non-energy impacts, and health and safety. This task resulted in 30 interviews completed with landlords and property managers, including 29 telephone interviews and one in-person interview. HES-IE Landlord and Property Manager Interviews – Project Characteristics Interviewers asked each landlord and property manager about one of their projects served by the program (referred to as their “key project”) between July 2013 and April 2015. If the landlord or property manager was involved with more than one participating project, interview questions focused on the project with the largest amount of gross electric savings as reported in the program database. Of the 30 projects, 24 were served by Eversource and six were served by UI. The 30 projects included 2,373 housing units, representing 7% of the projects and estimated number of units in the HES-IE participant population between July 2013 and April 2015, in total. The key projects ranged in size from five to 360 units, with an average of 79 units per project and a midpoint of 41 (Table 13)., Figure 2 compares the sample in terms of the number of units, projects, and unique landlord and property manager contacts to the participant population. Figure : HES-IE Landlord and Property Manager Interviews – Sample Comparison to HES-IE Participant Population 1 In the absence of unit-level data in UI’s participation database, the study estimated the total number of units in the UI population by multiplying the mean units per projects among the key projects (79.10) by the number of UI projects in the participation database (166). Table : HES-IE Landlord and Property Manager Interviews – Number of Units in Sample and Population HES-IE Units per Project Eversource UI1 Total1 Participant Population (n=429 projects) Average 78 - - Median 51 - - Interview Sample (n=30 projects) Average 81 72 79 Median 46 26 41 1 UI’s participation database does not capture the number of housing units in its projects; as such, the table does not report the average or median number of units among the full population. Figure 3 presents the program measure types installed at the 30 key projects. The key projects were most likely to have received incentives for refrigerators (7 projects) and insulation (7); for core services, they most often received light bulbs (29), water-saving equipment (21), and air sealing (19).
Data inconsistencies are not frequent enough to cause overwhelming concern, but the program may want to consider additional verification of measure quantities installed. As noted, the evaluation found some inconsistencies between measures recorded in the tracking data and those found on site, but these inconsistencies were not frequent enough to overwhelmingly point to any major program or measure-specific issues. Excess of measures. For all measures except LEDs, field technicians verified a few more measures on site than were reported in the tracking data. The evaluation found this to be the case for two sites with CFLs, four sites with hot water measures (faucet aerators and efficient-flow showerheads), and two sites with refrigerators. One speculation is that these measures may have been installed using a different pool of money (e.g., the Department of Energy’s Weatherization Assistance Program [WAP]) at the same time as the HES-IE measures. Tenants and landlords/property managers would be unable to distinguish between HES-IE and WAP lamps. Zero or low installation rates. Conversely, the visits found some sites where fewer measures were installed than were reported. Some examples are discussed below. The most severe of these discrepancies included one site where the tracking data showed seven CFL installations per unit for one site, but field technicians found only circline fluorescent fixtures installed through the program. The technicians’ conversations with both tenants and landlords or property managers at this site indicated that they never received CFLs through the program. CFLs The evaluation identified approximately 7% more CFLs on site than were reported in the program data. In most cases, the tenants stated that they had purchased and installed the additional lamps either before or after participating in the program, and the analysis excludes such bulbs. Additionally, only six tenants across all the buildings visited reported that any of the program CFLs had failed since installation.
Table 47 outlines survey questions, response options, and the range of resulting initial and influence-rating free ridership rates associated with specific responses. Table : End-user Participant Survey Respondents – Free Ridership Scoring Methods Question wording Response Initial Free Ridership Score Before learning about the program, did you have any specific plans to purchase and install the [MEASURE] that you installed through the program? Yes Automatic free rider (1.0) No / Don’t know Possible free rider (0.0 to 1.0) Inter-related Considerations in Determining Initial Free Ridership Score1 If you had not participated in the program, would you have purchased one that was a more / same / less efficient? More / Same Possible free rider (0.0 to 1.0) Less Possible free rider (0.0 to 0.75) Not at all Automatic non-free rider (0.0) If you had not participated in the program, when would you have purchased/installed the measure? Sooner / Same time / Within three months Possible free rider (0.75 to 1.0) Within six months Possible free rider (0.50 to 0.75) Within one year Possible free rider (0.25 to 0.50) More than one year Possible free rider (0.0 to 0.25) Question Wording Maximum Rating Influence Rating Free Ridership Score Please consider how influential the following elements were on your decision to install the measure. Please base your answer on a scale of 1 to 5, with 1 indicating “No influence on your decision to install it” and 5 indicating “had a great influence on your decision to install it.” The utility company’s incentive or rebate Installation contractor, if used Information from the utility company 1 Free rider (1.0) 2 Partial free rider (0.75) 3 Partial free rider (0.50) 4 Partial free rider (0.25) 5 Non-free rider (0.0) 1 Any measures for which the interviewee expressed they did not have specific plans to install the measure in the absence of the program were asked about these remaining three inputs.
Table 61: CEC Outreach Chi-Squared Test Results Total CCEC Points Residential Only Points All points accumulated through October, 2015 Town Utility Outreach Quartile Achieved Points Town Count Expected Points Town Utility Outreach Quartile Achieved Points Town Count Expected Points 1 (highest) 7,206 41 6,239 1 (highest) 4,748 41 3,979 2 6,295 39 5,935 2 3,736 39 3,785 3 3,364 20 3,043 3 2,248 20 1,941 4 (lowest) 8,852 69 10,500 4 (lowest) 5,668 69 6,696 Total 25,717 169 25,717 χ2, p < .001 Total 16,400 169 16,400 χ2, p < .001 Points accumulated 2012 through October, 2015 Town Utility Outreach Quartile Achieved Points Town Count Expected Points Town Utility Outreach Quartile Achieved Points Town Count Expected Points 1 (highest) 4,678 41 4,082 1 (highest) 2,816 41 2,481 2 4,199 39 3,883 2 2,416 39 2,360 3 2,246 20 1,991 3 1,494 20 1,210 4 (lowest) 5,704 69 6,870 4 (lowest) 3,500 69 4,175 Total 16,827 169 16,827 χ2, p < .001 Total 10,226 169 10,226 χ2, p < .001 ANOVA Test This subsection reports additional findings of the relationship between utility outreach and CEC point accumulation. Table 62 shows the results of a series of analysis of variance (ANOVA) tests in which the annual normalized outreach quartile groups are tested for joint significance (F statistic) as predictors of total points accumulated in each year (left-hand table) and as predictors of the percent of households in a town that are new participants in the HES program (right-hand table). This test was selected in order to identify the years in which the utility outreach grouping is, jointly, associated with each outcome. Statistical significance in these tests is shown with p-values less than 0.1. These ANOVA tests indicate a statistically significant relationship at the 90% confidence level between
Table : Evaluation Recommendations – Relationship with Program Document Review Findings # Recommendation Description Audience Documentation Review Findings Process Recommendations #4 Participants recommend increased advertising; consider new/additional opportunities with vendors to conduct co-op advertising Customers The document analysis did not find evidence of co-op advertising. #5 Future advertising should continue to emphasize energy and energy cost savings; address customer skepticism about need for improvements or "I haven't gotten around to it" attitudes Customers The program provides a great deal of advertising using a variety of mediums, but it does not appear to underscore the necessity of making the improvements and “act fast” language. Financing and Decision-making Recommendations #7 Provide vendors with additional or more detailed talking points/materials to encourage add-on measure upgrades Vendors The document review findings indicate that the program provides extensive print and online materials to support customers and vendors, but survey and interview findings indicate that additional details about program offerings may be needed to address customer concerns about information quality. Short-term survey respondents are significantly more satisfied with information quality than long-term respondents, possibly signaling program improvements or superior recall due to more recent participation. #8 Clarify program processes and offerings further for customers who are concerned about information quality Customers #11 Provide "everyday" language version of loan applications Customers The program's online financing tool already provides a great deal of assistance to customers, but the other evaluation efforts indicate that customers and vendors need clearer and/or further guidance. #12 Expand on materials that provide financing information Customers #13 Provide vendors with additional talking points/sales methods to address initial loan rejections Vendors The Implementation Manual asks vendors to direct customers to the Energize Connecticut website for more information about financing options, but does not encourage them to explain the options in detail to better ensure that that the customer understands the options and how best to take advantage of them. #14 Provide guidance to vendors about preferred language to use when referring to financing Vendors
Table : End-user Participant Survey Respondents – Low-income vs. NLI (based on HUD Median Income Limits) Income Type HES (n=61) HES-IE (n=44) Rebate-only (n=7) Low-income 31% 70% 33% Non low-income 61% 25% 67% Don’t know/Refused 8% 5% - Note: Responses are weighted End-user Nonparticipant Surveys – Housing Characteristics and Demographics Table : End-user Nonparticipant Survey Respondents – Age of Respondent Age Group Non-Low Income (n=140 ) Low-Income (n=100) 18 to 24 years old 3% 2% 25 to 44 years old 30% 29% 45 to 64 years old 47% 40% 65 years or older 19% 26% Don’t know / Refused 1% 2% Note: Responses are weighted. Table 101: End-user Nonparticipant Survey Respondents – Ownership Status of Respondent Ownership Status Non-Low Income (n=140) Low-Income (n=100) Own 93% 73% Rent 6% 22% Don’t know/Refused 1% 5% Note: Responses are weighted.