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 30th, 2013 USAEE Anchorage Conference 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. 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 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 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 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. 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 Data • Green buildings in NY • See some clustering effects in the analysis, using the fraction of buildings in a neighborhood 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 Models • The conditional mean of the share of green buildings in a zip code is exp(X i ) E ( sgi X i ) 1 exp(X i ) • Accordingly, the share of non-green buildings in a zip code has the following conditional mean: 1 E ( s0i X i ) 1 exp(X i ) 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) log(sˆgi ) X i log(1 exp(X i )) log(sˆ0i ) 0 log(1 exp(X i )) log(sˆgi ) log(sˆ0i ) X i 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 Models log(sˆgi ) log(sˆ0i ) X i wij log(sgj ) i j i • 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 Results • Summary statistics, zip code level-NY Variable Obs Number of Firms in a zip code 497 Per Sq Ft Total Value 497 Per Sq Ft Improvement Value 497 Average year built 497 Average per building sqft 497 Number of green buildings in a zip code 497 Number of non-green buildings in a zip code 497 Share of buildings occupied by owners 497 Share of green buildings by number 497 Share of non-green buildings by number 497 Share of green buildings by area 497 Share of non-green buildings by area 497 Mean Std.Dev. Min 679.47 916.68 3.00 53.07 50.92 0.14 42.24 38.88 0.09 1952.75 18.69 1874.00 70758.77 286208.20 560.00 1.16 2.32 0.00 295.13 0.28 0.02 0.98 0.05 0.95 362.34 0.15 0.13 0.13 0.15 0.15 0.00 0.00 0.00 0.00 0.00 0.00 Max 7241.00 429.39 303.97 2004.20 2812739.0 26.00 2258.00 1.00 1.00 1.00 1.00 1.00 Results • Summary statistics, zip code level-CO Variable Number of Firms in a zip code Per Sq Ft Total Value Per Sq Ft Improvement Value Average year built Average per building sqft Number of green buildings in a zip code Number of non-green buildings in a zip code Share of green buildings by number Share of non-green buildings by number Share of green buildings by area Share of non-green buildings by area Obs 174 174 174 174 174 174 174 174 174 174 174 Mean Std.Dev. Min 697.05 544.29 3.00 25.88 16.82 2.57 17.82 11.84 2.20 1975.58 18.18 1903.33 107601.0 40694.41 0 921.80 1.12 2.10 0.00 171.29 0.04 0.96 0.06 0.94 200.61 0.15 0.15 0.17 0.17 0.00 0.00 0.00 0.00 0.00 Max 2888.00 185.49 123.76 2009.00 929402.1 0 12.00 876.00 1.00 1.00 1.00 1.00 Share by number of buildings NY 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% AZ FL CO All four states 5 Model number 1 2 3 4 Spatially Weighted Green Share AZ 0.760 (0.389)** 1.159 (0.498)** 1.034 (0.340)*** 0.194 (0.529) 0.975 (0.205)*** 0.003 0.005 0.004 0.004 -2.315 (1.023)** -1.502 (0.834)* -0.273 (0.868) 0.003 (0.0004)*** -0.052 (0.030)* 0.068 (0.039)* (0.001)*** -0.085 (0.309) -0.028 (0.412) (0.001)*** -0.016 (0.017) 0.003 (0.003) (0.001)*** -0.257 (0.131)* 0.394 (0.190)** (0.0004)*** -0.006 (0.007) 0.003 (0.003) 6.85E-06 (1.48E-06) *** 0.044 -8.56E-06 (1.05E-05) 0.005 6.26E-05 (3.60E-05) * 0.006 2.04E-05 (8.55E-06) ** 0.057 7.17E-06 (1.30E-06) *** 0.049 (0.021)** -91.491 (40.555)** (0.052) -10.736 (102.548) (0.047) -13.464 (92.572) (0.036) -127.495 (69.805)* (0.015)*** -99.310 (29.658)*** CO FL Number of Firms Per Sqft Total Value Per Sqft Improvement Value Average per building sqft Average year built _cons # of obs. F Adj R-squared 497 165 146 174 14.18 6.03 8.6 8.7 0.1375 0.1554 0.2392 0.2107 Notes: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 982 20.6 0.1524 ual state and all states together. Table XXX. Results for individual state and all states together. mber of buildings AZ Share Shareby bysqft number of buildings FL CO Results 2 3 4 1.159 (0.498)** 1.034 (0.340)*** 0.194 (0.529) All four states Model number 5 Spatially 0.975 Weighted Green (0.205)*** Share AZ -2.315 (1.023)** CO -1.502 (0.834)* FL -0.273 (0.868) 0.004 Number 0.003 of Firms (0.001)*** (0.0004)*** -0.257 Per Sqft -0.006 Total (0.131)* Value(0.007) 0.394 Per Sqft 0.003 (0.190)** Improvement (0.003) Value 2.04E-05 Average 7.17E-06 per building sqft (8.55E-06) (1.30E-06) ** *** 0.057 Average 0.049 year built (0.036) (0.015)*** • Spatial models • Share by sqft • If the distance weighted share 0.005 0.004 of the(0.001)*** sqft of (0.001)*** -0.085 -0.016 green buildings in (0.309) (0.017) neighboring zip -0.028 0.003 (0.412) codes (0.003) increase by -8.56E-06 6.26E-05 10%, the share of (1.05E-05) (3.60E-05) sqft of green * 0.005 0.006 in a zip buildings (0.052) (0.047) code will increase -10.736 -13.464 -127.495 by 8~10% (102.548) (92.572) (69.805)* 165 146 174 6.03 8.6 8.7 0.1554 0.2392 0.2107 heses. *** p<0.01, ** p<0.05, * p<0.1 _cons-99.310 (29.658)*** # of obs. 982 F 20.6 Adj R-squared 0.1524 NY NY AZ AZ FL FL CO CO All Allfour four states states 105 61 72 83 94 1.194 0.760 (0.390)*** (0.389)** 0.830 1.159 (0.089)* (0.498)** 1.109 1.034 (0.377)*** (0.340)*** -0.684 0.194 (0.606) (0.529) 0.995 0.975 (0.215)*** (0.205)*** 0.006 0.004 -3.657 -2.315 (2.074)* (1.023)** -2.286 -1.502 (1.543) (0.834)* -0.457 -0.273 (1.492) (0.868) 0.005 0.003 (0.001)*** (0.0004)*** (0.002)*** (0.001)*** (0.002)*** (0.001)*** -0.035 -0.052 -0.506 -0.085 -0.047 -0.016 (0.052) (0.030)* (0.517) (0.309) (0.031) (0.017) 0.043 0.068 0.615 -0.028 0.006 0.003 (0.068) (0.039)* (0.691) (0.412) (0.005) (0.003) (0.002)*** (0.001)*** -0.358 -0.257 (0.224) (0.131)* 0.530 0.394 (0.325)* (0.190)** (0.001)*** (0.0004)*** -0.010 -0.006 (0.012) (0.007) 0.006 0.003 (0.005) (0.003) 6.85E-06 6.85E-06 (2.53E-06) (1.48E-06) *** *** 0.083 0.044 -2.48E-05 -8.56E-06 9.80E-05 6.26E-05 (1.76E-05) (1.05E-05) (6.81E-05) (3.60E-05) * -0.020 0.005 0.029 0.006 4.08E-05 2.04E-05 (1.44e-05) (8.55E-06) *** ** 0.056 0.057 8.47E-06 7.17E-06 (2.25E-06) (1.30E-06) *** *** 0.073 0.049 (0.087) (0.052) 30.058 -10.736 (171.161) (102.548) (0.061) (0.036) -145.864 -127.495 (119.661) (69.805)* (0.027)*** (0.015)*** -147.245 -99.310 (51.587)*** (29.658)*** 0.004 0.003 (0.037)** (0.021)** -160.983 -91.491 (70.405)*** (40.555)** 0.009 0.005 0.007 0.004 (0.089) (0.047) -58.795 -13.464 (175.217) (92.572) 497 497 165 165 146 146 174 174 11.63 14.18 5.77 6.03 7.86 8.6 6.47 8.7 0.1140 0.1375 0.1485 0.1554 0.2212 0.2392 0.1595 0.2107 Notes: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 982 982 17.84 20.6 0.1338 0.1524 Results-Owner Occupancy Table XXX. Results with the variable of owner occupancy State: NY Model number Spatially Weighted Green Share Number of Firms Per Sqft Total Value Per Sqft Improvement Value Average per building sqft Average year built Share of buildings occupied by its owner _cons # of obs. F Adj R-squared Share by number of buildings 11 12 0.641 (0.387)* 0.002 (0.0004)*** -0.055 (0.030)* 0.077 (0.039)** 7.94E-06 (1.50E-06)*** 0.040 (0.021)* 8.881 (2.664)*** -87.550 (40.161)** 1.170 (0.389)*** 0.004 (0.001)*** -0.038 (0.051) 0.054 (0.067) 7.89E-06 (2.57E-06)*** 0.080 (0.037)** 9.655 (4.615)** -158.463 (70.174)** 497 13.99 0.155 497 10.66 0.120 Notes: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 ! Share by sqft 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 Back up slides • The weight matrix is a normalized matrix: row-standardized weights matrix • (Philip A. Viton, 2010. Notes on Spatial Econometric Models)