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

Approach

Yueming Lucy QiuAshutosh Tiwari

Arizona State University

Yi David WangUniversity 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

• Accordingly, the share of non-green buildings in a zip code has the following conditional mean:

)exp(1

)exp()(

i

iigi X

XXsE

)exp(1

1)( 0 iii XXsE

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)

))exp(1log()ˆlog( iigi XXs

))exp(1log(0)ˆlog( 0 ii Xs

iigi Xss )ˆlog()ˆlog( 0

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

• The spatial weights matrix is row-standardized to have row-sums of unity in most empirical studies:

ij

igjijiigi swXss )log()ˆlog()ˆlog( 0

• 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-NYVariable Obs Mean Std.Dev. Min MaxNumber of Firms in a zip code 497 679.47 916.68 3.00 7241.00Per Sq Ft Total Value 497 53.07 50.92 0.14 429.39Per Sq Ft Improvement Value 497 42.24 38.88 0.09 303.97Average year built 497 1952.75 18.69 1874.00 2004.20Average per building sqft 497 70758.77 286208.20 560.00 2812739.0Number of green buildings in a zip code 497 1.16 2.32 0.00 26.00Number of non-green buildings in a zip code 497 295.13 362.34 0.00 2258.00Share of buildings occupied by owners 497 0.28 0.15 0.00 1.00Share of green buildings by number 497 0.02 0.13 0.00 1.00Share of non-green buildings by number 497 0.98 0.13 0.00 1.00Share of green buildings by area 497 0.05 0.15 0.00 1.00Share of non-green buildings by area 497 0.95 0.15 0.00 1.00

Results

• Summary statistics, zip code level-CO

Variable Obs Mean Std.Dev. Min MaxNumber of Firms in a zip code 174 697.05 544.29 3.00 2888.00Per Sq Ft Total Value 174 25.88 16.82 2.57 185.49Per Sq Ft Improvement Value 174 17.82 11.84 2.20 123.76Average year built 174 1975.58 18.18 1903.33 2009.00Average per building sqft 174 40694.41 107601.00 921.80 929402.10Number of green buildings in a zip code 174 1.12 2.10 0.00 12.00Number of non-green buildings in a zip code 174 171.29 200.61 0.00 876.00Share of green buildings by number 174 0.04 0.15 0.00 1.00Share of non-green buildings by number 174 0.96 0.15 0.00 1.00Share of green buildings by area 174 0.06 0.17 0.00 1.00Share of non-green buildings by area 174 0.94 0.17 0.00 1.00

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%

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%

Results-Owner Occupancy

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)