Master’s Thesis
“Energy Certificates and Rental Premiums: The Case of Dutch Commercial Real Estate”
Author:
Arantxa Tjon-A-Joe
Student number:
11171103
Thesis Supervisor:
Martijn Dröes
Program:
Finance
Specialization:
Real estate finance
1
Abstract
The aim of this research thesis is to identify the influence of eco-certification,
moreover BREEAM certification, on the rental prices of commercial real estate in the
Netherlands. BREEAM is a certificate that companies can freely opt for by doing an
assessment of the building’s sustainability. The importance of this is that often it is
difficult to demonstrate just how sustainable a particular property is. This thesis
examines if BREEAM-certified buildings acquire a rental premium. This is done by
utilizing rental transactions of BREEAM-certified properties supplied by data from
NVM Business and the Dutch Green Building Council. To examine this, an OLS
regression is executed to exactly determine the rental premium of a sustainable
certificate. In addition, another OLS regression is executed to determine the rental
premium for various degrees of sustainability. Lastly, a sharp regression discontinuity
analysis is performed to discover whether the impact of various certificates with
almost identical degrees of sustainability are valued variously. The regression results
indicate that occupants pay a rental discount of 0.4% on average for a BREEAMcertified building. This result is however statistically insignificant. Moreover, the
outcomes suggest that a greater degree of certification prompts an increase in rental
premiums.
Statement of Originality
This document is written by Arantxa Tjon-A-Joe, student number 11171103, who
declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document are original and that
no sources other than those mentioned in the text and its references have been used
in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of
completion of work, not for the contents.
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Table of Contents
Abstract ...................................................................................................................................2
Statement of Originality ..........................................................................................................2
Chapter 1. Introduction ...........................................................................................................4
Chapter 2. Literature review ...................................................................................................7
2.1 Sustainability in real estate and eco-certification..........................................................7
2.2 BREEAM certification in commercial real estate .........................................................8
2.3 Four-quadrant model .....................................................................................................9
2.4 Incorporating sustainability in business plans.............................................................11
2.4.1 Incorporating sustainability in business plans for companies ..............................11
2.4.2 Incorporating sustainability in business plans for owners of properties ..............12
2.4.3 Incorporating sustainability in business plans for offices and retail ....................12
2.4.4 Greenwashing.......................................................................................................12
2.5 Highlights of Existing literature ..................................................................................13
Chapter 3. Data .....................................................................................................................14
3.1.1.BREEAM .............................................................................................................14
3.1.2 NVM Business database ......................................................................................15
3.2 Descriptive statistics ...................................................................................................16
Chapter 4. Methodology .......................................................................................................19
Chapter 4.1 Ordinary Least Squares regressions and their variables ................................19
4.1.1 Variables ..............................................................................................................19
4.1.2 Regression models ...............................................................................................21
4.2 Regression discontinuity analysis ...............................................................................21
Chapter 5. Results .................................................................................................................23
5.1 Ordinary Least Squares regression results ..................................................................23
5.2 Regression discontinuity analysis results ....................................................................26
5.3 Robustness Check .......................................................................................................27
Chapter 6. Conclusion and Discussion..................................................................................28
6.1 Limitations ..................................................................................................................30
6.2 Further research...........................................................................................................30
Reference list.........................................................................................................................31
Appendix ...............................................................................................................................34
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Chapter 1. Introduction
As of late, a greater emphasis is being placed on sustainability in all areas of property
stakeholders, made up of occupants, developers, governments, investors, owners, and
the community (World Green Building Council, 2013). As a result, there has been an
increase in consciousness regarding sustainability in the real estate market. So much
so that before 2023, all office buildings bigger than 100 m² must have at minimum an
energy label C in the Netherlands. This corresponds to a maximum annual fossil
energy use of 235 kWh/m² or an Energy Index of 1.3 at minimum. The building is
not allowed to be occupied if it does not match these standards (NVM, 2022). Last
year, eleven buildings from the University of Utrecht were assessed for their
sustainable performance in nine out of ten sustainability categories using the
BREEAM method. According to their spokesperson, a BREEAM certification helps
the university on its way to a sustainable real estate portfolio (University of Utrecht,
2021). As sustainability is becoming more prominent nowadays this has led to an
increase in demand for sustainable buildings. Forty percent of the world’s greenhouse
gas emissions are attributable to the real estate industry (Forbes, 2022). Of this, about
half is from the commercial real estate sector. This makes commercial real estate one
of the biggest polluters worldwide (Fuerst & McAllister, 2011). Despite this,
advancements in technological developments facilitate the commercial real estate
sector to conserve energy costs and possibly cut down greenhouse gas emissions by
30% (Kats & Perlman, 2006). According to the Ministry of Economic Affairs (2021),
as of 2008, Dutch commercial properties must be rated by designating an energy
label. An energy label is a rating that administers a transparent and straightforward
indication of the energy efficiency as well as alternative crucial characteristics. The
energy labels run from A to G, with A being the most efficient and G being the most
inefficient (European Commission, 2021).
Companies that invest in the sustainability of their properties contribute to Corporate
Social Responsibility (CSR). CSR is an automated business model that assists
companies to become more socially responsible for themselves, the shareholders, and
society. In return, this business model can improve the reputation of companies by
acquiring eco-certification. This is not the only advantage of making a building more
4
sustainable. The companies can also reduce operating expenses, increase the
productivity of employees, and increase the financial situation. Taking all the abovementioned benefits into account, it becomes clear why some companies would invest
in sustainable properties.
In spite of the obvious necessity and increasing demand for sustainable buildings, it
can still be difficult to regulate and match just how sustainable some commercial
properties are. Sustainability certificates, like BREEAM, could be a solution to this
issue. However, this does not come without extra costs, a so-called rental premium.
Investors and developers in real estate will solely engage in sustainable properties or
activities when the rental premium is sufficient enough (Reichardt, 2016). According
to Eichholtz et al. (2010), in addition to this, companies will solely settle in the
aforementioned sustainable properties if it is advantageous for their economic
condition and reputation. There are several eco-certificates in the world, like the
LEED and Energy Star certificates. However, these certificates are uncommon in the
Dutch commercial market. Therefore, this research will focus on the BREEAM
certificate, which is predominately used in the Netherlands. This thesis’s primary
research question is as follows:
“To what degree does a BREEAM certificate lead to a rental premium on Dutch
commercial real estate?”
Each category of certification issued by BREEAM discloses a distinct degree of ecosufficiency, with acceptable being the minimum degree and excellent being the
maximum degree. Therefore, along with the primary research question, this thesis
also wishes to address to what extent the commercial real estate sector in the
Netherlands values various degrees of eco-efficiency differently. The main
hypothesis of this research reads as follows: buildings equipped with a BREEAM
certificate acquire a rental premium. How this hypothesis is derived is described in
Chapter 2.
This thesis will contribute to the existing literature in several ways. Firstly, this thesis
does not only investigate the effect of BREEAM certification on rent levels but also
examines possible rental premiums as a result of a specific degree of sustainability in
Dutch commercial real estate between the years 2009 and 2021. Existing literature
does not yet cover this time frame. Secondly, this paper contributes to the existing
5
literature by focusing on the Dutch office, retail, and industrial commercial real estate
segment. Most of the academic articles surrounding this topic focus on the office
segment or another individual segment. Finally, in previous studies on this topic,
sustainability is commonly determined by energy labels which are not available in the
Netherlands and are primarily centered around the UK and US markets (Eichholtz et
al., 2010; Fuerst & McAllister, 2009; Reichardt, 2012).
To test the research question an empirical analysis will be executed. The effect of a
BREEAM certification will be tested by means of two OLS regressions. To determine
if the BREEAM certificate leads to a rental premium, several datasets have been used.
Firstly, a data set originating from NVM Business, a Dutch real estate organization
conducting market research and real estate information. This data set provided
commercial real estate transactions between 2009 and 2021. This dataset from NVM
Business also provided building characteristics of observations such as the year of
construction. The second data set was supplied by the Dutch Green Building Council.
The sample includes a total of 4.684 observations of BREEAM-certified buildings.
Of this total, there are 2.749 existing buildings and 1.935 new construction buildings.
The outcome of this research demonstrates that there is no rental premium, but rather
a rental discount of circa 0.4% paid for a BREEAM-certified commercial real estate
property in the Netherlands. However, this result is statistically insignificant, so no
real conclusion can be drawn from it. Furthermore, the outcomes demonstrate that
rent levels indeed increase when the degree of sustainability increases. However, this
result is also statistically insignificant. The insignificance of both regression
outcomes is for the most part attributed to the low amount of observations in this
research. Lastly, to examine whether there exists a signaling effect, a regression
discontinuity analysis is performed. This research found evidence of regression
discontinuity around the 40% cut-off which is statistically significant. The results
demonstrate that occupants value the signal certificates provided, as buildings with
relatively equal degrees of sustainability, but with various degrees are valued
variously.
There are also some limitations in this research design. The most prominent limitation
is that this research was conducted with a relatively low amount of observations due
to the limited amount of rental transactions matched with BREEAM certificates.
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Secondly, the amount of building characteristics could be more extensive, although
this thesis does touch on the most important characteristics, the size of the building,
the location, and the age of the building (Kok & Jennen, 2012).
The structure of this research is arranged as follows: Chapter 2 discloses a
comprehensive literature review with regards to BREEAM certification. Chapter 3
discloses the description and sources of the various data utilized. Chapter 4 discloses
the research methodology which consists of the regression equations accompanied by
a detailed interpretation of the equations, descriptions of the regression variables, and
the research approach. In Chapter 5 the regression results will be presented. Lastly,
in Chapter 6, the research will come to a conclusion and includes a thorough
discussion of the findings of this research. Lastly, the limitations of this study will be
disclosed along with a few points of improvement for the purpose of future studies.
Chapter 2. Literature review
Chapter 2 focuses on the theoretical structure of this research. In this section, the most
important theories of past literature regarding sustainability in commercial real estate
are discussed. This chapter also includes a clear description of BREEAM and how
buildings are assessed according to this structure. Additionally, this section provides
a description of sustainability as a business plan. Lastly, the findings of comparable
studies are highlighted.
2.1 Sustainability in real estate and eco-certification
As mentioned in the last Chapter, sustainability has become a crucial component in
the real estate market, and commercial real estate especially is liable for a lot of
greenhouse gas emissions. However, just by looking at a building, you can’t make
out how sustainable the building really is. A building rating system administers a
mechanism by making buildings comparable to one another in terms of sustainability
(Giama & Papadopoulos, 2012). In the study by Giama and Papadopoulos (2012), the
rating system lends a building transparent declaration about their position on the
sustainability ladder and is thus very effective when doing research. The approach of
using a rating system zooms in on various characteristics of the properties being rated;
however, most rating systems focus on the analysis of the life cycle, and the energy
consumption of environmental reports (Giama & Papadopoulos, 2012). BREEAM
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and LEED are the more common and comprehensive rating structures, despite the
fact that most of the rating structures are comparable. According to Giama and
Papadopoulos (2012), this is because these rating systems are modified to the national
level. The BREEAM rating system was developed in the United Kingdom and is
applied mostly in European countries. The modified BREEAM certificate in the
Netherlands is termed the BREEAM-NL certificate and will be utilized in this
research paper. However, for simplicity, this thesis regards to a BREEAM-NL
certificate as a BREEAM certificate.
2.2 BREEAM certification in commercial real estate
BREEAM is an acronym for ‘Building Research Establishment Environmental
Assessment Method’ and was constructed by the Building Research Establishment
(BRE) in 1990. However, BREEAM was adopted in the Netherlands in 2009 (Dutch
Green Building Council, 2016). The BREEAM certificate is an optional rating
program for properties, which administers a prospect to diversify properties from
other properties by a refined reputation of the investor and the occupant. Investing in
a sustainable building administers a marketing strategy due to the signaling effect.
According to Fombrun and Shanley (1990), the reputation of a company improves
due to this signaling effect. In addition to this, tenants and investors of buildings that
score high on the sustainability chart encounter less backlash from activists, less
discussions about regulations, and increasing profitability due to the reduction in costs
(Eichholtz et al., 2010). In order to acquire a BREEAM certificate, the property in
question is evaluated by an independent and objective licensed evaluator. The
evaluator then evaluates ten various classifications of issues tackling from energy
efficiency and use of land to management and waste (Dutch Green Building Council,
2016).
A maximum score of Outstanding may be reached with a BREEAM accreditation.
The number is determined by how many sustainability criteria the project satisfies.
The greater the score, the higher the proportion of criteria satisfied. The Assessor
evaluates the BREEAM project using the information given. When the project is
finished, the Assessor pays a visit to inspect it. A random check is then conducted by
the Dutch Green Building Council. This is referred to as Quality Assurance (QA).
This QA is frequently tested by the parent organization BRE in terms of quality.
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Additional evidence of the project's long-term viability may be required. The
Assessor issues the final BREEAM certificate when all checks have been completed
(BREEAM, 2021).
2.3 Four-quadrant model
The four-quadrant model was conceptualized by DiPasquale and Wheaton in 1992.
This framework essentially splits the real estate market into two segments: the
segment for real estate assets and the segment for real estate space. This paragraph
will focus on the latter segment. In order to illustrate in which way the commercial
real estate market operates, the four-quadrant model will be utilized.
The prices of commercial properties are established by the demand for the properties.
(DiPasquale & Wheaton, 1992). Thus, the prices are based on the amount of tenants
who want to own such a commercial unit. Consequently, the prices impact supply of
the commercial buildings as these prices establish the supply of commercial
buildings. Prices are established in relation to construction- and replacement expenses
when new stock is added. Once this price surmounts the construction or replacement
expenses, it is crucial to add additional stock. In this framework, there are short-term
and long-term effects. Because of, among other things, delays in the construction
system, the real estate market is somewhat delayed. This causes an imbalance
between supply and demand in the short term. Moreover, the demand for commercial
properties is based on rent and alternative external economic factors. The rent for the
commercial properties is established on the market for real estate space. As a result,
the market for commercial real estate determines the rent based on the supply and
demand for properties.
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Figure 1. Four-quadrant model (DiPasquale & Wheaton, 1992)
Figure 1 discloses the four-quadrant model of DiPasquale and Wheaton (1992).
The occupier market, where the rents are determined, is situated in the upper right
quadrant. From there and counterclockwise, the investor market and construction
market follow suit. The upper left quadrant is the investor market. In this quadrant,
the rent establishes the asset price. In the lower-left quadrant, the construction market,
the determination of construction is dependent on prices. Finally the last quadrant, on
the lower right, is determined by the construction built and the amount of assets in
stock. It gives an indication of how much the stock contracts and expands.
When administering an exogenous shock, for example, a BREEAM certification, the
course begins in the first quadrant. In the Figure disclosed beneath, the repercussions
of an increase in rent as a result of BREEAM certification are illustrated. The higher
rent causes more construction, thus increasing the stock. The question is whether or
not BREEAM certification will result in a larger stock. This thesis assumes that this
is indeed the case. A larger stock of BREEAM-certified buildings, on the other hand,
does not always mean that they are all new stock. By restoring existing buildings,
they may be changed into more sustainable structures.
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Figure 2. Exogeneous shock to the market as illustrated by DiPasquale and
Wheaton (1992)
Figure 2 discloses the four-quadrant model of DiPasquale and Wheaton (1992) in the
midst of an exogenous shock.
2.4 Incorporating sustainability in business plans
2.4.1 Incorporating sustainability in business plans for companies
In addition to protecting the planet, sustainability can also be utilized as a strategic
business plan. According to Chiang et al. (2019) corporate social responsibility (CSR)
is a term that is often used by marketers to improve a company’s social appearance.
In addition to this, Chiang et al. (2019) disclosed that REITs participate in CSR if it
is clear that there will be an increase in opportunities to develop on the market. CSR
was created by companies to showcase that they care about society and want to
improve social welfare. Firms spend money on CSR to gain a competitive edge,
differentiate from the competition, and expand their investment options (Porter &
Kramer, 2006). The first authors to concentrate on the advantages of CSR on
company reputation were Eichholtz et al. (2010) who found that these advantages
weigh more than financial advantages that occur from, for example, a decrease in
operating costs. Furthermore, Eichholtz found that companies with meager financial
accomplishments undertake CSR to please shareholders and to compensate for the
negative attention to the company.
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2.4.2 Incorporating sustainability in business plans for owners of properties
Fuerst and McAllister (2011) illustrate the advantages of CSR for owners of
properties. They found that CSR utilized in the structure of building or renovating a
property equipped with an energy certificate leads to a higher rental premium, lower
vacancy rates, and a higher sales premium. However, even though there are clear
advantages, there are also quite some hurdles met by property owners when
considering sustainability upgrades. Crucial hurdles comprise of high introductory
installation costs, complications with raising investment funds, landlord and occupant
divide, transaction costs, and information hurdles (Gliedt & Hoicka, 2015).
According to Gliedt and Hoicka (2015), these hurdles can be resolved internally in
three various ways. Firstly, by emphasizing the advantages of energy improvements
to the subject property. Secondly, by enhancing admission to knowledge and
information. Lastly, by increasing access to financial resources (Gliedt & Hoicka,
2015).
2.4.3 Incorporating sustainability in business plans for offices and retail
According to Schweber (2013) sustainability certificates are generally utilized in
commercial real estate segments, where client interaction is critical to the company’s
success and profitability. The office segment in commercial real estate is where
companies reside with their core activities or lend their services. Lending services on
the day-to-day means interacting with clients in person. As a result, certified
properties can boost the reputation of companies. In the retail segment of commercial
real estate, certification may not instantly influence individual consumers (Preston &
Bailey, 2003). These authors also state that the retail and office segment is leading in
combining certification into their business plans and using it to their benefit.
Especially in today’s society, certification plays an important role as consumers are
becoming more and more environmentally conscious.
2.4.4 Greenwashing
There is however also a dark side to CSR termed greenwashing. Greenwashing is a
multi-faceted marketing strategy adopted by companies to promote the environmental
friendliness of their goods, services, properties, and company as a whole (Aggarwal
& Kadyan, 2014). Companies utilize greenwashing as a tool to contest in the global
marketplace. A few eco-certificates aid greenwashing. An example of such an eco-
12
certificate is the Energy Star certificate. According to Gromicko (2010), Energy Star
only appraises the final commodity, in this instance, a property. In this way, Energy
Star fails to acknowledge the company or occupants’ working circumstances.
Greenwashing diverts the attention of the public from the absence of investing in
sustainability. For this reason, this thesis also focuses on BREEAM certification and
not the other certification alternatives and BREEAM only appraises commercial
properties with rigorous regulations.
2.5 Highlights of Existing literature
This segment briefly touches upon the results of various studies related to the research
topic. The effects of eco-certificates on commercial real estate have been extensively
studied in academic literature. A prominent study conducted by Reichardt (2012)
studies two eco-certificates, the Energy Star and LEED from 2000 until 2010 in North
America. The authors discovered a large rent premium for certified properties, that
surged prior to the financial crisis and then declined during and following the
financial crisis. In addition to this, the authors found that there exists a favorable
correlation between the occupancy rate and an eco-certificate. Similar to Reichardt
(2012), studies conducted by Eichholtz et al. (2010) and Fuerst and McAllister (2009)
used Energy Star and LEED-certified buildings to determine if there was a rental
premium. Both papers found comparable rental premiums varying between 3.0% to
7.0% and 3.0% to 5.0% respectively. Furthermore, it is evident that investors and
occupants are not only prepared to pay a higher rental premium in North America but
also in China. According to Zhang et al. (2016), investors and occupants are prepared
to pay a rental premium of 6.9% for a green building label, which is an eco-certificate
that is similar to BREEAM. Kok and Jennen (2012) examined the effect of energy
labels and convenience on the rental prices of offices in the Netherlands. Even though
accessibility is not a segment mentioned in BREEAM certification, energy efficiency.
The research conducted by Kok and Jennen (2012) concluded that buildings that were
equipped with a higher efficiency level also had 6.5% higher rents.
However, not all studies conducted on this topic have shown to have significant
results. Fuerst and McAllister (2011) went on to investigate the efficiency of two
dozen BREEAM-certified buildings and examined if a BREEAM certificate led to a
rental premium and a higher market value. Their results were however insignificant,
due to a low number of observations. Another study that found insignificant results
13
was that of Kok and Jennen (2012). In their research, they found that offices equipped
with an eco-certificate indeed acquire a higher rent level but found that this result was
statistically insignificant. The authors attributed the insignificance of this result to the
crisis effect, meaning that under favorable economic circumstances the upper scale
of the market grows asymmetrically fast, whereas rents fall more quickly during more
difficult economic times (Kok & Jennen, 2012). Lastly, a study conducted by Feige
et al. (2013) on 2,500 properties in Switzerland, examined the impact of property
sustainability on the rent level. In their study, they found a surprising negative relation
between the rent level and the energy efficiency. Feige et al., (2013) credited this
result to the piling of energy expenses and rents in the lease framework in
Switzerland.
From the literature review described in the Chapter above, the following hypotheses
can be constructed:
Hypothesis 1: Buildings equipped with a BREEAM certificate acquire a rental
premium
Hypothesis 2: Buildings equipped with a higher BREEAM score acquire a higher
rental premium
Chapter 3. Data
In this Chapter, the data collection process will be disclosed. This research wishes to
expose if buildings equipped with a BREEAM certificate have higher rental values,
thus, a rental premium. To do this, two datasets have been utilized. These datasets
consist of panel data as they are made up of geographical data based on zip code
regions and are recorded over time.
3.1.1.BREEAM
The first dataset which will be utilized is the database that will supply information
about the BREEAM certificates in the Netherlands, The Dutch Green Building
Council. Furthermore, this company also reserves all the data and information with
respect to BREEAM certificates in the Netherlands. The sample includes a total of
4.684 observations of BREEAM-certified buildings. Of this total, there are 2.749
existing buildings and 1.935 new construction buildings. A BREEAM certificate is
given in variations spanning from 0% to 100% conditional on the measure of
14
sustainability. The degrees of sustainability and the corresponding scores for existing
stock and new construction are disclosed in Table 2.
Table 1. Amount of certified properties in BREEAM dataset
Certified
Number of certificates
New construction
2.749
Existing
1.935
Total
4.684
Table 1 discloses the amount of certified buildings provided by BREEAM. The dataset
is split into new construction and existing stock.
Table 2. Scores per level of sustainability
BREEAM certificate
Existing stock
New construction
Acceptable
10%
x
Pass
25%
30%
Good
40%
45%
Very good
55%
55%
Excellent
70%
70%
Outstanding
85%
85%
Table 2 discloses the various levels of sustainability within BREEAM certification.
The levels are split into existing stock and new construction.
3.1.2 NVM Business database
The second dataset utilized in this research paper is the NVM Business database. This
database supplied the rental transactions for the Dutch commercial real estate (retail,
offices, and logistics) as well as the needed variables such as the construction year,
the floor size, the location, and the commercial segment. This database was supplied
by CBRE the Netherlands as they are members and receive unique access to the NVM
database. CBRE is the biggest international all-around real estate services and
investment corporation. Furthermore, it is one of the “Big Four” commercial real
estate services corporations next to Colliers, JLL, and Cushman & Wakefield.
The dataset consists of observations between 2009 and 2021 and concerns rental
transactions from Dutch commercial real estate. The dataset contains observations
based on zip code, the most descriptive address level available in the datasets. The
15
fundamental dependent and independent variables were retrieved from this dataset.
The natural logarithm of the dependent variable, the rental price, was utilized in this
research. When utilizing the natural logarithm of a variable instead of the variable
itself, one simplifies the interpretation of the results.
Subsequently, the BREEAM dataset and the NVM Business dataset were merged to
form the final dataset. The final dataset was left with 321 observations. Noteworthy
is that existing buildings make up for 64% of the observations, while new construction
makes up for 36% of the observations.
Table 3. The final amount of observations in dataset
Segment
N
Existing stock
205
New construction
116
Total
321
Table 3 discloses the final amount of observations after merging the BREEAM dataset
with the NVM Business dataset.
3.2 Descriptive statistics
Table 3 demonstrates the descriptive statistics of BREEAM-certified commercial
properties in the Netherlands. Several building characteristics were added,
specifically, the age of the building, the size of the building, and the commercial
segment. The natural logarithm of the dependent variable, the rental price, was
utilized in this research.
Taking into account that the datasets were acquired from two distinct databases, the
datasets were merged to create one dataset. The final dataset was set to 321
BREEAM-certified properties. The mean rent per month for office buildings is
€5,404, the mean rent per month for retail buildings is €4,660, and the mean rent per
month for industrial buildings is €5,776. The next variable that will be discussed is
also the primary (independent) variable of interest, the BREEAM score of the
building. The average BREEAM score is 55.81%, together with a standard deviation
of 19.46. This indicates that the average BREEAM degree of sustainability was
around the Very Good degree for both Existing stock and New construction.
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Table 4. Descriptive statistics BREEAM certified properties in the Netherlands
Variables
Rent (€)
Rent office
properties (€)
Rent retail
properties (€)
Rent industry
properties (€)
BREEAM
score
Age (years)
Size(m²)
Dummy
variables
Office
buildings
Retail buildings
Industrial
buildings
Construction
year
1976 - 1980
1980 - 1990
1990 - 2000
2000 - 2010
2010 - 2021
Existing stock
New stock
N
321
101
Mean
St. Dev.
Min.
5471.67 21829.60 119.25
5404.07 9915.70
125
p25
1000
1100
Median
1950
2250
p75
3600
5560
Max.
365213.25
69213.33
54
4660.45
850
1875
2975
23216.67
166
5776.70 29256.07 119.25
975
1687.5
365213.25
321
55.81
19.46
11.5
41.40
50.16
4791.6
7
2916.6
7
73.88
321
321
16.58
1325.79
9.22
4583.25
2
19
10
133
16
264
23
630
46
41610
321
.315
.465
0
0
0
1
1
321
321
.168
.517
.375
.5
0
0
0
0
0
1
0
1
1
1
321
321
321
321
321
321
321
.003
.059
.274
.439
.327
.639
.361
.056
.236
.447
.497
.47
.481
.481
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
5102.81
93.7
Table 4 discloses the descriptive statistics of the 321 observations in this research
sample from the year 2009 through 2021 for the BREEAM-certified commercial
properties in the Netherlands. All the important variables are incorporated.
The second variable is a control variable, which is the age of the building. The age of
the properties was estimated by deducting the construction year from 2022. The
crucial drivers of the value of a real estate property are the location, size of the
property, and the age (Kok & Jennen, 2012). For this reason, this variable was added
to the model. It is anticipated that an older real estate property will have a lower rent
level in comparison to a newer (younger) real estate property. This variable is
particularly meaningful when examining BREEAM, as one might claim that the
majority of BREEAM certified properties are younger considering that BREEAM
technology is not that old. The average age of the properties is 17 years, with a
standard deviation of 9.22.
17
The third variable is also a control variable and is the size of the building. The rental
price of a property is set to increase as the size of the property increases (Kok &
Jennen, 2012). The size of the property also discloses possible economies of scale
that might appear in the larger transactions. The average size of the properties is 1.326
square meters, with a standard deviation of 4.188,68.
Furthermore, to discover whether the observations in the sample are representative
for the Netherlands, the various locations where the observations are situated are
presented in Table 5.
Table 5. Locations of the BREEAM certified properties in sample
Location
'S-Gravenhage
'S-Hertogenbosch
Alkmaar
Almelo
Alphen Aan Den Rijn
Amersfoort
Amsterdam
Angerlo
Apeldoorn
Arnhem
Barendrecht
Barneveld
Bergen Op Zoom
Berkel En Rodenrijs
Beverwijk
Bleiswijk
Botlek Rotterdam
Breda
Deventer
Dordrecht
Duiven
Ede
Eersel
Eindhoven
Enschede
Total
N
4
4
1
3
5
9
22
3
9
2
13
3
1
1
1
11
1
6
3
2
3
5
6
9
6
Location
Haaksbergen
Haarlem
Heemstede
Hellevoetsluis
Hendrik-Ido-Ambacht
Hengelo
Hilversum
Hoofddorp
Hoogeveen
Houten
Leiden
Leusden
Lijnden
Lochem
Maasdijk
Made
Moerdijk
Naarden
Nieuwegein
Nijkerk
Oldenzaal
Oosterhout
Oud Gastel
Oud-Beijerland
Raamsdonksveer
N
1
3
1
3
7
3
1
3
5
2
1
3
2
2
7
2
6
3
5
10
1
1
1
2
4
Location
Ridderkerk
Rockanje
Roelofarendsveen
Roosendaal
Rosmalen
Rotterdam
Son En Breugel
Stolwijk
Tiel
Tilburg
Uden
Utrecht
Veghel
Vianen
Vlaardingen
Vleuten
Voorthuizen
Waalwijk
Weert
Wormerveer
Zaandam
Zeewolde
Zwijndrecht
Zwolle
N
14
1
1
3
1
25
1
2
2
15
4
8
2
1
1
2
1
1
3
1
2
1
4
13
321
Table 5 demonstrates the locations of the 321 observations from the year 2009
through 2021 for the BREEAM certified commercial properties in the Netherlands.
Table 5 supplies information of the 74 locations where the observations were recorded
in the final dataset. Rotterdam and Amsterdam are the cities that have the highest
number of observations, namely 25 and 22 respectively. This is not surprising as
Rotterdam and Amsterdam are part of the Big Four (G4) cities in the Netherlands.
18
The other two G4 cities are Utrecht and The Hague, and have 8 and 4 observations
respectively. Tilburg also has quite a high number of observations, 15 to be exact.
From this table we can conclude that most observations were recorded in bigger cities.
Chapter 4. Methodology
In this Chapter the research methodology will be presented. This research wishes to
disclose whether the Dutch commercial real estate value sustainability certificates by
analyzing whether a rental premium is supplied to BREEAM certified commercial
buildings. An OLS regression containing BREEAM certificates is used to examine
this. Following this, another model is disclosed, this time the variable for the
sustainability certificate is dismissed by a dummy variable. This variable measures
the degree per level of sustainability. This is done in agreement with the BREEAM
procedure. The outcome of this model might shed light to whether various degrees of
sustainability are valued diversely. Consequently a regression discontinuity analysis
will be performed to examine if the property sustainability score itself is of
significance to the tenant to identify whether there is a signaling effect.
Chapter 4.1 Ordinary Least Squares regressions and their variables
4.1.1 Variables
In this subsection, the dependent variable, the independent variable of interest and the
various control variables will be discussed.
Rent (Log(Rentit))
Rent is the dependent variable in both regressions and demonstrates the rent of a
property of i at time t. Because the rent levels diverge significantly across the
Netherlands, as seen in the data supplied by NVM business the logarithm of rent is
utilized. By taking the logarithm of rent, the outcomes become normally distributed.
Certificate (Certit)
The certificate is an independent variable, and also the primary variable of interest. It
is a variable representing the certification score. The BREEAM scores were provided
by the Dutch Green Building Council. The BREEAM scores vary from 0% to 100%.
Age of the property (Ageit)
19
The age of the property is a control variable and discloses the maturity of the property
in years. The data provided by NVM business discloses the construction year.
Accordingly, the age is calculated by subtracting the construction year from 2022. By
taking the logarithm of age, the outcomes become normally distributed. According to
Kok and Jennen (2012), the age of a real estate property is among the three most
crucial drivers of the value of a real estate property. The other two drivers are the
location and the size of the property.
Property Size (Sizeit)
The size of the property is a control variable and discloses the magnitude of the
property in square meters. By taking the logarithm of size, the outcomes become
normally distributed. According to Kok and Jennen (2012), the size of a real estate
property is among the three most crucial drivers of the value of a real estate property.
The other two drivers are the location and the age of the property.
Location dummy (LOCit)
The location of the properties are included in the regression as a dummy variable that
discloses the various locations (Locit). According to Kok and Jennen (2012), the
location of a real estate property is among the three most crucial drivers of the value
of a real estate property. The other two drivers are the size and the age of the property.
The various locations are summarized in Table 5.
Commercial Real Estate Segments (OFF, RET, LOG)
The next variable is a dummy variable that discloses the three various commercial
real estate segments of interest to the regression. The office segment is termed OFF,
the retail segment is termed RET and the logistics segment is termed LOG. As this
research wishes to distinguish between the various commercial segments, the
segments are inserted into the regressions, instead of running the same regression
three times.
Years dummy (Yeart)
This variable, which is also included in the regression as a dummy variable, shows
the years of the specified time period over which the regression occurs. The dummy
20
variable is set equal to 1 for a particular year and 0 otherwise. Lastly, the model
includes an error term (𝜀it) which is expected to be zero on average.
4.1.2 Regression models
Subsequently, two OLS regressions will be performed:
1. 𝐿n(Rentit)=𝛼+𝛽1Certit+𝛽2Ln(Ageit)+𝛽3Ln(Sizeit)+𝛾𝑗LOCit+δ𝑗𝑂FF𝑖t+σ𝑗𝑅𝐸𝑇𝑖t
+λ𝑗LOG𝑖t+𝜏𝑗YEARt+𝜀it
2. 𝐿n(Rentit)=𝛼+𝜑𝑗∗Certit+𝛽2Ln(Ageit)+𝛽3Ln(Sizeit)+𝛾𝑗LOCit+δ𝑗𝑂FF𝑖t+σ𝑗𝑅𝐸𝑇𝑖t
+ λ𝑗LOG𝑖t +𝜏𝑗YEARt +𝜀it
Regression equation (1) will test if a BREEAM sustainability certificate leads to an
increase in the rent of a commercial property in the Netherlands. The dependent
variable in this model is the log of the rent (Rentit) of a property i at time t. Because
the rent levels diverge significantly across the Netherlands, as seen in the data
supplied by NVM business, the log of rent is utilized. The constant (α) displays the
baseline of necessary rent for a given building. In this model, the variable of interest
is 𝛽1 and is expected to be positive and statistically significant. This might indicate
that BREEAM-certified buildings acquire a rental premium.
By substituting the BREEAM certified properties variable with a dummy variable per
degree of BREEAM certification as disclosed in Table 2, regression equation (2)
expands the previous model. The various influences of the various degrees of
sustainability on the rent are investigated by running this regression. It is anticipated
that tenants are prepared to pay a premium for a property with a greater degree of
sustainability. The rest of the model is left identical. In this model, the variable of
interest is the dummy variable 𝜑𝑗 and is expected to be positive and significantly
different from zero. In addition to this, it is anticipated that various degrees of
BREEAM certification will result in various degrees of 𝜑𝑗. Therefore, it is anticipated
that higher degrees of certification lead to a higher rental premium paid for a
BREEAM-certified building.
4.2 Regression discontinuity analysis
To analyze the causal effect of BREEAM sustainability certificates, a regression
discontinuity analysis is performed supplementary to the OLS regressions in the
previous subchapters. The regression discontinuity analysis could disclose whether
21
occupiers genuinely value a distinct certificate variously even though the degree of
sustainability is approximately the same. This would emphasize that occupiers do not
specifically value the degree of certification, but are rather concerned with the
corresponding mark, which highlights the signaling effect. The data provided by
BREEAM does administer the means to examine this, as the data does not only
disclose the degree of the certificate but also the reciprocal score.
According to Imbens and Lemieux (2007), a regression discontinuity analysis is
accomplished by examining observations over a specific degree, cut-off point, or unit
i. This specific degree is characterized by the minimum scores provided in Table 2.
This study uses a sharp regression discontinuity analysis because it assumes that the
cut-off scores that are linked to the BREEAM certificates are strictly adhered to.
Subsequently, across the various cut-off points, the buildings around the cut-off point
that did not receive treatment are classified into the non-treatment group, while a
treatment group is defined as the observations of rent levels of BREEAM-certified
buildings just above the cut-off point. As seen in Table 2 there are multiple cut-off
points when looking at BREEAM certification. There are cut-off points for
Acceptable, Pass, Good, Very good, Excellent, and Outstanding respectively.
Nonetheless, for the various cut-off points, an amount of observations around the cutoff point are necessary so as to produce enough output. For this research, properties
between a scope of 5% around the cut-off are admitted to the regression discontinuity
analysis.
Table 6. Amount of observations between a scope of 5% around the cut-off point
Cut-off point
10%
25%
40%
55%
70%
85%
Amount of observations in scope
Treatment
5
10
63
43
64
30
5
8
38
0
64
24
No Treatment
0
2
25
43
0
6
Table 6 discloses the amount of observation between a scope of 5% around the cutoff point. The observations are divided under No treatment and Treatment.
A basic linear regression for a regression discontinuity analysis is disclosed by the
following regression:
(3) Y=𝛼 + Dτ + βX + e
22
In this linear regression Y is described as the outcome variable while X is the
assignment variable. D = 1 if X ≥ cutoff value and D = 0 otherwise. By reconstructing
the simple regression (3) the following model is displayed in accordance with
Cappelleri and Trochim (2015):
(4) 𝐸(𝑦𝑖)=𝛼 + 𝛽1Z𝑖 + 𝛽2X𝑖 + 𝛽3𝑉𝑖
In this sharp regression discontinuity analysis 𝛽1 is the treatment effect parameter
and the variable of interest. If this variable were to be significantly different from
zero, there would be a link between the status of the certificates and the value
occupants place on the properties. In other words, occupants appreciate the status of
a particular certificate above the real degree of sustainability of the property. Because
of the low number of observations around 10%, 25%, 55%, and 70%, these cut-off
points are excluded from the regression discontinuity analysis. Thus, only the 40%
and 85% cut-off points are included in this regression discontinuity analysis. The
variable Z𝑖 is the assignment variable which is 1 if included in the treatment and 0
otherwise. Furthermore, 𝛽2 is the linear slope parameter and 𝛽3 is the parameter
involving the control variables age, size, and location. This regression discontinuity
design is in line with Cappelleri and Trochim (2015).
Chapter 5. Results
In this Chapter of the research, the main empirical outcomes of the two OLS
regressions and the regression discontinuity analysis are presented, interpreted, and
discussed.
5.1 Ordinary Least Squares regression results
In column one the first regression consisting of the most crucial housing
characteristics is disclosed. These characteristics are the age and the size of the
property. The dummy variable for the location has been left out of this regression.
The dependent variable in this model is the log of the rent (Rentit) of property i at time
t. Because the rent levels diverge significantly across the Netherlands, as seen in the
data supplied by NVM business, the logarithm of rent is utilized. The location
dummies have been excluded from the model. The model has an R² of 0.158, which
indicates that circa 15.8% of the variation in the rent is warranted by the variables
included in the model. The variable age is positive and significant at the 5%
23
significance level with a coefficient of 0.197. The results indicate that the older the
property is, the higher the rent will be. The age of a property is generally accompanied
by a negative price impact. However, in actuality, there are variations of this behavior
known as vintage effects (Dambon et al., 2022). In their research, Dambon et al.
(2022), discovered strong vintage effects in cities compared to rural locations. As
most observations in this research were recorded in cities, this could explain the
positive coefficient for age. The following variable that will be discussed is the size
of the property. The variable of size is positive and significant at the 1% significance
level with a coefficient of 0.288. Thus, the size of a property has a positive and
significant impact on the rent level. This result is in line with Eichholtz et al. (2010).
The next regression is showcased in column two. This regression also consists of the
most crucial housing characteristics: the age and the size of the property; only now,
the BREEAM scores of the properties are added to the model, as disclosed in the
initial section of the regression model (1). This result shows that the age of the
property is no longer significant at any significance level. Furthermore, there is no
real change in the variable of size. The R² of the model went up to 17.9%, which
indicates that circa 17.9% of the variation in the rent is warranted by the variables
included in the model. The variable BREEAM score indicates that for BREEAMcertified properties, on average, a rental discount of 0.9% is paid by occupants. The
variable for the BREEAM score is negative and significant at all significance levels.
This implies that a BREEAM-certified property does not result in a higher rent paid
by occupants. This result was not expected. Various explanations for this surprising
result will be detailed in the discussion.
Following this, an additional regression was performed to control for possible
variations in the market circumstances per year of the transactions in column three,
while column four includes the different real estate segments as seen in the regression
model (1). In column three, there is significant evidence that various transaction years
have a significant impact on the rent paid by occupants. The variables age and size of
the property stay relatively equal. However, the variable BREEAM score increases
from minus 0.9% to minus 0.8%, which is not that much of a difference. The R² has
increased to 26.7%, due to the inclusion of the year dummies.
24
In column four, the real estate segments are added to the regression model to form the
complete model of regression (1). The variable BREEAM score is negative and
insignificant at 0.4%. Insignificant results have been found in studies conducted by
Fuerst and McAllister (2011) and Kok and Jennen (2012). Furthermore, a negative
relation between sustainability and rent levels has been found in a study conducted
by Feige et al., (2013). Possible explanations for this negative and insignificant result
will be explained in detail in the discussion section. The R² also increases again to
31%. This indicates that by adding the real estate segment dummies to the regression
model, the explanatory power of the model becomes higher. The rental premium for
the office segment is 11.8% and not significant at any level, while the rental premium
for the retail segment is 63.1% and significant at all levels.
Table 7. Regression results model
Dependent variable: lnRent
lnAge
lnSize
BREEAM score
(1)
.197**
(.077)
.288***
(.044)
Dummy Office
(2)
.117
(.081)
.297***
(.043)
-.009***
(.003)
(3)
.041
(.08)
.28***
(.042)
-.008**
(.003)
(4)
.033
(.08)
.328***
(.043)
-.004
(.003)
-.118
(.135)
.631***
(.168)
6.155***
(.369)
321
.179
.171
Yes
No
9.828***
(1.056)
321
.267
.234
Yes
No
9.511***
(1.033)
321
.31
.274
Yes
No
Dummy Retail
Pass
Good
Very good
Excellent
Outstanding
Constant
Observations
R2
Adj R2
Year Fixed Effects
Location Fixed Effects
5.502***
(.293)
321
.158
.153
Yes
No
(5)
.043
(.08)
.322***
(.044)
-.167
(.136)
.626***
(.169)
.471
(.34)
.444
(.329)
1.574
(1.036)
.173
(.341)
.385
(.375)
8.93***
(1.061)
321
.323
.278
Yes
No
Dependent variable is the natural logarithm of rent; lnAge is the natural logarithm
of age; lnSize is the natural logarithm of surface area; Standard errors are in
parentheses and clustered at the neighborhood level. *** p<.01, ** p<.05, * p<.1.
25
The fifth and final column discloses the results of regression model (2). In this model,
the variable BREEAM score is substituted by a dummy variable per degree of
sustainability. This regression model does not impact the results by much and the R²
also stays relatively equal. To prevent the dummy variable trap, the Acceptable degree
has been left out of the model. The coefficients gradually increase from Pass, Good,
to Very Good showing premiums of 44.4%, 47,1%, and 57,4% correspondingly. This
indeed shows signs that the rent premium becomes higher when the degree of
sustainability becomes higher. However, none of the variables which showcase the
degree of sustainability are significant. This could be due to the low amount of
observations in this research.
5.2 Regression discontinuity analysis results
Apart from the various regressions performed in Table 6, a regression discontinuity
analysis is executed regarding two different cut-offs. The cut-offs are set around 40%,
which coincides with Good, and around 85%, which coincides with Outstanding.
Although most of the observations have about an equal BREEAM score, some
observations are added to the treatment group, whilst some were added to the nontreatment group.
In Table 8, panel one discloses the results of the regression discontinuity analysis
around the 40% cut-off, while panel two discloses the results of the regression
discontinuity analysis around the 85% cut-off. In this table, the variable of interest is
the Average Treatment Effect (ATE).
The outcome disclosed in the table shows proof of regression discontinuity around
the 40% cut-off. The impact around the 40% cut-off is circa 110% and statistically
significant at all levels. This implies that there is strong evidence of signaling around
the 40% cut-off. However, the impact around the 85% level is negative and
insignificant, with an impact of circa 94%. The insignificance of this result could be
due to the low number of observations as discussed in the methodology.
26
Table 8. Regression discontinuity analysis results
Ln Rent
Coefficient
Robust
St. Err.
tvalue
Panel 1, Regression discontinuity analysis around the 40 percent cut-off point
.34
3.24
Average
Treatment
1.099***
Treatment
(1 vs 0)
Effect (ATE)
2.831
4.81
Constant
Treatment
13.632***
0
Panel 2, Regression discontinuity analysis around the 85 percent cut-off point
-.944
1.273
-0.74
Average
Treatment
Treatment
(1 vs 0)
Effect (ATE)
4.541
13.521
0.34
Constant
Treatment
0
pvalue
95%
Confidence
interval
.002
.42
1.779
.000
7.966
19.298
.465
-3.56
1.672
.740
-23.251
32.334
Tabel 8 discloses the regression discontinuity analysis results. Panel 1 discloses the
outcomes at the 40% level cut-off point, while Panel 2 displays the outcomes at 85%
level cut-off point. *** p<.01, ** p<.05, * p<.1.
5.3 Robustness Check
To identify whether the results of the empirical research are still supported, the
location dummies will be added to the model. This variable is disclosed in the
regression output as ‘Location Fixed Effects’. The results of this regression are visible
in Table 9. Remarkable is that now, the variable for age is negative in all regressions.
This indicates that the older a property is, the lower the rent will be. The opposite
result was found in the regression excluding the location dummies. The variable size
of the property has stayed relatively the same and is still highly significant.
The variable of interest, the BREEAM score is negative and insignificant in all
regressions performed. The variable BREEAM score indicates that for BREEAMcertified properties, on average, a rental discount of 0.7%, 0.7%, and 0.4% is paid by
occupants in columns two, three, and four respectively. This implies that a BREEAMcertified property does not result in a higher rent paid by occupants, but in lower rents.
This result was not expected in this research. Possible explanations have been
addressed in the discussion section.
Furthermore, the R² of the regressions in Table 9 is far higher than the R² in the
regressions disclosed in Table 7. This indicates that by adding the location dummies
27
to the regression model, the explanatory power of the model becomes higher. To
highlight this, the R² of regression (1) in column 4 of Table 7 was 32.1%, while the
R² of regression (1) in column 4 of Table 9 is 62.5%. The explanatory power has
almost doubled in the results.
Table 9. Regression results model including location dummies
Dependent variable: lnRent
lnAge
lnSurface
BREEAM score
(1)
-.047
(.132)
.273***
(.048)
Dummy Office
(2)
-.082
(.134)
.286***
(.048)
-.007
(.005)
(3)
-.209
(.129)
.286***
(.046)
-.007
(.004)
(4)
-.186
(.128)
.317***
(.047)
-.004
(.004)
-.168
(.146)
.359*
(.214)
7.294***
(.668)
321
.542
.399
Yes
Yes
11.663***
(1.113)
321
.615
.471
Yes
Yes
11.356***
(1.117)
321
.625
.481
Yes
Yes
Dummy Retail
Pass
Good
Very good
Excellent
Outstanding
Constant
Observations
R2
Adj R2
Year Fixed Effects
Location Fixed Effects
6.927***
(.623)
321
.537
.396
Yes
Yes
(5)
-.234*
(.13)
.322***
(.049)
-.161
(.148)
.406*
(.216)
.543
(.372)
.524
(.375)
1.195
(.943)
.46
(.412)
.062
(.453)
10.762***
(1.137)
321
.634
.484
Yes
Yes
Dependent variable is the natural logarithm of rent; lnAge is the natural logarithm
of age; lnSize is the natural logarithm of surface area; Standard errors are in
parentheses and clustered at the neighborhood level. *** p<.01, ** p<.05, * p<.1.
Chapter 6. Conclusion and Discussion
The purpose of this research was to acquire intuition into the impact of BREEAM
certification on rental premiums in the Dutch commercial real estate market. The
importance of this is that sustainability certificates, among which BREEAM, are
increasingly gaining popularity, particularly now that climate change is a major
subject in society.
28
The research question reads as follows:
“To what degree does a BREEAM certificate lead to a rental premium on Dutch
commercial real estate?”
To answer this question, the results of the regression analysis will be discussed. To
start off, the independent variable of interest in regression equation (1) will be
discussed which is disclosed in column 4 of Table 7. The regression results indicate
that occupants pay a rental discount of 0.4% on average for a BREEAM-certified
building. Even though this variable is negative, which was not expected, it is also not
significant at any level. The insignificance is in line with the studies conducted by
Fuerst and McAllister (2011) whose results were insignificant due to a low number
of observations. Thus, as this research only has 321 observations, the lack of
significance is attributed to the low number of observations. Another study conducted
by Kok and Jennen (2012) also found insignificant results when examining the impact
of eco-certificates on office rent levels. These authors attributed the insignificance of
these results to the crisis effect. Thus, another explanation for the insignificant results
could be that under favorable economic circumstances, the upper scale of the market
grows disproportionately fast, whereas rents fall more quickly during more difficult
economic times. Furthermore, the result indicates a negative effect on the rent level.
While this finding contradicts the majority of studies that have found strong
significance and rental premiums (Eichholtz et al., 2010; Fuerst & McAllister, 2009;
Reichardt, 2012) it is in line with Feige et al. (2013). The results of the regression
equation (1) leave the first hypothesis unconfirmed as there was no statistically
significant result found that properties equipped with a BREEAM certificate acquire
a rental premium.
In addition to this, regression equation (2), which is disclosed in column 5 of Table 7
will be discussed. This regression is an expansion of regression equation (1). This
model measures the various influences of the various degrees of sustainability. The
outcomes indeed demonstrate signs that the rent premium rises when the degree of
sustainability rises. However, none of the variables which showcase the degree of
sustainability were significant. The insignificance is in line with the studies conducted
by Fuerst and McAllister (2011) whose results were insignificant due to a low number
29
of observations. Thus, as this research only has 321 observations, the lack of
significance is attributed to the low number of observations.
To further examine rental premiums, the signaling effect was tested by running a
regression discontinuity analysis. The results indicate that there is indeed some
regression discontinuity present. The outcome shows proof of regression
discontinuity around the 40% cut-off, which is statistically significant at all levels.
This implies that there is strong evidence of signaling around the 40% cut-off.
However, the impact around the 85% level is negative and insignificant. The
insignificance of this result could be due to the low number of observations as
discussed in the methodology. Thus, the results demonstrate that occupants value the
signal certificates provided, as buildings with relatively equal degrees of
sustainability, but with various degrees are valued variously.
6.1 Limitations
The most prominent limitation of this research is the restricted amount of
observations. There has not been much research on the impact of BREEAM
certification on rental premiums in the Dutch commercial real estate market. The
findings may improve as the amount of observations rises. Fuerst and McAllister
(2011) investigated the efficiency of two dozen BREEAM-certified buildings and
examined if a BREEAM certificate led to a rental premium and a higher market value.
Their results were however insignificant, due to a low number of observations.
Moreover, another prominent limitation of this research is that some important
variables, such as property attributes, may have been left out of the analysis. This is
due to the lack of information on properties in the Netherlands. If the databases were
to improve their information on this matter, the findings could improve as well.
6.2 Further research
Because of the growing interest in sustainability certificates, there is a possibility that
providers of sustainability certificates will ultimately participate in price wars. These
price wars would cause the prices of sustainability certificates to drop and more
properties would become certified. For further research, one could examine whether
the surge in certified properties and its advantages would ultimately neutralize the
non-certified properties. That is to say , would rental premiums for certified properties
dissolve over time as the market for certificates would be in equilibrium.
30
Another suggestion for further research is to examine whether BREEAM certification
on commercial rental transactions is impacted by intelligence prior to the enforcement
of BREEAM in 2009. The study could center around the question of the
contemplation of a sustainability certificate could lead to an increase in rental
premiums. It could be permissible that occupants would be prepared to pay extra rent
or agree to extend their contract period for properties that will become certified in the
foreseeable future. The reverse could also be true. Intelligence on expired or canceled
certificates can have the reverse impact on the total rent an occupant is prepared to
pay. This could bring about rental discounts.
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Appendix
Appendix A: BREEAM Sustainability categories
Source: BREEAM.com
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