The Value of a Green Building Certificate for

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 & Mc. Allister (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 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 quality Iteration Iteration 0: 1: 2: 3: 4: log log log likelihood likelihood = = = -11. 46822 -10. 436821 -10. 328076 -10. 327686 Conditional (fixed-effects) logistic regression Log likelihood = -10. 327686 Number of obs LR chi 2(3) Prob > chi 2 Pseudo R 2 = = 40 7. 07 0. 0697 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 cert 01 | 1. 074909 1. 112476 0. 97 0. 334 -1. 105504 3. 255322 ---------------------------------------

First results Iteration 0: . . . Iteration 12: log likelihood = -13. 862944 log likelihood = -4. 938938 Conditional (fixed-effects) logistic regression Log likelihood = -4. 938938 Number of obs LR chi 2(8) Prob > chi 2 Pseudo R 2 = = 40 17. 85 0. 0224 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 cert 01 | 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 additional CEE countries