Monthly Archives: September 2018

The Green to Fund Green

By Megan Campbell

The last ten years have seen an explosion in the number of financing options available for energy efficiency and renewable energy upgrades. With more financing choices than ever, both homeowners and energy efficiency program implementers are looking for the best options for financing energy efficiency upgrades that lead to deeper energy savings.

New Financing Options
Traditionally, property owners have conventional financing options for energy efficiency projects, such as personal loans, home equity lines of credit (HELOC), credit cards, as well as energy-specific credit cards available through major banking institutions. However, these options do not address access to financing as a barrier to energy efficiency and, more recently, new options have become available. In 2008, the green bank concept was conceived as a way to facilitate clean energy development. Defined as a public or quasi-public financial institution, a green bank maximizes public funds and private-sector investment to create low-cost, long-term financing for clean energy measures. State green bank programs such as those in Connecticut and New York (the first states to adopt) have had great success in terms of lending volume. Since its establishment, the Connecticut Green Bank has surpassed $1 billion in clean energy investment and, to date, the NY Green Bank has made close to $458 million in overall investments. Alternatively, Property Assessed Clean Energy (PACE) programs allow a property owner to finance the up-front cost of energy improvements made to their property and then pay the costs back over time through their property tax bill (10 to 20-year terms). With all these options, which financial products are residential clients turning to? And are they getting the most bang (or energy efficiency) for their buck?

Baby Steps for Energy Efficiency Financing
At the time when energy efficiency financing products were emerging for the residential sector, Opinion Dynamics conducted a market characterization study for energy efficiency financing. Through a general population survey of California homeowners, we found that over a two-year period (2014-2015) 36% of homeowners made some upgrade to their homes that reduced energy use. Of these, a quarter used financing to pay for the upgrade, most commonly (over 80%) a conventional, non-energy efficiency financing product, such as loans offered through retailers, contractors, and credit cards. Extrapolating to the population, this means that only 1 to 2% of California homeowners in that two-year period used an energy efficiency financing product.

How Far Can EE Financing Grow?
This finding raises several important market questions. How many customers want or need new financing options and how many need financing at all? How can we encourage homeowners to invest in deeper retrofits that lead to deeper energy savings? Is it possible that more attractive financing options could entice more homeowners to invest in energy-efficient upgrades and potentially invest in larger projects at one time? With the plethora of existing energy efficiency and conventional financing options, what type of financing product would cut through the market noise and resonate with a homeowner?

Addressing Customer Needs
To help answer these questions, Opinion Dynamics recently conducted a latent-class, discrete-choice (LCDC) research study with over 400 homeowners to identify the financing product features that resonate most with those looking to upgrade their homes. In this study, customers completed online “shopping exercises” asking for their likelihood to pay for a given home improvement project with varying project costs, payment methods, underwriting criteria, financing services, energy savings thresholds, and rebate levels.

When faced with a marketplace full of options and features, the study revealed that most homeowners (78%) preferred some sort of financing over cash or credit card, suggesting that customers who used no financing in the baseline study (75%) may have chosen financing if they found an attractive option. The LCDC showed that a variety of financing models would be attractive in the market, though term loans were the most popular type of model. However, rather than financing type, the monthly payment amount, or more precisely, an affordable monthly payment amount (from the homeowner’s perspective) was the most critical factor in encouraging homeowners to take out a loan for home improvements.

Many utilities in the industry are struggling to make whole-home upgrade programs cost-effective under the traditional rebate model. Some utilities are attempting finance-only pathways to whole-home upgrades as a potentially more cost-effective solution to encouraging deeper savings. Based on the research to date, many homeowners may attempt a deeper retrofit if they find an affordable monthly payment solution.

Discrete Choice Modeling: Choices Speak Louder Than Words

By: Evan Tincknell

An important yet difficult challenge for any energy-efficiency program is the ability to quantify savings directly attributable to program offerings. To do so, one needs to understand the degree to which customers would have taken comparable energy-saving measures in the program’s absence. Other valuable insights such as which aspects of a product or offering people find most enticing or how much customers would be willing to pay are key to implementing an effective program. Every purchase involves choice, and those choices are rarely as simple as they appear. While it’s nearly impossible to define an individual’s exact purchase behavior, discrete choice modeling can reveal complex patterns in the choices that groups of people make.

Developed by economists and psychologists in the late 1970’s, discrete choice modeling pairs a specialized survey design with regression-based analytics to better understand and predict customer preferences under various market scenarios. A discrete choice survey presents participants with a series of ‘choice sets’ and then asks the participants to make a series of hypothetical purchase decisions, choosing between several products with varying characteristics. By aggregating the outcome of many individual purchase decisions, discrete choice analysis can predict the types of products customers find most attractive, how purchase tendencies would shift in the absence of discounts, and which products are most effective to discount. Common applications of this method include pricing analysis, product concept testing, product branding or positioning, and market share forecasting. In market research applications, discrete choice experiments are most often used with big-ticket items, such as airline tickets or cars.

One particularly powerful application for discrete choice modeling within the energy sector is to estimate the net impacts of energy-efficiency programs. This modeling can be used effectively to optimize rate structure offerings, pricing plans, design effective demand-response, and energy-efficiency program offerings, as well as assess market conditions and remaining market potential for a product, service, or program.

In recent energy-efficiency evaluations, we have used discrete choice surveys to assess price sensitivity of program-discounted energy efficient light bulbs. Although light bulbs are a relatively low-cost item and a fairly routine purchase decision for most shoppers, different customers focus on different product characteristics. Some might only consider price or gravitate toward the cheapest available product of a given wattage, while others may care more about light color, expected bulb life, or energy savings. The discrete choice survey presents respondents with a few product options with varying characteristics and uses hundreds of decisions from different customers to interpret preferences across products. The results allow us to simulate markets featuring either discounted or non-discounted energy efficient bulbs, in effect projecting how market shares would shift in the absence of the program.

In addition to modeling price sensitivity and predicting market shift with different price options available, the discrete choice model also measures the relative importance of various product characteristics. In the case of light bulbs, we can include light color or expected bulb life in the survey and then use the results to predict which light colors are most popular or how strong preferences are for light color relative to bulb life. We can also estimate how similar products fare or the relative importance of price for different types of products, which can help identify what products are most impactful to discount and what level of discounting is most effective.

While the discrete choice method relies on customer self-report, it’s methodology avoids many of the biases associated with more direct survey questions about decision-making processes or willingness to pay. By asking customers to make trade-offs between price and other non-price attributes, this method reveals the true effect of price or other considerations on customer choice and avoids much of the social desirability bias commonly associated with more direct questions on product pricing.

Discrete choice modeling analysis offers a wide array of analytic possibilities and insight into customer behavior. It is a time-proven method that holds tremendous promise in the relatively new area of research for energy-efficiency program evaluators. Energy-efficiency programs seeking to influence larger, more carefully considered purchase decisions may, therefore, look towards discrete choice modeling experiments offer a wide array of analytic possibilities and are readily customizable, making them a promising tool for informing both program design and program evaluation. In the context of constantly shifting markets, we look forward to helping our clients take advantage of this innovative and flexible approach to maximize program efficacy and provide their customers with the products they value most.