diffusion into new markets: economic returns required by
TRANSCRIPT
Diffusion into New Markets: Economic Returns Required by Households to Adopt Rooftop Photovoltaics
Ben Sigrin, Easan Drury National Renewable Energy Laboratory
Abstract
While the U.S. residential solar market is growing quickly, costs for acquiring customers are high--and this indicates the value of efforts to identify new market segments and predict areas ripe for adoption. To better understand how the next wave of solar diffusion could occur, we explore the range of economic thresholds that households without PV would require to consider solar adoption, finding that these households require more attractive payback times by 1-3 years to achieve comparable market share as current adopters. In contrast, non-adopters indicate they would be satisfied with equal or lower returns when the benefits of solar are expressed in terms of their monthly bill savings—as is the case for third-party owned systems. If true, this suggests that the leasing model fundamentally inverts the assumption that later adopters require higher economic benefits. Adopters, both buyers and leasers, are compared to their non-adopting peers across a range of demographic and attitudinal factors. We find that leasers appear to be more highly influenced by installer advertising (radio, TV) and marketing, while buyers were more influenced by personal contacts. Environmental concern, once a preeminent reason for adopting is decreasing in relative importance, whereas lowering total electricity costs and protecting one’s household from future increases in prices are now the two more important reasons. Understanding these dynamics, and how they are changing, offers installers low-cost opportunities to attract new customers and expand their market base.
Introduction
The U.S. residential solar market is expanding quickly, with installed capacity more than doubling between 2012 and 2014 (SEIA 2014). Several trends point to a maturing market—consolidation of market share among solar installers, increasing access to low-cost capital--particularly from institutional funding sources, and increased competition between market players. California, the largest market for solar in the U.S. stopped issuing
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state-issued rebates for residential systems in the second half of 2013 in the SCE and PG&E service territories, yet installations have continued. The U.S. Federal Installation Tax Credit, once an irreplaceable incentive for profitable installations, is expected to decrease from 30% to 10% in 2016—and the industry will live on. Yet installers and their industry are not completely in the clear. Customers still need to be recruited, and costs for acquiring customer are high, estimated at $0.49/W per customer, or roughly 10 - 20% of a system’s costs (GTM 2013). In part this is because rooftop solar is an unproven commodity for many households. Trusted contacts from social networks (friends, family, coworkers, and neighbors) combined with observations of existing systems does much of the heavy lifting in convincing unsure customers. In response, the industry has experimented with a number of innovative advertising and marketing methods to either recruit new leads or improve their conversation rate for existing ones. These methods range from door-to-door canvasing, to partnerships with established retailers, to purchasing customers leads wholesale from third party aggregators (GTM 2013). All of these point to a continued need for research that can help identify new market segments, predict areas ripe for adoption, and test effectiveness of marketing tactics (Davidson et al 2014).
Customer behavior has been a focus of recent research. In this, the main framework is of the consumer as a decision-maker, drawing on the behavioral economics, Diffusion of Innovations, and Value-Based Norms frameworks (Faiers and Neame 2006; Rogers 2003; Stern et al 1999; Wilson & Dowlatabadi 2007) to understand the economic, informational, social, and behavioral factors that predict adoption trends. Some early insights from this field are that social networks can help reduce customer uncertainty (Bollinger and Gillingham 2012; Rai and Robinson 2013) and that customers are motivated to adopt for a variety of reasons—not economics or environmental concerns alone (Schelly 2014; Zhai & Williams 2011). Finally, that a number of barriers may exist which inhibit adoption including high upfront costs, inadequate access to financing options, lack of awareness of available products,
Energy Market Prediction: Papers from the 2014 AAAI Fall Symposium
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concerns about required system maintenance, and the risk of PV negatively affecting home values (Hoen et al 2011; Margolis & Zuboy 2006).
Third-party ownership, or leasing, has been instrumental both in the market’s expansion and in mitigating some of the barriers outlined above. Most current lease contracts guarantee both production and operational and maintenance of the system, thus reducing risk and hassle to the consumer (Shih and Chou 2011). More importantly, the leasing business fundamentally inverts the financial proposition to the consumer by eliminating the need to take on debt or make a potentially large up-front payment. As many households do not have sufficient free cash to make these payments, leasing has both grown the market and attracted new demographics (Rai and Sigrin 2013; Drury et al 2012). To better understand how the next wave of solar diffusion could occur, we fielded two surveys in 2013 in the San Diego metro area to explore: i) demographic and attitudinal variations within current adopter populations; ii) differences between adopters and their non-adopting peers; iii) the range of economic thresholds that households without PV would require to consider solar adoption—and how these compare to the historic returns PV adopters have received.
Data Two surveys of San Diego households were conducted in 2013 for: (1) homeowners that had adopted PV (n=1234) and (2) homeowners that had not adopted PV (n=790). The survey instruments were designed to elicit new data exploring the factors that drive households to adopt PV, including household-level motivations (e.g., wanting to save money, wanting to lock in stable electricity costs, etc.), adoption barriers (e.g., upfront costs, impacts on home value, etc.), personal factors (e.g., political beliefs, demographics), social network characteristics (e.g., how many neighbors/friends have adopted), and access to information. In addition the surveys explored the economic thresholds that households would require to seriously consider solar adoption (non-adopting households) or seriously re-adopting solar again (solar adopting households), allowing us to compare these self-reported thresholds for adopters and non-adopters. Several survey questions were tested in a series of three focus groups composed of (1) PV adopters who owned their systems, (2) PV adopters who had leased their systems (or signed a power purchase agreement), and (3) PV non-adopters. Responses from focus group participants were used to clarify and improve the survey instrument.
Adopter Survey The PV adopter survey was administered in Oct/Nov 2013 as an online survey using SurveyGizmo. The survey was in the field for three weeks, and two reminders were sent at the end of weeks one and two. Invitations to complete the survey were emailed to 10,064 PV adopters in San Diego County who had applied for California Solar Initiative incentives from January 2007 through the first quarter of 2013. Of these, participation in individual sections of the survey ranged from about 880 – 1,230. The final response rate was approximately 15%, defined as the number of fully or partially completed surveys divided by the number of successfully-delivered solicitations. To ensure representativeness of survey respondents to the population of PV owners in San Diego, we looked at two main factors: (1) whether the respondent pool generally represented the breakdown between third-party owned PV customers and host owned PV customers; (2) whether respondents effectively represent adoption from early years (pre-2009) as well as more recent years (2012-2013). For (1), we find 29.7% of survey respondents leased compared to 30.6% of all PV adopters in San Diego (CSI 2014). For (2) we do find a small bias towards over-representing recent installations--28.8% of survey respondents reported adopting in 2012 versus 25.3% of actual installations in 2012, and 2.3% versus 1.5%, respectively, for the first quarter of 2013.
Non-Adopter Survey In additional to the PV adopter survey, we also fielded a survey through Qualtrics for PV non-adopters. This survey was sent to single-family homeowners in San Diego county that had not adopted rooftop solar systems. The non-adopter survey was administered during March and April 2014. The sampling method is somewhat different than the adopter survey in that responses were solicited until reaching a pre-determined number of 790 completed survey responses. The non-adopter’s instrument used many of the same questions from the PV adopters survey so that responses could be compared across the populations of PV adopters and non-adopters. These include demographics, relative importance of factors in the adoption decision, characteristic of the home, and economic thresholds that would entice homeowners to seriously consider adopting PV. The non-adopter survey also included additional questions exploring any contacts that homeowners have had with solar installers to control for exposure to the solar industry. For both the adopter and non-adopter surveys, the sampling design was limited to homeowners – since these are the households that benefit from installing PV – and we did not intend for the sampled populations to be
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representative of the underlying San Diego population. However by controlling for homeownership, this allows us to understand how PV adopters differ from their peers, and thus which customer segments are more likely to adopt.
Results Our first section of analysis focuses on understanding motivations for adoption and identifying differences in the characteristics of adopters that decide to lease versus buy--their motivations, how these have changed over time, and demographic variations.
Motivations for adoption Adopters were asked how important various factors were in their decision to install solar panels. Figure 1 shows the relative importance of multiple factors plotted longitudinally from 2007 to 2013, after converting categorical responses to a numeric scale (e.g. 1 = “Not at all important”, through 5 = “Very Important”). Lowering total electricity costs and protecting one’s household from future increases in prices were rated as the two most important factors, which affirms the importance of economic factors in driving adoption decisions. Compounding this, importance of economic factors increases over time, whereas we find that environmental concern decreases in relative importance. This indicates installers should continually evaluate their marketing strategies to stay competitive. The focus groups of PV adopters in San Diego highlighted the importance of events in stimulating initial interest in rooftop PV. Among all adopters surveyed the top five events leading them to seriously consider rooftop solar systems were increasing electricity rates (32%), planning for retirement (24%), talking to friends or family members with solar (21%), direct marketing by solar companies (16%), and planning a remodeling project (11%)1. The top two events reflect a common theme from survey respondents of general concern over rising electricity costs or economic concerns in general; influence from social groups is also strong (Bollinger & Gillingham 2012; Rai and Robinson 2013). A surprising result here was the relative importance of retirement planning in the decision to adopt rooftop solar systems. Prevalence of retirement planning as a trigger indicates potential for retirees or near-retirees as a significant market segment.
1 Since respondents were allowed to indicate more than one event, the percentages do not sum to 100%.
Differences in buy vs lease samples Within the San Diego market, momentum in adoption trends is heavily skewed towards third-party ownership (leasing), as opposed to host-ownership (buying) which led early adoption trends (CSI 2014). Because our survey covers adoption from 2007 – 2013, it is demonstrative of this shift—overall 317 adopters, or 26.3% leased their system, whereas for adoption in 2012 -2013 only, leasing comprises 52.2% of the sample. Therefore, it is instructive to understand differences in the third-party owned sample as compared to the host-owned as it reveals how customer demographics are changing. Customers adopting via host-ownership reported that different situations or events prompted their initial interest in installing solar panels as compared to third-party adopters (Figure 2). Specifically, for leasers “recent increases in prices”, “a conversation with a friend or family”, and “direct marketing by a solar company” were the three most likely events to prompt interest. By comparison, “thinking about retirement planning and conversations with friends or family” were the second and third most likely events for buyers. In general, leasers appear to be more highly influenced by installer advertising (radio, TV) and marketing, whereas buyers were more influenced by personal contacts.
Fig. 1: Evolution of important factors in adopting solar.
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Table 1: Comparison of demographic and adoption factors for buyers and leasers
Ha: μbuy ≥ μlease Unequal Var. Assumed
Buy Mean
Lease Mean t df p-value
2-tailed 95% CI of Difference
Lower Upper Age (years) 59.7 58.0 2.21 471.0 0.027* 0.20 3.27
Edu (years post-secondary) 4.64 4.23 2.91 479.4 0.003** 0.13 0.67
Income ($1,000) 168.4 155.2 1.55 459.7 0.121 -3.50 30.0
Imp. of lower elec. costs 4.58 4.50 1.41 470.0 0.158 -0.03 0.20
Imp. of protect increase in elec. prices 4.43 4.58 -2.44 566.8 0.015* -0.26 -0.02
Imp. of protect environment 3.89 3.78 1.36 506.1 0.173 -0.052 0.288
Imp. of increasing home value 3.20 3.03 1.90 488.6 0.058 -0.006 0.343
Imp. of home easier to sell 2.52 2.45 0.746 501.5 0.456 -0.114 0.255
Fig 2: Buy vs lease differences in events that prompted adoption interest Previous research by has reached different conclusions as to the demographic differences between host-owned versus third-party owned adopters. Drury et al (2011) found demographic differences in PV adoption in Southern Caifornia Edison’s service territory at the zip code level, with adoption by leasers associated with areas with lower mean incomes and educational levels. In contrast, Rai & Sigrin (2013) found no significant difference between the groups in the nascent Texas market when surveying individual households. To test differences in the sample, we conducted a series of Student’s t-test with the null
hypothesis that the mean of buyers’ responses equals that of the leasers’. Since the income and education variables were initially solicited as ordinal categorical measures, they are first converted to numeric responses. For income, the midpoint of the interval e.g. $125,000 for “$100,000 - $150,000” is used; Education is converted to the number of years of post-secondary instruction. For the remaining categorical variables that cannot be ordered, we use a Pearson Chi-Squared test to determine whether distribution of responses differ between the market segments We find somewhat mixed results (Table 1) with some demographic and attitudinal differences between customers from the two business models. Specifically, buyers are found to have higher incomes by $13,000 (in $2012) on average, though the result is not statistically significant. Buyers, however, are older on average than leasers by nearly two years and have nearly half a year of additional post-secondary education than leasers--and both results were significant. In addition, leasers were less likely to be retired (38% of sample vs. 45%) and more likely to have children living at home (37% vs 31%) though result are only significant at a 90% CI (χ2 = 3.21, df = 1, p = 0.073) and (χ2 = 2.97, df = 1, p = 0.085) respectively. For factors that adopters indicated were important in their decision to adopt PV, buyers rated “Lowering my total electricity costs” as being the most important, whereas “Protecting myself from future increases in electricity prices” was the most important factor for leasers (table 1). Aside from this difference, the two groups rated the remaining factors with comparable magnitude of importance.
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Table 2: Comparison of demographic and adoption factors for solar adopters and non-adopters (general homeowners)
Ha: μadopt ≥ μnonadopt Unequal Var. Assumed
Adopters Mean
Non-Adopt Mean t df p-value
2-tailed 95% CI of Difference
Lower Upper Age (years) 59.1 57.6 2.42 1608 0.015* 0.20 3.27
Edu (years post-secondary) 4.54 4.15 4.07 1666 5.0e-5**** 0.13 0.67
Income ($1,000) 164.9 114.8 10.4 1568 < 1e-5**** 40.6 59.5
Exp. remain in house (years) 33.7 15.2 3.96 1076 7.9e-5**** 9.39 27.79
Home size (sq. ft) 2676 2208 4.76 1229 < 1e-5**** 275.0 660.8
Imp. of lower elec. costs 4.56 4.59 -0.72 1684 0.472 -0.10 0.047
Imp. of protect increase in elec. prices 4.47 4.46 0.33 1816 0.745 -0.06 0.09
Imp. of protect environment 3.86 3.92 -1.05 1807 0.294 -0.164 0.050
Imp. of increasing home value 3.15 3.88 -13.39 1845 < 1e-5**** -0.831 -0.619
Imp. of home easier to sell 2.50 3.64 -18.97 1780 < 1e-5**** -1.26 -1.021
Differences in adopter vs non-adopter samples As the U.S. residential rooftop photovoltaics (PV) market matures, markets must necessarily diffuse into new populations and locations to continue growing. A key prediction from the Diffusion of Innovations literature is that there are attitudinal and demographic differences between early-adopting individuals and the rest that follow them (Rogers 2003; Wilson & Dowlatabadi 2007). For example, while early adopters are highly interested in the novelty of new technology, the general populace requires a clear degree of relative advantage between the old and new technology. Next we examine attitudinal and demographic differences between adopters (both buyers and leasers as one sample) and non-adopting households. Adopting households were found to have statistically-significantly demographic differences as compared to non-adopting households across an array of characteristics. Adopters were found to have higher incomes by $50,100 on average, be more highly-educated, and live in larger homes (Table 2). Adopters also expect to stay in their current home by nearly 20 years longer than non-adopters —a prerequisite for making a long-term investment in a PV system. For non-numeric factors, we again use a Pearson’s Chi-Squared test for differences in distribution of responses. Adopters were found to be significantly more likely to have children living in the household ( χ2 = 30.79, df = 1, p < 1e-05), with 32.5% of adopters reporting at least one child lives in their household, as compared to 19.5% of non-adopters. Interestingly, no difference was found in the likelihood of being retired, with 43.0% of adopters retired as compared to 42.7% of non-adopters. Adopters were also more likely to have air-conditioning (77.1% vs 63.9%) or a pool (37.3% vs 18.2%)—and both results were significant at a 95% CI. These results support the notion that installers
use heuristics to identify potential customers i.e. through ownership of energy-intensive appliances. Concerns over high electricity bills, in addition to concern about future rate changes is often highlighted as a motivation for adopting solar—supported by our results, particularly in California which has some of the highest retail rates of the nation. In both surveys, households were asked how they thought electricity rates would change over next 5 years. We find that a majority of respondents in both populations expect electricity costs to increase substantially, and at a faster pace than the long-term Consumer Price Index average (BLS 2014). There were also significant differences in expectations between groups. Specifically, near half of adopters (45.2%) expect rates to increase by at least 30% over the next five years, whereas only a quarter of non-adopters (25.2%) hold the same opinion. Interestingly, while non-adopters rated protecting their households from future rate increases as the most important factor they would consider if adopting solar, their responses above imply that they do not think this is a likely outcome. Adopters’ disproportionate concern over rate increases, therefore, could either be an outcome of the adoption process i.e. personal research, conversations with installers or a prior opinion which spurred their initial interest in adopting. Both samples were tested to compare for differences in the factors they considered important when adopting solar (Table 2). As in the previous comparison, lowering one’s bill and protection from future rate increases were considered the two most important factors in the decision. One insight is that the general populace considered the importance of home value--increasing home value and making it easier to sell, to be far more important than the adopting sample. An explanation for this could be that
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adopters, having already researched solar, judge the risk to their home to be manageable. Conversely, this suggests that the general populace considers PV installation to pose a potential risk to their home value (founded or otherwise). Efforts to provide additional information therefore could provide a low-cost opportunity to expand potential market size.
Economic returns required for adoption To understand how adopters and non-adopters evaluate the economics of a residential PV system, both surveys solicited a number of questions relating to the economic thresholds that individuals would require to seriously consider adopting solar for their home. Since adopters have actually already adopted PV for their home, the question is posed in two ways—the historic return they expected to receive at the time of adoption, and the return they would require to readopt. Non-adopters were asked a similar question regarding the level of returns they would require to seriously consider adopting solar. First, respondents were asked to select the economic metric they would/did use to evaluate whether solar panels made economic sense for their household. Again, for adopters this is a question about their previous evaluation, but for non-adopters it is a hypothetical question—“If you were seriously considering solar, how would you evaluate whether solar panels made sense”. A majority of all populations reported they would primarily use monthly bill saving ($/month) (MBS) to evaluate solar economics (Table 3), followed by payback period (years to investment payoff). Other metrics were reported to be used, such as net present value (NPV) and rate of return (RoR), though they are used by a minority of households. Given the variation in preference for different metrics—and that these metric show different price thresholds for when a PV investment becomes profitable (Drury et al 2011), this has strong implications for the price at which a solar PV system becomes appealing to different types of customers. Previously, the consumer behavior literature has suggested that residential customers primarily use a simple payback time to evaluate a new technology (Rai and Sigrin 2013; Camerer et al. 2004; Kempton & Montgomery 1982; Kirchler et al. 2008). However, with the strong growth of third-party owned systems, we expected that leasing customers are frequently being pitched PV systems based on the monthly bill savings rather than a payback time. Surprisingly, customers who bought PV systems are also increasingly using monthly bill savings. Use of the MBS metric is consistent with the importance respondents place on reducing their current and future bills.
Table 3: Economic metrics used to evaluate solar investment Buyers Leasers Non-Adopters Monthly bill savings
40.3% 60.5% 43.4%
Payback time 29.5% 16.1% 41.8%
Rate of return 17.1% 9.8% 6.3%
Net present value 2.2% 1.6% 3.5%
I would not estimate economics
3.0% 4.6% 3.7%
Other 7.8% 7.2% 1.4%
Based on the metric respondents indicated they would use, they are then asked a series of questions to evaluate the minimum economic return they would require to seriously consider adopting solar. As we assume most non-adopters have not substantially examined the potential solar returns, their question requires more finesse. Specifically, non-adopters are asked a series of questions implying an increasing or decreasing attractiveness e.g “I would seriously consider solar if the payback time was one year or less”, “…two years or less”, etc. Permissible responses are “Yes”, “Maybe”, “No”, or “I don’t know”. One expects the respondent to indicate in the affirmative for highly attractive returns, with a transition to “maybe” and then “no” as returns become less attractive. The respondent’s willingness-to-pay is taken as the average value for which they indicate “maybe”. For quality control, we discard all responses that imply a preference for lower returns over higher ones as well non-ordinal responses; for responses with no “maybe” response, the value is taken as the transition from “yes” to “no”. In addition, respondents were randomly assigned questions with either incrementally increasing or decreasing returns; willingness to pay was found invariant to the ordering of these questions. Economic thresholds are given in terms of the percent of the sample that indicated they would be willing to seriously consider solar at a given return or better (Figures 3- 4). Since the sample is small for the metrics other than payback period or MBS, the analysis will focus on these two metrics. Among respondents that used payback time to evaluate returns, non-adopters required more attractive paybacks by 1-3 years. That is, 50% of non-adopters would require a payback of 6 years or less to seriously consider adopting, whereas adopters would only require a 7.5 year payback. Expectations converge for paybacks greater than 10 years for both groups, where approximately 20% of all respondents indicated they would consider adopting at a 10-year payback.
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Fig 3: Customer willingness-to-adopt for given payback period or better Differences in responses for the monthly bill savings metric are opposite those of payback time, with non-adopters indicating they would be satisfied with lower savings when using the MBS metric. For example, only 24.7% of adopters indicate they would consider adopting with savings of $50/month, whereas 71.9% of non-adopters indicate that would at the same level of returns. Because monthly bill savings scales with both system size (larger systems offset more consumption) and the customer’s consumption prior to adoption (larger bills allow more potential for avoided cost), we normalized the MBS values by each customer’s reported summer bill; for adopters we use summer bills prior to adoption. Thus, the transformed metric is now the MBS as a percentage of a summer bill, or the fraction of avoided bill. Note that with this normalization, savings can exceed 100% if the respondent indicates they would only adopt if monthly savings exceed their monthly bill. Savings of roughly 15% of the average summer bill are required to entice 10% of both populations. Thereafter, between 20% and 90% of the summer bill, an additional 10% - 35% of the non-adopter population indicates they would seriously consider adopting. For savings above 90%, the pattern reverses, with adopters more likely to indicate they would adopt—though 85% of the potential market has been saturated at this level of returns. Differences in the adopter and non-adopter populations’ willingness to consider adoption for different metrics offers an intriguing insight into how each group perceives the relative benefits of adoption. If true, this suggests that the leasing model fundamentally inverts the traditional Diffusion of Innovations assumption that later adopters
require higher economic benefits. By framing the proposition for adopting solar as a series of monthly savings—as opposed to a large upfront payment, greater portions of the general population could be enticed than if projects’ returns were expressed in terms of the payback time. Conversely, the results suggest that there are portions of the general population that are either unaware of the potential MBS returns available, or are prevented from adopting for other reasons e.g. insufficient roof space, HOA restrictions, or low electricity bills. If activated, these groups could provide additional momentum to the growing solar market as they indicate they would be willing to adopt under current market conditions.
Conclusion The U.S. residential solar market is growing quickly, and to continue growing, it must expand into new populations. In the San Diego market motivations for adopting are evolving, with environmental concerns decreasing in priority, replaced with greater interest in saving money and, particularly, reducing exposure to higher future bills. Customers leasing their systems now constitute a majority of new installations in many national markets—and these customers are more representative of the general population than early adopters. Looking to future market growth, there are substantial demographic gaps between adopters and the general populace. A key insight is that non-adopting households are more concerned with the risk of solar negatively impacting their home’s value—reducing this concern could unlock additional market potential. Consistent with prior
Fig 4: Customer willingness-to-adopt for normalized monthly bill savings
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research, we find that the general populace would require more attractive payback periods by 1 – 3 years than current adopters to consider adopting. Surprisingly, the general populace would be satisfied with lower savings when adoption benefits are framed in terms of the monthly bill savings. For installers seeking to lower customer acquisition costs, framing the benefits of solar in this way could be a successful tactic.
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