1. The Economics of Green Building
Transatlantic Evidence
Nils Kok
UC Berkeley
Maastricht University
Connecticut Green Building Council
October 2010

2. Energy and buildings
Property sector consumes and pollutes
 Carbon emissions and buildings are closely related
 30% of global CO2 emissions
 70% of U.S. electricity consumption
 Impact of energy costs directly affects tenants and investors
 Energy costs are single largest expense in provision of office space
 30% of operating expenses
 Energy represents 10% of total occupancy cost
 Salience can only increase with rising energy prices
 Awareness is growing…
 Legislation
 US: Waxman-Markey bill
 Europe: EPBD, CRC
 Corporate real estate as part of CSR policy (e.g., Chevron, BoA, …)
 “Sustainable” property funds (e.g., Hines/CalPERS)

3. Usage of “green building” in the popular press…

4. …and visitors at the “Greenbuild” conference

5. “Green” building in the marketplace?
Top-10 “green” commercial office markets

6. Economic significance of “green” building
Implications upon the market for commercial space
 Trends in “green” building may have economic implications
 A higher initial outlay…
 Not clear how much higher (0 – 20%)
 ‘Smarter’ building managers/software
… may be compensated by “green” value drivers
 Cost savings
 Energy savings (up to 35%)
 Emission reduction
 Increased rents
 Reputation effects
 Improved indoor air quality
 Increased economic lives, reduced depreciation
 Case studies on the economic implications
 Focus often on new buildings
 Results are hard to generalize

7. Economic value of “green” label?
Academic research on Energy Star and LEED certification
Sample of 28,000 office buildings (2009 cross section), 3,000 of which
are certified by EPA or USGBC
1. Evidence on economic premium for green office buildings
 Rigorous control for quality differences (PSM)
 Label vintage
2. Identify the sources of rent and value increments
 Explicit link to
 EPAs measures of energy efficiency
 USGBC measures of “sustainability”
Sample of 8,000 office buildings (2007 – 2009 panel), 694 of which are
certified by EPAs Energy Star or the U.S. Green Building Council
3. Short-run price dynamics of green office buildings
 Returns during turbulent 2007 – 2009 period

9. “Green” buildings
Two programs: Energy Star (EPA) and LEED (USGBC)
 Dataset of existing sustainable office properties (U.S.)
 EPAs Energy Star for Commercial Buildings (1995)
 Efficiency in source energy use is in top quarter relative to CBECS
 Standardized for building use (occupancy, hours) and climate
 Certified by professional engineer
 Based on real energy consumption (at least one year of bills)
 USGBCs Leadership in Energy and Environmental Design (1999)
 Scoring systems based on 6 components of “sustainability”
 Energy efficiency is just one component
 Various systems and versions (eg. NC, EB, O&M, ...)
 Based on design stage (and now verified after construction)
 Similar programs exist in Australia, Japan, the UK, …

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

11. 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

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

13. Clusters of “green” and control buildings
Minimum of one control building per cluster
Chicago, IL Houston, TX Columbus, OH

15. Evidence on the “green” premium
Cross section of green buildings at the end of 2009
 October 2009 sampling of green office buildings
 Rental sample
 1,943 green buildings
 18,858 control buildings
 Transaction sample
 744 green buildings
 5,249 control buildings
 To further control for differences in quality, we estimate a propensity
score for each building (Rosenbaum and Rubin, 1983)
 The probability ρ that a building is certified, as a function of its hedonic
characteristics

16. Green buildings and conventional comparables
Propensity score weighting substantially reduces differences

17. Methodology
How to further correct for differences in location and quality?
 Standard real estate valuation framework
(1)
 Rin is the rent, effective rent, or transaction price per sq.ft.
 Xi is a vector of hedonic characteristics
 Size, age, renovation, class, amenities, public transport, …
 Percent change in employment in service sector (CBSA) to control
for regional variation in demand for office space
 Cluster cn dummies to control for location – 1,943 (744) separate
dummies in the rental (transaction) sample
 gi = dummy variable if building i has green label
 Dummy variables for year of sale in transaction sample

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

19. Conclusions and implications (I)
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 are the costs of “greening” properties?

20. The greener the better?
Unique premium for each “green” building
 What is the relation between the variation in the “green” premium
and the LEED-score or energy consumption?
 The increment to rent or market value for green buildings varies:

21. Detailed information on LEED-rated buildings
Standardized indices of buildings’ sustainability
 For 209 (103) LEED-rated buildings, we have information on:

22. The sources of economic premiums for “green”
Relating the premium to LEED and Energy Star data
 Relating increments in rent and market value to the characteristics of
LEED and Energy Star-rated buildings:
(2)
Estimated by GLS (Hanushek 1974)

23. 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
 LEED-certified, score 60: effective rent of 20 percent higher
 Energy Star and LEED are complementary

24. Interpretation of regression results
The increment for LEED rated buildings

25. Information on Energy Star-rated buildings
Emissions of efficient buildings are substantial…
 For 1,719 Energy Star-rated buildings, we have information on:
 Average emission of a building in our sample: 4,326 tons of CO2
 750 cars, 9,000 barrels of oil, …
 Energy Star-rated buildings emit at least a quarter less carbon as
compared to conventional office buildings

26. Regression results (I)
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

27. Regression results (II)
The transaction increment for Energy Star rated buildings
Energy efficiency is reflected in “green” price increment
 A $1 saving in energy costs is associated with a 4.9 percent premium
in market capitalization, which is equivalent to $13/sq.ft.
 This implies a cap rate of about 8 percent

28. Conclusions and implications (II)
LEED and Energy Star labels seem to be complimentary
 The green increment is systematically related to the underlying
characteristics of energy efficiency or “sustainability”
 Market seems to be relatively efficient in pricing these aspects
 Direct capitalization of energy efficiency important information for
investments in building retrofits
 LEED and Energy Star measure somewhat different aspects of
“sustainability” and complement each other
 Low correlation between LEED-score and EUI-score

29. 2007 – 2009 office market dynamics
Office rents, vacancy rate, and unemployment
Office rents –30%
Vacancy rate +40%
Unemployment +115%

30. 2007 – 2009 office market dynamics
Office rents, vacancy rate, and unemployment
Unemployment x2
Office rents –30%
Vacancy rate +30%

31. Short-run price dynamics of green buildings
Substantial increase in rated space in a contracting economy
 8,182 observations as of September 2007
 694 rated buildings and 7,488 nearby control buildings
 Rents, occupancy rates, effective rents
 Same sample matched to financial information in October 2009
 Drop buildings that were converted to “green” during the sample period
 We estimate developments in rents, occupancy rate, effective rents:
N
(3) log Rint = α0 + αt + βi Χ it + ∑γ n cn + δ t git + ε int
n=1
 Rint is the rent or effective rent per sq.ft.
 Xit is a vector of hedonic characteristics
€  Size, age, renovation, class, amenities, public transport, …
 Cluster cn dummies to control for location – 694 separate dummies
 git is a green dummy

32. Results: rental levels and green ratings
The “green” premium has slightly declined….

33. Regression results: logarithmic changes in rent
Returns to “green” comparable to (high quality) buildings

34. Conclusions and implications (III)
“Green” is getting mainstream
 Increased awareness of energy efficiency and the role of the real
estate sector have increased attention upon “green” building
 Energy efficient and sustainable office space is now a large share of
the commercial property sector -- getting mainstream
 This may have economic implications for investors, tenants, and
policymakers
 Buildings certified by Energy Star or LEED command higher rents
and prices in the marketplace
 The “green premium” has slightly decreased during a period of volatility
in property markets…
…but the relative rents of green buildings have remained unchanged
compared to identical conventional buildings

35. Conclusions and implications
LEED and Energy Star labels seem to be complimentary
 Market seems to be relatively efficient in pricing aspects of
“sustainability”
 Implications for investors:
 These developments will affect the existing stock of non-certified office
buildings
 Environmental characteristics are a risk factor that should be priced in
 Policy implications:
 Modest programs by government and non-profit institutes to provide
information are effective and incorporated by market participants
 Directly affects energy consumption (and emissions)
 More aggressive policies?

36. What happens in Europe… (?)

37. 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”

39. Program evaluation of energy labels…
Energy certificates, efficiency, and market pricing
1. The adoption process:
 Adoption rate dynamics
 Adoption determinants
2. The influence of energy certificates on the transaction prices of
dwellings
 Attempt to disentangle labeling effects in:
 Quality of home
 Energy features
3. The real energy use (gas and electricity) for all individual dwellings
 What determines energy consumption?
 How strong is the link between EPC and usage?
 Is energy use capitalized? (for labeled versus non-labeled dwellings)

40. The laboratory
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

41. Adoption rate
Diffusion slows down, but higher in “weak” regions

42. Adoption rate
Diffusion slows down, but higher in “weak” regions

43. Energy labels and home prices
Energy efficiency is clearly reflected…

44. Energy labels are incorporated in prices
Partial reflection of NPV energy savings
 Labels will become mandatory (per EU regulation)
 Private homebuyers take labels into account
 What about tenants?
 Labels will become part of rental policies
 Labels are “popular” in the commercial property market
 Green procurement GSAs (minimum label C, or two label steps)
 Pressure of corporate tenants

45. Questions/remarks?
kok@haas.berkeley.edu