LOCATION, LOCATION, GREEN. A SPATIAL
ANALYSIS OF GREEN BUILDINGS IN EUROPE?
Gunther Maier
Research Institute for spatial and Real Estate Economics
Vienna University of Economics and Business (WU)
Costin Ciora
The Bucharest University of Economic Studies (ASE)
Department of Financial Analysis and Valuation (AEEF)
Ion Anghel
The Bucharest University of Economic Studies (ASE)
Department of Financial Analysis and Valuation (AEEF)
Agenda
1. Introduction / Motivation
2. Research questions
3. Literature
4. Methodology / Results
5. Conclusions / Future improvements
2
Introduction
• Work in progress
• Organizing a summer school and ERSA2016
• “Europe” has shrunk to “Germany”
• Substantial room for improvement
• Improvement of data basis
• Improvement of analytical steps
• Improved methods
3
Motivation
• The high spread of green building certification across
Europe has become more visible with the new projects
that have been built in the last five years.
• About 35% of the EU's buildings are over 50 years old,
so there will be a wide spread of green buildings in the
future
4
Motivation
Source: DGNB
5
Research questions
• Where are the green buildings located?
• Where in the country?
• Where in the respective city?
• Is there a function towards distribution of green buildings?
6
Hypotheses:
• Green projects are more common in the SW of Germany
(DGNB office in Stuttgart)
• There is a common distance of 2-4 km from the city center,
in which green buildings are being developed. The
“green belt” - As the distance from the CBD increase, thus
the green project are more common (Green Building
District)
• In larger cities (larger CBD) the green belt is located further
away from the center
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Literature
Determinants of green building development
Eichholtz, Kok & Quigley (2010) - US market study on 10.000 commercial
buildings with LEED and/or Energy Star label, divided into 900 clusters, based
on their location, showed an increase in selling price of 16 percent.
Fuerst & McAllister (2009) – calculated a sale price premium of 31% for
energy Star certified buildings and 35% for LEED certified.
Miller, Spivey & Florance (2008) calculated a value premium of 9.9% for
LEED certified buildings and 5.3% for Energy Star.
Location of green buildings
Braun, Cajias & Bienert (2014) – ERES 2014, Bucharest
Argued that green buildings (US market) are located predominantly and
disproportionally in prime locations.
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Methodology / Results
• Green buildings in Germany with specific coordinates
• 343 buildings with DGNB certification and
coordinates (projects without address excluded)
with
• Point pattern analysis in order to test for density
differences of green buildings in Germany and for
spatial clusters of project in the country.
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Methodology / Results
• Green Buildings are more
frequent in the South-West
of Germany
Trend formula: ~polynom(x, y, 2)
Estimate
S.E. Ztest
(Intercept) -177.44985272 46.43045860
na
[x]
-0.43375161 0.78792202
[x^2]
-0.02690892 0.01275400
*
[y]
7.24675651 1.82216133
***
[y^2]
-0.07377680 0.01797647
***
[x.y]
0.01762523 0.01505135
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Methodology / Results
• Green Buildings are
spatially clustered
• L: transformation of
Ripley’s K
• Based on nearest neighbor
distance
11
Methodology / Results
• Location in the city / What is the respective city?
• “Labour Market Regions” in Germany (Eckey, Kosfeld,
Türck, 2006)
• Identify LMRs with green buildings
• Identify the largest city in each LMR – in few cases, also
close to the green building
• Define the center of this city as center of the region
• For every green building, calculate the distance to the
nearest center – “distance from CBD”
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Methodology / Results
• Location in the city /
distance from the center
• More central in larger cities
(ANOVA: p < 0.0005)
n
median
mean
stdev
All
337
4.90
8.47
9.41
<50,000
24
10.88
14.48
13.30
50-100,000
10
10.78
17.33
18.25
100-500,000
89
6.10
9.81
9.64
500-1Mio
109
4.93
8.05
8.96
1Mio-2Mio
73
4.26
5.82
5.80
>2Mio
32
3.28
4.91
4.66
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Methodology / Results
• Distance from the center declines with the size of the
city – but, a very weak relationship (R2 = 0.06)
• Effect levels off with larger cities
• But still negative at max population
Estimate
Std. Error
t value
Pr(>|t|)
signif
Intercept
12.11
0.93
12.99
< 2e-16
***
Pop
-6.8e-06
1.78e-06
-3.83
1.54e-4
***
pop^2
1.4e-12
5.0e-13
2.77
0.006
**
14
Methodology / Results
•
•
•
•
Define 1km distance bands around the center
Count the number of green buildings in each band
Expected from increasing area: linear increase (2rp)
Expected from hypothesis: increase – maximum –
decrease with maximum in 2-4km.
• Hypotheses (shape) are
clearly NOT confirmed for
whole sample
15
Methodology / Results
• Subdivide by city categories:
• < 50,000
• 50,000 – 100,000
• 100,000 – 500,000
• 500,000 – 1,000,000
• 1,000,000 – 2,000,000
• > 2,000,000
16
Methodology / Results
• Subdivide by city categories:
• < 50,000
• 50,000 – 100,000
• 100,000 – 500,000
• 500,000 – 1,000,000
• 1,000,000 – 2,000,000
• > 2,000,000
17
Methodology / Results
• Subdivide by city categories:
• < 50,000
• 50,000 – 100,000
• 100,000 – 500,000
• 500,000 – 1,000,000
• 1,000,000 – 2,000,000
• > 2,000,000
18
Methodology / Results
• Numbers of green buildings clearly decline with
distance from the city center
• Contrary to naïve expectation (linear increase)
• Strong concentration tendency
• whether this is stronger than the concentration of
office buildings / new office buildings needs to be
answered
• In 4 of 6 city classes is the modus in the first
kilometer; same for total sample
• Hypothesis of a green building band around the CBD
is rejected – for absolute numbers
• Could still show up in relative terms (relative to (new)
office buildings)
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Conclusions
• We see a clear pattern of spatial distribution of green
buildings in Germany, but not necessarily as
expected:
• GBs are more frequent in SW of Germany
(location of DGNB office?) – more outside the
larger cities
• GBs are clearly spatially concentrated in clusters
• Largest number in the first kilometer around the
city center – despite the lack of land and historic
building stock
• Hypothesis of a green band around the city was
not confirmed
20
Future improvements
• Point pattern analysis:
• Use German borders as window
• Currently rectangular window
• Get to know the method better, use more
adequate / sophisticated methods
• All analytical steps:
• Compare with a reference distribution – stock of
office buildings, new office buildings
• Currently, the reference is an equal distribution
• Spatial framework
• Expand beyond Germany
• Other countries, other certificates
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Thank you!
Gunther Maier
Research Institute for spatial and Real Estate Economics
Vienna University of Economics and Business (WU)
Costin Ciora
The Bucharest University of Economic Studies (ASE)
Department of Financial Analysis and Valuation (AEEF)
Ion Anghel
The Bucharest University of Economic Studies (ASE)
Department of Financial Analysis and Valuation (AEEF)
Corresponding author:
Gunther Maier
[email protected]
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