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© 2009 Dwight Jaffee and Nancy Wallace, Slide 1 Fisher Center for Real Estate at Haas School Market Mechanisms for Financing Green Real Estate Investments.

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Presentation on theme: "© 2009 Dwight Jaffee and Nancy Wallace, Slide 1 Fisher Center for Real Estate at Haas School Market Mechanisms for Financing Green Real Estate Investments."— Presentation transcript:

1 © 2009 Dwight Jaffee and Nancy Wallace, Slide 1 Fisher Center for Real Estate at Haas School Market Mechanisms for Financing Green Real Estate Investments Dwight Jaffee and Nancy Wallace Presentation to Conference on Green Building, the Economy, and Public Policy December 3, 2009

2 © 2009 Dwight Jaffee and Nancy Wallace, Slide 2 Fisher Center for Real Estate at Haas School  Starting point: –Buildings consume almost 40% of U.S. energy. –U.S. buildings appear less efficient than Europe.  Why is there not a major retrofit of existing stock? –No mystery: U.S. energy prices far less. –First best solution: Raise U.S. energy prices. Energy Consumption and U.S. Real Estate Table 1.1: Buildings Share of U.S. Primary Energy Consumption (Percent), 2006 Residential Buildings Commercial Buildings Total Buildings IndustryTransportationTotalTotal Consumption (quads) 20.9%18.0%38.9%32.7%28.4%100%99.5 Source: U.S. Department of Energy (2009)

3 © 2009 Dwight Jaffee and Nancy Wallace, Slide 3 Fisher Center for Real Estate at Haas School Government Interve ntions to Stimulate Energy-Efficient Investments  Building codes: –Primarily apply to new construction; –Prescriptive in nature, do not encourage innovation.  Disclosure certificates: –Will raise sales prices based on certificate or benefit; –But are the required investments NPV positive? –Do they stimulate energy-efficient investments?  Government subsidies: –Certainly limited with current fiscal deficits.  Overall, there are certainly benefits, but limited.

4 © 2009 Dwight Jaffee and Nancy Wallace, Slide 4 Fisher Center for Real Estate at Haas School Private Market Failures that Inhibit Energy Efficient Investments  Three key steps in investment process: 1)Identification of worthwhile investments; 2)Computation of comparative NPV; 3)Mortgage market funding of investments.  This identifies two frictions that inhibit energy- efficient investments in commercial real estate: –Limited information on efficient investments; –Limited mortgage loan funding of investments.

5 © 2009 Dwight Jaffee and Nancy Wallace, Slide 5 Fisher Center for Real Estate at Haas School Commercial Mortgage Underwriting  Commercial mortgage underwriting standards: –Loan to value ratio (LTVR, say 65%); –Debt service coverage ratio (DSCR, say 1.25); –Both ratios based on net operating income (NOI).  Energy costs disappear inside NOI: –With triple net lease, tenant pays own energy bill. –Standard ARGUS software has no energy input. –Lenders see no trace of energy costs.  Result is that loan rate, size, and default risk determined by NOI, LTVR, DSCR alone.

6 © 2009 Dwight Jaffee and Nancy Wallace, Slide 6 Fisher Center for Real Estate at Haas School Why Tenants Do Not Pay Higher Rents if Offered Lower Energy Costs  Three key conditions must be met: 1)Tenants must be confident of lower energy costs before they will offer to pay higher rents; 2)Landlords must be confident of higher rents before they will make energy-saving investments; 3)Actual investments must be demonstrably NPV >0 before (1) and (2) will occur.  All steps fail from imperfect information regarding the energy savings and investment productivity.  Thus both lenders and tenants require better tools to measure energy costs and future savings.

7 © 2009 Dwight Jaffee and Nancy Wallace, Slide 7 Fisher Center for Real Estate at Haas School Three Proposed Disclosures  Energy Efficiency Score will provide reliable indication of the impact of investments on energy costs, and therefore on rents and property NOI.  Energy Volatility Score will provide reliable indication of impact of energy cost volatility on rent and NOI volatility.  Energy Efficiency Action Ratio will provide comparative NPV information to evaluate alternative energy-saving investments.

8 © 2009 Dwight Jaffee and Nancy Wallace, Slide 8 Fisher Center for Real Estate at Haas School Embedding Energy Scores in Commercial Mortgage Lending

9 © 2009 Dwight Jaffee and Nancy Wallace, Slide 9 Fisher Center for Real Estate at Haas School Property Tax Assessed Energy Retrofit Financing  AKA as PACE (Property Accessed Clean Energy).  States now allow jurisdictions to create voluntary, individual, clean energy assessment districts. –Energy loans have same seniority as property taxes. –Greatly lower interest cost, expands availability. –Available for both residential and commercial.  But same information requirements still exist: –Cities must have means to evaluate investments. –Commercial lenders must be satisfied that NPV > 0 before they will allow creation of senior lien.

10 © 2009 Dwight Jaffee and Nancy Wallace, Slide 10 Fisher Center for Real Estate at Haas School Monte Carlo Simulation of Stochastic Energy Prices on Building NOI  Mortgage risk/default are based on tail risks: lender’s main concern is the worst outcomes. –Requires stochastic simulation of future energy price paths, with focus on the worst paths. –Alternative energy paths create NOI distribution, with worst outcomes creating default. –Default can be tenant/lease, or owner/mortgage.  Can be integrated with additional state variables, such as interest rates, economy, property prices. –This methodology is already well developed and accepted in commercial mortgage lending.

11 © 2009 Dwight Jaffee and Nancy Wallace, Slide 11 Fisher Center for Real Estate at Haas School Prototype Building in San Diego CA LocationSan Diego Square Feet225,000 Office TenantFIRE Sale Price$127 Million NOI at sale$8.86 Million

12 © 2009 Dwight Jaffee and Nancy Wallace, Slide 12 Fisher Center for Real Estate at Haas School Energy Prices  We model both electricity and natural gas prices, as the exponential of an Orenstein Uhlenbeck process, assuming correlation of 0.90: de t = α e [θ e (t) – e t ] dt + v e (e t, t, …) dw t e dg t = α g [θ g (t) – g t ] dt + v g (g t, t, …) dw t g  Processes calibrated to history:

13 © 2009 Dwight Jaffee and Nancy Wallace, Slide 13 Fisher Center for Real Estate at Haas School Representative Paths for Monthly Electricity Spot Price (2008-2016)

14 © 2009 Dwight Jaffee and Nancy Wallace, Slide 14 Fisher Center for Real Estate at Haas School Simulated Electric Power Costs

15 © 2009 Dwight Jaffee and Nancy Wallace, Slide 15 Fisher Center for Real Estate at Haas School Simulated Natural Gas Costs

16 © 2009 Dwight Jaffee and Nancy Wallace, Slide 16 Fisher Center for Real Estate at Haas School PV Simulations Almost 10% of paths create negative equity and possible default

17 © 2009 Dwight Jaffee and Nancy Wallace, Slide 17 Fisher Center for Real Estate at Haas School Conclusions  Existing U.S. buildings require significant energy saving investments: –Current government actions helpful but limited. –Market failures appear to inhibit private actions.  We focus on informational frictions that impede the recognition of investment opportunities by property owners, tenants and mortgage lenders.  Our prototype Monte Carlo simulation tool indicates it is practical to develop scores that measure the impact of alternative energy investments on property rents and NOI.


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