NewEnergyNews: TODAY’S STUDY: SOLAR BOOSTS HOME RESALE VALUES

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YESTERDAY

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  • QUICK NEWS, July 25: Climate Change In The Not-So-Funny Funnies; The Truth About Wind, Birds, And Bats; How Goes For 100% New Energy
  • THE DAY BEFORE

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    Anne B. Butterfield of Daily Camera and Huffington Post, f is an occasional contributor to NewEnergyNews

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  • Another Tipping Point: US Coal Supply Decline So Real Even West Virginia Concurs (REPORT), November 26, 2013
  • SOLAR FOR ME BUT NOT FOR THEE ~ Xcel's Push to Undermine Rooftop Solar, September 20, 2013
  • NEW BILLS AND NEW BIRDS in Colorado's recent session, May 20, 2013
  • Lies, damned lies and politicians (October 8, 2012)
  • Colorado's Elegant Solution to Fracking (April 23, 2012)
  • Shale Gas: From Geologic Bubble to Economic Bubble (March 15, 2012)
  • Taken for granted no more (February 5, 2012)
  • The Republican clown car circus (January 6, 2012)
  • Twenty-Somethings of Colorado With Skin in the Game (November 22, 2011)
  • Occupy, Xcel, and the Mother of All Cliffs (October 31, 2011)
  • Boulder Can Own Its Power With Distributed Generation (June 7, 2011)
  • The Plunging Cost of Renewables and Boulder's Energy Future (April 19, 2011)
  • Paddling Down the River Denial (January 12, 2011)
  • The Fox (News) That Jumped the Shark (December 16, 2010)
  • Click here for an archive of Butterfield columns

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  • TODAY AT NewEnergyNews, July 26:

  • ORIGINAL REPORTING: The Best Way To Do Community Solar
  • ORIGINAL REPORTING: What U.S. DER Can Learn Down Under
  • ORIGINAL REPORTING: Solar’s Newest Arizona Challenge

    Monday, February 10, 2014

    TODAY’S STUDY: SOLAR BOOSTS HOME RESALE VALUES

    Exploring California PV Home Premiums

    Ben Hoen, Geoffrey T. Klise, Joshua Graff-Zivin, Mark Thayer, Joachim Seel and Ryan Wiser, December 2013 (Lawrence Berkeley National Labs)

    Executive Summary

    Although photovoltaic (PV) penetration in the United States is increasing rapidly, properly valuing homes with PV systems remains a barrier to PV deployment. Previous studies show that PV homes command sales price premiums. Still, some appraisers and other home valuers assign no value to a home’s PV system, and those who do often cannot find comparable home sales to help determine the PV premium. This has spurred the development of alternative methods of valuing PV homes, including the use of an income approach (based on the present value of PV energy produced over its useful lifetime) and the replacement cost approach (based on the present installed cost equivalent of the PV system). However, those approaches have just begun to have been validated against actual market premiums. Moreover, the drivers underlying PV home premiums are not well understood, which may deter some appraisers from assigning value to PV systems.

    This study, which builds on a previous study conducted by the same authors (Hoen et al., 2011; 2013), helps fill both of those gaps by: 1) using regression analysis to examine actual PV home sales price premiums from a large dataset of California PV homes; 2) exploring the sensitivities of those estimated premiums to the size and age of the installed PV system at the time of home sale, and 3) comparing the actual premiums to predictions made with the income and cost approaches.

    Our analysis offers clear support that a premium exists in the marketplace; thus, PV systems have value, and their contribution to home values must be assessed. We find that premiums in California are strongly correlated with PV system size and weakly correlated with PV system age, in other words larger systems garner larger premiums and older systems garner smaller premiums. We estimate that each 1-kW increase in size equates to a $5,911 higher Premium (p-value 0.000) and each year systems age equates to a $2,411 lower premium (p-value 0.087).

    Additionally, the actual California premiums appear to erode over time (estimated to be approximately 9% per year), more quickly than either the income (approximately 0.5% per year) or cost approaches (5% per year) predict and thus the premiums for homes with older systems (e.g., between 6 and 10 years old) appear to be substantially smaller than predicted.

    Further, premiums appear to be substantially larger than predicted using the income (42% of premiums when the average income estimate is used, p-value 0.000) and cost approaches (65% of premiums, p-value 0.000). There are a number of plausible explanations for this disparity including: premiums might be larger because buyers were willing to pay more for the PV system owing to its green cachet; there could be transaction costs that are avoided by purchasing a home with a PV system already installed that are not incorporated in the cost estimates; the average utility-specific California residential electricity retail rates, which are used for the income estimates, might be lower than they should be in CA where steeply tiered rates are commonplace; and, the market-based Premium estimates could contain effects from omitted variables and therefore potentially overestimate the actual premiums.

    We conclude by proposing future research ideas to further improve understanding of the impact of PV systems on home values and therefore related barriers to deployment.

    Background

    Photovoltaic (PV) penetration in the United States is increasing rapidly, but challenges in properly valuing homes with PV systems remain a barrier to PV deployment. Solid empirical evidence shows that homes with PV garner a sales price premium (Farhar et al., 2004; Hoen et al., 2011; Dastrup et al., 2012; Desmarais, 2013; Hoen et al., 2013); Figure 1 shows a set of premium estimates from Hoen et al. (2011; 2013) derived from a variety of different hedonic models (e.g., “fixed” vs. “continuous”; “base” vs. “robustness”).

    Still, the banking, appraising, and assessment communities have been slow to establish a protocol for, and confidence in, valuing PV systems as part of appraising/assessing a home’s value (Klise et al., 2013b). This is due in part to the difficulty in transferring the average results from large study samples to individual homes. Although appraisers might expect, because of the existing analyses, to find some PV premium in the market, they also would be expected to rely on “comparable sales” near any target home (the most common method used by appraisers) to corroborate that expectation and determine the level of the premium of the target home based on market conditions at the time of appraisal. Rarely is there a high enough density of “comparable” PV home transactions near target homes to enable such an analysis.

    In part to fill this methodological gap, other valuation methods not typically used for residential properties have been proposed for PV homes, such as the income approach and the replacement cost approach, (referred to, for the remainder of the document, as simply the cost approach2) both of which are familiar techniques to appraisers, underwriters, and assessors and other valuers for use with commercial properties (Klise et al., 2013b). The income approach assumes that the value of an asset is developed using the discounted present value of the stream of income an asset produces over time. In the case of a PV system asset, this income is the avoided energy costs (i.e., energy cost savings). If a home costs less to “operate,” it should be, all else being equal, assigned a higher value than similar homes (Eichholtz et al., 2009).

    The cost approach assumes an asset should be worth approximately what the cost to replace it with a similar asset would be. Following this logic, a home with PV would enjoy a premium equal to what it would cost to install a PV system of similar age and size on a similar home without PV.

    Intrinsic in both of these valuation approaches is the expectation that, under normal situations, buyers and sellers will respond to these “income” and “cost” signals to value the properties in the market. To date, however, little has been done to test that assumption with PV homes, though that has begun to change (e.g., Desmarais, 2013).

    Using the income approach as a guide, Sandia National Laboratories and Energy Sense Finance created a Microsoft Excel-based downloadable worksheet that can be used by valuation professionals to predict the value a PV home might have in the market.3 The PV Value® tool has been received favorably by the lending and appraisal community (Klise et al., 2013b), in part because of its ease of use, relative transparency, and conformance to well-understood appraisal techniques. To date, however, the tool has only just begun to be verified against actual sales data to determine if its predicted sale-price premiums are in line with actual premiums in the marketplace. A recent study looked at 30 Colorado homes using various appraisal methods to value the home, finding, in most cases, their results to be similar (Desmarais, 2013).

    There is also evidence (in San Diego) that PV homes might experience a “green premium” that is above the amount that would be expected from energy savings alone (Dastrup et al., 2012), but this has not been explicitly investigated previously within a broader dataset of California PV homes. Further, there is evidence that the value of a PV system could be influenced by the age of the system (Hoen et al., 2011, 2013), with older systems receiving smaller premiums, all else being equal, but how sharply the market values of PV systems are influenced by age is not understood.

    The present work seeks to help close these gaps in current knowledge by answering the following research questions:

    1. Are there sensitivities to the size and age of PV systems in the California PV home Premiums found in the marketplace?

    2. Are estimates using the income and cost approaches strongly correlated with the California PV home sales price Premiums found in the marketplace?

    3. Do these results validate the PV Value® tool, and do they offer any insights for how to improve the income approach used in the PV Value® tool and/or to estimating algorithms based on the cost approach?

    The paper relies on data collected in earlier work by Hoen et. al. (2011, 2013), including data on 1,894 PV homes sold in California from 2000 through 2009, and 70,425 non-PV homes sold over the same time frame and in the same neighborhoods as the PV homes. These same homes are re-analyzed here, and the estimated premiums for these homes are compared to predictions made using the income and cost valuation approaches.

    The remainder of this report is structured as follows. Section 2 reviews the methodological approach for the analysis. Section 3 describes the data. Section 4 presents the results, and Section 5 provides discussion and concluding remarks…

    Conclusion

    Although PV penetration in the United States is increasing rapidly, and previous studies have found that market premiums exist for PV homes, the drivers of those premiums have not been explored adequately. Moreover, developing solid techniques for predicting PV premiums for individual homes has only begun, with little validation of the accuracy *of those predictions. This study helps fill both of those gaps by analyzing PV home premiums from a large dataset of California PV homes, exploring the sensitivities of those premiums to the size and age of the installed system at the time of sale, and comparing those premiums to two commonly used techniques for predicting the value of a PV system, using either the income or cost approach. The estimates using the income approach are derived using the PV Value® tool (Klise et al., 2013b), while the cost approach estimates use a replacement cost approach.

    Our analysis offers clear support that a premium exists in the marketplace; thus, PV systems have value, and their contribution to home values must be assessed. We find that premiums in California are strongly correlated with PV system size and weakly correlated with PV system age, in other words larger systems garner larger premiums and older systems garner smaller premiums. We estimate that each 1-kW increase in size equates to a $5,911 higher Premium (p-value 0.000) and each year systems age equates to a $2,411 lower premium (p-value 0.087).

    Additionally, the actual California premiums appear to erode over time (estimated to be approximately 9% per year), more quickly than either the income (approximately 0.5% per year) or cost approaches (5% per year) predict and thus the premiums for homes with older systems (e.g., between 6 and 10 years old) appear to be substantially smaller than predicted.

    Further, premiums appear to be substantially larger than predicted using the income (42% of premiums when the average income estimate is used, p-value 0.000) and cost approaches (65% of premiums, p-value 0.000). There are a number of plausible explanations for this disparity including: premiums might be larger because buyers were willing to pay more for the PV system owing to its green cachet; there could be transaction costs that are avoided by purchasing a home with a PV system already installed that are not incorporated in the cost estimates; the average utility-specific California residential electricity retail rates, which are used for the income estimates, might be lower than they should be in CA where steeply tiered rates are commonplace; and, the market-based premium estimates could contain effects from omitted variables and therefore potentially overestimate the actual premiums.

    A number of areas might be considered for future study: investigate premiums in other markets outside of California and across a broader set of PV homes and over a more recent period, including the recent market crash and recovery; investigate how premiums vary between customer owned and third-party owned PV systems; further explore the impact of system age, “green cachet”, and retail electricity rates on PV premiums; and, explore how these and other relationships change over time as the market for PV homes develops. These further investigations will help improve our collective understanding of PV premiums, and will help further tune the income and cost based valuation tools that develop to predict the impact of PV systems on homes prices.

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