Title
ASU Solar Housing Survey
 
Grant No.
1) QESST ERC, ASU Consortium (Project creation and data collection)
2) DE-EE0007664 (Final editing, publication, and dissemination)
 
Creators
Jason O’Leary <jason.oleary@asu.edu>
 
Contributors
Clark Miller, Project Lead <clark.miller@asu.edu> 
Jacqueline Hettel Tidwell, Data Manager <jacqueline.tidwell@asu.edu>

Contributing Organizations
Quantum Energy and Sustainable Solar Technologies Center (Arizona State University), Hosting Institution <qesst.asu.edu>
Social Energy Atlas (Arizona State University), Distributor <www.thewhysupply.org>
 
Completion Date
First data Import: 09/21/2015
Online Poll closed: 03/28/2016
Final Import: 03/28/2016
Publication for Public Use: 04/18/2017
 
Rights
This dataset is open access. Please use proper attribution when citing this study, including author, date, and DOI number. 
________________________________________

Research Study Description
The purpose of this study was to examine how rooftop solar PV affected residential real estate transactions in Maricopa County, Arizona. The primary goal was to determine which of three financing arrangements existed at the time the homes were sold. These arrangements included leases with “payments” remaining at the time of sale, “prepaid” solar leases, and “owned” solar PV systems. The hypothesis is that the different kinds of ownership models for solar PV are treated in a measurably different way by the marketplace and this results in different outcomes for those who purchase these homes. Through the questions in the survey we hope to quantitatively and qualitatively measure people’s experiences with pre-existing solar PV in the residential real estate market.

Sampling Frame

Temporal coverage
We sampled solar housing real estate sales where the close of escrow occurred between 01 January, 2014 through 01 July, 2015. Surveys were sent out in August and September of 2015. The second round of duplicate mailings served to remind people who had not already responded to the survey from the first round.
 
Spatial coverage
We sampled households in single-family residential homes in Maricopa County, Arizona. While some areas of Pinal County do fall within the realm of the Phoenix-Mesa-Scottsdale metropolitan statistical area (MSA), as defined by the US Office of Management and Budget, Maricopa County is definitely the “central county” and Pinal would be considered an “outlying county”. Further, the number of potential respondents in the MSA boundaries that overlapped Pinal County was very small, yet more difficult to capture and compare without using geospatial software or datasets to constrain the parameters to MSA boundaries.

Other Parameters for Inclusion
The homes sampled already had solar PV installed at the time of sale. Solar hot water heaters did not qualify. Respondents must have identified themselves as the current homeowner at the time of the survey.  While it might be possible for the mailed survey to be somehow forwarded to a landlord of a rented property, that landlord might not have a keen sense of how the solar has performed over time, nor a close interaction with the solar PV. However, if the solar PV element was a consideration in their purchase of the property, then we would want to hear about that as well. For instance, some landlords make utilities included in their rental agreement, thus might glean some profit by charging a flat monthly electricity fee to their tenants, yet paying less with a solar PV ownership or lease agreement on the property that they own. Some of the homes sampled had only solar hot water heaters but no solar PV. These respondents were excluded from the sample because we were only using solar PV as a technological focal point. Solar hot water heating is a very different technology and includes a very different set of interactions between actors and systems.
Instruments and measures

Protocols for the survey

This survey was distributed via US mail. The envelope contained three items. 1) Cover letter. 2) Double-sided survey questionnaire. 3) Metered business return envelope. No postage was necessary for respondent.
 
The questions are listed below, in the order in which they were presented to the respondents. The naming format for these questions (Q1…) was not sequential due to difficulties with the Qualtrics interface but were retained as identifiers anyway in order to maintain consistency between digital and paper responses. These “Q1” indicators were not printed on the survey. The paper version and the online version used the same ordering of questions. We also used skip-check logic that automatically skipped questions for online respondents if they did not apply to them based on their previous answers.
 
See cover letter and Questionnaire documents for exact layout of questionnaire. 

Survey Questions listed below:
 
Q1 - Please enter the 4-digit Secure ID code. (Located in the upper right hand corner of the survey you received)
_fill in the blank_
 
Q2 - Do you currently own the home at this address?
a) Yes
b) No
 
Q25 - What kind of financing did you use to purchase this home?
a) Conventional
b) FHA
c) VA
d) Cash
e) Other
 
Q4 - What is the size of your solar PV system? (in kW)
_fill in the blank_
 
Q5 - Was the solar PV system leased or owned outright by the previous homeowner?
a) Leased solar
b) Owned solar
 
Q6 - If the solar PV system was leased, did you take over payments, or was the lease prepaid by the seller of the home?
a) Took over payments on the lease
b) Lease was pre-paid
c) N/A - system is owned
 
Q7 - If you took over payments on the solar lease, then how much was the monthly payment? (in $USD) 
_fill in the blank_
 
Q8 - What is the name of the solar company who installed or owns the solar PV on your roof?
_fill in the blank_
 
Q9 - If the solar PV system was leased with outstanding payments, was the seller required
to pre-pay the lease before the sale of the home could be completed?
a) Yes, this was a stipulation
b) No, this was not a stipulation
c) I don't know
 
Q10 - Think back to before you purchased this home. When you thought about solar PV,
how did the following factors influence your decision on whether or not to purchase this
property?
 
This was a matrix of ten questions. The questions were on the Y axis and a modified Likert scale on the X axis.
 
The five Likert scale options included:
Very negative, Somewhat negative, Neutral, Somewhat positive, Very positive
 
The ten questions are listed below:
Q10_1 Good for the environment
Q10_2 Concerned about climate change
Q10_3 Cleaner energy
Q10_4 Lower electricity bills
Q10_5 Earn extra money from net-metering
Q10_6 Planning to rent the property
Q10_7 Someone I know had solar
Q10_8 Maintenance and repair costs
Q10_9 Adds value to the home
Q10_10 Would like to go off-grid
 
 
Q18 - How familiar are you with the idea of installing batteries in your home to store solar energy for later use? (This is often called “Solar Plus Batteries”)
a) Very familiar
b) Somewhat familiar
c) A little familiar
d) Vaguely familiar
e) Never heard of it
 
Q19 - If you were to purchase home batteries for energy storage, how would you most
likely use the batteries? (Rank from 1 to 3, with 1 = most likely, and 3 = least likely)
___ Backup (to provide energy in case of blackout)
___ Cycling energy on and off the grid (storing energy from your solar panels or the grid when it's cheapest, then
using it later when energy costs are more expensive)
___ To go off-grid (coupled with solar or other sources of electricity generation)
 
Q26 - In your current home, if such batteries had been installed in addition to solar PV,
how would the batteries have influenced your decision to purchase the house?
a) Less likely to buy
b) No influence
c) More likely to buy
 
Q20 - How likely are you to install a home battery for energy storage in your current home?
a) Very Unlikely b) Unlikely c) Undecided d) Likely e) Very Likely
 
Q14 - What is your gender?
Male, Female
 
Q13 - What is your age?
_fill in the blank_
 
Q17 - What is your combined annual household income? This includes all residents, regardless of whether they are related.
__ Less than $20,000 __ $20,000 – $29,999 __ $30,000 – $39,999 __ $40,000 – $49,999 __ $50,000 – $59,999
__ $60,000 – $69,999 __ $70,000 – $79,999 __ $80,000 – $89,999 __ $90,000 – $99,999 __ $100,000 or more
 
Q15 - Which of these best describes your race/ethnicity?
___ American Indian or Alaska Native
___ Asian
___ Black or African American
___ Hispanic or Latino
___ Native Hawaiian or other Pacific Islander
___ White
___ Other or multiple race/ethnicity
___ Prefer not to answer
 
Q16 - What is the highest level of school you have completed or the highest degree you
have received?
___ Grade school
___ Trade/technical/vocational training
___ Some high school, no diploma
___ High school graduate, diploma or equivalent (eg: GED)
___ Some college but no degree
___ Associate degree
___ Bachelor degree
___ Graduate degree
 
Q11 - Has the solar PV arrangement lived up to your expectations before purchasing this home? Why or why not?
_fill in the blank_
 
Q12 - Please tell us anything else you would like to share about your experience purchasing a solar home.
_fill in the blank_
 
 
Measures
See cover letter and Questionnaire documents for exact language of survey fixed responses.

Survey Question descriptions:
 
Q1 - Unique ID used to track respondents across databases while maintaining anonymity.
 
Q2 - Establishes ownership of home. If yes, response was included. If no, then response was automatically excluded since the recruitment cover letter states that only the homeowner may participate in this survey. All 280 responses were from homeowners. Non-compliant responses for this key question are not included in this data set. 

Q25 - Key question identifies financing model that respondent used to purchase home.
 
Q4 - Establishes the size of the solar PV system for comparison.
 
Q5 - Key question establishes basic financial model for solar PV system.
 
Q6 - Establishes whether respondent inherited payments from previous owner or if the lease was already paid off before final sale of the home.
 
Q7 - Monthly lease payment.
 
Q8 - Identifies solar installer.
 
Q9 - Key question establishes whether seller was required to pay off solar lease before home could be sold.
 
Q10 - Question introduction acts as a primer for memory and context, framing the subject and focusing on the thought process or heuristics that they used when thinking about solar PV on this home and how it might have impacted their decision to purchase the home.
 
This is a matrix of ten questions. The questions were on the Y axis and a modified Likert scale on the X axis.
 
The five Likert scale options include:
Very negative, Somewhat negative, Neutral, Somewhat positive, Very positive
 
The ten questions are classified below: (see questionnaire document for full text)
Q10_1 Environmental - global and conceptual
Q10_2 Environmental - global and conceptual
Q10_3 Environmental - local implication
Q10_4 Financial
Q10_5 Financial
Q10_6 Financial
Q10_7 Social networking interactions
Q10_8 Financial
Q10_9 Financial
Q10_10 Usage preferences, different from grid-tied solar. Sets up thinking for following questions on solar storage.
 
Q18 - Measures familiarity with solar storage technology.
 
Q19 - Measures preferences for different types of usage for solar batteries.
 
Q26 - Measures potential influence of solar batteries on home-buying decisions.
Note: This survey was performed in the year 2015, at a time when solar battery storage was emerging in mass media, but still a fairly new concept to most people. It was also a time just before the now-emerging trend of reducing or eliminating net-metering policies in Arizona and nation-wide.
 
Q20 - Measures potential influence of preferences on choice of whether to purchase solar storage technologies in the future.
 
Q14 - Demographics - gender
 
Q13 - Demographics - age
 
Q17 - Demographics - income
 
Q15 - Demographics - race/ethnicity

Q15_Other_TEXT_Demographics_Race_Ethnicity
This is a field into which people who indicated “other” had the option to enter multiple races or ethnicities. They were recorded here. 
 
Q16 - Demographics - education
 
Q11 - Open essay question - measures expectations vs perceived outcomes
 
Q12 - Open essay question - asks for any information that was not already represented in previous questions but that respondents felt were still important to them in their solar-home buying experience.

Note: The responses to Q11 and Q12 varied widely. While these questions did serve their intended purpose, respondents also included a great deal of comments and information related to things like solar PV markets, environmental preferences, social and policy preferences, among other topics.
 
Data Description

This survey is important for real estate evaluations because it seeks to create a set of empirical observations about many individual experiences that may not be fully or accurately captured through media reports or inter-personal storytelling. For example, one of the aspects not captured by other pre-existing data sets is the distinction between paid-off solar lease arrangements at the time of sale and leases that still had payments remaining. The public narrative treated all leases the same for the purposes of real estate sales (aka: “leased solar vs. owned solar”) but the sub-category of data within leases actually suggests very different outcomes. Potential homebuyers, realtors, assessors, utilities, and policy makers could all benefit from this information.
 
Data collection and data entry:
 
We included a short URL in the cover letter that respondents could enter into their web browser to fill out the survey via our Qualtrics portal. It was mobile friendly. Only a handful of respondents took advantage of this method, even though it also included a QR code that people could access with their mobile devices. The vast majority of respondents chose to return their responses via the paper survey. We then entered this raw information into the Qualtrics web portal manually. Once imported into one database, we performed quality-control which included checking the spelling of solar company names, translation of text into numbers for reported kW size, excluding respondents with solar hot water heaters only, and duplicate responses. The latter two measures accounted for those excluded from the survey.  
 
Notes on Q/A: 

Q19 - Solar storage rankings
One of the solar storage questions was supposed to be a ranked number question. People often answered this question improperly, in part due to the translation from digital to paper. The digital version had logic fail-safes to make sure people answered correctly while the paper version did not. Instead of ranking all three options together with a unique number from 1-3 as instructed, they used it more like a Likert scale and ranked each option individually from 1-3. This means that, while still somewhat useful to get a general sense of their preferences, this question (Q19) is neither consistently quantifiable no statistically reliable, due to the high number of non-standard responses. 

Q4 - What is the size of your solar PV system? (in kW)
This question required a great deal of quality control measures. Respondents often gave answers in what appeared to be watts (W), not kilowatts (kW). Sometimes they indicated that they weren’t sure or gave answers that had to do with some other calculation instead. For those that appeared to simply answer in W instead of kW, we checked the size of the home and panels using Google earth. Where the size of the home and its installed panels appeared to be logical and reasonable, we made the adjustment to comply with kW readings. In the few cases where the quality questions about this field were unresolvable or illegible, given the responses on paper, the entry was left blank. 

Q8: Solar Installer 
When people indicated their solar installer, sometimes the information was incomplete, incorrectly entered, or simply blank. For the records where we did have responses, we went onto Google to search for each one in order to verify the following information about each installer: a) Correct spelling of company name; b) Solar Installer actually existed and operated within Arizona. This was important because some installer names are quite similar, sometimes only a few characters different. By accurately listing the installers, we may then be able to calculate which companies did the most installations in a particular area. This could then be compared to other demographic data for analysis. 

Q11 & Q12 - Open essay questions
We took measures to maintain confidentiality for respondents in both of these questions by removing data where respondents included personal information such as name, phone number, or email address. In the few instances where this occurred, such personal information was replaced with the generic text code “#NAME?”
 
File architecture

This data set is in a flat file saved in .csv format. It is named: “SH_Survey_Data”. The vast majority of the data was collected via paper surveys and input into the online Qualtrics survey portal. It was then exported as .csv files and imported in batches into a single database for easy viewing and analysis. Once the data was input, organized, anonymized (eg. removing personal information such as name and phone number or other contact information that respondents provided in the survey, even though this information was not requested) it was exported again to .csv format. 

Variable Description

Solar financing 
The primary variables of this study have to do with ownership of the solar panels (Q5) and financing models at the time of home sale (Q6). For the two types of solar ownership respondents indicated: 151 “leased” solar arrangements and 128 “owned” solar arrangements. Of the leased solar, 95 of respondents took over payments on the lease, while 54 of the leases were pre-paid before completion of the home sale. These prepaid leases represent perhaps the most important variable in this study, as this data had not been previously captured and analyzed in this way. Another important variable is whether or not a pre-paid lease was a stipulation before the sale of the home could occur (Q9). Of the sales involving solar leases, prepaid leases were required in 13, while 85 of respondents indicated it was not a stipulation and 39 said they did not know if it was a stipulation or not. 

Home financing 
The third most important variable for analysis here is the question asking what kind of financing the respondents used to purchase their homes (Q25). These financing models each have different requirements and restrictions as to how or even whether real estate appraisers may value solar when determining a fair market value for the home at the time of sale. The responses for the different types of loans are as follows: 137 Conventional, 33 FHA, 32 VA, 74 Cash, 4 Other. 

Demographics
The demographic questions ask about the respondent’s gender (Q14), age (Q13), household income (Q17), race/ethnicity (Q15), and education (Q16). There were 175 Male and 102 Female respondents. The mean average age of these respondents was 54 years old. The greatest number of responses for income (128 responses)  showed that these households made over $100,000 of annual income. The vast majority (239) self-identified as being white. As for education levels, 213 respondents reported having an associate degree or higher. 

Variables for Organization and analysis:
new_primary_key
This is the primary key through which this data may be related to other data sets. It replaces the Q1 Unique ID and is also populated with a random number set.  

Misc. Comments
Comments about nonstandard responses or why respondent was omitted. For instance, several respondents had solar hot water heaters only, not solar PV. This was recorded in this field and those records were excluded from this data set. In other examples, people wrote comments or expanded on answers in the margins of the survey. This was made note of in this field. 

Zip_code
This is the zip code in which the home is located. 

Q17 Income Range Holder
This is a quantitative field that represents the textual range which people chose. For instance, “<$20,000” = 10 in this numbered field. “$20,000-$30,000” = 20. “$30,000-$40,000” = 30, etc… It exists only for simplicity in being able to easily search for and quantify the results for this variable, since the respondents were offered a range rather than asked to indicate an exact amount. 

Q15_Other_TEXT_Demographics_Race_Ethnicity
The people who indicated “other” for race or ethnicity had the option to enter their multiple races or ethnicities here in this field. 


