Voluntary Green Building Certification: Economic Decision or Following the Trend? A Spatial Approach

1. Voluntary Green Building Certification: Economic Decision or Following the Trend? A Spatial Approach Yueming Lucy Qiu AshutoshTiwari Arizona State University Yi David Wang University of International Business and Economics July 30th, 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 EnergyStarprogram • 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 logitmodel + 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

15. Results • Summary statistics, zip code level-CO

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 sqftof green buildings in neighboring zip codes increase by 10%, the share of sqftof 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)