1. Voluntary Green Building Certification: Economic Decision or Following the Trend? A Spatial Approach Yueming Lucy Qiu Ashutosh Tiwari Arizona State University Yi David Wang University of International Business and Economics July 30 th, 2013 USAEE Anchorage Conference

2. Introduction Two main voluntary green building certification programs in U.S. – Leadership in Energy and Environmental Design (LEED):developed by the US Green Building Council (USGBC) – Energy Star: jointly sponsored by two federal agencies, the US Environmental Protection Agency, and the US Department of Energy The total number and square footage of buildings that have obtained these two green certificates have increased dramatically since 1995.

3. Existing Literature There has been increasing number of economics literature in recent years that examine these voluntary green building certification programs. Eichholtz, Kok, and Quigley (2009), Miller, Pogue, Gough, and David (2009), and Fuerst and McAllister (2011)found a premium on rental rates and sale prices enjoyed by green buildings compared to similar non-green counterparts. One important gap in the literature: to analyse why buildings voluntarily choose to get the green certificates. – Is it because of the peer effects – that the similar buildings nearby are getting the green certificates so a building follows its neighbours? – Or is it because buildings anticipate higher economic return? – Or both? These are the questions we try to address in this paper, using data on commercial buildings from New York, Arizona, Colorado and Florida

4. Existing Literature Several recent studies analyzed the spatial patterns of green buildings. – Kahn and Vaughn (2009) found the clustering of LEED certified buildings. Using zip code level data, they used a zero inflated negative binomial model to analyze the count of registered LEED buildings of each zip code. – Using Michael Porter’s “Diamond” theory, Allen and Potiowsky (2008) explained the clustering of green buildings in Portland, Oregon. – Cidell and Beata (2009) analyzed the variation among the implementation of various LEED certification categories and variation across space and they found these variations to be statistically significant. However, existing studies did not directly estimate the spatial correlation of LEED buildings

5. Contributions of this study To provide quantitative evidence of spatial peer effects on getting green building certificates To examine what factors influence the diffusion of green building certificates

6. Data Compiled from three main data sources – Energy Star database from the EnergyStar program – LEED database from USGBC – A commercial building stock database from ProspectNow.com The Energy Star and LEED database gives information of the addresses of green buildings. ProspectNow.com is a commercial real estate data provider which has the most complete commercial real estate stock with over 8 million commercial properties. It provides detailed information of each commercial building, including address, square footage, year built, assessed property value, improvement value, etc.

7. Data Most of existing studies on green commercial buildings are focused on office buildings only. However, other types of commercial buildings have also obtained green certification, such as shopping stores, food store markets, hospitals, hotels, restaurants. – E.g., more than half of the green buildings in NY are non-office buildings. In this paper, because ProspectNow can provide the info on all types of commercial buildings, we are analyzing commercial buildings sector as a whole, rather than commercial office buildings only. NY, AZ, CO, FL

8. Data Green buildings in NY See some clustering effects  in the analysis, using the fraction of buildings in a neighborhood

9. Models Fraction logit model + Spatial model Fraction logit model: We model the diffusion of green buildings using fraction logit model (Papke and Wooldridge, 1996; Wooldridge 2002). Looking at the spatial correlation of fraction of green buildings among neighborhoods. Neighborhood: zip code

10. Models The conditional mean of the share of green buildings in a zip code is Accordingly, the share of non-green buildings in a zip code has the following conditional mean:

11. Models Fraction logit model is non-linear: however, accounting for spatial correlation is problematic in non-linear models Therefore, apply the inversion approach suggested by Berry (1994)

12. Models Spatial model The decisions of buildings to get green certification could be influenced by their peers  the share of green buildings in a neighborhood is not independent from other neighborhoods The closer the neighborhoods, the larger the influence will be We add a spatial autoregressive term to the fraction logit model

13. Models The spatial weights matrix is row-standardized to have row-sums of unity in most empirical studies: If λ>0, neighbors having more green buildings will increase the number of green buildings of a zip code  peer effects

14. Results Summary statistics, zip code level-NY VariableObsMeanStd.Dev.MinMax Number of Firms in a zip code497679.47916.683.007241.00 Per Sq Ft Total Value49753.0750.920.14429.39 Per Sq Ft Improvement Value49742.2438.880.09303.97 Average year built4971952.7518.691874.002004.20 Average per building sqft49770758.77286208.20560.002812739.0 Number of green buildings in a zip code4971.162.320.0026.00 Number of non-green buildings in a zip code497295.13362.340.002258.00 Share of buildings occupied by owners4970.280.150.001.00 Share of green buildings by number4970.020.130.001.00 Share of non-green buildings by number4970.980.130.001.00 Share of green buildings by area4970.050.150.001.00 Share of non-green buildings by area4970.950.150.001.00

15. Results Summary statistics, zip code level-CO VariableObsMeanStd.Dev.MinMax Number of Firms in a zip code174697.05544.293.002888.00 Per Sq Ft Total Value17425.8816.822.57185.49 Per Sq Ft Improvement Value17417.8211.842.20123.76 Average year built1741975.5818.181903.332009.00 Average per building sqft17440694.41 107601.0 0921.80 929402.1 0 Number of green buildings in a zip code1741.122.100.0012.00 Number of non-green buildings in a zip code174171.29200.610.00876.00 Share of green buildings by number1740.040.150.001.00 Share of non-green buildings by number1740.960.150.001.00 Share of green buildings by area1740.060.170.001.00 Share of non-green buildings by area1740.940.170.001.00

16. Results Spatial models Share by number of buildings If the distance weighted share of the number of green buildings in neighboring zip codes increase by 10%, the share of number of green buildings in a zip code will increase by 8~10%

17. Results Spatial models Share by sqft If the distance weighted share of the sqft of green buildings in neighboring zip codes increase by 10%, the share of sqft of green buildings in a zip code will increase by 8~10%

18. Results-Owner Occupancy

19. Conclusions Title of this paper: Voluntary Green Building Certification: Economic Decision or Following the Trend? A Spatial Approach We have found that in general spatial peer effects is more important than the potential economic gain from the increased property values of green buildings. States do vary in terms of the importance of spatial peer effects vs. economic effects: e.g., Colorado different Implications for policy makers: establishing early adopters and demonstration projects can be helpful in help the diffusion of green buildings

22. Back up slides

23. The weight matrix is a normalized matrix: row-standardized weights matrix (Philip A. Viton, 2010. Notes on Spatial Econometric Models)