The Value of a Green Building Certificate for Office Buildings in CEE
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Transcript of The Value of a Green Building Certificate for Office Buildings in CEE
The Value of a Green Building Certificate for Office Buildings in CEE
Michal Gluszak, Gunther Maier, Sabine Sedlacek, Malgorzata Zieba
Content
Introduction The project team Conceptual background Interviews Survey instrument First results
Introduction
Green Building movement in Central and Eastern Europe Green Building Councils in almost all CEE countries Various strategies, various certification schemes Internationally active developers and consultants got
involved
What is the value of green building certificates in the CEE market? Very few certified buildings Limited information on transactions Need to do a contingent valuation study
The Research Team
Currently Cracow (Poland) and Vienna (Austria) Interested in expansion to other countries
Team Cracow: Michal Gluszak, University of Economics, Cracow Malgorzata Zieba, University of Economics, Cracow
Team Vienna: Gunther Maier, WU Vienna Sabine Sedlacek, Modul University, Vienna
Conceptual Background
Evidence for a positive effect of Green Building certificates on values and rents Eichholtz, Kok & Quigley (2010) (US; LEED and Energy
Star): effective rent premium + 7%, sales price premium +16%; the label by itself has a positive value above the implied energy savings.
Fuerst & McAllister (2011) (US; LEED and Energy Star): rent premium +5% (LEED) and 4% (Energy Star); sales price premium +25% (LEED), +26% (Energy Star)
Wiley, Benefield & Johnson (2010) (US; LEED and Energy Star): rent premium +7% to +17%; higher occupancy by 10% to 18%; selling premium per sqft $30 (Energy Star) to $130 (LEED).
Conceptual Background
Positive image of Green Buildings Addae-Dapaah, Hiang & Shi (2009) (Singapore, survey
of occupants): No effect of awareness and appreciation of green benefits beyond cost savings and higher building values. Benefits are very uncertain.
Hypotheses: Green building certificates have a significant positive
effect on rents and sales prices. In less developed markets (CEE) awareness will be low
Conceptual Background
Method of choice Hedonic price estimation with certificate as explanatory
variable
Problem: Too few green buildings yet in CEE markets; very
limited information on rents and transactions
Solution: Expert interviews Contingent valuation survey
Interviews
Interviews with 16 commercial property professionals
Semi-structured in-depth interviews Main results
Certificate recognition: weak (LEED the most popular), some experts were not familiar with different certification schemes
Green profile: is not a key attribute in a decision process, only one expert spontaneously mentioned it as somewhat important when office space decisions are concerned
Interviews
Main results (cont.) no single expert expected higher rents in certified properties. no single expert expected that company would move to
certified building from not certified premises even if they were provided assistance.
some experts suspected green washing Barriers: supply; current economic conditions Differences by size (bigger companies are more interested in
standard and wellbeing of employees) and nationality (US and UK companies are used to green standards)
Development is driven by international investors (SKANSKA most eminent example)
Interviews
Most important factors in office space choice in Poland:
No Attribute Frequency
1 rent 10
2 location 9
3 structure and size 7
4 maintenance costs 6
5 parking lots 6
6 tenant-mix 4
7 Facilities 3
8 architectural design 1
9 green building certificate 1
Survey instrument
Survey of companies who have moved to new office space within the last 2 years
Goal: identify the WTP (implicit price) for green building certificate
Strategy: contingent valuation Compare current office space with a similar hypothetical
alternative – which one would you have chosen? Analysis by use of a conditional logit model
Survey (start page)
Survey (page 1)
Survey (page 2)
Survey (page 3, repeated 10 times)
Survey
Generating the hypothetical alternatives Criteria are sorted in decreasing expected
attractiveness (new before old, city center before periphery)
For all criteria except price, operating costs and certificate: For the new alternative, we either stay at the criteria value (40%) or go one step up (30%) or down (30%). When out of bounds, it is set to the boundary value.
For certificates: When certificate: 50% same certificate, 50% no certificate; when “no certificate”: 40% no certificate, LEED, BREEAM and DGNB with 20% each
Survey
Generating the hypothetical alternatives Sum of characteristics gives a rough measure of
attractiveness Randomly generated price deviations by 0%, 5%, 10%,
15% or 20% up or down Result centered around zero and shifted by difference in
attractiveness Correction over the experiment:
When only the original option is chosen, the alternative option becomes cheaper
When only the alternative option is chosen, it becomes cheaper
First results
Based on only TWO respondents from Vienna Therefore: no significant coefficients, limited
model qualityIteration 0: log likelihood = -11.46822 Iteration 1: log likelihood = -10.436821 Iteration 2: log likelihood = -10.328076 Iteration 3: log likelihood = -10.327686 Iteration 4: log likelihood = -10.327686
Conditional (fixed-effects) logistic regression Number of obs = 40 LR chi2(3) = 7.07 Prob > chi2 = 0.0697Log likelihood = -10.327686 Pseudo R2 = 0.2550
------------------------------------------------------------------------------ choice | Coef. Std. Err. z P>|z| [95% Conf. Interval]-------------+---------------------------------------------------------------- const | -.9396476 .8159093 -1.15 0.249 -2.5388 .6595052 rent | -.1215278 .0835022 -1.46 0.146 -.2851891 .0421335 cert01 | 1.074909 1.112476 0.97 0.334 -1.105504 3.255322------------------------------------------------------------------------------
First results
Iteration 0: log likelihood = -13.862944 .....Iteration 12: log likelihood = -4.938938
Conditional (fixed-effects) logistic regression Number of obs = 40 LR chi2(8) = 17.85 Prob > chi2 = 0.0224Log likelihood = -4.938938 Pseudo R2 = 0.6437
------------------------------------------------------------------------------ choice | Coef. Std. Err. z P>|z| [95% Conf. Interval]-------------+---------------------------------------------------------------- loc | -.6671478 1.751317 -0.38 0.703 -4.099666 2.765371 transp | 16.89953 3649.109 0.00 0.996 -7135.223 7169.022 age | -2.088071 3.2887 -0.63 0.525 -8.533804 4.357663 type | -1.473766 3.592481 -0.41 0.682 -8.5149 5.567368 qual | -38.38153 7382.253 -0.01 0.996 -14507.33 14430.57 const | -.5146494 2.223854 -0.23 0.817 -4.873324 3.844025 rent | -.296666 .306436 -0.97 0.333 -.8972694 .3039375 cert01 | 18.15025 3649.109 0.00 0.996 -7133.972 7170.272------------------------------------------------------------------------------
Conclusions
Important question Survey instrument is tested and ready Lot of additional work needs to be done Interesting to add additional CEE countries