to specifically determine (1) the effect of the PV systems on electricity 

consumption in SheaHomes with such systems and (2) the effect of gas consumption in SEE 

homes (which are energy efficient and have solar water heating). However, combined utility 

consumption could be reported in terms of Btu. Because consumption and cost are closely 

correlated, the discussion of combined utility cost (see below) can also serve as a surrogate for 

combined utility consumption. (See also Figures J-1 to J-16 in Appendix J [Scatter Diagrams of 

Average Monthly Electricity Cost Consumption by Average Cost for All Homes in the Study] 

and Figures K-1 to K-16 in Appendix K [Scatter Diagrams of Average Monthly Gas Cost 

Consumption by Average Cost for All Homes in the Study].) 

Utility cost is taken to mean either the total or average household expenditures for electricity, 

gas, or both over a specified period of time. Several measures of utility cost are considered here: 

1. Total electricity cost, with and without taxes and miscellaneous charges 

2. Average electricity cost, with and without taxes and miscellaneous charges 

3. Average electricity cost per square foot, with and without taxes and miscellaneous charges 

4. Total gas cost, with and without taxes and miscellaneous charges 

5. Average gas cost, with and without taxes and miscellaneous charges 

6. Average gas cost per square foot, with and without taxes and miscellaneous charges 

7. Total combined utility bill, with and without taxes and miscellaneous charges 

8. Average monthly combined utility bill, with and without taxes and miscellaneous charges 

9. Average monthly combined utility bill per square foot, with and without taxes and 

miscellaneous charges. 

Total cost represents the cost of electricity or gas (with or without taxes and miscellaneous 

charges) used by a household for a specified period of time. For example, total cost per year 

represents the cost of electricity or gas over a 12-month period and encompasses the year’s 

seasonal cycles. Average monthly cost represents the average cost of electricity or gas (with or 

without taxes and miscellaneous charges) used by a household each month. Twelve-month 

averages provide a measure of cost across seasonal variations experienced during the year. 

Average monthly cost per square foot facilitates comparisons between homes with more or less 

square footage. 

201
Calculating and communicating the concept of combined utility cost is quite easy, because both 

electricity cost and gas cost are expressed in terms of dollars. Combined utility cost can simply 

be stated as the sum of electricity and gas cost. Total combined utility cost, then, represents the 

total dollars spent by a homeowner on all utilities for a specified period of time, whereas average 

monthly combined utility cost represents the average monthly expenditures for all utilities. 

Again, average monthly combined utility cost per square foot facilitates energy cost comparisons 

among homes with more or less square footage. 

Other Terminology 

Throughout the following discussion, utility consumption and cost are attributed interchangeably 

to homes and households. Clearly, the homes or houses themselves do not consume energy, 

although the quality of home construction can certainly contribute to utility demand. Utility 

consumption, and hence cost, result from occupants’ use of energy-related equipment and 

appliances to produce light, condition space, power appliances and plug loads (such as computers 

and television sets), and heat water. The attribution of utility consumption and cost to homes is 

generally used to more fully explain analytical results. In all cases, the context of the situation 

should make the intention clear. 

Data Processing 

A typical monthly utility bill for one household consists of days of service in the billing month, 

amount of electricity used (in kWh), cost and tax for electricity used, and miscellaneous costs 

associated with electricity use, plus amount of gas used (in therms) and cost and tax for gas use. 

Other information associated with the dates of service is also included. Because respondents 

moved into their homes and initiated service on different dates, the number of months of utility 

data for each household varies. Table 5 in Chapter 3 (Study Methods and Data Resources), 

presents descriptive statistics on months of service for some home categories. In addition, the 

number of days per monthly billing cycle varies slightly. However, meters are read on the same 

day each month at all houses in the study neighborhoods. 

Restriction to a Minimum of 28 Service Days per Month 

Because some households began service in the middle of a month, the number of days in the first 

billing month is particularly variable; so, for consistency, all records in the data set were omitted 

for which the number of days per billing cycle was fewer than 28. After imposing this restriction, 

the average number of days per billing cycle across all households and all billing months was 

determined to be 30.5. 

Restriction to 12 Months of Data per Household 

Because the time frame during which SheaHomes and comparison homes were sold spans 

approximately 2 years, there is considerable variability in the number of months that these homes 

received utility service. This inconsistency complicates conceptual and analytical comparisons on 

the basis of average or total energy consumption and cost. In fact, several homeowners had been 

202
in their homes for less than 1 year (but at least 6 months) at the time of the survey. Because 

utility consumption is seasonal, the decision was made to restrict the data set to homes that had 

experienced at least 1 full year of utility service and to use the last 12 months (July 2003 through 

June 2004) of the study period to include as many homes as possible. As a result, the utility data 

set was reduced to records from 122 homes (40 PV homes, 51 SEE homes, and 31 comparison 

homes). This restriction precludes having to incorporate weighting in the statistical analysis to 

accommodate differences in the numbers of months of utility service per home. 

Graphical Comparisons of Month-to-Month Utility Consumption 

Figures L-1 to L-14 in Appendix L (Line Graphs of Month-to-Month Consumption of Electricity 

and Gas for Individual Homes in the Study) are time-series plots that show the monthly 

consumption of electricity for July 2003 through June 2004 for each home in each of several 

categories (e.g., SEE or PV). Figures M-1 to M-10 in Appendix M (Line Graphs for Month-toMonth Consumption of Electricity and Gas for Homes in the Study with Recreational 

Equipment) are similarly grouped time-series plots that show the monthly consumption of 

electricity and gas for July 2003 through June 2004 for homes with and without pools and hot 

tubs. Figures L-1 to L-14 and Figures M-1 to M-14 show substantial fluctuation in electricity and 

gas consumption among the homes—even homes within the same category. As expected, 

considerable variation is also evidenced in electricity and gas consumption among months for 

each home. In addition, for both sets of graphs some homes have utility consumption that is 

abnormally high or off-trend. These homes, which could be termed outliers, are addressed in 

more detail below. Because utility cost is closely tied to utility consumption (Figures J-1 to J-16 

in Appendix J and Figures K-1 to K-16 in Appendix K), graphs of month-to-month electricity 

and gas costs (not included) can reasonably be expected to demonstrate the same patterns. 

Computing Total and Average Utility Consumption and Cost 

The individual monthly records for each household were collated to compute monthly averages 

and totals (accounting for the varying days per billing cycle) for the amounts of gas and 

electricity use, gas and electricity costs, and combined utility cost. These values were 

subsequently merged with the survey responses of the respective households. 

Additional Data Screening 

Three SheaHomes owners who provided permission to access their utility data were owners of 

homes designated as early. These homes were constructed before solar water heating was 

incorporated as standard. Relative to energy use and consumption, these homes are not 

comparable to other SheaHomes, so their utility data records and the associated values of average 

monthly consumption and cost were omitted. 

203
Further, analysis of 12-month totals reveals a high degree of variation in electricity consumption 

and gas consumption among all the homes, a result suggested by the time-series plots of monthto-month consumption contained in Appendixes L and M. This finding indicated the need for a 

more detailed investigation. To facilitate additional study, side-by-side box plots5

 of 12-month 

total electricity consumption were constructed that depict the distribution of values for the PV 

homes, SEE homes, and comparison homes. Similar side-by-side box plots of 12-month total gas 

consumption were also constructed. These two sets of box plots are shown in Figures 8 and 9. 

Both sets of box plots indicate the presence of statistical outliers; some outliers are rather 

extreme. 

For electricity consumption, five homes (one PV, three SEE, and one comparison) are indicated 

as outliers. For gas consumption, seven homes are indicated as outliers (two PV, three SEE , and 

two comparison). 

Extensive investigation of the outlier homes revealed no consistent explanation in terms of 

equipment or appliances, larger families or different family compositions, or other readily 

identifiable causes, so the decision was made to exclude these homes. Two homes are indicated 

as outliers for both electricity and gas consumption, which brings the total number of outlier 

homes to 10. The utility records of these 10 homes were eliminated to ensure data consistency in 

the subsequent analysis and discussion of combined utility cost. As a result of this additional data 

screening, the utility data set was reduced to records from 109 homes (37 PV homes, or 33.9%; 

44 SEE homes, or 40.4%; and 28 comparison homes, or 25.7%). 

Analysis of Utility Consumption 

Several statistical techniques were used to analyze utility consumption. Descriptive statistics 

were first computed. These statistics were calculated for all homes combined and for various 

categories of homes. Independent sample t-tests were then used to compare means and to 

determine whether there were statistically significant differences between home categories. In 

some cases, P2

 tests of independence were used to compare home categories with respect to home 

and household characteristics. 

5

A box plot is a graphical portrayal of the distribution of data values. The box plot displays the location of 

the minimum and maximum values, along with the 25th, 50th, and 75th percentiles. The 50th percentile is equivalent to 

the median. The box, or rectangle, portion of the display represents the interquartile range (IQR), which 

encompasses the central 50% of the values or the distance between the 25th and 75th percentiles. The lines that extend 

below and above the box are sometimes called the whiskers, which extend to the minimum and maximum values or 

to hinge points in the distribution. The lower and upper hinge points are defined by (1) Q1-1.5*IQR, where Q1 is the 

first quartile (25th percentile), and (2) Q3 + 1.5*IQR, where Q3 is the third quartile (75th percentile), respectively. 

Values beyond the hinge points are considered to be statistical outliers and are so indicated as circles or stars, with 

stars being the most extreme. 

204 
Descriptive Statistics on Utility Consumption 

Descriptive statistics on the six utility consumption measures were computed for the various 

categories of homes; and the values are reported in the tables contained in Appendix N 

(Descriptive Statistics, 12-Month Utility Consumption and Cost Data). Each table contains the 

mean, median, minimum value, maximum value, standard deviation, and coefficient of variation6 

for the variable(s) of interest that pertains to utility consumption or cost. All computations were 

made with the 12-month data set after early and outlier homes were omitted. 

Utility Consumption for All Homes Combined 

Table N-1 in Appendix N presents the descriptive statistics on total 12-month electricity (kWh) 

and gas (therms) consumption for the households included in the utility analysis. Descriptive 

statistics on average monthly electricity (kWh) and gas (therms) consumption are also presented. 

Figures P-1 and P-18 in Appendix P (Histograms of 12-Month Utility Data, All Homes 

Combined) depict the frequency distributions of average monthly electricity (kWh) and gas 

(therms), respectively, for all homes combined. 

6

The coefficient of variation is a unitless quantity, reported as a percentage, that expresses variation in the 

data relative to the mean (i.e., the ratio of the standard deviation to the mean multiplied by 100). Data sets that are 

tightly clustered around the mean have small coefficients of variation that approach 0, which suggests a high degree 

of precision. Data sets with values widely spread around the mean have coefficients of variation that are large, which 

suggests low precision. Because it is expressed only in terms of percentage, the coefficient of variation is a 

particularly useful measure for comparing data sets with means and standard deviations that are both different. 

205
Figure 8. Side-by-Side Box Plots of Total 12-Month Electricity Consumption for Three Categories 

of Homes: PV Homes (n=40), SEE Homes (n=51), and Comparison Homes (n=31)7 

7

 A total of five outliers are indicated (the symbols are overlapping for two outlier SEE homes) 

206 
Figure 9. Side-by-Side Box Plots of Total 12-Month Gas Consumption for Three Categories of

Homes: PV Homes (n=40), SEE Homes (n=51), and Comparison Homes (n=31)8

Table N-2 in Appendix N presents corresponding descriptive statistics on average monthly 

electricity (kWh) and gas (therms) per square foot. The respective frequency distributions are 

shown in Figures P-2 and P-8 in Appendix P. These values represent average amounts of 

electricity or gas used by a household adjusted for home size. As indicated in Tables N-1 and N2, total 12-month consumption, average monthly consumption, and average monthly 

consumption per square foot for the homes encompassed in this study are quite variable, as 

indicated by the coefficients of variation, all of which exceed 35% for both electricity and gas. 

8 Seven outliers are indicated. 

207
Comparing Utility Consumption for SheaHomes and Comparison Homes 

Table N-3 presents the comparative descriptive statistics on electricity and gas consumption for 

SheaHomes and comparison homes. For SheaHomes, total 12-month electricity consumption 

ranges from 267 to 16,741 kWh, with a mean of 7,425.8 kWh. The average monthly electricity 

consumption in the SheaHomes ranges from 22.3 to 1,399.8 kWh, with a mean of 619.9 kWh. 

For comparison homes, total 12-month electricity consumption ranges from 3,716 to 17,707 

kWh, with a mean of 9,502.8 kWh. The average monthly electricity consumption in these homes 

ranges from 309.4 to 1,475.9 kWh, with a mean of 792.6 kWh. Figure Q-1 (a, b) in Appendix Q 

shows the frequency distributions of total and average monthly electricity consumption for 

SheaHomes and comparison homes. 

Table 75 reports the results of t-tests that statistically compare the mean total 12-month 

electricity consumption and mean average monthly electricity consumption for SheaHomes and 

comparison homes. The results given in Table 75 confirm that the differences in mean total 12-

month electricity consumption and the mean average monthly electricity consumption for the 

SheaHomes and comparison homes are statistically significant (t= –2.662, p=009; t= –2.652, 

p=.009; respectively). On average, SheaHomes households consume less electricity on both an 

annualized and average monthly basis than do comparison homes. 

Similar results are observed when average monthly electricity consumption is adjusted for square 

footage. As reported in Table 75 (also see Table N-4 in Appendix N), the mean average monthly 

electricity consumption per square foot for SheaHomes is .200 kWh versus .282 for comparison 

homes, and this difference is also statistically significant (t= –3.321, p=.002). 

The variance, and hence the standard deviation, of total 12-month electricity consumption and 

average monthly electricity consumption is not significantly different for SheaHomes and 

comparison homes (F=2.258, p=.136; and F=2.248, p=.137; respectively). However, the variance 

in average monthly electricity consumption per square foot is significantly lower for SheaHomes 

than for comparison homes (F=5.477, p=.021). This suggests less fluctuation, or higher stability, 

when average monthly electricity consumption is adjusted for square footage. On the whole, 

though, there is substantial variation in all three electricity consumption measures among 

SheaHomes and comparison homes, as indicated by the relatively high coefficients of variation 

reported in Table N-3 in Appendix N. 

208
Table 75. Comparison of Mean Utility Consumption Measures for 

SheaHomes and Comparison Homes

Utility Consumption Measure 

Mean for 

SheaHomes 

Homes 

(n=81*) 

Mean for 

Comparison 

Homes 

(n=28*) 

tstatistic pvalue 

Total 12-month electricity consumption (kWh) 7,425.8 9,502.8 –2.662 .009 

Average monthly electricity consumption 

(kWh) 619.9 792.6 –2.652 .009 

Average monthly electricity consumption per 

square foot (kWh) .200 .282 –3.321 .002 

Total 12-month gas consumption (therms) 391.9 497.3 –2.687 .011 

Average monthly gas consumption (therms) 32.8 41.6 –2.684 .011 

Average monthly gas consumption 

per square foot (therms) .011 .015 –3.618 .001 

*Early and outlier homes are omitted. 

On average, SheaHomes also consume significantly less gas in terms of both total 12-month 

consumption and average monthly consumption than comparison homes. Total 12-month gas 

consumption for SheaHomes ranges from 100 to 739 therms, with a mean of 391.9 therms (Table 

N-3 in Appendix N). Average monthly gas consumption for these homes ranges from 8.3 to 61.9 

therms, with a mean of 32.8 therms. When adjusted by square footage, average monthly gas 

consumption ranges from approximately 0 to .02 therms, with a mean of .011 therms (Table N-4 

in Appendix). For comparison homes, total 12-month gas consumption ranges from 202 to 893 

therms, with a mean of 497.3 therms; average monthly gas consumption ranges from 16.9 to 74.7 

therms, with a mean of 41.6 therms; and average monthly gas consumption per square foot 

ranges from .01 to .03 therms, with a mean of .015 therms. As indicated in Table 75, the 

difference in the mean values for each of these measures for SheaHomes and comparison homes 

is statistically significant; the mean values in each case are lower for SheaHomes. Figure Q-7 (a, 

b) in Appendix Q shows the frequency distributions of total and average monthly gas 

consumption for SheaHomes and comparison homes. 

The variance, and hence the standard deviation, of total 12-month gas consumption, average 

monthly gas consumption, and average monthly gas consumption per square foot also are 

significantly lower for SheaHomes than for comparison homes. This suggests significantly lower 

fluctuation, or higher stability, in these measures (F=4.293, p=.041; F=4.279, p=.041; and 

F=9.996, p=.002; respectively). Again, however, there is substantial variation in all three gas 

consumption measures among both SheaHomes and comparison homes, as indicated by the 

relatively high coefficients of variation reported in Table N-3 in Appendix N. 

209
The statistical conclusions drawn from the foregoing analysis are based on an implicit 

assumption that the primary difference in the two groups of homes is the home design itself 

along with all contributing factors (e.g., the builder’s implementation of the design) that may be 

confounded with the design. Clearly, among homes represented in this analysis, SheaHomes 

appear to consume less energy, on average, than comparison homes. Teasing out the effects of all 

the factors that contribute to this result is difficult, particularly those that are related to occupant 

behavior. Nonetheless, such an exercise is important in determining the extent to which the 

households in the two groups are comparable. 

One obvious consideration is the difference in square footage of SheaHomes and comparison 

homes. Because the collection of homes for which utility data are available is not identical to the 

collection of homes from which survey responses were obtained, the square footage of this subset 

of homes in the 12-month utility data analysis must be calculated. The mean square footage of 

these SheaHomes is 3,084.6 ft2

, whereas the mean square footage of these comparison homes is 

2,810.2 ft2

, and the difference is statistically significant (t=4.102, p<.001). Hence, in this subset, 

the SheaHomes are significantly larger (by about 275 ft2, on average) than the comparison 

homes. The mean square footages of PV and SEE homes (both constructed by the same builder) 

are not significantly different (t= –1.036, p=.303). Table 76 contains the associated descriptive 

statistics. 

Table 76. Descriptive Statistics on Square Footages for Various Categories of Homes* 

Home Category n Minimum Mean Maximum 

Standard 

Deviation 

SheaHomes 81 2,222 3,084.6 3,678 289.4 

PV homes 37 2,222 3,048.2 3,678 300.9 

PV homes with 1.2-kW PV systems 31 2,222 3,032.1 3,678 291.2 

PV homes with 2.4-kW PV systems 6 2,584 3,131.8 3,678 364.6 

SEE homes 44 2,584 3,115.1 3,678 279.2 

Comparison homes 28 2,486 2,810.2 3,502 347.5 

*Early and outlier homes are omitted. 

The larger mean square footage for the SheaHomes does not appear to have adversely affected 

mean household energy consumption (on either an average monthly or a 12-month total basis). 

As noted above, mean consumption for both electricity and gas is still lower for these households 

than for comparison households that, on average, have fewer square feet. 

This observation is further underscored in Figures 10–13, which depict the relationship between 

average total 12-month electricity consumption ($), excluding taxes and miscellaneous charges, 

210
and average home square footage.9

 The correlation10 between these two variables is not 

particularly strong for any of the three home categories, and it is particularly low for PV homes 

(the correlation is higher when only those PV homes with 1.2-kW systems are considered). 

Similar relationships (not shown here) are exhibited in scatter diagrams of average home square 

footage and the average values of other utility consumption measures. This observation suggests 

that—contrary to what might have been expected—utility consumption does not necessarily 

increase with home square footage. The question remains as to whether increasing home square 

footage in combination with other factors leads to higher utility consumption. 

Two other factors to consider are household composition and the number of occupants in each 

household (household size). Table 77 shows the comparative percentages of SheaHomes and 

comparison homes that are occupied by adults only and by adults plus children. Of the 

SheaHomes, 35% are occupied by adults alone, whereas 65% are occupied by adults and 

children. Of the comparison homes, 22% are occupied by adults alone, whereas 78% are 

occupied by adults and children. These distributions of percentages are not significantly different 

(P2

=1.614, p=.204), which suggests that household composition for the two groups of homes is 

essentially equivalent. 

Table 77. Comparative Percentages of SheaHomes and Comparison Homes 

Occupied by Adults Only and by Adults plus Children*

Development Adults Only Adults plus Children Total 

SheaHomes 35% (n=28) 65% (n=51) 100% (n=79) 

Comparison homes 22% (n=6) 78% (n=21) 100% (n=27)

 Total 32% (n=34) 68% (n=72) 100% (n=106) 

*This analysis is limited to homes for which actual utility data are available. Early and outlier homes are omitted. 

Information about household size is not available on three of the homes (two from SheaHomes and one from 

comparison homes). 

9

Because these graphs depict averages, each data point represents a different number of homes. 

10Here, the different numbers of homes that contribute to each average is ignored for purposes of computing 

the correlation coefficient, r. 

211 
PV Homes 

0 

4000 

8000 

12000 

16000 

2000 2500 3000 3500 4000 

Home Size (ft2) 

Total 12-Month Electricity 

Consumption ($)* 

r = .22 

*Excluding taxes and 

miscellaneous charges 

Figure 10. Scatter Diagram of Average Total 12-Month Electricity Consumption ($) versus

Average Square Footage for PV Homes (n=37).11

11The number of homes associated with each data point varies. For example, there may be more 3000-ft2 

homes than 2500-ft2 homes. 

212 
1.2 PV Homes 

0 

4000 

8000 

12000 

16000 

2000 2500 3000 3500 4000 

Home Size (ft2) 

Total 12-Month Electricity

Consumption ($)* 

*Excluding taxes and 

miscellaneous charges 

r=.47 

Figure 11. Scatter Diagram of Average Total 12-Month Electricity Consumption ($) versus

Average Square Footage for PV Homes with 1.2-kW Systems (n=31).12

12The number of homes associated with each data point varies. For example, there may be more 3000-ft2 

homes than 2500-ft2 homes. 

213 
SEE Homes 

0 

4000 

8000 

12000 

16000 

2000 2500 3000 3500 4000 

Home Size (ft2) 

Total 12-Month Electricity

Consumption ($)* 

r = .53 

*Excluding taxes and 

miscellaneous charges 

Figure 12. Scatter Diagram of Average Total 12-Month Electricity Consumption ($) versus

Average Square Footage for SEE Homes (n=44).13

13The number of homes associated with each data point varies. For example, there may be more 3000-

square-foot homes than 2500-square-foot homes.

214
Comparison Homes 

0 

4000 

8000 

12000 

16000 

2000 2500 3000 3500 4000 4500 5000 

Home Size (ft2) 

Total 12-Month Electricity

Consumption ($)* 

*Excluding taxes and 

miscellaneous charges 

r = .64 

Figure 13. Scatter Diagram of Average Total 12-Month Electricity Consumption ($) versus

Average Home Size (ft2

) for Comparison Homes (n=28)14

14The number of homes associated with each data point varies. For example, there may be more 3000-ft2 

homes than 2500-ft2 homes. 

215 
Table 78 shows the comparative percentages of SheaHomes and comparison homes that are 

occupied by three or fewer individuals and by four or more individuals. Among the SheaHomes, 

55% are occupied by three or fewer persons, and 45% are occupied by four or more persons—a 

relatively even split. Among the comparison homes, 33% are occupied by three or fewer persons; 

67% are occupied by four or more persons—a 2-to-1 ratio. 

Table 78. Comparative Percentages of SheaHomes and Comparison Homes Occupied by

Three or Fewer Individuals and by Four or More Individuals*

Development Three or Fewer Four or More Totals 

SheaHomes 55% (n=44) 45% (n=46) 100% (n=80) 

Comparison homes 33% (n=9) 67% (n=18) 100% (n=27) 

Total 49% (n=53) 51% (n=54) 100% (n=107) 

*This analysis is limited to homes for which actual utility data are available. Early and outlier homes are omitted. 

Information on number of occupants is missing for two homes (one each from SheaHomes and comparison homes). 

Although the higher percentage of comparison homes with four or more occupants is noteworthy 

in that it offers a potential explanation for the higher mean utility consumption by these 

households, the difference in the distribution of percentages indicated in Table 78 is not quite 

statistically significant (P2

=3.791, p=.052). Records from a greater number of comparison homes 

would be needed to determine if household size (number of occupants) is significantly larger for 

those homes. Indeed, considering together all the homes for which actual utility data are 

available, there is a nearly even split between the number occupied by three or fewer individuals 

and the number occupied by four or more individuals. This again suggests that household size 

may not be a discriminating factor. Figure 14 gives an expanded view of the comparative 

distributions of household size for the SheaHomes and comparison homes in terms of a side-byside percentage bar chart.15 

Energy-consuming equipment—particularly home recreational amenities such as pools and hot 

tubs—is also of interest when comparing the two groups of homes.16 Table 79 shows the 

percentages of SheaHomes and comparison homes that have either a pool or hot tub, or both, and 

those that have neither. Of the SheaHomes, 26.3% have a pool or hot tub, or both; 73.8% have 

neither. On the other hand, 57.7% of the comparison homes have a pool or hot tub, or both; 

42.3% have neither. Note that comparison homes did not come with pools or hot tubs standard. 

Figure 15 provides an expanded presentation of the ownership distribution for SheaHomes and 

comparison homes in terms of a side-by-side percentage bar chart. Clearly, a higher percentage 

of SheaHomes have no pool or hot tub, and this difference in the distribution of percentages is 

15Utility consumption and cost per person is an interesting concept that is not pursued here. 

16Solar hot water and solar PV are not considered at this point because no comparison homes have these 

features. Nonetheless, all SheaHomes (except early homes) have solar hot water systems, and some have solar PV 

systems. None of the comparison homes have solar hot water or solar PV systems. Consequently, these features may 

constitute the single most important reason why mean utility consumption is lower for SheaHomes. 

216 
statistically significant (P2

=8.65, p=.003). Such a strong difference may partially explain the 

higher mean utility consumption in comparison homes, and this possibility is considered in more 

detail below. 

Table 79. Comparative Percentages of SheaHomes and

Comparison Homes with a Pool and/or Hot Tub or with Neither

Development Neither Pool and/or Hot Tub Total 

SheaHomes* 73.8% (n=59) 26.3% (n=21) 100% (n=80) 

Comparison homes* 42.3% (n=11) 57.7% (n=15) 100% (n=26) 

Total 66.0% (n=70) 34.0% (n=36) 100% (n=106) 

*Early and outlier homes are omitted. Information about pools and hot tubs is missing for three homes (one from 

SheaHomes and two from comparison homes). 

Table 80 summarizes the results of statistical comparisons of the percentage of ownership of 

various other energy-related equipment and appliances among SheaHomes and comparison 

homes. The percentage distribution is significantly different for only two items: (1) two 

refrigerators and (2) hot water flow regulators. A significantly lower percentage of the 

comparison homes have two refrigerators, and a significantly higher percentage of these homes 

have hot water flow regulators. Both results might ordinarily be expected to contribute to lower 

utility consumption; but this is apparently not the case (at least the effect is not immediately 

apparent) because mean utility consumption in terms of both the 12-month total and the average 

monthly amount is higher for comparison homes than for SheaHomes (see Table 75). The two 

groups of homes are considered to be essentially equivalent on the basis of the other equipment 

listed in Table 80. 

217
Figure 14. Side-by-Side Percentage Bar Chart Showing the Comparative Distributions of

Household Size for SheaHomes (n=80) and Comparison Homes (n=27)17

17 Information on household size is missing for two homes (one each from SheaHomes and comparison 

homes). 

218 
Figure 15. Side-by-Side Bar Chart Depicting the Percentages of Shea Homes (n=80) and

Comparison Homes (n=26) with Hot Tubs and/or Pools18

18Information concerning pool and hot tub ownership is not available for three homes (one from 

SheaHomes and two from comparison homes). 

219 
Table 80. Statistical Comparisons of the Percentage of Ownership of Various Other

Energy-Related Equipment among SheaHomes and Comparison Homes

Home Feature 

SheaHomes* 

Ownership 

Comparison* 

Homes Ownership P2 p-value 

Ceiling fans 63.8% (n=51) 61.5% (n=16) .041 .839 

Dual zone heating/cooling 62.5% (n=50) 57.7% (n=15) .191 .662 

Dimmer switches for lights 57.5% (n=46) 57.7% (n=15) .000 .986 

Two refrigerators 45.0% (n=36) 23.1% (n=6) 3.942 .047 

Compact fluorescent light bulbs (CFLs) 43.8% (n=35) 53.8% (n=14) .805 .370 

Hot water flow regulator 10.0% (n=8) 30.8% (n=8) 6.605 .010 

Standalone freezer 8.8% (n=7) 15.4% (n=4) .929 .335 

*In each comparison, the total number of SheaHomes is 80 and the total number of comparison homes is 26. 

Information is not available for three homes (one from SheaHomes and two from comparison homes). Early and 

outlier homes are omitted. 

Comparing Electricity and Gas Consumption for PV and SEE Homes 

An important question for this study is the extent to which solar PV systems affect actual utility 

consumption. This question can be directly addressed when PV and SEE homes are compared. 

Of the 81 SheaHomes on which actual utility data are available, 37 (45.7%) have solar PV 

systems and 44 (54.3%) do not. The PV and SEE homes are in the same development and were 

constructed by the same builder with identical energy-efficiency features. Additionally, all homes 

in both categories are equipped with solar water heating. Further, there is no significant 

difference between the PV and SEE homes with respect to square footage (t= –1.036, p=.303), 

household size or composition (P2

=.690, p=.406; and P2=.086, p=.770; respectively), ownership 

of pools or hot tubs, or both (P2

=.627, p=.429), or ownership of any of the other equipment and 

appliances noted in Table 80.19 

Table N-5 in Appendix N presents descriptive statistics on total 12-month electricity and gas 

consumption and on average monthly electricity and gas consumption for PV homes and SEE 

homes. Table N-6 in Appendix N reports descriptive statistics on average monthly electricity and 

gas consumption per square foot for these same groups of homes. 

Figure R-1 (a, b) in Appendix R show the distributions of total and average monthly electricity 

consumption for PV and SEE homes. For the PV homes, total 12-month electricity consumption 

ranges from 267 to 12,868 kWh, with a mean of 6,368.9 kWh. Average monthly electricity 

consumption for these homes ranges from 22.3 to 1073.4 kWh, with a mean of 531.7 kWh. On a 

square-footage basis, average monthly electricity consumption ranges from .009 to .415 kWh, 

19At least four different floor plans were constructed by SheaHomes. All four floor plans are represented 

among the PV and SEE home categories. 

220 
with a mean of .174 kWh. For the SEE homes, total 12-month electricity consumption ranges 

from 3,333 to 1,6741 kWh, with a mean of 8,314.7 kWh; average monthly electricity 

consumption ranges from 277.7 to 1,399.8 kWh, with a mean of 694 kWh. On a square-footage 

basis, average monthly electricity consumption for homes without solar PV ranges from .090 to 

.489 kWh, with a mean of .222 kWh. These values suggest that, on average, electricity 

consumption is lower in the PV homes. 

With respect to gas consumption, the findings are somewhat different. Figure R-7 (a, b) in 

Appendix R shows the distributions of total and average monthly gas consumption for PV and 

SEE homes. For PV homes, total 12-month gas consumption ranges from 100 to 678 therms, 

with a mean of 366 therms; and average monthly gas consumption ranges from 8.3 to 56.6 

therms, with a mean of 30.7 therms (see Table N-5 in Appendix N). Average monthly gas 

consumption per square foot ranges from approximately 0 to .02 therms, with a mean of .01 

therms. For SEE homes, total 12-month gas consumption ranges from 128 to 739 therms, with a 

mean of 413.7 therms; and average monthly gas consumption ranges from 10.7 to 61.9 therms, 

with a mean of 34.6 therms. Average monthly gas consumption per square foot for SEE homes 

ranges from .011 to .020 therms, with a mean of .011 therms. These values suggest that, on 

average, gas consumption is approximately equivalent in PV and SEE homes, all of which have 

solar water heating and energy-efficiency measures. 

For both electricity and gas, the variances of total 12-month consumption, average monthly 

consumption, and average monthly consumption per square foot are statistically equivalent for 

PV homes and SEE homes. Nonetheless, there is substantial fluctuation in these values for both 

groups of homes, as indicated by the relatively high coefficients of variation reported in Tables 

N-5 and N-6 in Appendix N, all of which exceed 30%. In particular, the coefficients of variation 

for total 12-month electricity consumption, average monthly electricity consumption, and average 

monthly electricity consumption per square foot all exceed 50% for PV homes. This finding 

suggests that, for whatever reason, electricity consumption in these homes is fairly inconsistent, 

even though on average it is relatively low.20 

Table 81 presents the results of formal statistical comparisons of the means of three electricity 

consumption measures for both electricity and gas for PV and SEE homes. As suggested above, 

the PV homes have significantly lower electricity consumption, on average, than the SEE homes, 

in terms of each of three utility consumption measures: (1) 12-month total electricity 

consumption, (2) average monthly energy consumption, and (3) average monthly energy 

consumption per square foot (t= –2.713, p=.008; t= –2.711, p=.008; and t= –2.497, p=.015; 

respectively). There is no significant difference, on average, in gas consumption between PV and 

SEE homes with and without solar PV systems with respect to any of the three utility 

consumption measures (t= –1.549, p=.125; t= –1.541, p=.127; and t= –1.130, p=.171; 

respectively). These results are consistent with the expectation that solar PV systems reduce the 

need to extract electricity from the grid, but have no significant impact on gas consumption. 

20One possible explanation for the variability in electricity use, documented in line graphs in Appendix L, is 

that, at various times, PV systems were down for maintenance or inverter repair (Marks 2006; Nelson 2006). Further 

analysis of PV system downtime would be needed to clarify this possibility. Results in Chapter 21 on modeling could 

also be affected by such occurrences. 

221 
Table 81. Comparison of Mean Utility Consumption Measures for PV and SEE Homes* 

Utility Consumption Measure 

PV Homes 

(n=37) 

SEE Homes 

(n=44) 

tstatistic pvalue 

Total 12-month electricity consumption (kWh) 6,468.9 8,314.7 –2.713 .008 

Average monthly electricity consumption (kWh) 531.7 694.0 –2.711 .008 

Average monthly electricity consumption per 

square foot (kWh) .174 .222 –2.497 .015 

Total 12-month gas consumption (therms) 366.0 413.7 –1.549 .125 

Average monthly gas consumption (therms) 30.7 34.6 –1.541 .127 

Average monthly gas consumption 

per square foot (therms) .010 .011 –1.130 .171 

*Early and outlier homes are omitted 

As described elsewhere in this report, two capacities of PV systems (1.2-kW and 2.4-kW) were 

available to SheaHomes buyers. However, the statistical comparisons shown in Table 81 do not 

account for this difference. In general, homes with larger PV systems would be expected to 

exhibit lower mean electricity consumption than homes with smaller systems, which would make 

the comparison to SEE homes even more distinct. To test this hypothesis, Table 82 shows the 

results of formal statistical tests that contrast the mean values of the utility consumption 

measures for the two sizes of PV systems. Indeed, PV homes with 2.4-kW systems have 

significantly lower mean values (nearly 50% lower) for all three measures of electricity 

consumption than homes with 1.2-kW systems. There are no significant differences with regard 

to gas consumption. Appendix R1 contains comparative histograms of electricity consumption 

for PV homes with 1.2-kW systems and 2.4-kW systems. 

Because substantially more homes have 1.2-kW systems (n=31) than 2.4-kW systems (n=6), 

additional comparisons of utility consumption should be provided between SEE homes and PV 

homes with the smaller and larger PV systems. Hence, Table 83 provides the results 

corresponding to those given in Table 81 when the PV home category is restricted to those with 

1.2-kW systems. With this restriction, the differences between the mean values of the three 

electricity consumption measures for PV and SEE homes are not as large. In fact, the difference 

in mean total 12-month electricity consumption and the difference in mean average monthly 

electricity consumption are just barely significant; the difference in mean average monthly 

electricity consumption per square foot is not statistically significant. Clearly, the larger (2.4-kW) 

solar PV systems, in combination with the energy-efficiency package, have a substantial impact 

on the electricity consumption of PV homes compared with SEE homes. 

222
Table 82. Comparison of Mean Utility Consumption Measures for 

PV Homes with 1.2-kW and 2.4-kW Systems*

Utility Consumption Measure 

With 1.2-kW 

PV Systems 

(n=31) 

With 2.4-kW 

PV Systems 

(n=6) 

tstatistic pvalue 

Total 12-month electricity consumption (kWh) 6864.2 3809.5 2.254 .031 

Average monthly electricity consumption 

(kWh) 572.9 318.4 2.250 .031 

Average monthly electricity consumption per 

square foot (kWh) .189 .100 2.305 .022 

Total 12-month gas consumption 

(therms) 366.8 361.7 .082 .935 

Average monthly gas consumption (therms) 30.7 30.3 .086 .932 

Average monthly gas consumption 

per square foot (therms) .010 .010 .359 .722 

*Early and outlier homes are omitted 

Table 83. Comparison of Mean Utility Consumption Measures for 

PV Homes with 1.2-kW Systems and SEE Homes* 

Utility Consumption Measure 

PV Homes with 

1.2-kW Systems 

(n=31) 

SEE Homes 

(n=44) 

tstatistic pvalue 

Total 12-month electricity consumption 

(kWh) 6,864.2 8,314.7 –1.950 .055 

Average monthly electricity consumption 

(kWh) 572.9 694.0 –1.949 .055 

Average monthly electricity consumption 

per square foot (kWh) .189 .222 –1.682 .097 

Total 12-month gas consumption 

(therms) 366.8 413.7 –1.431 .157 

Average monthly gas consumption (therms) 30.7 34.6 –1.422 .159 

Average monthly gas consumption 

per square foot (therms) .010 .011 –1.166 .248 

*Early and outlier homes are omitted 

223
Comparing Electricity and Gas Consumption for SEE and Comparison Homes 

Since SheaHomes have lower electricity and gas consumption, on average, than comparison 

homes (in terms of total 12-month consumption, average monthly consumption, and average 

monthly consumption per square foot) and PV homes have lower electricity consumption (in 

terms of the same three measures), on average, than SEE homes, it is of further interest to 

compare utility consumption in comparison homes and SEE homes. Although these two groups 

of homes could be conceptually similar and might have essentially equivalent utility 

consumption, in fact, the SEE homes, as ComfortWise homes, meet a higher standard of energy 

efficiency than do the comparison homes, which were only built to California’s Title 24 

efficiency standard.21 In addition, SEE homes have solar water heating systems. Tables N-7 and 

N-8 in Appendix N present descriptive statistics on total 12-month consumption, average 

monthly consumption, and average monthly consumption per square foot for electricity and gas 

for the comparison and SEE homes. 

Table 84 presents the results of formal statistical comparisons of the means of the three 

electricity consumption measures for both electricity and gas for the two groups of households. 

On average, the mean total 12-month electricity consumption and mean average monthly 

electricity consumption are higher for comparison homes than for SEE homes, but the difference 

is not statistically significant (t= –1.364, p=.177; t= –1.355, p=.180; respectively). However, 

when adjusted for square footage, the difference is significant (t= –2.322, p=.025); that is, the 

mean average monthly electricity consumption per square foot is significantly lower for SEE 

homes than for comparison homes. This is particularly noteworthy because comparison homes 

are significantly smaller than SEE homes (the mean square footage for comparison homes is 

2,810.2 and the mean square footage for SEE homes 3,115.1, t=4.104, p<.001). 

The observed differences in electricity consumption between SEE and comparison homes may be 

partly because of differences in the numbers of homes involved, since fewer comparison homes 

are represented than SEE homes. The observed differences may also result from a significantly 

higher ownership percentage of pools and hot tubs among comparison homes. Of the 44 SEE 

homes, only 10 (23%) have pools and/or hot tubs, or both, whereas, of the 26 comparison homes, 

15 (59%) have pools and hot tubs (P2

=8.702, p=.003). Because the comparison homes have 

significantly less square footage, on average, than do the SEE homes, the ownership of pools and 

hot tubs may play a more important role in electricity consumption than other variables. 

21The energy-efficiency measures included in the ComfortWise high-performance home are described in 

Chapter 1 (Introduction and Background). The Title 24 building standards may be reviewed at 

http://www.energy.ca.gov/title24/ (accessed 9/20/05). 

224 
Table 84. Comparison of Mean Utility Consumption Measures for 

Comparison Homes and SEE Homes*

Utility Consumption Measure 

Comparison 

Homes 

(n=28) 

SEE 

Homes 

(n=44) 

tstatistic pvalue 

Total 12-month electricity consumption (kWh) 9,502.8 8,314.7 –1.364 .177 

Average monthly electricity consumption (kWh) 792.6 694.0 –1.355 .180 

Average monthly electricity consumption per 

square foot (kWh) .282 .222 –2.322 .025 

Total 12-month gas consumption (therms) 497.3 413.7 –2.015 .050 

Average monthly gas consumption (therms) 41.6 34.6 –2.012 .050 

Average monthly gas consumption per square foot 

(therms) .015 .011 –3.044 .004 

*Early and outlier homes are omitted. 

There are no other readily apparent differences between these two groups of homes. They are not 

significantly different in terms of household size or composition (P2

=2.715, p=.099; P2=2.253, 

p=.133, respectively); and there are no significant differences between them in terms of 

ownership of the other equipment and appliances noted in Table 80. 

The difference in the variance of total 12-month electricity consumption for comparison homes 

and SEE homes is not statistically significant (F=2.301, p=.134). The difference in the variance 

of average monthly electricity consumption per square foot for these two categories of homes is 

also not statistically significant (F=2.280, p=.136). However, when average monthly electricity is 

computed on a square-footage basis, the difference in the variances of the values for the two 

groups of homes is statistically significant (F=5.859, p=.018). This finding suggests that, in 

addition to the fact that the mean average monthly electricity consumption per square foot is 

significantly higher for the comparison homes, there is greater fluctuation, or inconsistency, in 

this quantity for the comparison homes than for the SEE homes. 

The results for gas are entirely different. As indicated in Table 84, total 12-month gas 

consumption, average monthly gas consumption, and average monthly gas consumption per 

square foot are all significantly lower, on average, for SEE households than for comparison 

households (t= –2.015, p=.05; t= –2.012, p=.05; and t= –3.044, p=.004; respectively). Three 

factors may explain much of this difference: (1) absence of solar hot water systems in 

comparison homes, (2) inclusion of more energy-efficiency features in the SEE high-performance 

homes, and (3) a higher percentage of ownership of pools and hot tubs in comparison homes. In 

many cases, gas is used for heating pools and hot tubs. Appendix M contains line graphs showing 

the total gas consumption by household since occupancy for comparison homes with pools. 

225
The variances of total 12-month gas consumption, average monthly gas consumption, and 

average monthly gas consumption per square foot are all significantly higher for comparison 

homes than for SEE homes (F=4.203, p=.044; F=4.203, p=.044; and F=7.576, p=.008, 

respectively). In addition to the finding that mean gas consumption is significantly higher for 

comparison homes, this additional finding suggests greater instability in the gas consumption 

measures as well. Such instability may again be a result of the higher rates of ownership of pools 

and hot tubs among comparison homes and the seasonal demands for gas that they create. 

Confidence Interval Estimates for Selected Utility Consumption Measures 

Because we could not obtain utility data for all the SheaHomes and comparison homes, the true 

mean values of the utility consumption measures cannot be known with certainty. The mean 

values reported in Tables 82–84 are sample-based values that are associated only with homes for 

which we could obtain data. In the language of statistics, such values are referred to as point 

estimates. The households for which data were obtained are assumed to be representative of all 

the SheaHomes and comparison homes; hence, the point estimates contained in Tables 82–84 are 

considered to be reliable. However, using an interval approach to estimate the true mean values 

of utility consumption in all the homes is somewhat more desirable, because there is some 

sampling uncertainty associated with the point estimates. 

Consequently, Table 85 presents 95% confidence interval estimates22 for the mean values of 

selected utility consumption measures for the PV, SEE, and comparison homes. These intervals 

account for the sampling error associated with the individual point estimates that arises because 

utility data could not be obtained for all homes. Each respective interval is expected to cover the 

true mean value of the utility consumption measure in question with statistical confidence of 

95%. For example, based on data from 37 homes, the interval 5,299.7 kWh to 7,428.0 kWh is 

expected to cover the true mean of total 12-month electricity consumption for PV homes with 

95% confidence. The interval endpoints provide low and high cases on which future planning 

scenarios can be based. Table 86 provides corresponding confidence intervals for only those PV 

homes with 1.2-kW systems. 

22The general form of the confidence interval is x ± t ⋅ MSE , where x is the mean, t is a multiplier 

taken from the t-distribution to guarantee statistical coverage of 95%, and MSE is the mean square error. The product 

t A MSE constitutes the margin of error. 

For each of these confidence intervals, the MSE is computed using only the data associated with the homes 

in the specific group or category of interest, and the finite population correction factor is not applied. Different 

confidence intervals could be computed by using a value of the MSE derived from the results of ANOVA, which 

considers the data from all three groups simultaneously. Although such intervals would not be expected to be 

substantially wider, they could lead to different interpretations. 

Because there are different numbers of homes in the three categories, statistical significance should not 

necessarily be attributed to the overlapping or non-overlapping confidence intervals for a specific utility 

consumption measure. 

226 
Table 85. 95% Confidence Intervals on the Mean Values of

Selected Utility Consumption Measures for Three Home Categories*

Utility 

Consumption 

Measure 

PV Homes 

(n=37) 

SEE Homes 

(n=44) 

Comparison Homes 

(n=28) 

Lower 

Limit 

Upper 

Limit 

Lower 

Limit 

Upper 

Limit 

Lower 

Limit 

Upper 

Limit 

Total 12-month electricity 

consumption (kWh) 5,299.74 7,437.99 7,334.63 9,294.69 7,898.02 11,107.55 

Average monthly electricity 

consumption (kWh) 442.46 620.84 612.12 775.91 658.69 926.45 

Total 12-month gas 

consumption (therms) 319.32 412.68 372.25 455.16 423.37 571.27 

Average monthly gas 

consumption (therms) 26.75 34.56 31.15 38.09 35.42 47.75

 *Early and outlier homes are omitted 

Table 86. 95% Confidence Intervals on the Mean Values of 

Selected Utility Consumption Measures for Three Groups of Homes* 

Utility 

Consumption 

Measure 

PV Homes 

(n=31) 

SEE Homes 

(n=44) 

Comparison Homes 

(n=28) 

Lower 

Limit 

Upper 

Limit 

Lower 

Limit 

Upper 

Limit 

Lower 

Limit 

Upper 

Limit 

Total 12-month electricity 

consumption (kWh) 5,728.13 8,000.32 7,334.63 9,294.69 7,898.02 11,107.55 

Average monthly electricity 

consumption (kWh) 478.12 667.72 612.12 775.91 658.69 926.45 

Total 12-month gas 

consumption (therms) 313.92 419.76 372.25 455.16 423.37 571.27 

Average monthly gas 

consumption (therms) 26.30 35.15 31.15 38.09 35.42 47.75

 *Early and outlier homes are omitted 

Impact of Energy-Intensive Household Equipment on Utility Consumption 

As previously noted, there are significant differences in the percentages of SheaHomes and 

comparison homes that have energy-intensive equipment, such as pools and hot tubs. Such 

equipment would be expected to have an impact on utility consumption. Tables S1- through S-6 

in Appendix S (Descriptive Statistics on Total and Average Monthly Electricity Consumption 

and Total and Average Gas Consumption with and without Selected Equipment, by Categories of 

227
Homes, 12-Month Data) contain descriptive statistics on six utility consumption measures for 

homes with various combinations of energy-intensive equipment within the three categories of 

homes (separate descriptive statistics are also included for PV homes having 1.2-kW systems). 

Figures 16–18 and Figures 19–21 graphically depict this same information. Within each of the 

three home categories, there are notable differences in the numbers of homes with specific 

combinations of equipment. In fact, the numbers of homes for several of the equipment 

combinations are quite small (indeed, within all three home categories some of the equipment 

combinations are not represented at all); and for this reason, care must be taken when 

extrapolating the statistics contained in these tables or making percentage comparisons. 

Nonetheless, the information can be used to establish general trends concerning the incremental 

impacts on utility consumption of adding energy-intensive equipment to homes. Such impacts are 

different depending on the home category. Appendix R2 contains comparative histograms of 

average monthly electricity and gas consumption for homes with and without pools or hot tubs. 

For example, one of the most notable observations is the impact of pools and hot tubs (alone, in 

combination with each other, or in combination with other equipment) on electricity 

consumption in comparison homes. For these homes, total 12-month electricity consumption 

(kWh), average monthly electricity consumption (kWh), and average monthly electricity 

consumption per square foot are all substantially higher than for corresponding homes without 

this equipment.23 Higher electricity consumption is also associated with pools and hot tubs in PV 

homes and SEE homes (electricity is used for pumps), but the increase is not so great; and 

particularly in the case of PV homes, some evidence suggests that the increase is no larger than 

for other energy-intensive equipment, such as extra refrigerators and standalone freezers. 

Interestingly, these same combinations of energy-intensive equipment are also associated with 

increased gas consumption. 

Base-Case Homes 

The most important information contained in Tables S-1 through S-6 in Appendix S and Figures 

16–18 and Figures 19–21 may be the statistics about utility consumption for homes with no 

energy-intensive equipment. In fact, for all home categories, those without any energy-intensive 

equipment constitute the largest subcategory in terms of number of homes. These homes could be 

considered base-case homes in the sense that the amounts of electricity and gas consumption 

reported for them are not confounded by energy-intensive equipment.24 Although there are other 

factors to consider (e.g., number of occupants), these particular homes provide the most direct 

comparison of utility consumption between PV, SEE, and comparison homes. 

23No information is available on the sizes and configurations of pools and hot tubs in these homes, which 

may also affect utility consumption. For purposes of this discussion, all pools are assumed to be equivalent and all 

hot tubs are assumed to be equivalent. 

24 Base-case homes are defined as homes without pools, hot tubs, standalone freezers, or additional 

refrigerators. These homes may still have one or more other kinds of energy-intensive equipment. 

228 
Figure 16 suggests that base-case comparison homes and base-case SEE homes have roughly the 

same mean average monthly electricity consumption (7.2% higher for SEE homes); the value for 

base-case PV homes is considerably lower (separate estimates are presented for base-case PV 

homes with 1.2-kW systems). Mean average monthly electricity consumption is 77.5% higher in 

SEE homes than in PV homes (59.9% higher in SEE homes than in PV homes with 1.2-kW 

systems) and 65.5% higher in comparison homes than in PV homes (49.1% higher in comparison 

homes than in PV homes with 1.2-kW systems). As indicated by the one-standard-deviation error 

bars, the variability in average monthly electricity consumption is about the same for all three 

home categories. 

On the other hand, Figure 19 suggests that base-case comparison homes have the highest mean 

average monthly gas consumption, followed by base-case SEE homes and PV homes, 

respectively. Mean average monthly gas consumption is 23.1% higher in comparison homes than 

in SEE homes and 81% higher than in PV homes (83.7% higher than in PV homes with 1.2-kW 

systems). Furthermore, mean average monthly gas consumption is 47.1% higher in SEE homes 

than in PV homes (49.2% higher than in PV homes with 1.2-kW systems). The figure also 

suggests the variability in average monthly gas consumption is highest for base-case comparison 

homes and lowest for base-case PV homes. 

Formal ANOVA applied to these base-case homes indicates that mean average monthly 

electricity consumption for SEE and comparison homes is not significantly different (589.40 

kWh vs 549.59 kWh). The ANOVA results also indicate that base-case PV homes have 

significantly lower mean average monthly electricity consumption (332.06 kWh) than base-case 

SEE or comparison homes. However, this is only true when all base-case PV homes are 

considered together (those with 1.2-kW systems and those with 2.4-kW systems combined). 

When PV homes are restricted to only those with 1.2-kW systems, the difference in mean 

average monthly electricity consumption for base-case PV homes and SEE homes is still 

significantly different (368.61 kWh vs 589.40 kWh), but the difference between base-case PV 

homes and base-case comparison homes is not (368.61 kWh vs 549.59 kWh) (p=.073). This 

finding seems counterintuitive, but the number of homes is small. As noted above, the mean 

average monthly electricity consumption for base-case SEE homes is about 7.2% higher than for 

base-case comparison homes. Apart from sampling variation and disparities in the numbers of 

homes involved, the reasons for the higher mean average monthly electricity consumption in 

base-case SEE homes is not completely understood. 

All these same results are obtained when ANOVA is applied to electricity consumption 

computed on a square-footage basis, except that mean average monthly electricity consumption 

for base-case PV homes (.115 kWh/ft2

; .128 kWh per square foot for PV homes with 1.2-kW 

systems) is significantly lower than mean average monthly electricity for base-case SEE (.190 

kWh/ft2) and comparison homes (.214 kWh/ft2), whether or not all PV homes or only those with 

1.2-kW systems are considered. Adjusting for home size removes any ambiguity in the 

comparison of base-case PV homes to base-case homes within the other categories. 

Similarly, formal ANOVA applied to base-case homes indicates that mean average monthly gas 

consumption for SEE and comparison homes is not significantly different (31.970 therms vs 

229
39.362 therms). The ANOVA results also indicate that base-case PV homes have significantly 

lower mean average monthly gas consumption (21.741 therms; 21.428 therms for PV homes with 

1.2-kW systems) than base-case SEE or comparison homes. This is true regardless of whether all 

base-case PV homes are considered together (both those with 1.2-kW systems and those with 

2.4-kW systems combined) or if only those with 1.2-kW systems are considered. All these same 

results are obtained when ANOVA is applied to gas consumption computed on a square-footage 

basis (.0075 therms/ft2 for PV homes, .0074 therms/ft2 for PV homes with 1.2-kW systems, .0105 

therms/ft2

 for SEE homes, and .0154 therms/ft2 for comparison homes), except that mean average 

monthly gas consumption for base-case SEE homes is significantly lower than mean average 

monthly gas consumption for base-case comparison homes. Again, adjusting for home size 

clarifies the distinction between base-case SEE and comparison homes illustrated in Figure 19. 

Based on P2 analysis, there is no significant difference among the three categories (PV, SEE, 

comparison) of base-case homes with respect to any of the demographic variables, such as 

household size and household composition. This adds credence to the reliability of the findings 

noted above because utility consumption is not confounded by differences attributable to these 

variables. Interestingly, while adjusting for home size in the computation of average monthly 

electricity consumption and average monthly gas consumption appears to clarify the relationship 

among the three home types somewhat, the size of base-case homes (in terms of square footage) 

in and of itself is not highly correlated with total or average utility consumption as illustrated in 

Table 87.25 

Table 87. Correlation* between Home Size (Square Footage) and Four

Utility Consumption Measures for Base-Case PV, SEE, and Comparison Homes

Utility Consumption 

Measure 

PV Homes 

(n=13) 

PV Homes with 

1.2-kW Systems 

(n=11) 

SEE Homes 

(n=19) 

Comparison 

Homes 

(n=7) 

Total 12-month electricity 

consumption (kWh) 

.14 !.01 .45 !.15 

Average monthly electricity 

consumption (kWh) .14 !.01 .45 !.15 

Total 12-month gas 

consumption (therms) .23 .08 !.11 !.18 

Average monthly gas 

consumption (therms) .23 .08 !.11 !.18 

*Pearson product moment correlation. 

25Some of the correlations in Table 87 are negative, suggesting that, as home size (square footage) 

increases, utility consumption decreases. This somewhat counterintuitive result is largely attributable to the small 

numbers of homes involved. 

230 
0 

200 

400 

600 

800 

1000 

1200 

1400 

1600 

PV 

1.2 PV 

SEE 

Comparison PV 

1.2 PV 

SEE 

Comparison 

PV 

1.2 PV 

SEE 

Comparison 

PV 

1.2 PV 

SEE 

Comparison 

No Special Features Standalone Freezer Tw o Refrigerators Standalone Freezer & Tw o 

Mean Average Monthly Electricity Consumption (kWh) 

Refrigerators 

Figure 16. Comparison of Mean Average Electricity Consumption for Four Categories of Homes:

No Energy-Intensive Equipment (Base-Case Homes), Standalone Freezer, Two Refrigerators, and

Standalone Freezer and Two Refrigerators26

26Different numbers of homes are associated with each graphed mean. The figure also includes onestandard-deviation error bars. Error bars are absent for means representing only a single home. 

231 
0 

200 

400 

600 

800 

1000 

1200 

1400 

1600 

PV

1.2 PV 

SEE

Comparison 

PV

1.2 PV 

SEE

Comparison 

PV

1.2 PV 

SEE

Comparison 

PV

1.2 PV 

SEE

Comparison 

Mean Average Monthly Electricity Consumption (kWh) 

Pool Pool & Standalone Freezer Pool & Tw o Refrigerators Hot Tub 

Figure 17. Comparison of Mean Average Electricity Consumption for Four Categories of Homes:

Pool, Pool and Standalone Freezer, Pool and Two Refrigerators, and Hot Tub27

27Different numbers of homes are associated with each graphed mean. The figure also includes onestandard-deviation error bars. Error bars are absent for means representing only a single home. 

232 
0 

200 

400 

600 

800 

1000 

1200 

1400 

1600 

PV

1.2 PV 

SEE

Comparison 

PV

1.2 PV 

SEE

Comparison 

PV

1.2 PV 

SEE

Comparison 

PV

1.2 PV 

SEE

Comparison 

Hot Tub & Tw o Refrigerators Pool & Hot Tub Pool, Hot Tub, & Standalone 

Freezer 

Pool, Hot Tub, & Tw o 

Refrigerators 

Mean Average Monthly Electricity Consumption (kWh) 

Figure 18. Comparison of Mean Average Electricity Consumption for Four Categories of Homes:

Hot Tub and Two Refrigerators; Pool and Hot Tub; Pool, Hot Tub, and Standalone Freezer; 

and Pool, Hot Tub, and Two Refrigerators28

28Different numbers of homes are associated with each graphed mean. The figure also includes onestandard-deviation error bars. Error bars are absent for means representing only a single home. 

233 
0 

10 

20 

30 

40 

50 

60 

70 

80 

PV

1.2 PV 

SEE

Comparison 

PV

1.2 PV 

SEE

Comparison 

PV

1.2 PV 

SEE

Comparison 

PV

1.2 PV 

SEE

Comparison 

No Special Features Standalone Freezer Tw o Ref rigerators Standalone Freezer & Tw o 

Refrigerators 

Mean Average Monthly Gas Consumption (therms) 

Figure 19. Comparison of Mean Average Gas Consumption (therms) for Four Categories of

Homes: No Energy-Intensive Equipment (Base-Case Homes), Standalone Freezer, 

Two Refrigerators, and Standalone Freezer and Two Refrigerators29

29Different numbers of homes are associated with each graphed mean. The figure also includes onestandard-deviation error bars. Error bars are absent for means representing only a single home. 

234 
0 

10 

20 

30 

40 

50 

60 

70 

80 

PV

1.2 PV 

SEE

Comparison 

PV

1.2 PV 

SEE

Comparison 

PV

1.2 PV 

SEE

Comparison 

PV

1.2 PV 

SEE

Comparison 

Pool Pool and Standalone Freezer Pool & Tw o Refrigerators Hot Tub 

Mean Average Monthly Gas Consumption (therms) 

Figure 20. Comparison of Mean Average Gas Consumption (therms) for Four Categories of

Homes: Pool, Pool and Standalone Freezer, Pool and Two Refrigerators, and Hot Tub30

30Different numbers of homes are associated with each graphed mean. The figure also includes onestandard-deviation error bars. Error bars are absent for means representing only a single home. 

235 
0 

10 

20 

30 

40 

50 

60 

70 

80 

PV 

1.2 PV 

SEE 

Comparison 

PV 

1.2 PV 

SEE 

Comparison 

PV 

1.2 PV 

SEE 

Comparison 

PV 

1.2 PV 

SEE 

Comparison 

Hot Tub & Tw o Refrigerators Pool & Hot Tub Pool, Hot Tub, & Standalone 

Freezer 

Pool, Hot Tub, & Tw o 

Refrigerators 

Mean Average Monthly Gas Consumption (therms) 

Figure 21. Comparison of Mean Average Gas Consumption (therms) for Four Categories of

Homes: Hot Tub and Two Refrigerators; Pool and Hot Tub; Pool, Hot Tub, and Standalone Freezer;

and Pool, Hot Tub, and Two Refrigerators31

31Different numbers of homes are associated with each graphed mean. The figure also includes onestandard-deviation error bars. Error bars are absent for means representing only a single home. 

236 
Analysis of Utility Cost 

Utility cost data can be analyzed in much the same way as consumption data. Cost is clearly 

driven by consumption, except that taxes and other miscellaneous charges are added to the utility 

bill that the consumer pays. Consequently, utility data must be analyzed from two perspectives: 

(1) the actual cost of the resource consumed (i.e., kWh of electricity and therms of natural gas) 

and (2) the total utility bill that the consumer pays, which includes all charges above and beyond 

the cost of the resource. 

Descriptive Statistics on Utility Cost 

Descriptive statistics on the nine utility cost measures (see p. 3 of this chapter) were computed for 

various categories of homes, and the values are reported in the tables found in Appendix N. Each 

table contains the mean, median, minimum value, maximum value, standard deviation, and 

coefficient of variation of the variable in question. As is the case of electricity and gas 

consumption, all computations were applied to the 12-month data set after early and outlier homes 

were omitted. 

Utility Cost for All Homes Combined 

Table N-9 in Appendix N presents descriptive statistics on total 12-month electricity cost, 

including and excluding taxes and miscellaneous charges, for the homes encompassed by the 

study. Descriptive statistics on average monthly electricity cost, including and excluding taxes and 

miscellaneous charges, are also presented. Figures P-3 and P-4 in Appendix P depict the 

frequency distributions of average monthly electricity cost, including and excluding taxes and 

miscellaneous charges, respectively. Similarly, Table N-10 in Appendix N presents descriptive 

statistics on total 12-month gas cost and average monthly gas cost, including and excluding taxes 

and miscellaneous charges. Figures P-9 and P-10 in Appendix P depict the frequency distributions 

of average monthly gas cost, including and excluding taxes and miscellaneous charges, 

respectively. 

Table N-11 in Appendix N presents descriptive statistics on combined utility bill, both on a total 

12-month basis and an average monthly basis, with and without taxes and miscellaneous charges. 

Figures P-15 and P-16 in Appendix P depict the frequency distributions of average monthly 

combined utility bill, with and without taxes and miscellaneous charges, respectively. Combined 

utility bill represents the overall utility cost to the consumer of both electricity and gas and may 

more accurately reflect the energy cost of a home. Combined utility bill also tends to somewhat 

level out differences in electricity and gas consumption among households, because some 

households may use large amounts of electricity and very little gas, or conversely, large amounts 

of gas and more moderate amounts of electricity. 

Table N-12 in Appendix N presents corresponding descriptive statistics on average monthly 

electricity cost, average monthly gas cost, and average monthly combined utility bill, excluding 

taxes and miscellaneous charges, on a square-footage basis. These values represent the average 

monthly costs of electricity or gas used by a household adjusted for home size. 

237
Figures P-5 and P-6 in Appendix P show the frequency distributions of average monthly 

electricity cost, with and without taxes and miscellaneous charges, respectively, on a squarefootage basis. Figures P-11 and P-12 in Appendix P depict the corresponding frequency 

distributions of average monthly gas cost on a square-footage basis. Figures P-17 and P-18 in 

Appendix P present the respective frequency distributions of average monthly combined utility 

bill on a square-footage basis. 

As indicated in Table N-12, average monthly electricity cost, average monthly gas cost, and 

average monthly combined utility bill, excluding taxes and miscellaneous charges, for the homes 

encompassed in this study are quite variable, as indicated by the coefficients of variation, all of 

which exceed 45%. Visual inspection of the line graphs of month-by-month electricity and gas 

consumption by individual homes (Appendix L) reveals this variability on a house-by-house basis. 

In addition, all the frequency histograms are skewed to the right. These two observations suggest 

that more detailed analysis of the utility cost measures is warranted. However, because average 

monthly combined utility bill is thought to more accurately reflect overall home energy cost, and 

because homeowners are more likely to identify with this quantity, all further analysis will be 

restricted to this single utility cost measure.

32 

Comparing Utility Cost for SheaHomes and Comparison Homes 

Table N-15 in Appendix N presents comparative descriptive statistics on average monthly 

combined utility bill for SheaHomes and comparison homes.33 Figures Q-15 (a, b) and Q-16 (a, b) 

in Appendix Q show the distributions of average monthly combined utility bill for these two home 

categories. For SheaHomes, average monthly combined utility bill, excluding taxes and 

miscellaneous charges, ranges from $16.59 to $265.08, with a mean of $122.96. When the taxes 

and miscellaneous charges are added, average monthly combined utility bill ranges from $20.91 to 

$289.12, with a mean of $134.21. In both cases, the range is quite wide, which is also 

corroborated by the relatively large values of the standard deviation and coefficient of variation. 

For comparison homes, average monthly combined utility bill ranges from $63.37 to $288.90, 

with a mean of $160.25, when taxes and miscellaneous charges are excluded. Including taxes and 

miscellaneous charges, the range is $69.29 to $307.63, with a mean of $173.57. For the 

comparison homes, the ranges are also quite wide, and the standard deviations and coefficients of 

variation are also relatively high. Nonetheless, inspection of the means suggests that average 

monthly combined utility bill is higher for comparison homes than for SheaHomes (30.3% higher, 

without considering taxes and miscellaneous charges; 29.3% higher including these additional 

amounts). 

32Further discussion and interpretation of electricity, gas, and combined utility costs using the 12-month

data is presented in the Epilogue to this report.

33Table N-15 also contains descriptive statistics on total 12-month combined utility bill, with and without

taxes and miscellaneous charges, but these statistics are not discussed further. In addition, Tables N-13 and N-14

present the corresponding descriptive statistics on total 12-month cost and average monthly cost, with and without

taxes and miscellaneous charges, for electricity and gas, respectively. These tables are presented for the sake of

completeness and are also not discussed here.

238 
A t-test was used to statistically compare the average monthly combined utility bill (with and 

without taxes and miscellaneous charges) for SheaHomes and comparison homes. The results, 

given in Table 88, confirm that the difference in mean average monthly combined utility bill for 

SheaHomes and comparison homes is statistically significant (t= –2.966, p=.004; t= –2.874, 

p=005; respectively). On a mean basis, SheaHomes have lower average monthly combined utility 

bills than do comparison homes, regardless of whether taxes and miscellaneous charges are taken 

into consideration. 

Similar results are observed when average combined monthly utility bill is considered on a 

square-footage adjusted basis. As reported in Table 88 and Table N-1634 in Appendix N, the mean 

average combined monthly utility bill per square foot, excluding taxes and miscellaneous charges, 

for SheaHomes is $0.040 versus $0.057 (a difference of 42.5%) for comparison homes, and this 

difference is also statistically significant (t= –3.548, p=.001). When taxes and miscellaneous 

charges are included, the difference in means is also significant (t= –3.464, p=.001). The 

associated distributions are shown in Figures Q-17 (a, b) and Q18 (a, b) in Appendix Q. 

The variance, and hence the standard deviation, of average monthly combined utility bill is not 

significantly different for SheaHomes and comparison homes (F=3.675, p=.058; and F=3.165, 

p=.078; respectively). However, the variance in average monthly combined utility bill per square 

foot is significantly lower for SheaHomes than for comparison homes, with or without taxes and 

miscellaneous charges (F=8.085, p=.005), which suggests less fluctuation, or higher stability, on 

this square-footage adjusted basis. On the whole, though, there is considerable variation among 

households in both communities as indicated by the relatively high coefficients of variation 

reported in Tables N-15 and N-16 in Appendix N. 

As expected, these results track those presented earlier for electricity and gas consumption for 

SheaHomes and comparison homes. On average, both electricity and gas consumption is lower for 

SheaHomes than for comparison homes, translating to a lower average monthly utility costs, 

whether or not taxes and miscellaneous charges are considered. This is also true when an 

adjustment for home size (square footage) is made (recall that SheaHomes in this data set are 

significantly larger than comparison homes). 

34Table N-16 also contains descriptive statistics on average monthly cost for electricity and gas, excluding 

taxes and miscellaneous charges, which are not discussed here. 

239 
Table 88. Comparison of Mean Average Monthly

Combined Utility Bill for SheaHomes* and Comparison Homes

Utility Cost Measure 

SheaHomes 

(n=81) 

Comparison 

Homes 

(n=28) 

tstatistic pvalue 

Average monthly combined utility bill (excluding 

taxes and miscellaneous charges) $122.96 $160.25 –2.874 .005 

Average monthly combined utility bill per square 

foot (excluding taxes and miscellaneous charges) $0.040 $0.057 –3.548 .001 

Average monthly combined utility bill (including 

taxes and miscellaneous charges) $134.21 $173.57 –2.966 .004 

Average monthly combined utility bill, per square 

foot (including taxes and miscellaneous charges) $0.043 $0.062 –3.464 .001 

*Early and outlier homes are omitted. 

Comparing Utility Cost for PV and SEE Homes 

Table N-1935 in Appendix N presents descriptive statistics on average monthly combined utility 

bill for PV and SEE homes. Table N-2036 in Appendix N reports descriptive statistics on average 

monthly combined utility bill on a square-footage basis. The associated distributions are shown in 

Figures R-15 (a, b) and R-16 (a, b) in Appendix R. 

For the PV homes, average monthly combined utility bill, excluding taxes and miscellaneous 

charges, ranges from $16.59 to $219.24, with a mean of $106.53. On a square-footage basis, the 

range is $0.006 to $0.079, with a mean of $0.035. When taxes and miscellaneous charges are 

included, average monthly combined utility bill for these homes ranges from $20.91 to $238.49, 

with a mean of $166.25. In both cases, the range is quite wide, which is again corroborated by the 

relatively large values of the standard deviation and coefficient of variation. 

For SEE homes, average monthly combined utility bill, excluding taxes and miscellaneous 

charges, ranges from $56.23 to $265.08, with a mean of $136.77. On a square-footage basis, the 

range is $0.018 to $0.093, with a mean of $0.044. When taxes and miscellaneous charges are 

factored in, average monthly combined utility bill for these homes ranges from $61.32 to $289.12, 

with a mean of $149.32. For the PV homes, the ranges are also quite wide (both with and without 

taxes and miscellaneous charges), and the standard deviations and coefficients of variation are 

also relatively high. Despite the high variation, inspection of the means suggests that average 

35Table N-19 also contains descriptive statistics on total 12-month combined utility bill, with and without

taxes and miscellaneous charges, which are not discussed here. In addition, Tables N-17 and N-18 present the

corresponding descriptive statistics on total 12-month cost and average monthly cost, with and without taxes and

miscellaneous charges, for electricity and gas, respectively. Again, these tables are presented for the sake of

completeness and are not discussed here.

36Table N-20 also contains descriptive statistics on average monthly cost for electricity and gas, excluding 

taxes and miscellaneous charges, which are not discussed here. 

240 
monthly combined utility bill is higher, on average for SEE homes than for PV homes (28.1% 

higher, without considering taxes and miscellaneous charges; 28.4% higher including these 

additional amounts). 

Table 89 presents the results of formal statistical comparisons of average monthly combined 

utility bill for PV homes and SEE homes. As suggested above, on a mean comparison basis, the 

PV homes have significantly lower average monthly combined utility bills than SEE homes, both 

with and without taxes and miscellaneous charges (t= –2.652, p=.01; t= –2.635, p=.01; 

respectively). Similarly, on a square-footage basis, the PV homes have a significantly lower mean 

average combined monthly utility bill than the SEE homes, both with and without taxes and 

miscellaneous charges (t= –2.458, p=.016; t= –2.444, p=.017; respectively). 

Table 89. Comparison of Mean Average Monthly

Combined Utility Bill for PV Homes and SEE Homes*

Utility Cost Measure 

PV Homes 

(n=37) 

SEE Homes 

(n=44) 

tstatistic pvalue 

Average monthly combined utility bill (excluding 

taxes and miscellaneous charges) $106.53 $136.77 –2.635 .010 

Average monthly combined utility bill, per square 

foot (excluding taxes and miscellaneous charges) $0.035 $.044 –2.444 .017 

Average monthly combined utility bill (including 

taxes and miscellaneous charges) $116.25 $149.32 –2.652 .010 

Average monthly combined utility bill, per square 

foot (including taxes and miscellaneous charges) $.038 $.048 –2.458 .016 

*Early and outlier homes are omitted. 

Table 81 shows that, for all three utility consumption measures, PV homes exhibit significantly 

lower electricity consumption, on average, than do SEE homes; there is no significant difference, 

on average, for three utility consumption measures with regard to gas. As expected, the 

significantly higher average monthly combined utility bills for SEE homes closely track the 

average monthly electricity consumption. 

As suggested above, average combined monthly utility bill is still quite variable, whether or not 

taxes and miscellaneous charges are taken into account, and whether an adjustment on the basis of 

square footage is considered. When taxes and miscellaneous charges are excluded, the variance, 

and hence the standard deviation, of average monthly combined utility bill is not significantly 

different for PV homes and SEE homes (F=0.121, p=.729). Considering these additional amounts, 

the variance of average monthly combined utility bill is also not significantly different (F=0.109, 

p=.742) for the two home categories. Similarly, on a square-footage basis, the variance of average 

monthly combined utility bill is not significantly different for the two home categories, with or 

without taxes and miscellaneous charges (F=.001, p=.975; F.0, p=.985; respectively). 

On the whole, there is considerable variation in average monthly combined utility bill among all 

SheaHomes as indicated by the relatively high coefficients of variation reported in Tables N-19 

241
and N-20 in Appendix N. Such instability in utility cost is driven, in turn, by the relatively high 

variation in gas and electricity consumption among all these homes. 

Table 90 contains the results of statistical comparisons of mean average monthly combined 

utility bill for PV homes with 1.2-kW systems and those with 2.4-kW systems (also see the 

related histograms of utility cost in Appendix R1). The mean values for the four utility cost 

measures are lower for PV homes with 2.4-kW systems than for those with 1.2-kW systems and 

mimic the results shown for utility consumption in Table 82. For example, average monthly 

combined utility bill, excluding taxes and miscellaneous charges, is 35.2% lower for PV homes 

with 2.4-kW systems than for homes with 1.2-kW systems. However, these differences are not 

statistically significant, except almost so when average monthly combined utility bill is computed 

on a square-footage basis. The fact that the differences are not statistically significant may be 

attributable to two different situations: (1) as shown in Table 82, the contribution of the cost of 

gas to the combined utility bill is not significantly different in these homes and (2) the number of 

PV homes with 2.4-kW systems is much smaller than the number of PV homes with 1.2-kW 

systems. Also note that the mean square footage of the PV homes with 1.2-kW systems and 2.4-

kW systems is not significantly different, a result that also undoubtedly plays a role. 

Table 90. Comparison of Mean Average Monthly Combined Utility Bill for 

PV Homes with 1.2-kW and 2.4-kW PV Systems*

Utility Cost Measure 

PV Homes with 

1.2-kW Systems 

(n=31) 

PV Homes with 

2.4-kW Systems 

(n=6) 

tstatistic pvalue 

Average monthly combined utility bill 

(excluding taxes and miscellaneous 

charges) 

$112.98 $73.23 1.828 .076 

Average monthly combined utility bill, 

per square foot (excluding taxes and 

miscellaneous charges) 

$0.037 $.023 2.020 .051 

Average monthly combined utility bill 

(including taxes and miscellaneous 

charges) 

$123.19 $80.36 1.813 .078 

Average monthly combined utility bill, 

per square foot (including taxes and 

miscellaneous charges) 

$0.041 $.025 2.002 .053 

*Early and outlier homes are omitted 

Since there are substantially more homes with 1.2-kW systems than with 2.4-kW systems, it is 

important to compare utility cost for PV and SEE homes when the PV homes with 2.4-kW 

systems are omitted. Table 91 provides the results that correspond to those given in Table 89 

when the PV home category is restricted in this way. The mean average monthly combined utility 

bill for PV homes with 1.2-kW systems is significantly lower (but just barely so) than for SEE 

homes, whether or not taxes and miscellaneous charges are included. The difference in the means 

is not statistically different, however, when average monthly combined utility bill is computed on 

a square-footage basis, which again suggests that home size is not a major contributing factor. 

242
With reference to the results shown in Table 89, the 2.4-kW systems clearly affect the 

comparison of utility cost between PV homes and SEE homes. Although the difference in mean 

average monthly combined utility bill is significantly lower for PV homes than for SEE homes, 

the difference is not nearly so substantial when the PV homes with 2.4-kW systems are excluded 

from consideration. 

Table 91. Comparison of Mean Average Monthly Combined Utility Bill for 

PV Homes with 1.2-kW PV Systems and SEE Homes*

Utility Cost Measure 

PV Homes with 

1.2-kW Systems 

(n=31) 

SEE Homes 

(n=44) 

tstatistic pvalue 

Average monthly combined utility bill 

(excluding taxes and miscellaneous charges) $112.98 $136.77 –1.973 .052 

Average monthly combined utility bill, per 

square foot (excluding taxes and 

miscellaneous charges) $.037 $.044 –1.729 .088 

Average monthly combined utility bill 

(including taxes and miscellaneous charges) $123.19 $149.32 –1.991 .050 

Average monthly combined utility bill, per 

square foot (including taxes and 

miscellaneous charges) $.041 $.048 –1.745 .085 

*Early and outlier homes are omitted. 

Comparing Utility Cost for Comparison and SEE Homes 

Table 92 presents the results of formal statistical comparisons of mean average monthly 

combined utility bill, with and without taxes and miscellaneous charges, for comparison and SEE 

households. Figures Q-15b and Q-16b in Appendix Q and Figures R-15b and R16b in Appendix 

R show the corresponding distributions. There is no significant difference in mean average 

monthly combined utility bill between comparison and SEE homes, with or without taxes and 

miscellaneous charges (t= –1.564, p=.122; t= –1.650, p=.103; respectively). The mean costs are 

higher for comparison homes, but not by statistically significant amounts (16.2% and 17.2%, 

with and without taxes and miscellaneous charges, respectively). However, when adjusted for 

square footage, the respective differences are significant (t= –2.516, p=.016; t= –2.593, p=.013; 

respectively). The mean average monthly combined utility bill per square foot for comparison 

homes is significantly higher than for SEE homes (29.2% and 29.5% higher, with and without 

taxes and miscellaneous charges, respectively). As noted previously, the SEE homes in this data 

set are significantly larger than the comparison homes. 

On average, electricity consumption is not significantly different for comparison and SEE homes, 

except on a square-footage basis (SheaHomes have significantly lower electricity consumption, 

on average, on a square-footage basis). Also, on average, SEE homes have significantly lower 

gas consumption on all three utility consumption measures. Consequently, these findings about 

utility cost closely track those reported earlier for electricity and gas consumption, particularly on 

a square-footage basis. 

243
Table 92. Comparison of Mean Average Monthly

Combined Utility Bill for Comparison and SEE Homes*

Utility Cost Measure 

Comparison 

Homes 

(n=28) 

SEE 

Homes 

(n=44) 

tstatistic pvalue 

Average monthly combined utility bill (excluding 

taxes and miscellaneous charges) $160.25 $136.77 –1.650 .103 

Average monthly combined utility bill, per square foot 

(excluding taxes and miscellaneous charges) $.057 $.044 –2.593 .013 

Average monthly combined utility bill, (including 

taxes and miscellaneous charges) $173.57 $149.32 –1.564 .122 

Average monthly combined utility bill, per square foot 

(including taxes and miscellaneous charges) $.062 $.048 –2.516 .016 

*Early and outlier homes are omitted 

The difference in the variance of average monthly combined utility bill for comparison and SEE 

homes is statistically significant only when square footage is taken into account. On a squarefootage basis, the variance and, hence, the standard deviation of average monthly combined 

utility bill is significantly higher for comparison homes than for SEE homes, with and without 

taxes and miscellaneous charges (F=7.527, p=.008; F=6.828, p=.011; respectively). This finding 

suggests that there is greater fluctuation, or inconsistency, in utility cost for comparison homes 

than for SEE homes. 

Confidence Interval Estimates for Selected Utility Cost Measures 

Table 93 presents 95% confidence interval estimates37 for the mean values of selected utility cost 

measures for the PV, SEE, and comparison homes. These intervals account for the sampling error 

associated with the individual point estimates of utility cost that arises because we could not 

obtain data from all the households. Each respective interval is expected to cover the true mean 

value of the utility cost measure in question with statistical confidence of 95%. For example, 

based on data from 37 homes, the interval $98.03 to $134.46 is expected to cover the true mean 

of average monthly combined utility bill (including taxes and miscellaneous charges) for all PV 

homes with 95% confidence. The interval endpoints provide low and high cases on which future 

planning scenarios can be based. Table 94 provides the corresponding comparison of 95% 

confidence intervals when PV homes are restricted to those with 1.2-kW systems. 

37Again, each confidence interval is constructed with a margin of error that is derived from the data 

associated with the specific category of homes in question. Different confidence intervals could be constructed by 

using a margin of error obtained via ANOVA, which considers the data from all three categories of homes 

simultaneously. 

244 
Table 93. 95% Confidence Intervals on the Mean Values of

Selected Utility Cost Measures for PV, SEE, and Comparison Homes*

Utility Consumption 

Measure 

PV Homes 

(n=37) 

SEE Homes 

(n=44) 

Comparison Homes 

(n=28) 

Lower 

Limit 

Upper 

Limit 

Lower 

Limit 

Upper 

Limit 

Lower 

Limit 

Upper 

Limit 

Average monthly electricity 

cost (excluding taxes and 

miscellaneous charges) 

$62.83 $91.17 $89.29 $117.13 $97.07 $142.27 

Average monthly electricity 

cost per square foot 

(excluding taxes and 

miscellaneous charges) 

$.021 $.030 $.029 $.037 $.035 $.050 

Average monthly combined 

utility bill (excluding taxes 

and miscellaneous charges) 

$89.76 $123.31 $120.85 $152.69 $133.91 $186.58 

Average monthly combined 

utility bill per square foot 

(excluding taxes and 

miscellaneous charges) 

$.029 $.040 $.039 $.049 $.048 $.066 

Average monthly electricity 

cost (including taxes and 

miscellaneous charges) 

$69.31 $100.23 $98.37 $128.75 $105.54 $155.15 

Average monthly electricity 

cost per square foot 

(including taxes and 

miscellaneous charges) 

$.023 $.032 $.032 $.041 $.038 $.055 

Average monthly combined 

utility bill (including taxes 

and miscellaneous charges) 

$98.03 $134.46 $132.00 $166.65 $144.83 $202.30 

Average monthly combined 

utility bill per square foot 

(including taxes and 

miscellaneous charges) 

$.032 $.044 $.042 $.053 $.052 $.072

 *Early and outlier homes are omitted. 

245
Table 94. 95% Confidence Intervals on the Mean Values of Selected

Utility Cost Measures for PV (with 1.2-kW Systems), SEE, and Comparison Homes*

Utility Consumption 

Measure 

PV Homes with 1.2-kW 

PV Systems 

(n=31) 

SEE Homes 

(n=44) 

Comparison 

Homes 

(n=28) 

Lower 

Limit 

Upper 

Limit 

Lower 

Limit 

Upper 

Limit 

Lower 

Limit 

Upper 

Limit 

Average monthly electricity 

cost (excluding taxes and 

miscellaneous charges) $68.02 $98.66 $89.29 $117.13 $97.07 $142.27 

Average monthly electricity 

cost per square foot 

(excluding taxes and 

miscellaneous charges) 

$.022 $.032 $.029 $.037 $.035 $.050 

Average monthly combined 

utility bill (excluding taxes 

and miscellaneous charges) 

$89.76 $123.31 $120.85 $152.69 $133.91 $186.58 

Average monthly combined 

utility bill per square foot 

(excluding taxes and 

miscellaneous charges) 

$.029 $.040 $.039 $.049 $.048 $.066 

Average monthly electricity 

cost (including taxes and 

miscellaneous charges) 

$74.83 $108.38 $98.37 $128.75 $105.54 $155.15 

Average monthly electricity 

cost per square foot 

(including taxes and 

miscellaneous charges) 

$.025 $.036 $.032 $.041 $.038 $.055 

Average monthly combined 

utility bill (including taxes 

and miscellaneous charges) 

$98.03 $134.46 $132.00 $166.65 $144.83 $202.30 

Average monthly combined 

utility bill per square foot 

(including taxes and 

miscellaneous charges) 

$.032 $.044 $.042 $.053 $.052 $.072

 *Early and outlier homes are omitted. 

246
Impact of Energy-Intensive Equipment on Utility Cost 

As in the case of utility consumption, various combinations of energy-intensive equipment in 

homes would be expected to have an impact on utility cost. Tables U-1 through U-12 in 

Appendix U (Descriptive Statistics on Average Monthly Electricity Cost, Average Monthly Gas 

Cost, and Average Monthly Utility Bills with and without Selected Equipment, by Categories of 

Homes, 12-Month Data) contain descriptive statistics on several utility cost measures for homes 

with various combinations of energy-intensive equipment within the three categories of homes 

(separate descriptive statistics are also included for PV homes with 1.2-kW systems). Figures 

22–24 and Figures 25–27 graphically depict this same information for one utility cost measure, 

mean average monthly combined utility bill, with and without taxes and miscellaneous charges, 

respectively. Again, within each home category, there are notable differences in the numbers of 

homes having specific combinations of equipment and, for some of the equipment combinations, 

few or no homes are represented. As previously noted, care must be taken when extrapolating the 

statistics contained in these tables or making percentage comparisons. However, this information 

might be used to establish trends concerning the incremental impacts on utility cost of adding 

energy-intensive equipment to homes. Such impacts vary depending on the home category. 

Pools and hot tubs (alone, in combination with each other, or in combination with other 

equipment) have a notable impact on utility cost, whether or not taxes and miscellaneous charges 

are considered.38 This is particularly true for comparison homes. For these homes, the mean 

average combined utility bill, with or without taxes and miscellaneous charges, is substantially 

higher than for corresponding homes without this equipment. Higher utility cost is also 

associated with pools and hot tubs in SEE homes and, to a lesser extent, in PV homes. In PV 

homes, standalone freezers lead to about the same increase in mean average monthly combined 

utility bill (with or without taxes and miscellaneous charges) as hot tubs, whereas the impact of 

two refrigerators is not as great. The combined effect of multiple items is difficult to resolve 

because of the small numbers of households involved. 

Base-Case Homes 

The most important information contained in Tables S-1 to S-12 in Appendix S, Figures 22–24, 

and Figures 25–27 may be the statistics about utility cost for homes with no energy-intensive 

equipment. These are, again, referred to as base-case homes because the utility costs reported for 

them are not confounded by energy-intensive equipment. As previously noted, there are other 

factors to consider (e.g., number of occupants); but these particular homes provide the most 

direct comparison of utility cost between PV, SEE, and comparison homes. 

Figures 22 and 25 suggest that base-case comparison homes and base-case SEE homes have 

about the same mean average monthly combined utility bill, but the amount for base-case PV 

homes is considerably lower (again, separate estimates are presented for base-case PV homes 

with 1.2-kW systems). Including taxes and miscellaneous charges, mean average monthly 

38However, information obtained in the qualitative phase of this study suggests that, due to cost, some 

homeowners do not heat their pools/hot tubs except on special occasions or unless they are having guests. 

247 
combined utility bill is 76.3% higher in base-case SEE homes than in base-case PV homes 

(64.7% higher in base-case SEE homes than in base-case PV homes with 1.2-kW systems) and 

76.5% higher in comparison homes than in base-case PV homes (64.9% higher in comparison 

homes than in base-case PV homes with 1.2-kW systems). Excluding taxes and miscellaneous 

charges, the results are similar: the mean average monthly combined utility bill is 76.3% higher 

in base-case SEE homes than in base-case PV homes (64.3% higher in base-case SEE homes 

than in base-case PV homes with 1.2-kW systems) and 77.5% higher in comparison homes than 

in base-case PV homes (65.4% higher in comparison homes than in PV homes with 1.2-kW 

systems). As indicated by the one-standard-deviation error bars, the variability in mean average 

monthly combined utility bill is also somewhat lower for base-case PV homes (and 1.2-kW PV 

homes) than for base-case SEE and base-case comparison homes. 

Formal ANOVA applied to these base-case homes confirms that PV homes have significantly 

lower mean average combined monthly utility bills than SEE or comparison homes. This is true 

when all PV homes or only those with 1.2-kW systems are considered, whether or not taxes and 

miscellaneous charges are included (excluding taxes and miscellaneous charges, $66.20 for PV 

homes, $71.04 for PV homes with 1.2-kW systems, $116.72 for SEE homes, and $117.52 for 

comparison homes; including taxes and miscellaneous charges, $72.46 for PV homes, $77.56 for 

PV homes with 1.2-kW systems, $127.75 for SEE homes, and $127.92 for comparison homes). 

Furthermore, the ANOVA results indicate that the mean average combined monthly utility bills 

are essentially equivalent for SEE and comparison homes. Finally, these same results are 

obtained when ANOVA is applied to average combined monthly utility bill computed on a 

square-footage basis, with or without taxes and miscellaneous charges (excluding taxes and 

miscellaneous charges, $0.023/ft2

 for PV homes, $0.037 ft2 for PV homes with 1.2-kW systems, 

$0.038 ft2

 for SEE homes, and $0.046 ft2 for comparison homes; including taxes and 

miscellaneous charges, $0.025 ft2

 for PV homes, $0.041 ft2 for PV homes with 1.2-kW systems, 

$0.041 ft2 for SEE homes, and $0.050 ft2 for comparison homes). 

248
0 

50 

100 

150 

200 

250 

300 

350 

400 

PV

1.2 PV 

SEE

Comparison 

PV

1.2 PV 

SEE

Comparison 

PV

1.2 PV 

SEE

Comparison 

PV

1.2 PV 

SEE

Comparison 

No Special Features Standalone Freezer Tw o Refrigerators Standalone Freezer & Tw o 

Ref rigerators 

Mean Average Monthly Combined Utility Bill ($),

Including Taxes and Miscellaneous Charges 

Figure 22. Comparison of Mean Average Monthly Combined Utility Bill, Including Taxes and

Miscellaneous Charges, for Four Categories of Homes: No Energy-Intensive Equipment,

Standalone Freezer, Two Refrigerators, and Standalone Freezer, and Two Refrigerators39

39Different numbers of homes are associated with each graphed mean. The figure also includes onestandard-deviation error bars. Error bars are absent for means representing only a single home. 

249 
0 

50 

100 

150 

200 

250 

300 

350 

400 

PV

1.2 PV 

SEE

Comparison 

PV

1.2 PV 

SEE

Comparison 

PV

1.2 PV 

SEE

Comparison 

PV

1.2 PV 

SEE

Comparison 

Pool Pool & Standalone Freezer Pool & Tw o Refrigerators Hot Tub 

Mean Average Monthly Combined Utility Bill ($),

Including Taxes and Miscellaneous Charges 

Figure 23. Comparison of Mean Average Monthly Combined Utility Bill, Including Taxes and 

Miscellaneous Charges, for Four Categories of Homes: Pool, Pool and Standalone Freezer, Pool 

and Two Refrigerators, and Hot Tub40 

40Different numbers of homes are associated with each graphed mean. The figure also includes onestandard-deviation error bars. Error bars are absent for means representing only a single home. 

250 
0 

50 

100 

150 

200 

250 

300 

350 

400 

PV 

1.2 PV 

SEE 

Comparison 

PV 

1.2 PV 

SEE 

Comparison 

PV 

1.2 PV 

SEE 

Comparison 

PV 

1.2 PV 

SEE 

Comparison 

Hot Tub & Tw o 

Refrigerators 

Pool & Hot Tub Pool, Hot Tub, & Standalone 

Freezer 

Pool, Hot Tub, & Tw o 

Refrigerators 

Mean Average Monthly Combined Utility Bill ($),

Including Taxes and Miscellaneous Charges 

Figure 24. Comparison of Mean Average Monthly Combined Utility Bill, Including Taxes and

Miscellaneous Charges, for Four Categories of Homes: Hot Tub and Two Refrigerators; Pool and

Hot Tub; Pool, Hot Tub, and Standalone Freezer; and Pool, Hot Tub, and Two Refrigerators41

41Different numbers of homes are associated with each graphed mean. The figure also includes onestandard-deviation error bars. Error bars are absent for means representing only a single home. 

251 
0 

50 

100 

150 

200 

250 

300 

350 

400 

PV

1.2 PV 

SEE

Comparison 

PV

1.2 PV 

SEE

Comparison 

PV

1.2 PV 

SEE

Comparison 

PV

1.2 PV 

SEE

Comparison 

No Special Features Standalone Freezer Tw o Refrigerators Standalone Freezer & Tw o 

Ref rigerators 

Mean Average Monthly Combined Utility Bill ($),

Excluding Taxes and Miscellaneous Charges 

Figure 25. Comparison of Mean Average Monthly Combined Utility Bill, Excluding Taxes and

Miscellaneous Charges, for Four Categories of Homes: No Energy-Intensive Equipment,

Standalone Freezer, Two Refrigerators, and Standalone Freezer and Two Refrigerators42

42Different numbers of homes are associated with each graphed mean. The figure also includes onestandard-deviation error bars. Error bars are absent for means representing only a single home. 

252 
0 

50 

100 

150 

200 

250 

300 

350 

400 

PV

1.2 PV 

SEE

Comparison 

PV

1.2 PV 

SEE

Comparison 

PV

1.2 PV 

SEE

Comparison 

PV

1.2 PV 

SEE

Comparison 

Pool Pool & Standalone Freezer Pool & Tw o Refrigerators Hot Tub 

Mean Average Monthly Combined Utility Bill ($),

Excluding Taxes and Miscellaneous Charges 

Figure 26. Comparison of Mean Average Monthly Combined Utility Bill, Excluding Taxes and 

Miscellaneous Charges, for Four Categories of Homes: Pool, Pool and Standalone Freezer, Pool 

and Two Refrigerators, and Hot Tub43 

43Different numbers of homes are associated with each graphed mean. The figure also includes onestandard-deviation error bars. Error bars are absent for means representing only a single home. 

253 
0 

50 

100 

150 

200 

250 

300 

350 

400 

PV 

1.2 PV 

SEE 

Comparison 

PV 

1.2 PV 

SEE 

Comparison 

PV 

1.2 PV 

SEE 

Comparison 

PV 

1.2 PV 

SEE 

Comparison 

Hot Tub & Tw o 

Refrigerators 

Pool & Hot Tub Pool, Hot Tub, & Standalone 

Freezer 

Pool, Hot Tub, & Tw o 

Refrigerators 

Mean Average Monthly Combined Utility Bill ($),

Excluding Taxes and Miscellaneous Charges 

Figure 27. Comparison of Mean Average Monthly Combined Utility Bill, Excluding Taxes and

Miscellaneous Charges, for Four Categories of Homes: Hot Tub and Two Refrigerators; Pool and

Hot Tub; Pool, Hot Tub, and Standalone Freezer; and Pool, Hot Tub, and Two Refrigerators44

44Different numbers of homes are associated with each graphed mean. The figure also includes onestandard-deviation error bars. Error bars are absent for means representing only a single home. 

254 
Summary and Discussion 

This chapter compares actual utility consumption and cost for three specific categories of homes: 

homes with solar PV systems constructed by SheaHomes, energy-efficient homes constructed by 

SheaHomes (these do not have solar PV systems), and homes in a nearby comparison community 

constructed by another builder. Differences in utility consumption and cost between comparison 

homes and all SheaHomes combined are also evaluated. 

Utility consumption and cost were found to be highly variable quantities for all the homes and 

households encompassed by the study. The variability derives from month-to-month seasonal 

variation, as well as differences among homes and households within the same home category. 

Month-to-month seasonal variation is to be expected. Differences among households that may be 

attributable to occupant behavior or the addition of energy-intensive equipment or amenities are 

less easily explained. Hence, the chapter investigates the effects of household demographics and 

several home characteristics on utility consumption and cost and compares and contrasts the 

results for homes and households included in the various home categories. 

The results of the analyses reported here suggest that SheaHomes households (PV and SEE 

homes combined) consume less electricity and gas on average than comparison households. 

Several measures of electricity and gas consumption were analyzed and led to essentially the 

same conclusion. In addition, the average monthly amounts of electricity and gas consumed by 

SheaHomes households were found to be more stable (less variable) than the amounts consumed 

by comparison homes, particularly when adjusted for home size. Similarly, SheaHomes 

households incur lower utility cost, stated in terms of average monthly combined utility bill (with 

or without taxes and miscellaneous charges), than comparison households. The variability in the 

amounts for SheaHomes households is about the same as for comparison households (unless 

square footage is taken into account, in which case it is lower for SheaHomes). 

The utility consumption and cost advantages realized by the SheaHomes households seem even 

more remarkable because the SheaHomes (in the utility data set) are larger than the 

corresponding comparison homes. The larger size of the SheaHomes apparently has little or no 

impact on utility consumption and cost relative to the comparison homes. As noted throughout 

the analyses reported in this chapter, home size (square footage) has an inconsistent and generally 

limited effect on utility consumption and cost. 

Furthermore, there are no statistically significant differences between SheaHomes and 

comparison households relative to household composition or size (number of occupants). Again, 

this makes the findings about utility consumption and cost even more meaningful because it 

suggests the advantages achieved by SheaHomes are not specifically the results of fewer 

occupants or a different household makeup. On the other hand, a significantly higher percentage 

of comparison households have pools and hot tubs than SheaHomes households, which suggests 

that the presence of energy-intensive equipment and amenities is an important consideration (see 

the discussion below). 

255
The preceding observations are further informed by considering the distinctions in utility 

consumption and cost between the PV and SEE homes constructed by SheaHomes, and between 

SEE homes and comparison homes. Summary findings are presented below: 

• Mean average electricity consumption is significantly lower in PV homes with 2.4-kW 

systems than in PV homes with 1.2-kW systems, while mean average gas consumption is 

essentially equivalent in the two types of homes. Clearly, the size or capacity of the PV 

system has a notable impact on electricity consumption, but because gas consumption is not 

affected, the impact on overall utility cost is not so clear cut. However, in this study the 

number of homes with the larger systems is quite small, and so it was not possible to 

investigate utility consumption and cost in these homes from all possible perspectives. (See 

the Epilogue to this report for further discussion.) 

• When PV homes with both sizes of systems are considered together, the mean average 

monthly electricity consumption for such homes is significantly lower than for SEE homes, 

and the variability in average electricity consumption among PV homes and SEE homes is 

about the same. Mean average monthly gas consumption is also about the same. These 

findings translate into lower overall utility cost for PV homes; that is, the mean average 

monthly combined utility bill for PV homes is significantly lower than for SEE homes, 

whether or not taxes and miscellaneous charges are considered. 

• When only PV homes with 1.2-kW systems are considered, the results are somewhat 

different. Mean average monthly electricity consumption is lower for PV homes than for SEE 

homes, but the difference nears statistical significance (p=0.55); plus there is no significant 

difference in average monthly gas consumption. In terms of overall cost, mean average 

monthly combined utility bill is lower for PV homes than for SEE homes, whether or not 

taxes and miscellaneous charges are considered, and the difference is just barely significant; 

however, on a square-footage basis, the average monthly combined utility bill for these PV 

homes is not significantly different than the average monthly combined utility bill for SEE 

homes, whether or not taxes and miscellaneous charges are considered. 

Another advantage enjoyed by PV homeowners is that when the net electricity consumed was 

lower than 300 kWh before March 2004, which was the breakpoint in SDG&E’s tiered rate 

structure, the amount they paid for every kilowatt-hour they use is lower than for those whose net 

electricity is higher than the breakpoint. The latter pay more per kilowatt-hour for all the 

electricity they use.45 

As an example of how energy efficiency and solar PV systems could affect electricity costs over 

time, the 2006 residential summer rates for electricity, which are based on a daily baseline 

allowance, are as follows (Anders 2006): 

45The monthly threshold changed to $500 in March 2004, according to the SDG&E web site. 

256 
Table 94a. SDG&E’s Residential Summer Rates for Electricity 

Electricity Rate Categories Cents per kWh 

Daily baseline energy 

allowance 

12.867 

101%–130% of baseline 14.884 

131%–200% of baseline 25.664 

201%–300% of baseline 26.571 

Over 300% of baseline 28.154 

SDG&E provided energy consumption data on a monthly basis; therefore, it was not possible to 

compute the effect of the SheaHomes’ energy efficiency, solar water heating, and PV package on 

compliance with daily electricity allowances. 

These findings are somewhat surprising. As expected, an overall savings in utility cost is 

achieved in PV homes relative to SEE homes. Such savings are realized solely on the basis of a 

reduction in electricity consumption since gas consumption is essentially equivalent in the two 

types of homes. The amount of the reduction is especially noteworthy in PV homes with the 

larger capacity systems (2.4-kW systems) (see Epilogue). 

Mean average gas consumption is significantly lower in SEE homes than in comparison homes. 

Average gas consumption is also less variable for SEE homes than for comparison homes, 

indicating greater stability. On the other hand, the difference in mean average electricity 

consumption in SEE and comparison homes is not statistically significant unless it is computed 

on an average square footage basis. Furthermore, in terms of overall cost, mean average monthly 

combined utility bill is not significantly different for SEE and comparison homes, whether or not 

taxes and miscellaneous charges are consider, except when it is computed on a square footage 

basis. 

These findings suggest there is little difference between SEE homes and comparison homes on 

the basis of utility consumption and cost, unless home size (square footage) is taken into account. 

Since the comparison homes are smaller, on average, than the SEE homes, comparison homes 

might be expected to have lower utility consumption and cost (assuming they had the same 

energy efficiency features). However, the opposite situation is true. The utility requirements for 

SEE homes are the same, on average, as those for much smaller homes that do not have the same 

energy efficiency features. 

The presence of various energy-intensive appliances and amenities has an adverse impact on 

utility consumption in all home categories. Pools and/or hot tubs have a particularly detrimental 

effect, but other equipment combinations also contribute to higher utility consumption. Because 

the effects of various equipment tend to interact with, or compound, each other, and because the 

numbers of homes with distinct equipment combinations are small, formulating precise estimates 

of the contributions of these equipment combinations to utility consumption and cost is difficult. 

257
However, it is possible, and perhaps even more desirable, to contrast utility consumption and 

cost in PV, SEE, and comparison homes that do not have any such equipment ( base-case 

homes). In fact, when only base-case homes are considered, PV homes have significantly lower 

average monthly electricity consumption and significantly lower average monthly gas 

consumption irrespective of the capacity of the PV system, when computed on a square footage 

basis. While some ambiguity about average electricity consumption remains when home size, or 

square footage, is not considered, there is no ambiguity when utility consumption gets translated 

to cost. Mean average monthly combined utility bill is significantly lower in base-case PV homes 

than in SEE homes, irrespective of the capacity of the PV system, with and without the 

adjustment for home size (square footage). This is an important finding because it strongly 

validates the PV concept. 

Comparing utility consumption in base-case SEE and comparison homes suggests the two types 

of homes are not significantly different in terms of average monthly electricity consumption, and 

that they are significantly different in terms of average monthly gas consumption only when 

home size (square footage) is taken into account. When the consumption amounts are translated 

into cost, mean average monthly combined utility bill is not significantly different for SEE and 

comparison homes, whether or not home size (square footage) is taken into consideration, and 

whether or not taxes and miscellaneous charges are included. This suggests that SEE and 

comparison homebuyers who do not complement their homes with energy-intensive equipment 

or amenities may not experience much difference in utility consumption and cost, on average. 

Based on all these findings, the building/construction/marketing concept initiated by SheaHomes 

in its two Scripps Highlands developments appears to have been validated. Overall, utility 

consumption and cost is lower, on average, for SheaHomes than for comparison homes. 

However, much of this difference is attributable to PV homes, specifically those with the larger 

capacity PV systems (2.4-kW systems). While they have many obvious benefits, the SEE homes 

do not necessarily have lower utility consumption and cost, on average, relative to the 

comparison homes. This is particularly apparent when base-case homes (those without additional 

energy-intensive equipment or amenities) are considered. These observations about SEE homes 

do not necessarily support the concept envisioned by SheaHomes relative to comparison homes. 

Additional study involving larger numbers of homes is needed, however, to substantiate the 

findings. 

258
Chapter 21

Modeling of Utility Consumption and Cost

Introduction 

For planning and marketing purposes, 

developers and builders have a keen 

interest in being able to precisely and 

accurately forecast energy consumption 

and costs in the new homes they 

construct and sell. Likewise, consumers 

are interested in the energy requirements 

of the new homes they purchase. The 

availability of reliable estimates of 

energy cost and consumption can greatly 

improve the confidence of builders and 

buyers and enhance overall 

marketability. Such estimates are 

perhaps even more critical to the market, 

and to consumer demand, when new or 

alternative energy systems and 

technology are incorporated into 

housing. 

Estimates, projections, or forecasts of 

residential energy consumption and 

costs can be obtained in a number of 

ways. However, they are often 

developed from models (equations) that 

involve various inputs that, in 

combination with one another, are 

believed to have important impacts. 

This chapter presents the results of 

Chapter Highlights 

C Models that predict utility consumption and cost 

can be useful for builders and consumers. 

C In this study, different predictive models were 

constructed for PV homes, SEE homes, and 

comparison homes using regression analysis 

because there is a small number of homes with a 

wide range of energy-intensive equipment and 

amenities. 

C All the models are dominated by variables that 

represent the presence of energy-intensive 

equipment and amenities, such as pools, hot tubs, 

and two refrigerators. Yet the models are clearly 

affected by homeowner beliefs, attitudes, and 

perception. The most reliable models include 

these variables. 

C The predictive models are better for the PV and 

comparison homes (R2

 of .7 or higher) than they 

are for the SEE homes. 

C Models of electricity consumption have better 

predictive power than models of gas 

consumption. These difference are mirrored in 

the electricity and gas cost models. 

C Models are better for homes with 1.2-kW PV 

systems than for all homes with PV systems 

because homes with 2.4-kW systems have lower, 

more variable electricity use and cost. 

statistical modeling of actual utility consumption and costs1 for three groups of homes 

encompassed by the present study: PV homes, SEE homes, and comparison homes. Table 72 in 

Chapter 20 (Comparative Analysis of Utility Consumption and Cost) provides descriptions of the 

home categories. A total of 109 homes are represented: 37 PV homes, 44 SEE homes, and 28 

comparison homes. Thirty-one of the PV homes have 1.2-kW systems, and the remaining six 

have 2.4-kW systems. 

1

In this context, utility consumption and cost are used synonymously with energy consumption and cost. 

259
The empirical models and modeling results presented here were developed by applying statistical 

methods to the available utility consumption and cost data. Other modeling approaches— 

specifically, those of an engineering nature—could have been taken but were not pursued in this 

study. 

The consumption and cost data that were used in this modeling effort—called the utility data 

set—are identical to those described in Chapter 20 after editing and screening. The procedures by 

which the data were originally obtained and subsequently screened are reported in Chapter 20. 

Also as noted in Chapter 20, the actual utility consumption and cost data available for study 

exhibit considerable variation. Homes vary within and between categories. Such variation can 

present analytical challenges to the empirical modeling process. 

Three types of variables were identified as potential explanatory model inputs: (1) physical 

characteristics of the home (such as square footage and the presence or absence of a pool); (2) 

household demographic characteristics (such as total number of occupants, age of head of 

household, and the presence or absence of children); and (3) energy-related occupant behaviors 

and attitudes (such as adjusting thermostats and turning off lights and computers).2

 Among these 

three types, home and household characteristics are fairly distinguishable and factual records or 

observations about them are generally easy to obtain. Occupant behaviors and attitudes are much 

more intangible, and therefore, more difficult to measure and analyze. Data on the variables used 

in this analysis were obtained from various sources that are described in Chapter 3 (Study 

Methods and Data Resources). 

Again, the effects of climate are automatically controlled. The boundaries of the SheaHomes 

communities (which encompass PV and SEE homes) and the comparison community are within 

a few blocks of each other, and there are effectively no weather or climate differences. Further, 

the utility data for SheaHomes and comparison homes encompass the same 12-month period (see 

the discussion in Chapter 20, which eliminates the possibility of variation caused by seasonality 

or timing of the data collection process. Hence, the modeling results reported here include no 

further adjustment for climate or seasonality. 

Modeling Approach 

Stepwise multiple regression analysis, a well-known statistical approach, was selected as the 

most direct means of modeling utility consumption and cost. Based on the analysis of the 

empirical consumption and cost data presented in Chapter 20, separate regression models needed 

to be developed for PV, SEE, and comparison homes rather than constructing a single, overall 

model using the approach sometimes referred to as “dummy variables” or “indicator variables.” 

2

As previously noted, several variables that contribute to home energy consumption were not measured in this study, 

such as number of rooms; the number, size, location, and glazing of windows; the orientation of the home; the 

orientation of solar thermal and PV panels; and so on (for example, Christensen and Barker 2001). 

260 
Preliminary modeling investigations that used the “dummy variables” approach indicated there 

would be insufficient data to establish meaningful results.3,4 

Because of the difference in consumption units, electricity and gas consumption were modeled 

separately. Two dependent, or response, variables were considered: average monthly 

consumption and average monthly consumption per square foot.5

 Hence, separate regression 

models of average monthly electricity consumption, average monthly electricity consumption per 

square foot, average monthly gas consumption, and average monthly gas consumption per square 

foot were constructed for PV, SEE, and comparison homes. Further, separate models were 

constructed for PV homes and for 1.2 PV homes.6 

Because cost may ultimately be of greater interest to builders and consumers, it was decided to 

investigate utility cost on the basis of the following expanded set of response variables7

: 

• Average monthly electricity cost, with and without taxes and miscellaneous charges 

• Average monthly electricity cost per square foot, with and without taxes and miscellaneous 

charges 

• Average monthly gas cost, with and without taxes and miscellaneous charges 

• Average monthly gas cost per square foot, with and without taxes and miscellaneous charges 

• Average monthly combined utility bill, with and without taxes and miscellaneous charges 

• Average monthly combined utility bill per square foot, with and without taxes and 

miscellaneous charges 

Consequently, a separate model using each of these utility cost measures as the response variable 

was constructed for PV, SEE, and comparison homes. Again, separate models were constructed 

for PV homes and for 1.2 PV homes. 

3

The sufficiency of the data is impaired by the different numbers of homes within the three categories and associated 

inequities in the numbers of observations associated with various home and homeowner categories or classifications, 

as well as varying degrees of homeowner nonresponse to survey questions, moderate to high variation in the 

consumption and cost data, and other factors. 

4

A disadvantage of not using the “dummy variables” approach is that it precludes estimation of the common effect 

that a particular variable would have on all types of homes being considered. For example, a hot tub might increase 

gas consumption by some specified amount, on average, in all kinds of homes; but, because of other factors, the total 

increase in one type of home might be more than in another. Without using the “dummy variables” approach, the 

average amount by which a hot tub increases gas consumption regardless of home type cannot be determined. 

5

Other measures of utility (electricity and gas) consumption could have been considered. See Chapter 20 for a 

description of these measures. 

6

1.2 PV homes are PV homes with 1.2-kW PV systems. There are insufficient data to construct similar models for 

PV homes with 2.4-kW systems. 

7

These measures of utility cost were also previously described in Chapter 20. 

261 
Preliminary Considerations 

We undertook several preliminary analyses and investigations before we proceeded to the model 

development phase. This work validated some underlying assumptions and provided better focus 

for the overall modeling effort. The modeling effort is also informed by additional results 

presented in Chapter 20. 

Verifying the Consumption-Cost Relationship 

In general, utility consumption and cost are expected to be directly related. However, this 

relationship must be empirically confirmed at the outset so the modeling efforts are not impaired 

by a faulty assumption that could adversely affect the selection of explanatory variables. 

When there is a strong linear relationship between consumption and cost, the variables or 

features that drive electricity and gas consumption probably also drive their associated costs. In 

this situation, modeling consumption alone (without the additional effort of modeling cost) may 

be sufficient. Appendix V contains a number of figures that indicate the data contained in the 

utility data set exhibit this expected relationship. Figure 28 is illustrative. Here, the relationship 

between average monthly electricity consumption and average monthly electricity cost, excluding 

taxes and miscellaneous charges, for PV homes is nearly a straight line indicating a one-to-one 

relationship between the two quantities. The slight upward curvature reflects the tiered rate 

structure in effect during the study period.8 

Relationship between Electricity and Gas Consumption 

An additional preliminary question is if the same variables can be used to model both electricity 

and gas consumption. The degree to which electricity and gas consumption track each other in 

the study homes over time is an important clue. This question can first be addressed by reviewing 

Figures L-1 to L-10 in Appendix L and M-1 to M-10 in Appendix M described in Chapter 20. 

These graphs show the month-to-month total consumption (July 2003–June 2004) of electricity 

and gas for homes encompassed by the study. In some homes, electricity and gas consumption 

are strongly and positively correlated (as electricity consumption increases, so does gas 

consumption); in others, electricity and gas consumption are strongly and negatively correlated 

(as electricity consumption increases, gas consumption decreases). However, in many homes the 

association is relatively weak, which suggests that a consistent, strong relationship between 

electricity and gas consumption is absent over time. In fact, the correlation exceeds .5 in only 39 

(35.8%) of all the homes represented in the utility data set, and the overall correlation (data from 

8

www.sdge.com/tariff/com-elec/DR-LI.pdf, “Residential Customer Rate Information Schedule” (accessed 4-20-06) 

and www2.sdge.com/tariff/com-gas/GR.pdf, “Residential Customer Gas Rate Information Schedule” (accessed 

4-20-06). 

262 
all homes combined) is only .029.9

 Consequently, electricity and gas consumption do not 

necessarily increase or decrease in concert with each other; however, differences in home size, 

number of occupants, the presence or absence of a pool, and other variables have not yet been 

taken into account. 

Figures 29–32 depict the relationship between average monthly electricity consumption and 

average monthly gas consumption for PV, 1.2 PV, SEE, and comparison homes. Figure 33 shows 

the relationship for all the homes in SheaHomes communities, and Figure 34 depicts the overall 

relationship for all homes combined. The computation of average monthly electricity and gas 

consumption smooths out the ups and downs of consumption over time, such as those depicted in 

the figures contained in Appendixes L and N. Nevertheless, Figures 29–34 also suggest that, 

even on an average monthly basis, electricity and gas consumption are not strongly related in the 

homes encompassed by this investigation. Again, these figures do not reflect any adjustments for 

the effects of specific factors known to affect utility consumption. 

Table 95 presents the actual numerical values of the correlation10 between average monthly 

electricity consumption and average monthly gas consumption, both with and without an 

adjustment for square footage, for the respective home categories. In each case the correlation is 

positive, but relatively modest to low (values range from .43 to .58). 

These modest to low correlations suggest that, for all home types represented in Figures 29–34, 

the variables or features that drive electricity consumption are somewhat different from those that 

drive gas consumption. This finding indicates that the optimum models of electricity and gas 

consumption will not necessarily contain the same components, or if they do, that such 

components will not necessarily have the same importance. 

9

Correlation ranges from 0 (total absence of relationship) to 1 (perfect relationship, or total alignment) in either the 

positive or negative direction. Hence, a correlation of .5 would be the minimum value to associate with a moderate 

correlation. Values below .5 (either positive or negative) suggest the relationship is weak to nonexistent. 

10Pearson product moment correlation. 

263 
Figure 28. Scatter Diagram of Average Monthly Electricity Consumption (kWh) versus Average

Monthly Electricity Cost ($), Excluding Taxes and Miscellaneous Charges, for PV Homes (n=37)

264
Further, the correlation values reported in Table 95 are not necessarily improved when home 

size, or square footage, is taken into account. This suggests that adjusting for the effects of home 

size, a variable that initially might be considered to be an important contributor to utility 

consumption, may not necessarily lead to improved understanding of consumption patterns by 

itself, or that the improvement may be limited. This limitation of square footage in predicting 

energy consumption was discussed in Chapter 20. 

Table 95. Correlation* between Average Monthly Electricity and Gas Consumption, 

with and without Adjustment for Square Footage, for Various Categories of Homes 

Home Category Electricity versus Gas Electricity versus Gas (ft2) 

PV Homes (n=37) .558 .545 

PV Homes with 1.2-kW Systems (n=31) .577 .549 

SEE Homes (n=44) .467 .427 

SheaHomes (n=81) .530 .501 

Comparison Homes (n=28) .503 .505 

All Homes Combined (n=109) .553 .569 

*Pearson production moment correlation. 

Identification of Prospective Explanatory Variables 

The various sources of data encompassed by the study yielded information on a large number of 

variables that could potentially help explain utility consumption and cost in the homes. As noted 

previously, some of these variables are demographic in nature, some represent physical 

characteristics of the homes, some reflect the presence of energy-intensive equipment and 

amenities, and still others pertain to the attitudes, perceptions, opinions, and behaviors of 

homeowners.11 An assessment of all these variables led to a subjective determination about each 

one concerning its potential to directly affect utility consumption. As a result of this assessment, 

the number of prospective explanatory variables was reduced to 44. Table 96 lists these 44 

variables grouped into three categories: household/demographic characteristics; physical home 

characteristics/features; and homeowner attitudes, behaviors, or perceptions. 

11Items pertaining to the attitudes, perception, opinions, and behaviors of homeowners represent questions contained 

in the homeowner questionnaires (see Appendixes A, B, C, and D). 

265 
Figure 29. Scatter Diagram Depicting the Relationship between Average Monthly Electricity

Consumption and Average Monthly Gas Consumption for PV Homes (n=37)

266
Figure 30. Scatter Diagram Depicting the Relationship between Average Monthly Electricity

Consumption and Average Monthly Gas Consumption for PV Homes with 1.2-kW Systems (n=31)

267
Figure 31. Scatter Diagram Depicting the Relationship between Average Monthly Electricity

Consumption and Average Monthly Gas Consumption for SEE Homes (n=44)

268
Figure 32. Scatter Diagram Depicting the Relationship between Average Monthly Electricity

Consumption and Average Monthly Gas Consumption for Comparison Homes (n=28)

269
Figure 33. Scatter Diagram Depicting the Relationship between Average Monthly Electricity

Consumption and Average Monthly Gas Consumption for SheaHomes (n=81)

270
Figure 34. Scatter Diagram Depicting the Relationship between Average Monthly Electricity 

Consumption and Average Monthly Gas Consumption for All Homes Combined (n=109) 

Preliminary Correlation Analysis 

Correlation analysis was used as an initial screening approach to investigate the relationship 

between the variables listed in Table 96 and average monthly electricity and gas consumption. 

The corresponding correlations between the variables listed in Table 96 and average monthly 

electricity and gas cost were not computed and analyzed because Figure 28 suggests this step 

would be redundant. 

271
The complete list of all possible pairwise correlations is quite lengthy, and many of the 

correlations are low and uninformative. Hence, for the sake of brevity, only selected correlations 

are reported here. 

Tables 97–100 contain the empirical correlations between average monthly electricity 

consumption (with and without an adjustment for square footage) and several potential 

explanatory variables for PV, 1.2 PV, SEE, and comparison homes, respectively. These tables 

also contain the correlations between these same variables and average monthly gas consumption 

(with and without an adjustment for home size in terms of square footage). Each table represents 

only those variables in Table 96 for which the correlation with at least one of these utility 

consumption measures12 exceeds .3 (in absolute value).13 

Although .3 is recognized to be a low correlation value and suggests a relatively weak statistical 

relationship, it does represent a somewhat useful cutoff point for presentation purposes. Note 

that, in Tables 97–100, the correlation is as large as .55 in only three instances. This finding 

suggests that no individual item or variable is an adequate descriptor of electricity or gas 

consumption by itself, and that a multivariable approach is needed to modeling is needed. 

Tables 97–100 also indicate that different variables are correlated with average monthly 

electricity consumption (with or without an adjustment for square footage) than with average 

monthly gas consumption (with or without an adjustment for square footage). This finding 

corroborates the situation portrayed in Figures 31–34. For the homes in this study, electricity and 

gas consumption are apparently driven by somewhat different usage circumstances, home 

characteristics, and occupant behaviors. 

Furthermore, Tables 97–100 indicate that the variables that correlate with average monthly 

electricity consumption are not necessarily the same as those that correlate with average monthly 

electricity consumption per square foot, and that the variables that correlate with average 

monthly gas consumption are not necessarily the same as those that correlate with average 

monthly gas consumption per square foot. For many variables listed in these tables, the 

correlation with average monthly electricity or gas consumption is higher than the corresponding 

correlation between average monthly electricity or gas consumption per square foot, regardless of 

the home category. This finding again suggests that square footage may not be a particularly 

important factor, by itself, when modeling utility consumption in these homes. 

12Average monthly electricity consumption, average monthly electricity consumption per square foot, average 

monthly gas consumption, or average monthly gas consumption per square foot. 

13Spearman rank correlation was used to compute the correlation coefficient between the utility consumption 

measures and variables whose values are recorded on the ordinal (discrete) scale; Pearson product moment 

correlation was used to compute the correlation coefficient between the utility consumption measures and variables 

whose values are continuous. 

272 
Table 96. Features, Characteristics, and Items Considered as 

Prospective Explanatory Variables, by Category

Household/Demographic 

Characteristics 

Physical Home 

Characteristics/Features* 

Homeowner Attitudes, 

Behaviors, or Perceptions 

• Household size (number of 

occupants) 

• Household composition (adults 

versus children) 

• Age of head of household 

• Gender of head of household 

• Educational level of head of 

household 

• Household income before taxes 

• Marital status of head of household 

• Size of home (square footage) 

• Own pool 

• Own hot tub 

• Own dual-zone heating/airconditioning system 

• Own hot water flow regulator 

• Own two refrigerators 

• Own standalone freezer 

• Own ceiling fans 

• Own dimmer switches for lights 

• Own compact fluorescent light 

bulbs 

• Size of PV system 

• In summer, set thermostat to 75B

 or 

higher 

• In winter, set thermostat to 70B

 or 

lower 

• When away, modify thermostat 

settings 

• Turn off computers when not in use 

• Use air conditioning less than in

 previous home 

• More energy conscious than before 

• Tend to be thrifty 

• When away, turn off lights 

• Regularly recycle paper, plastic, or 

cans 

• Conserve water 

• Practice xeriscaping 

• More energy conscious than before 

• Use less air conditioning than in 

previous home 

• Drive fuel-efficient vehicles 

• Threats to the environment are 

exaggerated 

• Individuals need to take responsibility 

for the environment 

• Household energy consumption is not 

a major contributor to environmental 

problems 

• Individuals should work to preserve 

the environment for future 

generations 

• Federal regulations provide adequate 

protection for the environment 

• Concern for the electricity crisis in 

California 

• Like to be as independent as possible 

• Like to experiment with new ways of 

doing things 

• Seen as leader in social, work, or 

volunteer settings 

• Buy environmentally friendly 

products even if they cost more 

• Willing to modify lifestyle to help the 

environment 

• Intrigued with new technology 

*Ownership of solar water heating systems is omitted because no homes from the comparison community have these 

systems. Size of PV water system is applicable only to homes with this feature. 

Finally, Tables 97–100 suggest that average monthly electricity and gas consumption (with and 

without an adjustment for square footage) are correlated with different variables in PV homes 

than in SEE and comparison homes, and that the total number of variables exceeding the 

subjective correlation threshold of .3 is different for the three home categories. Only five 

273 
variables that exceed the .3 correlation threshold are identified in Table 97 for PV homes, 

whereas 13 variables exceeding the .3 correlation threshold are identified in Table 99 (SEE 

homes) and 15 variables are identified in Table 100 (comparison homes). In addition, no single 

variable is common to all three groups of homes (i.e., no variable is common to all three tables). 

However, two variables (pool ownership and income) are common to the tables for PV homes 

and comparison homes (Tables 97 and 100); and seven variables (household size, household 

composition, square footage, willing to modify lifestyle to help environment, turn off computers 

when not in use, conserve water, use air conditioning less than in previous home) are common to 

the tables for SEE homes and comparison homes (Tables 99 and 100). 

These last results principally indicate the distinctions and commonalities among PV, SEE, and 

comparison homes. The information presented in these tables tends to underscore the belief that 

the three groups of homes/households are different in many ways with respect to utility 

consumption and cost. This finding has an important impact on model development. 

As suggested earlier, the results of the correlation analysis indicate that no individual variable 

identified in Table 96 could likely serve as the sole descriptor of electricity or gas consumption 

(and cost) in PV, SEE, or comparison homes. Also, because the correlations are empirical and 

can be directly affected by small numbers of observations, some of them may seem illogical at 

first glance. Nonetheless, subsets of the variables might be identified that can be used in 

conjunction with one another to establish reasonable models of utility consumption. In any such 

endeavor, care must be taken to account for intercorrelations among all the variables, some of 

which may not be readily apparent. Some combinations of variables may need to be disregarded 

because they essentially carry the same information content (i.e., multicollinearity). Furthermore, 

some variables associated with higher correlations in Tables 97–100 may become less important 

in the presence of intercorrelation. The use of stepwise multiple regression analysis helps identify 

the most important combinations of variables and unravel their associated relationships. 

In summary, the correlation analysis provides a first impression of the relationship between 

average monthly electricity and gas consumption and the variables listed in Table 96. These 

results are used to inform the modeling building process which, as indicated below, is highly 

iterative in nature. 

Development of Models 

Model development is an iterative process. Even with a powerful tool like stepwise multiple 

regression analysis, the procedure sometimes requires considerable trial and error to find an 

optimal solution. Modeling also involves the application of sound scientific judgment. The 

models reported here resulted from numerous iterations and represent the current state of 

knowledge about the available data. 

As previously indicated, different models were developed for average monthly electricity 

consumption, average monthly electricity consumption per square foot, average monthly gas 

consumption, and average monthly gas consumption per square foot for PV, 1.2 PV, SEE, and 

comparison homes. 

274
Table 97. Variables that Are Most Highly Correlated1

 with Average Monthly Electricity and

Gas Consumption (with and without an Adjustment for Square Footage) for PV Homes

Variable 

Correlation with: 

Average 

Monthly 

Electricity 

Consumption 

Average Monthly 

Electricity 

Consumption (ft2

) 

Average 

Monthly 

Gas 

Consumption 

Average 

Monthly Gas 

Consumption 

(ft2

) 

Two refrigerators2 .377 .355 .463 .441 

Pool2 .402 .359 .301 .237 

In summer, set thermostat 

to 75B

 or higher3 !.214 .200 !.362 !.365 

Gender of head of 

household respondent4 !.297 !.314 !.189 !.151 

Annual household income 

before taxes .308 .225 .183 .117 

1

Only variables for which the Spearman rank correlation or Pearson product moment correlation exceeds .3 (in 

absolute value) for at least one of the utility consumption measures are included. The value of n is different for each 

cell. 

2

Presence=1; absence=0 

3

Recorded on a 1-5 scale, where 1=strongly disagree and 5=strongly agree 

4

Male=1; female=2 

275
Table 98. Variables that Are Most Highly Correlated1

 with Average Monthly Electricity and

Gas Consumption (with and without the Square Footage Adjustment) for 1.2 PV Homes

Variable 

Correlation with: 

Average 

Monthly 

Electricity 

Consumption 

Average 

Monthly 

Electricity 

Consumption 

(ft2) 

Average 

Monthly Gas 

Consumption 

Average 

Monthly Gas 

Consumption 

(ft2) 

Two refrigerators2 .442 .413 .522 .515 

Ceiling fans2 !.008 .000 !.241 !.309 

Stand-alone freezer2 .146 .110 .317 .281 

Pool2 .471 .422 .275 .226 

Regularly recycle paper, 

plastic, or cans3 !.230 !.215 !.341 !.326 

In summer, set thermostat 

to 75B

 or higher3 !.163 !.141 !.371 !.348 

Turn off computers3 !.137 !.182 !.306 !.315 

Gender of head of 

household4 !.401 !.417 !.201 !.185 

Annual household income 

before taxes .384 .279 .145 .065 

1

Only variables for which the Spearman rank correlation or Pearson product moment correlation exceeds .3 (in 

absolute value) for at least one of the utility consumption measures are included. The value of n is different for each 

cell. 

2

Presence=1; absence=0 

3

Recorded on a 1-5 scale, where 1=strongly disagree and 5=strongly agree 

4

Male=1; female=2 

276
Table 99. Variables that Are Most Highly Correlated1

 with Average Monthly Electricity and

Gas Consumption (with and without the Square Footage Adjustment) for SEE Homes

Variable 

Correlation with: 

Average 

Monthly 

Electricity 

Consumption 

Average 

Monthly 

Electricity 

Consumption 

(ft2) 

Average 

Monthly Gas 

Consumption 

Average 

Monthly Gas 

Consumption 

(ft2) 

Ceiling fans2 .323 .361 .081 .150 

Stand-alone freezer2 .336 .342 .168 .131 

Dimmer switches for 

lights2 .298 .309 .323 .360 

Hot water flow regulator2 .136 .104 .344 .360 

Turn off computers when 

not in use3 !.512 !.500 !.168 !.112 

Conserve water3 !.321 !.391 !.204 !.209 

Use air conditioning less 

than in previous home3 !.482 !.468 !.358 !.300 

Intrigued with new 

technology3 .407 .422 .116 .129 

Like to be as independent 

as possible3 .412 .451 .352 .395 

Household size .436 .436 .034 !.019 

Household composition .433 .387 .184 .123 

Willing to modify lifestyle 

to help environment3 .351 .353 .434 .351 

Federal regulations provide 

adequate protection for the 

environment3 

!.377 !.353 !.153 !.174 

1

Only variables for which the Spearman rank correlation or Pearson product moment correlation exceeds .3 (in 

absolute value) for at least one of the utility consumption measures are included. The value of n is different for each 

cell. 

2

Presence=1; absence=0 

3

Recorded on a 1-5 scale, where 1=strongly disagree and 5=strongly agree 

277
Table 100. Variables that Are Most Highly Correlated1

 with Average Monthly Electricity and

Gas Consumption (with and without the Square Footage Adjustment) for Comparison Homes

Variable 

Correlation with: 

Average 

Monthly 

Electricity 

Consumption 

Average 

Monthly 

Electricity 

Consumption 

(ft2) 

Average 

Monthly Gas 

Consumption 

Average 

Monthly Gas 

Consumption 

(ft2) 

Compact fluorescent lights2 .031 .134 !.329 !.247 

Hot tub2 .442 .422 .226 .093 

Pool2 .569 .527 .285 .211 

Tend to be thrifty3 !.365 !.369 !.408 !.381 

Turn off computers when not in 

use3 !.297 !.263 !.390 !.320 

Conserve water3 .014 !.006 !.274 !.309 

More energy conscious than 

before3 !.499 !.484 !.359 !.268 

Use air conditioning less than in 

previous home3 !.330 !.325 !.172 !.096 

Drive fuel-efficient vehicles3 !.453 !.391 !.390 !.165 

Household size .343 .333 .535 .484 

Household composition .149 .137 .377 .366 

Annual household income 

before taxes .420 .394 .503 .477 

Like to experiment with new 

ways of doing things3 !.371 !.391 !.018 !.018 

Willing to modify lifestyle to 

help environment3 !.336 !.398 !.037 !.168 

Threats to the environment are 

exaggerated3 .618 .645 .524 .437 

Household energy consumption 

is not a major contributor to 

environmental problems3

 .311 .299 .293 .269 

1

Only variables for which the Spearman rank correlation or Pearson product moment correlation exceeds .3 (in 

absolute value) for at least one of the utility consumption measures are included. The value of n is different for each 

cell. 

2

Presence=1; absence=0 

3

Recorded on a 1-5 scale, where 1=strongly disagree and 5=strongly agree 

278 
Similarly, different models were developed for each of the following utility cost measures: 

• Average monthly electricity cost (including and excluding taxes and miscellaneous charges) 

• Average monthly utility cost per square foot (including and excluding taxes and 

miscellaneous charges 

• Average monthly gas cost (including and excluding taxes and miscellaneous charges) 

• Average monthly gas cost per square foot (including and excluding taxes and miscellaneous 

charges) 

• Average combined monthly utility bill (including and excluding taxes and miscellaneous 

charges) 

• Average combined monthly utility bill per square foot (including and excluding taxes and 

miscellaneous charges). 

Version 13 of the Statistical Package for the Social Sciences (SPSS) served as the primary 

computational tool, although various other software programs (including Microsoft Excel and the 

Statistical Analysis System, or SAS) were used in the modeling work. The results reported are 

largely based on the stepwise multiple regression routines available in SPSS. As noted in Chapter 

20, the PRISM system was not used to make any further adjustments to the data because the 

design of the study already controls for weather and climate differences. 

The multiple regression approach to model development is a highly iterative process, requiring 

the evaluation of numerous combinations of explanatory variables. For the present work, the 

method of maximizing the coefficient of determination, R2

, was used to obtain an optimum 

combination of variables, all of which are statistically significant. To begin the process, all the 

variables in Table 96 are included in the model, and those that are not found to be statistically 

significant, or that are deemed to have illogical coefficients, are omitted. A new model is then 

constructed from the reduced set, and again those variables that are not found to be statistically 

significant, or that are determined to have illogical coefficients, are omitted. This process is 

iterated until no more terms are eliminated. The final model consists of the set of statistically 

significant variables for which R2

 is maximum. 

Models of Utility Consumption 

Tables 101–104 present the summary results of the regression analysis for average monthly 

electricity consumption, average monthly electricity consumption per square foot, average 

monthly gas consumption, and average monthly gas consumption per square foot, respectively. 

Each table contains information indicating the best model14 (explanatory variables, coefficients, 

and associated goodness-of-fit statistics) for PV, 1.2 PV, SEE, and comparison homes. For 

example, in Table 101, the best model of average monthly electricity consumption (Y) for PV 

homes is 

14In this context, the best model is the one with the highest value of R2

, the multiple correlation coefficient. The 

values of R2

 range from 0 to 1. If R2=1 the model completely explains the data (or it is said to fit the data perfectly). 

279
1

2

3

4

5

Y = 741.85 + 322.40X1 - 104.37X2 + 277.55X3 + 466.58X4 - 151.73X5 +e, where 

X =Own pool 

X =Turn off lights when away 

X =Own two refrigerators 

X =Own standalone freezer 

X =Age of head of household, and, 

e= unexplained remainder (error). 

This is the specific combination of statistically significant variables for which R2 is maximum. 

The names of the explanatory variables and their associate coefficients are given in the columns 

labeled “Variables” and “Coefficients.” As indicated in the first column of Table 101, R2

 is .693, 

which suggests that 69.3% of the variation in average monthly electricity consumption is 

attributable to its relationship with this specific linear combination of explanatory variables, 

leaving 30.7% of the variation unexplained. In addition, the standard error, SE, is 169.40 kWh, 

which is another measure of the size of the remaining variation in average monthly utility 

consumption.15 Ultimately, the value of SE could be substituted into an appropriate equation for 

computing the standard error of future values of average monthly electricity consumption (Y) 

given specific values of X1–X5, or for constructing confidence limits on predicted future values. 

The sizes of the p-values (see the last column of Table 101) associated with each of X1–X5 in the 

above model confirm that all the explanatory variables are statistically significant (for each 

variable p<.05). Furthermore, the relative importance of the individual explanatory variables can 

be best evaluated by inspecting the standardized coefficients (fourth column in Table 101), which 

account for differences in the units or scales associated with all the explanatory variables.16 In 

Table 101, the variable “Own two refrigerators” has the largest effect in the overall prediction of 

average monthly consumption because it has the largest standardized coefficient. The presence of 

a pool, a standalone freezer, and two refrigerators all tend to increase average monthly electricity 

consumption (since their coefficients and standardized coefficients are positive), whereas turning 

off lights and age of head of household have a dampening effect on average monthly electricity 

consumption. The directionality of the coefficients adheres to the expected patterns. 

Table 105 shows additional details of the stepwise multiple regression modeling of average 

monthly electricity consumption (without a square footage adjustment for home size) for PV 

homes. The table illustrates the progressive improvement in R2

 and SE as individual terms are 

added to the model. The “Regression Mean Square” in the fourth column is an estimate of the 

variance in average monthly consumption explained by the model, whereas “Residual Mean 

Square” in the fifth column is an estimate of the variance remaining. The standard error, or SE, is 

the square root of this number. “Residual Degrees of Freedom” in the sixth column is the number 

15In regression terminology, SE is the square root of the error mean square and is sometimes referred to as the 

“standard error of estimate.” For a model that fits the data perfectly, SE=0. 

16Standardized coefficients have values that range from +1 to -1. 

280 
of observations, n, minus the number of terms included in the model. In this particular case, n=33 

(i.e., data were available on all the variables for only 33 of the 37 PV homes). For the sake of 

brevity, these additional details are omitted for all other models described in Tables 101–104. 

Further explanation of stepwise multiple regression results can be found in Draper and Smith 

(1998) or Kleinbaum, Kupper, and Muller (1988). 

Note that the best model of average monthly electricity consumption for PV homes (Table 101) 

involves only two of the variables identified in Table 97. This further confirms that univariate 

correlation analysis is insufficient to characterize the variation in the data. 

The models depicted in Tables 101–104 contain relatively few variables. However, it is not to be 

construed that the remaining variables have no effect. They do have an effect; but the effects are 

not statistically significant. Additional terms or variables could be added to these models in every 

case, and the R2

 value would increase by an incrementally small amount. However, there would 

be little or no improvement in predictive power. For the sake of simplicity and parsimony, such 

variables should be left out of the model. 

Among all the models represented in Tables 101–104, those for average monthly electricity 

consumption shown in Table 101 have the highest values of R2

, as a group. The model of average 

monthly electricity consumption for PV homes has an R2 value of .693; the model for 1.2 PV 

homes has an R2 value of .731. The model of average monthly electricity consumption for 

comparison homes has an even higher R2 value of .917. Hence, the models of PV, 1.2 PV, and 

comparison homes are all reasonably good. On the other hand, the model of average monthly 

electricity consumption for SEE homes has an R2 value of .525, which is lower than desired. The 

reason for this lower R2

 value is not entirely understood. Data on some of the key variables may 

be missing, or additional variables that are key to explaining electricity consumption in SEE 

homes may be absent because they were beyond the scope of the study. 

On the whole, the models for average monthly electricity consumption per square foot are not as 

good as the corresponding models of average monthly electricity consumption without the square 

footage adjustment, as indicated by their correspondingly lower values of R2. The R2 value 

associated with the model for PV homes is only .290, whereas the R2

 value for the corresponding 

model of average monthly electricity consumption without the square footage adjustment is .693. 

The R2

 value associated with the model of average monthly consumption per square foot for SEE 

is .493, whereas it is .525 for the corresponding model of average monthly electricity 

consumption without the square footage adjustment. On the other hand, the values of R2

 for the 

two models of electricity consumption with and without the square footage adjustment for 

comparison homes are .929 versus .917, respectively. The comparisons are not direct because the 

models of electricity consumption with and without the square footage adjustment involve 

different explanatory variables (see the further discussion on this issue below); however, the 

computational adjustment for home size clearly has a debilitating effect on modeling capability, 

except for comparison homes. The true effect of home size or square footage may be confounded 

with the effects of other factors that operate in tandem. Also, in the San Diego climate regime, 

and at the specific geographic location of the study homes, space conditioning may be less 

important to electricity consumption. 

281
All the models that pertain to electricity consumption (Tables 101 and 102) involve six or fewer 

explanatory variables, and many encompass four or fewer. That more than 40 variables identified 

in Table 96 can be reduced to this relatively small subset for purposes of modeling average 

monthly electricity consumption is rather amazing. 

Average monthly gas consumption is more difficult to model than average monthly electricity 

consumption, a conclusion that is again suggested by lower corresponding R2 values. The value 

of R2

 associated with the models of average monthly gas consumption is less than .5 for all three 

categories of homes, both with and without the computational adjustment for home size; again, 

the home size adjustment has a debilitating effect on modeling capability, except for comparison 

homes. The R2 values associated with the models of average monthly gas consumption, both with 

and without the square footage adjustment, are particularly low for SEE homes. Again, the 

reason for these lower R2 values is not completely understood, although plausible explanations 

are the absences of data on some of the key explanatory variables, or the exclusion of additional 

variables because they were beyond the scope of the study. 

Two overarching observations can be made about the modeling process and results that pertain to 

utility consumption. First, the best models of utility consumption (e.g., average monthly 

electricity consumption) for PV, SEE, and comparison homes encompass different sets of 

explanatory variables. This ultimate outcome was suggested by the results of the preliminary 

correlation analysis. Different variables apparently drive electricity and gas consumption for PV, 

SEE, and comparison homes/households. 

Although this situation underscores the distinction between the three home categories relative to 

these important measures, it is somewhat awkward from an interpretive standpoint because it 

suggests that factors such as hot tubs that are known to contribute to higher utility consumption 

and cost are unimportant or missing in some scenarios. This seemingly contradictory situation is 

easily explained by the fact that, in this specific utility data set, there is an unequal number of 

homes in the three home categories with data on all variables of interest. For example, as noted 

in Chapter 20, the number of homes with various combinations of energy-intensive equipment is 

quite disparate across the three categories of homes, and some combinations are missing 

altogether (see, for example, Table S-1 in Appendix S). In addition, there are varying degrees of 

nonresponse associated with the variables linked to items taken from the homeowner survey. 

This is also the reason the “dummy variable” approach to regression cannot be effectively 

applied to these data, a situation that often arises in observational studies.17 

Second, although suggested by the preliminary correlation analysis, it was not entirely known 

beforehand that the four different utility consumption measures—average monthly electricity 

consumption, average monthly electricity consumption per square foot, average monthly gas 

consumption, and average monthly gas consumption per square foot—cannot be modeled by the 

17In investigations based on designed experiments or planned studies, the numbers of observations, or sample sizes, 

for various combinations of variables are more tightly controlled and can be more directly modeled with the “dummy 

variables” approach. In observational studies, the researcher usually has little control on the number of responses or 

data values per variable. 

282 
same set of explanatory variables. That is, different variables apparently drive gas and electricity 

consumption. In addition, the best set of descriptors for average monthly electricity consumption 

is somewhat different from the best set of descriptors for average monthly electricity 

consumption per square foot, and the best set of descriptors for average monthly gas 

consumption is somewhat different from the best set of explanatory variables for average 

monthly gas consumption per square foot. This finding again suggests that the home size 

adjustment on the basis of square footage is not a major explanatory variable for utility 

consumption. On the other hand, in the case of SEE homes, if average monthly electricity 

consumption is taken to be the response variable of interest rather than average monthly 

electricity consumption per square foot, and home size (square footage) is included as one of the 

prospective explanatory variables, it is determined to be statistically significant, a finding that is 

somewhat contradictory. Most likely, the effect of home size is confounded with other variables 

whose effects act in conjunction with one another. Even so, relative to comparison homes, the 

SEE homes (high performance homes without PV systems) appear to be somewhat unique with 

regard to utility consumption. 

283
Table 101. Multiple Regression Models of Average Monthly Electricity Consumption 

for Three Categories of Homes (without Adjustment for Square Footage) 

Home 

Category Variable Coefficient 

Standard 

Error 

Standardized 

Coefficient 

tstatistic pvalue 

PV Homes Constant 741.85 109.68 ! 6.76 <.001 

n=33* 

R2

=.693 

SE=169.40 

Own pool 322.40 86.66 .42 3.72 .001 

Turn off lights !104.37 40.68 !.29 !2.57 .016 

Own two refrigerators 277.55 63.37 .50 4.38 <.001 

Own standalone freezer 466.58 120.24 .49 3.88 .001 

Age of head of household !151.73 45.14 !.40 !3.36 .002 

1.2 PV Homes Constant 553.85 104.49 ! 5.30 <.001 

n=29* 

R2

=.731 

SE=151.91 

Own two refrigerators 310.40 61.12 .59 5.08 <.001 

Own pool 352.73 83.01 .51 4.25 <.001 

Own standalone freezer 399.49 98.48 .47 4.06 <.001 

Turn off computers !72.59 21.22 !.41 !3.42 .002 

Gender of head of 

household !146.19 66.02 !.26 !2.21 .037 

SEE Homes Constant !120.46 392.90 ! !.31 .761 

n=37* 

R2

 =.525 

SE=194.91 

Turn off computers !103.42 25.10 !.51 !4.12 <.001 

Tend to be thrifty !87.21 31.95 !.34 !2.73 .010 

Square footage of home .30 .13 .28 2.34 .025 

Comparison 

Homes 

Constant 961.51 107.82 ! 8.92 <.001 

Own pool 525.82 53.38 .76 9.85 <.001 

n=21* 

R2

=.917 Turn off computers !129.75 18.51 !.61 !7.01 <.001 

SE=111.96 Own two refrigerators 342.64 64.39 .47 5.32 <.001 

Turn off lights !173.84 58.51 !.24 !2.97 .010 

Own dual-zone heating/airconditioning system !141.10 50.33 !.21 !2.80 .013 

*Not all homes/households have values for all variables, so the number of cases encompassed by the analysis is 

reduced. R2

 is the coefficient of determination, or multiple correlation coefficient. SE is the standard error. 

284 
Table 102. Multiple Regression Models of Average Monthly Electricity Consumption 

per Square Foot for Three Categories of Homes 

Home 

Category Variable Coefficient 

Standard 

Error 

Standardized 

Coefficient t-statistic p-value 

PV Homes Constant .26 .05 ! 4.76 <.001 

n=33* 

R2

=.290 

SE=.081 

Own two 

refrigerators .07 .03 !.37 2.38 .024 

Size of PV 

system !.08 .04 !.35 !2.24 .033 

1.2 PV 

Homes Constant .15 .02 ! 7.73 <.001 

n=29* 

R2

=.218 

SE=.079 

Own two 

refrigerators .08 .03 .47 2.75 .011 

SEE Homes Constant .26 .01 ! 20.64 <.001 

n=37* 

R2

=.493 

SE=.062 

Turn off 

computers !.04 .01 !.55 !4.36 <.001 

Tend to be 

thrifty !.03 .01 !.34 !2.68 .011 

Comparison 

Homes 

Constant .43 .04 ! 9.65 <.001 

Own pool .18 .02 .71 9.32 <.001 

n=21* 

R2

=.925 

SE=.040 

Turn off 

computers !.05 .01 !.61 !6.90 <.001 

Own two 

refrigerators .13 .02 .50 5.71 <.001 

Own dual-zone 

heating/airconditioning 

system 

!.08 .02 !.33 !4.32 .001 

Gender of head 

of household !.06 .02 !.22 !2.87 .012 

Turn off lights !.06 .02 !.21 !2.58 .022 

*Not all homes/households have values for all variables, so the number of cases encompassed by the analysis is 

reduced. R2

 is the coefficient of determination, or multiple correlation coefficient. SE is the standard error. 

285 
Table 103. Multiple Regression Models of Average Monthly Gas Consumption 

for Three Categories of Homes (without Adjustment for Square Footage) 

Home 

Category Variable Coefficient 

Standard 

Error 

Standardized 

Coefficient t-statistic p-value 

PV Homes Constant 27.53 2.74 ! 10.05 <.001 

n=33* 

R2

=.434 

SE=9.534 

In summer, set 

thermostat 75N

or higher 

!3.46 1.33 !.34 !2.40 .023 

Own two 

refrigerators 11.38 3.50 .47 3.25 .003 

Own standalone 

freezer 17.27 6.01 .42 2.87 .008 

1.2 PV 

Homes 

n=29* 

Constant 21.46 2.66 ! 8.08 <.001 

Own two 

refrigerators 13.24 3.64 .55 3.64 .001 

R2

=.494 

SE=9.234 

Own standalone 

freezer 21.04 5.96 .53 3.53 .002 

Own pool 9.87 4.62 .31 2.14 .042 

SEE Homes 

n=37* 

R2

=.168 

SE=10.116 

Constant 33.79 1.76 ! 119.19 <.001 

Own hot water 

flow regulator 14.126 5.36 .41 2.66 .012 

Comparison 

Homes 

Constant !25.25 21.35 ! !1.18 .250 

n=25* 

R2

=.387 

SE=13.335 

Household size 17.22 5.72 .50 3.01 .006 

Own pool 12.55 5.56 .38 2.26 .034 

*Not all homes/households have values for all variables, so the number of cases encompassed by the analysis is 

reduced. R2

 is the coefficient of determination, or multiple correlation coefficient. SE is the standard error. 

286
Table 104. Multiple Regression Models of Average Monthly Gas Consumption 

per Square Foot for Three Categories of Homes 

Home 

Category Variable Coefficient 

Standard 

Error 

Standardized 

Coefficient t-statistic p-value 

PV Homes Constant .009 .001 ! 10.53 <.001 

n=33* 

R2

=.393 

SE=.003 

In summer, set 

thermostat 75N

or higher 

!.001 <.001 !.37 !2.53 .017 

Own two 

refrigerators .003 .001 .43 2.88 .007 

Own standalone 

freezer .005 .002 .36 2.36 .025 

1.2 PV 

Homes 

n=29* 

Constant .009 .001 ! 9.24 <.001 

Own two 

refrigerators .004 .001 .52 3.28 .003 

R2

=.445 

SE=.003 

Own standalone 

freezer .005 .002 .41 2.59 .016 

In summer, set 

thermostat to 

75N

 or higher 

!.001 <.001 !.33 !2.18 .039 

SEE Homes 

n=43* 

R2

=.114 

SE=.003 

Constant .011 .001 ! 19.71 <.001 

Own hot water 

flow regulator .003 .001 .34 2.30 .027 

Comparison 

Homes 

Constant !.004 .008 ! !.52 .609 

Household size .006 .002 .44 2.77 .012 

n=25* 

R2

=.469 

SE=.005 

Own dual-zone 

heating/airconditioning 

system 

!.005 .002 !.41 !2.58 .017 

Own pool .004 .002 .36 2.25 .035 

*Not all homes/households have values for all variables, so the number of cases encompassed by the analysis is 

reduced. R2

 is the coefficient of determination, or multiple correlation coefficient. SE is the standard error. 

287 
Table 105. Stepwise Multiple Regression Modeling of Average Monthly

Electricity Consumption for PV Homes (without Adjustment for Square Footage), n=33

Model R2 

Standard 

Error 

Regression 

Mean 

Square 

Residual 

Mean 

Square 

Residual 

Degrees of 

Freedom F p-value 

1a .209 253.81 528780.32 64421.46 31 8.21 .007 

2b .347 234.52 437927.69 54999.67 30 7.96 .002 

3c .471 214.69 396386.41 46092.63 29 8.60 <.001 

4d .565 198.12 356708.79 39250.37 28 9.09 <.001 

5e .693 169.40 350205.79 28696.91 27 12.20 <.001 1

Model 1: 477.44 + 353.05 (Own pool)

2

Model 2: 659.77 + 412.54 (Own pool) – 134.35 (Turn off lights)

3

Model 3: 576.78 + 369.71 (Own pool) – 130.94 (Turn off lights) + 199.49 (Own two refrigerators)

4

Model 4: 474.00 + 378.23 (Own pool) – 95.22 (Turn off lights) + 249.40 (Own two refrigerators)

 + 323.70 (Own standalone freezer) 5

Model 5: 741.85 + 322.40(Own pool) – 104.37 (Turn off lights) + 277.55 (Own two refrigerators)

 + 466.58 (Own standalone freezer) – 151.73 (Age of head of household) 

Models of Utility Cost 

In several senses, modeling utility cost is a more straightforward exercise than modeling utility 

consumption. Since consumption and cost closely track each other (see Figure 28), once a 

consumption model has been developed and the explanatory variables identified, that model can 

immediately be used as the starting point for identifying the most important explanatory variables 

in the corresponding cost model. Models for electricity and gas costs can also be more directly 

compared because the disparity in consumption units does not need to be rectified. Furthermore, 

utility cost models developed on the basis of combined monthly expenditures are somewhat more 

competent from a statistical standpoint, and the results are somewhat more interpretable. A 

primary reason for this is that, on a combined or overall cost basis, the differing amounts of 

electricity and gas consumption, translated into dollars, are pooled into a single number that 

masks some of the variability. For example, homes that use more gas than electricity are 

essentially indistinguishable from homes that use more electricity than gas because the total cost 

amount tends to obscure such distinctions and the associated variations. 

Tables 105–109 contain the summary results of using stepwise multiple regression analysis to 

model average monthly electricity cost, with and without taxes and miscellaneous charges, and 

average monthly electricity cost per square foot, with and without taxes and miscellaneous 

charges, for PV, SEE, and comparison homes. Separate model results are also shown for 1.2 PV 

homes. As suggested in Chapter 20, homeowners are more likely to consider their utility bills to 

consist of total monthly utility costs including taxes and other charges, yet excluding these 

amounts may more accurately reflect the true costs of energy consumption. 

288
Tables 110–113 report corresponding summary modeling results for average monthly gas cost, 

with and without taxes and miscellaneous charges, and average monthly gas cost per square foot, 

with and without taxes and miscellaneous charges. Finally, Tables 114–117 present the summary 

modeling results for average monthly combined utility bill, with and without taxes and 

miscellaneous charges, and average monthly combined utility bill per square foot, with and 

without taxes and miscellaneous charges for PV, 1.2 PV, SEE, and comparison homes. This last 

set of results pertaining to combined utility bill is of specific interest here. The modeling results 

for average monthly electricity and gas cost are included only for completeness and are not 

discussed further. 

Except for SEE homes, the R2 values associated with all the models reported in Tables 114–117 

are moderately high. None of the models involves more than six explanatory variables, and many 

encompass four or fewer. These findings combined suggest that, except for SEE homes, 

modeling average monthly combined utility bill is fairly straightforward and the results are 

reasonably good. Again, the reasons for the poorer R2 values associated with SEE homes are not 

completely known, although this finding closely mimics that for average monthly electricity and 

gas consumption in these homes. 

Because of the strong relationship between average monthly consumption and average monthly 

cost observed in the figures presented in Appendix V, the modeling results for average monthly 

combined utility bill are expected to somewhat mimic those for average monthly electricity and 

gas consumption. Indeed, in Table 114, the R2 values associated with models of average monthly 

combined utility bill (including taxes and miscellaneous charges) for PV, 1.2 PV, SEE, and 

comparison homes are close to the corresponding values reported in Table 101 for average 

monthly electricity consumption (but not for average monthly gas consumption). There is also a 

fairly high degree of correspondence between the variables that constitute the corresponding 

models. 

The modeling results summarized in Table 115, which pertains to average monthly combined 

utility bill excluding taxes and miscellaneous charges, are almost the same as those shown in 

Table 114, which pertains to average combined monthly utility bill including taxes and 

miscellaneous charges. The corresponding R2

 values are extremely close; the only practical 

differences in the models found in the sizes of the coefficients are associated with the individual 

explanatory variables. Including or excluding taxes and miscellaneous charges apparently makes 

little or no difference to modeling capability. 

Tables 116 and 117 present similar summary modeling results for average monthly combined 

utility bill per square foot, when taxes and miscellaneous charges are included and excluded, 

respectively. For PV and 1.2 PV homes, the R2

 values associated with the models of average 

monthly combined utility bill per square foot, with and without taxes and miscellaneous charges, 

are lower than those associated with the corresponding models of average monthly combined 

utility bill, with and without taxes and miscellaneous charges, computed without the square 

footage adjustment. The opposite is true for SEE and comparison homes. This finding once again 

289
suggests that, at least for PV homes, adjusting for home size has a deleterious effect on modeling 

capability. Also, the respective models of average monthly combined utility bill, with and 

without taxes and miscellaneous charges, comprise different variables than those of average 

monthly combined utility bill, with and without taxes and miscellaneous charges, computed on a 

square footage basis. 

Further inspection of Tables 114–117 permits an assessment of the relative importance of 

individual explanatory variables to be made on the basis of the standardized coefficients. For PV 

homes, owning two refrigerators has the largest impact, followed closely by owning a standalone 

freezer. Both features lead to an increase in average monthly combined utility bill. For 1.2 PV 

homes, owning a pool has the largest impact, followed closely by owning two refrigerators. Both 

lead to an increase in average monthly combined utility bill. For SEE homes, turning off 

computers has the largest impact leading to a decrease in average monthly combined utility bill. 

Finally, for comparison homes, owning a pool has the largest impact, resulting in an overall 

increase in average monthly combined utility bill. 

Two additional overarching observations can be made. To the extent that such models can be 

constructed, the identified explanatory variables largely make good physical and practical sense. 

None of the models represented in Tables 114–117 contain explanatory variables that are 

illogical or whose coefficients have signs that are opposite the anticipated direction. This was 

also the case for the models of average monthly electricity and gas consumption. Also, the best 

models of utility cost (e.g., average monthly combined utility bill) for PV, SEE, and comparison 

homes encompass different sets of explanatory variables. Different variables apparently drive 

electricity and gas costs for PV, SEE, and comparison homes or households. Again, this was also 

the case for average monthly electricity and gas consumption. 

290
Table 106. Multiple Regression Models of Average Monthly Electricity Cost, 

Including Taxes and Miscellaneous Charges, for Three Categories of Homes

Home 

Category Variable Coefficient 

Standard 

Error 

Standardized 

Coefficient 

tstatistic pvalue 

PV Homes Constant 120.35 18.94 ! 6.35 <.001 

n=33* 

R2

=.695 

SE=$29.254 

Own pool 57.53 14.97 .43 3.84 .001 

Turn off lights !19.23 7.03 !.31 !2.74 .011 

Own two refrigerators 47.82 10.94 .49 4.37 <.001 

Own standalone freezer 78.46 20.77 .47 3.88 .001 

Age of head of household !24.75 7.80 !.38 !3.18 .004 

1.2 PV Homes Constant 75.64 13.47 ! 5.61 <.001 

n=29* 

R2

=.718 

SE=$27.500 

Own two refrigerators 51.50 11.23 .56 4.59 <.001 

Own pool 73.05 14.76 .60 4.95 <.001 

Turn off lights !15.48 6.99 !.27 !2.21 .037 

Own standalone freezer 57.68 18.95 .38 3.04 .006 

Turn off computers !0.01 3.85 !.29 !2.34 .028 

SEE Homes Constant !52.59 72.27 ! !.73 .472 

n=37* 

R2

=.533 

SE=$35.851 

Turn off computers !19.04 4.61 !.50 !4.13 <.001 

Tend to be thrifty !16.08 5.88 !.33 !2.74 .010 

Square footage of home .06 .02 .31 2.57 .015 

Comparison 

Homes 

Constant 187.80 18.93 ! 0.02 <.001 

Own pool 97.40 8.27 .77 11.78 <.001 

n=21* 

R2

 =.944 Turn off computers !23.79 2.95 !.62 !8.07 <.001 

SE=$17.306 Own two refrigerators 61.94 10.05 .47 6.16 <.001 

Turn off lights !32.57 9.12 !.25 !3.57 .003 

Own dual-zone heating/airconditioning system !24.56 7.86 !.20 !3.13 .007 

Gender of head of 

household !21.55 8.84 !.16 !2.44 .029 

*Not all homes/households have values for all variables, so the number of cases encompassed by the analysis is 

reduced. R2

 is the coefficient of determination, or multiple correlation coefficient. SE is the standard error. 

291 
Table 107. Multiple Regression Models of Average Electricity Cost, 

Excluding Taxes and Miscellaneous Charges, for Three Categories of Homes

Home 

Category Variable Coefficient 

Standard 

Error 

Standardized 

Coefficient 

tstatistic pvalue 

PV Homes Constant 109.95 13.77 ! 6.33 <.001 

n=33* 

R2

=.695 

SE=$26.836 

Own pool 52.56 13.73 .43 3.83 .001 

Turn off lights !17.56 6.45 !.31 !2.73 .011 

Own two refrigerators 43.77 10.04 .49 4.36 <.001 

Own standalone freezer 72.10 19.05 .47 3.79 .001 

Age of head of household !22.91 7.15 !.38 -3.20 .003 

1.2 PV Homes Constant 68.82 12.31 ! 5.59 <.001 

n=29* 

R2

=.718 

SE=$25.131 

Own two refrigerators 46.99 10.27 .55 4.58 <.001 

Own pool 66.71 13.49 .60 4.95 <.001 

Turn off lights !14.16 6.39 !.27 !2.22 .037 

Own standalone freezer 52.62 17.32 .38 3.04 .006 

Turn off computers !8.21 3.52 !.29 !2.34 .029 

SEE Homes Constant !42.85 66.67 ! !.64 .525 

n=37* 

R2

=.527 

SE=$33.075 

Turn off computers !17.42 4.26 !.50 !4.09 <.001 

Tend to be thrifty !14.84 5.42 !.34 !2.74 .010 

Square footage of home .05 .02 .30 2.46 .019 

Comparison 

Homes 

Constant 171.72 16.66 ! 10.31 <.001 

Own pool 87.29 7.28 .75 12.00 <.001 

n=21* 

R2

=.948 Turn off computers !22.84 2.59 !.65 !8.80 <.001 

SE=$15.233 Own two refrigerators 62.26 8.85 .52 7.04 <.001 

Gender of head of 

household !22.19 7.78 !.18 !2.85 .013 

Turn off lights !27.95 8.03 !.23 !3.48 .004 

Own dual-zone heating/airconditioning system !20.83 6.91 !.19 !3.01 .009 

*Not all homes/households have values for all variables, so the number of cases encompassed by the analysis is 

reduced. R2

 is the coefficient of determination, or multiple correlation coefficient. SE is the standard error. 

292 
Table 108. Multiple Regression Models of Average Monthly Electricity Cost per Square Foot,

Including Taxes and Miscellaneous Charges, for Three Categories of Homes

Home 

Category Variable Coefficient 

Standard 

Error 

Standardized 

Coefficient t-statistic p-value 

PV Homes Constant .041 .007 ! 5.74 <.001 

n=33* 

R2

=.602 

Own two 

refrigerators .016 .004 .49 3.76 .001 

SE=$0.011 Own pool .015 .006 .35 2.69 .012 

Own standalone 

freezer .024 .008 .43 3.04 .005 

Age of head of 

household !.009 .003 !.40 !2.95 .006 

Turn off lights !.006 .003 !.30 !2.27 .031 

1.2 PV 

Homes Constant .024 .003 ! 6.80 <.001 

n=29* 

R2=.214 

SE=$0.014 

Own two 

refrigerators .014 .005 .46 2.71 .011 

SEE Homes Constant .043 .002 ! 18.34 <.001 

n=37* 

R2

=.486 

SE=$0.012 

Turn off 

computers !.006 .001 !.55 !4.32 <.001 

Tend to be 

thrifty !.005 .002 !.33 !2.62 .013 

Comparison 

Homes Constant .033 .004 ! 7.82 <.001 

n=21* 

R2

=.531 

SE=$0.016 Own pool .034 .007 .73 4.64 <.001 

*Not all homes/households have values for all variables, so the number of cases encompassed by the analysis is 

reduced. R2

 is the coefficient of determination, or multiple correlation coefficient. SE is the standard error. 

293
Table 109. Multiple Regression Models of Average Monthly Electricity Cost per Square Foot,

Excluding Taxes and Miscellaneous Charges, for Three Categories of Homes

Home 

Category Variable Coefficient 

Standard 

Error 

Standardized 

Coefficient t-statistic p-value 

PV Homes Constant .037 .007 ! 5.72 <.001 

n=33* 

R2

=.602 

Own two 

refrigerators .014 .004 .48 3.76 .001 

SE=$0.010 Own pool .014 .005 .34 2.69 .012 

Own 

standalone 

freezer

 .022 .007 .44 3.06 .005 

Age of head of 

household !.008 .003 !.40 !2.98 .006 

Turn off lights !.005 .002 !.30 !2.26 .032 

1.2 PV 

Homes Constant .022 .003 ! 6.77 <.001 

n=29* 

R2

=.214 

SE=$0.013 

Own two 

refrigerators .013 .005 .46 2.71 .011 

SEE Homes Constant !.039 .002 ! 18.17 <.001 

n=.37* 

R2

=.485 

SE=$0.011 

Turn off 

computers !.006 .001 !.54 !4.30 <.001 

Tend to be 

thrifty !.005 .002 !.33 !2.63 .013 

Comparison 

Homes Constant .031 .004 ! 7.93 <.001 

n=21* 

R2

=.528 

SE=$0.015 

Pool .031 .007 .73 4.61 <.001 

*Not all homes/households have values for all variables, so the number of cases encompassed by the analysis is 

reduced. R2

 is the coefficient of determination, or multiple correlation coefficient. SE is the standard error. 

294
Table 110. Multiple Regression Models of Average Monthly Gas Bill, Including Taxes and

Miscellaneous Charges, for Three Categories of Homes

Home 

Category Variable Coefficient 

Standard 

Error 

Standardized 

Coefficient t-statistic p-value 

PV Homes Constant 28.22 3.01 ! 9.36 <.001 

n=33* 

R2

=.428 

SE=$10.494 

In summer, set 

thermostat to 

75N

 or higher 

!3.82 1.58 !.34 !2.41 .022 

Own two 

refrigerators 12.24 3.85 .47 3.18 .003 

Own standalone 

freezer 18.73 6.62 .41 2.83 .008 

1.2 PV 

Homes 

n=29* 

Constant 21.48 2.92 ! 7.35 <.001 

Own two 

refrigerators 14.24 4.00 .54 3.56 .002 

R2

=.492 

SE=$10.150 

Own standalone 

freezer 22.92 6.55 .53 3.50 .002 

Own pool 11.34 5.08 .32 2.23 .035 

SEE Homes 

n=37* 

R2

=.172 

SE=$11.027 

Constant 34.83 1.92 ! 18.14 <.001 

Own hot water 

flow regulator 15.76 5.84 .42 2.70 .011 

Comparison 

Homes 

Constant !29.98 23.77 ! !1.26 .221 

n=25* 

R2

 =.391 

SE=$14.847 

Household size 18.75 6.37 .49 2.95 .007 

Own pool 14.86 6.19 .40 2.40 .025 

*Not all homes/households have values for all variables, so the number of cases encompassed by the analysis is 

reduced. R2

 is the coefficient of determination, or multiple correlation coefficient. SE is the standard error. 

295
Table 111. Multiple Regression Models of Average Gas Utility Bill, 

Excluding Taxes and Miscellaneous Charges, for Three Categories of Homes

Home 

Category Variable Coefficient 

Standard 

Error 

Standardized 

Coefficient t-statistic p-value 

PV Homes Constant 26.47 2.84 ! 9.33 <.001 

n=33* 

R2

=.427 

SE=$9.882 

In summer, set 

thermostat to 

75N

 or higher 

!3.60 1.49 !.34 -2.42 .022 

Own two 

refrigerators 11.52 3.62 .47 3.18 .004 

Own standalone 

freezer 17.61 6.23 .41 2.83 .008 

1.2 PV 

Homes 

n=29* 

Constant 20.12 2.75 ! 7.32 <.001 

Own two 

refrigerators 13.40 3.77 .54 3.56 .002 

R2

=.492 

SE=$9.556 

Own standalone 

freezer 21.57 6.16 .53 3.50 .002 

Own pool 10.71 4.78 .33 2.24 .034 

SEE Homes 

n=37* 

R2

=.172 

SE=$10.382 

Constant 32.69 1.81 ! 18.09 <.001 

Own hot water 

flow regulator 14.82 5.50 .42 2.70 .011 

Comparison 

Homes 

Constant !28.38 22.37 ! !1.27 .218 

n=25* 

R2

=.392 

SE=$13.975 

Household size 17.67 5.99 .49 2.95 .007 

Own pool 13.99 5.83 .40 2.40 .025 

*Not all homes/households have values for all variables, so the number of cases encompassed by the analysis is 

reduced. R2

 is the coefficient of determination, or multiple correlation coefficient. SE is the standard error. 

296
Table 112. Multiple Regression Models of Average Monthly Gas Bill per Square Foot, Including

Taxes and Miscellaneous Charges, for Three Categories of Homes

Home 

Category Variable Coefficient 

Standard 

Error 

Standardized 

Coefficient t-statistic p-value 

PV Homes Constant .010 .001 ! 9.87 <.001 

n=33* 

R2

=.393 

SE=$0.003 

In summer, set 

thermostat to 

75N

 or higher 

!.001 .001 !.37 -2.56 .016 

Own two 

refrigerators .004 .001 .43 2.85 .008 

Own standalone 

freezer .005 .002 .36 2.37 .025 

1.2 PV 

Homes 

n=29* 

Constant .009 .001 ! 8.61 <.001 

Own two 

refrigerators .004 .001 .51 3.23 .003 

R2

=.443 

SE=$0.003 

Own standalone 

freezer .006 .002 .41 2.59 .016 

In summer, set 

thermostat to 

75N

 or higher 

!.001 .001 !.33 !2.21 .037 

SEE Homes 

n=37* 

R2

=.126 

SE=$0.004 

Constant .011 .001 ! 17.91 <.001 

Own hot water 

flow regulator .004 .002 .35 2.24 .031 

Comparison 

Homes 

Constant !.005 .009 ! !.61 .546 

Household size .006 .002 .43 2.70 .013 

n=25* 

R2

=.472 

SE=$0.005 

Own dual-zone 

heating/airconditioning 

system 

!.006 .002 !.41 !2.58 .017 

Own pool .005 .002 .38 2.38 .027 

*Not all homes/households have values for all variables, so the number of cases encompassed by the analysis is 

reduced. R2

 is the coefficient of determination, or multiple correlation coefficient. SE is the standard error. 

297 
Table 113. Multiple Regression Models of Average Monthly Gas Bill per Square Foot, Excluding

Taxes and Miscellaneous Charges, for Three Categories of Homes

Home 

Category Variable Coefficient 

Standard 

Error 

Standardized 

Coefficient t-statistic p-value 

PV Homes Constant .009 .001 ! 9.83 <.001 

n=33* 

R2

=.394 

SE=$0.003 

In summer, set 

thermostat to 

75N

 or higher 

!.001 <.001 !.37 -2.56 .016 

Own two 

refrigerators .003 .001 .43 2.85 .008 

Own 

standalone 

freezer 

.005 .002 .36 2.36 .025 

1.2 PV 

Homes 

n=29* 

Constant .008 .001 ! 8.58 <.001 

Own two 

refrigerators .004 .001 .51 3.23 .003 

R2

=.443 

SE=$0.003 

Own 

standalone 

freezer 

.005 .002 .41 2.59 .016 

In summer, set 

thermostat to 

75N

 or higher 

!.001 <.001 !.33 !2.21 .036 

SEE Homes 

n=.37* 

R2

=.125 

SE=$0.003 

Constant .001 .001 ! 17.86 <.001 

Own hot water 

flow regulator .004 .002 .35 2.24 .032 

Comparison 

Homes 

Constant !.005 .008 ! !.62 .541 

Household size .006 .002 .43 2.70 .013 

n=25* 

R2

=.471 

SE=$0.005 

Own dual zone 

heating/air 

conditioning 

system 

!.005 .002 !.41 !2.58 .018 

Own pool .005 .002 .38 2.38 .027 

*Not all homes/households have values for all variables, so the number of cases encompassed by the analysis is 

reduced. R2

 is the coefficient of determination, or multiple correlation coefficient. SE is the standard error. 

298 
Table 114. Multiple Regression Models of Average Monthly Combined Utility Bill, 

Including Taxes and Miscellaneous Charges, for Three Categories of Homes

Home 

Category Variable Coefficient 

Standard 

Error 

Standardized 

Coefficient 

tstatistic pvalue 

PV Homes Constant 155.36 20.96 ! 7.41 <.001 

n=33* 

R2

=.734 

SE=$32.378 

Own pool 68.16 16.56 .43 4.12 <.001 

Turn off lights !22.45 7.78 !.31 !2.89 .008 

Own two refrigerators 59.91 12.11 .52 4.95 <.001 

Own standalone freezer 100.94 22.98 .51 4.39 <.001 

Age of head of household !28.85 8.63 !.37 !3.34 .002 

1.2 PV Homes Constant 102.33 14.84 ! 6.90 <.001 

n=29* 

R2

=.761 

SE=$30.284 

Own two refrigerators 64.88 12.37 .58 5.24 <.001 

Own standalone freezer 77.64 20.87 .43 3.72 .001 

Own pool 85.79 16.25 .59 5.28 <.001 

Turn off lights !18.83 7.70 !.28 !2.45 .022 

Turn off computers !9.04 4.24 !.24 !2.14 .044 

SEE Homes Constant 162.75 9.08 ! 17.92 <.001 

n=37* 

R2

=.429 

SE=$44.068 

Turn off computers !24.57 5.53 !.58 !4.44 <.001 

Own hot water flow 

regulator 59.18 23.34 .33 2.54 .016 

Comparison 

Homes 

Constant !12.28 56.19 ! !.22 .830 

Own pool 122.24 15.14 .80 8.08 <.001 

n=21* 

R2

 =.849 Household size 51.85 14.85 .34 3.49 .003 

SE=$32.004 In winter, set thermostat to 

70N

 or lower !21.87 6.78 !.33 !3.23 .005 

Own dual-zone heating/airconditioning system !43.02 14.56 !.30 !2.96 .009 

*Not all homes/households have values for all variables, so the number of cases encompassed by the analysis is 

reduced. R2

 is the coefficient of determination, or multiple correlation coefficient. SE is the standard error. 

299
Table 115. Multiple Regression Models of Average Monthly Combined Utility Bill, 

Excluding Taxes and Miscellaneous Charges, for Three Categories of Homes

Home 

Category Variable Coefficient 

Standard 

Error 

Standardized 

Coefficient 

tstatistic pvalue 

PV Homes Constant 142.82 19.32 ! 7.39 <.001 

n=33* 

R2

=.734 

SE=$29.831 

Own pool 62.61 15.26 .43 4.10 <.001 

Turn off lights !20.60 7.16 !.31 !2.88 .008 

Own two refrigerators 

55.15 11.16 .52 4.94 <.001 

Own standalone freezer 93.25 21.18 .51 4.40 <.001 

Age of head of household !26.77 7.95 !.37 !3.37 .002 

1.2 PV Homes Constant 93.84 13.62 ! 6.89 <.001 

n=29* 

R2

=.761 

SE=$27.803 

Own two refrigerators 59.58 11.36 .58 5.25 <.001 

Own standalone freezer 71.40 19.16 .43 3.73 .001 

Own pool 78.74 14.92 .59 5.28 <.001 

Turn off lights !17.32 7.07 !.28 !2.45 .022 

Turn off computers 

!8.25 3.89 !.24 !2.12 .045 

SEE Homes Constant 149.01 8.34 - 17.87 <.001 

n=37* 

R2

=.430 

SE=$40.462 

Turn off computers !22.54 5.08 !.58 !4.44 <.001 

Own hot water flow 

regulator 

54.85 21.43 !.33 2.56 .015 

Comparison 

Homes 

Constant !18.60 51.40 ! !.36 .722 

Own pool 111.61 13.85 .79 8.06 <.001 

n=21* 

R2

=.851 Household size 50.37 13.59 .36 3.71 .002 

SE=$29.276 In winter, set thermostat to 

70N

 or lower 

!21.30 6.20 !.35 !3.44 .003 

Own dual-zone heating/airconditioning system !38.73 13.32 !.29 !2.91 .010 

*Not all homes/households have values for all variables, so the number of cases encompassed by the analysis is 

reduced. R2

 is the coefficient of determination, or multiple correlation coefficient. SE is the standard error. 

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Table 116. Multiple Regression Models of Average Monthly Combined Utility Bill per Square Foot,

Including Taxes and Miscellaneous Charges, for Three Categories of Homes

Home 

Category Variable Coefficient 

Standard 

Error 

Standardized 

Coefficient 

tstatistic pvalue 

PV Homes Constant .053 .008 ! 6.60 <.001 

n=33* 

R2

=.636 

SE=$0.012 

Own two refrigerators .019 .005 .51 4.12 <.001 

Own standalone freezer .030 .009 .46 3.41 .002 

Age of head of household !.010 .003 !.39 !3.01 .006 

Own pool .018 .006 .35 2.87 .008 

Turn off lights !.007 .003 !.30 !2.41 .023 

1.2 PV Homes Constant .037 .006 ! 6.01 <.001 

n=29* 

R2

=.589 

SE=$0.013 

Own two refrigerators .019 .005 .52 3.71 .001 

Own standalone freezer .020 .009 .33 2.26 .033 

Own pool .020 .006 .43 .313 .005 

Turn off lights !.007 .003 !.31 !2.21 .037 

SEE Homes Constant .055 .003 ! 20.73 <.001 

n=37* 

R2

=.469 

SE=$0.013 

Turn off computers !.007 .002 !.51 !4.01 <.001 

Tend to be thrifty !.006 .002 !.35 !2.75 .009 

Comparison 

Homes 

Constant .119 .015 ! 8.19 <.001 

Own pool .042 .006 .73 7.42 <.001 

n=21* 

R2

=.865 

SE=$0.012 

Own dual-zone heating/airconditioning system 

!.027 .005 !.50 !5.04 <.001 

Educational level of head of 

household 

!.008 .002 !.35 !3.59 .003 

Tend to be thrifty !.009 .003 !.29 !2.96 .010 

Turn off lights !.012 .006 !.21 !2.14 .049 

*Not all homes/households have values for all variables, so the number of cases encompassed by the analysis is 

reduced. R2

 is the coefficient of determination, or multiple correlation coefficient. SE is the standard error. 

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Table 117. Multiple Regression Models of Average Monthly Combined Utility Bill per Square Foot,

Excluding Taxes and Miscellaneous Charges, for Three Categories of Homes

Home 

Category Variable Coefficient 

Standard 

Error 

Standardized 

Coefficient 

tstatistic pvalue 

PV Homes Constant .049 .007 ! 6.60 <.001 

n=33* 

R2

=.637 

SE=$0.011 

Own two refrigerators .018 .004 .51 4.13 <.001 

Own standalone freezer .028 .008 .47 3.443 .002 

Age of head of household !.009 .003 !.39 !3.03 .005 

Own pool .017 .006 .35 2.87 .008 

Turn off lights !.007 .003 !.30 !2.41 .023 

1.2 PV Homes Constant .034 .006 ! 6.01 <.001 

n=29* 

R2

=.590 

SE=$0.012 

Own two refrigerators .017 .005 .52 3.72 .001 

Own standalone freezer .018 .008 .33 2.27 .032 

Own pool .019 .006 .43 3.14 .004 

Turn off lights !.006 .003 !.31 !2.21 .037 

SEE Homes Constant !.050 .002 ! 20.63 <.001 

n=.37* 

R2

=.468 

SE=$0.012 

Turn off computers !.006 .002 !.51 !3.99 <.001 

Tend to be thrifty !.006 .002 !.36 !2.77 .009 

Comparison 

Homes 

Constant .097 .010 ! 9.40 <.001 

Own pool .041 .005 .79 7.67 <.001 

n=21* 

R2

=.839 

SE=$0.011 

Own dual-zone heating/airconditioning system 

!.025 .005 !.51 !4.77 <.001 

Educational level of head 

of household 

!.009 .002 !.41 !3.92 .001 

In winter, set thermostat to 

70N

 or lower 

!.007 .002 !.31 !2.91 .010 

*Not all homes/households have values for all variables, so the number of cases encompassed by the analysis is 

reduced. R2

 is the coefficient of determination, or multiple correlation coefficient. SE is the standard error. 

Summary and Discussion 

This investigation illustrates that statistical models of utility consumption and costs can be 

constructed for several types of homes that consist of relatively few input variables and have 

reasonably good explanatory power. As might be expected, most of the models reported here are 

dominated by variables that represent various energy-intensive equipment, but others tend to 

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represent homeowner demographics, opinions, attitudes, and behaviors associated with energy 

conservation. In particular, the models of utility consumption and cost associated with SEE 

homes often include more attitudinal and behavioral inputs than equipment-oriented inputs. 

Each section of Tables 101–104 and Tables 106–117 provides the information necessary to write 

the equation for the respective model, along with the associated goodness-of-fit statistics, for the 

utility consumption or cost measure in question. The tables contain different models for PV, 

SEE, and comparison homes. The tables also provide modeling results specifically for homes 

with 1.2-kW PV systems so distinctions can be made that involve this specific size of PV system 

(models for PV homes with 2.4-kW systems cannot be developed because of the small number of 

cases). Each model presented has the largest R2 that can be obtained using the original collection 

of prospective explanatory variables listed Table 96, with the caveat that some variables were 

ultimately omitted from some models because they seemed inappropriate for predicting the 

quantity of interest or their coefficients were presumed to be in the wrong direction (e.g., ceiling 

fans lead to an increase in gas consumption). Other terms could be added to every model reported 

here, but the usefulness and practicality of such terms would be highly suspect. 

The explanatory variables encompassed by these models as the “best” ones do not necessarily 

correspond to those with the highest individual correlations indicated in Tables 97–100, and the 

variables with the highest correlations in Tables 97–100 do not necessarily have the largest 

effects in the corresponding models, if, in fact, they appear at all. This result is not surprising, 

since intercorrelations among variables are to be expected. Clearly, variables that indicate the 

presence of energy-intensive equipment seem most important across all models. 

Regardless of which measure of utility consumption or cost is designated as the response 

variable, there are notable differences in the models developed for PV, SEE, and comparison 

homes. There is some, but not extensive, overlap among the sets of explanatory variables for PV, 

SEE, and comparison homes for either utility consumption or cost. 

With regard to electricity consumption, the models associated with PV and comparison homes 

are moderately good to very good (R2

 values are approximately .7 or higher). The models 

associated with SEE homes are poorer quality (they have the lowest corresponding values of R2

, 

on the order of about .5), which suggests that electricity consumption is most difficult to explain 

for these homes. As noted earlier, this result may be due to inequities in the numbers of responses 

and data values for individual variables or to the absence of important variables that were beyond 

the scope of the study. Increased variability in average monthly electricity consumption for SEE 

homes is not the issue, as indicated by the comparative coefficients of variation shown in the 

tables of Appendix W. 

The models of gas consumption are generally of poorer quality (considerably lower R2

 values and 

higher SE values on a comparative basis) than the corresponding models of electricity 

consumption. Average monthly gas consumption apparently cannot be modeled as well as 

average monthly electricity consumption with the variables identified in Table 96, which 

suggests that other inputs may be more important. Again, as indicated by the comparative 

303
coefficients of variation shown in the tables of Appendix W, average monthly gas consumption is 

not more variable than average monthly electricity consumption. 

These differences among models of electricity and gas consumption for PV, SEE, and 

comparison homes are mirrored in those for cost. The same patterns of differences among models 

are present when cost is stated in terms of average monthly combined utility bill. The models of 

average monthly combined utility bill for PV and comparison homes are fairly good (in terms of 

R2

 and SE); the models for SEE homes are of poorer quality. 

All models of electricity and gas consumption and cost for 1.2 PV homes, as well as those for 

costs stated in terms of average monthly combined utility bill, are better (higher R2 and lower SE) 

than the corresponding models for all PV homes combined. This result is not unexpected because 

there are substantially fewer 2.4 PV homes in the data set, and the 2.4 PV homes have lower, but 

more variable, average electricity consumption and cost (see Table W-1 in Appendix W). 

The effect of home size on utility cost and consumption is somewhat enigmatic. At the outset of 

the study, home size, expressed in terms of square footage, was thought to be an important 

explanatory variable. However, as the results of this modeling work demonstrate, home size is 

probably not that important, in and of itself; at least not in this set of homes. The impact of home 

size is likely confounded with one or more additional factors that act sequentially or in parallel to 

affect utility consumption and cost. 

The models reported here are really best used as mathematical expressions with which to portray 

overall relationships. However, such expressions are not very interesting or practical in and of 

themselves. The ultimate objective would be to use them to predict or forecast utility 

consumption and costs in future home construction. For planning and decision-making purposes, 

such predictions would be highly useful to builders and new homebuyers. The idea is to 

substitute into the model equations values of the model inputs associated with prospective 

homeowners and compute an estimate of utility consumption or cost. 

For example, suppose it is proposed to use the best-fit model of average monthly electricity 

consumption for PV homes to estimate average monthly electricity consumption for a 

prospective buyer of a new PV home. This model, as previously noted, is given in Table 101 by: 

Y = 741.85 + 322.40X1 - 104.37X2 + 277.55X3 + 466.58X4 - 151.73X5 +e, where 

X1=Own pool 

X2=Turn off lights when away 

X3=Own two refrigerators 

X4=Own standalone freezer 

X5=Age of head of household 

e= unexplained remainder (error) 

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To obtain the requisite prediction, the prospective buyer has to report his/her age as the head of 

household, state whether or not he/she intends to own a pool, two refrigerators, and a standalone 

freezer, and indicate his/her level of agreement with a question or survey item about turning off 

lights. 

Though fairly straightforward, this approach can be problematic because the equations 

encompass input variables for which values may not be readily obtained from prospective 

homeowners. As noted earlier, factual records and physical measurements, like gender, number 

of home occupants, and home square footage, can be fairly easily collected. Measurements and 

responses that represent opinions, attitudes, beliefs, behavioral intentions, and perceptions are 

somewhat more difficult to procure or replicate, and they can often only be acquired via surveys 

or focus groups. For more complex models, obtaining the necessary inputs to the equation from 

all prospective homebuyers—particularly those related to beliefs, opinions, and intended 

behaviors–may present difficulties. 

A related concern has to do with the consistency and compatibility of the input data. The models 

reported here are developed from a fairly static data set; some of the variables represent 

homeowner responses to cross-sectional survey questions that were administered at a fixed point 

in time. Prospective homeowners outside the original survey set would not necessarily have the 

same conditions and circumstances as those encompassed by this study, nor would they 

necessarily be expected to respond in the same way. Consequently, caution would need to be 

exercised to avoid inappropriate extrapolation beyond the range and applicability of the original 

data. 

Models would be better, and more direct, if equations like those reported here could be 

constructed solely from factual or physical measurements that are easily obtained or replicated. 

Unfortunately, this seems impossible to do and still obtain high goodness-of-fit. The implication 

is that utility consumption and cost cannot be adequately modeled or predicted simply on the 

basis of factual or physical measurements. Homeowner beliefs, attitudes, and perceptions clearly 

affect utility consumption and cost in tangible (e.g., a purchase decision involving energyefficient appliances) and intangible ways (such as changes in daily energy behavior), and 

including such variables is required to develop reliable models with acceptable predictive power. 

In addition, these models would be applicable only in the climates with the same heating and 

cooling degree days that San Diego has. 

With these caveats in mind, the models of utility consumption and cost reported here for PV and 

comparison homes clearly can be used to provide reasonably good estimates for future homes of 

these types. At the very least, they can be used to develop general guidelines and trends for 

prospective builders and homebuyers. On the other hand, the models associated with SEE homes 

are really not optimum and probably should not be used for predictive purposes. Their low R2 

values suggest that the resulting predictions would not be very reliable. 

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Clearly, the ability to derive good models of utility consumption and cost (in the sense of 

goodness-of-fit) is largely data driven. The end results of all modeling exercises are only as good 

or as adequate as the available data. Data quality encompasses both the amount of information, in 

terms of numbers of observations and measurements, and the accuracy or representativeness of 

the measurements. Identification of all relevant variables, and availability of data on all such 

variables, are also key. With a larger set of households and an even higher response rate to 

pertinent homeowner questionnaires,18 the modeling approaches and strategies described here 

would probably yield even stronger results in future studies. The ultimate goal would be to 

develop a straightforward prediction scheme, such as a simple nomograph, to use in estimating 

utility consumption and cost for prospective homebuyers. Such a device would be particularly 

helpful in familiarizing prospective buyers of PV homes with the ultimate savings attributable to 

PV systems as they consider the up-front costs of including such equipment in their new home 

purchases. 

In future work, a number of other analytical approaches could be pursued to further evaluate the 

model quality. One such approach that might yield valuable insights is to investigate plots of 

model residuals to determine if any explainable patterns can be detected. Investigation of patterns 

in the residuals often leads to the identification of other effects that the model does not already 

address. However, such investigations can be time consuming, particularly when numerous 

models are involved. In addition, results from the analyses of other utility data sets might be 

combined to help improve overall model results. 

18The total number of respondents who agreed to allow their utility data to be obtained was substantially smaller than 

the total number of study respondents. Also, the performance of multiple regression is sensitive to missing values 

that often result from item nonresponse. Item nonresponse is the situation in which a respondent selectively responds 

to survey questions, completing some and omitting others. When using multiple regression, if the response or value 

for only a single item in a set of items is missing, the entire case is deleted from the analysis. 

306 
Chapter 22

Perceived and Actual Utility Bills

Introduction 

Chapter Highlights 

A key facet of this study involves homeowners’ C All homeowners in the study tend to significantly perceptions of their utility bills compared to the overestimate their actual average monthly utility actual amounts. This chapter describes important bills. 

findings about this relationship. 

C SheaHomes respondents estimate monthly utility 

As noted previously, utility data were obtained bills ranging from $50 to $475, with a mean 

monthly estimate of $143. from SDG&E for 132 study households whose 

respondents signed a utility release form (URL). C Comparison respondents estimate monthly utility In the discussion which follows, data from all bills ranging from $4 to $540, with a mean 

these homes are included. Although the homes monthly estimate of $184.55. 

had received utility service for varying numbers C PV owners estimate monthly utility bills ranging of months, data from every month of service for from $11 to $475, with a mean estimate of $116. every home were utilized (except as noted 

below).1 The rationale for this approach is that, C PV owners estimate significantly lower monthly 

when asked to estimate their average utility bills, utility bills than do non-PV owners. 

most homeowners, including those surveyed for C Comparison of estimated utility bills of non-PV this study, likely conceptualize a number that owners and comparison owners result in no encompasses the entire time they have resided in significant differences. the home, automatically factoring in the 

variations experienced across months and C PV owners are more likely to accurately estimate 

their utility bills than are comparison seasons. Hence, using the actual utility data for respondents, who have the least accurate, and the entire period of occupancy is thought to yield highest overestimates, of their utility bills. amounts that are most comparable to those 

perceived and estimated by the respondents. 

To obtain the most direct and reliable comparison possible, it would be necessary to control for 

the influence of perceptions about utility bills and home size at the respondents’ previous 

residences. Consequently, all respondents were asked to estimate the utility bills and square 

footage at their previous homes, as well. Fortunately, no significant differences emerged on these 

variables for households in the SheaHomes and comparison communities, nor for households 

with and without PV systems in the SheaHomes communities. This finding is important because, 

on average, homes in the SheaHomes communities are larger than those in the comparison 

community. 

1

By way of comparison, in Chapter 19 (Comparative Analysis of Utility Consumption and Cost), which more 

specifically addresses actual utility consumption and cost, only data from the period July 2003–June 2004 are used to 

establish a consistent time frame for comparison. Homes without a full 12 months of service were omitted from the 

analysis, along with homes determined to be outliers with regard to electricity or gas consumption. The utility data 

from the homeowners were included here. 

307 
Methodology for Computing Actual Average Monthly Utility Bills 

To accurately compute the actual average monthly utility bill for each home, additional length-ofservice issues had to be addressed. Specifically, it was determined that the number of days per 

monthly billing cycle varied. Since some households began service in the middle of a month, the 

number of days in the first billing month was particularly variable, so, for consistency purposes, 

all records in the data set were omitted for which the number of days per billing cycle was fewer 

than 28. After applying this filter, the average number of days per billing cycle across all 

households and billing months was calculated to be 30.5 (also see the discussion in Chapter 20, 

Comparative Analysis of Utility Consumption and Cost). The individual monthly records for 

each household were then collated to compute monthly averages (accounting for the differing 

days per billing cycle and differing months of service) for electricity and gas cost, and for 

combined utility cost, and these values (weighted averages) were subsequently merged with the 

survey responses of the respective households. 

Prior Utility Bills and Sizes of Homes 

Respondents were asked “Approximately what was your household’s average total monthly 

utility bills at your prior residence?” Respondents’ answers were reported in dollars per month 

for a residence of specific size (in square feet). The mean estimated combined monthly utility 

cost for all responding households was $271.48, with a range of $25 to $300 per month. The 

mean square footage was 1,786.4, with a range of 1,000 to 4,850 square feet. 

SheaHomes owners estimated lower utility bills at their prior residences (mean=$246.09) than 

did comparison homeowners (mean=$353.63); the difference approaches statistical significance 

(t= –1.892; p=.062). The reason for this difference is not altogether clear. The difference in 

estimated mean square footage for the prior residences between SheaHomes owners 

(mean=1,814.16 square feet) and comparison homeowners (mean=1,728.94 square feet) is not 

statistically significant, so the significantly lower average utility bills reported for previous 

residences by SheaHomes owners is not likely due to home size. Further, there is no significant 

difference in estimated mean monthly utility bills for prior residences, nor in estimated mean 

square footage of prior residences, when households in the SheaHomes communities with and 

without PV systems are compared. 

Reported Utility Bills 

Because they are billed separately for electricity and natural gas, respondents in SheaHomes 

communities with PV systems were asked: “Approximately what is your household’s average 

monthly electricity bill now?” and “Approximately what is your household’s average monthly 

natural gas bill now?” Other SheaHomes comparison respondents were asked to state their 

household’s “average monthly utility bill now.” These questions result in self-reported data 

representing point-in-time perceptions of the homeowners and may not accurately reflect actual 

monthly amounts. Table 118 summarizes these results. 

308
Table 118. Estimates of Monthly Utility Bills Reported by Respondents 

by Household Category 

Household Category Mean 

Standard 

Deviation 

All SheaHomes (n=143) $143.08 $84.32 

SheaHomes with PV systems (n=55) $116.44 $77.74 

SheaHomes without PV systems (n=88) $159.73 $84.40 

Comparison homes (n=44) $184.55 $100.68 

Table 118 shows that the SheaHomes respondents report significantly lower average monthly 

utility bills than respondents from the comparison community, and that the amounts are less 

variable. The mean of the average monthly utility bills (gas and electricity combined) reported by 

SheaHomes respondents is $143.08 with a standard deviation of $84.32 (range of $50 to $475), 

and the mean of the average monthly utility bills reported by respondents from the comparison 

community is $184.55 with standard deviation of $100.68 (range of $55 to $540). The difference 

in the two mean values is statistically significant (t= –2.721; p=.007). The standard deviation in 

reported amounts for both groups is quite high. The coefficient of variation of average monthly 

utility bills reported by SheaHomes respondents is 59%, and the corresponding coefficient of 

variation of average monthly utility bills reported by respondents from the comparison 

community is 55%. 

If PV owners are omitted from the SheaHomes respondent pool, the results are somewhat 

different. The mean of the average monthly utility bills reported by the remaining SheaHomes 

respondents is $159.73 with a standard deviation of $84.40 (range of $50 to $450). The 

difference in mean values associated with the restricted pool of SheaHomes respondents and the 

respondents from the comparison community is not statistically significant (t= –1.492; p=.138). 

This result suggests that PV systems may have a marked impact on the perceived lowering of 

average monthly utility bills in these types of homes. 

The mean reported monthly gas bill for SheaHomes respondents who are PV owners is $41.30 

(range of $6 to $125) and the mean monthly electricity bill is $80.54 (range of $5 to $350). The 

mean total utility bill (gas and electricity combined) for SheaHomes respondents who are PV 

owners is $116.44 (range of $11 to $475).2

 When subjected to a t-test, the mean reported monthly 

utility bill is significantly lower for owners of homes with PV systems than for owners of homes 

without PV systems in the SheaHomes communities (t= –3.075; p=.003). 

The broad range of reported utility bill values may be the result of differences in respondent 

perceptions or actual household energy usage attributable to differences in household 

composition, energy usage behaviors, or appliance installations. It is possible, although unlikely, 

that a few of the PV systems are not functioning properly or at all. 

2

The mean overall utility bill is not identical to the sum of the mean electricity and mean gas bills because the 

denominator is different in each case due to missing data in one or the other of the variables. 

309 
Table 119 summarizes the percentages of responses with respect to three intervals of average 

monthly utility cost reported by homeowners from the SheaHomes communities and the 

comparison community. The three intervals are (1) less than $100 a month, (2) $100 to $199 a 

month, and (3) greater than or equal to $200 a month. The percentage of SheaHomes respondents 

(27%) reporting average monthly utility bills in the lowest cost interval (less than $100) is more 

than double the percentage of respondents from the comparison community (13%) who report 

having average monthly utility bills this low. The difference in percentages among the three 

intervals for SheaHomes respondents and respondents from the comparison community 

approaches statistical significance (P2=5.878; p=.053). 

Table 119. Comparison of the Percentage Distributions of Average Monthly Utility Bills

Reported by Homeowners in the SheaHomes and Comparison Communities

Monthly Utility Cost Interval 

Percentages of Homeowners 

in the SheaHomes 

Communities (n=177) 

Percentages of Homeowners 

in the Comparison 

Community (n=54) 

< $100 a month 27% 13% 

$100-$199 a month 37% 35% 

$$200 a month 36% 52%

 Totals 100% 100% 

Again, if the PV owners are omitted from the SheaHomes respondent pool, the distribution of 

percentages among the three intervals for the two respondent groups is not statistically significant 

(P2=2.53; p=.282). However, a higher percentage of SheaHomes respondents report average 

monthly utility bills in the lowest cost interval than respondents from the comparison community 

(Table 120); and, as was discussed in Chapter 10 (Homebuyer Satisfaction with the Purchased 

Home), SheaHomes respondents perceive their homes to be more energy efficient than do 

respondents from the comparison community. 

Clearly, within the SheaHomes communities, owners of homes with PV systems report 

significantly lower overall monthly utility bills than do owners of homes without PV systems. 

Table 121 summarizes the percentages of responses with the three intervals of reported average 

monthly utility cost by PV system owners and non-PV system owners within the SheaHomes 

communities. The distribution of percentages among the three intervals for the two respondent 

groups is statistically significant (P2=7.548; p=.023). 

310
Table 120. Comparison of the Percentage Distributions of Average Monthly Utility Bills Reported 

by the Homeowners in the SheaHomes and Comparison Communities (PV Owners Omitted) 

Monthly Utility Cost Interval 

Percentages of Homeowners 

in the SheaHomes 

Communities (n=105) 

Percentages of Homeowners 

in the Comparison 

Community (n=54) 

< $100 a month 19% 13% 

$100–$199 a month 42% 35% 

$$200 a month 39% 52%

 Totals 100% 100% 

Table 121. Comparison of the Percentage Distributions of Average Monthly Utility Bills Reported 

by Homeowners in the SheaHomes Communities with and without PV Systems 

Monthly Utility Cost Interval 

Percentages of 

Homeowners with 

PV Systems (n=72) 

Percentages of 

Homeowners without 

PV Systems (n=105) 

< $100 a month 38% 19% 

$100–$199 a month 31% 42% 

$ $200 a month 32% 39%

 Totals 101%* 100% 

*Percentage does not add to 100 because of rounding 

Relationship between Perceived and Actual Utility Bills 

Most homeowners tend to overestimate the size of their combined monthly utility bills compared 

with the actual amounts obtained from SDG&E. As shown in Table 122, the average combined 

monthly utility bill reported by 81 homeowners in the SheaHomes communities was $165.44, 

and the average actual bill for these same households was only $139.11—a difference of $26.33. 

Based on a paired t-test, the size of the difference is statistically significant (t=5.680; p<.0001). 

The corresponding differences in average reported and actual combined monthly utility bills for 

households in SheaHomes communities with PV systems and those without are both statistically 

significant, though the overestimation for households with PV systems is slightly less than half as 

much ($13.01 versus $34.65). Further, 41 owners of homes in SheaHomes communities 

designated as main reported an average combined monthly utility bill of $190.88 compared to the 

average actual amount of $157.95—a statistically significant difference of $32.93—whereas six 

owners of homes designated as ineligible reported an average combined monthly utility of 

$136.05 versus an average actual amount of $133.38—a difference of only $2.67 that is not 

statistically significant. The difference for three homes designated as early is much larger, but is 

not statistically significant. 

311
Table 122. Comparison of Reported Combined Monthly Utility Bills 

with Actual Monthly Utility Bills by Categories of Households* 

Household 

Category n 

Mean** 

Reported 

Monthly 

Utility Bill 

Mean*** 

Actual Avg. 

Monthly 

Utility Bill Difference 

t-test 

Results**** 

All households 110 $177.13 $145.65 $31.48 t=5.946; p<.0001 

All SheaHomes households 84 $165.44 $139.11 $26.33 t=5.680; p<.0001 

PV households 34 $118.55 $105.54 $13.01 t=2.294; p=.029 

Non-PV households 50 $194.73 $160.08 $34.65 t=5.358; p<.0001 

Ineligible households 6 $136.05 $133.38 $2.67 t=.311; p=.768 

Early households 3 $283.67 $204.15 $79.52 t=1.774; p=.218 

Main households 41 $190.88 $157.95 $32.93 t=5.075; p<.0001 

Comparison households 26 $210.01 $164.03 $45.98 t=2.885; p=.008 

*Only households for which there is both a reported (estimated) combined monthly utility bill and an average actual 

combined monthly utility bill obtained from SDG&E; therefore, the number of households in each category is 

different in this table than in the previous tables in this chapter 

**To facilitate comparison to the mean average monthly utility cost, this is a weighted mean to account for the 

different number of months of occupancy (partial months excluded). For this reason, as well as the smaller numbers 

of households, these values are not identical with those reported in Table 118 

***The average monthly utility cost for each household is weighted to account for the different numbers of days per 

service month, and then a weighted mean of these averages for all households is computed to account for the 

different number of months of occupancy (partial months excluded) 

 A weighted paired t-test 

Finally, homeowners in 26 comparison households reported an average combined utility bill of 

$210.01 versus an actual amount of $164.03, for a statistically significant difference of $45.98. 

Interestingly, except for the three homes in the SheaHomes communities designated as early, the 

overestimation was highest for homeowners in the comparison community. 

Therefore, the ownership of a home with a PV system appears to contribute to less of an 

overestimation of monthly household utility costs—that is, to a more accurate perception of 

actual utility costs—than other SheaHomes and comparison home owners have. The least 

accurate, most overestimated perceptions of utility bills are those of comparison homeowners. 

Summary 

As anticipated, the respondents’ estimates of their current utility bills are highly variable. On 

average, SheaHomes respondents report significantly lower utility bills than do respondents from 

the comparison community. However, when homeowners with PV systems are omitted from the 

analysis, the difference in reported average monthly utility bills between the remainder of the 

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SheaHomes owners and owners of comparison homes is not statistically significant. Owners of 

homes with PV systems report significantly lower monthly utility bills, on average, than do 

owners of SheaHomes without PV systems. 

When weighted data are considered, although SheaHomes respondents estimate, on average, their 

monthly utility bills as $165.44, their actual average monthly utility bills are $139.11. Although 

comparison respondents, on average, estimate their monthly utility bills as $210.01, their actual 

average monthly utility bills are $164.03. Although PV owners, on average, estimate their 

monthly utility bills as $118.55, their actual average monthly utility bills are $105.54. 

Comparisons between perceived and actual utility bills show that all homeowners in the study 

tend to overestimate their actual average monthly utility bills to an extent that is statistically 

significant. However, ownership of a home with a PV system is associated with a closer-toaccurate perception of actual utility costs than for the other homeowner categories. Homeowners 

in the comparison community have the least accurate, or highest overestimates, of average 

monthly utility bills. 

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Chapter 23

Findings, Conclusions, and Discussion

Introduction 

This study was a natural field experiment. It was an empirical and statistical approach to the data 

and did not involve engineering or economic analysis. The research situation resulting in the 

findings in this report is unique. We were able to work closely with the SheaHomes staff, and to 

locate a comparison community adjacent to the high-performance homes that matched the San 

Angelo and Tiempo homes well. Before the research began, there was a good deal of media 

attention to and public interest in the high-performance homes project. Even though the 

SheaHomes owners were fatigued by contacts from reporters and other researchers, they were 

generous with their time in granting lengthy qualitative interviews and in completing complex 

questionnaires. The comparison homebuyers were almost equally cooperative and generous with 

their time. SDG&E staff were also patient and helpful in providing utility data and background 

information on questions related to utility billing, interconnectivity issues, and net metering. 

Research at other sites might not be conducted in such an ideal situation. 

Because the study’s findings are highlighted and summarized in the executive summary and in 

each chapter, this chapter will not focus on them. Instead, this chapter emphasizes discussion of 

the meanings and implications of the findings. Offering conclusions on such a substantial study is 

a daunting task; thus, not every possible conclusion is included here. The focus is on the most 

important implications that we believe should be highlighted. 

The conclusions are discussed in several sections covering topics as follows: who the 

homebuyers are, their reasons for purchase, and their satisfaction with their homes; PV owners’ 

characteristics, decision-making, satisfaction, information levels, and perceived benefits of PV 

ownership; aspects of utility consumption and cost including the delivery of high-performance 

homes on their promise of saving energy costs, the interactive effect between technology and 

behavior, and modeling utility consumption and cost; and the business aspects of highperformance homes, including their cost relative to other homes, the benefits and costs to the 

builder, the role of the builder staff, the SheaHomes discontinuance decision, and the value of 

resold high-performance homes. 

Who Are These Homebuyers? 

SheaHomes and comparison homebuyers are very much the same. They comprise a 

homogeneous population of new homebuyers looking for upscale homes in north San Diego. The 

similarities between these groups as they went through their home search process far outweigh 

any differences detected. The two categories of homebuyers are similar in their reasons to 

purchase, designation of energy as a low priority reason for purchase, satisfaction with the sales 

staff, and satisfaction with their homes’ comfort. Respondents are male and female heads-ofhousehold, although 56% are male. They are original owners of the new homes and 90% had 

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previously owned homes. Three-quarters of the respondents are between 25 and 50 years of age, 

and two-thirds have families. They are highly educated with professional, business, and scientific 

occupations. A significantly higher percentage of SheaHomes owners than comparison owners 

have annual incomes that exceed $200,000. 

Most of the buyers in the study came from San Diego, so they were already aware of the 

desirability of the Scripps Highlands location before the developments began. Majorities of the 

buyers became aware of the developments as they drove through the area. Word-of-mouth was 

the second most common source of information in learning about the new homes. There was so 

much interest that SheaHomes held a lottery to give potential buyers a place in line to select their 

lots and floor plans. 

Environmentalism 

Although environmentalism and early adopter characteristics are often associated with purchase 

of innovative “green” products, and high-performance homes could be seen as both innovative 

and protective of the environment, apparently these motivations do not distinguish the purchasers 

of high-performance homes from other new homebuyers. Indeed, support for environmental 

protection is so widespread in the population that we would be unlikely to discern differences 

among the categories of homebuyers at Scripps Highlands. The one difference found among the 

environmental variables is that SheaHomes purchasers significantly more frequently link 

household energy consumption with environmental problems than do comparison home 

purchasers. As noted, other home features are far more influential in home purchase decisions 

than are energy and environmental characteristics. 

Surprisingly, respondents under 40 years of age exhibit lower support for the environment (and 

may even be characterized as anti-environmental in their attitudes), whereas those 40–49 years of 

age are more supportive toward the environment. Those 50+ in age are the most environmentally 

supportive. A significantly higher percentage of homeowners more than 40 years of age than of 

younger homeowners indicate they would take actions to preserve and improve the environment; 

the reason for this is not that older homeowners have higher incomes and could therefore be 

more financially able to purchase environmental products because statistical tests show that 

annual income does not vary by age category. So that is not the explanation. Instead, it could be 

that younger homebuyers are busier raising children and may expect sustainable attributes to be 

built into their homes 

The Purchase Decision Process 

Importance of Reasons for Purchase and Home Features in Purchase Decision 

When considering 24 important reasons for purchase, responses are virtually identical between 

the SheaHomes and comparison homeowners. Only two reasons elicit a significantly different 

response. Comparison respondents rated the desirability of the area as more important to them, 

and SheaHomes buyers rated reputation of the builder as more important to them. Energy was not 

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a very important factor in the purchase decision for most of these new homebuyers. Concern for 

the electricity crisis in San Diego was also not an important factor. 

When considering 15 home features, almost all were important to the purchase decision of the 

SheaHomes and comparison buyers. SheaHomes buyers assign higher importance ratings to 

quality of construction, availability of a three-car garage, and granite counter tops as standard 

than comparison buyers. Because SheaHomes prides itself on the quality of its homes’ 

construction and positions its homes in the market based on quality, the company’s marketing 

message about quality has apparently reached the home buying market. 

From the study’s qualitative data, we know that the aesthetics of solar features are not considered 

problematic by the SheaHomes buyers. However, those who might have been concerned about 

aesthetics probably would not have purchased the high-performance homes and their views 

would not be represented in this study. 

Barriers to PV Purchase 

If PV systems are offered optionally, the most important barriers to the purchase of optional PV 

systems are that potential buyers perceive the systems as too expensive and that payback would 

be too long. Main homebuyers who chose not to purchase homes with PV systems also indicate 

concerns about maintenance and system reliability. 

Homebuyer Information Sources 

The sales staff of both builder companies provided information on the homes, and the majority of 

buyers are satisfied with the job the sales staff did. The SheaHomes sales staff was the source of 

information on the energy features of high-performance homes, and SheaHomes buyers were, on 

the whole, satisfied with the job they did. The staff was also the single most important source of 

information on PV systems; 61% of PV owners relied on them for PV information. Other 

sources, used by far fewer PV homebuyers, include AstroPower, Inc., word-of-mouth, and 

SDG&E. 

Satisfaction 

Both categories of homeowners are quite satisfied with their new homes in general, as expected. 

A significantly higher percentage of SheaHomes owners than of owners of comparison homes 

(77% versus 67%) indicate they would buy the same house over again. Both categories of 

homeowners are satisfied with their homes’ investment potential, location, size, and layout. A 

significantly higher percentage of SheaHomes owners than owners of comparison homes is 

satisfied with lot size, builder reputation, storage space, and quality of construction. 

The evidence suggests that SheaHomes owners are more satisfied with their homes than the 

comparison owners are with theirs. Although this would not be the only factor affecting 

satisfaction, the comparison homeowners report significantly higher average monthly utility bills 

than do the SheaHomes owners. Whereas both categories of homeowners find their homes to be 

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comfortable, comparison buyers pay significantly higher utility costs to maintain their comfort 

levels than do SheaHomes buyers. The homeowners appear to perceive differences in their utility 

costs: SheaHomes buyers give their homes significantly higher ratings on energy efficiency (a 

mean score of 7.35 on a 10-point scale) than do comparison buyers (a mean score of 6.31). 

After their experiences in living in their new homes, SheaHomes owners, and especially PV 

owners, indicate they are significantly more knowledgeable about savings on utility bills, tax 

credits, rebates, interconnectivity issues, and system performance than they were before they 

moved in. 

We believe the findings from the utility analysis (discussed in this conclusions chapter) are 

directly related to homeowner satisfaction. In response to the homeowner questionnaires, 52% of 

respondents from SheaHomes with PV systems agree or strongly agree that they are satisfied 

with the savings on their utility bills, whereas 16% disagree or strongly disagree and 32% are 

unsure. Of the SheaHomes respondents without PV systems, only 28% agree or strongly agree 

that they are satisfied, while 25% disagree or strongly disagree and 47% are unsure. Clearly, a 

higher percentage of PV homeowners are satisfied with the savings on their utility bills than nonPV homeowners, but among both groups, the percentage who are unsure is also quite high. 

Further, 49% of respondents from SheaHomes with PV systems agree or strongly agree that their 

gas bills are lower than they would have been without their solar preheating water system, 

whereas 21% disagree or strongly disagree and 30% are unsure. Fifty-one percent of non-PV 

SheaHomes owners agree or strongly agree that their utility bills are lower than they would have 

been without solar preheating water systems, while 17% disagree or strongly disagree and 32% 

are unsure. 

PV Ownership 

By and large, those who knowingly selected homes with solar PV systems are very much like all 

other homebuyers in the SheaHomes and comparison communities. The few differences in the 

survey responses detected through detailed data analysis apply mostly to the PV owners. The 

findings suggest that those consciously opting for homes with PV systems tend to be male headsof-household in their 40s with training as scientists and engineers. 

Characteristics of PV Homebuyers 

The PV homebuyers are not early adopters of an innovation: they do not have higher education, 

occupation, or income levels than other homebuyers; they do not display more early-adopter 

characteristics, such as opinion leadership, than others; and they are not more environmentally 

oriented than others. These are regular homebuyers buying new upscale homes. 

We proposed a new category of high-performance homebuyers that we term the unwitting 

adopter. Although they bought homes with PV systems, these homebuyers were unaware of the 

fact until after they had been living in their new homes. The existence of unwitting adopters has 

important implications because it suggests that ordinary homebuyers can and do purchase high317
performance homes on the basis of non-energy criteria, which in turn suggests that highperformance homes with PV systems can be sold to ordinary homebuyers without offering PV 

systems as special options. 

The PV Purchase Decision Process 

Patterns of response on the importance of reasons for purchase differ by PV ownership. On 

average, PV owners rate all reasons included in the study as less important than do owners of 

PV-eligible homes who chose not to purchase PV systems, except the “Availability of a PV 

system” and “The package of energy features.” These are rated significantly higher by PV owners 

than by the buyers of PV-eligible homes who chose not to purchase PV systems. These findings 

lead us to conclude that those who purchased PV homes brought a greater concern about energy 

to the home purchase decision than did main buyers who chose not to purchase PV homes or 

comparison buyers. Most homebuyers may not have been very knowledgeable about the energy 

features when they purchased their homes, but more than three-quarters of the PV owners (77%) 

feel knowledgeable after living in their homes—a significantly higher percentage than non-PV 

owners (64%). 

Homeowner Satisfaction with the Home by PV Ownership 

Non-PV owners, on average, assign significantly higher mean satisfaction ratings than PV 

owners to the location, home size, lot size, layout, and storage space of their homes. PV owners 

assign higher mean satisfaction ratings to energy features than do non-PV owners, and 77% of 

them rate themselves satisfied or very satisfied with the package of energy features, compared 

with 67% of non-PV SheaHomes owners. However, these differences are not statistically 

significant. 

Information Levels of PV Homebuyers 

Most of the PV buyers (57%) feel they were not very well informed when they made their 

decision about PV ownership. Although some information on PV systems was available through 

a fact sheet, an operating manual on PV systems, web sites, and a video on operations and 

maintenance, it has not been easy for PV owners to locate and obtain information on their PV 

systems. Based on our field work, once homes were sold, SheaHomes university did not routinely 

include energy efficiency, solar water heating, and PV systems in its training curriculum for new 

homebuyers. Because approximately 10 PV respondents contacted researchers with questions 

about their systems and how to get information about them, NREL prepared and mailed a letter 

to SheaHomes PV owners providing information on points of contact for them to pursue, 

including SheaHomes customer service and AstroPower, Inc. 

PV owners seem to have a continued need for technical assistance that could be filled by PV 

brokers working with new homebuilders. This finding is important because misperceptions about 

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PV systems could be corrected,1

 levels of satisfaction could be increased, and the beneficial 

interactive effects between the PV owners and their systems relative to energy efficiency of the 

home could be reinforced. 

Perceived Benefits of PV Ownership 

PV owners were presented with 15 statements on potential benefits from owning PV systems; 

when factor analyzed, these responses result in three factors. The first factor reflects responses 

for “altruistic” benefits of PV ownership, such as helping to reduce global warming, helping the 

local economy, benefitting future generations, and helping to improve air quality in the area. This 

factor closely resembles the first factor of perceived benefits of retrofit grid-tied PV ownership in 

the study of Colorado homeowners (Farhar and Coburn 2000). 

The second factor reflects responses on the financial advantages of PV ownership, such as 

reduced electricity bills, free electricity once the system is paid for, selling electricity back to the 

utility company, and increasing the home’s resale value. This factor is quite similar to the second 

factor of perceived benefits of PV ownership termed “financial advantages” in the Colorado 

study. 

The third dimension reflects responses that appear to focus on the personal satisfaction that can 

be derived from owning and living with a PV system, such as increased self-sufficiency, being 

technologically innovative, and feeling good about owning it. Although not identical to the third 

factor in the Colorado study, this dimension is similar in that the items defining it pertain to 

personal satisfaction provided by being first on the block with a new PV system and enjoying a 

new technology. 

The fact that these factors emerge from both analyses of two different sets of respondents 

—owners of new grid-tied PV homes in San Diego and owners of existing homes in Colorado 

responding to the idea of grid-tied PV retrofits—suggests that these perceptions of benefits apply 

to both new and retrofit markets for PV systems. In the San Diego case, homeowners were 

responding after experiencing PV systems. In the Colorado case, homeowners were responding 

only to the idea of owning them. 

We believe that the energy features, and in particular the PV systems, were icing on the cake for 

the SheaHomes buyers. These factors seem to describe the flavors of that icing—the aspects that 

various PV owners appreciate the most: altruistic benefits, financial advantages, and personal 

satisfaction. 

1

One such misperception is that if a PV system is sized at 2.4 kW, it should produce 2.4 kW of electricity. Not so, 

according to AstroPower, Inc.; technically, the system produces less. Presenting the technical details about why this 

is so is beyond the scope of this report. But it is an important point bearing on the perception of PV systems by 

owners who are carefully monitoring what is going on with their systems and homes. It should not be ignored. 

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Satisfaction with Utility Bills 

A significantly higher percentage of PV owners than non-PV SheaHomes owners (not 

comparison owners) are pleased with utility billing processes and believe that electricity rates 

have come down. Most of them have bragged to others about their utility bills (67% compared 

with 26% of non-PV owners bragging about theirs). PV owners, in particular (52%), indicate that 

their expectations for utility bills have been met, compared with 28% of non-PV owners. 

Living in PV homes has resulted in significantly lower utility bills, by owners’ estimates,2

 than 

those reported by the rest of the homebuyers. The PV experience has also apparently resulted in 

an even more positive attitude toward the desirability of energy efficiency and solar features in 

new housing, and an intention to buy such housing in the future should the PV homeowners 

move. 

Although the analysis suggests that PV owners are somewhat less satisfied than other buyers with 

the investment potential of their homes, objective analysis of home resale values shows that PV 

homes more than hold their own in the resale market. The PV owners will very likely become 

aware of this advantage over time, if they have not already. 

The ZEH Experience 

The findings reasonably support a conclusion that, once homebuyers experience living in highly 

energy-efficient homes with solar water heating and PV systems, they become more favorable 

toward their homes. As noted, buyers of high-performance homes with PV systems are, by and 

large, like the buyers of other nearby homes of similar qualities and in a similar price range. It is 

not qualities they brought to the home purchase decision, but rather the experience of PV 

ownership that changes their attitudes and perceptions. 

Surprisingly, despite experiencing some difficulties with interconnectivity agreements, PV 

owners have more positive attitudes toward SDG&E than do SheaHomes non-PV owners. PV 

owners are significantly more pleased with utility billing processes than are non-PV owners, and 

they are significantly more likely to believe that electricity rates have come down than are nonPV owners. This appears to be another sound reason for utility companies to actively support net 

metering for new and retrofit housing. 

Conclusions from the Utility Analysis 

One of the most unusual and significant aspects of this study is that actual utility data were 

available for analysis rather than estimates or homeowner perceptions. This information, which 

consists of records of gas and electricity cost and consumption obtained directly from SDG&E, 

represents “real world” measurements recorded for homes that encompass a wide range of 

household types, homeowner lifestyles, and equipment and amenity configurations. 

2

As well as by our analysis of utility bills. 

320 
Utility consumption and cost were found to be highly variable quantities for all the homes 

included in the study. Some of this variation is attributable to normal seasonal cycles, but 

occupant behavior also plays a role, as do homeowner decisions to improve their homes with 

energy-intensive equipment and amenities such as pools, hot tubs, and multiple refrigerators. 

An interesting finding relative to variability comes from visual inspection of the line graphs of 

electricity and gas consumption of the individual homes in the study. The line graphs on energy 

consumption of the individual SheaHomes show that the houses built earlier in the project 

exhibit greater variability in electricity and gas consumption than do the houses built later in the 

project. This decrease in variability over time suggests that SheaHomes became more effective in 

implementing the high-performance home designs with more practice. In other words, the builder 

got better at building the high-performance homes, and this improvement is reflected in the 

energy consumption data for individual homes. 

In addition, it became clear over time that certain PV systems were down for maintenance at 

various times during the study period. Although we have no data on the exact dates of 

downtimes, we infer that at least some of the higher months in the electricity consumption of 

solar PV homes coincide with periods of maintenance. 

The Delivery of High-Performance Homes on the Promise of Saving Energy Costs 

The original SheaHomes concept has been borne out by the utility analysis. The homes were 

originally advertised as providing homebuyers with the potential to reduce their utility bills from 

30% to 50% over conventionally built homes. At the time San Angelo and Tiempo were planned, 

ConSol, Inc., estimated that the homes would save 38% of heating, cooling, and water-heating 

energy beyond the California Title 24 guidelines in effect at that time (Hammon 2004).3

 The 38% 

energy savings was estimated to convert to 14% of actual cost savings over standard Title 24 

houses without solar electric systems. Electricity cost savings attributable to the solar electric 

system would be in addition to the 14% savings. 

Recall that all SheaHomes were more energy efficient than were ENERGY STAR homes, that 

296 of them had solar water heating standard, and that 120 of them had PV systems. Among the 

homes studied, SheaHomes were found to consume less electricity and gas, on average, than 

comparison homes. Similarly, SheaHomes incur lower utility costs (electricity, gas, and 

combined utility bill), on average, than comparison households. This finding, in and of itself, 

essentially validates the SheaHomes construction concept. 

Results from comparisons of average monthly combined utility bills (including taxes and 

miscellaneous charges) among various categories of homes are itemized below: 

• When we examine the data for SEE homes (that is, SheaHomes that are highly efficient with 

solar water preheating systems but without solar PV systems) versus comparison homes, we 

3

These were the older Title 24 guidelines in effect prior to 2005. 

321 
find 14% actual average monthly cost savings (including taxes and miscellaneous charges), 

as had been predicted. 

• When we examine the data for SheaHomes versus comparison homes, we find 23% lower 

combined average monthly utility bills (including taxes and miscellaneous charges) for 

SheaHomes than for comparison homes. 

• When we compare all PV homes (both 1.2-kW and 2.4-kW) to comparison homes, we find a 

36% saving in average monthly electricity costs and a 27% saving in average monthly gas 

cost, and a combined average monthly utility cost saving (including taxes and miscellaneous 

charges) of 33%. 

• The combined average monthly utility bill for homes with 1.2-kW systems is 35% lower than 

for the comparison homes. 

• The combined average monthly electricity cost for homes with 2.4-kW systems is 63% lower 

than for the comparison homes. 

• The combined average monthly total utility bill for homes with 2.4-kW systems is 54% lower 

than for the comparison homes. 

These findings are all statistically significant at p=.05, except for the difference in average 

monthly combined utility cost between the SEE and comparison homes (p=.122). Thus, the story 

on energy and cost savings is more complex than was understood when San Angelo and Tiempo 

were designed and built. 

The utility consumption and cost advantages realized in SheaHomes are even more remarkable 

because SheaHomes (for which actual utility data are available) are larger than comparison 

homes, and there are no statistically significant differences between the two with regard to 

household makeup or number of occupants. On the other hand, a significantly higher percentage 

of the comparison homes include pools and hot tubs.4 This finding suggests that, as anticipated, 

homeowner choices about energy-intensive equipment and amenities have an important bearing 

on actual utility consumption and cost, and that the presence of such features is critical to 

interpretation of the data. 

Energy efficiency and solar features help energy costs in another way. SDG&E calculates energy 

charges using a daily baseline allowance that varies by climate zone and seasonal time of the 

year, among other variables. Between May and October 31, the daily baseline allowance for 

electricity is 11.8 kWh and between November 1 and April 30, it is 11.5 kWh. Similarly, the 

summer daily baseline allowance for natural gas is 493 therms, and for winter it is 1.546 therms. 

4

Fifty-eight percent of comparison homes and 26% of SheaHomes for which there are utility data had pools and/or 

hot tubs. The comparison homes did not come with pools or hot tubs standard. Of the 44 SEE homes, only 10 (23%) 

have pools and/or hot tubs, whereas of the 26 comparison homes, 15 (59%) have pools and/or hot tubs. 

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Electricity costs rise based on the amount of electricity used above the baseline allowance. “The 

cost per-unit increases as energy use increases.”5 

A closer investigation of the SheaHomes data suggests that most of the utility consumption and 

cost advantages realized among that group of homes is found in those with PV systems. In 

particular, SheaHomes with PV systems have significantly lower average monthly electricity 

consumption and cost than SEE homes,6

 but average monthly gas consumption and cost for the 

two groups, although 17% lower for PV homes, are statistically equivalent at p=.05. Both 

categories of SheaHomes (those with PV systems and SEE homes) are highly efficient homes 

with solar water preheating systems. 

Further, the most significant savings among SheaHomes relative to comparison homes are 

realized in those equipped with the larger, 2.4-kW systems. In fact, although the number of PV 

homes in the study with 2.4-kW systems is small, the mean savings in average monthly 

electricity cost is approximately 63% relative to comparison homes, an amount that is consistent 

with the reductions reported in other studies, and about 57% relative to the high-performance 

SEE homes. If only the PV homes with 1.2-kW systems are considered, the mean savings in 

average monthly electricity cost is about 30% relative to comparison homes and about 19% 

relative to the SEE homes. Because of the rigorous nature of our investigation, we believe our 

results validate and strengthen our claim that 2.4-kW or larger systems on top of high energy 

efficiency levels and solar water heating are needed to effect the most significant cost savings. 

Additionally we found that average monthly electricity consumption and cost were not 

significantly different for comparison homes and SEE homes, except when computed on a 

square-footage basis. On the other hand, we found that the mean differences in average monthly 

gas consumption and cost for these two groups of homes, though not large, were significant, with 

the mean amounts for the SEE homes being lower (in the 17%-18% range for both consumption 

and cost). While the SheaHomes in this particular comparison are not equipped with PV systems 

that can lower electricity consumption, they do have solar water heating systems that help reduce 

gas consumption. 

Taken together, the results reported in the section on satisfaction discussed earlier in this chapter 

suggest that most homeowners believe their solar preheating water systems are helpful, but that 

adding solar preheating water systems alone to highly efficient homes is not enough to effect a 

level of savings of monthly utility cost that is obvious to them. On the other hand, the effect on 

cost of the energy package together is much more readily apparent to homeowners because of the 

savings realized especially on electricity consumption. 

5

www.sdge.com/customer/baseline.shtml (accessed 7/18/06). 

6

SheaHomes without PV systems, excluding early homes (which had no solar water preheating systems) and outliers 

from the analysis. 

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At the time solar features were added at San Angelo and Tiempo, the houses were evaluated for 

suitability for PV. An effort was made to install solar water heating and PV systems on the side 

and rear roof exposures. An examination of the site maps suggests that the houses with PV 

systems standard may have had more optimal orientation for solar water heating systems. This is 

because if the house had a PV system it probably had a south-facing solar water heating system. 

The percentage difference in monthly gas cost between base-case PV homes and base-case 

comparison homes is 50% (with the PV home costs lower) almost double the 27% difference 

when all PV and comparison homes are compared. 

Although the results reported above reflect real-world conditions in homes representing a broad 

spectrum of features, the presence of energy-intensive equipment and amenities confounds the 

interpretation of the data, as suggested above. Hence, we believe it is necessary to consider 

additional comparisons of utility consumption and cost in homes that do not include any of these 

features. Such homes, which are here referred to as base-case homes, would be purchased in their 

“raw” or “natural” state before any additional equipment or features are installed. Among these 

homes, we found base-case SheaHomes with energy efficiency, solar water heating, and PV 

systems had significantly lower average utility bills than either the base-case comparison homes 

or the base-case SEE homes with energy efficiency and solar water heating. The mean difference 

in average monthly electricity cost between base-case PV homes and base-case comparison 

homes is approximately 42%, and for average monthly gas cost it is about 47%. The mean 

difference in average monthly electricity cost between base-case PV and SEE homes is 

approximately 47%, and for average monthly gas cost it is about 34%. Although the numbers of 

homes involved in these comparisons are relatively small, the findings are especially significant 

because they fundamentally validate the overall benefits of PV added to high-performance homes 

with solar water heating for the residential market in terms of savings in both electricity and gas 

costs. 

On the other hand, we find the mean difference in average monthly electricity cost is not 

statistically significant at p= .05 for base-case comparison homes and base-case SEE homes, nor 

is the mean difference in average monthly gas cost. In fact, average monthly electricity cost is 

slightly higher (about 9%) for the base-case SEE homes than for the base-case comparison 

homes, whereas average monthly gas cost is about 20% lower for base-case SEE homes with 

solar water heating than for base-case comparison homes. Again, the number of homes involved 

is small, but the results are somewhat surprising; perhaps part of the reason for this finding is that 

the comparison homes were built to the Title 24 building code in effect in 2001. We conclude 

that buyers of basic high-performance homes (such as the SEE homes) may not experience much 

difference in their average monthly electricity cost relative to buyers of Title 24 comparison 

homes, but that they may experience somewhat lower average monthly gas cost. However, in 

terms of average monthly combined utility bill, base-case SEE homes are not significantly 

different from base-case comparison homes, indicating the apparent reduction in average 

monthly gas cost (presumably the result, in part, of the presence of solar water heating systems) 

is not enough to offset higher average monthly electricity cost in the SEE homes. 

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Feedback and the Interactive Effect between Technology and Behavior 

Considering all these findings and conclusions together, it appears that, relative to more 

conventional comparison homes, true savings in utility consumption and cost can only 

consistently be obtained when energy-producing technology (such as PV systems) is in place on 

top of energy efficiency and water heating technologies. Interestingly, other findings from this 

study suggest that PV ownership tends to foster increased interest in, and enthusiasm about, the 

technology, which may translate into energy-saving behaviors. In fact, the presence of a physical 

feedback device (the digital display) in the PV homes is closely linked to satisfaction with these 

systems and to an expression of pro-solar-energy beliefs and behaviors. When PV systems are 

producing more electricity than is being consumed, the electric meter runs backwards, which 

provides additional feedback and satisfaction to PV owners. 

Additional analysis suggests that living with the systems, whether or not there is specific intent to 

acquire them from the outset, promotes increased familiarity with, and interest in, those systems 

that ultimately leads to heightened awareness of energy consumption and conservation and 

changes in energy consumption behaviors. Hence, we conclude that, although the presence of the 

PV systems is very important, the behavioral interaction of the consumer with the technology 

based on the digital display—and to some extent the electric meter—provides feedback that 

produces the most pronounced effect on homeowners. The fact that the base-case SEE owners do 

not have feedback devices and that their energy consumption and costs, though lower, are not on 

average, significantly lower at p=.05 than comparison homeowners adds further evidence to the 

significance of feedback in optimizing energy and cost savings. 

Perceived versus Actual Utility Bills 

Homeowner estimates of their monthly utility bills are notoriously inaccurate. The study 

provided a rare opportunity to compare the amounts of monthly utility bills that homeowners 

estimated they were paying with the amounts they were actually paying, at least for those 

homeowners who released their utility bills. Because homeowners tend to think about what their 

utility bills are running per month, this analysis used monthly averages of the total utility costs 

since the homeowners moved in. We found that SheaHomes respondents report significantly 

lower average monthly utility bills ($143.08) than do comparison respondents ($184.55), and that 

their estimates are less variable than those of comparison respondents. 

When we compare perceived to actual mean monthly utility bills (limiting the analysis to those 

whose utility data we have and using weighted means), SheaHomes respondents estimate average 

monthly utility bills of $165.44, but their actual average monthly bills are significantly lower at 

$139.11 (p=.000). Similarly, comparison respondents report average monthly utility bills of 

$210.01, but their actual average monthly bills are significantly lower at $164.03 (p=.008). 

Clearly, PV owners report significantly lower average monthly utility bills ($116.44) than do 

SheaHomes non-PV owners ($159.73) (p=.003). When we compare perceived to actual utility 

bills (limiting the analysis to those whose utility data we have and using weighted means), PV 

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owners estimate average monthly utility bills of $118.55, but their actual mean monthly utility 

bills are significantly lower at $105.54 (p=.029). Owners of SheaHomes non-PV homes estimate 

their mean monthly utility bills at $194.73, but their actual mean monthly utility bills are 

significantly lower at $160.08 (p=.000). 

We conclude that all homeowners tend to overestimate their average monthly utility bills to an 

extent that is statistically significant. Ownership of homes with PV systems is associated with a 

more accurate perception of utility costs than other SheaHomes and comparison homeowners 

have. Comparison homeowners have the highest overestimates of their average monthly utility 

bills. 

Conclusions from the Modeling Work 

The primary objective of our modeling exercise was to develop straightforward and logical 

equations with which to forecast or predict utility consumption and cost in new construction. 

Such forecasts or predictions would be extremely beneficial to homebuilders as they plan and 

market new homes and developments, as well as to consumers who are contemplating the 

purchase of a new home. The ultimate goal of this work would be to provide a straightforward 

and reliable way to calculate utility consumption and cost under a variety of home/household 

configurations and lifestyles. 

We demonstrate that, at least in the case of PV and comparison homes, utility consumption and 

cost can be reliably modeled by an equation containing a relatively small number of variables (on 

the order of three to six). However, even though most of these variables involve the presence of 

tangible equipment or amenities, some relate to occupant behaviors, attitudes, or perceptions that 

may be more difficult to pin down. On the whole, the best models of utility consumption and cost 

that can be constructed involve the interaction or interface of homeowners with various 

equipment and amenities, and we conclude that consideration of both kinds of variables is 

necessary to optimally model the utility response in homes. 

The utility response in SEE homes is much more difficult to model. These homes/homeowners 

appear to be a unique category with utility response patterns that are more closely tied to 

occupant behaviors, attitudes, and perceptions than to the presence of specific energy-intensive 

equipment and amenities. Such variables are certainly more intangible and are likely interrelated 

in ways that are not completely known. Both situations can counteract and even defy the 

development of mathematical equations that reliably explain and predict utility consumption and 

cost. 

The Business Aspects of High-Performance Homes 

The builder’s experience and perception of the project were documented through in-depth 

interviews with the executives and staff, as well as review of the SheaHomes contractor reports 

and media coverage, and through public records. SheaHomes, in completing its San Angelo and 

Tiempo developments, accomplished a complex technical and institutional achievement. Besides 

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selling all 306 homes in 31 months, the builder also sold almost half of them with solar PV 

systems. SheaHomes sold three times as many homes as the comparison builder in the same 

length of time. In this section, we discuss conclusions about the competitiveness of highperformance homes, the business experience of SheaHomes in offering high-performance homes, 

the role of the builder staff, the uptake of PV systems, the optimal development of highperformance homes, and the resale value of high-performance homes. 

Are High-Performance Homes Competitive on the Market? 

The study’s findings do not support a widely held belief that new high-performance homes are 

more expensive than conventional homes on the market. The mean price per square foot of the 

high-performance homes in this study was significantly lower than the mean price per square foot 

of the comparison homes, which came with no extra amenities standard. 

Benefits and Costs to the Builder 

SheaHomes enjoyed economic advantages for building high-performance homes. The company 

received a 50% subsidy on the cost of the PV systems from the CEC—the first time a residential 

builder in California had received the subsidy from the state. The company also received a $750 

rebate from SDG&E for the installation of solar water preheating systems at each home and 

enjoyed a 15% tax credit for energy-efficient housing that was more than 15% more efficient 

than Title 24 housing (the standard in effect in 2001). 

SheaHomes also enjoyed several other benefits from completing its Scripps Highlands project, 

including (1) partnerships with organizations interested in solar energy and energy efficiency, (2) 

extensive media coverage of its innovative developments, (3) enhanced reputation by becoming 

an innovator with high-performance home technology, and (4) greater exposure to the home 

buying market. Other benefits may have also accrued to the company through the contacts the 

company forged in its work with DOE, NREL, the State of California, and with San Diego city 

government. These benefits, with time, could translate into economic advantages. 

SheaHomes management said that the San Angelo and Tiempo homes sold out a year faster than 

expected, and attributed the accelerated sales pace to the desirable location. This is interesting 

because the comparison development had model homes for potential buyers to visit, whereas 

many of the earlier SheaHomes buyers, when they bought their homes, could see only the 

undeveloped land, pictures of elevations, and drawings of floor plans. SheaHomes did not build 

its model homes until late in the sales process. This made the sales process between SheaHomes 

and comparison homes an uneven playing field in favor of the comparison homes; therefore, it 

seems remarkable that the two developments sold out in the same length of time. This suggests 

that, all other things being equal, the high-performance homes would sell more quickly than 

conventional homes (despite the management view in July 2003). 

SheaHomes management also told us that the company did not lose money on the energy 

efficiency and solar energy attributes of the high-performance homes, but they seemed to indicate 

that the homes were not that profitable, either. The company did not share proprietary 

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information with the researchers on its expected or actual profits from the San Angelo and 

Tiempo developments. 

SheaHomes staff indicated, however, that they hoped that the City of San Diego would view the 

Scripps Highlands communities favorably. The company planned to work with the City on a 

project to provide affordable housing with high-performance features in the San Diego area. This 

became the Bella Rosa affordable housing development. 

The Scripps Highlands experience was something of a double-edged sword for SheaHomes. 

Several of the benefits also involved costs. These included costs of (1) building highperformance homes, despite rebates; (2) climbing the learning curve, including new language and 

acronyms; (3) selling and scheduling installation of optional solar PV systems; (4) obtaining the 

rebates for the PV systems; (5) dealing with interconnectivity issues; (6) dealing with tax 

implications for customers; and (7) educating visitors and new homebuyers about the homes’ 

innovative energy features. In addition, SheaHomes was concerned about whether highperformance homes could be sold at prices that were competitive with conventional new home 

prices in the area. 

SheaHomes managers also pointed out that they took some informal complaining and grumbling 

from members of the San Diego homebuilders association for building the Scripps Highlands 

project. To speculate on this phenomenon, in building San Angelo and Tiempo, SheaHomes took 

a highly innovative step. It is the normal social process in any social group to sanction members 

who are perceived as deviating from group norms (in either positive or negative directions). This 

dynamic is similar to the sanctions that hourly-paid factory workers impose on a “rate-buster.”7

 It 

must be acknowledged that innovative builders stand out from the builder community within 

which they are embedded, and they could face informal sanctions from other builders. This 

phenomenon should be offset with higher financial incentives for ZEH builders. 

What Was the Uptake of Optional Solar PV? 

Ryan Green and the company were interested in knowing what the uptake of homes with PV 

systems would be. Of the total 306 homes, only 260 were PV-eligible. Of these 260 homes, 120, 

or 46%, were actually sold with some sort of PV system. Of these 120, 96 were sold with 1.2 PV 

systems standard. The remainder of the homebuyers chose to purchase either 1.2 PV systems or 

2.4 PV systems optionally. In addition, eight of the 96 buyers that purchased homes with PV 

systems standard chose to upgrade their 1.2 PV systems to 2.4 PV systems. Hence, a total of 32 

homebuyers made an optional PV purchase. These 32 home represent 27% of all the homes sold 

with PV systems or 12% of all PV-eligible homes. 

Clearly, homes with PV systems standard can be sold, since not one of these homes remain 

unsold today. However, the findings presented above paint a picture of only limited market 

interest in solar PV systems offered as optional features. This picture influenced SheaHomes 

7

A “rate-buster” is a worker who works much harder and produces much more than the group norm. 

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management when it made its decision to discontinue building high-performance homes in other 

projects it was planning (see Chapters 2 and 4). 

Our research suggests several reasons why homebuyers may be reluctant to consider an optional 

purchase of a PV system. However, we believe the seemingly lackluster sales of optional systems 

was due more to ineffective marketing than lack of homebuyer interest. In the course of our 

research, we discovered that only 44% of buyers of PV eligible homes remembered being offered 

an optional system. Thus, 56% of buyers of PV-eligible homes at Scripps Highlands were 

apparently not offered such systems. When we examine the data with this knowledge in hand, we 

observe that, of those who remember being offered optional PV systems, 46% actually purchased 

them. Extrapolating to all the PV-eligible homes at Scripps Highlands, we estimate that 44% 

would have purchased the systems had they been offered. Obviously, this is a much higher 

percentage than the 12% figure reported above. So the market interest in optional PV systems 

appears to have been much higher than these figures would indicate. They suggest that, had 

buyers been aware of solar PV options, another 40 to 50 homebuyers at San Angelo and Tiempo 

would have purchased 1.2-kW systems or would have upgraded to 2.4-kW systems. 

The Roles of the SheaHomes Sales and Options Staffs 

The question might be asked why 56% of the buyers of PV-eligible homes were not offered the 

option. Although the lead sales agent was enthusiastic about solar PV, in the end the sales staff 

were more concerned about finalizing home sales, for which they were rewarded with 

commissions, and they were less focused on sales of PV systems that were considered 

“extracurricular” and might complicate the deals. Sales people learned that homebuyers in 

general are not likely to be well informed about solar PV systems (although a few were 

sophisticated about energy features), and that educating them would take a fair amount of time. 

Also, the sales staff was the major source of information for homebuyers, and they, themselves, 

were not fully informed about the utility cost savings that could be expected from the homes and 

the PV systems. This is not to fault the sales staff, who are quite effective at what they do, and 

who are much appreciated by the homebuyers. More needs to be known about the energy and 

cost savings of high-performance homes and ZEHs, and the sales staff were probably as informed 

as they could be at the time the Scripps Highlands project was going on. 

We also learned through qualitative work that the individuals whose job it was to offer optional 

features to the buyers were apparently uninformed about the PV systems (including their 

placement on the roofs and other aspects). Undoubtedly, this prevented some systems from being 

sold or upgraded because buyers could not get answers to their questions rapidly, and they had 

hundreds of other decisions to make at the time. 

Management Decisions 

To continue our discussion, then, upper management at SheaHomes-San Diego realized that only 

a small percentage of PV-eligible homes were selling with optional PV systems. On the other 

hand, they were undoubtedly unaware that the majority of the buyers of PV-eligible homes had 

not been offered the PV option. Thus, SheaHomes management may have decided to discontinue 

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its pursuit of high-performance homes in its upcoming developments based on inaccurate or 

incomplete information. 

Based on this analysis, we conclude that inadequate information on the part of the builder can 

lead to premature withdrawal from the market. In addition, when the innovation champion (in 

this case, Ryan Green) leaves the company, such projects will likely flounder. For highperformance homes projects to succeed, their champions must be heirarchically located at or near 

the top of the company, have the support of the top management team, and provide followthrough to the end of, and possibly even beyond completion of, the projects. 

SheaHomes is an industry leader in offering quality upscale energy-efficient solar homes. The 

company was a participating builder in the Ladera Ranch project. Its reputation still remains, 

even though SheaHomes is not pursuing ZEH concepts in any of its current developments in the 

San Diego area. If another San Diego large-production builder aggressively pursues the 

development of PV homes and establishes a reputation, SheaHomes could eventually lose this 

specific market advantage that it enjoyed because of Scripps Highlands. However, SheaHomes 

has kept the door open to future use of ZEHs, although management said the company wants to 

better understand costs, benefits, and market response before committing to another project. 

Optimal Development of High-Performance Homes by Large-Production Builders 

From this experience we learned that offering energy efficiency and solar features standard does 

not interfere with homes sales; in fact, it may have accelerated home sales. We learned that this is 

not a “niche market,” as is commonly believed. However, because PV technology is complex, 

unfamiliar, and costly, buyers have difficulty making decisions about whether to purchase it. 

When PV systems are offered optionally, customers weigh them against aesthetic features of 

their homes, such as granite counter tops. Yet PV systems are part of a home’s basic equipment 

and structure, so to many customers the decision felt like comparing apples and oranges. 

Also, offering optional PV systems seems to be burdensome for large-production builders 

because the transaction costs of scheduling system installation are higher than if the installations 

were routine for each house, and sales staff have to sell the solar PV systems in addition to the 

home itself. These considerations lead us to believe that PV systems should be offered standard. 

Including PV in the price of the home streamlines PV purchases, and PV homebuyers will 

experience lower utility (especially electricity) bills than they would have otherwise. The home 

price does not necessarily increase noticeably where subsidies are in effect. 

Therefore, from a business perspective, future new home developments should feature highly 

energy-efficient new homes with solar water preheating and tankless water heating, and PV 

systems standard. PV service providers, broker companies, or installers trained to provide 

turnkey packages rather than builders, should handle the technical details of PV installation. 

These include ordering or bulk purchasing the PV systems, providing qualified installers, postinstallation inspection, dealing with interconnectivity for net metering, dealing with rebates and 

tax credits, and handling any callbacks related to PV systems. 

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In addition, larger PV systems are needed (at least 2.4-kW and preferably larger yet) so that 

homeowners can clearly perceive the effects of the PV systems on their utility bills. The PV 

owner perceptions were discussed in an earlier section of this chapter. 

The heating and air-conditioning systems in these homes should be highly energy efficient. Dualzone heating and air-conditioning systems should be provided standard for two-story homes. The 

new home package would ideally include ENERGY STAR appliances. If appliances are not 

included, homebuyers should be encouraged to select energy-efficient appliances, for which their 

utility company could provide rebates.8 Highly efficient homes with solar water heating and PV 

systems standard will be more profitable for builders and sales staff, more beneficial for utility 

load profiles, and more cost-effective for homebuyers.9 

The Increase in Property Values of High-Performance Homes over Time 

High-performance homes not only hold but increase their value at a faster rate than do 

conventional homes. Both SheaHomes and comparison homes increased markedly in value at 

resale. Five percent of SheaHomes had been held an average 22.5 months and 13% of 

comparison homes 28.1 months before resale at some 42 months after the developments were 

begun in the spring of 2001. Owners of SheaHomes realized a higher percentage of financial gain 

when compared to the nearby comparison homes, despite the similarities between the two groups 

of homes in location, original sales price, and square footage. Resale prices for 29 homes 

analyzed (the first 29 homes sold in the area10) show that the increase in value for SheaHomes 

averaged 55.4% and 44.7% for the comparison homes. The mean gain in sales price for 

SheaHomes was $306,509 over original price. The most expensive home sold for $1.1 million. 

The mean gain in sales price for the comparison homes was $264,562 over the original price. The 

most expensive comparison home sold for $995,000. Comparison homes also apparently turn 

over sooner than do SheaHomes—that is, two and one-half times the number of comparison 

homes than of SheaHomes were sold in the 42 months—which could be related to their owners’ 

somewhat lower levels of satisfaction. Although we do not have data in this study on all of the 

variables that could affect resale value, it seems reasonable to partially attribute the difference to 

the energy features of the SheaHomes. 

Answers to the Advisory Group’s Questions 

As mentioned in Chapter 2 (Guiding Ideas), the study’s advisory group recommended that the 

research address a specific set of questions. Briefly, the answers to these questions are presented 

here. 

8

Federal tax credits are currently provided tax credits for energy efficiency features, including windows and water 

heaters. 

9

The caveat that this is not an economic analysis must be repeated here. This refers to a utility bill savings effect that 

is large enough to get the notice of homeowners, but is not a reference to results of a cost-benefit analysis. 

10By 2/7/05. 

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1. How much did buyers know about the energy features of the homes? How well do the 

consumers understand them? What messages are the sales staff communicating about the 

energy features? 

The buyers were relatively uninformed about the energy features of the homes before they 

bought them. SheaHomes respondents rate themselves with a mean score of 5.73 on a scale 

of 1 to 10, and comparison respondents rate themselves at 4.81, on average. Indeed, a 

handful of buyers actually bought homes with PV systems and did not know it! SheaHomes 

buyers are, in general, satisfied with the information received from sales staff on energy 

features, giving average ratings around 7 on a 1 to 10 scale. 

2. What is the role of the home builder “image” and reputation in the sales of the ZEHs? 

The reputation of the builder was significantly more important in the home purchase 

decision to SheaHomes than to comparison homebuyers. Its average importance rating was 

3.96 on a 1 to 5 scale among SheaHomes purchasers and 3.57 among comparison buyers, a 

difference that is statistically significant at p=.05. 

3. Do ZEHs have more market value than conventional homes and resale homes? Did energy 

features bring out people who were originally shopping for resale homes as well as new 

homes? 

The SheaHomes at Scripps Highlands originally sold for somewhat less than the comparison 

homes. The mean price of the SheaHomes was $556,344; the mean price of the comparison 

homes was $598,028. The most expensive home at SheaHomes sold for $701,184, and the 

most expensive comparison home sold for $711,887. 

However, the situation is reversed at resale. SheaHomes experienced a mean dollar gain of 

55.4% for a mean length of 22.5 months of ownership. Comparison homes experienced a 

mean dollar gain of 44.7% for a mean length of 28.1 months of ownership. The mean resale 

prices of the SheaHomes and comparison homes were nearly identical; for SheaHomes the 

mean resale price was $862,853 and for comparison homes it was $862,590. The most 

expensive resale home at SheaHomes sold for $1.1 million by February 2005. The most 

expensive resale comparison home sold for $995,900 by that date. The mean dollar gain per 

month owned was $14,492 for SheaHomes and $9,301 for comparison homes. 

More than double the percentage of comparison homes than SheaHomes were resold in the 

first 42 months of ownership. Comparison owners are, on average, less satisfied than are the 

owners of SheaHomes. Seventy-seven percent of SheaHomes owners say they would be 

willing to purchase their same homes all over again, compared with 67% of comparison 

owners; 5% of SheaHomes owners would be unwilling to do so, whereas 15% of comparison 

owners would be unwilling to purchase their same homes over again. 

Based on the survey data, the energy features did not result in visits by more buyers who 

were looking at both new and resale homes. In fact, 72% of comparison buyers indicate they 

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visited resale housing, whereas 57% of SheaHomes buyers visited resale housing, a 

difference that is statistically significant (P2=3.835; p=.05). The lost lookers study (Collins 

2003) suggested that visitors were not necessarily drawn by energy features, and in fact some 

were unaware, even after their visit to Scripps Highlands, that SheaHomes offered special 

energy features in their homes. 

4. What is the additional value to the customer of these systems? What price could be added to 

the price of a ZEH over a conventional home? 

It is, of course, interesting that the high-performance homes sold for less than the 

comparison homes. The data on willingness-to-pay, collected from non-PV and comparison 

owners (that is, buyers who did not purchase PV homes), suggest a cut-off point of 

approximately $5,000 for a system that would save 60% to 70% of electricity costs. 

However, this level of savings would require a larger PV system. SheaHomes buyers who 

upgraded their PV systems from 1.2-kW to 2.4-kW paid an additional $4,000; those who 

purchased optional 1.2-kW systems paid $6,000 (later raised to $7,000); those who 

purchased optional 2.4-kW systems paid $10,000 (later raised to $11,000). Reasons for not 

purchasing a PV system tend to center around the expense. Subsidies and amortization 

would be required to permit installation of the larger 2.4-kW to 3-kW systems that would be 

needed to reduce electricity costs by 60% to 70%. 

5. To what extent are energy performance features important in drawing people to look at the 

homes? To buy the homes? 

The energy features—and the media attention they generated—drew significant numbers of 

people to the SheaHomes Sales Center, but many of these people were not buyers. They were 

interested in solar energy and how it worked, but they were not actively looking. In fact, 

some mentioned they were thinking of building their own homes and wanted more 

information. 

The data from the buyers showed that energy features are far less important in the purchase 

decision than issues like location, the safety and security of the area, and the quality of the 

neighborhood. The mean importance rating for the “package of energy features,” although 

lower than most other features listed, is positive at 3.56 on a 1-to-5 scale. The “package of 

energy features” had a higher mean importance rating than solar water heating (3.49) or 

availability of PV system (3.34 on a 1-to-5 scale). A majority of SheaHomes buyers (58%) 

indicate that the package of energy features was important or very important in their 

purchase decision; 52% indicate that the availability of solar water heating was important or 

very important in their purchase decision, and 49% indicate that the availability of solar PV 

was important or very important in their decision. 

The sense of the responses seems to be that the energy features were “icing on the cake” for 

most of the SheaHomes buyers. 

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Seventy-seven percent of comparison buyers indicate they visited SheaHomes while they 

were shopping for their new homes; however, a majority of 57% of comparison buyers 

indicate they were unaware of the homes’ energy features. 

6. Should solar features be standard or optional? Are optional upgrades a good idea? 

As discussed earlier, our research suggests that solar PV systems of at least 2.4-kW should 

be standard, and optional upgrades are not a good idea because they complicate the 

transaction. 

7. How are ZEH purchasers different from purchasers of conventional homes in motivation, 

attitudes, and demographics? 

In general, purchasers of high-performance homes are upscale, and they are not different 

from purchasers of upscale conventional homes in motivation, attitudes, and demographics. 

8. Among energy features, which are the most important to homebuyers—efficiency features, 

solar water heating systems, or solar electric systems? Which feature has the most appeal? 

Or does an integrated ZEH with all features have the most draw? 

The package of energy features including the PV system appears to have the most appeal. PV 

buyers give a mean importance rating relative to their purchase decision of the package of 

energy features as 3.75 on a 1-to-5 scale and the availability of PV systems as 3.60. These 

mean scores are significantly higher than those of all main owners, and even more so than 

those of main owners who were offered PV systems and chose not to purchase them. 

9. Is aesthetics a barrier? Is it positive, negative, or neutral? How important was it in the 

purchase decision? Does it matter if solar equipment is on the front or back of the house? 

Neither the qualitative nor the quantitative studies identified aesthetics as a barrier to highperformance ownership. However, because the study is of homeowners who bought these 

homes, it cannot be concluded that no one objects to the aesthetics of high-performance 

homes. It seems fair to conclude that the market is large enough that it does not matter if 

some people object. Plenty of buyers do not object. No one mentioned a problem with the 

placement of the PV systems, and the solar water preheating systems look like a pleasant 

skylight. 

10. How important is the feedback device (showing the amount of electricity the house is using 

and the amount the PV systems is producing)? 

The feedback device is very useful to PV owners. This device is the link between PV 

technology and the home’s occupants that results in the interaction effects discussed above. 

The digital display shows owners how and when they are using electricity, permitting them a 

modicum of control over task scheduling. They use it to monitor if appliances or lights have 

been left on as they are leaving the house so they can turn them off. They report that they 

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change their behavior because of this device. Without it, PV owners would have no way to 

monitor their electricity production and consumption in real time. We conclude that the digital 

display, showing both production and consumption of electricity, is critical in optimizing the 

interaction between ZEH technology and energy-consuming behavior. 

11. How satisfied are customers with their home purchases? 

Most buyers are satisfied with their new homes, but SheaHomes buyers, and especially PV 

buyers, are more satisfied than are comparison buyers. Several pieces of evidence in the 

study support this conclusion. 

Summary Remarks 

These findings and conclusions are believed to be valuable to builders, policy-makers, utility 

companies, trade and professional organizations, and the energy-efficiency and solar-energy 

communities, as well as to marketers, researchers and energy analysts, and homebuyers. 

Recommendations for some of these groups are discussed in the next chapter. 

In conclusion, this study is replete with findings that support the rapid development of highperformance homes with PV systems, near-ZEHs, and ZEHs. Once offered standard, the costs of 

these homes to the builder appear to be manageable, the product provides differentiation on the 

market, and ordinary homebuyers want to buy these homes. Once they live in them, homeowners 

become even more enthusiastic. Policies that support the deployment of ZEHs, such as netmetering legislation, simplified interconnectivity agreements, building codes and standards, 

utility rebates, and subsidies for solar water heating and PV systems, will be rewarded by rapid 

diffusion of an idea whose time has come. 

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Chapter 24

Recommendations and Summary Remarks

This study has produced an extensive and invaluable body of evidence and information that 

should help guide an array of government and private industry organizations and industries for 

years to come. The findings increase our understanding about the complex relationships among 

large-production builders, energy efficiency companies, water-heating companies, the PV 

industry, utility companies, government organizations, and buyers of high-performance homes. 

The study can also serve as a model for obtaining similar information concerning builder and 

consumer acceptance of ZEHs in other areas. 

Many recommendations could be formulated based on the extensive findings of this report. The 

ones included here represent the authors’ best judgments on approaches that are likely to foster 

an upsurge in building ZEHs that effectively save utility-supplied energy and that will enjoy 

market popularity. These recommendations are organized into several categories to underscore 

the need for collaborative work and contributions from many parties. Recommendations for the 

business community, organizations involved in information dissemination and environmental 

concerns, and government agencies are highlighted, as are suggestions for future research. Each 

recommendation is presented as a specific action that we think should be taken, along with some 

associated discussion. Potential audiences for the report are also listed. Some final summary 

remarks about the study are provided at the end of the chapter. 

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Part One: Recommendations for the 

Residential Real Estate Marketplace

In order for ZEHs to proliferate, there has to be a definable market. Consumers have to be 

interested in the concept, builders must be willing to construct the homes, lenders have to adopt a 

pro-ZEH financing stance, PV manufacturers must be able to produce enough equipment and at 

reasonable prices, utility companies have to embrace the idea of residentially-generated 

electricity, and secondary market mortgage lenders must recognize the value that PV technology 

brings to the home over time. In the end, the homebuyer wants to obtain a quality product at a 

reasonable price, and each of the other entities wants to recoup its costs and receive a reasonable 

return on its investment. With these things in mind, we offer a number of recommendations that 

we believe would promote a more robust marketplace for ZEHs based on our research findings. 

Builders 

Builders, themselves, may have the greatest potential impact on the ZEH market. Without 

builders who are willing to adopt and experiment with the ZEH concept, there is no way for the 

ZEH market to grow unless the state or federal government becomes the builder. Hence, we offer 

a number of specific recommendations to encourage builder participation. 

Orientation of Homes 

Builders should plan developments with primarily east-west streets to maximize opportunities for 

the optimal placement of solar features on new homes. 

Offer PV Systems, Efficiency, and Solar Water Heating as Standard Equipment 

and Focus on Larger PV Systems 

Builders should offer ZEHs with efficiency, solar water heating, and PV systems as standard 

(rather than optional) equipment on all homes in large-scale developments. All PV systems 

should be at least 2.4 kW in size and should include digital feedback/readout displays. 

Transactions costs are too high when homes and PV systems are sold separately, and homebuyers 

have difficulty determining the value of PV systems as home options when juxtaposed with other 

optional features. Our research suggests that it is simply more effective from a sales and 

marketing perspective to use this standard-package approach when offering homes with specific 

energy packages. 

Foster Management Commitment 

The top management of home builders must, themselves, be committed to the ZEH concept, and 

must acknowledge that building ZEHs may require a shift in construction practice and the 

company’s usual operating model. Ideally, all major home builders should establish a ZEH 

concept team that is responsible for following market trends and consumer preferences, 

researching the latest technology, pursuing vendor and governmental partnerships, and reporting 

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to top management on a regular basis. Top management should establish communication 

channels by which ZEH champions can step forward to provide leadership in this area. Top 

management should also recognize that even a partial switch to building ZEHs will require 

steadfast decision-making and follow-through for at least five years. 

Provide Training and Incentives for Sales Staff 

To accurately and completely address questions from potential and actual homebuyers, all sales 

staff involved in marketing ZEHs need formal training on ZEH features and their impacts. For 

example, such individuals need to be able to distinguish Title 24 homes, ZEHs, and near-ZEHs if 

they are to maximize sales. Specifically, the sales staff needs to be able to provide realistic 

projections of energy savings. In addition, if the decision is to offer PV systems as optional 

equipment, contrary to our recommendation, sales staff need to be able to provide accurate 

information about their costs, benefits, and operational aspects. As noted elsewhere, a labeling 

system would help. To maximize the buy-in from sales staff, we recommend they be offered 

incentives for completing training courses about ZEHs and premiums on sales commissions for 

selling them. 

Become Trained in the ZEH Building Concept 

Builders and their construction staff should become trained in the ZEH building concept so that 

they can fully understand and appreciate market expectations, construction performance 

expectations, benefits, and costs. Such training could be offered by homebuilder associations, 

involving companies with special expertise, such as DOE’s Building America partners. In 

addition to construction and business details, the builder training should also include information 

about the new market paradigm for ZEHs. 

Contract for Turnkey Packages to Provide PV Management Services 

Builders unaccustomed to PV installations in their projects should contract with PV installers to 

provide turnkey procurement, installation, and management services for the PV systems in their 

high-performance homes and ZEHs. These companies specialize in PV systems procurement, 

installation, interconnectivity, and technical assistance. This role is analogous to that of ConSol, 

Inc., for energy-efficiency packages. Although one of the primary responsibilities of PV brokers 

is to arrange for the bulk purchase and scheduling of systems installations, such individuals and 

organizations can fill many other important and necessary roles, including the following: 

• Serving as the homebuyers’ ombudsman for ZEH features, answering questions, inspecting 

systems, arranging for repairs, if necessary, and arranging for interconnectivity between the 

homeowner and the utility company 

338
• Applying for rebates or other financial incentives to which the builder and homeowners are 

entitled (e.g., tax credits, rebates, or subsidies) 

• Providing a warranty for the entire system, including inverters 

• Processing and handling homeowner callbacks regarding PV system problems, educating PV 

owners, and helping to train sales staff regarding the costs, benefits, and operating 

expectations of PV systems. 

Create More Effective Marketing Messages 

Builders must market ZEHs more effectively. Model homes should be available that feature ZEH 

attributes, and those attributes should be clearly highlighted, labeled, and explained. Strong, 

appealing marketing materials must be available, and the sales staff need to be able to use these 

materials to educate potential buyers. Our research suggests that potential ZEH buyers are 

ordinary new home shoppers who are not likely to be sophisticated about the homes’ attributes. 

Consequently, even if PV systems are standard, the buyers need to be more informed about them. 

SheaHomes has a reputation for building quality homes, and it succeeded in selling its homes at 

Scripps Ranch by offering a quality product. However, many of the visitors to the sales center, as 

well as many buyers, remained unaware of the energy features of their homes. Consequently, to 

sell more ZEHs, builders should consider positioning themselves as “green” companies or as 

companies committed to energy efficiency. Essentially, they need to re-brand themselves to be 

most successful. 

Explore Opportunities to Build Affordable ZEHs 

In collaboration with governmental agencies (as suggested above) and non-governmental 

organizations (NGOs), builders should explore opportunities to build affordable housing with 

ZEH features. At this point in time, building affordable ZEHs would undoubtedly require 

subsidization; however, reduced utility bills would clearly benefit lower-income homeowners 

and renters. Use of ZEH technologies in affordable housing would also help bring down the cost 

of these technologies as the result of larger-scale manufacturing. Investments in affordable 

housing would be a much better alternative than subsidizing high utility bills for lower-income 

families. 

Photovoltaics Manufacturers 

PV manufacturers should foster large-scale ZEH construction by offering PV panels at 

discounted prices for bulk purchases. PV manufacturers should also provide training and 

technical assistance for installers offering turnkey packages, builder sales staffs, real estate 

agents, and owners of PV systems. Turnkey packages could include obtaining permits, handling 

interconnectivity agreements, ordering PV and solar water heating systems, installing systems, 

339
applying for relevant rebates for the builder, training builder sales staffs and real estate agents, 

and handling customer education and callbacks. Warranty enforcement should also be part of the 

package. Alt-aire Energy, Ecobroker, and Powerlight, for example, are developing and offering 

turnkey services. 

Utility Companies 

Utility companies have a special role to play in the development of ZEH markets, and, as a result, 

they can enjoy the unique benefits of a zero-peak community. One direct benefit is the potential 

for using residentially supplied electricity from PV systems to ease peak-load requirements on 

hot summer afternoons. Additionally, our research suggests that an increased number of ZEHs 

will likely result in an improved perception of the utility companies on the part of homeowners 

leading to better public relations overall. Finally, utility companies have substantial expertise and 

resources that, when turned toward solving problems related to ZEH deployment, can enhance 

implementation of the zero-peak community concept. 

We recommend that utility companies perfect the interconnectivity process by simplifying it. 

SDG&E reported that its lawyers were working to create user-friendly forms especially designed 

for residential PV homeowners to make it easy for them to interconnect. This single step would 

go a long way to improving relations between ZEH buyers and their utility providers 

Utility companies should foster net metering, and should agree to the one-meter concept of net 

metering. Offsetting any resulting revenue losses, utility financial benefits can come from peak 

shaving. Another is that PV owners have a more positive view of utility companies than other 

homeowners, which is a public relations advantage. Finally, utility companies have substantial 

expertise and resources that, when turned toward solving problems in deploying ZEHs while 

improving the grid, can be a substantial positive force in increasing the sustainability of 

electricity supply. Such a role could be rewarded by public utilities commissions, for example. 

Utilities should conduct analyses of the benefits that ZEH-produced electricity offers them 

relative to peak loads and easing strain on the utility grid. Grids need upgrading and repair, and 

analyses should be conducted to define how integration of ZEHs with the grid can be 

simultaneously developed with grid renovation projects. 

Utilities should take the initiative to educate consumers, builders, and other parties with 

brochures, videos, fact sheets, and web sites. Every utility should have a ZEH ombudsman to 

deal with the transition to the ZEH new home market. 

Utilities should continue to promote vigorous demand-reduction programs, including rebates on 

energy-efficient appliances for builders and homeowners. 

340
Solar Water Heating Manufacturers 

Manufacturers of solar water heating should seek to participate in the market for turnkey 

packages for new ZEHs. If, as is hypothesized in this study, new homes with PV systems also 

provide better orientation for solar water heating systems, the benefits of solar water heating in 

reducing utility costs for customers will be even higher. Solar water heating, along with features 

that promote high levels of energy efficiency, should be offered standard with all ZEHs in the 

future. 

Lenders 

In this study, we found that the SheaHomes appreciated more on a percentage basis than 

comparison homes. Consequently we strongly recommend that lenders consider ZEHs to be a 

particularly lucrative mortgage-lending market because of the likelihood of improved cash flow 

for the buyers. There exists the potential for some innovative lenders to develop a niche in ZEH 

financing and successfully differentiate themselves within the broader market. Approaches to 

ZEH lending could be based on previously established models of energy-efficient mortgages 

(EEMs). 

Secondary Mortgage Markets 

Fannie Mae, Freddie Mac, and other secondary mortgage organizations have responsibilities to 

the public. These organizations should educate lenders and underwriters nationwide on the 

benefits of lending to ZEH homebuyers. 

Real Estate Agents and Agencies 

As the ZEH market develops, more and more of these homes will come into the secondary 

market. In order for them to be properly valued and re-marketed, real estate agents and agencies 

are going to have to be (1) made aware of their existence and (2) become as familiar with them as 

the original sales agents. This situation presents an excellent opportunity for a niche in the 

secondary market to be developed. Agents will undoubtedly be asked questions about taxes, 

insurance, longevity of the PV systems, and other related issues from prospective buyers who no 

little or nothing about ZEHs. Hence, we recommend that real estate agents and agencies take the 

initiative to obtain training about ZEHs and to engage their professional organizations to support 

such training activities. 

341
Part Two: Information Dissemination Organizations and

Environmental Concerns

Public awareness of ZEHs and their benefits will go a long way to promoting more availability. 

As the public demands access to ZEHs, builders will be forced to provide them, and a natural 

market will develop. Hence, the dissemination of information about ZEHs is extremely 

important. However, such information must be appropriate and reliable, and it must be easily 

accessible in order to have an impact. Not only does the public need information about 

equipment costs and energy savings, it needs information about the environmental benefits of 

ZEHs and solar PV technology. DOE should add a prominent ZEH section to both the Buildings 

and Solar Web sites at eere.gov. In this section, we link recommendations that have to do with 

information dissemination about ZEHs with those that have to do with environmental awareness. 

Information Dissemination Organizations 

We strongly encourage journalists and broadcast media to become aware that there is a win-winwin feature story to be reported about the value and desirability of ZEHs for builders, utility 

companies, the renewables industry, and homeowners. As examples, the Today Show of May 26, 

2006 featured a segment on the Premier Gardens near-ZEH development in Sacramento, 

California. Newsweek featured a story called, “No More Electric Bills” on August 18, 2005. Such 

feature stories would help educate the public and the homebuying markets about the potential for 

ZEHs. Journalists and broadcast media should become aware of the value and desirability of 

ZEHs for builders, utility companies, the renewables industry, and homeowners. This would help 

educate the public and the homebuying markets about the potential for ZEHs, thus benefitting 

builders, homebuyers, utility companies through the zero-peak load to cut residential bills, the 

environment, the emerging photovoltaics industry, and others. 

Environmental Organizations 

Voluntary environmental organizations at all levels (local, state, and national) should be actively 

engaged in the promotion of ZEHs in all communities. Such efforts must be based on sound data 

and analyses to be credible. They will be most effective if environmental organizations 

coordinate their work, portraying a consistent picture about the environmental implications of 

ZEHs. Claims concerning the effects on the environment do not need to be exaggerated—the 

data substantiate the claims that these homes use less utility-supplied energy and help protect the 

environment. This is particularly critical in the arena of concern about the “carbon footprint” and 

global warming. Environmental organizations should actively engage the media to tell the full 

story about ZEHs. 

Nonprofit Initiatives for Clean Energy and “Green” Building Initiatives 

States, foundations, and nongovernmental organizations should better acquaint themselves with 

the potential of ZEHs in their communities. Such initiatives as state “clean energy funds” in 10 

342
states1

 have provided support for PV, including buy-down programs for customer-sited PV 

(Barbose, Wiser, and Bolinger 2006). These funds can be used to provide higher incentives for 

builders to adopt PV and high-efficiency homes with solar water heating. They can also be used 

to cultivate the turnkey approach to PV and solar water heating, installation, builder champions, 

and to educate and train large production builders. Considerable potential exists for action among 

state, local and nonprofit partnerships to foster widespread ZEH construction. 

1

California, Connecticut, Illinois, Massachusetts, Minnesota, New Jersey, Oregon, Pennsylvania, Rhode 

Island, and Wisconsin. 

343 
Part Three: Recommendations for Government Agencies

A number of advances in federal, state, and local government programs and policies are needed 

to insure proliferation of ZEHs. Below we present some specific ways in which government 

entities can work to promote the development of a robust ZEH market. 

Federal Government 

Several agencies within the federal government can impact the uptake of ZEHs, including the 

U.S. Department of Energy (DOE) , the U.S. Environmental Protection Agency (EPA), the U.S. 

Department of Housing and Urban Development (HUD), and the Federal Energy Management 

Agency (FEMA). 

U.S. Department of Energy (DOE) 

DOE has a buildings R&D program and regulatory activities to improve building codes and to 

assist states in improving the energy efficiency of buildings. It also has a Solar Powers America 

Initiative. Through its Building America program, DOE should continue R&D to achieve true 

ZEHs before the current 2020 goal and continue its Building America Program to assist builders 

in designing, constructing, and monitoring ZEH subdivisions in various climates. 

U.S. Environmental Protection Agency (EPA) 

Based on its experience with the ENERGY STAR program, EPA should develop labeling for 

ZEHs so that consumers would know that the claims made for energy savings are realistic and 

accurate. ZEHs could be labeled similar to the same way that ENERGY STAR commodities are 

labeled, or they should receive some kind of approval similar to a Good Housekeeping Seal of 

Approval. However, such approval should only be awarded when the homes meet standards that 

have been established under best practices and meet the approval of appropriate industry and 

professional groups. 

Federal Energy Management Agency (FEMA) 

In working on redevelopment of areas devastated by natural disasters, FEMA should cooperate 

with builders, developers, and other federal agencies to foster sustainable rebuilding using ZEH 

principles and concepts. Homes that are rebuilt according to quality standards and best ZEH 

practices will result in significant savings in electricity consumption and costs, and will support 

the utility grid in traumatized areas, especially where the electricity load for air-conditioning is 

substantial. 

The opportunity is not to reduce the potential hazards, which can only be achieved through better 

siting decisions, but to take advantage of the opportunity to build new ZEHs as replacement 

homes. The performance results would be superior to retrofitting older homes. Because 

homeowners who are victimized by natural disasters would have a difficult time building ZEHs 

on their own, they would need technical assistance from FEMA, builders, and others. 

344
State and Local Governments 

State and local governments can also take some very specific actions to foster the construction of 

more ZEHs. 

As noted elsewhere in the report, our results suggest that SEE and comparison homes, which 

were built to California’s Title 24 building code, have similar utility consumption patterns. 

Hence, we conclude that California’s program is successful in conserving energy. The California 

initiative to provide utility surcharge funds for a million solar roofs statewide by 2010 appears to 

be well-advised, especially with California’s need to shave peak loads, and may be mimicked in 

other states with peaking problems. 

Additional recommendations for state and local governments follow: 

• State legislation for net metering should be enacted to foster the use of the utility grids for 

residential and commercial PV-produced power to reduce peak loads. The homes at Scripps 

Highlands have a single meter, which seems to be the most effective approach. 

• States with Renewable Portfolio Standards (RPSs) should mandate a set-aside for small scale 

residential net-metered electricity. A certain percentage of each utility’s electricity supplies 

should come from net metered residential solar electricity by a certain date. 

• The process for issuing building permits should be altered to encourage construction of ZEHs. 

Also, permits for ZEHs should “go to the head of the line” for permitting (as in the Expedite 

Program in San Diego) since they cut energy use and related emissions by at least 50%. 

345
Part Four: Research Recommendations

Although our study was very extensive, there are still aspects of the ZEH concept that need to be 

explored. In particular, our ability to answer all the questions we wanted to ask was limited by 

the number of homes and homeowners available for study. Consequently, we recommend that a 

more controlled, statistically designed field experiment be conducted that encompasses more 

homes and a broader spectrum of home types. Provisions should also be made to study homes 

and homeowners longitudinally. It would be very desirable to observe utility behavior in ZEHs 

and comparison homes over time with the same occupants. The difficulty in planning such an 

experiment is that even large-production developments tend to have only a few hundred homes, 

and homeowner cooperation and response rates are notoriously low, although our study had a 

63% response rate. The only way around these real-world limitations is to conduct more carefully 

designed longitudinal comparative studies with several new ZEH home developments near 

conventional developments. It would be ideal if the “experimental” and the comparison 

communities were built adjacent to each other and the utility data analyzed were for identical 

time periods. 

There is a limit to how many variables can be included in a single study. Yet we discovered 

questions we didn’t ask the homeowners that we wish we had. Additional variables would 

strengthen an investigation similar to the one we conducted. For example, in retrospect we would 

like to have asked homeowners about their expectations for their utility bills in the their new 

homes in real dollar terms. In addition, we did not collect data on the floor plans and orientations 

of the houses, which would be useful, and we should have obtained more details about the 

number of stories, bedrooms, living areas, and garages, as well as the actual ages of all 

occupants. 

We recommend that a formal study of builders’ attitudes toward ZEHs be conducted that would 

investigate the barriers and opportunities perceived by builders. However, finding the correct 

people within the company to answer such questions would be difficult; and the responses from 

the CEO, CFO, site manager, sales manager, options manager, and others would likely be 

different. Other questions of interest would include: (1) Are there ZEH innovation champions in 

the builder communities? and (2) What is the attitude and role of homebuilder associations 

relative to ZEHs? 

Our study did not analyze the peak-demand impacts of the SheaHomes Scripps Highlands 

development. However, it is important to understand the peak-demand impacts of ZEH-induced 

electricity savings as well as ZEH output to the grid on hot summer afternoons. Relative to ZEH 

impacts on utility peak-demand, SMUD estimates that, if all new homes built during 2004 had 

been built to ZEH standards with PV systems oriented west of south, the utility could have 

realized a significant peak load reduction—up to 20 MW (Keesee 2005). Further analyses to 

establish these effects are needed. 

346
Mortgage-lender requirements for cash-flow benefits of ZEH ownership relative to qualifying 

mortgages for ZEH loans also need to be analyzed. This would be similar to an EEM concept, 

except that it needs to go much further in considering in the economic impacts of electricity and 

gas cost savings in homes with PV systems of at least 2.4 kW. 

Formal economic analyses should be performed on the costs of ZEHs. Variables to be addressed 

include: subsidies, tax credits, and utility bill savings in real dollar terms, expected length of stay 

in the households, mortgage payments and their increases (if any) in amortizing the cost of ZEH 

features, energy cost increases, and consideration of payback questions. Past economic analyses 

have focused on individual homeowners and addressed the question of whether PV purchase 

makes sense for individual households. We recommend a shift in the focus of economic analysis 

toward the costs and benefits of having a thriving ZEH industry to global, national, state, and 

local economies and the environment. 

Further market research is needed on how to brand ZEHs. This research should address the kinds 

of terminology that should be used to capture public attention about the benefits for individual 

families purchasing these homes, as well as builders, utility companies, and other associated 

entities. 

Research should explore further whether tankless water heating in combination with solar water 

preheating systems would be a better alternative to solar preheating water systems with gas-fired 

storage tanks. Tankless water heating has been successfully used in near-ZEH projects in the 

Sacramento area. The NAHB Research Center instrumented and monitored the heating, cooling, 

and water heating energy consumption of five SheaHomes without PV systems at Scripps 

Highlands (Moore 2003). The report, sponsored by PATH, showed that combining solar water 

preheating systems with tankless water heating resulted in the greatest energy savings among the 

various alternatives studied (45% to 77% savings from the base case used in their study). 

Analysis of how ZEHs can be made available in affordable housing is needed. Such analysis 

should address the kinds of programs that would be needed to foster the routine inclusion of ZEH 

features in affordable housing and apartments, as well as the kinds of incentives that might be 

needed in rebuilding efforts, such as Katrina redevelopment, to build only sustainable housing. 

347
Part Five: Audiences for the Report

Stakeholder groups know best how they can use the findings from this study to pursue productive 

and profitable activities. Following is a list of stakeholders for whom we think the report will be 

most beneficial. 

Building Industry 

• Large-production builders 

• Home builders association 

• California Building Industries Institute (BII) 

• NAHB 

• NAHB Research Center 

• HVAC contractors 

• Electricians, electricians’ unions, International Association of Electrical Inspectors 

• Other trade unions 

Code Officials 

• State building code officials 

• Local building code officials 

• Professional associations of state and local building code officials 

Home Inspectors 

• Building inspectors 

• American Society of Home Inspectors 

• National Association of Certified Home Inspectors 

Marketing Companies and Market Research 

• Home sales 

• PV system sales 

• Water heating systems sales 

• Related sales 

• Market research companies 

Environmental Organizations 

• Environment California 

• Natural Resources Defense Council 

• Sierra Club 

• Other environmental organizations 

Solar Energy Industries Associations 

• California Energy Industries Association 

• Colorado Solar Energy Industries Association 

• Other solar energy industries associations 

348
Professional Associations 

• American Solar Energy Society 

• American Council for an Energy-Efficient Economy 

• Alliance to Save Energy 

• Other professional associations 

PV Manufacturers, Installers, and Brokers 

• PV manufacturers (e.g., Alt-aire Energy, Powerlight, GE, Sharp, and Kyocera Solar, Inc.) 

• Other installers and PV manufacturers 

Water Heating Industry 

• Solar water heating systems manufacturers (e.g., Sun Systems, Inc.) 

• Tankless water heating manufacturers 

Home Design Professionals 

• Architects 

• Architectural engineers 

• Designers 

Utility Companies 

• SDG&E 

• SMUD 

• Other utility companies 

• EPRI 

• GRI 

Federal Power Management Agencies 

• WAPA 

• BPA 

• TVA 

Energy Education 

• Colorado Energy Science Center 

• San Diego State University 

• San Diego Regional Energy Office 

• Vocational education 

• Other educational institutions and organizations 

Communicators 

• Journalists; science writers 

• Time/Newsweek, etc. 

• Broadcast media (NPR, PBS, CNN, etc.) 

• PR firms 

349
Financial Interests 

• Home mortgage lenders 

• Investors 

• Secondary mortgage markets (Fannie Mae, Freddie Mac, etc.) 

• EEMs 

Home Energy Raters and Energy Efficiency Designers 

• RESNET 

• ConSol, Inc. 

• Energy Star Colorado 

Federal Policy-Makers 

• DOE Buildings Program; Million Solar Roofs Initiative 

• HUD Energy Office 

• EPA ENERGY STAR program 

• NSF Climate Change Program 

• CEQ Council on Environmental Quality 

• OSTP Office of Science and Technology Policy 

• U.S. military (military housing) 

Regional Policy-Makers 

• Western Governor’s Association energy initiative 

• Other regional policy-makers 

State Policy-Makers and Associated Organizations 

• State legislatures 

• National Conference of State Legislatures (NCSL) 

• Clean Energy States Alliance 

• California Energy Commission 

• Colorado Office of Energy Management (e.g.) 

• California Governor’s Office (million solar roofs initiative) 

• Other state energy offices 

• California PUC 

• National PUC organization 

• Other PUCs 

Local Policy-Makers 

• City of San Diego 

• City of Aspen 

• City of Boulder 

• Other municipalities that are working on energy efficiency and renewable energy programs 

• Local energy offices 

350
Analysts/Researchers 

• Climate change analysts (carbon footprint) 

• Energy analysts/national laboratory and university researchers and research centers 

• Environmental analysts 

• Environmental sociologists, environmental psychologists, behavioral scientists 

• National laboratory staffs 

Nongovernmental Organizations 

• Habitat for Humanity 

• Consumer organizations (e.g., Consumers Union) 

• Other NGOs and nonprofits 

Other Interested Parties 

• Coal, nuclear industries 

• BP 

• Alternative energy companies and manufacturers 

• Lobbyists 

351
Part Six: Final Summary Remarks 

The creativity of SheaHomes in pursuing the high-performance homes project at Scripps 

Highlands has made possible a unique study on the market response and utility performance of 

these homes and helped to jump-start ZEH developments. The results of the study are 

encouraging for those who want to stimulate ZEH developments in California, throughout the 

United States, and abroad. If organizations become dedicated to the widespread building of 

ZEHs, the residential markets will be there. The degree to which the markets develop, however, 

depends on the collective and sustained response of all concerned. 

This study provides extensive information for the many groups that will be involved in building 

ZEHs, providing equipment for them, and implementing policies to ensure their widespread 

acceptance. Effectively employed, this information can serve as the fuel for the engine of change 

in home building and utility net metering for years to come. 

352
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Moore, Michael. 2003. “Final Report for Field Evaluation of PATH Technologies, SheaHomes, 

San Diego, California.” Washington, D.C. National Association of Home Builders (NAHB) 

Research Center, for the Partnership for Advancing Technology in Housing (PATH), U.S. 

Department of Housing and Urban Development. October. 35 pp. 

Morrison, Donald F. 1976. Multivariate Statistical Methods. 2nd Ed. New York: McGraw-Hill 

Book Company. 

Murr, Andrew. 2005. “No More Electric Bills.” Newsweek, August 15, p. 43. 

Nelson, Les. 2006. Personal communication. 

RESNET. 2006. “Adopted 2005 Enhancements of the National Home Energy Rating Standards.” 

Residential Energy Services Network www.natresnet.org/amendments/default.htm. 

Rivera, Diane. 2002, 2003, 2004. Marketing Director, SheaHomes, San Diego. Personal 

interviews. 

Rogers, Everett M. 1995. Diffusion of Innovations. Fourth Edition. New York: The Free Press, 

519 pp. 

Rogers, Everett M. and F. Floyd Shoemaker. 1971. Communication of Innovations. 2nd Edition. 

New York: The Free Press. 

Roper, Marc. 2002. AstroPower, Inc. Personal interviews. 

Roper, Marc. 2006. Personal communication. 

Shusterman, Teri. 2001, 2002. Marketing Director, SheaHomes, San Diego. Personal interviews. 

Starrs, Thomas J. 1996. “Net Metering: New Opportunities for Home Power.” Renewable Energy 

Policy Project (REPP) Issue Brief, No. 2, College Park: University of Maryland, September, 

11 pp. 

Stokes, Kirk. 2004. Altair Energy, Denver, Colorado. Personal interviews. 

356
Van Liere, Kent. 2003. Personal communication concerning the best items to use in the 

questionnaires to measure environmental values from the New Environmental Paradigm. 

U.S. Department of Energy. n.d. “Building America Research Is Leading the Way to Zero 

Energy Homes.” Fact Sheet. Washington, D.C.: Office of Energy Efficiency and Renewable 

Energy, Building Technologies Program. 

U.S. Department of Energy Web site for renewable energy and energy efficiency: 

http://www.eren.doe.gov/solarbuildings 

357
Epilogue

Introduction 

Considerable activity by production home builders has moved the concept of near zero-energy 

homes (ZEHs) closer to reality since this study began in 2001. This epilogue summarizes recent 

builder activity in near-ZEHs. It also compares findings on electricity bill savings from other 

solar home projects to results from the current study on the SheaHomes Scripps Highlands 

development using 12 months of utility billing data for the SheaHomes and comparison home 

communities. 

New Production Solar Home Developments in California 

Since this study began, large-production builders other than SheaHomes have initiated new solar 

home projects in California. According to the 2006 Mortgage Industry National Home Energy 

Rating Standards (HERS), tomorrow’s ZEH is a home that saves 100% of its energy 

consumption compared to a HERS reference home of the same size (RESNET 2006). The newly 

adopted “HERS Index” uses a score of “0” for a net zero energy home while a score of “100” 

equates to the HERS reference home. None of the solar PV homes built in California since the 

SheaHomes Scripps Highlands development meet the true ZEH standard; however, at least 13 

projects in California have been identified that feature homes with solar-electric (solar-PV) 

systems. Table 123 summarizes these developments. At least six large-production builder 

companies offer these solar homes: Centex Homes, Clarum Homes, Morrison Homes, Pardee, 

Premier Homes, SheaHomes, and US Homes. Hundreds of today’s ZEHs have been built and 

thousands have been planned in California communities as of this writing. Today’s ZEH cuts 

utility bills at least 50%. 

Clearly, at least a small percentage of production builders in California has recognized a market 

for new solar homes in California. These “early adopter” builders are climbing the necessary 

learning curve to build and offer these innovative homes. The homes provide the builders with 

market differentiation, free advertising through media interest, fast sales, a market edge because 

of substantially reduced utility bills, and community goodwill (Hammon 2005). The market 

experience for these homes appears to be positive. For example, Clarum Homes reportedly sold 

60% of the 257 solar homes at Vista Montana before its grand opening and sold out completely 

one year ahead of schedule (Hering 2005). In fact, the builder has decided to build only solar 

homes in the future (Hammon 2005). 

Table 123 shows a relatively modest number of solar homes compared to the estimated 150,000 

new housing starts in California during 2005 (Hering 2005). As noted in Table 123, Lennar 

Communities is involved in the Bickford Ranch development in Placer County. In addition, 

Lennar is developing a 1,600-home community with 1.2 PV systems at Hunters Point Naval 

Shipyard in San Francisco. 

New technology is helping to spur builder interest in solar homes. Roof-integrated PV systems— 

such as PV tiles or shingles—blend seamlessly into roofs. These building-integrated PV systems 

are preferred by homebuilders for aesthetic reasons. 

358
Table 123. Solar Home Developments in California in 2005 

Name of 

Development 

Builder and 

Location Price Range 

Number of Homes; 

PV System Sizes 

Premier Gardens 

Rancho Cordova 

Premier Homes 

Sacramento $150,000 to $270,000 99 homes 

2.4-kW PV systems 

Terramore at Ladera 

Ranch 

SheaHomes 

Orange County 

$900,000 to $1 million 

starting price 

87 homes 

2.4-kW PV systems 

Vista Santa Barbara 

Pardee Homes 

San Diego –**  – 

Evergreen at 

Ladera Ranch 

Pardee Homes 

Orange County –** 77 homes; 29% with 

2.4-kW PV systems 

Ladera Ranch 

San Diego Other builders –** 

122 homes with 1.2-kW 

and 2.4-kW PV 

systems*** 

Grupe Homes Sacramento – – 

Vista Montana Clarum Homes 

Watsonville $340,000 to $380,000 

257 homes 

1.2- to 2.4-kW PV 

systems 

Shorebreeze I, II, III, and 

IV 

Clarum Homes 

Palo Alto $595,000 (Phase IV) 39 homes 

Hamilton Park Clarum Homes 

Menlo Park –** 47 homes 

KD Development 

San Diego –** –** 100-150 homes 

Premier Oaks Premier Homes 

Roseville –** 49 homes 

2-kW PV systems 

Bickford Ranch 

Hunter’s Point 

US Homes 

Lennar Communities 

Placer County 

–** 917 proposed 

homes 

Lakeside 

Elk Grove Morrison Homes –** 120 homes; only 12 

with 2-kW PV systems 

Centex Homes Livermore 

San Ramon 

– – 

*Other builders of solar homes mentioned by Hering (2005) are Centex Homes and Standard Pacific Homes 

**Information not available 

***Hering (2005) reports that GE Energy is supplying 350 systems to Terramore Village at Ladera Ranch in Orange 

County 

Sources: http://environmentcalifornia.org/ accessed 9/1/05; 

unpublished case studies by Bruce Baccei, ConSol, Inc., (2005); and Pratsch (2006).

359
Performance of Near-ZEHs: The Premier Gardens Case 

Keesee (2005) reports that Premier Gardens developed by Premier Homes in Sacramento, 

California, is one of the first “all ZEH” communities.1

 Keesee also claims that these homes have 

a package of energy-efficiency measures that reduces heating and water heating energy use by 

more than 15% and cooling energy use by 50%, compared to California’s Title-24 building 

energy standards in a cooling-dominated climate. 

Results reported by the Sacramento Municipal Utility District (SMUD) from an analysis of 10 

months of utility data comparing the 95 Premier Gardens homes with 95 nearby conventional 

homes (Keesee 2005) are as follows:2 

• On average, 16% less electricity was used by the Premier Gardens Homes compared to the 

comparison homes during the first 10 months of occupancy.3 

• PV systems supply more than 47% of the electricity in the Premier Gardens Homes. 

• Average monthly electricity bills are 54% lower in the Premier Gardens Homes than in the 

comparison homes. 

• Average monthly electricity bills for the Premier Gardens Homes are 50% lower than the 

average monthly SMUD residential bill. 

A corresponding analysis of April 2005 bills conducted by Keesee (2005) showed that the 

average electricity bills for this one month were 78% lower than the “typical SMUD bill” and 

63% lower than the comparison homes’ bills. A SMUD analysis of December 2004 bills shows 

that the Premier Gardens’ electricity bills were 42% lower than SMUD’s “typical residential 

customers” for this one month (Hering 2005). 

Hammon (2005) also analyzed a comparison of one month of electricity bills in September 2004 

for Premier Gardens and comparison homes. He reports that 22 of the Premier Gardens “nearZEHs” used 60% to 70% less electricity than 17 neighboring homes in the Sacramento area 

during that month. The size of the PV systems was not reported, but other sources suggest that it 

was 2 kW (Table 123). Data on the homes’ square footage, occupancy, equipment, and other 

factors that affect energy consumption—and therefore bills—are not reported. 

1

Technically, this is not accurate because these homes cannot provide all the energy they use. Data reported 

in Keesee (2005). 

2

The method was a comparison of simple averages of the electricity bill data from the two communities. 

3

Hammon (2006) later clarified that the 16% decrease in electricity use was the result of energy efficiency 

measures only; the effects of the PV systems were in addition to the 16%. The Premier Gardens homes all had two 

electric meters, one for PV generation and one for home net, so that the contributions of energy efficiency and PV 

could be calculated separately. More analyses have been completed on the Premier Gardens experience and they may 

be found in the Proceedings of the American Council for an Energy-Efficient Economy 2006 Summer Study, 

authored by Hammon and others. 

360 
Results from the Premier Gardens development were also reported in a Newsweek article by 

Andrew Murr on August 15, 2005, entitled “No More Electric Bills” (page 43). The article 

features a claim that the average power saving for those living in these homes is 60%. The story 

describes the energy features of the community, including 2-kW solar modules on the roof, 

spectrally selective windows, “fluorescent bulbs” (probably referring to compact fluorescent 

lights), and tankless water heating. The homes are net metered. SMUD’s Keesee is quoted as 

saying that the project “helps us lower usage at peak power times. That lets us avoid building 

costly plants or buying expensive power at peak usage time.” 

The article reported that the Premier Gardens’ energy features add $18,000 to the purchase price 

of the home; in other locations these features can add $25,000 or more. California subsidizes 

approximately 50% of the cost of the solar PV systems.4 

The current study did not analyze the peak-demand impacts of the SheaHomes Scripps Highlands 

development. However, it is important to understand the peak-demand impacts of ZEH-induced 

electricity savings, as well as ZEH output to the grid on hot sunny afternoons. Relative to ZEH 

impacts on utility peak demand, SMUD analyses show that peak electrical demand has been 

reduced by as much as 13% in homes that participated in the PV Pioneer retrofit and Solar 

Advantage Home PV programs. SMUD estimates that, if all new homes built during 2004 had 

been built to ZEH standards with PV systems oriented west of south, the utility could have 

realized a significant peak load reduction—up to 20 MW (Keesee 2005). SMUD plans continued 

analysis of electrical usage, bills, and peak-demand impacts of the ZEH homes over the years 

ahead to determine the impact of ZEHs on the utility’s system peaks. 

Percentages of Energy Cost Savings at the SheaHomes Scripps Highlands 

Relative to Comparison Homes 

Tables 124 through 128 present data from the current study on the percentages of savings on bills 

for electricity and gas for various categories of homes. In all cases, the SheaHomes highperformance homeowners enjoyed lower electricity and gas bills, on average, than did the 

comparison homeowners for the 12-month period from July 1, 2003, through June 30, 2004. 

Table 124. Mean Monthly Gas Costs for PV, SEE, and Comparison Homes 

Gas Costs 

PV 

Homes 

(n=31) 

SEE 

Homes 

(n=44) 

Comparison 

Homes 

(n=28) 

Total 

(n=103) 

Mean monthly* gas cost including taxes and 

miscellaneous charges 

(F=4.970; p=.009) 

$31.59 $35.76 $43.22 $36.53 

Mean monthly* gas cost excluding taxes and 

miscellaneous charges 

(F=4.960; p=.009) 

$29.64 $33.56 $40.58 $34.29 

*12-month average 

4

As of July 2006, California’s subsidy stood at $2.60/W (Nelson 2006). 

361 
Table 124 presents data on the average monthly gas costs for PV, SEE, and comparison homes. 

As Table 124 shows, the ANOVA results in highly significant differences in mean gas cost 

among the three categories of homes. The mean monthly gas costs for PV homeowners including 

and excluding taxes and miscellaneous charges are significantly lower than those of comparison 

homeowners. The mean monthly gas costs for SEE homeowners both including and excluding 

taxes and miscellaneous charges are also significantly lower than the mean monthly gas costs of 

comparison homeowners (p=.009). The mean monthly gas costs including and excluding taxes 

and miscellaneous charges for PV homeowners are not significantly different from those of SEE 

homeowners. 

As Table 125 shows, the mean annual electricity cost was 13% lower for the SEE homes5 than 

for the comparison homes when taxes and other charges are included, and the mean monthly 

electricity cost with all charges was 14% lower. The gas costs were even more advantageous to 

the SEE owners, fully 17% lower than the gas costs in the comparison community homes, on 

average. These savings are attributable to the energy-efficiency features of the SEE homes, which 

save electricity and gas that would otherwise have been used for space conditioning and water 

heating. All these differences are statistically significant. The savings in gas costs near statistical 

significance (p=.059). 

Table 126 compares data on the same variables for SheaHomes with PV systems6 and the 

comparison homes. In this instance, the mean electricity savings are higher, with the SheaHomes 

PV owners saving, on average, 35% on electricity costs compared to the comparison 

homeowners. Interestingly, the gas savings of the PV homes are even higher than for the 

SheaHomes in general, averaging 27%, or 10 points higher than for SheaHomes as a whole. 

These savings may be attributed to the energy efficiency and solar PV features of the 

SheaHomes, as well as other factors. The cost savings could result simply from greater 

consciousness of energy consumption on the part of solar PV residents, the floor plans or 

orientations of solar PV homes, or other factors. 

Table 127 compares data on these variables for SheaHomes with PV systems7 to SEE homes. 

The PV homeowners enjoy 25% lower electricity bills, on average, than do the non-PV homes. 

They also enjoy a 17% cost saving on their gas bills, on average. 

Table 128 compares data on the same set of variables for SheaHomes with 1.2-kW PV systems 

and SheaHomes with 2.4-kW PV systems. The percentage differences in electricity cost are the 

highest of all the comparisons. All these differences are statistically significant. SheaHomes 

owners of 2.4-kW solar PV systems save an average of 46% on their annual electricity costs 

compared with SheaHomes owners of 1.2-kW solar PV systems. Clearly, this finding is a 

function of PV system size. Further, although the numbers are small, the owners of the larger PV 

systems average an incremental saving of 2% on their annual gas bills. 

5

SEE homes are SheaHomes energy-efficient homes with solar water preheating systems. 

6

Ignoring size of PV system 

7

Ignoring size of PV system 

362 
Table 125. Percentage Differences in Electricity and

Gas Costs of SEE Homes and Comparison Homes

Electricity Costs 

SEE homes 

(n=44) 

Comparison 

Homes 

(n=28) 

Percentage 

Difference 

Mean annual electricity cost including 

taxes and miscellaneous charges 

(t-test n.s.; p=.214) 

$1,360.43 $1,562.98 13% 

Mean annual electricity cost excluding 

taxes and miscellaneous charges 

(t-test n.s.; p=.183) 

$1,236.37 $1,434.87 14% 

Mean monthly* electricity cost including 

taxes and miscellaneous charges 

(t-test n.s.; p=.217) 

$ 113.56 $ 130.34 13% 

Mean monthly* electricity cost excluding 

taxes and miscellaneous charges 

(t-test n.s.; p=.186) 

$ 103.21 $ 119.67 14% 

Gas Costs 

Mean annual gas cost including taxes and 

miscellaneous charges 

(t= –1.940; p=.059**) 

$ 427.17 $ 516.61 17% 

Mean annual gas cost excluding taxes and 

miscellaneous charges 

(t= –1.937; p=.059**) 

$ 400.95 $ 485.05 17% 

Mean monthly* gas cost including taxes 

and miscellaneous charges 

(t= –1.938; p=.059**) 

$ 35.76 $ 43.22 17% 

Mean monthly* gas cost excluding taxes 

and miscellaneous charges 

(t= –1.936; p=.059**) 

$ 33.56 $40.58 17% 

*12-month average 

**These results near significance. 

363
Table 126. Percentage Differences in Electricity and Gas Costs

 of SheaHomes with PV Systems and Comparison Homes 

Electricity Costs SheaHomes with 

PV Systems 

(n=37) 

Comparison 

Homes 

(n=28) 

Percentage 

Difference 

Mean annual electricity cost including 

taxes and miscellaneous charges 

(t= –3.196; p=.002) 

$1,015.38 $1,562.98 35% 

Mean annual electricity cost excluding 

taxes and miscellaneous charges 

(t= –3.422; p=.001) 

$ 922.32 $1,434.87 36% 

Mean monthly* electricity cost including 

taxes and miscellaneous charges 

(t= –3.190; p=.003) 

$ 84.77 $ 130.34 35% 

Mean monthly* electricity cost excluding 

taxes and miscellaneous charges 

(t= -3.415; p=.001) 

$ 77.00 $ 119.67 36% 

Gas Costs 

Mean annual gas cost including taxes and 

miscellaneous charges 

(t= –3.105; p=.003) 

$ 375.72 $ 516.61 27% 

Mean annual gas cost excluding taxes and 

miscellaneous charges 

(t= –3.102; p=.003) 

$ 352.52 $ 485.05 27% 

Mean monthly* gas cost including taxes 

and miscellaneous charges 

(t= –3.098; p=.003) 

$ 31.48 $ 43.02 27% 

Mean monthly* gas cost excluding taxes 

and miscellaneous charges 

(t= –3.095; p=.003) 

$ 29.53 $ 40.58 27% 

*12-month average 

A Comparative Analysis 

It is useful to discern whether the data from our study of electricity cost savings support the 

findings of Keesee (2005) and Hammon (2005) that compare Premier Gardens homes with 

neighboring comparison homes. As noted above, Keesee (2005) reports mean monthly electricity 

bills that are 54% lower than the bills of comparison homes for the first 10 months of occupancy 

(number of homes not reported). Also as previously noted, Keesee reports a 63% lower 

electricity bill for the month of April 2005 compared with the bills of comparison homes. Also 

Murr (2005) reports a 60% saving on electricity bills, and Hammon (2005) reports 60% to 70% 

lower electricity bills for the month of September 2004 compared with comparison homes. 

364
Table 127. Percentage Differences in Electricity and Gas Costs 

 of SheaHomes with PV Systems and SEE homes 

Electricity Costs 

SheaHomes with 

PV Systems 

(n=37) 

SEE Homes 

(n=44) 

Percentage 

Difference 

Mean annual electricity cost including taxes 

and miscellaneous charges 

(t= –2.671; p=.009) 

$1,015.38 $1,360.43 25% 

Mean annual electricity cost excluding taxes 

and miscellaneous charges 

(t= –2.653; p=.010) 

$ 922.32 $1,236.37 25% 

Mean monthly* electricity cost including 

taxes and miscellaneous charges 

(t= –2.669; p=.009) 

$ 84.77 $ 113.56 25% 

Mean monthly* electricity cost excluding 

taxes and miscellaneous charges 

(t= –2.651; p=.010) 

$ 77.00 $ 103.21 25% 

Gas Costs 

Mean annual gas cost including taxes and 

miscellaneous charges (t-test n.s.) $ 375.72 $ 427.17 17% 

Mean annual gas cost excluding taxes and 

miscellaneous charges (t-test n.s.) $ 352.52 $ 400.95 17% 

Mean monthly* gas cost including taxes and 

miscellaneous charges (t-test n.s.) $ 31.48 $ 35.76 17% 

Mean monthly* gas cost excluding taxes and 

miscellaneous charges (t-test n.s.) $ 29.53 $ 33.56 17% 

*12-month average 

An analogous case that uses our study’s data compares electricity bills from the homes with 2.4-

kW PV systems with the electricity bills of the comparison homes. The findings should be 

similar because the PV system size reported for Premier Gardens is 2-kW AC, approximately 

equivalent to the 2.4-kW PV DC systems in the current study. Table 129 exhibits the costs and 

percentage differences for electricity and gas bills, as well as for combined utility bills, for the 

comparison homes and the SheaHomes with 2.4-kW PV systems only. The data span a 

comparable period of 12 months. As Keesee’s and Hammon’s studies would predict, the savings 

on electricity bills for these small groups of homes average 62% to 64%, depending on which 

cost variable is examined. 

Taking the analysis further than earlier analyses reported by Keesee and Hammon, Table 128 

compares the gas costs and differences as well, showing that, on average, the SheaHomes with 

2.4-kW PV systems save 29% more than the comparison homes on their gas bills, in addition to 

their electricity cost savings. 

365
Table 128. Percentage Differences in Electricity and Gas Costs of SheaHomes with 

1.2-kW PV Systems and SheaHomes with 2.4-kW Systems 

Electricity Costs 

SheaHomes 

with 1.2-kW PV 

Systems 

(n=31) 

SheaHomes 

with 2.4- kW 

PV Systems 

(n=6) 

Percentage 

Difference 

Mean annual electricity cost including 

taxes and miscellaneous charges 

(t=2.140; p=.039) 

$1,097.42 $ 591.48 46% 

Mean annual electricity cost excluding 

taxes and miscellaneous charges 

(t=2.169; p=.037) 

$ 998.42 $ 529.13 47% 

Mean monthly* electricity cost including 

taxes and miscellaneous charges 

(t=2.137; p=.040) 

$ 91.60 $ 49.25 46% 

Mean monthly* electricity cost excluding 

taxes and miscellaneous charges 

(t=2.166; p=.037) 

$ 83.34 $ 44.24 47% 

Gas Costs 

Mean annual gas cost including taxes and 

miscellaneous charges (t-test n.s.) $ 376.98 $ 369.21 2% 

Mean annual gas cost excluding taxes and 

miscellaneous charges (t-test n.s.) $ 353.73 $ 346.30 2% 

Mean monthly* gas cost including taxes 

and miscellaneous charges (t-test n.s.) $ 31.59 $ 30.91 2% 

Mean monthly* gas cost excluding taxes 

and miscellaneous charges (t-test n.s.) $ 29.64 $ 28.99 2% 

*12-month average 

Indeed, the combined total utility costs for the 12-month period of analysis shows that the 

SheaHomes with 2.4-kW PV systems save 54% on their overall combined utility bills for the 12-

month period, on average, compared with the overall combined utility bills for the comparison 

homes. Thus, the claims made by prior researchers, engineering estimates, and analyses of actual 

performance of near-ZEHs are borne out by the data in this study. 

Our findings are not strictly comparable to those reported from the Premier Gardens studies. For 

one thing, the analyses in this report measure an annual utility billing cycle rather than one month 

or 10 months of billing data, which were the foci of previous investigations. In addition, two 

different sets of homes are involved with different characteristics and located in somewhat 

different climates. Perhaps their only common characteristics are certain energy features. Also, 

the Premier Gardens solar PV systems are larger than are most of the 1.2-kW solar PV systems 

on SheaHomes. 

366
To set the findings discussed above into a broader context, Table 130 presents data on the 

average monthly combined utility bills for PV, SEE, and comparison homes. As Table 8 shows, 

the analysis of variance results in highly significant differences in mean combined utility bills 

among the three categories of homes. The mean combined monthly utility bills for PV 

homeowners (1.2-kW and 2.4 kW combined) both with and without taxes and miscellaneous 

charges are significantly lower than those of SEE homeowners, and the latter enjoy significantly 

lower mean combined monthly utility bills than do comparison owners. These results are 

particularly pronounced when the costs are analyzed on a square-footage basis. This set of 

findings provides additional support to the concept of high-performance homes’ reducing overall 

monthly utility bills. 

Concluding Remarks 

These findings have implications for policy makers. In addition to homebuilders, policy makers 

are taking notice of the investment and business potential of solar PV homes. Hering (2005) 

reports that the Million Solar Roofs initiative (now the Solar Powers America Initiative) has set 

the goal of one million solar roofs by 2017. The recently authorized California Solar Incentive 

(CSI) program has similar goals, setting more than $3 billion aside over 10 years for construction 

and retrofit of commercial and residential buildings. The residential new construction share is 

$350 million. 

Thus, the claims made by prior researchers, engineering estimates, and analyses of actual 

performance of near-ZEHs are borne out by the data in this study reducing average monthly 

utility costs by at least 50% when 2.4 PV systems are used in highly energy-efficient homes with 

solar water heating systems. 

367
Table 129. Percentage Differences in Electricity and Gas Costs of

Comparison Homes and SheaHomes with 2.4-kW PV Systems

Electricity Costs 

Comparison 

Homes 

(n=28) 

SheaHomes 

with 2.4-kW

 PV Systems 

(n=6) 

Percentage 

Difference 

Mean annual electricity cost including 

taxes and miscellaneous charges $1,562.89 $ 561.48 64% 

Mean annual electricity cost excluding 

taxes and miscellaneous charges $1,434.87 $ 529.13 63% 

Mean monthly* electricity cost including 

taxes and miscellaneous charges $ 130.35 $ 49.45 62% 

Mean monthly* electricity cost excluding 

taxes and miscellaneous charges $ 119.69 $ 44.24 63% 

Total kWh cost, 12 months 

(t= –3.054; p=.005) $1,434.87 $ 529.13 63% 

Total electricity bill, 12 months 

(t= –2.990; p=.005) $1,562.89 $ 591.48 62% 

Gas Costs 

Mean annual gas cost including taxes and 

miscellaneous charges $ 516.61 $ 369.21 29% 

Mean annual gas cost excluding taxes and 

miscellaneous charges $ 485.05 $ 346.30 29% 

Mean monthly* gas cost including taxes 

and miscellaneous charges $ 43.22 $ 30.91 29% 

Mean monthly* gas cost excluding taxes 

and miscellaneous charges $ 40.58 $ 28.99 29% 

Total therm cost, 12 months 

(t-test n.s.; p=.116) $ 485.05 $ 346.30 29% 

Total gas bill, 12 months 

(t-test n.s.; p=.116) $ 516.61 $ 369.21 29% 

Combined Utility Costs 

Total combined utility bill including taxes 

and miscellaneous charges, 12 months 

(t= –3.010; p=.005) 

$2,079.50 $ 960.69 54% 

*12-month average 

368
Table 130. Average Monthly Combined Utility Bills Comparing PV, SEE, and Comparison Homes 

Cost Measure 

PV 

Homes 

(n=37) 

SEE 

Homes 

(n=44) 

Comparison 

Homes 

(n=28) 

Total 

(n=109) 

Mean monthly* combined utility bill including 

taxes and miscellaneous charges 

(F=7.269; p=.001) 

$116.25 $149.33 $173.57 $144.32 

Mean monthly* combined utility bill excluding 

taxes and miscellaneous charges 

(F=7.517; p=.001) 

$106.53 $136.77 $160.25 $132.54 

Mean monthly* combined utility bill per ft2 

including taxes and miscellaneous charges 

(F=11.095; p=.000) 

$.038 $.048 $.062 $.048 

Mean monthly* combined utility bill per ft2 

excluding taxes and miscellaneous charges 

(F=11.451; p=.000) 

$.035 $.044 $.057 $.044 

*12-month average 

369
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A New Market Paradigm for Zero-Energy Homes: The Comparative 

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The housing industry is in flux. Today’s landscape differs from the buyer’s market in San Diego we have studied for 

the past five years. Demand for housing varies in different parts of California and the nation, and interest rates are on 

the upswing. The value of our study does not lie in describing the motivations of today’s new homebuyers, but rather 

it lies in suggesting a conceptually fresh alternative paradigm for the building and marketing of zero-energy homes 

(ZEHs) based on experience. When this paradigm is used, builders, new homebuyers, and utility companies will 

benefit. When appropriately applied to business practice and public policy, this new paradigm will help builders create 

the sustainable communities so necessary for our well-being and that of future generations. 

15. SUBJECT TERMS 

zero-energy home; zeh; SheaHomes; new homebuyers 

16. SECURITY CLASSIFICATION OF: 17. LIMITATION 

OF ABSTRACT 

UL 

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OF PAGES 

19a. NAME OF RESPONSIBLE PERSON 

a. REPORT 

Unclassified 

b. ABSTRACT 

Unclassified 

c. THIS PAGE 

Unclassified 19b. TELEPHONE NUMBER (Include area code) 

Standard Form 298 (Rev. 8/98) 

Prescribed by ANSI Std. Z39.18 

F1147-E(12/2004)