DEGREE PROJECT,
REAL ESTATE AND CONSTRUCTION MANAGEMENT
BUILDING AND REAL ESTATE ECONOMICS
MASTER OF SCIENCE, 30 CREDITS, SECOND LEVEL
STOCKHOLM, SWEDEN 2016
Stock Market Behavior in Publicly Traded
Real Estate Companies
– How does the choice of allocation strategy affect publicly traded real estate companies
volatility, risk, risk-adjusted return and correlation?
Niklas Langer & David Koller
2016
ROYAL INSTITUTE OF TECHNOLOGY
1
DEPARTMENT OF REAL ESTATE AND CONSTRUCTION MANAGEMENT
TECHNOLOGY
DEPARTMENT OF REAL ESTATE AND CONSTRACTION MANAGEMENT
2
Master of Science thesis
Title
The Effect of Portfolio Allocation Strategy on
Stock Market Behavior in Publicly Traded
Real Estate Companies
Niklas Langer & David Koller
Real Estate and Construction Management
TRITA-FOB-Byf-MASTER-2016:31
437
Han-Suck Song
Real Estate, Correlation, Focused Portfolio,
Diversified Portfolio
Author(s)
Department
Master Thesis number
Archive number
Supervisor
Keywords
Abstract
Within the real estate asset class, most companies own and operate properties. How the companies
construct their property portfolio, in respect of property type and geographical focus, differ. Some
companies have chosen to be focused while the holdings of some companies are well diversified.
Depending on which strategy is chosen, the underlying assets of the companies will be different and
affected by different factors. This paper investigates if, in Sweden, the composition of the publicly
traded real estate companies’ property portfolios affects how their stocks behave on the stock market.
Four hypotheses about the behavior is stated, each hypothesis is linked to certain key financial figures
that is calculated and analyzed over time. The financial figures that are investigated are the correlation
between the companies as well as the correlation with the stock market portfolio, the risk and the riskadjusted return of the companies. All figures are tested over either a 36 month or a 12 month rolling
time period.
The results show that the diversified companies display a higher correlation with each other as well as
the market since the beginning of the 21st century. Companies that are diversified across property
types but focused geographically also display higher correlation. Hence, if a company is focused or
diversified geographically doesn’t seem to affect the level of correlation between the companies.
Another result is that the smaller, often more focused, companies have a low correlation with each
other as well as with the market. However, there are exceptions among the diversified as well as the
focused companies.
The risk is measured with the help of two variables, the beta coefficient and standard deviation. The
results of the rolling beta coefficients show that the companies that are diversified over property type
have a higher market risk compared to those that are focused. Whether a company is diversified or
focused geographically doesn’t seem to matter. The results of the standard deviation measurements do
not show this result as all companies moved in similar fashion.
Risk-adjusted return is measured with the help of the Sharpe ratio. The results show that the riskadjusted return is independent of the composition of the companies’ portfolios. However, in the crisis,
the risk-adjusted return of all companies are compressed regardless of how well they performed prior
to the crisis.
3
Acknowledgement
This Master Thesis is our final work at the Royal Institute of Technology and the Department of Real
Estate and Construction Management in Stockholm, Sweden.
We would like to thank our supervisor Han-Suck Song for his guidance throughout this Master Thesis.
Niklas Langer & David Koller
May 29, 2016
Stockholm
4
5
Table of Contents
1
Introduction ..................................................................................................................................... 8
2
Aim .................................................................................................................................................. 9
3
Theory............................................................................................................................................ 10
4
5
3.1
Modern Portfolio theory ........................................................................................................ 10
3.2
Volatility and variance........................................................................................................... 10
3.3
Correlation and covariance .................................................................................................... 11
3.4
Risk and diversification ......................................................................................................... 11
3.5
Beta (β) .................................................................................................................................. 12
3.6
Herfindahl Index .................................................................................................................... 12
3.7
IFRS ....................................................................................................................................... 12
3.8
REIT ...................................................................................................................................... 13
Literature review and hypothesis development ............................................................................. 14
4.1
Hypothesis nr. 1 ..................................................................................................................... 14
4.2
Hypothesis nr. 2 ..................................................................................................................... 15
4.3
Hypothesis nr. 3 ..................................................................................................................... 16
4.4
Hypothesis nr. 4 ..................................................................................................................... 18
4.5
Diversifying internationally ................................................................................................... 19
4.6
Correlation between real estate stocks and direct real estate ................................................. 20
4.7
The listed real estate companies ............................................................................................ 20
Method, Data and Methodology .................................................................................................... 21
5.1
Method ................................................................................................................................... 21
5.2
Data........................................................................................................................................ 21
5.2.1
5.3
6
Omitted data................................................................................................................... 21
Methodology .......................................................................................................................... 22
5.3.1
Time-periods .................................................................................................................. 22
5.3.2
Financial Methods ......................................................................................................... 22
5.3.3
Restrictions .................................................................................................................... 22
5.3.4
Geographical breakdown ............................................................................................... 23
Description of the Real Estate Companies’ Holdings ................................................................... 24
6.1
Atrium Ljungberg .................................................................................................................. 25
6.2
Balder..................................................................................................................................... 26
6.3
Castellum ............................................................................................................................... 27
6.4
Catena .................................................................................................................................... 28
6.5
Diös........................................................................................................................................ 29
6.6
Fabege .................................................................................................................................... 30
6
6.7
Fastpartner ............................................................................................................................. 31
6.8
Heba ....................................................................................................................................... 32
6.9
Hufvudstaden ......................................................................................................................... 33
6.10
Klövern .................................................................................................................................. 34
6.11
Kungsleden ............................................................................................................................ 35
6.12
Sagax ..................................................................................................................................... 36
6.13
Wallenstam ............................................................................................................................ 37
6.14
Wihlborgs .............................................................................................................................. 38
7
Results and Analysis ...................................................................................................................... 39
7.1
Herfindahl Index .................................................................................................................... 39
7.2
Hypothesis nr. 1 ..................................................................................................................... 40
7.3
Hypothesis nr. 2 ..................................................................................................................... 43
7.4
Hypothesis nr. 3 ..................................................................................................................... 45
7.5
Hypothesis nr. 4 ..................................................................................................................... 50
8
Conclusion ..................................................................................................................................... 52
9
Future Research ............................................................................................................................. 53
10
References ................................................................................................................................. 54
7
1 Introduction
Modern portfolio theory (MPT), which was developed by Harry Markowitz in 1952, attempts to
maximize the return of a portfolio given a certain level of risk, or minimize risk given a certain level
of return (Markowitz, 1952). This is, according to the theory, accomplished by allocating capital in
different asset classes that has a correlation less than one. The lower the correlation between an asset
and the existing portfolio, the more diversification is gained by including the asset in the portfolio.
Different market segments can behave in very different ways. Some markets follow the general index
closer than others, some move in opposite directions etc. To create a well-diversified portfolio,
investors typically invest their capital in different asset categories to reap greater diversification
benefits. However, investors do also often allocate their capital in multiple assets within each asset
category. Creating an optimal portfolio requires having an optimal allocation strategy on the “asset
class level” as well as within each asset class.
Within real estate, most companies own and operate properties. These properties can be of a very
different nature and situated in highly different areas. Depending on the type of property as well as
geographical location, the underlying forces which ultimately determine the return of the property,
will vary from region to region and property type to property type. A retail property has different
opportunities as well as risks compared to for example an industrial property. Different geographical
regions (within a country) are dependent on different type of industries. For example northern Sweden
is more dependent on industrial companies in the mining and forestry sectors and their success
compared to southern Sweden. Each listed real estate company has specialized in some way in order to
stay competitive. There are companies which only own and operate certain types of properties and
there are companies which only own and operate properties in a certain region (Eichholtz, et al.,
1995).
In theory, real estate companies with different allocation strategies should be treated differently by the
stock market. At first glance, a company focused on residential properties should have less volatility
than for example a company that owns a lot of industrial properties, given the stable nature of
residential properties, at least in urban areas. Do the investors take the composition of the companies’
property portfolios in to account when evaluating them, or are all property companies regarded as one
entity in the investors’ eyes?
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2 Aim
The aim of this article is to investigate if, and if so; how, the allocation strategy of the major listed real
estate companies affects their volatility, correlation with the market and risk-adjusted return.
Allocation strategy means how the companies’ portfolios are constructed, geographically as well as
across property types. The purpose of this investigation is to gain further insight into the real estate
market in Sweden and how different property types are correlated with the Stockholm all-share index.
The idea is that this information will improve the understanding for both institutional and private
investors of how to efficiently allocate capital in real estate shares. Four hypotheses will be formed
and tested:
1. Companies with similar property portfolios in respect of type and region, have a
high correlation with each other.
2. Companies with a diversified property portfolio will have a higher correlation
with the market than companies with a focused portfolio.
3. Companies with a focused property portfolio will have a higher volatility
compared to companies with a more diversified portfolio.
4. It is better (from a risk-adjusted return perspective) to have a diversified
property portfolio
In 2013, Medhat Khalil (Khalil, 2013) published an article about the correlation and volatility of the
listed Swedish real estate companies. This article aims to dig deeper and extend upon his and others
previous work in this subject and hopefully find a new perspective on the Swedish real estate market.
9
3 Theory
3.1
Modern Portfolio theory
When Harry Markowitz developed the foundation for what is known today as “modern portfolio
theory”, he created a whole new field of finance. According to modern portfolio theory investors
should combine different assets in a portfolio instead of holding one asset (Markowitz, 1952). Putting
all wealth in one asset results in a high exposure to loss when compared with a portfolio with different
assets. The natural reason for this is simply that a poorly performing asset does not generate the same
level of negative effect if the other assets in the portfolio perform well. This way of thinking is
consistent with the old adage "don't put all your eggs in one basket". Return, standard deviation and
correlation are crucial factors in portfolio theory. By taking these three factors into consideration when
combining assets, it is possible to gain a higher risk-adjusted return. Hence, the idea with portfolio
theory is to minimize portfolio volatility, at a given level of return or to maximize return at a given
volatility, through efficient portfolio allocation. Modern portfolio theory, as well as the celebrated
Black-Litterman model for portfolio allocation (Black & Litterman, 1991), are today utilized in most
aspects of the financial spectrum, not only with stocks and bonds.
Application of modern portfolio theory to real estate began in the 1970s when institutional investors
started to include the asset in portfolios together with stocks and bonds. About a decade later ideas
about applying portfolio theory on real estate level, through efficient allocation, developed (Geltner, et
al., 2014). There exist two conventional categories of diversification strategies within real estate
portfolios. The first category is diversification among different property type such as residential, retail,
offices and warehouse. The second category is geographical diversification. There is an ongoing
discussion if it is more effective to focus on one property type and diversify across regions or focus on
one region and diversify among property types (Eichholtz, et al., 1995) (Peng & Schultz, 2013).
3.2
Volatility and variance
The risk of a stock or a portfolio is commonly measured by standard deviation (volatility). Standard
deviation is a measure of how much the return for a stock deviates from its mean. A higher standard
deviation means a higher variability of the return. Standard deviation is calculated by taking the square
root of the variance SD(R) =�𝑉𝑉𝑉𝑉𝑉𝑉(𝑅𝑅) and the formula for variance is:
=
Var ( R )
T
1
(R t − R ) 2
∑
T − 1 t =1
Equation 1 - Variance
Where Rt is the realized return of a stock in year t and R is the average annual return. T represents
number of years (Berk & DeMarzo, 2014).
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3.3
Correlation and covariance
Co-movement of securities can either be measured by covariance or by correlation. This section will
shed more light on correlation, since it is the most common way to measure co-movement in the world
of finance.
The formula for historical covariance between the return for the stocks Ri and Rj is:
𝐶𝐶𝐶𝐶𝐶𝐶�𝑅𝑅𝑖𝑖 , 𝑅𝑅𝑗𝑗 � =
1
� (𝑅𝑅 − 𝑅𝑅�𝚤𝚤 ) (𝑅𝑅𝑗𝑗,𝑡𝑡 − 𝑅𝑅�𝚥𝚥 )
𝑇𝑇 − 1 𝑡𝑡 𝑖𝑖,𝑡𝑡
Equation 2 - Covariance
Where T represents the time and 𝑅𝑅� is the average return for each stock.
The correlation of two stocks is calculated through dividing the covariance (see formula above) of the
two returns Ri and Rj with the standard deviation for each return:
Corr (Ri, Rj) =
Cov(Ri, Rj)
SD ( Ri ) SD ( Rj )
Equation 3 - Correlation
The correlation coefficient ranges between -1 and 1. Two securities that have a correlation of 1 are
perfectly correlated and will move in the same direction, if one goes up the other does as well. Stocks
that are highly correlated are usually operating in the same industry since they are often affected by
similar economic events. Two securities that have a correlation of -1 are perfectly negatively
correlated and will move in opposite directions, if one goes up, the other is bound to go down. Two
securities with a correlation of 0, is said to have no correlation or to be perfectly uncorrelated, which
means that the movements are completely random. If one security goes up, the other might go up as
well as down, there is no clear pattern. When studying actual securities, none are perfectly positively
or negatively correlated. There is always some degree of uncertainty involved. Lower correlation
between stocks in a portfolio generates a lower volatility i.e. lower risk (Berk & DeMarzo, 2014). It is
important to bear in mind that correlation is not consistent over time and it tends to increase in a bear
market (When the general stock market is declining) (Schindler, 2012).
3.4
Risk and diversification
The risk associated with a security is often divided into two categories, market risk, or systematic risk,
as well as firm-specific risk, or idiosyncratic risk. Firm-specific risk is the risk associated with the
individual company or investment. In a real estate perspective, it could be risks such as vacancies or
maintenance. As these risks are specific to each asset, by combining multiple assets, the overall risk
declines as the probability of all assets performing poorly is lower. By gradually increasing the
number of stocks in a portfolio the specific risk will be diversified away until only the market risk
remains. Market risk consists of factors that affect economy as a whole. Accordingly, all shares have
some market risk. Some examples of these factors are inflation, interest rates, economic growth,
unemployment, changes in the trade balance or changes in corporate tax rates, to name a few. As these
risks affect the whole market and all companies within, it cannot be diversified away (Berk &
DeMarzo, 2014).
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3.5
Beta (β)
Securities have different sensitivity to systematic risk and it is measured by its beta coefficient (β).
The beta of a security describes how many percentage points (%) the return of the security is expected
to change if the market portfolio changes with 1%. When a security’s beta is below 1 it is less
sensitive to market risk and when the beta exceed 1 it is more sensitive to market risk. It is important
to bear in mind that the beta coefficient for a security is not consistent over time. The beta for a
security is calculated by dividing the covariance, between the return on the security and market
portfolio, with the variance of the market portfolio (Berk & DeMarzo, 2014):
𝛽𝛽 =
𝐶𝐶𝐶𝐶𝐶𝐶(𝑅𝑅𝑖𝑖 , 𝑅𝑅𝑀𝑀𝑀𝑀𝑀𝑀 )
𝑉𝑉𝑉𝑉𝑉𝑉(𝑅𝑅𝑀𝑀𝑀𝑀𝑀𝑀 )
Equation 4 - Beta coefficient
3.6
Sharpe ratio
The sharpe ratio measures the performance of a portfolio or a stock in relation to its risk. This is done
by subtracting the return of the asset with the risk-free return and then dividing by its standard
deviation, or risk. The higher the sharp ratio is, the better the risk-adjusted return is. (Berk &
DeMarzo, 2014)
𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺 𝒓𝒓𝒓𝒓𝒓𝒓𝒓𝒓𝒓𝒓 =
3.7
𝑬𝑬�𝑹𝑹𝒑𝒑 � − 𝑹𝑹𝒇𝒇
𝑺𝑺𝑺𝑺(𝑹𝑹𝒑𝒑 )
Herfindahl Index
The Herfindahl index is used to measure how focused a portfolio is in an easy and comparable way. It
is done by adding the square of each observation with each other. The result is a number between 0
and 1 which by itself doesn’t say much, but when compared with other results it is easy to see which
firms are more focused or diversified. For example, a company with equal amount of properties in
four cities, 25 percent in each, will get a Herfindahl index of 0,25 (0,252+0,252+0,252+0,252) while a
company with equal shares in three cities get a Herfindahl index of 0,33. The second company is more
focused than the first. (Cronqvist, et al., 2001)
𝑵𝑵
𝑯𝑯 = � 𝒔𝒔𝟐𝟐
𝒊𝒊=𝟏𝟏
Equation 5 - Herfindahl index
3.8
IFRS
In 2005, a new accounting standard was introduced in the EU. The major difference for real estate
companies is that as of January 1, 2005, in their financial reports, they must declare the “fair value”
which in reality means the market value. Before 2005, companies often declared the acquisition value
of their properties. This change has made comparing companies easier as the companies true value has
become more transparent (Ball, 2012).
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3.9
REIT
A REIT, or Real Estate Investment Trust, is a company that owns or finances income-producing real
estate. REITs have special taxation rules as long as they qualify as a REIT. To qualify as a REIT the
company must, among other things, own at least 75 percent real estate assets and pay at least 90
percent of the taxable income as dividend to its shareholders. (NAREIT, u.d.)
13
4 Literature review and hypothesis development
In the following section earlier research and literature in the topic will be discussed and presented for
each hypothesis. The aim is to gain further insight before testing the hypotheses.
4.1 Hypothesis nr. 1
- Companies with similar property portfolios in respect of type and region, have
a high correlation with each other
Khalil (2013) investigates the correlation of different Swedish real estate shares as well as their betas
during bear and bull markets to see if they are asymmetric. Khalil categorizes the Swedish real estate
companies in three categories; commercial, residential and industrial. He then investigates how the
companies in each category correlate with each other to determine whether there are any
diversification benefits within the real estate asset class. He found that while the real estate market had
a high correlation with the general market (0.91), there do exist diversification possibilities within real
estate, even within each sub-category of real estate. The correlation among the commercial companies
ranged between 0.4 and 0.89 and between the two residential companies, the correlation was 0.62. The
correlation between the residential companies and the commercial companies ranged between 0.49
and 0.91. As there seems to be no relationship between the correlation and the categories, the results
are non-conclusive.
It is worth noting that Khalil investigates the correlation between these shares on an annual basis and
between 2003 and 2012. Other results might be found if the correlation between these companies is
studied at a monthly, weekly or even daily level and during longer time span. It is also possible that
the underlying dataset in Sweden is too limited, which might affect the result. Research conducted in
the US, for example the one below, hints at a possible relationship between type of property holding
and correlation.
Gyourko and Nelling (1993) examine the systematic risk and diversification properties of publicly
traded REITs vary with type and location of the companies’ property holdings. Two main results are
presented. First, the beta of the firms varies depending on what type of properties the firm is focusing
on. Specifically, retail-oriented REITs have a significantly higher beta, i.e. greater systematic risk,
compared to REITs owning primarily warehouse and industrial properties. Second, diversification by
property type and broad economic region is unrelated to a stock-market based measure of
diversification. The first result implies that the market takes the underlying property holdings into
account when valuing the companies. If that is the case, then at least theoretically, companies with
similar portfolios should display an increased correlation with each other. That would imply that there
exist a potential for diversification, however the authors cannot draw any conclusions, since their data
is insufficient, about the benefits of diversifying across property types and across geographical
regions.
Chong, Krystalogianni and Stevenson (2012) study the correlation between REIT sub-markets in the
US. About 90 percent of REITs in the US are not classified as diversified but are instead focused on a
single submarket (such as residential, office or healthcare). The authors find that the correlation
between these submarkets has been increasing since 1990, ultimately resulting in reduced
diversification when investing across these property types. The authors suggest that a higher trading
volume in REIT shares, increased number of listed REITs and increased amount of institutional
capital invested in REITs is the key factors behind the last decades increased correlation between
different submarkets in the REIT industry.
14
To summarize the findings from the previous literature, there is no support for the hypothesis that
companies with similar property portfolios will have a higher correlation among them.
4.2 Hypothesis nr. 2
- Companies with a diversified property portfolio will have a higher correlation
with the market than companies with a focused portfolio
In 2011, Leimdörfer (2011), published a short article investigating whether real estate shares can be a
proxy for direct real estate investments. The results in the article shows that real estate shares have a
low correlation with direct real estate and a high correlation with the stock market in the short run (< 4
years). However, in the long run (> 4 years), real estate shares tend to have a high correlation with
direct real estate and a negative correlation with the stock market. The results suggest that an investor
that holds real estate shares for more than 4 years is exposed towards the real estate market instead of
the stock market. If real estate shares have a high correlation with the stock market in the short run but
a high correlation with direct real estate in the long run, that should imply that the correlation between
real estate shares in the short run is independent of the companies’ real estate portfolios. It should also
imply that in the long run, as correlation increases with direct real estate, the correlation between real
estate companies with more similar portfolio allocation ought to be higher than portfolios with
different allocation strategies. However, it is important to notice that Leimdörfer only have
investigated Swedish real estate companies in this report.
The results presented by Leimdörfer doesn´t present an answer to hypothesis number two, however it
does give some interesting food for thought. If the correlation between direct and indirect real estate
increases over time, what does that mean for the hypothesis? In the short run, when correlation with
the stock market is dominating, there shouldn’t be any major difference between the diversified
companies and the focused companies. In the long run, when the correlation according to Leimdörfer
is high with direct real estate, there might be a difference between the companies.
Lee and Stevenson (2005) shows in their report that the diversification benefits of REITs increases as
the holding period increases. This implies that REITs, in this regard, behave similar to what
Leimdörfer found on listed Swedish real estate companies. Thus, Lee and Stevenson finds that the
correlation between securitized real estate and the market portfolio decreases as the holding period
increases. If there is a difference between companies with a diversified portfolio and companies with a
focused portfolio, it should appear and strengthen when looking at longer time periods.
Boer et al. (2005) analyze 275 real estate companies, over 17 years, in the US, the UK, France, the
Netherlands and Sweden to investigate if corporate focus translates into superior stock performance.
The focus in the article is both in regard to property types as well as geographic concentration. The
authors find difference focus priorities for real estate firms in Europe and REITs in the US. In the US,
REITs are often more focused in a particular property type and diversified among different
geographical regions, while the European companies are more focused geographically and diversified
among different property types. The authors noticed that increasing concentration of property type for
REITs in the US was the trend during the 90’s. In Europe, the trend towards more geographic focus
existed but it was not as strong as its counterpart in the US. Worth mentioning is that the Dutch
submarket is not a part of this European trend, possibly as a result of their limited home market. In
most cases, a shift towards corporate focus leads to an increase in the risk-adjusted stock
outperformance. However, the authors also find that the increased focus also tends to lead to an
increase in the firm specific risk but a, not so certain, decrease in systematic risk.
If the share of firm specific risk associated with a company is high, that should theoretically imply that
15
the firm has a lower market risk. A lower market risk for a firm results in a lower correlation with the
market portfolio, since the firm reacts more to news that has little impact on the market as a whole. As
Boer et al. found that focused companies has a, on average, higher portion of firm specific risk, which
implies that these firms have a lower correlation with the market compared to more diversified
companies. This result is in line with the hypothesis but it must be mentioned that in reality, this might
not be the case.
Ro and Ziobrowski (2011) investigate if REITs that are specialized on property type outperform
diversified REITs. Thus, providing evidence of superior management expertise associated with
specialized REITs. The authors’ finds no statistical evidence on that specialized REITs outperform
those who are diversified. However, the authors do find that the specialized REITs have a significantly
higher market risk compared to diversified REITs but with the same return. This result suggests that
the specialized REITs pay a risk-premium for their lack of diversification while not gaining any
additional return. These findings are contrary to the results presented by Boer et al (2005) which found
that focused real estate firms bears a lower market risk. The data used by Ro and Ziobrowski is more
recent which might have an impact on the result. Ro and Ziobrowski also finds some evidence on that
the size of the REIT might have an effect on the performance, which means that this kind of
investment vehicle can become too large.
In the results presented by Khalil, the diversified companies have a correlation with the market that
ranges from 0.59 to 0.83. The focused companies, Fabege and Hufvudstaden, have a correlation with
the market of 0.83 and 0.53, respectively. These results are non-conclusive but the limited data
available makes it hard to draw any conclusions based upon them.
To summarize the previous literature that in some way deals with hypothesis nr. 2, there are both
results arguing that companies with a specialized or focused portfolio have a lower correlation with
the market as well as the opposite. What is more interesting is the results presented by Leimdörfer and
Lee and Stevenson (Lee & Stevenson, 2005), where both articles indicates that the magnitude of the
correlation is rather a question of time horizon. A longer timespan shows an increased correlation
between the property shares and direct real estate and a shorter timespan shows an increased
correlation between the property shares and the market portfolio.
4.3 Hypothesis nr. 3
- Companies with a focused property portfolio will have a higher risk compared
to companies with a more diversified portfolio
The results from Ro and Ziobrowski (Ro & Ziobrowski, 2011), which is discussed in the section
above, confirms the hypothesis that focused property portfolios will have a higher market risk than a
diversified portfolio. This is since their findings in the investigation showed that specialized REIT
portfolios roughly have the same return but higher market risk.
Hoesli and Serrano (2006) analyze the behavior of securitized real estate betas in 16 countries and
their link with financial assets and real estate. They found that the betas in 14 countries have decreased
dramatically since 1990, especially in Sweden where it was above 2 until the 2000s when it went
below 0.5. The authors also tested three different variables to see if they could explain the differences
in betas. The first tested variable was the standard deviation of securitized real estate. The second
tested was the standard deviation of common stocks, and the third, the correlation between common
stocks and securitized real estate. In Sweden, all three variables helped explain the decrease in beta.
The results suggest that real estate, as an asset class, has become a lot less volatile in almost all 16
countries since the 90s.
16
Clayton and MacKinnon (2003) investigate the link between REIT, financial assets and real estate
return. A multi-factor return generating approach was utilized in order to break down the volatility of
REITs into different sub-categories; large cap stocks, small cap stocks, bonds and unsecuritized real
estate. Over the entire period (1979-1998), large cap stocks account for the greatest proportion of
REIT market volatility with small cap stocks second, bonds third and contribution of unsecuritized
real estate was almost negligible. The authors then investigated three sub-periods, namely 1979-1984,
1985-1991 and 1992-1998. In the first sub-period, 72 % percent of the REIT return volatility could be
explained by large cap stocks while in the last sub-period only 7 percent of the volatility could be
explained by large cap stocks. The proportion related to real estate increased from a meagre 1 percent
to 15 percent in the las sub-period. Notable is also the increase of idiosyncratic risk which increased
from 35 percent to 63 percent during the entire time period.
Clayton and MacKinnon (2003) found that over the period 1979-1998, the volatility of REITs is less
explained by large cap stocks and more by small cap stocks as well as real estate and idiosyncratic
risk. They called this a maturation process in which the REITs are beginning to more than before
reflect the underlying asset, the securitized real estate. If the volatility of the REITs more now than
before reflects the underlying assets, the different attributes between those assets should mean an
increased difference between REITs with different portfolios. As these results cannot give an answer
to the hypothesis, it implies that the possibility of a difference between companies with diversified and
focused portfolios has increased since 1979.
Khalil (2013) also investigates the volatility of the listed Swedish real estate stocks. He had divided
his total period (2003-2012) into three sub-periods depending on the direction of the economy of that
period. 2003-2007 is a period of economic growth, a bull market. 2007-2009 is a period of economic
decline, a bear market and 2009-2012 is a period of economic recovery, again a bull market. As with
the correlation there are no conclusions to be drawn if a focused property portfolio can be a factor of
increased risk.
17
4.4 Hypothesis nr. 4
- It is better (from a risk-adjusted return perspective) to have a diversified
property portfolio.
Lind and Nyström (2012) create an optimal portfolio using data from the Swedish IPD property index.
This portfolio is then compared to the actual portfolios of several Swedish listed real estate companies
to check for discrepancies. The portfolio with the highest risk-adjusted return, for the period of 1993 –
2010, comprises of a major weight in residential properties in Stockholm, Gothenburg and Malmo,
with a small weight allocated in industrial properties. For the period of 2005 – 2010, after the
implementation of the new IFRS rules, the efficient portfolio still has a major weight in residential
properties but with offices in Malmo and Stockholm instead of industrial properties. During the latter
time period, a majority of the listed real estate companies held a portfolio far below the efficient
frontier. This, according to modern portfolio theory, means that the companies can gain a higher return
without taking on any more risk simply by diversifying their portfolios. Lind and Nyström present
several possible explanations to these results. Firstly they argue that the financial risk models only
work in “normal” conditions which means that some risk are not included in the models and thus the
efficient portfolios might only be so according to IPD data and not in the actual market. The second
explanation is that as the real estate market is illiquid and inefficient, which makes it difficult to make
informed calculated decisions and therefore the companies rely more on gut feeling and local market
knowledge. Thirdly, as each property is associated with different risks, it might be difficult to fully
apprehend what the risk and return would be on the aggregated portfolio level. Instead of focusing on
how good each new property is for the portfolio, each investment is treated more as an individual
investment.
The optimal portfolio, based upon IPDs data was a portfolio with a major weight in residential
properties in the major cities. According to Lind and Nyström’s findings, diversifying across
geographical regions seems as the best strategy. But as the authors mentioned, these results might not
be the same in the actual market for a number of reasons.
Hellström and Karlsson (2010) studies publicly listed real estate companies in Sweden during the
years of 1998 to 2008. Their objective is to investigate how the shareholder value is affected by a
diversified or focused strategy. They found that between 1998 and 2005, companies with a focused
allocation strategy gain a higher shareholder value, which is in line with what Cronqvist et al. (2001)
found in their research. Furthermore, the investigation indicated that the choice of strategy had little
impact on the shareholder value.
Leimdörfer (1997, as cited by Hellström & Karlsson, 2010) published a report in which they
investigated how diversification across geographical regions and property type affected how real
estate companies were valued. They found that the companies whose property portfolio was the most
focused both geographically and by property type, had a 24 % higher value than the companies who
were the most diversified. Leimdörfer presented a couple of possible explanations for the result as
well as an explanation as to why not all companies are focused. Operational and financial efficiency
was presented as two reasons why focused companies were valued more. Operational efficiency
means that the companies can run their business more effectively as the properties are similar as well
as concentrated, resulting in lowered maintenance costs and increased revenues. Financial efficiency
means that the shares of a focused company are easier to include in a portfolio as they are more easily
analyzed and diversified. They will thus be more popular and more valuable.
Eichholtz, Hoesli, MacGregor and Nanthakumaran (1995) investigate, starting with one property type
18
in one region, whether it is more effective to diversify across regions but within a property type or
across property types but within a region. In their analysis, they analyze data from the UK as well as
the US. In other researches, which are cited in this article, there are articles that argue that greater
diversification is gained by diversifying across property type (Miles and McCue, two studies) as well
as articles arguing that it is better to diversify across regions (Hartzell et al). The conclusions drawn
from the earlier research by Eichholtz and his colleagues is that the earlier research favors
diversification across regions instead of across property types. In their own analysis, three different
analyses were used; analysis of correlation matrices, analysis of efficient frontiers and principal
components analysis. The results of the analyses vary between property types as well as between the
US and the UK. In the US, the correlation matrices shows slight advantage for regional diversification
but it is marginal. The analysis of the efficient frontier paints a clearer picture; office properties
benefitted more from diversification across property types, retail properties gained greater
diversification across regions and with industrial properties, it was best to diversify across both region
and property type. In the UK, the analysis of the correlation matrices as well as the analysis of the
efficient frontiers found mixed results but the conclusion is that it is better to diversify across both
region and property type. The principal component analysis yields mixed results as well. To
summarize the findings, it is often best to diversify across regions except in some cases, for example
retail in the US or for riskier portfolios in London.
Benefield et al. (2009) finds that property type diversified REITs outperform property-type specialized
REITs during the period of 1995-2006. The findings were not in line with the, of that time,
mainstream corporate finance literature. The authors put forward two possible explanations that might
explain the findings. Firstly, the diversified REITs seemed to have, on average, a greater exposure
towards office properties. If office properties performed better than the other property types during the
time period, it would explain the results. Secondly, property-type specialized REITs, on average, had a
greater exposure towards residential and retail properties. If these were to perform worse, that would
also explain the findings. The authors did not check any of these hypotheses mentioned. These results
are also contrary to those presented by Leimdörfer(1997).
To summarize the earlier research, focused portfolios are often favored over diversified portfolios and
geographical diversification is better than diversifying across property types. There are articles
presenting opposite results to both statements.
4.5
Diversifying internationally
In 1996, Eichholtz (Eichholtz, 1996) showed that real estate stock returns were less strongly
internationally correlated than common stocks and bonds. This means that international diversification
can reduce the risk of indirect real estate portfolios more than portfolios consisting of only common
stocks and bonds. This result is probably connected to the nature of real estate. Real estate markets
tend to be more local compared to other markets. In many European countries, international investors
on the real estate market are a new phenomenon. It is easy to derive the local nature of real estate to a
decreased correlation, and thus increased diversification benefits between real estate markets in
different countries.
Glaslock and Kelly (2007) tests whether it is best to diversify internationally or across property types.
They find that diversification benefits across property types is small and that diversification benefits
across countries is a more effective tool of diversification. However, they do find evidence that the
relative importance of country effects is decreasing while that of property type effects is increasing,
probably as a result of the increasing globalization of the real estate market.
19
4.6
Correlation between real estate stocks and direct real estate
The idea that investing in securitized real estate can substitute direct real estate investments is
strengthened by Falk (Falk, 2012). He finds, by analyzing the Swedish real estate market, that
securitized real estate has outperformed direct real estate investments between 2000 and 2010. More
specifically, direct real estate has on an annual basis returned 9.33 % with a standard deviation of 9.22
%. Meanwhile, indirect real estate has had an annual return of 16.95 % and a standard deviation of
24.43 %. However, even if the indirect investments have yielded a higher return, the risk-adjusted
return is slightly higher for direct real estate. The author suggests that the actual standard deviation of
direct real estate might be higher but that as a result of its illiquid nature, it is not shown in the
statistics.
4.7
The listed real estate companies
Lagelius and Pikosz (2013) perform a study of real estate companies listed on the Stockholm Stock
Exchange. They analyse the strategies and performance of these companies during the period of 20022011. The authors find a clear trend of specialization among the companies. Most, but not all, have
specialized over the years either in property type or in geographical focus and the trend started before
2008 which means that it is not a result of the financial crisis. On the other hand, as a result of the
financial crisis in 2008, the companies have deemed the cash flow and management of their properties
as more important than before. Before 2008, the rapid increase in property values as well as the easy
access to capital, both foreign and domestic, made the transaction market attractive. As that market
“died” after 2008, the companies had to refocus and thus, the cash flows became more important.
Another result of the financial crisis is that the banks have adopted a more conservative lending
policy. This has made capital a more valued commodity which has made alternative ways of financing
more attractive. Thus, most companies today have a more diversified financing, utilizing for example
bonds and preferred shares in a more active way than before 2008.
Since this research was released in 2013, a lot has happened on the international financial arena. As
the central banks of the US, the EU, the UK, Japan and others has utilized quantitative easing as a way
of tackling low inflation. The cost of capital has decreased and it has been fairly easy to gain financing
as the market is flooded with capital. As inflation is low, the interest rates have also been historically
low. The situation today is more similar to how it was before 2008 in terms of capital accessibility.
Starting in the late 2015, this trend has declined somewhat as the uncertainty has decreased. (IMF,
2015)
20
5 Method, Data and Methodology
5.1
Method
This study used a quantitative method since it was purely data driven. Indeed, the statistical nature of
the above mentioned hypotheses means that the best way to answer them was to collect data from
many different companies and then analyze those using quantitative methods. In order to include as
many companies as possible and to analyze the results as thoroughly as possible, it would have been
difficult (from a resource perspective) to include any qualitative methods such as interviews. The
results of such a qualitative method would have been interesting but that research is left for others to
conduct (Saunders, et al., 2009).
5.2
Data
The primary data which is used in this research is the daily closing price of the real estate companies’
stocks as well as the Stockholm all-share index. The data was gathered from the Nasdaq OMX Nordic
website and contains daily closing prices as well as percentage change compared to the day before
(Nasdaq OMX, u.d.). This data was adjusted to account for eventual splits or other similar events that
have had an effect on the stock price. Today, there are more than 15 real estate companies listed on the
Stockholm stock exchange. These companies are all of different age and the time that they have been
publicly traded vary. The oldest in the sample, Hufvudstaden, dates back to 1987 while some
companies in the sample were listed as recently as 2007. The rest were listed somewhere in between.
The other dataset that was used in this study was information about the property portfolio of each
company. This data was gathered from the annual reports released by the companies and contains
information about how their properties are divided geographically as well as by property type. Due to
different company policies, the available annual reports vary from company to company. During the
studied time periods, most companies have all annual reports available but some do not. How each
company has reported its property holdings has also varied over time. Sometimes they have changed
as a result of modernization and sometimes as a result of new legislation. These complications further
limit the available data.
5.2.1
Omitted data
In the 90s and early 2000s, the datasets of the companies’ closing prices are in many cases not
complete. Many observations (closing prices) are missing, some companies has more data missing and
some less. Before any statistical analyses can be exercised, the corresponding datasets must be
synchronized. If for example the correlation between company A and the OMX all-share index is
investigated, all daily closing prices that exist in one of the datasets only must be omitted so that the
datasets contains the data of the exact same dates. In some cases, up to 30 percent of the observations
have been omitted as a result of gaps in the datasets. The most affected companies in this regard are
Atrium Ljungberg and Heba. These two companies lacked closing prices in the late 90s and in the
early 2000s.
Atrium Ljunberg is excluded from the two earlier time periods (in hypothesis 1) due to the large
amount of missing data. Atrium Ljunberg was instead introduced in the two later time periods, starting
in 2007. If Atrium Ljungberg were to be included in the earlier data series, too many observations had
to be omitted.
21
5.3
5.3.1
Methodology
Time-periods
Four hypotheses have been stated and will be tested. How these are tested, and what data that will be
used, varies between the hypotheses. The first hypothesis tests the correlation between the companies
themselves. This data is limited by the different lengths of the time series and to maximize the use of
the data it has been divided into four time periods, 2000-2003, 2003-2007, 2007-2009 and 2009-2016.
The correlation between the companies in each sub-period is then calculated.
To utilize the longer but fewer, time series as well as the more recent more numerous but shorter time
series, it made sense to divide the total period into several sub-periods. This is also motivated from an
economical perspective. Since the early 2000s, the world economy has experienced very different
economical climates. Some periods characterized by economic growth and some by economic decline,
often referred to as bull and bear markets. In a bull market, the market is in an upwards trend with the
opposite being true for a bear market.
What is known today as the IT-bubble, burst in the beginning of the new millennia when the market
realized that many of the new IT Fcompanies would not be able to deliver on the investors’
expectations. The bubble burst and the years from 2000 to the beginning of 2003 were characterized
by economic decline. In 2003, the weak economy started growing and for four years the economy was
strong. However, in 2007 the housing market in the US crashed sending the world into another
downward spiral of economic decline. The lucrative business of subprime mortgages had created a
castle of air. This spiral continued until 2009 when the bear turned into a bull and the economic
growth started increasing yet again and today, the bull is still alive. Worth mentioning is that some
believe that the bull is being kept alive by life-supporting services such as low interest rates and
quantitative easing. If this is the fact and the bull is actually already beyond saving, only time will tell.
(Blanchard, et al., 2013)
5.3.2
Financial Methods
To answer the hypothesis, various financial methods were used. To answer hypothesis one, correlation
is calculated for each time period. A rolling correlation was used in hypothesis 2. The time window is
36 months and it is "rolled forward" one day at a time. In similar fashion, a rolling standard deviation
and beta value calculation is used to answer hypothesis three. To answer hypothesis four, a rolling
Sharpe ratio is used with a window of 12 months instead of 36. The 10 year Swedish Government
bond was used as the risk free interest rate. To help determine whether the companies are focused or
diversified, a Herfindahl index is calculated for the geographic data as well as the property type data.
If the index is above 0.5, the portfolio is deemed as focused, if it is less than 0.5, the portfolio is
regarded as diversified. There are no general practices regarding “time window length” or Herfindahl
index values. The 36 month window was chosen as it is a frequently used time window and it fit well
with the length of the time periods.
All calculations have been carried out with the Excel 2013 program.
5.3.3
Restrictions
When collecting the portfolio data from the annual reports, several restrictions had to be made to keep
the task doable and the results replicable. In all cases except one, no affiliated companies were
included in the portfolio of the companies, in other words, only the directly owned properties were
included. The exception is the share in Hemsö owned by Kungsleden. This was because of two
reasons. Firstly, in their annual reports, Kungsleden presented the properties owned by Hemsö as their
own making them easy to include. Secondly, the share was rather substantial, about 50 percent, which
22
made it hard to ignore.
Only companies which have been listed, as a pure real estate company, for at least nine years are
included in this research.
5.3.4
Geographical breakdown
To be able to compare and analyze the companies’ real estate portfolios, the holdings have been sorted
into seven different geographical regions. The first region, Stockholm, consists of all areas that are
located within the Stockholm County. The second region is Gothenburg and it ranges to Kungälv in
the north, Lerum in the east and Mölndal in the south. The third region Öresund, consists of Skåne
County, Halmstad and Denmark. Götaland consists of all cities within the region except those cities
that are located in the Öresund region and Gothenburg, see map 1. Svealand consist of all cities within
the region except Stockholm County. Norrland ranges from Gävle and up to the national border in the
north. The last geographical region is abroad and consists of all countries except Sweden and
Denmark. The majority of the properties that are located abroad in this research are located in
Norway, Finland and Germany.
Map 1 - The geographical regions
23
6 Description of the Real Estate Companies’ Holdings
Companies are, based upon a few conditions, chosen to be analyzed and compared. The company’s
primary business must be the owning and operating of properties. They must also have been active,
and released, at minimum 9 annual reports. 14 companies fulfill these conditions. Below, the
companies are presented along with a brief accountant of their history as well as some information
about their portfolio over time.
24
6.1
Atrium Ljungberg
The Ljungberg group was established in 1946 by Tage Ljungberg. In the end of 2006 the Ljungberg
group merged with Atrium Fastigheter. This merging is apparent in figure 1 and 2 below since their
lettable area increased with approximately 650 000 m2. The company is mainly focusing on owning,
developing and operating retail and office properties. Atrium Ljungbergs property portfolio has been
fairly consistent over time, both by property type and geographically. A majority of their holdings has
throughout the years been located in the Stockholm region. In 2014, approximately 55% of their
properties in the Stockholm region were located in the inner city. However, Atrium Ljungberg also
has property holdings in other major cities such as Gothenburg, Malmo and Uppsala. A larger part of
the classification “other” in figure 1 below consists of garage space (Atrium Ljungberg AB, 20032014).
Atrium Ljungberg property types
1 200
% of lettable area
90%
1 000
80%
70%
800
60%
50%
600
40%
400
30%
20%
200
10%
0%
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Office
Retail
Industrial/Warehouse
Public property
Other
Area(Right axis)
2013
Total area in thousand m2
100%
0
2014
Residential
Figure 1 - Atrium Ljungberg Property Types
Atrium Ljungberg regions
1 200
% of lettable area
90%
1 000
80%
70%
800
60%
50%
600
40%
400
30%
20%
200
10%
0%
2003
Total area in thousand m2
100%
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
0
2014
Stockholm
Göteborg
Öresund
Götaland
Svealand
Norrland
Abroad
Area(Right axis)
Figure 2 - Atrium Ljungberg Regions
25
6.2
Balder
Balder was established in 2005 through the knowledge assurance company Enlight AB. During the
initial years their main holdings consisted of office, retail and industrial/warehouse. The majority of
the properties were located in Stockholm (60%) and Gothenburg (15%). In 2007 balder sells a
majority of their industrial and warehouse holdings to Corem property group and simultaneously
acquires shares in the company. However, this research does not consider holdings in affiliate
companies. During 2009 Balder implement a large change when they acquire the residential company
Din Bostad AB. Since then, residential has been the main property type in Balders portfolio, which
can be seen in both figures below. In 2013 balder increases their residential portfolio by acquiring
Bovista Invest AB which have a property portfolio of 4300 apartments. Balder were established on the
Finnish market in 2014 with the purchase of a retail portfolio of 65000 m2. This is their first holdings
that are classified as abroad in this research since their holdings in Copenhagen is included in the
Öresund region (Balder AB, 2005-2014).
Balder property types
2 500
% of lettable area
90%
80%
2 000
70%
60%
1 500
50%
40%
1 000
30%
20%
500
Total area in thousand m2
100%
10%
0%
2005
2006
2007
2008
2009
2010
2011
2012
Office
Retail
Industrial/Warehouse
Public property
Other
Area(Right axis)
2013
0
2014
Residential
Balder regions
100%
2 500
% of lettable area
90%
2 000
80%
70%
1 500
60%
50%
40%
1 000
30%
20%
500
10%
0%
2005
2006
2007
2008
2009
2010
2011
2012
2013
0
2014
Stockholm
Göteborg
Öresund
Götaland
Svealand
Norrland
Abroad
Area(Right axis)
Figure 4 - Balder Regions
26
Total area in thousand m2
Figure 3 - Balder Property Types
6.3
Castellum
Castellum was founded in the wake of the financial crisis in Sweden in the early nineties. In the crisis,
many banks received properties as a result of defaulted mortgages. To save the banking sector and get
the economy going, the Swedish government bought the properties and placed them in two companies,
one being Castellum. The properties of the portfolio which were situated in better locations ended up
being Castellum. Today Castellum own, operate and develop commercial properties in growth
regions. Castellums distribution across regions has been quite consistent between 1997 and 2013.
During these years Gothenburg has been their major region with Öresund and Stockholm not far
behind in respect of area, see figure 6 below. In 2014, there was a trend reversal in Castellums history
of continuous increase of lettable area. This is since Castellum decided to sell properties in regions
were growth is expected to be insufficient (Castellum AB, 1997-2014).
Castellum property types
4 000
% of lettable area
3 500
3 000
2 500
2 000
1 500
1 000
500
Total area in thousand m2
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
0
Office
Retail
Industrial/Warehouse
Public property
Other
Area(Right axis)
Residential
Castellum regions
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
4 000
3 500
3 000
2 500
2 000
1 500
1 000
500
0
Stockholm
Göteborg
Öresund
Götaland
Svealand
Norrland
Abroad
Area(Right axis)
Figure 6 - Castellum Regions
27
Total area in thousand m2
% of lettable area
Figure 5 - Castellum Property Types
6.4
Catena
What is today known as Catena, started as AB Volvator in the 60’s. During that time it was the
company of Volvo and its main business was to sell cars. In 1984 the company was split into two
parts, one consisting of the retail business, Bilia, and the second part consisted of the properties and is
today known as Catena. In 2010, the board, together with the shareholders, decides to change its focus
to project development. All properties except two in Stockholm, which Catena planned to develop,
were sold. In 2013, Catena acquires Brinova Logistik AB which were a logistic company with
approximately 650 000 m2. Today the company mainly operates, maintains and develops logistical
facilities in Stockholm, Gothenburg and the Öresund region (Catena AB, 2006-2014).
Catena property types
800
700
% of lettable area
600
500
400
300
200
100
2007
2008
2009
2010
2011
2012
Office
Retail
Industrial/Warehouse
Public property
Other
Area(Right axis)
2013
Total area in thousand m2
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
2006
0
2014
Residential
Figure 7 - Catena Property Types
Catena regions
800
% of lettable area
90%
700
80%
600
70%
60%
500
50%
400
40%
300
30%
200
20%
100
10%
0%
2006
2007
2008
2009
2010
2011
2012
2013
0
2014
Stockholm
Göteborg
Öresund
Götaland
Svealand
Norrland
Abroad
Area(Right axis)
Figure 8 - Catena Regions
28
Total area in thousand m2
100%
Diös
6.5
Diös property types
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
2005
1 600
Total area in thousand m2
% of lettable area
Diös was founded in 2005 and today, it is the prevalent private real estate company in northern
Sweden. Diös business idea is to own, operate and develop commercial and residential properties in
northern Sweden. The properties are located from Borlänge in the middle part of Sweden to Luleå in
the northern part of Sweden. Offices have been Diös largest holding during the years when
considering property types and it has ranged between 40-50%. In the beginning, Diös owned a lot of
properties in Svealand, however, this changed in 2007 when Diös decided to focus their portfolio to
the northern parts. Since 2007 Diös have had the majority of their holdings located in Norrland. In
2011, Diös AB merged with Norrvidden AB, which were primarily focused in northern Sweden and
had a lettable area of 827 000 m2. Diös were noted on Stockholm mid cap the following year. In 2014,
Diös major holdings consisted of offices and industrial/warehouse and their properties are mainly
located in cities like Gävle, Sundsvall and Östersund (Diös AB, 2005-2014).
1 400
1 200
1 000
800
600
400
200
2006
2007
2008
2009
2010
2011
2012
Office
Retail
Industrial/Warehouse
Public property
Other
Area(Right axis)
2013
0
2014
Residential
Figure 9 - Diös Property Types
Diös regions
1 600
% of lettable area
90%
Total area in thousand m2
100%
1 400
80%
1 200
70%
60%
1 000
50%
800
40%
600
30%
400
20%
200
10%
0%
2005
2006
2007
2008
2009
2010
2011
2012
2013
0
2014
Stockholm
Göteborg
Öresund
Götaland
Svealand
Norrland
Abroad
Area(Right axis)
Figure 10 - Diös Regions
29
Fabege
6.6
In 2004, the real estate company Whilborg completed the acquisition of Fabege. This Fabege was the
commercial part of the real estate company Drott. Following the takeover, Wihlborg decided to create
two real estate companies with the main focus on commercial real estate. The first company was
Fabege and focused on properties in Stockholm. The other one was Wihlborgs, but instead of
Stockholm they were focused in the Öresund region. Whilborg changed their name to Fabege during
this consolidation. At the same time the board decided to list the properties in Öresund as Wihlborgs
on the Stockholm Stock Exchange. Thus, the data between 1998 and 2003 represents Wihlborg, see
figure 11 and 12 below. Fabege has since 2005, operated and developed properties in the center of
Stockholm. Today, no property is located farther from the city center than 5km and their holdings
mainly consist of offices (Fabege AB, 1998-2014).
Fabege property types
3 500
% of lettable area
3 000
2 500
2 000
1 500
1 000
500
0
Office
Retail
Industrial/Warehouse
Public property
Other
Area(Right axis)
Total area in thousand m2
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Residential
Fabege regions
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
3 500
% of lettable area
3 000
2 500
2 000
1 500
1 000
500
0
Stockholm
Göteborg
Öresund
Götaland
Svealand
Norrland
Abroad
Area(Right axis)
Figure 12 - Fabege Regions
30
Total area in thousand m2
Figure 11 - Fabege property Types
6.7
Fastpartner
The real estate company Fastpartner was founded in 1987 by ICA, Skandia and Skanska. The
company was listed at the Stockholm stock exchange in 1994. Between 1999 and 2004, Fastpartner
mainly focused on owning and operating industrial/warehouse, retail and residential properties in
Stockholm and Gävle. During this time Fastpartner decides to invest in information technology and
medical technology. The following two years, 2005 and 2006, Fastpartner decided to reduce their
residential holdings to be able to increase their focus on commercial real estate, especially
industrial/warehouse properties. From 2011 and until 2014, Fastpartner increased their property stock
from approximately 650 000 to 1 200 000, see both figures below. Today the business idea is to own,
operate and develop mainly commercial properties, particularly in Gävle, Norrköping and Stockholm
(Fastpartner AB, 1999-2014).
Fastpartner property types
1 400
% of lettable area
1 200
1 000
800
600
400
200
Total area in thousand m2
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
0
Office
Retail
Industrial/Warehouse
Public property
Other
Area(Right axis)
Residential
Figure 13 - Fastpartner Property Types
Fastpartner regions
1 400
Total area in thousand m2
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
% of lettable area
1 200
1 000
800
600
400
200
0
Stockholm
Göteborg
Öresund
Götaland
Svealand
Norrland
Abroad
Area(Right axis)
Figure 14 - Fastpartner Regions
31
6.8
Heba
Heba property types
100%
260
% of lettable area
90%
250
80%
70%
240
60%
230
50%
40%
220
30%
210
20%
200
10%
0%
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Office
Retail
Industrial/Warehouse
Public property
Other
Area(Right axis)
2013
Total area in thousand m2
Heba was founded in 1952 by Karl Holmberg and Folke Eriksson. Ever since the company was
founded, it has focused on owning, developing and operating residential properties. Heba has through
the years had a smaller percentage of lettable area in commercial real estate and parking space.
Parking space is labeled as "other" in figure 15. Geographically, almost all of their properties have
during the years been located in Stockholm. Their holdings in Stockholm are located both in the city
center and in the suburbs. However, between 2002 and 2010 they had approximately 10% of their
holdings located in Borlänge (Svealand). The significant change in Hebas lettable area in 2006 is due
to a sale of their properties in Hässelby. Today, Heba is solely active in Stockholm and mainly in its
suburbs such as Lidingö, Huddinge and Täby to name a few (Heba AB, 2002-2014).
190
2014
Residential
Figure 15 - Heba Property Types
Heba regions
260
% of lettable area
90%
250
80%
70%
240
60%
230
50%
40%
220
30%
210
20%
200
10%
0%
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
190
2014
Stockholm
Göteborg
Öresund
Götaland
Svealand
Norrland
Abroad
Area(Right axis)
Figure 16 - Heba Regions
32
Total area in thousand m2
100%
Hufvudstaden
6.9
Hufvudstaden was founded in 1915 with the known magnate Ivar Kreuger as chairman of the board.
Since the company started it has been a major owner of commercial properties in Stockholm CBD.
Between 1993 and 1997, Hufvudstaden mainly owned properties in Stockholm and Gothenburg.
However the company also had a few properties in cities such as Paris, London, Tokyo and Berlin. In
1996 the board decides that the company should only be focused in Sweden, whereupon all properties
abroad are sold. In 1998, Hufvudstaden acquires NK Cityfastigheter with two shopping malls, one in
Stockholm and one in Gothenburg. Besides the shopping malls, the portfolio also contained holdings
in four other cities in Sweden. These four properties were sold the following year. The classification
other in figure 17, consist mainly of car park and storage. Today, Hufuvudstaden owns, operate and
develops properties in prime locations in both Stockholm and Gothenburg (Hufvudstaden AB, 19932014).
Hufvudstaden property types
700
% of lettable area
600
500
400
300
200
100
Office
Retail
Industrial/Warehouse
Public property
Other
Area(Right axis)
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
0
Total area in thousand m2
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Residential
Figure 17 - Hufvudstaden Property Types
Hufvudstaden regions
700
% of lettable area
90%
600
80%
70%
500
60%
400
50%
40%
300
30%
200
20%
100
10%
0
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
0%
Stockholm
Göteborg
Öresund
Götaland
Svealand
Norrland
Abroad
Area(Right axis)
Figure 18 - Hufvudstaden Regions
33
Total area in thousand m2
100%
6.10 Klövern
Klövern was founded in 2002 when the IT-company Adcore decided to divide the company into
different businesses. The board decided to list Klövern on the Stockholm stock exchange the
following year. During the first three years as a listed company, Klövern more than doubled their
lettable area. Klöverns increase in lettable area was mainly due to acquisitions of office and
industrial/warehouse in Norrköping, Linköping, Västerås and Borås. Klövern entered the Stockholm
real estate market in 2006, through the acquisition of 46 properties in Kista and Täby. In 2012,
Klövern acquired the real estate company Dagon and become exposed to Gothenburg and Öresund.
Two years after the acquisition, Klövern decides to issue a b-share. Today the company is mainly
focused in the office and industrial/warehouse segment. However, they also own retail and public
properties. Geographically, the majority of their lettable area is located in Götaland, Svealand and
Stockholm (Klövern AB, 2002-2014).
Klövern property types
3 000
% of lettable area
90%
2 500
80%
70%
2 000
60%
1 500
50%
40%
1 000
30%
20%
500
10%
Total area in thousand m2
100%
0
0%
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Office
Retail
Industrial/Warehouse
Public property
Other
Area(Right axis)
Residential
Klövern regions
% of lettable area
100%
3 000
2 500
80%
2 000
60%
1 500
40%
1 000
20%
0%
2002
500
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
0
2014
Stockholm
Göteborg
Öresund
Götaland
Svealand
Norrland
Abroad
Area(Right axis)
Figure 20 - Klövern Regions
34
Total area in thousand m2
Figure 19 - Klövern Property Types
6.11 Kungsleden
Kungsleden property types
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
4 000
3 500
3 000
2 500
2 000
1 500
1 000
500
0
Office
Retail
Industrial/Warehouse
Public property
Other
Area(Right axis)
Total area in thousand m2
% of lettable area
Kungsleden was founded as a result of the economic crisis in the 90’s when the banks were sitting
with large property portfolios which they wanted to dispose of. The portfolio was very varied and
distributed all over Sweden. Kungsleden increased its property portfolio significantly in 2005 when
they acquired both Weland Fastigheter and Fogelvik Holding. The residential property which was
included in the deal was sold the following year. In addition to the company acquisitions, Kungsleden
also acquired the remaining shares in the jointly owned Hemsö Älderboende, which contained
retirement housing. Starting in 2008, the third AP-fund, acquired shares in Hemsö in several phases.
In 2012, it purchased the remaining 50% of the shares in Hemsö. In 2011, Kungsleden acquires 36
office and industrial properties from Nordic and Russian properties Ltd. The acquisition results in that
Västerås becomes one of Kungsledens main regions in respect of lettable area. Kungsleden increases
their holdings in Stockholm, Gothenburg and Öresund in 2013 when they acquire 84 properties from
GE Capital Real Estate. Today, Kungsleden owns, operates and develops commercial and industrial
properties across whole Sweden (Kungsleden AB, 1999-2014).
Residential
Figure 21 - Kungsleden Property Types
Kungsleden regions
4 000
Total area in thousand m2
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
% of lettable area
3 500
3 000
2 500
2 000
1 500
1 000
500
0
Stockholm
Göteborg
Öresund
Götaland
Svealand
Norrland
Abroad
Area(Right axis)
Figure 22 - Kungsleden Regions
35
6.12 Sagax
Sagax was founded out of a transformation of the technology company Effenet Group AB in 2004.
The company has through acquisitions expanded considerably since they was founded and during the
years they have focused on light industry and warehouse, with a majority located in Stockholm. The
company has throughout its history also had properties abroad and the majority of these cross-border
investments have been located to Finland, particularly in Helsingfors. However, they have also had
smaller holdings in Germany. In 2013 Sagax decided to issue B-shares and preferred shares and at the
same year Sagax acquires 15% of the shares in Hemsö. Today the company owns, operates and
develops industrial properties, mainly warehouse and light industry, in Sweden and Finland (Sagax
AB, 2004-2014).
Sagax property types
1 800
90%
1 600
80%
1 400
70%
1 200
60%
1 000
50%
800
40%
600
30%
20%
400
10%
200
0%
2004
2005
2006
2007
2008
2009
2010
2011
2012
Office
Retail
Industrial/Warehouse
Public property
Other
Area(Right axis)
2013
Total area in thousand m2
% of lettable area
100%
0
2014
Residential
Figure 23 - Sagax Property Types
Sagax regions
1 800
90%
1 600
80%
1 400
70%
1 200
60%
1 000
50%
800
40%
600
30%
20%
400
10%
200
0%
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
0
2014
Stockholm
Göteborg
Öresund
Götaland
Svealand
Norrland
Abroad
Area(Right axis)
Figure 24 - Sagax Regions
36
Total area in thousand m2
% of lettable area
100%
6.13 Wallenstam
Wallenstam property types
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
1 400
1 200
1 000
800
600
400
200
Total area in thousand m2
% of lettable area
Wallenstam was founded by Lennart Wallenstam in Gothenburg 1944. In the beginning of the
company’s history it was focused on residential development, primarily in Gothenburg. Wallenstam
decided to phase out its construction activity in the 70’s and instead focus on owning, operating and
developing residential properties. However, the construction activity would be resumed in the 2000s.
Wallenstam were listed at the Stockholm stock exchange in 1984 and has since then been mainly
focused in Gothenburg and Stockholm. Wallenstam’s property portfolio has, during the years, also
consisted of office, retail and industrial properties. Today, Wallenstam have approximately 68% of
their properties located in Gothenburg and 30% in Stockholm (Wallenstam AB, 1999-2014).
0
Office
Retail
Industrial/Warehouse
Public property
Other
Area(Right axis)
Residential
Figure 25 - Wallenstam Property Types
Total area in thousand m2
% of lettable area
Wallenstam regions
100%
1 400
90%
1 200
80%
1 000
70%
60%
800
50%
600
40%
30%
400
20%
200
10%
0%
0
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Stockholm
Göteborg
Öresund
Götaland
Svealand
Norrland
Abroad
Area(Right axis)
Figure 26 - Wallenstam Regions
37
6.14 Wihlborgs
Wihlborgs was founded in 1924 as a construction company. This changed in 1985 when the company
became a traditional property company. In 2005, the company was divided into two companies,
Fabege (see description above) and Whilborgs. Since Wihlborgs was split, its main geographical focus
has been in the Öresund region with 100% of the holdings. When considering property types,
Whilborgs main focus has been office and industrial/ warehouse but they also have had retail and
public properties. Whilborgs lettable area has been rather stable over time with minor property
acquisitions and divestments. Today, offices and industrial/warehouse is Wihlborgs largest holding
and together they account for approximately 80% of the lettable area (Wihlborgs AB, 2005-2014).
90%
1 600
80%
1 400
% of lettable area
1 800
70%
1 200
60%
1 000
50%
800
40%
600
30%
20%
400
10%
200
0%
2005
2006
2007
2008
2009
2010
2011
2012
Office
Retail
Industrial/Warehouse
Public property
Other
Area(Right axis)
2013
Total area in thousand m2
Wihlborg property types
100%
0
2014
Residential
Figure 27 - Wihlborgs Property Types
Wihlborg regions
1 800
90%
1 600
80%
1 400
70%
1 200
60%
1 000
50%
800
40%
600
30%
20%
400
10%
200
0%
2005
2006
2007
2008
2009
2010
2011
2012
2013
0
2014
Stockholm
Göteborg
Öresund
Götaland
Svealand
Norrland
Abroad
Area(Right axis)
Figure 28 - Wihlborgs Regions
38
Total area in thousand m2
% of lettable area
100%
7 Results and Analysis
7.1
Herfindahl Index
Table 1 - Herfindahl Index Property Types
Company
Atrium Ljungb.
Balder
Castellum
Catena
Diös
Fabege
Fastpartner
Heba
Hufvudstaden
Klövern
Kungsleden
Sagax
Wallenstam
Wihlborgs
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
0,32
0,26
0,23
0,39
0,33
0,41
-
0,34
0,25
0,22
0,38
0,37
0,42
-
0,35
0,27
0,22
0,38
0,35
0,41
-
0,36
0,28
0,22
0,71
0,38
0,34
0,33
0,40
-
0,24
0,37
0,30
0,23
0,71
0,38
0,37
0,34
0,35
-
0,24
0,37
0,39
0,23
0,71
0,39
0,31
0,30
0,68
0,37
-
0,26
0,27
0,38
0,32
0,39
0,37
0,73
0,39
0,32
0,21
0,73
0,37
0,39
0,33
0,26
0,40
0,32
0,30
0,43
0,47
0,76
0,38
0,35
0,29
0,72
0,37
0,39
0,33
0,27
0,39
0,31
0,26
0,48
0,46
0,76
0,38
0,35
0,34
0,72
0,38
0,41
0,34
0,28
0,38
0,31
0,27
0,46
0,47
0,75
0,38
0,35
0,32
0,71
0,38
0,41
0,33
0,34
0,39
0,29
0,27
0,46
0,46
0,76
0,38
0,35
0,30
0,71
0,38
0,42
0,32
0,35
0,39
0,29
0,25
0,55
0,45
0,76
0,38
0,35
0,27
0,70
0,38
0,36
0,31
0,33
0,38
0,96
0,26
0,54
0,40
0,76
0,38
0,35
0,29
0,72
0,33
0,36
0,31
0,34
0,38
0,96
0,26
0,56
0,42
0,76
0,38
0,34
0,40
0,72
0,35
0,37
0,31
0,38
0,38
0,84
0,26
0,55
0,40
0,76
0,40
0,34
0,36
0,71
0,33
0,36
0,30
0,31
0,39
0,90
0,26
0,55
0,38
0,74
0,40
0,34
0,36
0,72
0,31
0,37
Mean
0,30
0,31
0,37
0,58
0,27
0,42
0,35
0,74
0,38
0,35
0,32
0,71
0,37
0,38
In table 1 above, Heba (0.74) and Sagax (0.71) display the highest average values throughout the years
with accessible data for each company. In recent years, Catena has become the most concentrated
company by property type with a Herfindahl index on 0.90. This is since they started to focus on
industrial/ warehouse properties. Fabege has during the last years obtained a Herfindahl index around
0.50. This causes Fabege to be a borderline case, since it’s hard to determine if the company is
diversified or focused. The most diversified companies when considering property types are Atrium
Ljungberg, Balder, Diös and Kungsleden, with a Herfindahl index around 0.30. Values above 0.50 are
seen as focused and are marked with a green color.
Table 2 - Herfindahl Index Regions
Company
Atrium Ljungb.
Balder
Castellum
Catena
Diös
Fabege
Fastpartner
Heba
Hufvudstaden
Klövern
Kungsleden
Sagax
Wallenstam
Wihlborgs
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
0,22
0,40
0,23
0,72
0,24
0,40
-
0,22
0,42
0,26
0,72
0,19
0,42
-
0,22
0,39
0,27
0,72
0,20
0,42
-
0,22
0,48
0,29
0,82
0,72
0,49
0,19
0,42
-
0,89
0,22
0,50
0,37
0,82
0,72
0,49
0,20
0,45
-
0,89
0,22
0,57
0,40
0,82
0,70
0,48
0,19
0,45
0,44
-
1,00
0,43
0,22
0,56
1,00
0,49
0,82
0,70
0,50
0,18
0,39
0,46
1,00
0,54
0,23
0,22
0,23
0,51
1,00
0,58
0,80
0,66
0,35
0,19
0,35
0,47
1,00
0,54
0,30
0,22
0,29
0,52
1,00
0,55
0,80
0,66
0,37
0,19
0,25
0,46
1,00
0,57
0,31
0,22
0,28
0,51
1,00
0,54
0,81
0,66
0,37
0,18
0,26
0,46
1,00
0,64
0,24
0,22
0,28
0,52
1,00
0,54
0,80
0,66
0,37
0,18
0,26
0,46
1,00
0,63
0,22
0,22
0,36
0,53
1,00
0,50
1,00
0,67
0,36
0,19
0,25
0,46
1,00
0,63
0,21
0,22
1,00
0,71
1,00
0,47
1,00
0,66
0,36
0,20
0,28
0,49
1,00
0,65
0,20
0,22
1,00
0,71
1,00
0,37
1,00
0,66
0,24
0,23
0,28
0,48
1,00
0,60
0,20
0,22
0,21
0,70
1,00
0,35
1,00
0,61
0,24
0,20
0,29
0,53
1,00
0,61
0,19
0,23
0,20
0,70
1,00
0,35
1,00
0,61
0,24
0,22
0,28
0,55
1,00
Mean
0,68
0,25
0,22
0,43
0,60
0,80
0,41
0,88
0,68
0,37
0,20
0,30
0,46
1,00
When observing the Herfindahl index by region in table 2 above, Wihlborg (1.00), Heba (0.88) and
Fabege (0.80) display the highest concentration on average. This trio is closely followed by Atrium
Ljungberg (0.68) and Hufvudstaden (0.68). Diös has in recent years emerged as one of the more
concentrated companies geographically with a Herfindahl index around 0.70. This increase in the
index is a result of Diös decision to be more focused in the north of Sweden. Kungsleden (0.20),
Castellum (0.22) and Balder (0.25) are the companies with highest diversification geographically.
39
7.2 Hypothesis nr. 1
- Companies with similar property portfolios in respect of type and region, have
a high correlation with each other
Table 3 - Correlation Matrix
2000-02-28 to 2003-03-12
Cast.
Castellum
Fabege
Fastpartner
Heba
Hufvudstaden
Kungsleden
Wallenstam
Fabege
1,00
0,23
0,09
0,09
0,16
0,19
0,16
Fastp.
1,00
0,16
0,05
0,19
0,18
0,21
1,00
0,08
0,12
0,04
0,10
Heba
1,00
0,12
-0,05
0,09
Hufv.
1,00
0,09
0,13
Kungsl.
1,00
0,22
Wall.
1,00
In table 3 above, it is seen that the overall correlation during this time period is low. According to
Leimdörfers report, the correlation tends to be low in the short term perspective and tend to increase in
the long term. This might be the reason for the low correlation in this case. However, the highest
correlation is found between Castellum and Fabege followed closely by Wallenstam-Kungsleden as
well as Wallenstam-Fabege. The lowest correlation is between Heba and Kungsleden as well as
Fabege-Heba and Fastpartner-Kungsleden. The highest correlations are marked with a green color; the
lowest are marked with a red color. The average correlation this time period is 0.13.
Table 4 - Correlation Matrix
2003-03-12 to 2007-07-16
Castellum
Fabege
Fastpartner
Heba
Hufvudstaden
Klövern
Kungsleden
Wallenstam
Cast.
Fabege Fastp. Heba Hufv. Klövern Kungsl. Wall.
1,00
0,46
1,00
0,07
0,13
1,00
0,09
0,09
0,08
1,00
0,30
0,32
0,03
0,13 1,00
0,27
0,28
0,14
0,11 0,22
1,00
0,39
0,41
0,09
0,04 0,24
0,28
1,00
0,30
0,29
0,10
0,14 0,20
0,23
0,31 1,00
Klövern is listed and included in the bull market of 2003-2007, as seen in table 4 above. The average
correlation during this time period is 0.21, which is significantly higher compared to the last period.
The highest correlation is again between Castellum and Fabege (0.46) followed by Fabege and
Kungsleden at 0.41. Worth mentioning is that all three consist to a larger part of office in respect of
area during this period of time. The lowest correlation is found between Fastpartner and Hufvudstaden
at 0.03 and this pair is closely followed by Heba and Kungsleden with a correlation on 0.04. Heba is
during this period of time the most focused company both by property type and geographically, at the
same time Kungsleden is the most diversified in both categories.
40
Table 5 - Correlation Matrix
2007-07-16-2009-03-06
Atrium Ljungberg B
Balder
Castellum
Catena
Diös
Fabege
Fastpartner
Heba
Hufvudstaden
Klövern
Kungsleden
Wallenstam
Wihlborgs
Atrium L. Balder Cast. Catena Diös Fabege Fastp. Heba Hufv. Klövern Kungsl. Wall. Wihl.
1,00
0,12
1,00
0,30
0,16
1,00
0,24
0,13
0,19
1,00
0,11
0,07
0,21
0,17 1,00
0,30
0,23
0,77
0,21 0,23
1,00
0,26
0,13
0,24
0,14 0,06
0,29
1,00
0,04
0,10
0,10
0,02 0,10
0,07
0,07 1,00
0,33
0,10
0,65
0,19 0,14
0,58
0,19 0,05 1,00
0,31
0,09
0,55
0,21 0,23
0,54
0,13 0,12 0,47
1,00
0,29
0,19
0,74
0,13 0,19
0,71
0,25 0,08 0,61
0,55
1,00
0,43
0,19
0,48
0,23 0,17
0,49
0,29 0,06 0,51
0,40
0,47
1,00
0,38
0,08
0,70
0,20 0,23
0,66
0,20 0,12 0,62
0,63
0,68
0,53 1,00
This time period, seen in table 5 above, a whole new bunch of companies has joined in. Atrium
Ljungberg, Balder, Catena and Diös. The average correlation is 0.29, which is higher compared to the
previous period, which was considerably longer. This increase is consistent with the theory which
states that the correlation increases in a downturn market (Schindler, 2012). The highest correlation is
once again between Castellum and Fabege (0.77) followed by Castellum – Kungsleden (0.74). The
lowest correlation during this measurement period is found between Catena and Heba. Catena is
during this time focused on retail and industrial/warehouse and Heba are focused on residential. In
addition to this Heba displays a low correlation with all companies.
Table 6 - Correlation Matrix
2009-03-06 to 2016-02-02
Atrium Ljungberg B
Balder
Castellum
Catena
Diös
Fabege
Fastpartner
Heba
Hufvudstaden
Klövern
Kungsleden
Sagax A
Wallenstam
Wihlborgs
Atrium L. Balder Cast. Catena Diös Fabege Fastp. Heba Hufv. Klövern Kungsl. Sagax Wall. Wihl.
1,00
0,26
1,00
0,33
0,42 1,00
0,04
0,07 0,08
1,00
0,13
0,19 0,22
0,08 1,00
0,28
0,41 0,74
0,09 0,22
1,00
0,13
0,18 0,20
0,06 0,16
0,22
1,00
0,10
0,11 0,16
0,04 0,06
0,15
0,07 1,00
0,34
0,42 0,75
0,11 0,28
0,70
0,20 0,16 1,00
0,28
0,41 0,57
0,11 0,25
0,58
0,22 0,09 0,57
1,00
0,29
0,38 0,63
0,07 0,22
0,60
0,18 0,11 0,60
0,52
1,00
0,09
0,15 0,18
0,10 0,07
0,15
0,13 0,08 0,18
0,16
0,13 1,00
0,36
0,46 0,60
0,10 0,24
0,59
0,23 0,14 0,62
0,55
0,53 0,18 1,00
0,33
0,46 0,65
0,10 0,27
0,64
0,22 0,12 0,67
0,58
0,59 0,16 0,62 1,00
In the final time period, seen in table 6 above, the average correlation is still 0.29. A couple of new
companies have been added, Sagax and Wihlborg. The highest correlation is found in the triangle of
Castellum-Fabege-Hufvudstaden while the lowest is found in Heba-Catena and Catena-Atrium
Ljungberg. The correlation with other companies for Catena, Heba and Sagax does not exceed 0.20.
This result interesting since all three is focused by property type. Another interesting outcome is
Balders correlation which has increased significantly since last period with a majority of the
companies.
When studying all four time periods, some patterns can be found. Firstly, the overall correlation has
been increasing in the last 15 years with the average correlation increasing in all four time periods.
This is in line with the results found in the US REIT market by Chong et al, that correlation between
41
the different sub-markets have been increasing since the 1990s. The results only date back to 2000 and
so this cannot be fully compared but one theory is that the real estate market in Europe is lagged to
that of the US. Chong et al suggests that higher trading volume, increased number of listed companies
as well as an increased amount of institutional capital is the reason for the increase in correlation.
Looking at the situation in Sweden today, all those three conditions are, at first glance, fulfilled. The
low yield of bonds has forced the institutional investors to increase their investments in real estate,
there have been many IPOs in recent years and finally, the stock market as a whole have become more
and more popular. None of this can be confirmed by the data in this research but could be the subject
of future research.
Secondly, the companies which have the highest as well as the lowest correlation are mostly the same
in all time periods. In the top spectrum we find Castellum, Fabege and Kungsleden for the first three
periods, table 3-5. In the fourth period, table 6, Kungsleden is replaced by Hufvudstaden although the
correlation between these four companies has been generally high and similar. Contrary, Heba have
had a relatively low correlation with the rest of the companies in all four periods. Worth mention is
that Heba is the only company which has been focused in all these periods.
The companies can be divided into two distinct groups. The first group has a generally high
correlation with each other and consists of Atrium Ljungberg, Balder, Castellum, Fabege,
Hufvudstaden, Klövern, Kungsleden, Wallenstam and Wihlborg. The second group has a low
correlation with each other or with anyone else and consists of Catena, Heba and Sagax, all three
focused by property type. Then there are two companies which do not fit to any of the classifications,
Diös and Fastpartner. Diös is the one not easily categorized as their correlation is significantly lower
than the first group but still much higher compared to the second group. What separates Diös from all
other companies is the geographic location of their properties. This is since Diös is exclusive with its
large lettable area in north of Sweden. Fastpartner is the other company that neither has a low or high
correlation with all other companies. However, Fastpartners portfolio is quiet similar to Catena and
Sagax, a majority in industrial properties spread out in the country but with a major share in
Stockholm. Why do they not correlate more with each other? The simple answer to this question is
that there are other forces which dictate the movements of the stocks.
42
7.3 Hypothesis nr. 2
- Companies with a diversified property portfolio will have a higher correlation
with the market than companies with a focused portfolio
Figure 29 - Rolling Correlation, All Companies
The 36-month rolling correlation with the Stockholm all-share for the 14 real estate companies is
displayed in figure 29 above. Between 1999 and 2006, the correlation ranged from 0.0 to 0.4 with
Heba and Atrium Ljungberg in the bottom region and Kungsleden and Fabege as climax. After this
period, several companies are added to the chart and the correlation started to spread out and is now
ranging between 0.1 and 0.7. Sagax, Heba and Catena have the lowest correlation in the second half
of the chart. The common characteristics of these three companies are that they are focused by
property type. The majority of the companies that can be seen in the higher regions of the chart are
companies which are diversified according to the Herfindahl index. This will be more obvious in the
subdivided charts below. Furthermore, one can also see that the correlation with Stockholm all-share
increased as an effect of the financial crisis in 2007 and this correlation has stayed on high levels since
the crisis.
Figure 30 - Rolling Correlation, Focused Companies
43
Figure 30 above consists of companies that are focused by property type or geographically. In this
chart it is clearer, that companies with a focus on property type have low correlation with OMX
Stockholm all-share. These three companies correlation with the market portfolio has ranged between
0.1 and 0.2 the past years. The top of the chart consist of Fabege, Hufvudstaden and Whilborgs. A
majority of lettable area in retail and office is the common denominator for these three companies.
Furthermore, Fabege and Whilborgs derived from the same company in 2005 and both are only
focused in one region today. Atrium Ljungberg which have had a correlation that have ranged between
0.3 and 0.04 the latter half of the chart is also heavily invested in office and retail. The 36-month
correlation for Atrium Ljungberg increases sharply in 2009. This event could be derived from
Atriums merging with Ljungberg in 2006. Another interesting occurrence in the chart is that almost all
companies, except for Heba, display an ascending correlation trend from 2014 until today (2016).
Figure 31 - Rolling Correlation, Diversified Companies
Figure 31 above displays the 36-month rolling correlation, with Stockholm all-share, for the
diversified real estate companies. All diversified companies showed a low and compressed correlation
in the first third of the chart, with values ranging between 0.1 and 0.3. This correlation ascended in
four out of six companies during the financial crisis in 2007. Balder, who did not follow this up going
trend during the crisis, joined the other companies in the top during 2010. This increase in correlation
occurs in conjunction with Balders acquisition of the residential company Din Bostad. This acquisition
results in that Balder increase their geographical diversification. Fastpartner is in this case the only
company which remains on low levels of correlation during the entire measurement period. However,
there exist three companies with the same low levels of correlation in the last third of the chart as the
focused companies. All three was according to Herfindahl index focused by property type and
Fastpartner is not far from obtaining the same classification during this period of time. Thus, there
exists a linkage between these four companies.
44
7.4 Hypothesis nr. 3
- Companies with a focused property portfolio will have a higher risk compared
to companies with a more diversified portfolio
Figure 32 - Rolling Beta, All Companies
The 36-month rolling beta, with Stockholm all-share as benchmark index, has been calculated for each
real estate firm. The beta coefficients are rather compressed in the first third of the chart and the
greatest difference between the highest and lowest beta is approximately 0.40 during this period of
time. A majority of the beta values is in a downward trend between 1999 and 2004. The decreasing
betas in the beginning of the 21st century are in line with what Hoesli and Serrano found in their
research 2006. The decrease in volatility, which can be seen in figure 35 below, is one of the factors
behind the declined beta value according to Hoesli and Serrano. However, the beta values starts to
spread out in conjunction with the boom in the real estate market during 2006 and the financial crisis
in 2007-2008. Fabege will together with Kungsleden reach the highest point, during the whole
measurement period, in 2009. The difference between the highest and lowest beta is now around 1.00.
Catena, Diös, Sagax and Wihlborg are added to the chart after the financial crisis. The top region,
which is roughly ranging from 0.80 to 1.00 between 2012 and 2016, consist to a high degree of
diversified companies with exception for Fabege.
45
Figure 33 - Rolling Beta, Focused Companies
Figure 33 above which displays the 36-month rolling beta of focused companies, shows a downward
trend for all companies in the beginning years. However, the beta values for Fabege and Hufvudstaden
starts to increase during 2006. Fabege, which increases most of the two and will have a beta above
1.00 over a time period, merged with Wihlborgs in 2005 and became solely focused to Stockholm
inner city. Wihlborg, which was an outcome of this merger and consist merely of properties in
Öresund region, will join Fabege in the top region of the chart. Worth mentioning is that Fabege
displays a standard deviation around 90% between 2008 and 2011. Atrium Ljungbergs increase in the
beta value occurs in conjunction with the acquisition of the Ljungberg group in the end of 2006. The
sharp increase of Diös beta in the end of the measurement period could be explained by their
acquisition of Norrvidden in 2011. Diös lettable area more than doubled and in addition to the
acquisition, the company was noted on the Stockholm mid cap the following year. Another company
that displays a sharp increase in the beta value in the last third of the chart is Sagax. In 2013, Sagax
acquires 15 % of Hemsö and at the same year they issue b-shares and preferred shares. The higher
exposure towards the capital market could be an explanation to the increased beta for Sagax during
this period of time. Another explanation to Sagax sharp increase in beta could also be explained by
their increase in volatility which is displayed in figure 34 below. Two companies which can be seen at
the bottom of the chart are Heba and Catena. Worth mentioning is that both of them are focused by
property type according to Herfindahl index. The result, which shows that focused companies have a
lower market risk, is also found in the research conducted by Boer et al. (2005).
46
Figure 34 - Rolling Beta, Diversified Companies
Wallenstam and Castellum display a descending trend during the initial years of the chart. This trend
is in line with what Hoesli and Serrano found in their research and this is described in more detail
above. However, Fastpartners beta coefficient develops in the opposite direction during these years.
This could be explained by their high volatility in the beginning of the measurement period, which is
presented in Figure 35 below. Nevertheless, the betas for all companies are compressed around 0.3 in
the end of 2005. Immediately after this compression the betas started to increase sharply for all
companies except Fastpartner. Kungsleden followed by Castellum are found in the top of the chart
after this sharp increase. The interesting thing about this result is that Kungsleden and Castellum
belongs to the most diversified companies, both geographically and by property type, in this sample
according to the Herfindahl index. Fastpartner shows a rather low beta value, in comparison with the
other diversified companies, in the second half of the chart. What separates Fastpartner from the other
companies in this case is that Fastpartner is on the verge of being classified as a focused company,
both geographically and by property type.
47
Figure 35 - Rolling Standard Deviation, All Companies
All companies except Fastpartner display a descending trend during the initial years in the chart with
36-month rolling standard deviation. This will be discussed more in detail below figure 36. However,
all companies become compressed between approximately 0.40 and 0.50 towards the end of 2006. The
standard deviation starts to increase for all companies during the financial crisis in 2007. This increase
will reach its peak in the end of 2009 with Fabege and Kungsleden in the top region. After the crisis,
the standard deviation has started to decrease for almost all companies with exception for Catena and
Sagax. Today, the decline has started to even out, though some companies indicate a possible increase.
Figure 36 - Rolling Standard Deviation, Focused Companies
All focused companies are following each other quite closely during the whole measurement period,
with the exception of Catena and Sagax. The increase in Catenas volatility occurs between 2010 and
2013. During these years, Catena sells its whole property portfolio with except of two properties in
Solna. After the initial turmoil, the volatility decreases to same levels as the other companies in the
sample. Sagax displays an increased volatility during those years were they issue a b-share and a
48
preferred share. Throughout these years they also expand their property portfolio. Worth mentioning is
the standard deviation for Stockholm all- share, which has been located in the bottom region for the
whole measurement period.
Figure 37 - Rolling Standard Deviation, Diversified Companies
Fastpartner deviates, with a standard deviation above 100%, from the other companies during the
initial years of the chart with diversified companies. This high volatility for Fastpartner in the initial
year was partly due to their investments in the IT-industry, which bubble burst in the beginning of the
21th century. After the turbulent years following the burst of the dotcom bubble, Fastpartners volatility
started to decline to same levels as all other companies and since then no company has deviated as
significantly as Fastpartner did. The Stockholm all-share is located in the bottom region during the
whole measurement period and this is also the case for the focused companies.
49
7.5 Hypothesis nr. 4
- It is better (from a risk-adjusted return perspective) to have a diversified
property portfolio.
Figure 38 - Rolling Sharpe Ratio, All Companies
The first observation to be made regarding figure 38 above is the clear stock return compression that
takes place during times of financial instability. In 2007, the sub-prime mortgage bubble burst and in
2012, the Eurozone experienced severe turmoil as several countries needed bailout loans to avoid
declaring bankruptcy. Both of these events are clearly represented in the graph above and one can see
that during these times, all companies take a heavy hit on the stock market. In the years before 2008 as
well as between 2008 and 2012 and after 2012, the market seems to price the companies in different
ways as their risk-adjusted return varies greatly from company to company. However, during the
crises, all companies receive an equal treatment. Probably, these companies are very different and
have very different balance sheets thus making them more or less susceptible to financial turmoil. One
example is Hufvudstaden which has very low level of debt financing compared to most other
companies and even though they aren’t in the absolute bottom, they are far lower than what could be
expected considering their stable financial situation.
The second observation is the decreasing trend in the last year that all companies suffer from. The
market has seen some volatility and year-highs followed by year-lows. Once again it is interesting to
see that the Sharpe ratio of all companies is decreasing, once again the market doesn’t seem to
consider the attributes of the different companies.
50
Figure 39 - Rolling Sharpe Ratio, Focused Companies
Before 2008, there is no clear distinguishable pattern except a trend of increased volatility in Atrium
Ljungberg, Hufvudstaden and Fabege, all three with a large share in offices while Heba, with almost
90 % in residential is more stable. After the financial crisis of 2008, the newcomers exhibit the largest
variations in risk-adjusted return. All companies except Heba have periods of quite high risk-adjusted
return as well as low risk-adjusted return. Heba is the only company that does not show this behavior
between 2008 and 2016. As shown in hypothesis one, Heba displays low correlation with the other
companies.
Figure 40 - Rolling Sharpe Ratio, Diversified Companies
Apart from Balder, which has performed better than average since the financial crisis, it is difficult to
determine whether the diversified companies perform better than the focused companies. When
comparing to the Sharpe ratio of the market portfolio, the focused companies seem to perform slightly
better but it is well within the margin of error. The literature favors focused companies although there
are some research that oppose this.
51
8 Conclusion
The objective of this research was to investigate how the stock market treats the different real estate
companies. The attributes that were investigated were the correlation between the companies and the
Stockholm all-share index as well as the correlation between the companies themselves. The
correlation between the companies were tested over four time periods, 2000-2003, 2003-2007, 20072009 and 2009-2016. Rolling Beta values, standard deviation and Sharpe ratio were also calculated to
see how the shares of these real estate companies behaves and if there is any relationship to their
property holdings. Four hypotheses were formulated and tested.
The first hypothesis states that companies with a similar portfolio allocation strategy would have a
higher correlation compared to companies with different allocation strategies. The results showed that
some companies had a higher correlation in all four test periods while some companies had no
correlation with any other companies in all time periods. The companies that had a high correlation
with each other are often the more diversified companies such as Castellum and Kungsleden. The
companies with low correlation are the smaller, property type focused companies Catena, Heba and
Sagax. As Leimdörfer (2011) mentioned, the smaller focused companies are more easily analyzed
which might explain why they tend to have a lower correlation with other companies. Contrary,
diversified companies are more difficult to separate which could explain why their stocks tend have a
higher correlation between themselves, compared to the focused companies. Also, Sagax, Catena and
Fastpartner have a similar property portfolio yet there is no correlation between them. The market
doesn’t seem to care about their similar structure or our categorization is too crude to see it.
The second hypothesis states that companies with a diversified portfolio will have a higher correlation
with the broad stock market. A 36-month rolling correlation was used to investigate this. Among the
diversified companies, all but one, Fastpartner, which is almost classified as focused, has a high,
above 0.5, correlation with the market since 2008. Of the focused companies, a major share has a low
correlation with the market, although three companies, which are focused geographically, do not.
Thus, the degree of geographical diversification doesn’t seem to be a contributing factor to higher
correlation with the market portfolio. There are no indications, except for Diös, that geographical
focus matters. Diös had a low correlation with the market during the most part of their time as a
publicly traded company, though in recent years, their correlation has increased to higher levels.
The third hypothesis states that companies with a focused property portfolio have a higher risk
compared with companies with a diversified portfolio. Two methods were used to measure the risk of
the companies, 36-month rolling standard deviation and 36-month rolling beta-values. The analysis of
the rolling beta values show that the companies that are diversified over property type have a higher
market risk compared to those that are more focused. As in previous results, whether a company is
diversified geographically doesn’t matter. Sagax is a special case as their market risk has increased in
the last couple of years which can be explained by their increase in standard deviation in the same
time. The standard deviation of the companies is much more correlated and no conclusions can be
drawn based upon them.
The fourth hypothesis states that diversified companies will have a higher risk-adjusted return
compared to companies with a focused property portfolio. Risk-adjusted return was measured by
calculating a 12-month rolling Sharpe ratio of the companies. The results are mixed and the riskadjusted return seems independent of the composition of the companies’ property portfolios. The only
conclusion is that the Sharpe ratio of all companies, with no exceptions, is severely compressed in
times of financial distress.
52
In the results, Fastpartner often stands out as an exception. They had a low correlation with the market
even though their profile fits that of the diversified companies. One reason for this could be due to the
presence of a strong ownership. The majority owner of Fastpartner is also its executive director which
could be one explanation. Something that speaks against this is that other companies with similar
situations (though not as extreme) such as Balder or Klövern is not behaving in the same way.
To conclude, the results are mostly mixed in hypothesis one and four which, as argued below, might
be because of the limited dataset. In hypothesis two, the results are in line with the predictions
although some exceptions exist. In hypothesis three, the results were not in line with the predictions.
Overall, the companies that are focused on a specific property type stands out generally.
A problem with conducting this kind of research in Sweden is that the data available is limited. The
real estate industry is relatively large but the number of companies is almost too small for a datadriven research. As only publicly traded companies were relevant for this research, the number of
available companies decreases even more. In the recent years (too recent for this research) a number of
new companies have been listed which will hopefully make future research better and easier to
conduct as well as the results more trustworthy.
9 Future Research
In this research, the property portfolios were compared by investigating their lettable area. However,
this was only done as this data is easily gathered. What truly drives the company’s stock performances
and their results is the income they receive from these lettable areas. Instead of comparing square
meters, it would have been more interesting to compare based upon rental income. To perform a
similar study based upon income or net-operating income and then compare the results to the results
based upon lettable area is in interesting topic for future research.
53
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