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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.

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The Value of a Green Building Certificate for Office Buildings in CEE

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  1. The Value of a Green Building Certificate for Office Buildings in CEE Michal Gluszak, Gunther Maier, Sabine Sedlacek, Malgorzata Zieba

  2. Content • Introduction • The project team • Conceptual background • Interviews • Survey instrument • First results

  3. 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

  4. 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

  5. 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).

  6. 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

  7. 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

  8. 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

  9. 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)

  10. Interviews • Most important factors in office space choice in Poland:

  11. 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

  12. Survey (start page)

  13. Survey (page 1)

  14. Survey (page 2)

  15. Survey (page 3, repeated 10 times)

  16. 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

  17. 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

  18. First results Based on only TWO respondents from Vienna Therefore: no significant coefficients, limited model quality Iteration 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 = 0.0697 Log 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 ------------------------------------------------------------------------------

  19. 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 = 0.0224 Log 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 ------------------------------------------------------------------------------

  20. Conclusions Important question Survey instrument is tested and ready Lot of additional work needs to be done Interesting to add additional CEE countries

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