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)