Nils Kok Maastricht University

An Overview of

The Economics of Green Building

(everything you always wanted to know, and more….)

Electricity consumption and the built environment Residential and commercial sector consume 74% of US total

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Energy Consumption in Buildings (LHS)

Substantial environmental externalities Consequences are a global (economic) threat 1.  Small improvements in buildings can have large effects

q  Carbon emissions and buildings are closely related q  30-40 percent of global GHG emissions

q  Built environment offers largest potential for greenhouse gas abatement q  IPCC (2007), Stern (2008), McKinsey cost abatement curve

2.  Impact of energy costs directly affects tenants and investors q  30 percent of operating expenses, 10 percent of total housing costs q  Salience can only increase with rising energy prices

3.  Awareness is growing q  Corporate real estate as part of CSR policy (e.g., Chevron, BoA, …) q  Investor focus on energy efficiency q  Legislation

Some policy responses Energy efficiency debate prominent in policy circles

How to reduce energy consumption in the property sector?

0. Raise energy prices q  Cap-and-trade in California, Europe, UK, and Australia

1.  Stricter building codes and subsidizing retrofits q  Works, but mostly for new construction, and effects are small

q  Building codes for residential homes are effective at saving energy (Jacobsen and Kotchen, in press)

q  Fiscal tight-belting constrains subsidies and effects unclear 2.  Stimulating market efficiency through transparency (energy labels)

q  Investments in energy efficiency may lead to: q  Save on current resources, insure against future price increases q  Higher transaction prices

Alternatively: voluntary labels

“Green” buildings in the US Two programs: Energy Star (EPA) and LEED (USGBC)

§  EPAs Energy Star for Commercial Buildings (1995) q  Efficiency in source energy use is in top quarter relative to CBECS q  Standardized for building use (occupancy, hours) and climate q  Certified by professional engineer q  Based on real energy consumption (at least one year of bills)

§  USGBCs Leadership in Energy and Environmental Design (1999) q  Scoring systems based on 6 components of “sustainability” q  Energy efficiency is just one component q  Various systems and versions (eg. NC, EB, O&M, ...) q  Based on design stage (and now verified after construction)

§  Similar schemes exist internationally (BREEAM, HQE, DGNB, CASBEE, Green Star, HK BEAM, etc.)

A hype? Visitors at the annual “Greenbuild” (USGBC) conference

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Visitors at "Greenbuild" conference

“Green” building diffusion in the marketplace Trends in 48 MSAs, 1995 – 2010 (Kok et al., 2011)

§  Energy Star, 2010: q  10 percent of office buildings q  30 percent of stock

§  LEED, 2010: q  5 percent of office buildings q  10 percent of stock

§  Registered: 27,000 buildings (6b sq.ft.)

§  Size effect (Snyder, et al., 2003)

Incidence of Green Space Utilization by Major Tenants Fraction of Firm’s Office Space Housed in Green Buildings

Space Occupied (1) (2) (3)

Tenant Name Green Office Space Total Space

CoStar Green as

Fraction of Total Space CoStar

x 1000 sq. ft. x 1000 sq. ft. % Wells Fargo Bank 2,741 7,343 37.33% United States Government 2,415 14,631 16.50% Bank of America 2,124 18,695 11.36% ABN AMRO 1,724 2,993 57.60% State of California 1,568 5,706 27.49% Deloitte & Touche 1,554 5,131 30. 28% Best Buy 1,500 2,104 71.31% U.S. Dept. of Health & Human Sc. 1,442 1,662 86.72% Shell 1,362 3,989 34.14% Chevron 1,229 6,181 19.88% Blue Cross & Blue Shield 1,211 12,251 9.89% Adobe Systems 1,158 1,388 83.43% Compuware Corporation 1,094 1,300 84.18% American Express 1,018 6,754 15.07% The Vanguard Group 990 1,569 63.07% Cal/EPA 950 950 100.00% Mitre Corporation 944 1,293 73.02% JP Morgan Chase 907 10,670 8.50% Skadden 889 1,751 50.77% Ernst & Young 864 4,149 20. 83%

Incidence green space utilization major tenants Fraction of firm’s office space housed in green buildings

Incidence of Green Space Utilization by Industry Fraction of Office Space Housed in Green Buildings by Four -Digit SIC

Space Occupied (1) (2) (3)

SIC Code Industry Description Green Office Space

Total Office Space CoStar

Green as Fraction of Tota l Space

CoStar x 1000 sq. ft. x 1000 sq. ft. % 8111 Legal Services 25,593 217,097 11.79% 6021 National Commercial Banks 9,436 86,782 10.87% 9199 Executive, Legislative and General Office 9,035 67,081 13.47% 1311 Crude Petroleum and Gas 7,076 11,304 62.60% 6282 Investment Advice 6,532 100,939 6.47% 8721 Accounting, Auditing, and Bookkeeping Services 5,158 136,766 3.77% 5731 Radio, Television, and Consumer Electronics Stores 1,531 3,888 39.37% 9311 Public Finance, Taxation, and M onetary Policy 822 14,491 5.67% 7373 Computer Integrated Systems Design 816 19,487 4.19% 3812 Search, Detection, Navigation, Guidance, … 291 4,869 5.97% 2759 Commercial Printing, NEC 287 3,996 7.17% 3069 Fabricated Rubber Products, NEC 285 769 37.08% 4731 Arrangement Transportation of Freight and Cargo 282 8,348 3.38% 9621 Regulations and Adm. of Transportation Programs 280 9,115 3.07% 7997 Membership Sports and Recreation Clubs 274 1,696 16.15% 8641 Civic, Social, and Fraternal Asso ciations 274 14,362 1.91% 2086 Bottled and Canned Soft Drinks, Carbonated Waters 261 5,037 5.19% 5411 Grocery Stores 253 8,363 3.03% 4724 Travel Agencies 252 7,539 3.34% 6552 Land Subdividers and Developers, 250 9,676 2.58%

Incidence of green space utilization per industry Fraction of office space located in green buildings

Economic significance of “green” building? Trends in “green” building may have economic implications §  The supply side

q  Incremental cost still unclear (Davis Langdon: no difference) q  “Smarter” building managers/software

§  The demand side q  Direct cost savings

q  Energy savings q  But also: lower insurance premiums (Fireman’s Fund: 5% discount)

q  Stronger rent-roll (investors) q  Reputation effects q  Corporate preferences (IAQ, corporate policies)

q  Higher value q  Increased economic lives q  Lower risk (reduced depreciation)

§  Limited systematic evidence q  Case studies on the economic implications focus often on new buildings q  Some first evidence: Eichholtz, et al. (2010), Fuerst and McAllister (2011)

Research design (Eichholtz et al, 2013) Investment dynamics and the source of “green” increments

Sample of 28,000 office buildings (2009 cross section), 3,000 of which are certified by EPAs Energy Star or the U.S. Green Building Council

1.  Evidence on the economic premium for green office buildings q  Rigorous control for quality differences (PSM) q  Label vintage

2.  Identify the sources of rent and value increments q  Explicit link to:

q  USGBC measures of “sustainability” q  EPAs measures of energy efficiency

Sample of 8,000 office buildings (2007 – 2009 panel), 694 of which are certified by EPA or USGBC

3.  Short-run price dynamics of green office buildings q  Returns during turbulent 2007 – 2009 period

Example: 101 California St, San Francisco Energy Star certified, LEED Gold

Defining conventional comparables Systematic match on location

§  Based upon longitude and latitude, we use GIS to identify all conventional office buildings in a 0.25 mile radius

§  One green building per cluster, control buildings can be in sample more than once

Example: 101 California St, San Francisco Energy Star certified, LEED Gold

Propensity-weighted regression results Market implications of Energy Star and LEED

So… Eco-investment real estate sector is not only “doing good”

§  Ceteris Paribus, green buildings 1.  Have higher rents by 2-6% 2.  Have higher effective rents by 6-8% 3.  Have higher selling prices by 11-13%

§  The average non-green building in the rental sample would be worth $5.6 M more if it were converted to green

§  The average non-green building sold in 2004-2009 would have been worth $11.1 M more if it had been converted to green

§  The implied cap rate (3%) suggests that property investors value the lower risk premium inherent in certified commercial office buildings

§  The missing piece…what is the cost of “greening” properties?

Generalization of the model Unique premium for each “green” building

§  The increment to rent or market value for the green building in cluster n, relative to the prices of other buildings in that cluster (i.e., controlled for location, climate, and quality):

(2)

Regression results (I) The rental increment for LEED rated buildings

§  LEED-certified, score 40: effective rent of 2 percent higher than otherwise identical, registered building

§  Energy Star certification is complementary to LEED certification

Regression results (II) The rental increment for Energy Star rated buildings

§  A $1 saving in energy costs is associated with an increase in effective rent of 95 cents

§  A $1 saving in energy costs is associated with an increase in effective rent of 95 cents

§  A $1 saving in energy costs is associated with a 4.9 percent premium in market capitalization, which is equivalent to $13/sq.ft. q  This implies a cap rate of about 8 percent

§  Market seems to be relatively efficient in pricing these aspects q  Energy efficiency incorporated in rents and prices q  Direct capitalization of energy efficiency also important information for

investments in building retrofits

§  LEED and Energy Star measure somewhat different aspects of “sustainability” and complement each other q  Low correlation between LEED-score and EUI-score (more later)

Interpretation of results Energy efficiency is capitalized quite precisely….

2007 – 2009 office market dynamics Office rents, vacancy rate, and unemployment

Office rents –30%

Vacancy rate +40%

Unemployment +115%

Short-run price dynamics of green buildings Substantial increase in rated space in a contracting economy

§  Green as a luxury good (Bils and Klenow, 1998) or “the chilling effect” of the recession on environmental concerns (Kahn and Kotchen, 2010)?

§  8,182 observations as of September 2007 q  694 rated buildings and 7,488 nearby control buildings q  Rents, occupancy rates, effective rents q  Same sample matched to financial information in October 2009

§  We estimate developments in rents, occupancy rate, effective rents:

q  Dependent variable is the logarithmic change in rent between times τ and T. q  (αT – ατ) measures the nominal change in log rents during the interval τ - T. q  (XiT – Xiτ) is the change in the hedonic characteristics of building i q  is the change in the average rental increment for a green-rated

building at times T and τ, q  We include cluster dummies to control for location – 694 separate dummies

logRinT − logRinτ[ ] = αT −ατ( )+βi Χ iT −Χ iτ( )+ δTgiT −δτgiτ( )+ εinT −εinτ( )

δTgiT −δτgiτ( )

Rent (per sq. ft)

Effective Rent# (per sq. ft)

(1) (2) (3) (4) (5) (6) Green Rating in 2007 and 2009

(1 = yes) -0.030** [0.012]

-0.014 [0.013]

0.005 [0.013]

-0.052*** [0.014]

-0.032** [0.016]

-0.010 [0.016]

Change in CBSA Vacancy Rate 2007 – 2009 (percent)

-0.094*** [0.013]

-0.065*** [0.014]

-0.121* [0.071]

-0.165*** [0.019]

-0.110*** [0.020]

-0.075 [0.118]

Renovated Between 2007 – 2009 (1 = yes)

0.031 [0.024]

0.019 [0.024]

0.068*** [0.026]

0.064 [0.043]

0.048 [0.042]

0.086** [0.040]

Building Class: Class A (1 = yes) -0.041***

[0.015] -0.032* [0.019] -0.065***

[0.022] -0.043 [0.026]

Class B (1 = yes) -0.022*

[0.012] -0.014 [0.014] -0.036**

[0.018] -0.013 [0.020]

Age: 0 – 10 years (1 = yes) -0.052**

[0.024] -0.029 [0.028] -0.099***

[0.033] -0.050 [0.040]

Amenities (1=yes)## -0.012

[0.009] -0.023*** [0.009] -0.043***

[0.012] -0.053*** [0.012]

Constant -0.005 [0.006]

-0.089 [0.059]

0.066 [0.080]

0.003 [0.007]

-0.258*** [0.084]

-0.174* [0.105]

Location Clusters### No No Yes No No Yes Observations 4,541 4,541 4,541 4,541 4,541 4,541 R2 0.014 0.034 0.233 0.023 0.046 0.221 Adj R2 0.0134 0.0301 0.124 0.0223 0.0425 0.110

Regression results Logarithmic changes in rent and effective rent, 2007-2009 Relative rents remain unchanged

What about the residential real estate sector?How energy literate are private consumers?

§  Current policies to reduce energy consumption assume rational decision-making by informed investors

§  That seems to hold for sophisticated investors in commercial property…

q  Labels have financial implications (Eichholtz et al., 2010, Fuerst and McAllister, 2011, etc.)

q  Efficient capitalization of energy bill (Eichholtz et al., 2012)

…but not necessarily for private consumers q  Residential “energy literacy” is low (Brounen et al., 2012) and nudges inform

consumers (Alcott, 2011)

§  Solar is capitalized into home prices (Dastrup et al., 2012)

§  Labeling programs in Europe and the US are becoming more prevalent q  Mandatory disclosure of EU energy label (Brounen and Kok, 2011) q  Voluntary disclosure of Energy Star/LEED label in the US

So, what happens in Europe…?

EU Energy Performance of Buildings Directive Originated January 2003, revised December 2009

“Member states shall ensure that, when buildings are constructed, sold or rented out, an energy performance certificate is made available by the owner to the prospective buyer or tenant”

The laboratory (Brounen and Kok, 2011) The Netherlands introduced energy certificates in Jan 2008

Stylized facts: Population: 16.5 mln Homes: 7.2 mln Ownership: 55% Temperature: 50 F

(34 F–64 F) Average home price: $322,000 Net mortgage: $1,120/month Gas bill: $133/month Electricity bill: $74/month

Adoption of the energy label Diffusion slows down, curve follows “media index” But adoption rates are higher in “weak” regions

Adoption of the energy label Diffusion slows down, curve follows “media index” But adoption rates are higher in “weak” regions

Heckman model to assess price impact Transaction discount for inefficient dwellings

“Green” homes in California (Kahn and Kok, 2012) Energy Star (EPA), LEED (USGBC), and GreenPoint Rated

§  Green labels for homes: reflection of steady state efficiency q  EPAs Energy Star for Homes (1995)

q  Asset rating (i.e., does not account for actual performance) q  For new construction only q  Changed in 2006 and 2012 q  Certified by professional engineer

q  USGBCs LEED for Homes (2005) q  Scoring systems based on 6 components of “sustainability” q  Energy efficiency is just one component q  Based on design stage (and now verified after construction)

q  GreenPoint Rated q  Comparable to LEED for Homes q  Primarily marketed in California q  Also for existing homes

§  Diffusion of green home labels substantially lags the commercial sector

Model specification (II) Hedonic model expanded with interaction terms §  Market implications of “green” certification for residential dwellings:

8.7 percent premium q  Is the willingness to pay affected by climate, energy prices? But also:

role of ideology and competition?

§  Recover heterogeneous effects of green home labels:

(1)

q  N is an interaction term that reflects: q  Local climatic conditions q  Local electricity prices q  Consumer ideology q  Green density

§  Caveat q  Green homes are mostly production homes, not high-end custom

homes, but…we have no information about the developer – possibility of bundling valuable amenities with green attributes (appliances, etc.)

log(Rijt ) =α0greenit +α1Ngreenit +βXi +γ jt +εijt

Heterogeneity in capitalization of green labels Weather and ideology matter, price and competition do not

§  Distinguish effects of energy-savings aspect of rating from other, intangible effects of label itself

Discussion of results The costs and benefits of green homes

§  Ceteris paribus, green homes have higher selling prices by 9%

§  The average non-green home in the sample would be worth $34,800 more if it were converted to green

§  What about relative input costs? §  Anecdotal evidence shows cost is $10,000 higher (at most), to construct

a dwelling that is 35 percent more efficient than code

§  What about the value of energy savings? §  30 percent savings on a typical $200/month energy bill translate in a

simple payback period of 48 years for the green increment

§  Other features seem to add value §  Unobservables – savings on resources other than energy, but also:

advanced ventilation systems, higher comfort, better IEQ §  Some homeowners attribute non-financial utility to a green label

(comparable to heterogeneity in solar premium)

What does all of this mean for investors? Energy efficiency and the capital market

§  Debt q  Higher risk for buildings more exposed to energy shocks

q  Lower LTVs, higher DSCRs q  Additional PACE "lien" on building not necessarily bad news

§  Equity q  Opportunity for ”green" real estate funds (Hines-CalPERS) q  Screen existing investments on environmental performance

§  Eichholtz et al. (2012) study on the effect of portfolio greenness on the financial performance of REITs

q  Dynamic measure of portfolio greenness q  Two channels

q  Benefits at property level q  Benefits from making CSR investment

q  Causality issues

Model specification Causality issues are main concern

§  We apply two-stage regressions, instrumenting the measures of “greenness” q  Exogenous measures that influence greenness

q  The weighted locational greenness (WLG) q  The weighted locational green policy (WLGPL)

q 

where i stands for REIT i, j stands for MSA j and t stands for year t

Model Portfolio greenness and financial performance

§  We then estimate the following equation:

§  Greenness stands for Number_Certified, Sqft_Certified and Score_Certified for both LEED and Energy Star certifications.

§  Financial Performance is proxied by ROA, ROE, Funds from Operations (FFO)/Total Revenue, Alpha and Beta.

§  Z covers a vector of control variables.

Stock performance No significant effect on “alpha”

Stock performance But beta’s are significantly lower for “greener” REITs

Summary of main findings REITs are affected by energy efficiency/sustainability §  We identify that, on average, 1% and 6% of the REIT property

portfolios are green-certified in 2010 for LEED and Energy Star, respectively

§  We find that greenness enhances operating performance when we estimate ROA, ROE and FFO/Total Revenue q  Different from analysis of financial performance of green properties,

these measures are net of costs.

§  We partially find an effect of greenness on abnormal returns

§  Greenness decreases market risk q  Possibly, due to sustainable returns of green properties. q  Properties in the portfolios are less exposed to market fluctuations.

Two practical applications 1. A green property index

Two practical applications 2. A real estate “ESG” benchmark

But…how effective are labels really?

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1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

Energy Consumption in Buildings (LHS) Of Which Commercial (RHS)

Energy conservation in commercial property An understudied area (in economics)

§  Much of current debate on energy efficiency focuses on residential sector (regulation, incentives, nudges, shocks, …) q  Brounen et al. (2012, in press), Costa and Kahn (2011), Reiss and White

(2005), Alcott (2011)

§  Literature on energy efficiency in commercial real estate focuses mostly on financial implications of (green) labels… q  Eichholtz et al. (2010, in press) …but how effective are these labels?

§  Commercial buildings are chunky so large effects by “treating” a small group, but…what determines electricity consumption in commercial buildings? q  Information from CBECS and engineering sources is limited, technical

and outdated

New paper of Kahn et al. (2012) Explaining commercial building electricity consumption

Commercial building electricity consumption is a function of: 1.  Construction characteristics

q  Square footage q  Vintage (price of electricity at time of consumption) q  Unobservables (e.g., architecture, amenities)

2.  Equipment quality and occupants q  Quality of HVAC systems, lighting, etc.

q  Does technological progress reduce energy consumption (Knittel 2012)?

q  Occupants and their behavior (tenants, appliances)

3.  Lease contracts q  Defines how payments are allocated and may affect economic

performance (Gould et al., 2005) q  Full gross (zero marginal cost) q  Modified gross (pro-rated share, free rider problem) q  (Triple) net

Empirical framework (II) Explaining commercial building electricity consumption

Commercial building electricity consumption is a function of:

4.  Human capital q  On-site building manager may affect energy consumption (comparable

to human capital of managers in manufacturing plants, Bloom et al., 2011)

5.  Macro conditions q  Climatic conditions

q  Tenant response dependent on building: “rebound effect” (Van Dender and Small, 2007; Davis, 2008)

q  Economic conditions (business cycle)

Data Unique panel on consumption, quality and contracts

50,000 commercial accounts in service area of a utility, merged with CoStar database – 38,906 accounts in 3,521 buildings over 2000 – 2010 period.

§  Energy consumption Billing information Electricity use per account per building (kWh)

monthly data transformed into daily consumption

§  Structure data Hedonic characteristics CoStar Vintage, size, property type (no multi-family), location, quality Occupancy rate

§  Behavioral data Property “demographics” CoStar Tenant (SIC code), building manager, lease contract (triple

net, full gross, …)

§  Other data Climatic conditions (NOAA) measured by average maximum temp, business cycle (unemployment rate)

Descriptive statistics Commercial stock is young relative to residential dwellings

Model specification (I) Cross-sectional analysis: consumption variation

§  The cross-sectional variation in commercial building energy consumption:

(1)

q  Eit is the average daily energy consumption in month t (in kWh)

q  Zt is a vector of structural characteristics of building i q  controls for locational variation in energy consumption, measured by

distance to city center q  Month-fixed effects

§  We assume no tenant sorting based on energy efficiency or contract characteristics. No information on electricity prices.

§  This empirical framework has some similarities with the model used by DOE and the EPA in calculations for the Energy Star program – but includes many more covariates

di

ln Ei( ) = βZi +γdi +εi

Regression results Cohort effects and building quality

§  Some economies of scale in larger buildings q  But discontinuity for buildings

> 50,000sq.ft. §  Vintage negatively related to

electricity consumption q  Exception: < 1970 q  Strongly contrasting findings for

residential dwellings q  Very recent buildings seem to

perform better

Regression results Cohort effects and building quality

§  Building quality and electricity consumption are complements, not substitutes. Comparable to vehicle weight and engine power (partially) offsetting technological progress in vehicles (Knittel, 2012)

§  Facing a marginal cost for energy consumption matters for tenants

(Levinson and Niemann, 2004) §  Soft budget constraints increase energy consumption §  Human capital seems to be important in building energy optimization

(Bloom et al., 2011) and is more likely to be present in gross buildings

Regression results Contract terms and human capital

Model specification (II) Panel analysis: consumption dynamics

§  The longitudinal variation in commercial building energy consumption:

(2)

q  Eit is the average daily energy consumption in month t (in kWh)

q  TEMPt is a vector of temperature dummies q  OCCit is the occupancy rate in building i in month t q  EMPLt is the local unemployment rate (reflecting business cycle) q  capture building-fixed effects, year-fixed effects and month-

fixed-effects, respectively q  Standard errors clustered at the property level

§  We implicitly assume no self-selection of heterogeneous tenants into different types of buildings – based on energy efficiency characteristics

ln Eit( ) = βTEMPt +γOCCit +δEMPLi +αi +βy +τm +εit

αi,βy,τm

Regression results Concave effect occupancy rate on electricity consumption

Regression results explained Dynamics have important effect on consumption

§  Non-linear relation between occupancy and energy use – empty buildings consume energy as well…

§  Building transaction increase energy consumption: investments in new systems offset by behavior of tenants

§  Beyond affecting occupancy rates, effect of business cycle is reflected on energy consumption (Henderson et al., 2011). May reflect the lower use-intensity of space (for instance, corporations having reduced presence in the space they occupy)

Temperature response estimations – type Office buildings more responsive to shocks

§  Temperature split in deciles, decomposing upper and bottom decile in 1st, 5th, 95th and 99th percentile

§  Temperature increase of 32F (99th percentile) leads to 35 (23)

percent higher electricity consumption for office (industrial)

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Temperature response estimations – leases Zero marginal cost induces earlier and more cooling

§  In buildings where tenants face a zero marginal cost for energy

consumption, the response to increases in outside temperature starts at lower temperatures and increases more rapidly

Temperature response estimations – age Recently constructed buildings more responsive to shocks

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§  More recently constructed buildings react stronger to change in temperature – confirming “behavioral hypothesis” on rebound effect. (Other structural effects may play a role as well.)

§  Higher quality buildings react stronger to change in temperature – confirming “behavioral hypothesis” on rebound effect. (Other structural effects may play a role as well.)

Temperature response estimations – quality Higher quality buildings more responsive to shocks

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Macro trends in energy consumption (’00-’10) Commercial building trend is flat

§  Long-term trends in energy consumption are rising, due to increase in stock of durable capital and increasing use intensity q  California stands out: “Rosenfeld Curve” (Charles, 2009)

§  What happened to electricity consumption in our sample commercial buildings?

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Conclusions and implications Future policies should focus more on commercial sector §  We document an inverse relation between building vintage (and

quality) and the electricity consumption q  Contrasts with evidence on residential structures, so policymakers might

be lulled… q  Comparable to technological progress in automobiles

§  Some explanations for our results 1.  Building codes have been developed for commercial buildings (targeting

25 percent savings), but these mostly affect energy consumption for heating (Belzer et al., 2004);

2.  The composition of the fuel mix has shifted away from gas and heating oil (the “electrification” of society);

3.  Accelerated diffusion of personal computers, printers and other equipment may comprise a significant amount of the recent increase in electricity consumption (the “computerization” of society);

4.  The behavioral response of building tenants may lead to more intensive use of more efficient equipment as marginal price of “comfort” is lower – the rebound effect.

Wrapping up Large increases in electricity consumption ahead…

§  Durable building stock is a major consumer electricity, and this is bound to increase. Between 2005 and 2030: q  Residential electricity use is predicted to increase with 39 percent q  Industrial electricity use is predicted to increase with 17 percent q  Commercial electricity use is predicted to increase with 63 percent (!!)

§  Energy efficient and sustainable office space is now a large share of the commercial property sector – getting mainstream

§  Policy implications of (early) findings: q  Market seems to be relatively efficient in pricing aspects of

“sustainability” q  Modest programs by government to provide information are effective and

incorporated by market participants q  This holds for residential as well as commercial real estate

§  But how efficient are new, “green” buildings? Future policies should focus more on commercial sector

q  Mandatory disclosure of “in use” energy labels q  Targeted subsidies or interventions using predictive model for energy

“hogs”

§  Vendors/building managers q  Payback period too narrow

q  Efficiency measures have indirect return q  Lower utility bill reflected in higher rents

q  Important for "triple net" leases

§  Building owners q  Environmental performance affects building value

q  Portfolio risk management q  Optimize equity yield

Wrapping up (II) Environmental characteristics are a risk factor