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Energy Efficiency

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01.10.2015 | Review Article

Integrating value and uncertainty in the energy retrofit analysis in real estate investment—next generation of energy efficiency assessment tools

verfasst von: Alireza Bozorgi

Erschienen in: Energy Efficiency | Ausgabe 5/2015

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Abstract

Real estate professionals, such as investors, owner-occupants, and lenders who are involved in the investment decision-making process, are increasingly interested in energy efficiency investment. However, inability of current tools and techniques for a thorough financial assessment of energy retrofits has led the decision makers to focus on simple energy cost analyses as the primary basis for making energy retrofit investment and/or lending decisions. Thus, substantial non-energy cost savings as well as value implications associated with energy efficiency are often ignored and energy retrofits are undervalued. Through an extensive literature review, interviews with real estate experts, and the author’s experience with energy efficiency analytics and tools development, this paper sets forth the rationales and foundations for the development of an integrated energy retrofit assessment tools to include value, risk, and uncertainty. It suggests a systematic value-based assessment process to analyze full costs and benefits associated with energy retrofit options in the context of value, while clearly articulating the risk and uncertainty. The outcome of the proposed process enables users make more informed investment decisions and drives major energy retrofit investments. As a result of applying such a value-based approach, many investment opportunities that were otherwise ignored may be realized, and therefore, the breadth and depth of energy efficiency investment will increase for the benefit of various stakeholders, including real estate professionals, governments, utilities, or other energy efficiency program sponsors.

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A list of future potential risk factors associated with inefficient buildings is presented in “Costs, benefits, risks, and uncertainties associated with energy efficiency investment” section.

Real estate valuers refer to real estate appraisers, underwriters, due diligence people throughout the paper.

According to Cooremans (2011), an investment is strategic if it contributes to create, maintain, or develop a sustainable competitive advantage. The more an investment decision contributes to competitive advantage, the more strategic it is. The author suggested that competitive advantage is a three-dimensional concept, formed of three interrelated constituents: costs, value, and risks. Thus, to measure strategic character (or “strategicity”) of an energy efficiency investment, one has to measure its contribution to competitive advantage in each dimension: value, costs, and risk.

Today, Liberty Mutual Insurance, Fireman’s Fund, and others offer pricing discounts to qualifying green commercial properties (Mincer 2009). Mills (2003) identified nearly 80 examples of energy-efficient and renewable energy technologies that offer loss prevention benefits (such as improved fire safety) through an examination of the connection between risk management and energy technology and listed some of the insurance companies/brokers that were offering energy savings insurance. Mills (2012) further described trends, activities, and promising avenues for the insurance industry in managing climate change risks and supporting greenhouse gas reductions.

U.S. Department of Energy (2014) conducted a comprehensive review of over 50 studies in the market on the impact of energy efficiency on financial performance of commercial building to determine the extent to which empirical evidence gathered via existing studies demonstrates that efficiency contributes to better financial performance.

Valuation models and inputs are discussed in “Sustainable property valuation,” “Uncertainty modeling,” and “Step 3: value-based modeling” sections.

Lawrence Berkeley National Laboratory (2014), http://www.iaqscience.lbl.gov/vent-summary.html. Published papers include papers by Fisk et al. (2009), Mendell and Heat (2005), and Seppänen et al. (2006)

Energy Related Building Investment Decision Support (eBID) (2004), http://cbpd.arc.cmu.edu/ebids/

Mills and Rosenfeld (1996) presented a framework for characterizing non-energy benefits into seven categories for several energy efficient technologies and provided specific examples for energy efficient windows, lightings, improved ventilation, and indoor air quality. The categories include the following: (1) improved indoor environment, comfort, health, and safety; (2) reduced noise; (3) labor and time savings; (4) improved process control; (5) increased amenity or convenience; (6) water savings and waste minimization; and (7) direct and indirect economic benefits from downsizing or elimination of equipment.

The non-energy benefits may also contribute to company market value (e.g., in case of for-profit investors)

The discount rate is an appropriate risk-adjusted rate that is used in the DCF methodology to discount future cash flow. This is the rate of return that investors require to achieve in their investment given the level of risk that they are taking, the higher degree of risk, and uncertainty results in higher discount rate, which means that higher return is required to attract investors. Higher return requirements (higher discount rates) translate to the lower the value for a given stream of actual or projected cash flows.

The capitalization rate is the ratio of net operating income (NOI) to current market value. In the DCF method, it is used to determine residual value of a stream of cash flows at the assumed time of sale or sale price. Last year’s income will be capitalized using this rate to estimate the sale price (typically 11th year income will be capitalized into a 10-year analysis). The capitalization rate is a measure of investor demand that reflects the return required by investors to acquire the stream of NOIs from a property and can significantly affect the rate of return of a property.

The “Risk analysis framework for cost estimation” report by Yoe (2000) includes a full discussion on using Monte Carlo analysis for cost estimation.

Guidance on quantification of various non-energy cost savings including heath costs and promotion and marketing costs is discussed in the “How to calculate and present deep retrofit value” report by Rocky Mountain Institute (2014).

Examples of studies on reduced absenteeism include (1) A study of 31 green buildings by Berk and Associates (2005) found that absenteeism reduced by 40 %. (2) A (Dunckley 2007) study “Green works wonders” in Australia found that sick days reduced by 39 % overall, to 0.28 days per month, after moving to a highly rated green building. (3) A survey of 534 tenants in 154 office buildings in 2009 found that tenants in LEED or Energy Star buildings reported an average of 2.88 less sick days per year resulting in an average impact of $1228 per worker or $4.91 per square foot, “Green Buildings and Productivity” by Miller et al.

Rollover risk refers to the risk of not being able to secure new tenants at favorable rates and terms when existing tenant leases in a building terminate. The risk also incorporates the leasing and tenant improvement costs to resign new tenants if tenants choose not to renew their leases. The rollover risk of a property will be unique to its particular portfolio of leases and markets conditions (Muldavin, 2010, p. 130).

As discussed previously, a range of utility expenses will be calculated directly using BPSs as part of step 2.

A report by IMT and AI (2012) includes an example for a medium-sized motel that underwent a rather standard energy efficiency upgrade. In this case, an annual reduction of energy costs by 45 % led to an increase in the calculated value by 8.5 %, assuming no change in any other line items or in capitalization rate. Note, furthermore, that an appraiser might even choose to adjust cap rate downward in a case like this, because of reduction in operating risk after retrofit. In this case, the incremental value would be even higher.

A study by Eichholtz et al. (2009) has shown that a 10 % decrease in energy consumption leads to an increase in effective rent of about 20 basis points and an increase in value of about 2 %, over and above the rent and value premium for a labeled building.

The triangular distribution or three point estimates, which is a continuous probability distribution, is widely accepted by valuation experts such as French (2007) as a simple, understandable, and appropriate approach to express the uncertainty associated with valuation inputs in the Monte Carlo analysis.

Probability distributions of energy use could be generated by using a combination of the Monte Carlo simulation and BSP. The Monte Carlo method randomly draws values from within distribution of input uncertainties for simulating energy use of a building. A detailed discussion regarding using the Monte Carlo method along with Energy Plus for modeling uncertainties has been presented by Wang et al. (2012) in “Uncertainties in energy consumption introduced by building operations and weather for a medium-size office building.” Through a case study on an office building, Bozorgi and Jones (2013) have also proposed a step-by-step procedure for deriving and including the uncertainty associated with various factors in energy retrofit option assessment. Their paper “Improving energy retrofit decisions by including uncertainty in the energy modelling process” clearly demonstrates how to generate probability distributions when creating and calibrating an energy model in order to provide decision makers with more insight into the risks associated with achieving the expected energy performance outcomes.

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Titel: Integrating value and uncertainty in the energy retrofit analysis in real estate investment—next generation of energy efficiency assessment tools
verfasst von: Alireza Bozorgi
Publikationsdatum: 01.10.2015
Verlag: Springer Netherlands
Erschienen in: Energy Efficiency / Ausgabe 5/2015
Print ISSN: 1570-646X
Elektronische ISSN: 1570-6478
DOI: https://doi.org/10.1007/s12053-015-9331-9

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