real estate prices and bank...

GHENT UNIVERSITY

FACULTY OF ECONOMICS

AND BUSINESS ADMINISTRATION

ACADEMIC YEAR 2014 – 2015

REAL ESTATE PRICES

AND BANK VALUATION

Master dissertation submitted in fulfillment

of the requirements for the degree of

Master of Science in

Business Engineering, Main subject Finance

Robin Bracke

under the supervision of

Prof. dr. Rudi Vander Vennet

II

Confidentiality clause

PERMISSION

I declare that the contents of this master dissertation can be consulted and / or reproduced, provided the

source is acknowledged.

Name student: Robin Bracke

III

Dutch summary

De laatste jaren werden heel wat nieuwsberichten de wereld ingestuurd die de indruk wekten dat de

vastgoedmarkten een significante invloed uitoefenen op de financiële markten. Dit deed de vraag

ontstaan of dit verband waargenomen en onderbouwd kan worden met grondige kwantitatieve analyses

op basis van een hedendaagse steekproef. Deze studie maakt gebruik van een steekproef die zestien

Europese landen bevat en die de periode 2005Q1-2014Q4 omsluit.

Eén centrale onderzoeksvraag vormt de kern van dit werk en kan omschreven worden als: Is er een

significante invloed van de vastgoedmarkt op de bancaire sector? Op basis van deze vraag en met behulp

van bevindingen uit de bestaande literatuur worden vijf onderzoekshypothesen naar voren geschoven.

Gegeven dat deze studie een beperkte tijdsspanne beschouwt en gebaseerd is op kwartaaldata werd

geopteerd om gebruik te maken van panelanalyses en rollende regressies op basis van deze panelanalyses

naast enkele grafische ontledingen. Dit werk is gebaseerd op een basismodel dat gelijkaardig is aan de

indexmodeluitdrukking van het capital asset pricing model en dat het uitbreidt met een vastgoedfactor.

Voorts vullen de specifieke regressieanalyses dit basismodel aan met relevante elementen afhankelijk van

de hypotheses die behandeld worden.

De resultaten en conclusies die uit dit onderzoek voortkomen, kunnen als volgt samengevat worden. Dit

onderzoek nuanceert de bestaande literatuur die suggereert dat een positief causaal verband

waarneembaar is tussen de vastgoedmarkt en de bancaire sector. Dit verband blijkt inderdaad significant

en positief te zijn tijdens de aanvang van de financiële crisis en van de soevereine crisis. Het is echter

insignificant wanneer geen crisis plaatsvindt en wanneer de crisissen verder gevorderd zijn. Deze

vaststelling geeft enige indicatie dat het waarnemen van een significant verband een signaal is van een

mogelijk dreigende crisis. Deze studie stelt verder ook vast dat de financiële crisis werd voorafgegaan door

een stijging van vastgoedprijzen wat de bevindingen van eerdere studies bevestigt. Deze observatie kan

ertoe aanzetten om behoedzaam te zijn jegens de bancaire sector wanneer stijgende vastgoedprijzen

worden waargenomen aangezien deze mogelijks leiden tot een vastgoedbubbel en eventueel zo ook tot

een financiële crisis. Bovendien wordt waargenomen dat hypotheek-intensieve banksectoren beter in

staat zijn om hun blootstelling tegenover de vastgoedmarkt te beperken ten opzichte van banksectoren

met relatief minder hypotheekactiviteiten. Overwaardering van de vastgoedmarkt blijkt dan weer een

cruciale factor te zijn voor een grotere blootstelling van de banksector tegenover de vastgoedmarkt

tijdens de aanvang van crisissen hoewel deze studie enkel de overwaardering tijdens het jaar 2007

beschouwt. Tot slot dient nog vermeld te worden dat geen significant verschil geobserveerd wordt in het

causale verband wanneer opwaartse vastgoedprijstrends vergeleken worden met neerwaartse

vastgoedprijstrends.

IV

Acknowledgements

Through the next few words, I would like to sincerely thank all the people who have been supportive in

the creation of this master dissertation and who have encouraged me throughout the process. First and

foremost, I want to express my gratitude toward Prof. dr. Rudi Vander Vennet without whom this

dissertation would not have come into being. He supervised this study and was always available to answer

questions concerning this dissertation. Furthermore, I want to thank Ms. Elien Meuleman for her

continuing support throughout this entire journey by providing valuable suggestions and by giving

practical guidelines regarding the considered analyses. During the beginning of this project, Mr. Frederik

Mergaerts made some recommendations so as to get a hold of the analyses that are implemented in this

study, for which I am also very grateful. And finally, I want to genuinely thank my parents for their patience

and for their support they have given me during the course of my whole life in everything I do. Without

them, none of this would have ever been possible. Moreover, my father is to thank for proofreading this

entire dissertation.

Robin Bracke

August 2015

V

Contents

Confidentiality clause ............................................................................................................................. II

Dutch summary ..................................................................................................................................... III

Acknowledgements ............................................................................................................................... IV

Contents ................................................................................................................................................. V

List of abbreviations ............................................................................................................................. VII

List of tables ........................................................................................................................................ VIII

List of figures ......................................................................................................................................... IX

Introduction ........................................................................................................................................... 1

Real Estate Prices and Bank Valuation ................................................................................................... 4

1 Existing literature ....................................................................................................................... 4

1.1 Real estate market characteristics ....................................................................................... 4

1.1.1 Dynamics and volatility ................................................................................................ 4

1.1.2 Real estate cycles ......................................................................................................... 5

1.1.3 Cross-country integration ............................................................................................. 6

1.2 Real estate markets and the macro-economy ...................................................................... 8

1.2.1 Procyclicality ................................................................................................................ 8

1.2.2 Correlation and causality .............................................................................................. 9

1.2.3 Real estate data as input for monetary policies ............................................................ 9

1.3 Real estate markets and financial markets ..........................................................................11

1.3.1 Importance of real estate markets ..............................................................................11

1.3.1.1 Importance of real estate markets: commercial real estate .....................................13

1.3.1.2 Importance of real estate markets: loan markets ....................................................13

1.3.1.3 Importance of real estate markets: irrationality and speculation .............................15

1.3.2 Monitoring real estate markets ...................................................................................16

1.4 Real estate data ..................................................................................................................18

1.4.1 Real estate data set characteristics .............................................................................18

1.4.2 International comparability of real estate indices ........................................................19

2 Hypotheses ................................................................................................................................22

3 Data and methodology ..............................................................................................................24

3.1 Model .................................................................................................................................24

3.2 Data ....................................................................................................................................25

3.2.1 Real estate returns ......................................................................................................26

3.2.2 Market returns ............................................................................................................30

VI

3.2.3 Banking sector returns ................................................................................................ 30

3.3 Analysis software ................................................................................................................ 34

3.4 Methodology ...................................................................................................................... 34

3.4.1 Fixed effects model or random effects model ............................................................. 36

3.4.2 Hypothesis 1 ............................................................................................................... 37

3.4.3 Hypothesis 2 ............................................................................................................... 38

3.4.4 Hypothesis 3 ............................................................................................................... 39

3.4.5 Hypothesis 4 ............................................................................................................... 40

3.4.6 Hypothesis 5 ............................................................................................................... 43

4 Results ....................................................................................................................................... 45

4.1 Hypothesis 1 ....................................................................................................................... 45

4.2 Hypothesis 2 ....................................................................................................................... 50

4.3 Hypothesis 3 ....................................................................................................................... 53

4.4 Hypothesis 4 ....................................................................................................................... 57

4.5 Hypothesis 5 ....................................................................................................................... 59

Conclusion ............................................................................................................................................. 62

Limitations and suggested future research ........................................................................................... 64

Reference list ........................................................................................................................................ 66

Appendices ........................................................................................................................................... 71

Appendix 1: Real estate data series ............................................................................................... 71

Appendix 2: Banking sector data series .......................................................................................... 73

Appendix 3: Trend reversals in the real estate data series ............................................................. 87

Appendix 4: Rolling regression graphs of the market factor........................................................... 91

Hypothesis 1 .................................................................................................................................. 91

Hypothesis 2 .................................................................................................................................. 91

Hypothesis 3 .................................................................................................................................. 92

Hypothesis 4 .................................................................................................................................. 93

Hypothesis 5 .................................................................................................................................. 94

VII

List of abbreviations

BIS: Bank for International Settlements

CAPM: capital asset pricing model

CDO: collateralized debt obligation

ECB: European Central Bank

ESRB: European Systemic Risk Board

EWS: early warning system

FSI: financial soundness indicator

IMF: International Monetary Fund

OECD: Organisation for Economic Co-operation and Development

VIII

List of tables

Table 1: Hypotheses............................................................................................................................... 23

Table 2: Sources of real estate data series.............................................................................................. 28

Table 3: Summary statistics of real estate return series .......................................................................... 29

Table 4: Summary statistics of market factor return series ..................................................................... 30

Table 5: Number of banking organization data sets per selected country ............................................... 32

Table 6: Summary statistics of banking sector return series ................................................................... 33

Table 7: Mortgage activity per country .................................................................................................. 40

Table 8: Real estate downtrend periods and cumulative returns ............................................................ 42

Table 9: Overvalued real estate markets in 2007 .................................................................................... 44

Table 10: Regression estimates - Hypothesis 1 ....................................................................................... 48

Table 11: Significant rolling regression estimates of real estate factor - Hypothesis 1 ............................. 50

Table 12: Regression estimates - Hypothesis 2 ....................................................................................... 52

Table 13: Regression estimates - Hypothesis 3, 4 and 5 .......................................................................... 55

Table 14: Significant rolling regression estimates of real estate factor - Hypothesis 5 ............................. 61

Table 15: List of all included banking organizations ................................................................................ 74

IX

List of figures

Figure 1: Mean and standard deviation of real estate return series ........................................................29

Figure 2: Mean and standard deviation of banking sector return series ..................................................33

Figure 3: Real estate index including trend reversal (Ireland) .................................................................41

Figure 4: Scatter plot of real estate and banking sector returns ..............................................................46

Figure 5: Mean of real estate and banking sector return series before and during crisis .........................46

Figure 6: Scatter plot of real estate and banking sector returns before and during crisis ........................47

Figure 7: Rolling regression estimates of real estate factor - Hypothesis 1 ..............................................49

Figure 8: Real estate indices (Northern Europe) .....................................................................................50

Figure 9: Rolling regression estimates of real estate factor - Hypothesis 2 ..............................................53

Figure 10: Rolling regression estimates of real estate factor - Hypothesis 3 ............................................56



Figure 13: Real estate indices (Northern and Eastern Europe) ................................................................71

Figure 14: Real estate indices (Southern and Western Europe) ..............................................................72

Figure 15: Histogram of ratio of deposits over total assets .....................................................................73

Figure 16: Histogram of ratio of equity over total assets ........................................................................73

Figure 17: Banking sector indices (Northern and Eastern Europe) ..........................................................85

Figure 18: Banking sector indices (Southern and Western Europe) .........................................................86

Figure 19: Real estate indices including trend reversal ...........................................................................87

Figure 20: Rolling regression estimates of market factor - Hypothesis 1 .................................................91





1

Introduction

‘Historical experience shows that there is a close connection between a slump in real estate values

and a financial market crisis’ (Walterskirchen, 2010).

‘When America’s housing market turned, a chain reaction exposed fragilities in the financial system’

(“The origins of the financial crisis: Crash course,” 2013).

It has often been suggested that there is a close connection between the real estate markets and the

financial markets, as the two foregoing quotes indicate. This raises the question whether this vulnerability

of the financial sector can be confirmed and substantiated with a thorough quantitative analysis in a

contemporary time setting. This master dissertation investigates whether there is a causal relationship

from the real estate market to the banking sector. It considers sixteen European countries and is based

on the time frame 2005Q1-2014Q4, which is reduced to 2005Q2-2014Q4 due to the implementation of

return series rather than nominal index series.

This study builds upon the existing literature as it focuses on a present-day sample period that fully

encloses the 2008 financial crisis. Hence, this study enables to examine hypotheses that are based on

findings from existing research and that are verified for the modern setting. Moreover, this dissertation

utilizes real estate data sets that are recently made available through the Bank for International

Settlements (BIS). These data sets were already obtainable before via national organizations. However, as

there has not been a lot of consensus regarding the collection and compilation methodologies of real

estate data, these national data sets are subject to a lot of heterogeneity and thus to issues related to

cross-comparability. The collection and thorough documenting of these data sets in one place has vastly

boosted research capabilities and has reduced the possible introduction of biases in results that are due

to the varying characteristics of the national data sets. Furthermore, the Handbook on Residential

Property Prices Indices has been a great improvement for research as well since it intends to harmonize

the methodologies regarding the collection and compilation of real estate data. This handbook is being

used by the BIS for the selection of the real estate data sets that are implemented in this study.

One central research question is maintained throughout this study:

Does the real estate market substantially affect the banking sector?

This question serves as the basis for five hypotheses that are formulated in this study. The main hypothesis

consists of two sub-hypotheses and deals with whether there indeed is a significant observable

relationship between the real estate market and the banking sector, whereas, on the other hand, it also

considers the dynamics within this relationship. Furthermore, this study investigates if the 2008 financial

crisis is preceded by an increase in real estate prices, as is observed in the existing literature for past crises.

2

Moreover, three additional hypotheses, which are based on certain categorizations of the observations,

are formulated. A first of these hypotheses considers the mortgage market. More specifically, it posits a

theory that mortgage-intensive banking sectors are more exposed toward the real estate market than

banking sectors with relatively less mortgage activities. As regards the hypotheses that consider the

categorizations, this study furthermore investigates whether banking sectors are harder hit by real estate

slumps than they benefit from real estate increases. And finally, a last hypothesis that is discussed in this

dissertation examines if overvaluation of the real estate market leads to a higher exposure of the banking

sector toward the real estate market.

The analyses in this dissertation are founded on a base model that is similar to the index model

representation of the capital asset pricing model and that extends it by including a real estate factor. All

analyses utilize three categories of data sets: real estate return series, banking sector return series, and a

market return series. The real estate return series are based on data sets that are obtained from the BIS

Residential Property Price database and that represent the real estate market of an entire country. The

banking sector returns, on the other hand, are a weighted average of individual banking organization

returns per country and are calculated using stock price series whereas the weights are based on the

market values of the individual banking organizations. Both the stock price series and the market values

are retrieved from the Datastream Professional database. The third category of data sets, the market

return series, are also calculated using series obtained from the Datastream Professional database and

are based on the MSCI Europe index. Besides some rudimentary graphical analyses, such as scatter plot

analysis and analysis of graphs containing means, this study is mainly built upon the implementation of

panel regression analysis and rolling regression analysis founded on these panel regressions. Taking the

whole sample into account, the full panel of this study contains sixteen cross-sections, represented by the

sixteen selected countries, and thirty-nine time series observations given the 2005Q2-2014Q4 return

sample period.

As is most often the case, this dissertation too is subject to several limitations which are summarized in

the following. A first limitation is already mentioned above and considers the restriction of the sample

period, which is the result of limits in the availability of real estate data series. Furthermore, another

limitation is established on the cross-sectional panel dimension because of the availability of banking

sector data, which reduces the considered countries within this study to sixteen. It is also unfortunate

that countries such as Greece, Spain and Portugal are not included in this study due to the same

limitations. Moreover, whereas this study considers the real estate markets in general, it does not look at

differences in the analyses regarding specific real estate market characteristics such as residential versus

commercial real estate and houses versus flats. And finally, as concerns the banking sector return series

that are used in this study, another restriction is formed by the implementation of stock price series and,

3

thus, only of listed and delisted banking organizations, whereas organizations that are not listed on any

exchange are not taken into account.

The remainder of this master dissertation is structured as follows. The succeeding section, which is called

Real Estate Prices and Bank Valuation, contains the core content of this study. This section first examines

the Existing literature concerning the subject that is being examined. Then, the Hypotheses that are based

on the central research question and that underpin this study are introduced. After that, the implemented

Data and methodology are elaborately discussed. In that part, the base model that is applied throughout

the study is explained and substantiated, the sources of the utilized data are examined, the software used

to execute the analyses is briefly described, and, lastly, the methodologies that are applied to investigate

the formulated hypotheses are considered. The concluding part of the core section provides an overview

of the Results that are obtained from all considered analyses. Succeeding the core section, another section

can be found that summarizes the Conclusions that are formed through this dissertation. The Limitations

and suggested future research regarding this study are discussed in the subsequent section. To conclude

this master dissertation, a Reference list containing all consulted sources, as well as Appendices, which

comprise further valuable information and to which is referred in the relevant sections of this work, are

included.

4

Real Estate Prices and Bank Valuation

1 Existing literature

The goal of this dissertation is to examine the causality between changes in real estate markets and the

valuation of shares of banks in a contemporary time setting. This literature research section seeks to

provide a comprehensive overview of the literature that is currently available on the topic and intends to

provide a more thorough understanding of the related fundamentals. Providing an in-depth and

exhaustive overview of all associated matters is beyond the scope of this dissertation and should be a

research in itself since there is a vast body of literature dealing with real estate markets, financial markets,

and related areas.

This section is structured as follows. Section 1.1 Real estate market characteristics provides an overview

of the specifics of real estate markets such as its recent evolution, cyclic behavior, and cross-country

integration. The subsequent section, 1.2 Real estate markets and the macro-economy, discusses the

connection between real estate markets and the macro-economy and covers topics such as procyclical

behavior and the use of real estate market information within monetary policies. A larger part of the

literature research can be found in the third section, 1.3 Real estate markets and financial markets, and

deals with the relationship between real estate markets and financial markets. That section also focuses

on the more specific topics of loan markets and irrationality, as well as the monitoring of real estate

markets, among other subjects. Finally, section 1.4 Real estate data discusses some typical issues related

to real estate data.

1.1 Real estate market characteristics

Real estate markets are a centerpiece within this master dissertation, and for that reason, an analysis of

the existing literature dealing with the specific characteristics of real estate markets is discussed within

the following paragraphs. First of all, an overview of relevant studies considering the specific dynamics

and volatility of real estate markets is provided. After that, there is a more detailed examination of a

generic real estate cycle as well as a short discussion of some closely related studies. And finally, cross-

country integration of real estate markets is discussed in the last paragraph of this section.

1.1.1 Dynamics and volatility

Since the 2000s, interest in the real estate markets has vastly increased. In their 2004 study of real estate

markets, Terrones, Otrok, & Carcenac (2004) observed that real estate prices of most industrial countries

had rapidly risen since the mid-1990s. These findings raised the question whether those increases were

5

in line with market fundamentals and whether a possible house market bubble was likely to burst. The

researchers found evidence of substantial deviations from the market fundamentals and warned for a

possible severe drop in house prices. Whereas these authors have focused their attention on the direction

of real estate markets and the deviations from fundamentals, others have investigated the volatility of

real estate markets: while Niinimäki (2009) states that, in general, real estate markets fluctuate

considerably, Terrones et al. (2004) observe a decline in volatility over time. This is, according to Terrones

et al. (2004), due to a weakening in macroeconomic volatility as well as a low inflation rate that is kept

stable across countries. Moreover, they find some evidence showing that house price volatility is related

to how the financial markets are structured institutionally. A final remarkable characteristic of real estate

markets is pointed out by Girouard, Kennedy, van den Noord, & André (2006), and concerns the

downward stickiness of the market, which refers to a prolonged decline once the prices have started

dropping. This is, according to these authors, caused by the illiquidity of these types of assets, stemming

from their high transaction costs and search costs as well as their heterogeneous nature.

1.1.2 Real estate cycles

It is interesting to consider whether a standard real estate cycle pattern exists and what it looks like as

compared to a typical business cycle. This is what Ahearne, Ammer, Doyle, Kole, & Martin (2005) have

examined. They investigated eighteen major industrial countries for a sample period from 1970 to 2005,

and they were able to describe a generic real estate cycle. This cycle description can be summarized as

follows. The characterization starts with an upward phase of the real estate cycle, when the monetary

policy is generally at ease. The real property market is co-moving with the business cycle, inflation rates

rise until they become less bearable for the economy, and the output growth has surpassed its potential

level. The central banks begin tightening their monetary policy in order to smooth off the business cycle.

Both the business cycle and real estate cycle tend to further increase to a certain extent, but at more or

less the same point in time, they both reach their summit. Subsequently, reversal takes place and the real

estate market ends up in a declining phase which takes five years on average. Simultaneously, the real

GDP growth falls for about one year, and consumption and investments drop back to a lower growth rate.

Once the business cycle has started falling, the monetary policy is eased in order to stimulate the economy

again. The policy rates are reduced to a bare minimum, and about three years before the real estate

market reaches its summit, the house prices start rising again and inflation rate starts growing. For a year,

the policy rates are kept at their minimum, and they are being raised steadily after that. At that point the

real estate cycle starts again from the beginning.

While not refuting the existence of general real estate cycle patterns, Hilbers, Zacho, & Lei (2001) mention

that the length of real estate cycles may differ across countries. Otrok & Terrones (2005) furthermore

6

observe that, even though there is an observable co-movement of house prices, stock returns and real

variables among countries, their cycle lengths tend to differ: house prices seem to display the longest

cycles while stock returns demonstrate the shortest. On the other hand, Herring & Wachter (1998) point

out that despite a clear connection between property cycles and banking crises the one does not

necessarily involve the other. In other words, there are some cases in which property cycles occur without

a banking crisis being present.

In another study of fourteen industrial countries for a thirty-year period (1970-2001), Helbling (2005) finds

evidence as well of the occurrence of boom-bust cycles in real estate markets. He, however, argues that

booms and busts are not as closely connected as is often assumed. Rapid house price upswings over a

period as short as two years, or even shorter, appear to be a better indicator of forthcoming busts than

price increases over a longer period of time are.

1.1.3 Cross-country integration

Several studies have investigated the cross-country integration of real estate markets and have found

evidence in favor of a certain interconnectedness. Some relevant studies that examined this cross-country

integration are discussed below.

Beltratti & Morana (2010) argue that housing markets should not be seen as individual markets, but, on

the contrary, as markets which are interconnected via shocks in macroeconomic variables. According to

them, this implies that international diversification of real estate investments is less advantageous. They

also stress the need for policy-makers to not only focus on their local markets, yet instead take the

international business cycles as well as real property effects into account. A similar study is conducted by

Case, Goetzmann, & Rouwenhorst (1999). They find that, even though country-specific factors tend to

influence real estate market returns more, these returns are to a large extent influenced by global factors

which leads them to the conclusion that cross-border correlations are indeed important when considering

real estate markets. On the other hand, Terrones et al. (2004) claim that rising trade and growing financial

relationships are the likely cause of the increased synchronization across countries. They state that the

co-movement of real estate markets takes place through the influence from global factors, such as co-

movement in interest rates and economic activity. This implies that macroeconomic fluctuations in one

country might transfer to another country, possibly influencing real estate prices. They furthermore

mention that the degree of synchronization of house prices across countries is closely linked with the

extent to which those countries are interconnected through the global factors. Therefore, the

interconnectedness between real estate markets is not exactly the same for each pair of countries, as all

countries demonstrate different sensitivities toward those global factors. Another remarkable point that

7

is discussed in this study is that the connection between the global factors and house price factors appears

to be increased over the last years up to 2004, which is the year in which the study has been conducted.

Evidence of the interconnectedness of real estate markets can be found in other studies as well. Ahearne

et al. (2005), for example, state that several factors, such as interest rates and global business cycles,

influence real estate markets worldwide. In another study, which focuses on thirteen industrialized

countries for the period from 1980 to 2004, Otrok & Terrones (2005) show that real house prices in the

examined countries tend to move in the same direction to an extent similar to the co-movement of

financial asset returns and macroeconomic variables. A study by Holly, Pesaran, & Yamagata (2006)

investigates the interconnectedness within the US by comparing 49 US states with one another. They

observe that the real estate markets of the investigated US states are closely intertwined. Similar research

has been carried out in the UK. Cameron, Muellbauer, & Murphy (2006), for example, examine the

existence of a house price bubble in the UK for the period 1972-2003 and find no evidence in favor of such

a bubble. They additionally consider the linkage of real estate markets among nine regions within the UK,

and they report that there is an observable interconnectedness between those regions. Furthermore,

they discuss the so-called ‘ripple effect’ in which a delay of house price movements is observed across the

regions, with London as the leading region. Girouard et al. (2006) confirm the correspondence of real

estate price dynamics across countries and, moreover, point out that there is an increase in this co-

movement over the years.

While a lot of studies provide evidence in favor of the international synchronization of real estate markets,

Hilbers, Banerji, Shi, & Hoffmaister (2008) argue that diverging trends in real estate prices can be observed

when considering countries within Europe. They studied several countries in order to find out why this

divergence is present and to discover what factors may lie at the basis of this phenomenon. By dividing

their country sample into three groups, based on the rate of real estate price increases since the mid-

1980s, they were able to discern some explanatory variables. For the groups that experienced the highest

increase in real estate prices, standard models containing user costs1, demographics, and output as

explanatory variables seem to properly explain the house price movements. For the third group, which is

characterized by hardly any rise in property prices, the models seem to be unreliable. The authors

nevertheless state low home ownership and less complete mortgage markets as some of the likely

contributing factors within that group.

1 User costs can be described as the expected costs of home ownership (Hilbers et al., 2008).

8

1.2 Real estate markets and the macro-economy

It is important to have an understanding of factors that influence the real estate markets in order to be

better able to grasp the interactions with financial markets. Whereas another separate and more in-depth

section (cf. 1.3 Real estate markets and financial markets) is devoted to the literature research on the

links between financial markets and real estate markets, a discussion of the relation between real estate

markets and the macro-economy is part of this rather brief section. It is structured as follows. First, the

procyclical behavior of real estate markets is discussed. Then, an overview is given of some studies that

discuss the correlation and causality between real estate markets and the macro-economy. And finally,

the practical use of real estate information within monetary policies is examined.

1.2.1 Procyclicality

Real estate cycles do not necessarily occur by themselves, yet, in contrast, appear to be frequently

correlated with other variables such as macroeconomic indicators (e.g., Ahearne et al. (2005), Girouard &

Blöndal (2001), Helbling (2005), and Terrones et al. (2004)). Nevertheless, other studies find evidence of

the absence of such a link between real estate markets and business cycles (e.g., Girouard et al. (2006)).

This section provides an overview of the mentioned studies.

In their paper, which also includes the description of a generic real estate cycle (cf. 1.1.2 Real estate

cycles), Ahearne et al. (2005) seek to get an understanding of which dynamics take place in real estate

markets. They find that real estate prices are procyclical, which means that they tend to move in line with

macroeconomic variables, such as real GDP, consumption, and investments. A possible reason for this

procyclicality, as they indicate, is the deregulation of financial markets in the late 1980s. This is positively

confirmed by Girouard & Blöndal (2001) who lend support to the finding that mortgage market

deregulation was in several cases of major importance for the macroeconomic fluctuations of the 1980s.

The existence of procyclicality of real estate markets is confirmed by Terrones et al. (2004). However, they

mention that the intensity of the procyclicality tends to vary across countries. In addition, these authors

note that there is a positive correlation between real house prices and output and consumption variables,

while a negative relationship is present between real house prices and interest rates. When considering

the correlation between real estate markets and stock markets, they find no significant relationship, but

real stock price movements, nevertheless, appear to precede house price movements. Helbling (2005)

additionally observes that real estate market crashes often occur simultaneously with downturns in the

economic activity, and that the beginning of both output falls and drops in real estate prices appear to

regularly coincide as well.

9

A conflicting study on the topic of procyclicality is one by Girouard et al. (2006), who point out that, after

having examined eighteen OECD countries for the period 1970-2005, the real estate cycle appears to

become disconnected from the business cycle.

1.2.2 Correlation and causality

There have been several studies examining to some extent the correlation and causality between real

estate markets and the macro-economy, of which the most relevant ones are discussed below.

Beltratti & Morana (2010) investigated the two-way link between general macroeconomic conditions and

the real estate markets from a perspective of the G-7 area. They observe a bidirectional relationship and

acknowledge that investments generally experience a larger impact from real estate shocks than

consumption and output do. In addition, they find evidence that real property fluctuations have a greater

influence on the macroeconomic situation than stock market fluctuations. When considering the direction

from the macroeconomic circumstances to real estate markets, the researchers find proof that roughly

40 % of global house price fluctuations originate from global macroeconomic movements, while 20 % of

global house price fluctuations result from movements in the real estate markets themselves. On the

contrary, Otrok & Terrones (2005) find some conflicting evidence in their study on thirteen industrialized

countries. They report that there certainly is a causality from real estate prices toward macroeconomic

variables, such as real output, residential investment and consumption. Yet, on the other hand, they do

not find significant evidence in favor of the causality from macroeconomic conditions toward housing

markets.

A study of Chirinko, De Haan, & Sterken (2008) moreover points out that there is an impact of housing

and equity shocks on private consumption, residential investment, and business investment, although the

extent of the impact on those three aspects is dissimilar across countries, and is related to the financial

structure within a specific country. The authors likewise mention that, in general, the impact of housing

shocks is bigger than the impact of equity shocks.

1.2.3 Real estate data as input for monetary policies

Taking into account the existing literature on the correlation and causality between real estate markets

and the macro-economy, a closer look at some of the available studies on the links with monetary policies

delivers additional insights into how real estate prices affect the macro-economy.

In their paper, Ahearne et al. (2005) investigate the reaction of central banks toward deviations of real

estate markets from their fundamentals. They report that there is no overall agreement on how central

banks should respond. Real estate information is often not taken into account, apart from assessing its

10

implications for inflation and GDP growth. This disagreement in appropriate repercussions is, according

to the researchers, due to several possible reasons. First and foremost, the central banks often have

different views on the matter. Some argue, for example, that they should counteract the emergence of

real estate bubbles, while others claim that they should focus on their core objectives (pursuing low

inflation and securing output stability) and that they should not be questioning the market. Second,

identifying and assessing real estate bubbles is a very intricate task because of the uncertainty a bubble

entails of what might happen, especially when the bubbles are occurring at the exact same moment as

when central banks are trying to identify them. Furthermore, since real market dynamics are observed a

posteriori, the central banks would be constrained to take repressive measures rather than pre-emptive

ones. Therefore, lags toward real estate markets would be introduced into their policies, and they would,

thus, experience difficulties taking effective actions. Chirinko et al. (2008) report similar findings, and

according to them, monetary policy makers tend to take equity shocks into account and act accordingly.

There is, however, no significant evidence that monetary policy makers react appropriately to housing

price dynamics.

As an advice toward policy makers, Terrones et al. (2004) advocate that, in times of rising housing prices

and a likely burst of a real estate market bubble, the best option for central banks is to deploy an early

but modestly tightening policy, since rapid tightening of monetary policies turned out to be a possible

cause of market collapses in the past, and should, thus, be avoided. Helbling (2005) mentions as well that

plummets after a boom in real estate markets can regularly be related to preceding monetary policy

tightening. Terrones et al. (2004) additionally note that central banks should increase the regulation of

the financial sector and that they should tighten lending requirements as well as financial surveillance.

As a word of caution, Trichet (2003) mentions that the inclusion of asset price information, such as

property prices, into systems of monetary policies, banks, price stability, and inflation should not be done

thoughtlessly. He states that multiple asset categories exist which could be included, such as stock prices,

housing prices, and exchange rates. Nevertheless, it is not clear which type of asset information should

be included and which should not. He furthermore argues that, besides the diverse nature, asset prices

contain information different from the information comprised in the prices of goods and services and they

should therefore not be used as indicators for these prices of goods and services. Another important point

of his work is that he argues that a sound monetary policy should not be based on asset prices, because

these are predominantly characterized by volatility, and a volatile input variable is not preferable for

monetary policies.

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1.3 Real estate markets and financial markets

The core of this master dissertation is built up around the relationship between the real estate markets

and financial markets. An overview of the existing literature on this topic is therefore indispensable within

this study. This section is structured as follows. First, the importance of real estate markets for financial

markets is discussed, including a more in-depth analysis of the literature on some more explicit topics,

namely, commercial real estate, loan markets, and irrationality and speculation. The second part

considers the more practical use of real estate information within certain mechanisms, such as financial

soundness indicators and early warning systems.

1.3.1 Importance of real estate markets

Several explanations can be given why real estate markets can have a large impact on financial markets.

Some of these explanations are stated by the IMF (2006), of which four are briefly discussed in this

paragraph. One cause of the real estate exposure is the real property owned by the financial organizations

themselves since this is directly subject to real estate price variations. Real estate is furthermore often

used as collateral for financial products and is one of the most important elements of bank loans, which

makes those products especially vulnerable toward fluctuations in real estate prices. Yet another

explanation might be the practice of securitization2 in which the securities are backed by real estate, as is

the case for mortgage backed securities for example. The exposure is, however, not limited to own real

estate, mortgages and securities. The financial organizations are indirectly influenced as well through their

customers and business partners who hold real estate or have provided funds to invest in real estate.

Since real property makes up a substantial share of private wealth, one should not underestimate these

indirect exposures. The IMF (2006) additionally states that the exposure of financial markets to the

volatility of the real estate sector is larger as compared with other asset classes, and this appears to be

due to the infrequent trade of real estate, the high transaction costs, and the legal and other regulations

that are characteristic for the real estate sector, among other reasons.

Koetter & Poghosyan (2008) also acknowledge that the use of real estate as collateral is an important

reason for the relevance of real estate markets for financial markets. They investigated the relationship

between the real estate market and bank distress in Germany for 1995-2004. In order to estimate the

probability of distress, they used a logistic regression model conditional on a set of explanatory variables,

which are based on CAMEL3 measures. They identify two paths through which real estate prices can

2 Securitization can be characterized as the conversion of assets that are rather illiquid into liquid securities (Keys, Mukherjee, Seru, & Vig, 2008). 3 CAMEL respectively stands for capital adequacy, asset quality, managerial quality, earnings, and liquidity (Koetter & Poghosyan, 2008).

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influence the distress probability of banks. A first path is named the ‘collateral effect’ and shows the

pattern in which increasing house prices raise the value of collateral which leads to a reduction of the

distress probability. The second route that is described is that, assuming rents being equal, a rise in real

estate prices will cause an increase of price-to-rent ratios which are, in turn, likely to inflate expectations

of future cash flows from housing. And these overstated expectations furthermore appear to be

correlated with a heightened probability of distress. The first path turns out to be substantially important

for those banks that are heavily involved in mortgage lending while the second route affects all sorts of

banks. The authors however mention that there are other factors as well that will influence the probability

of bank distress, and that the described effects might significantly differ among different areas.

Other studies, such as Baele, De Bruyckere, De Jonghe, & Vander Vennet (2015) and Case & Wachter

(2005), confirm the importance of real property in the financial markets. Case & Wachter (2005) note that

besides owning real estate themselves, banks tend to relax their lending conditions when asset prices rise

in the short term. Another point they mention is that incentive structures in banks often motivate toward

behavior that aggravates boom-bust cycles in real estate markets. They also remark that banks frequently

have to deal with little information on the real estate markets, or that they are only able to obtain

transaction data after transactions have been closed and data that is subject to short term fluctuations. It

is therefore of major importance for the financial system to acquire real estate market information that

is of high quality and that is timely. A study by Baele et al. (2015) shows that the stock returns of US bank

holding companies over the period 1986 to 2010 seem to be related to only three out of their investigated

twelve risk factors: a market factor, the high-minus-low Fama-French factor and a real estate factor. To

get these results a Bayesian model averaging technique was applied. The researchers furthermore state

that there is limited evidence that the relevant set of risk factors differs between different bank holding

companies.

A comment should be made that not all studies agree with the findings that real estate markets are

noticeably important for financial markets. Ahearne et al. (2005), for example, argue that, even though

mortgage lenders are substantially exposed to real estate market dynamics, this usually does not pose a

significant risk for the financial markets. One reason for this limited risk is, as is claimed by the authors,

that banks are assumed to have an adequate amount of capital at their disposal to bear the risks they

take. They furthermore state that a lot of banks securitize parts of their mortgages which can be perceived

as a second reason for their limited risk exposure. In addition, they mention that the outstanding debt of

a loan usually declines over time as the loan gets closer to its maturity date whereas the value of the

collateral usually increases, which leads to a lower exposure of banks toward defaulting. They also argue

that even if the collateral value drops below the loan value, households are generally not willing to default

on their loan and prefer to continue paying off their debt.

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Other researchers have focused their studies on specific areas within the financial markets and the real

estate markets when considering the relationship between the two markets. The subsequent paragraphs

discuss three of these areas: commercial real estate, loan markets, and irrationality and speculation.

1.3.1.1 Importance of real estate markets: commercial real estate

As pointed out by Zhu (2005), commercial real estate often seems to pose a bigger threat toward financial

markets than residential real estate does. As a possible reason he states that the repayment of commercial

property loans is dependent on the sale price or rents that are generated by the property built, and can

thus be subject to major fluctuations while, on the other hand, the borrowers of residential property

loans, which are typically households, earn in most cases a relatively stable income which lowers the risk

of default on those loans. A study of Federal Deposit Insurance Corporation (1997) furthermore shows

that banks that failed during the 1980s and the 1990s were significantly more exposed to commercial real

estate as opposed to the banks that did not fail during that period indicating that this type of exposure is

important to consider when assessing the risks of financial systems.

More in-depth studies on the topic of commercial real estate markets were conducted by Davis & Zhu

(2009, 2011). As they state, one of the characteristics of commercial property loans is that it is an

important and frequently the most volatile component of the assets of many banks. Banks furthermore

have a big exposure toward commercial property because they often serve as collateral for several other

loans. Davis & Zhu (2011) find that lending decisions within banks are positively influenced by commercial

property prices. This relationship is, however, bidirectional: it appears that real estate prices tend to be

positively influenced by lending decisions in the short run, whilst a longer horizon demonstrates a negative

relationship. Nevertheless, the effect from property prices on credit seems to be significantly higher than

the causality in the other direction. The authors moreover note that these dynamics may significantly

differ across countries, as well as when considering different stages in real estate cycles. In an earlier

study, Davis & Zhu (2009) observe that commercial property prices tend to be positively correlated with

banking profitability and bank lending, whereas they incline to be negatively correlated with the net

interest margins and default risks. They also note that bank size matters: bigger banks tend to be more

exposed to commercial real estate markets than smaller banks are.

1.3.1.2 Importance of real estate markets: loan markets

In the article ‘Lending behavior and real estate prices’ Hott (2011) examines the feedback effect between

the mortgage loans that banks provide, real estate prices and default rates on those mortgages. He

observes that banks not only suffer from real estate crises but they also contribute to them. His reasoning

of how banks could contribute to those real estate crises can be summarized as follows. When the

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economy does well and real estate prices increase, banks tend to raise their mortgage supply. As a

consequence of a high economy and rising real estate prices people become wealthier and, therefore, the

mortgage default rates drop. Providing mortgages becomes more profitable for banks and they increase

the mortgage availability. By doing so, they augment the demand for real estate, and, hence, cause real

estate prices to increase even more. A bubble might be created this way, and once such a bubble suddenly

bursts banks may suffer big losses. Oikarinen (2009) who focused his research on Finland, and other more

general studies, such as BIS & IMF (2005), BIS (2001) and IMF (2000), confirm this two-way relationship

between real estate markets and loan markets.

Zhu (2005) states that, even though evidence of the bidirectional relationship between real estate prices

and bank credit is found, the causality from property markets toward bank credit is much more clear than

the causality in the other direction. Some of the mechanisms that may underlie these causalities are also

mentioned in his work. A drop in real estate prices might, for example, lead to a deterioration of the value

of own property as well as of the financial conditions for both borrowers and banks. It furthermore may

increase the amount of non-performing loans, cause a slowdown of economic activity, and lead to a

decrease of the amount of financial transactions. These mechanisms can create a worsened financial

system. On the other hand, a declined bank credit provisioning can cause imbalances in supply and

demand on the real estate markets, and can generate price fluctuations. Gerlach & Peng (2005), who

researched both bank lending and the residential property market in Hong-Kong over a period covering

the 1980s and the 1990s, find very similar results and conclude that bank lending was not the major cause

of the real estate boom-bust cycles in Hong-Kong during the two investigated decades. The importance

of credit risk, and in particular real estate lending, is also observed by BIS (2004), who examined thirteen

banking crisis episodes that occurred in eight different countries in the 1980s and the 1990s, and of which

eleven episodes demonstrated major influence from real estate lending. The investigation of crisis

occurrences and the specifics of causal factors has been the subject of many other studies as well, such

as Davis & Karim (2008b) and Reinhart & Rogoff (2008). Reinhart & Rogoff (2008) considered multiple

subprime mortgage crises, like the 2008 crisis, and found that these types of crises are typically preceded

by a climb in both equity and real estate prices. The same phenomenon is observed by Davis & Karim

(2008b) who note that debt accumulation, of both corporates and households, and asset price booms

usually take place during the rise of a crisis; subsequently, this possibly leads to unsustainable situations

in which a decline in asset prices could lead to weak balance sheets and potentially to succeeding defaults

and insolvency.

Daglish (2009), on the other hand, investigated more in detail how real estate markets influence the

probability of default on mortgage loans for subprime borrowers, and hence provides a better insight into

the transmission of property price movements into loan markets. He finds that interest rates and house

15

prices vastly influence the default probabilities of these subprime borrowers. Subprime borrowers often

hope to be able to refinance their loans at lower interest rates once the underlying house price of the

mortgages has increased. They therefore frequently borrow at variable interest rates, making them

vulnerable to interest rate increases as well as house price decreases. The author furthermore mentions

that the abundant use of collateralized debt obligations4 (CDOs) has often caused a lot of distress for the

financial markets in the past. The CDOs can provide a decent and relatively low-risk payoff as long as

defaults of the underlying assets are weakly correlated. Nevertheless, he states that the correlation is

often underestimated, particularly in situations of interest rate changes.

1.3.1.3 Importance of real estate markets: irrationality and speculation

A topic that has often been discussed in literature of irrationality and speculation is the literature

investigating the deviations of prices from their fundamentals. As stated by Hott (2011), banks are able to

dissect movements in real estate markets to some extent. However, Koetter & Poghosyan (2010) mention

that not all fluctuations can be justified, especially the deviations of the real estate prices from their

fundamental values. They focused their research specifically on Germany, using several regional data sets

with a time frame from 1995 to 2004. In their research they acknowledge that real estate markets do

influence banking stability, both in times of crisis and periods without crises. They further investigated

that mechanism and found that nominal price levels and price changes in real estate markets do not

significantly pose any issues for the banking stability. A significant threat to the stability of banks is

according to them however posed by the deviations of real estate prices from their fundamental values,

which is confirmed by Goodhart & Hofmann (2007). In other words, bets on deviations from fundamental

values should be avoided. And yet, banks tend to increase their supply of mortgages when real estate

prices are rising. This behavior can, according to Hott (2011), be explained through three irrational

expectation formations, namely, mood-swings, momentum forecasts and disaster myopia. Girouard et al.

(2006) nevertheless report that, after having investigated a sample of eighteen OECD countries with a

time frame from 1970 to 2005, overvaluation of real estate prices was only existing for a small number of

countries, so this might not be such a big issue after all.

Deviations from fundamentals has not been the only topic that has been considered in the literature in

view of irrationality and speculation. Niinimäki (2009), for example, delved into the problem of moral

hazard as a possible cause for banking crises. He argues that banks tend to speculate on real estate prices

through the use of real estate as collateral for mortgage loans. He analyzed whether and how the value

4 A collateralized debt obligation (CDO) is a structured asset-backed security that consists of a pool of several asset classes and that is divided into multiple tranches, whereas the proceeds from the pool are distributed to its tranche investors through prioritization (Duffie & Garleanu, 2001).

16

of real estate could influence the credit decisions that banks make. He notes that a lot of banks tend to

rely more on the value of the collateral rather than profound credit analysis such as cash flow valuations,

especially during the run-up to the 2008 crisis as well as other financial crises in the past. If the value of

collateral has increased over time and the borrower defaults on his loan, banks are able to earn decent

profits on that default since they can seize the collateral and sell it which can, according to Niinimäki

(2009) and other authors (e.g., Collyns & Senhadji (2003) and Hilbers et al. (2001)), be a possible

explanation why banks allow financing to borrowers whose projects are unlikely to succeed. However, in

situations of a downturn in real estate markets, banks can be hard-hit by those slumps, and these gambles

can even cause them to fail.

Real estate cycles furthermore often become amplified as a result of the behavior of financial systems, as

is mentioned in an article by Pavlov & Wachter (2011). They argue that, especially in times of financial

innovation and deregulation, aggressive mortgage lending instruments appear to be generated which

make borrowing easier, and which lead to amplifications of both price rises and price declines in the real

estate market. Hilbers et al. (2001) confirm these findings and note that prices of real estate markets have

a tendency to keep dropping during the crises, which indicates that financial crises further aggravate the

decline in prices. They additionally state that, on the other hand, the stock prices of real estate companies

tend to be at a low once financial crises have commenced. In summary, the stock price expectations seem

to adapt more quickly to the changes in the real estate markets than real estate price expectations do.

1.3.2 Monitoring real estate markets

Several researchers (e.g., Borio & Drehmann (2009)), institutions (e.g., IMF (2006)), and others have

recognized the importance of real estate markets for financial markets. It has therefore been proposed

by many authors to include real estate indicators in several mechanisms, such as mechanisms to assess

financial stability (e.g., Financial Soundness Indicators (IMF, 2006)) and to predict banking crises (e.g.,

Early Warning Systems (Davis & Karim, 2008b)). The following paragraphs give an overview of the research

that has been conducted considering these topics.

The IMF (2006) has devoted a lot of attention to the substantial exposure of financial markets toward real

estate markets. As recognized by the IMF and experienced worldwide during crises in the 1990s, the real

estate sector has proven to be a persistent influence for both local and international economies. Before

that, real estate markets were often treated as independent markets (Heath, 2005). So as to achieve its

goals5, having a reliable, efficient and stable international financial system is crucial for the IMF; therefore,

one of their main tasks is to gather statistical information which enables the assessment of financial

5 The role of the IMF is to support and strengthen the international financial system (BIS & IMF, 2005).

17

system health (BIS & IMF, 2005). The IMF bundles that information within so-called financial soundness

indicators (FSIs), as explained in their book ‘Financial Soundness Indicators: Compilation Guide’ (IMF,

2006). These FSIs contain information about the financial institutions themselves as well as information

about the markets in which they operate; this market information commonly includes real estate data,

due to the large exposure of financial markets toward real estate markets, both directly and indirectly

(Heath, 2005). An IMF (2011) country report confirms this and highlights the importance of the use of real

estate market information, among other indicators, as an important element of the FSIs. The IMF (2006)

furthermore states that the main goals of such FSIs are to prevent crises from happening, to aid policy

makers in setting up and conducting their macroprudential analyses, and to detect the vulnerabilities of

financial systems. FSIs are, however, not only valuable to evaluate financial systems, but can also be of

importance for the assessment of the health and soundness of households and corporates (Heath, 2005).

Not only the IMF, but other organizations as well have been taking into account the real estate markets

when considering financial market stability. OECD et al. (2013), for example, mention in their Handbook

on Residential Property Prices Indices that real estate has an important role in debt financing and financial

crises. Borio & Drehmann (2009), who looked into several possible leading indicators of financial crises,

confirm the importance of property price information. They also mention the relevance of cross-border

exposure of most banking systems. Therefore, when considering predictors of a financial crisis in a certain

country one should also take into account the exposure of the country toward other countries. During a

conference of BIS & IMF (2005), a BIS member furthermore stated that the BIS started looking at the role

of asset prices for monetary policy makers during the 1980s and the 1990s, and that not much later asset

markets became of major interest when assessing financial stability.

A lot of studies have focused on the issue of predicting banking crises and have come up with early

warning systems (EWSs) for banking crises (e.g., Borio & Drehmann (2009), Davis & Karim (2008a, 2008b)

and Kaminsky & Reinhart (1999)). However, Demirgüç-Kunt & Detragiache (2005) mention that the

identification of determinants of past crises is often a much easier task than the identification of those of

possible forthcoming crises. Davis & Karim (2008b) additionally claim that, despite the voluminous

literature available on the subject, EWSs are infrequently applied in practice. In their research they

moreover conclude that, even though the subprime crisis that started in 2007 displayed some features

that were not present in previous crisis episodes, similarities with previous episodes can be observed as

well. They, therefore, advocate the optimization of existing prediction models, rather than rejecting them

and starting from scratch. EWSs might, besides assessing upcoming banking crisis episodes, serve other

purposes as well, such as the prediction of debt crises (Ciarlone & Trebeschi, 2005). These authors

moreover propose a more general definition of EWSs, stating that these are used to identify weaknesses

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and vulnerabilities on the one hand, but can also be used to generate acceptable and appropriate signals

of upcoming distress episodes on the other.

There is a vast body of evidence of a significant relationship between housing markets and financial

markets, as is clear from many of the articles mentioned in this dissertation (e.g., Hott (2011), Koetter &

Poghosyan (2010) and Oikarinen (2009)). It might therefore be a good idea to include real estate market

information in early warning models for banking crises, as is investigated by Barrell, Davis, Karim, & Liadze

(2010). They question the use of the traditional generic variables, such as inflation and GDP growth, when

drawing up EWSs for OECD countries. Conversely, they advocate the use of three more relevant indicators,

namely, unweighted bank capital adequacy, bank liquidity, and real estate price growth. Davis & Karim

(2008a) nevertheless mention that real estate prices have not been implemented in EWSs very often due

to a lack of adequate information on real estate markets.

1.4 Real estate data

The central analysis of this master dissertation is based on data from real estate markets of several

countries. Providing an overview of some of the studies that have been conducted so far and which deal

with the topic of real estate data and the issues thereof should therefore not be excluded from this

literature research. This section is divided into two subsections. The first one deals with studies that

include the discussion of real estate data set characteristics, whereas the second subsection discusses the

international comparability of these data sets.

1.4.1 Real estate data set characteristics

Arthur (2005) poses several characteristics that should be taken into account when obtaining real estate

data and constructing real estate price indices. The first and most obvious criterion that is considered is

that the data should be available, and it should preferably be updated regularly. Secondly, a constructed

index should be representative of the information it wants to include to date. To make this criterion

somewhat clearer the example is given that in order to be representative a contemporary consumer price

index should not include the prices of horses in its pool of products. Continuity is yet another condition

that needs to be considered. For several reasons, such as an improvement of compilation methodology

or a disappearance of a certain source, a data set may display data splits that can bias analyses. Another

criterion is that the length of the obtained data set should be long enough so as to conduct valuable

analyses. A second to last condition that is mentioned is the data frequency, which should be at least

quarterly if used for monetary or financial stability analyses. However, in some cases a yearly frequency

may be sufficient as well, since property prices do not seem to be that volatile over the quarters of one

year. Finally, obtained data on real estate markets should possess a characteristic of timeliness.

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Similar reasons why the collection of high quality real estate statistics is an intricate task are discussed by

Paul Van den Bergh during a joint conference of the BIS and the IMF in 2003 (BIS & IMF, 2005). Some of

these reasons are discussed within this paragraph. While real estate data is not always available on a

regular basis, the representativeness of the data is often questionable due to poorly explained

methodologies, which moreover might change over time and which may obscure data sources.

Furthermore, the timeliness and reporting frequency are often inappropriate, and continuity of the data

sets is mostly not available. He furthermore points out that the availability of data on real estate markets

has frequently been an issue for researchers. The data is often only available through commercial

resources and can thus usually not be obtained without cost. And even if the data can be gathered,

conducting valuable analyses is often inhibited through the limits and weaknesses of the data sets, as

explained above. Another study that discusses some of the concerns that come with real estate market

data collection is one by Terrones et al. (2004). Besides the questionable quality of the data, the lack of

standardization, and the use of dissimilar methodologies of collecting data and drawing up indices, they

also point out that the coverage of the indices is often different and that in most countries a correction

for variations in housing quality over time is not included.

The enumeration of all of these issues emphasizes the fact that the collection of valuable data of real

estate markets is a very challenging assignment. The BIS (2015c) has done a good job so far collecting real

estate data from multiple national sources and providing these data via a central database free of charge.

They furthermore have put effort into the selection and formation of representative indices per country.

An in-depth description of the existing BIS housing data sets is available in the Data and methodology

section (cf. 3.2.1 Real estate returns). The following subsection discusses more in detail the issues when

comparing real estate indices.

1.4.2 International comparability of real estate indices

The IMF (2006) states that the measurement of real estate prices is subject to specific characteristics that

make it harder to conduct comparable analyses, even at regional levels. One of these characteristics is

that real estate is an asset class that is typically heterogeneous and illiquid. The high degree of

heterogeneity should not at all be neglected when comparing the different countries with one another.

Several characteristics in which real property can differ are: location, building materials, age, size, number

of rooms, availability of utilities, physical condition, and other characteristics. Another feature that is put

forward and which is related to the heterogeneity is the lack of standardization. The IMF furthermore

points out that real estate prices are hard to measure and compare accurately due to differences in price

information, proprietary control of such information, and dissimilarities in the point of the purchasing

process when the price information gets listed. Another important feature is that, in order to be valuable,

20

an index needs to be the result of calculations on an adequate amount of observations. Unfortunately,

the number of observations is often an issue for real estate data. Besides the heterogeneity, the incidental

trades of houses, and the common use of real estate as collateral, Hilbers et al. (2001) also mention the

absence of a central trading market, the high transaction costs, legal and taxation differences among

countries, and the frequent need of financing through borrowing, which often implies the use of real

estate as collateral, as important elements that should be taken into account. Moreover, real estate data

is frequently not that representative due to poorly explained methodologies and obscure data sources

(cf. 1.4.1 Real estate data set characteristics), which makes an international comparison of real estate

markets even more difficult (BIS & IMF, 2005). The area coverage and the divergence in the compilation

methodologies used by different national authorities appear to be yet another source of lack of

homogeneous comparability (Arthur, 2005).

Similar as well as some other factors which should be considered when comparing indices of different

countries are mentioned by OECD et al. (2013), namely, the availability of data, the reporting frequency,

the area covered by the index, the unit of measurement (e.g., per dwelling or per square meter), the time

lags between observation and reporting, the point in the purchasing process at which the price is quoted,

as well as other factors. According to the OECD et al. (2013), the three challenges that should especially

be taken into account when compiling real estate property prices are that real estate is a very

heterogeneous asset, prices of real estate are typically negotiated and thus the price can vary along the

purchasing process, and last but not least, real estate is usually sold relatively infrequently.

The case of the European Central Bank (ECB) illustrates some of the comparability issues very well and is

described below. The ECB frequently compiles a euro area indicator representing the situation of the

residential property market in the euro area by utilizing data from national sources and is, thus,

confronted with the aggregation issues as well, as stated by Ahnert & Page (2005). They mention that the

ECB is particularly interested in the real property prices in the euro area because price developments have

both an effect on the existing wealth of households, and on the budget and savings decisions of potential

buyers of residential property. Housing furthermore plays an important role in consumer price indices.

Another point that is stated is that housing, to some extent, determines the stability of the financial

system, as it is a contributing factor of credit quality and collateral value. The authors mention some of

the issues that are inherent to the aggregation of the different national data sources. A first concern that

has to be tackled is the geographical coverage which should be similar for the compared countries. In case

of the ECB this should be as broad as possible for each country. The ECB also assumes that the provided

national indices are a representative average for the geographical differences within a country. A second

issue is the property type. Prices of new properties can behave significantly different from prices of

existing properties, as different rules in taxation and subsidies may apply, and the land that is available

21

may be a determining factor as well. The distinction between houses and flats, on the other hand, should

also be taken into account since these are subject to different market conditions as well. Another aspect

that should be considered is the adjustment for quality so as to be able to compare property over time

due to, for example, differences in size of property and to take the heterogeneity into account. A final

concern worth mentioning is the timing in the purchasing process when prices are reported. Indices based

on the prices when the property is first offered on the market as compared with contract prices, for

example, might lead to substantially different conclusions.

The issues of comparability have urged several organizations to come up with the Handbook on

Residential Property Prices Indices (OECD et al., 2013). This handbook provides guidelines for compiling

real estate price indices and, thereby, vastly enhances cross-country comparability and the usefulness of

real estate information in modeling, such as when assessing the soundness of the financial system through

Financial Soundness Indicators (IMF, 2006). The handbook (OECD et al., 2013) serves as a complement of

the already existing international guides used for compiling indices, such as consumer price indices, and

it is the result of a collaboration between OECD, IMF, Eurostat, and other organizations. Its goal is to bring

clarity in how a housing index should be compiled, and it poses some methodologies that appear to be

effective. There is, however, no one single index that serves all purposes well, as is stated in the book.

Measurements of real investments in the real estate sector, for example, ask for indices based on new

properties, while wealth assessments and FSIs typically desire the input of prices of both new and existing

properties. A successful implementation of this handbook by several real estate data sources will lead to

a substantial amelioration in research capabilities worldwide. It enables to utilize real estate prices in

predicting and assessing financial stability, and thus financial crises, more efficiently, which is one of the

goals of the IMF (BIS & IMF, 2005). The BIS utilizes this handbook in order to select and compose real

estate indices which are representative for specific countries and which are mutually comparable

(Szemere, Scatigna, & Tsatsaronis, 2014). The BIS database with property price indices will be discussed

in the Data and methodology section.

22

2 Hypotheses

This dissertation attempts to find an answer to the question whether real estate markets significantly

influence financial markets. This study focuses on the banking sector specifically and represents it by

considering returns on share prices of banks. While the Existing literature section gives an overview of the

relevant literature that has dealt with this topic and with related matters, this shorter section discusses

the reasoning behind the hypotheses that are tested within this study. It relates to relevant sections and

studies that are mentioned in the literature research.

When going over the existing literature, an overall impression arises that real estate markets substantially

affect financial markets. Not only has the importance of real estate markets for financial markets been

studied by many researchers (e.g., Baele et al. (2015), Borio & Drehmann (2009) and Niinimäki (2009)),

yet even official institutions use real estate indicators in certain mechanisms and systems, such as financial

soundness indicators (e.g., Heath (2005) and IMF (2006)) and early warning systems (e.g., Barrell et al.

(2010)). The literature furthermore suggests that the real estate markets positively affect financial

markets through the property that is owned by the banks, their business partners, and their clients (IMF,

2006), through the use of real estate as collateral which lowers the bank distress probability in case of

rising real estate prices (Koetter & Poghosyan, 2008), as well as through other mechanisms that are

mentioned in paragraph 1.3 Real estate markets and financial markets. Taking everything into

consideration, one would expect a substantial and positive causality from real estate markets toward the

banking sector (Hypothesis 1a).

The observed relationship between real estate markets and financial markets is furthermore likely to vary

over time as dynamics within and between both markets have been changing over time (e.g., Davis & Zhu

(2011), Girouard et al. (2006) and Hott (2011)). Reinhart & Rogoff (2008) and Davis & Karim (2008b)

moreover mention that subprime mortgage crises appear to be preceded by rises in real estate prices,

which strengthens the hypothesis that the relationship between the real estate market and the banking

sector is not constant over time, but, on the contrary, subject to substantial changes (Hypothesis 1b).

Another hypothesis that can be formulated given the studies that are mentioned in this paragraph is that

the 2008 financial crisis is likely to be preceded by a rise in house prices (Hypothesis 2).

Three additional hypotheses can be formulated following indications of significance for certain

categorizations. One such categorization regards mortgage lending. As the significance of collateral and

mortgage loans has been the research topic of multiple studies that deal with the examination of the

interaction between real estate markets and financial markets (e.g., Collyns & Senhadji (2003), Hott (2011)

and Koetter & Poghosyan (2008)), one would expect that for countries with a more mortgage-intensive

banking sector a substantially stronger positive relationship should be present (Hypothesis 3).

23

Yet other studies, such as Terrones et al. (2004), caution for the dangers and possible consequences of

severe drops in real estate prices. After a certain period of house price increases, and especially when the

prices have deviated from their fundamentals, there is a real chance of a severe drop that may cause harm

to the financial sector, as is suggested by, for example, Goodhart & Hofmann (2007), Koetter & Poghosyan

(2010) and Niinimäki (2009). It can thus be hypothesized that the banking sector is likely to substantially

suffer more from real estate market declines than it benefits from increases of real estate prices

(Hypothesis 4). Related to this hypothesis, one could furthermore assume that the banking sector of

countries with overvalued real estate markets is significantly more exposed toward their real estate

markets because of the probable severe drops in real estate prices, as opposed to countries without

overvalued real estate markets (Hypothesis 5).

The hypotheses that are discussed above are summarized in the following table. This table is used to

structure both the Methodology and Results sections.

Table 1: Hypotheses

This table provides an overview of the hypotheses that are tested within this study. This overview is used to structure

the following sections within this dissertation: 3.4 Methodology and 4 Results.

Hyp. N° Hypothesis

1a There is a significant and positive causality from the real estate market toward the banking sector.

1b The relationship between the real estate market and the banking sector is subject to variation over time.

2 The 2008 financial crisis is preceded by an increase in house prices.

3 Mortgage-intensive banking sectors are more exposed toward real estate markets than banking sectors with limited mortgage activities.

4 The banking sector suffers substantially more from real estate price declines than it benefits from real estate price rises.

5 The banking sector of countries with an overvalued real estate market is relatively more exposed toward the real estate market as opposed to the countries with no overvalued real estate markets.

24

3 Data and methodology

This section discusses the data that is used and the methodologies that are applied in order to investigate

the hypotheses that are substantiated in section 2 Hypotheses. Given the available data and provided that

this dissertation is written within the context of a European university, the choice has been made to

emphasize the European market. Further studies might, however, enlarge this viewpoint by investigating

the relationship from a perspective focusing on another geographical market, or even from a global

viewpoint.

In order to include as much European countries as possible, yet not to reduce the sample period to a bare

minimum, and given the available data, the sample period has been set to the period from 2005Q1 to

2014Q4. As will be discussed in section 3.1 Model, return series are used as input for the analyses within

this study. So as to obtain a return for 2005Q1 one needs the values of both 2004Q4 and 2005Q1. Since

not all of the real estate series that are used in this study contain values for 2004Q4, the actual sample

size of the index series (2005Q1-2014Q4) is reduced to 2005Q2-2014Q4 regarding the return series. For

the remainder of this dissertation the sample period that will be referred to is the return sample period

of 2005Q2-2014Q4.

This section is structured as follows. First, in paragraph 3.1 Model the model that forms the basis of this

study is presented. Then, a thorough discussion of all data series that are used can be found in paragraph

3.2 Data. Paragraph 3.3 Analysis software briefly describes the software tools that are used to conduct

this study. And finally, in the last paragraph, 3.4 Methodology, the methodologies that are used for all

analyses within this work are described.

3.1 Model

The model that underlies this study is based on the index model representation of the capital asset pricing

model (CAPM). The CAPM is a central theory within modern financial economics and provides an

expression for the expected return-risk relationship of securities (Bodie, Marcus, & Kane, 2011). The best-

known representation of the model is the following:

𝐸[𝑟𝑖] = 𝑟𝑓 + 𝛽𝑖 ∗ (𝐸[𝑟𝑚] − 𝑟𝑓)

with 𝐸[𝑟𝑖] the expected return of an individual security 𝑖, 𝑟𝑓 the return of a risk-free asset, 𝛽𝑖 the exposure

of the individual security return toward the expected market excess return, and 𝐸[𝑟𝑚] the expected return

of the market. The CAPM, however, has its limits when considering practical implementation, as explained

by Bodie et al. (2011): the CAPM assumes that the market portfolio is mean-variance efficient which

cannot be tested in practice, whereas it furthermore states the relationship between expected returns

25

although only realized returns can be observed. Therefore, an index model has been proposed so as to

overcome these limits. It can be represented as follows (Bodie et al., 2011):

𝑟𝑖 − 𝑟𝑓 = 𝛼𝑖 + 𝛽𝑖 ∗ (𝑟𝑚 − 𝑟𝑓) + 𝑒𝑖

with 𝑟𝑖 the realized return of an individual security 𝑖, 𝑟𝑓 the return of a risk-free asset, 𝛼𝑖 the abnormal

return of the security, 𝛽𝑖 the exposure of the security return toward the realized market excess return, 𝑟𝑚

the realized market return, and 𝑒𝑖 the equation error term.

The model used within this dissertation is very similar to the index model representation of the CAPM and

is extended with an additional real estate factor. It is represented by the following equation6:

𝐵𝐴𝑖𝑡 = 𝛼 + 𝛽 ∗ 𝑟𝑚𝑡 + 𝛾 ∗ 𝑅𝐸𝑖𝑡 + 𝑒𝑖𝑡 ( 1 )

with 𝐵𝐴𝑖𝑡 the banking sector return for country 𝑖 at time 𝑡, 𝛼 the abnormal return of the banking sector

index, 𝛽 the exposure of the banking sector return toward the market return, 𝑟𝑚𝑡 the market return at

time 𝑡, 𝛾 the exposure of the banking sector return toward the real estate market return, 𝑅𝐸𝑖𝑡 the real

estate market return of country 𝑖 at time 𝑡, and 𝑒𝑖𝑡 the equation error term of country 𝑖 at time 𝑡. Another

point worth mentioning is that all returns within this dissertation are reported as decimal numbers (i.e.,

a return of + 1 % is represented by + 0.01).

Equation ( 1 ) can be estimated using regression analysis if the following data series are available: return

series of indices representing the real estate market, return series of indices representing the banking

sector, and a market index return series that reflects the return on the relevant market portfolio. The

collection and preparatory activities of all three data series for each of the countries that is analyzed in

this study are discussed in the following paragraphs.

3.2 Data

This section gives an overview of the data sets that are used for all of the analyses conducted within this

master dissertation. As has been made clear in section 3.1 Model, three types of data sets are needed for

the model that is used, namely, real estate returns, banking sector returns and market returns. The input

for the real estate returns per country is retrieved from the BIS Residential Property Price database (BIS,

2015c), whereas the banking sector returns and market factor returns are obtained and formed using the

Bankscope database (Bureau van Dijk, 2015) and the Datastream Professional database (Thomson

Reuters, 2015b). The first paragraph of this section discusses the available data sets on the BIS Residential

Property Price database and the selection of the real estate indices that are used within this dissertation,

6 A pooled OLS regression is conducted to estimate the base model, as discussed in paragraph 3.4 Methodology. Therefore, the coefficients are not assigned any subscripts.

26

while the second paragraph briefly deals with the market index that is used to represent the European

market. The third and last paragraph of this section furthermore discusses the collection of organizations

that could be used to construct banking sector indices for the selected countries, the filtering thereof,

and, finally, the construction of the banking sector indices themselves.

Worth mentioning is that the data that is obtained from the Datastream database has a monthly

frequency. However, the used series from the BIS Residential Property Price database are only stated on

a quarterly basis. As a consequence, all analyses have been based on quarterly data. Moreover interesting

to note is that all returns within this study are calculated as logarithmic returns.

3.2.1 Real estate returns

The gathering, formation, and reporting of information about real estate markets has been plagued with

a lot of issues, including the heterogeneous nature of real assets, the discontinuity of real estate data sets,

and the area coverage. A more detailed overview of these and other issues can be found in section 1.4

Real estate data. Studies that considered real estate markets could not be performed without data-related

difficulties due to these concerns. However, as of July 2010, the BIS7 started publishing residential

property price series on a regular basis so as to enhance cross-country comparability (Scatigna & Szemere,

2014). Currently, three collections of data series are available on the BIS Residential Property Price

database: a collection of detailed series, a collection of long series, and a collection of selected nominal

and real residential property price series.

The collection that contains the detailed series consists of more than 300 series for 57 countries and is

constructed using data sets from local central banks that all focus on a different national segment

(Szemere et al., 2014). The selected series are data sets that are selected from the detailed data series

and that are intended to approximate as much as possible a nationwide index for each country that is as

homogeneous as possible (BIS, 2015b). These selections are based on the guidelines of the Handbook on

Residential Property Prices Indices. On the other hand, the long series collection contains long-term data

sets for eighteen advanced economies and five emerging economies, of which some start in 1970, and of

which each is a combination of multiple data sets from varying sources that use differing methodologies

(BIS, 2015a).

As has been made clear in the Handbook on Residential Property Prices Indices (OECD et al., 2013), due

to a lack of international guidelines in the past, authorities have been using divergent input,

methodologies and data sources to come up with real estate indices. It is therefore challenging to realize

7 The BIS, which is short for Bank for International Settlements, is an organization that was founded in 1930 with the purpose of supporting central banks in their quest for monetary and financial stability, enhancing international collaboration of the participating regions, and serving as a bank for the central banks (“About BIS,” 2005).

27

international comparisons of real estate markets based on these data. Since the intention within this

dissertation is to analyze the real estate markets based on indices with a broad coverage, the raw detailed

data series have not been selected as input for the analyses. That way, no assumptions have to be made

to combine the raw detailed data sets in order to come up with valuable input, which otherwise could

have led to the introduction of biases. The long data series collection has not been utilized either, despite

the long time frame that could have provided additional insights. The motivation of this choice is that

these series have been the result of a merging of data sets that often have a heterogeneous coverage and

that are obtained from several sources using differing methodologies. These are thus likely to include

altering pattern behavior due to the various sources and methodologies, which is not preferable for the

analyses conducted in this study.

The most suitable data sets for this master dissertation appear to be the selected series data sets for

multiple reasons. First, most of the data sets within this collection are not constructed using a combination

of multiple series, yet they are merely a selection of the most representative data set for each country.

This has the advantage that this collection contains more or less smooth sets in which the continuity is

maintained. Furthermore, the international comparability of the series is enhanced as the selection of the

series is made taking into account the guidelines mentioned in the Handbook on Residential Property

Prices Indices. The selection has moreover been conducted with the purpose of providing data sets with

an as broad national coverage as possible per country. A drawback of using these series is that they are

reported as quarterly data sets, whereas a higher frequency could enable even more valuable analyses.

Taking the sample period of this study (2005Q2-2014Q4) into account, the BIS selected series data

collection provides real estate data for twenty countries. However, of these twenty countries, four cannot

be included due to a lack of useful complementary data regarding their banking sector. These four

countries are Croatia, Estonia, Iceland, and Macedonia FYR. An overview of the remaining sixteen

countries that are incorporated in the dissertation are provided in alphabetical order of ISO 31668 code in

the table below. This table also provides the coverage and sources of the real estate data sets for each of

the countries. An overview of descriptive statistics of the return series of these data sets can be found in

Table 3 whereas the two graphs in Figure 1 represent the overall mean return and standard deviation for

each country.

8 ISO 3166 is the international standard that provides codes for countries and their subdivisions (International Organization for Standardization, n.d.).

28

Table 2: Sources of real estate data series

This table provides an overview of the real estate data sets that are analyzed within this master dissertation. This

overview includes the coverage of each data set as well as the national sources. This table is constructed using

information that is available on the BIS website via which the BIS Residential Property Price database can be accessed

as well (BIS, 2015c).

ISO 3166

Country Coverage Source

AT Austria All types of new and existing dwellings in the whole country

Austrian National Bank (http://www.oenb.at)

BE Belgium All types of dwellings in the whole country Statistics Belgium (http://statbel.fgov.be/fr/statistiques/chiffres/)

CH Switzerland Unweighted average of owner occupied flats and houses in the whole country

Wüest und Partner (http://www.wuestundpartner.com)

DE Germany All types of owner occupied new and existing dwellings in the whole country

Deutsche Bundesbank (http://www.bundesbank.de)

DK Denmark All types of new and existing dwellings in the whole country

Statistics Denmark (http://www.dst.dk)

FI Finland All types of owner occupied new and existing dwellings in the whole country

Statistics Finland (http://www.stat.fi)

FR France All types of new and existing dwellings in the whole country

National Institute of Statistics and Economic Studies (http://www.insee.fr)

GB United Kingdom All types of new and existing dwellings in the whole country

Office for National Statistics (http://www.ons.gov.uk)

IE Ireland All types of new and existing dwellings in the whole country

Central Statistics Office (http://www.cso.ie)

IT Italy All types of new and existing dwellings in the whole country

Bank of Italy (http://www.bancaditalia.it)

LT Lithuania All types of new and existing dwellings in the whole country

Centre of Registers (http://www.registrucentras.lt)

MT Malta All types of new and existing dwellings in the whole country

Central Bank of Malta (http://www.centralbankmalta.org/site/excel/statistics/house_prices.xls?20131120180159)

NL Netherlands All types of new and existing dwellings in the whole country

The Dutch Land Registry Office (Kadaster) (http://www.kadaster.nl)

NO Norway All types of new and existing dwellings in the whole country

Statistics Norway (http://www.ssb.no)

SE Sweden All types of new and existing dwellings in the whole country

Statistics Sweden (http://www.scb.se)

SK Slovakia All types of new and existing dwellings in the whole country

National Bank of Slovakia (http://www.nbs.sk)

29

Table 3: Summary statistics of real estate return series

This table provides summary statistics for the return series based on the real estate data series that are obtained

from the BIS Residential Property Price database (BIS, 2015c) for the return sample period 2005Q2-2014Q4. The last

column contains the Jarque-Bera test values. Significance at the 1%, 5%, and 10% levels is respectively indicated by

***, **, and *.

ISO 3166

Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis Jarque-Bera

AT 0.0113 0.0097 0.0796 -0.0276 0.0188 1.2761 6.3748 29.0920***

BE 0.0092 0.0098 0.0350 -0.0187 0.0123 -0.2932 2.6382 0.7715

CH 0.0086 0.0080 0.0235 -0.0103 0.0066 -0.2264 3.5560 0.8353

DE 0.0040 0.0066 0.0208 -0.0144 0.0082 -0.4605 2.4797 1.8183

DK 0.0057 0.0038 0.0653 -0.0748 0.0295 -0.1644 3.5574 0.6807

FI 0.0075 0.0097 0.0299 -0.0437 0.0139 -1.1060 5.9280 21.8821***

FR 0.0061 0.0068 0.0497 -0.0380 0.0188 -0.0763 3.1183 0.0606

GB 0.0082 0.0114 0.0405 -0.0547 0.0218 -0.7805 3.5908 4.5264

IE -0.0054 -0.0130 0.0658 -0.0721 0.0371 0.2068 1.9618 2.0294

IT 0.0001 0.0000 0.0193 -0.0218 0.0114 -0.0623 2.0371 1.5318

LT 0.0110 0.0086 0.2520 -0.1696 0.0822 0.3209 3.8417 1.8207

MT 0.0030 0.0055 0.0376 -0.0458 0.0213 -0.5635 2.6999 2.2100

NL -0.0018 0.0010 0.0192 -0.0396 0.0137 -0.8608 3.0576 4.8213*

NO 0.0147 0.0175 0.0609 -0.0727 0.0280 -0.7297 3.8604 4.6635*

SE 0.0158 0.0163 0.0595 -0.0533 0.0199 -1.1909 5.8010 21.9673***

SK 0.0098 -0.0005 0.0855 -0.0516 0.0329 0.5640 2.8203 2.1202

Figure 1: Mean and standard deviation of real estate return series

The two graphs represent the mean returns and standard deviations of the real estate return series per country,

ordered from the highest to lowest values and given the return sample period of 2005Q2-2014Q4. As one can

observe, the highest average returns appear to be achieved by Sweden, Norway, and Austria, whereas Ireland,

Netherlands, and Italy are the worst average performers. Lithuania, Ireland, and Slovakia are, on the other hand, the

most volatile over the sample period, while Switzerland, Germany and Italy demonstrate the lowest volatility.

An overview of the index evolutions for all countries is provided in Appendix 1: Real estate data series

(Figure 13 and Figure 14). Providing them all in the same graph would lead to a complex figure. Therefore,

they are divided into four groups following the segmentation of the United Nations Statistics Division

(2013). Furthermore, two vertical lines are provided in each graph. These represent the beginning of the

2008 financial crisis and the start of the sovereign crisis. As mentioned by Davis & Karim (2008a), there

are often differences among researchers of what they perceive as the start and duration of a crisis due to

the use of divergent identifying indicators. Without the intention to contradict other studies that use

-0,010

-0,005

0,000

0,005

0,010

0,015

0,020

SE NO AT LT SK BE

CH

GB FI FR DK

DE

MT IT NL IE

Mean

0,00

0,01

0,02

0,03

0,04

0,05

0,06

0,07

0,08

0,09

LT IE SK DK

NO

GB

MT SE FR AT FI NL

BE IT DE

CH

Standard deviation

30

alternative crisis start dates, the following two dates are used as the start of the 2008 financial crisis and

the succeeding sovereign crisis: August 9, 2007 (Elliott, 2011) and October 20, 2009 (Barber, 2009; Nelson,

Belkin, Mix, & Weiss, 2012), respectively. Since this study focuses on quarterly return data, these crisis

periods are defined as follows: 2005Q2-2007Q2 (pre-crisis), 2007Q3-2009Q3 (financial crisis), and

2009Q4-2014Q4 (sovereign crisis), provided that the quarterly dates represent the return over the quarter

preceding that date9.

3.2.2 Market returns

The real estate data series are complemented by a market index data set that is obtained via the

Datastream Professional10 database. As this dissertation focuses on European countries only, a relevant

market index would be an official index that focuses on Europe as a whole. This series is retrieved via the

Datastream Professional database (Thomson Reuters, 2015b), and given the data sets that are available

on the database, the MSCI11 Europe index has been selected to use as the market index series for all

conducted analyses. The MSCI Europe index is an index that was launched in 1969 and that covers fifteen

developed markets in Europe (MSCI Inc., 2015a). An overview of the descriptive statistics of the MSCI

Europe series can be found in Table 4.

Table 4: Summary statistics of market factor return series

This table provides summary statistics for the return series based on the MSCI Europe market index for the return

sample period 2005Q2-2014Q4. The information is obtained from the Datastream Professional database (Thomson

Reuters, 2015b). The last row contains the Jarque-Bera test values. Significance at the 1%, 5%, and 10% levels is

respectively indicated by ***, **, and *.

MSCI Europe

Mean 0.0146

Median 0.0373

Maximum 0.1781

Minimum -0.2542

Std. Dev. 0.0881

Skewness -1.0973

Kurtosis 4.2539

Jarque-Bera 10.3811***

3.2.3 Banking sector returns

A third category of data sets that are used as input for the analyses conducted within this study are

banking sector return series. These series are based on stock price series of a collection of listed and

9 The return reported at 2005Q2, for example, is based on the index value of 2005Q2 in comparison with 2005Q1. 10 Datastream is an application providing access to a huge database containing global financial data; it is provided by Thomson Reuters, a company specialized in products and services concerning information about the financial and other sectors (Thomson Reuters, 2015a, 2015b). 11 The MSCI is an organization that provides research-based indices and analysis tools that aid investors and researchers, among other actors, in their research activities (MSCI Inc., 2015b).

31

delisted banks for each of the sixteen selected countries. The list of banks is retrieved via the Bankscope

database while the stock price data is obtained from the Datastream database. This list has been acquired

on June 30, 2015, whereas it contains 305 organizations and is composed of active organizations that have

a specialization as either commercial banks, savings banks, cooperative banks, real estate and mortgage

banks, investment banks, bank holdings and holding companies, or finance companies.

Nevertheless, not all of the 305 organizations are suitable for the analyses of this dissertation. Therefore,

several organizations are filtered out using the following filters:

Only the organizations for which the primary business line indicates that the organization is mainly

conducting banking activities are included.

Organizations whose shares are held by other organizations that are also included in the list are

omitted from further analyses so as to avoid recurrent presence of the same data.

Organizations that were delisted before 2007 are not included because for these companies only

a very limited time frame (eight quarterly observations or less) is provided for the available data

sets.

The presence of banking activities can typically be identified using the ratio of deposits over total

assets. This ratio should be high enough so as to indicate that a part of the assets are financed

through deposits. A histogram of the ratio that takes into account all data sets (cf. Appendix 2:

Banking sector data series, Figure 15) shows that organizations with a ratio lower than 20 %

appear to be outliers. Therefore, only organizations with a ratio higher than 20 % are included.

This is a rather low cut-off value, yet in order not to exclude too many organizations, it cannot be

set too high. This value furthermore seems to be high enough so as to exclude exceptional

outliers.

Other outlier organizations can be identified using the ratio of equity over total assets.

Organizations with a very high ratio are unlikely to be suitable organizations for the analyses

conducted in this study since these cannot be perceived as actual banks. A histogram of this ratio

(cf. Appendix 2: Banking sector data series, Figure 16) shows that outliers appear to have a ratio

higher than 50 %. Therefore, these organization are excluded from further analyses.

The organizations for which no valid stock prices series are found are excluded.

Shares that are illiquid, i.e., they are confronted with a lot of zero returns, are excluded from

further analyses as these do not properly represent the dynamics of the financial sector of a

country.

32

Two of the Datastream data sets are excluded because one provides a value of 100 over the whole

sample period and thus seems to be subject to some error whereas the other consists of data for

only the last two months.

This thorough filtering results in a remaining set of 148 data series for which enough data is available for

further analyses. For each selected organization two series are retrieved from the Datastream database:

the total return index and the market value series, both stated in euros. The two series are then used to

construct banking sector indices per selected country following the procedure that is described as follows.

The monthly total return indices and market value series are transformed into quarterly series, after which

the quarterly logarithmic returns are calculated for the index series. Then, the quarterly market value

series are used to weigh the bank returns per country so as to get one representative banking sector

return data set for each country.

The table below gives an overview of the number of bank data sets that are available for each of the

selected countries. A detailed list of all included data sets is provided in Appendix 2: Banking sector data

series, Table 15. This list includes specifics such as bank name, specialization, primary business line,

listed/delisted status, and ratios of equity over total assets and deposits over total assets. An overview of

descriptive statistics of the return series of the banking sector data sets per country is given below in Table

6, while Figure 2 provides the means and standard deviations of the return series for each country. It is

remarkable that in Figure 2 Ireland stands out in both the means graph and standard deviations graph by

having achieved the lowest average return over the sample period while also being the most volatile.

Table 5: Number of banking organization data sets per selected country

This table lists the number of data sets that are used to construct one banking sector data set per country. The data

sets are retrieved from the Datastream Professional database based on a list of banking organizations obtained

through the Bankscope database (Bureau van Dijk, 2015; Thomson Reuters, 2015b).

ISO 3166

Number of data sets

ISO 3166

Number of data sets

AT 8 IE 3

BE 2 IT 24

CH 7 LT 3

DE 17 MT 4

DK 29 NL 4

FI 3 NO 23

FR 7 SE 4

GB 8 SK 2

33

Table 6: Summary statistics of banking sector return series

This table provides summary statistics for the return series of the banking sector indices for each of the investigated

countries, and for the return sample period 2005Q2-2014Q4. The banking sector returns are calculated using data

obtained from the Datastream Professional database (Thomson Reuters, 2015b) based on a list of organizations

acquired from Bankscope (Bureau van Dijk, 2015). The last column contains the Jarque-Bera test values. Significance

at the 1%, 5%, and 10% levels is respectively indicated by ***, **, and *.

ISO 3166

Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis Jarque-Bera

AT -0.0067 0.0118 0.4086 -0.6799 0.2029 -1.1814 6.0323 24.0142***

BE -0.0069 0.0342 0.8865 -1.0517 0.3082 -0.5492 6.7020 24.2308***

CH 0.0026 0.0317 0.2809 -0.4729 0.1479 -1.0376 4.5143 10.7248***

DE -0.0041 0.0232 0.3200 -0.4961 0.1581 -1.0830 5.3984 16.9720***

DK 0.0074 0.0309 0.4857 -0.7777 0.1944 -1.2664 8.6267 61.8706***

FI 0.0271 0.0514 0.2649 -0.4519 0.1259 -1.2819 6.7805 33.9065***

FR 0.0030 0.0332 0.3144 -0.6570 0.2017 -1.5264 6.0956 30.7161***

GB -0.0093 0.0097 0.3954 -0.7258 0.1756 -1.6362 8.9425 74.7853***

IE -0.0985 -0.0564 1.0357 -1.2996 0.4476 -0.4240 4.2101 3.5479

IT -0.0140 -0.0202 0.3004 -0.4270 0.1556 -0.3351 3.5280 1.1830

LT -0.0230 -0.0089 0.4771 -0.9621 0.2366 -1.5033 8.5138 64.0922***

MT 0.0151 0.0107 0.3457 -0.2297 0.1182 0.5447 3.7147 2.7584

NL -0.0068 0.0477 0.4479 -0.7040 0.2298 -0.7741 4.2687 6.5101**

NO 0.0212 0.0276 0.4124 -0.6246 0.1853 -0.9780 5.5712 16.9599***

SE 0.0242 0.0223 0.3155 -0.5528 0.1486 -1.4279 7.3874 44.5320***

SK 0.0098 0.0177 0.2106 -0.2122 0.1033 -0.1902 2.4363 0.7515

Figure 2: Mean and standard deviation of banking sector return series

The two graphs represent the mean returns and standard deviations of the banking sector series per country,

ordered from highest to lowest value and given the return sample period of 2005Q2-2014Q4. In order to position

the mean return and standard deviation of each country as opposed to the MSCI market index, the market index

values are provided as well. Finland, Sweden, and Norway seem to have realized the highest mean returns over the

sample period, while Ireland, Lithuania, and Italy attained the lowest. On the other hand, Ireland, Belgium and

Lithuania experienced the largest standard deviations, whereas Slovakia, Malta, and Finland achieved the smallest.

All data sets are retrieved from Datastream with a monthly periodicity to better enable the filtering

analyses discussed in this section. However, only the quarterly observations are used from these series as

the real estate data series from the BIS Residential Property Price database are solely provided as

quarterly data. Another possibility is to linearly interpolate the real estate data series so as to obtain

-0,12

-0,10

-0,08

-0,06

-0,04

-0,02

0,00

0,02

0,04

FI SE NO

MT

MSC

I

SK DK

FR CH DE

AT

NL

BE

GB IT LT IE

Mean

0,00

0,05

0,10

0,15

0,20

0,25

0,30

0,35

0,40

0,45

0,50

IE BE LT NL

AT

FR DK

NO

GB

DE IT SE CH FI

MT

SK

MSC

I

Standard deviation

34

monthly series. Nevertheless, this interpolation would likely introduce a bias as opposed to when the

actual monthly series would be utilized if they were available and is therefore not considered.

An overview of the index evolution of the banking sector indices per country is provided in Appendix 2:

Banking sector data series (Figure 17 and Figure 18), categorized into four graphs based on the geographic

partition of Europe into Northern, Eastern, Southern, and Western Europe, as implemented by the United

Nations Statistics Division (2013). Each of the graphs is moreover supplemented with two vertical red lines

representing the start of the 2008 financial crisis and the subsequent sovereign crisis.

A remark should be made that for some of the data sets certain observations are excluded in order to

avoid biases within further analyses as much as possible. These exclusions are discussed below.

Datastream provides constant return indices after delisting for the data sets that are delisted during the

sample period. Including these observations would wrongfully lead to zero return observations during

these periods, and these observations are thus excluded. Similarly, for shares that are liquid, but that

encounter a certain period of illiquidity, the observations of the illiquid period are excluded as well.

3.3 Analysis software

The analyses within this master dissertation are executed using two software tools, namely, Microsoft

Excel 2013 (version 15.0.4727.1000) and EViews 9 Enterprise Edition (version of April 3, 2015). Microsoft

Excel is used to prepare all data for analyses, which includes the filtering of data sets, and to perform

some more basic descriptive analyses, such as the histogram analyses mentioned in the paragraph above.

EViews 9 is a statistical package that is used to conduct all regression analyses.

3.4 Methodology

This section describes the methodologies of the analyses that are used in order to verify the hypotheses

that are formulated and summarized in Table 1. That table furthermore serves as a structure for the rest

of this subsection. The first paragraph briefly deals with the test that is used to distinguish between a fixed

effects model and a random effects model, whereas the subsequent paragraphs discuss the analyses used

to test all of the formulated hypotheses. The remainder of this introduction provides an overview of

general remarks that apply to all analyses.

All regression analyses within this study are based on panel data for which the cross-section dimension is

formed by the sixteen selected countries, while the time dimension follows the return sample period

35

2005Q2-2014Q412. This results in 16x39, or 624, observations for each of the three return categories13.

There are several reasons that underpin the choice of panel data. So as to enhance the statistical power

of all used tests, a panel analysis appears to be a better option than mere single time series analyses since

for the individual data sets only 39 observations are available per country. Moreover, panel analysis

enables to obtain more and richer insights considering the relationships that are present within the data.

Other advantages of panel data are stated by Baltagi (2013): the ability to take heterogeneity explicitly

into account, the provision of more valuable information, the availability of a higher number of degrees

of freedom, higher efficiency, among other advantages. From a literature point of view, there seems to

be an increasing international synchronization of real estate markets (e.g., Ahearne et al. (2005), Beltratti

& Morana (2010) and Otrok & Terrones (2005)), even though not all studies fully agree with this

observation, such as Hilbers et al. (2008). This increasing synchronization additionally justifies the choice

of panel analysis as it takes on a broader perspective rather than focusing on individual countries. Since

regression analyses of individual countries lack statistical power, one could consider to investigate the

hypotheses based on geographical clustering of the countries. However, as the possible significant

relationships through geographical clustering could not be observed in the existing literature and so as

not to implement random clustering, no such clustering analyses are included in this study. Another point

that is worth mentioning is that all regression analyses are executed using White robust cross-section

standard errors and covariances in order to correct for contemporaneous correlation and

heteroscedasticity.

The base model that is used for the analyses is discussed in section 3.1 Model and utilizes logarithmic

return series as input. For each of the analyses the base model is extended and adapted with the relevant

elements given the hypotheses that are tested. All the extended models are specified in the appropriate

paragraphs below. One may notice in the Results section that the regression estimates of the base model

are not constant over all of the analyses, even though the exact same model is being applied. The reason

for this is that some of the analyses are based on less than the sixteen countries due to data limitations.

Hence, the base model is adapted in order to take these data limitations into account. Furthermore, the

base model does include an intercept and a market factor besides the real estate factor. And although

both factors are reported in the regression tables, these will not be discussed as this would fade the focus

of this study which deals with the specific interaction between the real estate market and the banking

sector.

12 Recall that the index sample period (2005Q1-2014Q4) is reduced to 2005Q2-2014Q4 regarding the return series due to a lack of real estate observations for 2004Q4. 13 Notice that there is only one market return series that is used for all of the countries. Hence, the 16x39 panel of market returns in fact consists of sixteen times the same return series.

36

Several of the following analyses moreover include rolling regressions with one-quarter step sizes, and

even though the equations that are used are dissimilar, they do share some common characteristics,

namely, the rolling window size and the reporting methodology, which are both discussed in the following.

All rolling regressions are conducted using the same window size of eight quarters (i.e., two years). Eight

quarters appears to be an optimal window size since it enables to gather more valuable and somewhat

smoother rolling estimates than when a shorter window size would be used. A longer window size of ten

or more quarters, on the other hand, would result in the fact that the first window (e.g., 2005Q2-2007Q3

in case of a 10-quarter window size) is already bigger than the pre-crisis period that has been defined.

This would thus possibly lead to the inability to recognize certain dynamics in the beginning of the sample

period. Regarding the reporting of the rolling regressions, they are all graphically represented in a similar

way: for all the rolling regressions, both the relevant coefficient (solid line) and the 95 % confidence

interval (two dotted lines) are shown, indicating at which point in time the coefficient becomes significant.

Furthermore worth mentioning is that all values obtained through the individual regressions are reported

at the end of each regression sub-sample period. Hence, the coefficient and confidence interval

represented at, for example, 2007Q1 are based on the sample window 2005Q2-2007Q1 given the eight-

quarter window.

3.4.1 Fixed effects model or random effects model

The regression analyses that are performed in the following paragraphs are based on panel data. A choice

that needs to be made for panel regressions is whether to use a random effects model or a fixed effects

model. A test that is often used to verify which of the models is most appropriate for the panel data is the

Hausman test. This is a test that is available in Eviews, and for which the null hypothesis states that the

fixed and random effects model estimators do not differ significantly. More specifically, if the null

hypothesis is rejected, the fixed effects model is assumed to be most appropriate.

As the p-value of the Hausman test based on the pooled OLS regression of the base model is equal to

0.0321, this test appears to favor the fixed effects model using a five percent level of significance. For this

particular study, the choice has been made to use the fixed effect least-squares dummy variable (LSDV)

model, since that model provides coefficients that are easily interpretable.

37

3.4.2 Hypothesis 1

Hypothesis 1a: There is a significant and positive causality from the real estate market toward the banking

sector.

Hypothesis 1b: The relationship between the real estate market and the banking sector is subject to

variation over time.

The analyses conducted to verify Hypothesis 1a focus on the base model of this study. First, a visual

inspection takes place based on a scatter plot that considers both the real estate returns (x-axis) and

banking sector returns (y-axis) while a linear trendline is drawn through the observations. Even though

the scatter plot omits the market factor of the base model, it might already indicate the relationship

between the real estate market and the banking sector. Given the available literature, the trendline is

expected to be sloped positively. Additionally, the observations are divided14 into pre-crisis observations

and observations during crisis, and the average returns are calculated per country for both groups. Taking

hypothesis 1a into account, the real estate return means are expected to be lower during crisis as opposed

to the period before the crisis, as this is the case for the banking sector returns. On the other hand, a more

thorough analysis is conducted by applying a pooled OLS regression analysis on the base model

(regression 1). And as is the case for the scatter plot, this regression too is expected to show a positive

relationship between the real estate returns and the banking returns. The regression analysis furthermore

enables to determine whether this relation is significant or not.

To complement the scatter plot of the base model, the two groups of observations based on the pre-crisis

period and the period during crisis are plotted on a scatter plot as well. This scatter plot contains two

separate trendlines in order to distinguish the relationship between the real estate market and the

banking sector for both groups. However, as hypothesis 1b suggests, it is unlikely that this relationship is

constant over time. In order to investigate whether there is significant variation, the base model is

extended using dummy variables that distinguish between the pre-crisis period and the crisis period in

regression 2, and between the pre-crisis period, the financial crisis period, and the sovereign crisis period

in regression 3.

Even though regression 2 and 3 already provide an idea of the variation in the relationship between the

real estate market and the banking sector, the period boundaries are rather arbitrary. Therefore, for the

14 Recall that, as mentioned in the Data section, this study uses August 9, 2007 (Elliott, 2011) and October 20, 2009 (Barber, 2009; Nelson et al., 2012) as start dates for respectively the 2008 financial crisis and the succeeding sovereign crisis, which leads to the identification of the pre-crisis period (2005Q2-2007Q2), the financial crisis period (2007Q3-2009Q3), and the sovereign crisis period (2009Q4-2014Q4), given the quarterly nature of the data sets.

38

base model a rolling regression is applied as well as it likely provides additional insights into particular

variations that are present during the whole sample period.

3.4.3 Hypothesis 2

Hypothesis 2: The 2008 financial crisis is preceded by an increase in house prices.

One way to analyze hypothesis 2 is to examine the real estate graphs with regard to the 2008 financial

crisis period that has been identified. However, so as to substantiate this hypothesis quantitatively a

regression analysis is considered as well. Similarly to the model implemented by Barrell et al. (2010), this

study includes a lagged real estate factor into the base model. And as the 2008 financial crisis, which is

characterized by an overall downturn in the banking sector indices, is assumed to be preceded by a rise

in house prices, one would expect the bank returns to be negatively affected by the lagged real estate

returns during the beginning of the crisis. Whereas the study of Barrell et al. (2010) utilizes a real estate

factor that is lagged by three years, this dissertation implements a one-year lag due to data limitations.

An issue related with including lagged factors is that the sample size is reduced with the length of the lag

since at every point in the sample period the lagged value of that point minus the lag is needed. Utilizing

a lag of one year (i.e., four quarters) hence means that the sample size for this particular analysis is

reduced to 2006Q2-2014Q4. Implementing a higher lag size would lead to a loss of too much information

in the beginning of the sample period and thus possibly to a lack of verification capabilities regarding

hypothesis 2.

Three consecutive regression analyses are executed in order to gather insights into the dynamics of the

relation between the banking sector and the lagged real estate market factor. Regression 4 considers the

whole sample period after lag inclusion and is not expected to show a significant relationship between

the real estate returns and the banking returns as it does not focus on the crisis period but takes the whole

sample into account. Therefore, regression 4 is extended by distinguishing between the pre-crisis and

crisis period for regression 5, and between the pre-crisis period, the financial crisis period, and the

sovereign crisis period for regression 6. Whereas for the crisis period that is considered in regression 5

the lagged real estate factor coefficient is assumed to be significant and negative, the pre-crisis lagged

real estate factor coefficient is likely to be insignificant. On the other hand, regression 6 poses issues due

to the fact that the pre-sovereign crisis period is not a non-crisis period but is characterized by the financial

crisis. As Hypothesis 1 postulates that the real estate market positively and substantially affects the

banking sector, this hypothesis can be used to underpin the expectations about the lagged real estate

factor for the sovereign crisis period of regression 6: both the current and lagged real estate factor

coefficient are expected to be positive and significant. Moreover, similarly to the expectations for

39

regression 5, the lagged factor is expected to be insignificant for the pre-crisis period while it is anticipated

to be negative and significant for the financial crisis period.

It should be taken into account that the period during crisis (2007Q3-2014Q4) is still a longer period that

not only focuses on the start of the financial crisis but that takes the whole crisis, and thus also the lags

during crisis, into account. This is particularly the case for regression 5. Regarding regression 6, a similar

issue is present since it does not explicitly focus on the beginning of the financial crisis period. For that

reason, these analyses are complemented by considering a rolling regression that is based on regression

4. The graph of this rolling regression should be able to identify the significant negative lagged real estate

factor coefficient if hypothesis 2 would indeed pose a plausible theory. On the other hand, the rolling

regression graph of the current real estate factor coefficient is subject to the same expectations as

discussed in the methodology paragraph of Hypothesis 1.

3.4.4 Hypothesis 3

Hypothesis 3: Mortgage-intensive banking sectors are more exposed toward real estate markets than

banking sectors with limited mortgage activities.

In order to test the validity of hypothesis 3, a distinction is needed between those countries that have a

mortgage-intensive banking sector as opposed to those that are less mortgage-concentrated. For this

particular study, the mortgage activity is identified by considering the banking balance sheets per country.

More specifically, the ratio of ‘loans for house purchase’ over ‘total assets’ should give a good indication

of the mortgage activities of a country its banking sector. Both values are available through the European

Central Bank Statistical Data Warehouse under the category ‘MFI balance sheets’ (ECB, 2015).

Nevertheless, two of the sixteen countries are not available via the Statistical Data Warehouse, namely,

Norway and Switzerland. Moreover, the database does not provide usable data for Denmark. A solution

to circumvent this issue would be to obtain the relevant data from other sources. However, as different

sources do not always use the exact same definitions for similar data series, this could introduce a bias

into the results. Hence, the analyses considered to verify hypothesis 3 are based on the thirteen countries

that are reported in the table below. This table furthermore provides specific ratios for each of the

countries, calculated as the average ratio over the sample period and ordered from the lowest to the

highest value. The last column breaks down the thirteen countries into tertiles.

40

Table 7: Mortgage activity per country

This table provides an overview of the thirteen countries that are considered for the verification of hypothesis 3. The

mortgage activity of the banking sector of each country is represented by the ratio of ‘loans for house purchase’

over ‘total assets’, averaged over the sample period. The last column furthermore divides the countries into three

groups based on tertile values of the ratios. The data used to set up this table is obtained from the ECB Statistical

Data Warehouse (ECB, 2015).

ISO 3166

Loans for house purchase / Total assets

Tertiles

MT 5.73% 1

IE 7.25% 1

AT 7.97% 1

BE 8.27% 1

IT 8.34% 2

FR 9.48% 2

GB 12.13% 2

DE 12.82% 2

FI 17.26% 2

NL 17.31% 3

SK 17.61% 3

SE 19.63% 3

LT 24.12% 3

A regression analysis is executed to analyze the sample in view of hypothesis 3. Regression 7 takes the

thirteen countries into account and compares the two higher tertiles with the lowest tertile (i.e., the

lowest tertile is used as the reference group) in order to look for differences between the three clusters.

If hypothesis 3 is true, it should be possible to identify significantly greater real estate factor coefficients

for the upper tertiles. Furthermore, regression 7 is also performed as a rolling regression with a focus on

the difference between the third and first tertile as that difference is expected to be most significant. This

analysis is then complemented by the rolling regressions focusing on only tertile one and tertile three so

as to additionally consider the absolute estimates of both groups.

3.4.5 Hypothesis 4

Hypothesis 4: The banking sector suffers substantially more from real estate price declines than it benefits

from real estate price rises.

As hypothesis 4 makes a distinction between uptrends and downtrends in the real estate markets these

trends need to be identified. A possible method to recognize those trends is to perform a visual inspection

on the real estate index graphs. However, while pattern recognition using visual inspection is dependent

on relatively high subjectivity, there are other more objective and consistent techniques to analyze trend

behavior. The method that is applied within this study is the dual moving average crossover technique as

discussed by Park & Irwin (2004). It is a technique that is widely used to generate trading signals for

investment decisions. It is grounded on two moving averages that are based on the same index: a short-

term moving average and a long-term moving average. An uptrend is identified once the short-term

41

moving average crosses and becomes bigger than the long-term moving average, whereas a downtrend

is acknowledged when the short-term moving average crosses the long-term moving average from above.

One issue related to the use of the dual moving average crossover technique is that it is based on lagging

moving averages. Trend reversal is therefore identified with a lag after the actual trend has reversed.

For the application of the dual moving average crossover technique within this study the short-term

moving average is set to eight quarters while the long-term moving average is set to twelve quarters.

Utilizing moving averages with a shorter horizon would lead to the identification of a highly fluctuating

trend pattern and thus also to the identification of modest downtrends which are less relevant for these

analyses. Moving averages with a longer periodicity, on the other hand, would smoothen the trend

pattern too much and would cause a loss of ability to identify the more severe trends. Furthermore, in

order to compensate for the lag that is introduced by using moving averages, the trend pattern is moved

backward by eight quarters (i.e., the short-term moving average periodicity), which appears to be justified

in this case as the purpose here is not to generate trading signals based on historical information. Applying

this methodology to the sample and plotting the trend reversals (i.e., the peaks and troughs) on the real

estate index graphs appears to perform well considering the identification of the actual trend reversals.

As an illustration the real estate index graph including the trend reversal lines for Ireland is provided

below. The resulting graphs of the methodology applied to all countries for which a downtrend is

identified can be found in the Appendix 3: Trend reversals in the real estate data series.

Figure 3: Real estate index including trend reversal (Ireland)

This figure shows the trend reversal identification based on the methodology described under 3.4.5 Hypothesis 4

and applied to the real estate index of Ireland during the sample period of this dissertation.

In order to analyze the downtrends more in detail the cumulative return15 is calculated for all of the

downtrend periods. Furthermore, for those countries that show multiple downtrends the most severe

15 Recall that all returns are calculated as logarithmic returns. Obtaining the cumulative return can thus easily be done by aggregating the returns over the relevant periods.

020406080

100120140160180

1/03

/200

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1/10

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5

1/05

/200

6

1/12

/200

6

1/07

/200

7

1/02

/200

8

1/09

/200

8

1/04

/200

9

1/11

/200

9

1/06

/201

0

1/01

/201

1

1/08

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4

Ireland

42

cumulative downtrend is identified. The results are provided in the table below. The table moreover

provides the tertiles based on the highest negative cumulative return of the countries.

Table 8: Real estate downtrend periods and cumulative returns

This table shows the downtrend periods and the corresponding cumulative returns of the real estate markets as

identified by applying the methodology discussed in 3.4.5 Hypothesis 4. Furthermore, for each country the largest

negative cumulative return is selected. These returns are then divided into three groups based on tertile values. The

rows of the countries for which no downtrend is observed during the sample period are left empty.

ISO 3166

Downtrend 1 Downtrend 2 Highest neg. cum. return

Tertiles Start End

Cum. return

Start End Cum.

return

AT -

BE -

CH -

FI -

SE -

DE 1/04/2009 30/09/2009 -0.0102 -0.0102 1

NO 1/04/2008 30/09/2008 -0.0225 -0.0225 1

FR 1/01/2008 30/09/2009 -0.0715 1/01/2012 31/12/2014 -0.0569 -0.0715 1

MT 1/04/2007 31/12/2009 -0.0797 -0.0797 2

GB 1/01/2008 30/09/2009 -0.0997 1/04/2011 30/06/2011 -0.0035 -0.0997 2

IT 1/10/2008 31/12/2014 -0.1605 -0.1605 2

DK 1/07/2007 31/12/2009 -0.1820 1/10/2010 30/06/2012 -0.0542 -0.1820 2

NL 1/04/2008 31/12/2014 -0.1969 -0.1969 3

SK 1/10/2008 31/12/2014 -0.2357 -0.2357 3

LT 1/04/2008 30/09/2012 -0.6215 -0.6215 3

IE 1/07/2007 31/12/2012 -0.6824 -0.6824 3

The verification of hypotheses 4 is split up into two regressions. Regression 8 compares the downtrend

periods against the uptrend periods (i.e., the uptrend periods are used as the reference group) while

regression 9 focuses on the more extreme observations by only considering the countries that

experienced either no downturn at all or a severe downturn (i.e., the countries to which no tertile or the

highest tertile is assigned in the table above). As the hypothesis suggests, it is expected that the

downtrend factors have significantly higher16 coefficients than the uptrend factors.

Furthermore, a rolling regression is performed as well in order to analyze the variation over time. This

rolling regression does not make a distinction between the uptrends and downtrends, as some of

coefficients of the individual regressions could not be estimated due to a lack of either uptrend of

downtrend observations in specific periods. By contrast, the rolling regression compares the overall

factors of the countries showing the most severe downtrends (cf. the countries with the highest tertile in

the table above) with the overall factors of the countries showing no downtrends at all (cf. the countries

16 This expectation maintains the assumption of hypothesis 1 that there is a positive causal relationship from the real estate returns toward the banking sector returns. In other words, a positive real estate return is expected to correspond with a positive banking sector return, while a negative real estate return is expected to correspond with a negative banking sector return.

43

with no attached tertile in the table above). Hypothesis 4 raises the expectations that the countries with

the highest downtrends have a higher exposure toward the real estate factor as opposed to the countries

without any downtrends.

3.4.6 Hypothesis 5

Hypothesis 5: The banking sector of countries with an overvalued real estate market is relatively more

exposed toward the real estate market as opposed to the countries with no overvalued real estate markets.

As a starting point of the validation of hypothesis 5, one should make a distinction between the countries

that are confronted with overvalued real estate markets as opposed to the countries without overvalued

real estate markets. Within this dissertation, the source that is used to identify the overvalued real estate

markets is the European Systemic Risk Board (ESRB) risk dashboard which is published multiple times per

year by the ESRB and ECB and which consists of a collection of risk factors in order to continually analyze

systemic risk within the European Union (ESRB, 2015). More specifically, this study utilizes the valuation

measures per country that are reported in the residential property prices section under the credit risk

panel of the risk dashboard. Additionally, the data that is used to construct the risk dashboards is also

available via the ECB Statistical Data Warehouse (ECB, 2015). Unfortunately, the risk dashboard data dates

back to 2012 and, hence, only covers a sub-period (2012Q1-2014Q4) of the sample that is considered in

this study. All risk dashboard publications, however, also mention the valuations of the 2007 real estate

markets. The latter ones are used within this dissertation as these are most relevant in the view of the

2008 financial crisis that is fully embedded in the sample period. One should nevertheless take into

account that these only consider one point in time rather than the whole sample period.

A range of valuation estimates is provided based on four different valuation methods for each of the

reported countries in the ESRB risk dashboard (ESRB & ECB, 2014). This study considers a real estate

market of a country to be overvalued if the lowest estimate is above the zero line17. The table below

summarizes the countries for which the real estate markets are estimated to have been overvalued in

2007. Note that this table only lists eleven countries since, of the thirteen countries in the risk dashboard,

only eleven are involved in this study. Hence, the analyses regarding hypothesis 5 are limited to only those

countries.

17 The ESRB risk dashboards represent the overvaluation estimates of real estate markets as dots relative to a zero line. Estimates which are located above the zero line indicate overvaluation whereas estimates below the zero line indicate undervaluation. For this particular study, no distinction is made between the real estate markets that are undervalued and those that are neither undervalued nor overvalued.

44

Table 9: Overvalued real estate markets in 2007

This table provides an overview of the countries that are estimated to have had an overvalued real estate market in

2007. The information to determine which real estate markets were likely overvalued in 2007 is available in all of

the ESRB risk dashboards that are available (ESRB, 2015).

ISO 3166

Overvalued 2007

BE YES

DK YES

FR YES

GB YES

IE YES

IT YES

NL YES

SE YES

AT NO

DE NO

FI NO

In regression 10 the base regression is extended with dummies in order to compare the countries that

are estimated to have been overvalued in 2007 as compared with those that were not. And as hypothesis

5 puts forward, the countries with overvalued real estate markets are expected to have a substantially

higher exposure toward the real estate markets as opposed to those countries that are not overvalued.

Nevertheless, as mentioned above, the measures of overvaluation are only considered for 2007 due to

data limitations. Regression 10 could therefore provide somewhat biased results. As a consequence,

regression 10 is also executed as a rolling regression so as to gather insights into the dynamics of the

relationship. Besides the rolling regression that compares the overvalued real estate markets with the

non-overvalued real estate markets, two graphs are provided that show the rolling regression estimates

of the base regression applied to both groups individually.

45

4 Results

This section presents the results and corresponding interpretations in the light of the hypotheses that are

verified within this dissertation. Recall that the main question of this study is whether there is a significant

causal relationship between the real estate market and the banking sector. The investigation of this

relationship has been divided into five separate hypotheses that are introduced and extensively

substantiated in section 2 Hypotheses. Furthermore, the data that is used as input and the analyses that

are conducted so as to examine these hypotheses are discussed in section 3 Data and methodology. The

current section follows a similar structure as the one that is used in 3.4 Methodology since it is also based

on the summarizing table of the Hypotheses section (cf. Table 1).

The results that are reported within this section all focus on the significance of the real estate factor

coefficient, as the discussion of the intercept and market factor coefficient would make this section too

bulky. The tabulated regressions nevertheless also report the intercepts and market factor coefficients so

as to enable the reader to examine these specific factors if desired. Furthermore, the market factor graphs

for all rolling regressions that are performed in this section are provided in Appendix 4: Rolling regression

graphs of the market factor.

4.1 Hypothesis 1

Hypothesis 1a: There is a significant and positive causality from the real estate market toward the banking

sector.

Hypothesis 1b: The relationship between the real estate market and the banking sector is subject to

variation over time.

Given the available literature, one would expect that there is a positive causal relationship from real estate

returns toward banking sector returns. And as the scatter plot below shows, there is indeed some

indication of a positive correlation between the two variables. One should however take into account that

such a graph is only able to indicate a certain correlation but no causality. It furthermore only considers

the correlation between the two variables, whereas it omits the influence from the market factor.

Moreover, this graph is not able to analyze whether the slope of the trendline in this figure is significantly

different from zero.

46

Figure 4: Scatter plot of real estate and banking sector returns

In this scatter plot the real estate returns are plotted against the banking sector returns for all observations included

in the sample of this study. A linear trendline is provided as well.

The graphs below present a similar figure as the graphs considered in Figure 1 and Figure 2 (cf. 3.2 Data).

It however distinguishes between the observations before crisis and those during crisis. This graph is

different from the scatter plot portrayed above but the insights obtained from it are similar: there appears

to be a certain correlation between the real estate market and the banking sector. Indeed, most of the

mean real estate returns during crisis are lower than those before crisis, which is also the case for the

average banking sector returns. This confirms the observation of positive correlation between both

variables. There are nevertheless three countries that seem to demonstrate different patterns: for

Austria, there is almost no difference between the returns before crisis and those during crisis, while for

Switzerland and Germany the returns during crisis are even higher than those before crisis.

Figure 5: Mean of real estate and banking sector return series before and during crisis

The two graphs represent the mean returns per country of the real estate market and banking sector data series,

ordered from the highest to lowest observations before crisis.

The regression equation below provides the estimates of the base regression model of this study. It

supports a more quantitative foundation as opposed to the mere graphical representations above, and it

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turn

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LT SK DK SE NO IE FR BE

GB FI IT AT

NL

MT

CH DE

Real estate returns - Mean

Before crisis During crisis

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DE

MT LT FR CH IT DK

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SE FI BE

NO IE GB SK

Banking sector returns - Mean

Before crisis During crisis

47

does take the omitted market factor into account. As one can observe, the relationship from the real

estate market toward the banking sector appears to be insignificant, which is in contrast with the expected

positive relationship. A possible explanation for this observation is that the general relationship that is

perceived in previous studies does not prove valid anymore during the sample period of this study, which

often has not been included in the existing literature.

𝐵𝐴𝑖𝑡 = −𝟎. 𝟎𝟐𝟗𝟗∗∗ + 𝟏. 𝟔𝟒𝟔𝟖∗∗∗ ∗ 𝑟𝑚𝑡 + 0.3284 ∗ 𝑅𝐸𝑖𝑡 + 𝑒𝑖𝑡

(𝟎. 𝟎𝟏𝟐𝟔) (𝟎. 𝟏𝟖𝟎𝟖) (0.3019)

( 1 )

with 624 panel observations, 𝑅2 = 0.4693, 𝑎𝑑𝑗𝑢𝑠𝑡𝑒𝑑 𝑅2 = 0.4676, and Durbin − Watson statistic =

2.1441. Standard errors are reported in parentheses. Significance at the 1%, 5%, and 10% levels is


A more in-depth graphical inspection of the sample is provided in the scatter plot below, which

differentiates between the pre-crisis period and the period during crisis. It appears that a similar trend is

observed as in Figure 4: Scatter plot of real estate and banking sector returns regarding the period during

crisis. However, a remarkable observation is that the relationship seems to be weaker, and even slightly

negative, during the pre-crisis period. Further analysis will shed some light on this observation.

Figure 6: Scatter plot of real estate and banking sector returns before and during crisis

This scatter plot sets out the real estate returns against the banking sector returns for all observations included in

the sample of this study. It however distinguishes between those observations that occur before crisis and those

that occur during crisis. A linear trendline is provided as well for both groups of observations.

Similar to the remarks that are stated above, this mere graphical representation only gives an indication

of the correlation and not of the causality, whereas it also omits the market factor. Hence, a more

quantitative approach is considered in the table below, representing the base regression, which is also

stated above, complemented with two additional regressions that distinguish between the pre-crisis

period and the periods during crisis. As the table indicates, no significance can be found for the two-period

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Real estate return

During crisis Before crisis

48

split-up regarding the real estate factor coefficient. However, when considering three separate periods it

becomes apparent that the real estate factor coefficient is weakly significant during the sovereign crisis

period, indicating that only in this period there is an actual positive relationship between the real estate

market and the banking sector.

Table 10: Regression estimates - Hypothesis 1

This table provides the regression results of the base regression (regression 1) and the two regressions discussed

under 3.4.2 Hypothesis 1. Standard errors are reported in parentheses. Significance at the 1%, 5%, and 10% levels is


(1) (2) (3)

Intercept -0.0299** (0.0126)

before crisis -0.0134 (0.0163)

-0.0134 (0.0164)

during crisis -0.0273* (0.0143)

financial crisis 0.0273 (0.0167)

sovereign crisis -0.0410*** (0.0148)

Market factor 1.6468*** (0.1808)

before crisis 1.4205*** (0.1468)

1.4205*** (0.1472)

during crisis 1.6603*** (0.1865)

financial crisis 1.9710*** (0.1947)

sovereign crisis 1.4839*** (0.1984)

Real estate factor 0.3284 (0.3019)


-0.3291 (0.2728)

during crisis 0.6881 (0.4182)


sovereign crisis 0.8672* (0.4968)

R-squared 0.4693 0.4741 0.4958

Adj. R-squared 0.4676 0.4699 0.4893

Durbin-Watson statistic 2.1441 2.1533 2.1439

Panel observations 624 624 624

Sample period 2005Q2 2014Q4 2005Q2 2014Q4 2005Q2 2014Q4

The rolling regression of the base model offers even more insights into the relationship between the real

estate market and the banking sector (cf. Figure 7). It appears that the real estate factor coefficient is

insignificant for most of the sample period. However, there are some sub-periods during which significant

49

real estate factor coefficients are observed. Those are summarized in Table 1118. One may conclude that

in one particular period before the 2008 financial crisis (2005Q4-2007Q3) the overall rise in the real estate

markets, which is apparent from the real estate index graphs (cf. Appendix 1: Real estate data series),

appears to have negatively impacted the banking sector. This is probably due to the small negative shock

in the banking sector that, for most of the countries, took place between 2006Q1 and 2006Q2 (cf.

Appendix 2: Banking sector data series). The other significant estimates are all positive and take place

during the beginning of the 2008 financial crisis, when the most severe drops in stock prices occurred in

the banking sector, and during the earlier and most intense period of the sovereign crisis19. Overall, it

appears that the real estate market significantly and positively influences the banking sector during the

starts of crises, whereas the causal relationship is insignificant during the more advanced periods of the

crises and during periods without crises, providing confirmation of hypothesis 1b, and partly of hypothesis

1a as well.

Figure 7: Rolling regression estimates of real estate factor - Hypothesis 1

The graph shows the estimates of the real estate factor coefficient for the rolling regression that is based on

regression 1 (solid line). It furthermore provides the 95 % confidence interval for each estimate (dotted lines). The

portrayed estimates are reported at the end of each individual regression sample period.

18 Recall that the rolling regression estimates are reported at the end of each individual regression sample period. The estimates reported at 2007Q1, for example, are thus based on 2005Q2-2007Q1. 19 This period is, among other events, characterized by persistent protest in Greece against impending severe austerity measures as a result of unbearable government budget deficits (e.g., “Greece strike protests turn violent” (2010) and “Greek workers strike over pay freeze” (2010)), the provision of financial aid to Greece from multiple institutions (e.g., Barroso (2011)), the bailout of Ireland (e.g., “Ireland confirms EU bailout deal” (2010) and Neuger & Brennan (2010)), and a general worsening economic outlook (e.g., “New slump fears rock stock markets” (2011)).

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Table 11: Significant rolling regression estimates of real estate factor - Hypothesis 1

This table reports the coefficients and standard errors of those periods for which the real estate factor coefficient is

significant, based on the rolling regression that is shown in the top graph of Figure 7.

Period Real estate factor

Coefficient Standard error

2005Q4-2007Q3 -0.4805 0.2072

2007Q1-2008Q4 1.2668 0.4275

2009Q3-2011Q2 2.0313 0.7713

2009Q4-2011Q3 2.2616 0.7358

2010Q2-2012Q1 1.1144 0.5373

2010Q4-2012Q3 0.9591 0.4707

4.2 Hypothesis 2

Hypothesis 2: The 2008 financial crisis is preceded by an increase in house prices.

As mentioned in the Methodology section, the visual inspection of the index graphs may give some idea

of the real estate market evolution preceding the 2008 financial crisis. And as the graph below suggests,

there indeed seems to be an upsurge in the real estate markets with regard to the period foregoing the

first vertical line, which represents the start of the 2008 financial crisis. Even though the figure below only

shows the sample countries that are part of Northern Europe, the other graphs do display the rise in real

estate markets as well during that period, as can be observed in Appendix 1: Real estate data series.

Figure 8: Real estate indices (Northern Europe)

This graph provides the real estate index evolution over the sample period for the countries of Northern Europe that

are included in this study. The indices are normalized to 100 units in 2010. The input data series for the graphs are

obtained from the BIS Residential Property Price database (BIS, 2015c). The two vertical red lines in the graphs

represent the beginning of the 2008 financial crisis and the beginning of the subsequent sovereign crisis.

Nevertheless, in order to substantiate this observation quantitatively a more thorough analysis is needed.

The results of the regressions that are considered to analyze the validity of hypothesis 2 are shown in

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Northern Europe

Denmark Finland United Kingdom Ireland

Lithuania Norway Sweden

51

Table 12 below. As expected, when considering the whole sample period there does not seem to be a

significant relationship between the lagged real estate returns and the current banking sector returns

(regression 4). The division of the sample period into the pre-crisis period and the period during crisis

(regression 5) and into the pre-crisis period, financial crisis period, and sovereign crisis period (regression

6) only provides some evidence of a weakly significant lagged real estate factor coefficient during the crisis

period, whereas no significance is found when the crisis period is split up into the financial and sovereign

crisis. However, the significant coefficient is positive which is probably caused by the inclusion of the

sovereign crisis period that is preceded by the financial crisis period and for which a positive relationship

is thus expected between the lagged real estate factor and the banking sector. Another point worth

mentioning is that the current real estate factor coefficient is insignificant for all regressions as opposed

to the regressions conducted for hypothesis 1 and for which weak significance is found for the sovereign

crisis period.

A more detailed study of the precedence of the 2008 financial crisis by a rise in real estate prices is

performed by conducting a rolling regression that is based on regression 4. The results of this rolling

regression are presented in Figure 9, which portrays the rolling regression estimates of both the current

and lagged real estate factor coefficients. The current real estate factor coefficient shows a very similar

trend to the one observed within the analyses of Hypothesis 1, which is no surprise as regression 4 is a

mere extension of regression 1. On the other hand, the second graph, which is the one of the lagged real

estate factor coefficient, demonstrates a pattern which is in line with the expectations as a significant and

negative lagged real estate factor coefficient (value = -0.4720, standard error = 0.2198) is found in the

period just before and during the beginning of the 2008 financial crisis (2006Q4-2008Q3), while a positive

coefficient (value = 2.7869, standard error = 0.8610) is found in advance of and through the awakening of

the sovereign crisis (2008Q1-2009Q4), which is preceded by the downturn of the 2008 financial crisis. In

conclusion, the 2008 financial crisis does indeed appear to be preceded by a rise in real estate prices.

52

Table 12: Regression estimates - Hypothesis 2

This table provides the regression results of the base regression (regression 1) and the regressions discussed under

3.4.3 Hypothesis 2. Standard errors are reported in parentheses. Significance at the 1%, 5%, and 10% levels is


(1) (4) (5) (6)

Intercept -0.0299** (0.0126)

-0.0334** (0.0133)

before crisis -0.0300*** (0.0028)

-0.0300*** (0.0028)

during crisis -0.0296** (0.0142)


sovereign crisis -0.0398*** (0.0144)

Market factor 1.6468*** (0.1808)

1.6654*** (0.1812)

before crisis 1.2756*** (0.1277)

1.2756*** (0.1282)

during crisis 1.7031*** (0.1799)

financial crisis 2.0274*** (0.2035)

sovereign crisis 1.4834*** (0.2015)


0.2420 (0.3627)


-0.0632 (0.3106)

during crisis 0.3937 (0.4036)


sovereign crisis 0.6467 (0.4861)

Real estate factor (1-yr lag) 0.5054 (0.3752)

before crisis 0.0358 (0.2882)

0.0358 (0.2892)

during crisis 0.7770* (0.4393)


sovereign crisis 0.3439 (0.4095)

R-squared 0.4693 0.4720 0.4793 0.5008

Adj. R-squared 0.4676 0.4692 0.4727 0.4908

Durbin-Watson statistic 2.1441 2.1563 2.1701 2.1447

Panel observations 624 560 560 560

Sample period 2005Q2 2014Q4 2006Q2 2014Q4 2006Q2 2014Q4 2006Q2 2014Q4

53


The graphs show the estimates of both the current and lagged real estate factor coefficient for the rolling regression

that is based on regression 4 (solid line), which is a regression that extends the base model by including a lagged real

estate factor. It furthermore provides the 95 % confidence interval for each estimate (dotted lines). The portrayed

estimates are reported at the end of each individual regression sample period.

4.3 Hypothesis 3

Hypothesis 3: Mortgage-intensive banking sectors are more exposed toward real estate markets than

banking sectors with limited mortgage activities.

The regression performed in order to verify hypothesis 3 is presented in Table 13 (panel A). None of the

two groups with a more mortgage-intensive banking sector seems to show a significantly different

relationship from the real estate market toward the banking sector relative to the group of countries with

the fewest mortgage activities, which conflicts with the expectations. However, as the analyses of

hypothesis 1 suggest that the relationship between the real estate market and the banking sector

significantly differs over time, a rolling regression considering the difference between the most and the

least mortgage-intensive banking sectors may provide additional insights. The graph of the real estate

factor of this rolling regression is shown below in Figure 10 (top). This figure furthermore shows the graphs

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54

of the rolling regressions focusing on the least mortgage-intensive countries (middle) and the most

mortgage-intensive countries (bottom) individually.

As the top graph of Figure 10 suggests, the real estate factor coefficient of the mortgage-intensive

countries appears to either not differ at all from the least mortgage-intensive countries or to be

significantly lower, except for one period (2012Q3-2014Q2) at the end of the sample for which there is a

positive and significant real estate factor coefficient (value = 4.9656, standard error = 2.2093). However,

when the individual graphs of the least and most mortgage-intensive countries are examined, it appears

that, during the period of significant difference, the most-mortgage intensive countries do not

demonstrate a significant causal relationship between the real estate market and the banking sector

whereas the countries with the least mortgage-intensive banking sectors experience negative coefficients.

Therefore, hypothesis 3 can be rejected regarding the sample of this dissertation.

Concerning the mortgage-intensive banking sectors, one may notice that these results provide some

support to the study of Ahearne et al. (2005) who emphasize the insignificant relationship between real

estate markets and financial markets because of reasons such as the availability of enough capital in order

to bear risks related to the banking activities, the act of securitization of mortgages which helps to limit

risk exposure, among other reasons. It can thus be assumed that mortgage-intensive banking sectors are

better able to limit their exposure toward real estate markets, possibly through mechanisms that are

mentioned above.

55

Table 13: Regression estimates - Hypothesis 3, 4 and 5

This table provides the regression results of the base regression (regression 1) and the regressions discussed under 3.4.4 Hypothesis 3 (panel A), 3.4.5 Hypothesis 4 (panel B), and 3.4.6

Hypothesis 5 (panel C). Standard errors are reported in parentheses. Significance at the 1%, 5%, and 10% levels is respectively indicated by ***, **, and *.

PANEL A (mortgage activity) PANEL B (real estate downtrends) PANEL C (real estate overvaluation)

(1) (7) (1) (8) (9) (1) (10)

Intercept -0.0326*** (0.0126)

-0.0596*** (0.0211)

Intercept -0.0299** (0.0126)

-0.0176 (0.0110)

-0.0121 (0.0129)

Intercept -0.0384** (0.0158)

-0.0063 (0.0111)

Market factor 1.6828*** (0.1784)

2.1960*** (0.2819)

Market factor 1.6468*** (0.1808)

1.4779*** (0.1777)

1.5192*** (0.2109)

Market factor 1.8646*** (0.2160)

1.4931*** (0.1431)


0.7221 (1.0536)


0.1404 (0.2918)

-0.0910 (0.3186)


-1.3206 (1.0662)

Differential intercept

Differential coefficients of downtrends

Differential coefficients of overvalued countries

medium mortgage 0.0380** (0.0154)

intercept

intercept -0.0394** (0.0174)

high mortgage 0.0397** (0.0192)

all -0.0316** (0.0150)

market factor 0.5119*** (0.1584)

Differential market factor

severe -0.0777*** (0.0243)

real estate factor 2.1237 (1.4402)

medium mortgage -0.7212*** (0.1847)

market factor

high mortgage -0.7764*** (0.2230)

all 0.3889*** (0.1076)

Differential real estate factor

severe 0.6465*** (0.1784)

medium mortgage -0.6041 (1.1589)

real estate factor

high mortgage -0.6940 (1.1758)

all -0.0274 (1.0505)

severe -0.2143 (1.1998)

R-squared 0.4507 0.4749 R-squared 0.4693 0.4782 0.4404 R-squared 0.5175 0.5315

Adj. R-squared 0.4485 0.4665 Adj. R-squared 0.4676 0.4740 0.4323 Adj. R-squared 0.5152 0.5259

Durbin-Watson statistic 2.1151 2.1675 Durbin-Watson statistic 2.1441 2.1546 2.1528 Durbin-Watson statistic 2.1625 2.1940

Panel observations 507 507 Panel observations 624 624 351 Panel observations 429 429

Countries included 13 13 Countries included 16 16 9 Countries included 11 11

Sample period 2005Q2 2014Q4

2005Q2 2014Q4


2005Q2 2014Q4

2005Q2 2014Q4


2005Q2 2014Q4

56


The figure shows the estimates of the real estate factor coefficient for the rolling regression that is based on

regression 7 (solid line), yet leaving out the category with medium mortgage activities. It furthermore provides the

95 % confidence interval for each estimate (dotted lines). The first graph provides the rolling regression regarding

the difference between the most and least mortgage-intensive banking sectors, whereas the latter two graphs show

the rolling regressions based on only the least mortgage-intensive banking sectors (middle) and the most mortgage-

intensive banking sectors (bottom). The portrayed estimates are reported at the end of each individual regression

sample period.

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4.4 Hypothesis 4

Hypothesis 4: The banking sector suffers substantially more from real estate price declines than it benefits

from real estate price rises.

With respect to hypothesis 4 two regressions have been performed of which the results are reported in

Table 13 (panel B). However, no significant differences can be identified between the downtrends and the

uptrends with regard to the real estate factor. Therefore, a rolling regression is performed as well. As

discussed in 3.4.5 Hypothesis 4, this rolling regression does not distinguish between the downtrends and

uptrends, yet it differentiates between those countries that have experienced the most severe

downtrends in their real estate markets and those that experienced no downtrends at all. The graph

(Figure 11, top) shows no significant differences between both groups, except for one period, namely,

2007Q1-2008Q4, which contains the start of the 2008 financial crisis. A look at the graph of the countries

with severe downtrends (Figure 11, bottom) and the graph of the countries without downtrends (Figure

11, middle) learns that, during the period of significant difference, the countries with severe downtrends

in their real estate markets were significantly exposed to their real estate market, whereas the real estate

factor coefficient of the countries without downtrends is insignificant during that period. The real estate

index graphs of the countries that experienced severe downtrends (Netherlands, Slovakia, Lithuania, and

Ireland) indeed shows a similar pattern as their banking sector index graphs, except for the Netherlands

for which no similar pattern can be observed visually. However, this period is to the greater part not

characterized by real estate slumps in these four countries. Hence, no support can be lend to the

hypothesis that banking sectors suffer substantially more from real estate price declines than they benefit

from real estate price increases.

58


The figure shows the estimates of the real estate factor coefficient for the rolling regression that is founded on the

base model and that considers the differences between the countries that have experienced the highest real estate

downturns and those that were not confronted with any real estate downturn (solid line). It furthermore provides

the 95 % confidence interval for each estimate (dotted lines). The first graph deals with the difference between the

two groups while the latter two graphs individually focus on the countries that experienced no downtrends (middle)

and those that experienced the most severe downtrends (bottom). The portrayed estimates are reported at the end

of each individual regression sample period.

-20,0

-15,0

-10,0

-5,0

0,0

5,0

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15,0

1/03

/200

7

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-8,0-6,0-4,0-2,00,02,04,06,08,0

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4

Real estate factor (no downtrend)

-6,0

-4,0

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8,0

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/200

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1/08

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4

Real estate factor (severe downtrend)

59

4.5 Hypothesis 5

Hypothesis 5: The banking sector of countries with an overvalued real estate market is relatively more

exposed toward the real estate market as opposed to the countries with no overvalued real estate markets.

Similar to the segmentations considered under hypothesis 3 and 4, hypothesis 5 makes a distinction based

on certain characteristics. In this case the observations are split up into two groups relying on the fact

whether the real estate markets of the countries were overvalued in 2007 or not, as discussed in

paragraph 3.4.6 Hypothesis 5. The regression that is used to test hypothesis 5 is reported in Table 13

(panel C). No significant distinction is identified when considering the whole sample period. However,

when the rolling regression based on regression 10 is examined (Figure 12, top), there appears to be

multiple periods during which a significant difference in the real estate factor coefficient is perceived.

These periods of significance are summarized in Table 14, which furthermore also reports the coefficients

and standard errors of the real estate factor during each specific significant period. As the middle and

bottom graphs in Figure 12 indicate, the banking sectors of the countries with overvalued real estate

markets during 2007 are substantially more exposed to their real estate markets around the 2007-period,

as opposed to the countries that did not have real estate overvaluation during 2007 and which then even

experienced some negative real estate factor coefficients. This observation tends to confirm hypothesis 5

with regard to this specific period.

A remarkable additional observation is that the countries with overvalued real estate markets during 2007

were also confronted with significantly higher real estate exposure during the run-up of the sovereign

crisis, as the top graph in Figure 12 shows. One may assume that the countries that experienced

overvaluation of their real estate market during 2007 perhaps still had overvalued real estate markets20

during the rise of the sovereign crisis, although this is a rather presumptuous assumption that would need

further investigation that is not considered in this study.

20 Recall that the source that is used to identify overvaluation in real estate markets only provides data for 2007 and for the time frame of 2012Q1-2014Q4 (ESRB, 2015).

60


The figure shows the estimates of the real estate factor coefficient for the rolling regression that is based on

regression 10 (solid line). It furthermore provides the 95 % confidence interval for each estimate (dotted lines). The

first graph considers the difference between the countries with overvalued real estate markets in 2007 and those

without overvalued real estate markets, whereas the latter two graphs individually focus on the countries without

overvalued real estate markets (middle) and with overvalued real estate markets (bottom). The portrayed estimates

are reported at the end of each individual regression sample period.

-10,0

-5,0

0,0

5,0

10,0

15,0

1/03

/200

7

1/08

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7

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8

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8

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9

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9

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/201

0

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/201

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1/03

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1/08

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3

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4


-14,0-12,0-10,0

-8,0-6,0-4,0-2,00,02,04,06,0

1/03

/200

7

1/08

/200

7

1/01

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8

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8

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8

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9

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9

1/02

/201

0

1/07

/201

0

1/12

/201

0

1/05

/201

1

1/10

/201

1

1/03

/201

2

1/08

/201

2

1/01

/201

3

1/06

/201

3

1/11

/201

3

1/04

/201

4

1/09

/201

4

Real estate factor (not overvalued)

-6,0-4,0-2,00,02,04,06,08,0

10,012,0

1/03

/200

7

1/08

/200

7

1/01

/200

8

1/06

/200

8

1/11

/200

8

1/04

/200

9

1/09

/200

9

1/02

/201

0

1/07

/201

0

1/12

/201

0

1/05

/201

1

1/10

/201

1

1/03

/201

2

1/08

/201

2

1/01

/201

3

1/06

/201

3

1/11

/201

3

1/04

/201

4

1/09

/201

4

Real estate factor (overvalued)

61

Table 14: Significant rolling regression estimates of real estate factor - Hypothesis 5

This table reports the coefficients and standard errors of those periods for which the difference in real estate factor

coefficient between the countries with overvalued real estate markets and those without is significant, based on the

rolling regression that is shown in the top graph of Figure 12.

Period Real estate factor

Coefficient Standard error

2006Q1-2007Q4 2.2212 0.5214

2006Q2-2008Q1 1.3933 0.6057

2006Q3-2008Q2 1.2958 0.6394

2006Q4-2008Q3 1.5121 0.7259

2007Q1-2008Q4 6.6055 3.0008

2009Q3-2011Q2 5.9608 1.8721

2009Q4-2011Q3 8.5426 2.2226

62

Conclusion

This master dissertation has covered five hypotheses that are founded on one central question: Does the

real estate market substantially affect the banking sector? It extends the existing literature as it utilizes

the recently made available real estate data sets from the BIS Residential Property Price database,

whereas it is furthermore focused on a more contemporary time setting (2005Q1-2014Q4). Sixteen

European countries have been examined within this study through the implementation of both visual

inspection and panel data analysis. Nevertheless, the most insightful conclusions were formed based on

rolling regressions, which take the dynamics over the time dimension into account. The base model of this

study has been founded on the index model representation of the capital asset pricing model, yet extends

it by including a real estate factor. This resulted in the fact that three types of data series are used in this

study: real estate market returns, banking sector returns, and market returns. As regards the banking

sector returns, these have been constructed by obtaining stock price series of listed and delisted banking

organizations from the Datastream Professional database. Furthermore, this study has utilized the MSCI

Europe index as a proxy for the European market index.

Even though a vast body of literature suggests that there is a positive causal relationship between the real

estate market and the financial market, this observation has been refined within this study. Whereas the

visual inspection of a scatter plot and mean graphs tend to confirm the positively sloped relationship

concerning the whole sample period, this finding could not be substantiated through regression analysis.

However, when the observations are divided into three groups considering the pre-crisis period, the 2008

financial crisis period, and the sovereign crisis period, there is some indication of positive causality for the

sovereign crisis. Nevertheless, as already mentioned, the rolling regressions provided additional insights:

during the beginning of the financial crisis and the sovereign crisis, the banking sectors of the investigated

countries are substantially exposed toward their real estate markets as opposed to the periods before

crisis and the more advanced crisis periods. As the foregoing suggests, the observation of significant real

estate market exposure of the banking sector might indicate a looming crisis, may it be either a financial

or a sovereign crisis. If implemented in practice, this study its base model may be used as an early warning

system for crises, although it would be preferable to identify possible crises before they have started

rather than when they have already begun. One should nevertheless take into account that this study

focused on the 2008 financial crisis and the sovereign crisis with regard to the sixteen selected countries.

Therefore, the same relationship may or may not be present for crises that are not considered in this

study.

Furthermore, another finding of this study is that the 2008 financial crisis appears to be preceded by

increases in real estate prices, which inclines to confirm the observations of earlier studies that have

63

searched for early warning signals of banking crises. It might therefore be advisable to adopt a cautious

approach regarding the banking sector when real estate price rises are observed as they may signal the

ascent of a possible house bubble and the proximity of a potential crisis, which is also suggested by Barrell

et al. (2010).

A hypothesis that could not be confirmed in this study is that mortgage-intensive banking sectors are

more exposed toward their real estate markets than banking sectors that are less focused on mortgage

activities. Nevertheless, some support could be lent to the study of Ahearne et al. (2005) who conversely

state that banks are able to limit their exposure toward the real estate market because of securitization

and capital adequacy, among other reasons. Hence, the mortgage-intensive banking sectors seem to be

better able to control their real estate risk exposure.

In addition, overvaluation of real estate markets in 2007 appears to have been a causal factor of

substantial exposure of banking sectors toward real estate markets during the beginning of the 2008

financial crisis for those countries that were experiencing real estate overvaluations. The countries that

were not confronted with overvalued real estate markets during that period experienced a significantly

lower exposure. Moreover, although not further investigated in this study, the countries confronted with

overvalued real estate markets during 2007 seem to be significantly more exposed toward their real

estate markets during the early period of the sovereign crisis as well. Future research may elaborate on

this analysis by considering real estate markets that were overvalued in the periods outside 2007 in order

to verify whether this hypothesis is generalizable. If these observations prove to be valid in general, a

recommend next step would be to implement systems that are able to analyze, or even predict, real estate

overvaluation in order to be better prepared to suppress possible upcoming crises. Related to the analysis

of overvalued real estate markets, another hypothesis postulated that banking sectors suffer more from

real estate price declines than they benefit from real estate price rises. Although overvaluation seems to

be a crucial factor with regard to the real estate exposure of banking sectors, this related hypothesis could

not be confirmed within this study.

64

Limitations and suggested future research

This study has been subject to several limitations. One of the limitations of this study is the availability of

real estate data. Although local organizations often collect real estate data, it is hard to perform

international analyses based on these data due to the application of diverging collection methodologies,

compilation methodologies, real estate definitions, and other issues. Great progress has been made with

the appearance of the Handbook on Residential Property Prices Indices that is used by the BIS in order to

provide cross-comparable residential property price indices. This has been a great improvement that has

made this study possible. The series that are used from the BIS Residential Property Price database

restricted the sample of this study to 2005Q1-2014Q4 and to the availability of real estate data sets for

twenty countries. Nevertheless, this number was later reduced to sixteen countries because of limitations

that were present in the banking sector input. The real estate series that were most suitable for this

dissertation are furthermore only provided as quarterly data sets, which has been another limitation of

this study. Hence, for each country only 39 observations were available which made the use of panel

analysis preferable over individual time series analysis. The absence of individual time series analyses is

regrettable as the consideration of these single time series could have provided additional insights.

Although most studies dealing with the synchronization of real estate markets point out that an increasing

synchronization is found, Hilbers et al. (2008) note that real estate markets within Europe demonstrate

diverging trends, indicating that additional insights may indeed be obtained when considering individual

countries. Future research may take the potential decline of synchronization into account. This study may

furthermore be extended by enlarging the two mentioned dimensions: the time dimension, regarding

both the sample length and the periodicity, and the geographical dimension.

It is unfortunate that countries such as Portugal, Greece and Spain, are not included in this study due to

data limitations. As these countries have had or currently have issues with severe budget deficits and

government debt levels, especially during the sovereign crisis (Hinde, 2010), the inclusion of these

countries could have provided additional valuable insights. It might thus be suggested for future research

to expand this dissertation by taking these countries into account.

Moreover, this dissertation has been based on the selected series from the BIS Residential Property Price

database, as already mentioned above. This collection of data sets are based on more detailed real estate

series that are available via the same database but that are subject to high heterogeneity. The intention

of the selected series is to provide real estate indices that comprise a representative and comparable

index for each of the countries. However, one might also consider investigating whether banking sectors

are more exposed toward real estate regarding specific real estate characteristics. A characteristic that

can be suggested for such studies is the new/existing status of real estate, as Ahnert & Page (2005)

65

observe that property prices of new and existing property can be subject to significantly different

dynamics due to dissimilarities in, for example, taxation, subsidy rules and available land. The same

authors furthermore mention that houses and flats are dependent on different market conditions.

Therefore, a distinction between those two types of real estate might provide valuable insights as well.

Another characteristic that could be worth examining is the division of real estate into residential and

commercial real estate, as Zhu (2005) observes that commercial real estate tends to be a bigger threat

toward financial markets than residential real estate is. Although these characteristics might be hard to

analyze at this moment due to the data limitations, it might become easier to investigate them in the

future when and if the Handbook on Residential Property Prices Indices is being implemented globally by

all organizations that collect real estate data. Hence, this study advocates a better implementation of such

international real estate data collection and compilation methodologies so as to further enhance research

capabilities with regard to the real estate market. Moreover, the availability of real estate data through

one source, such as the BIS Residential Property Price database, is much appreciated since it massively

simplifies the collection effort of researches, who, hence, can focus on the analyses themselves. It is

therefore desirable that such organizations sustain their efforts in maintaining and improving these

valuable sources of data.

A final point that is worth mentioning concerns the banking sector data. Whereas the banking sector

returns are composed by relying on stock price series of banking organizations that are listed or delisted

during the sample period, organizations that are not listed on any exchange are not taken into account.

This resulted in the fact that for some countries only a few banking organizations are included which may

have led to sub-optimality concerning the representativeness of the banking sector return series.

Although this seems to be a rather straightforward limitation, other researchers may try to represent the

values of unlisted organizations in some way so as to compose banking sector return series that even

better represent the banking sector of a whole country than is the case in this study.

66

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71

Appendices

Appendix 1: Real estate data series

Figure 13: Real estate indices (Northern and Eastern Europe)

These graphs provide the real estate index evolution over the sample period for the countries of Northern and

Eastern Europe. The indices are normalized to 100 units in 2010. The input data series for the graphs are obtained

from the BIS Residential Property Price database (BIS, 2015c). The two vertical red lines in the graphs represent the

beginning of the 2008 financial crisis and the beginning of the subsequent sovereign crisis.

60

80

100

120

140

160

180

1/03

/200

5

1/08

/200

5

1/01

/200

6

1/06

/200

6

1/11

/200

6

1/04

/200

7

1/09

/200

7

1/02

/200

8

1/07

/200

8

1/12

/200

8

1/05

/200

9

1/10

/200

9

1/03

/201

0

1/08

/201

0

1/01

/201

1

1/06

/201

1

1/11

/201

1

1/04

/201

2

1/09

/201

2

1/02

/201

3

1/07

/201

3

1/12

/201

3

1/05

/201

4

1/10

/201

4

Northern Europe



60

80

100

120

140

160

180

1/03

/200

5

1/08

/200

5

1/01

/200

6

1/06

/200

6

1/11

/200

6

1/04

/200

7

1/09

/200

7

1/02

/200

8

1/07

/200

8

1/12

/200

8

1/05

/200

9

1/10

/200

9

1/03

/201

0

1/08

/201

0

1/01

/201

1

1/06

/201

1

1/11

/201

1

1/04

/201

2

1/09

/201

2

1/02

/201

3

1/07

/201

3

1/12

/201

3

1/05

/201

4

1/10

/201

4

Eastern Europe

Slovak Republic

72

Figure 14: Real estate indices (Southern and Western Europe)

These graphs provide the real estate index evolution over the sample period for the countries of Southern and

Western Europe. The indices are normalized to 100 units in 2010. The input data series for the graphs are obtained

from the BIS Residential Property Price database (BIS, 2015c). The two vertical red lines in the graphs represent the

beginning of the 2008 financial crisis and the beginning of the subsequent sovereign crisis.

60

80

100

120

140

160

180

1/03

/200

5

1/08

/200

5

1/01

/200

6

1/06

/200

6

1/11

/200

6

1/04

/200

7

1/09

/200

7

1/02

/200

8

1/07

/200

8

1/12

/200

8

1/05

/200

9

1/10

/200

9

1/03

/201

0

1/08

/201

0

1/01

/201

1

1/06

/201

1

1/11

/201

1

1/04

/201

2

1/09

/201

2

1/02

/201

3

1/07

/201

3

1/12

/201

3

1/05

/201

4

1/10

/201

4

Southern Europe

Italy Malta

60

80

100

120

140

160

180

1/03

/200

5

1/08

/200

5

1/01

/200

6

1/06

/200

6

1/11

/200

6

1/04

/200

7

1/09

/200

7

1/02

/200

8

1/07

/200

8

1/12

/200

8

1/05

/200

9

1/10

/200

9

1/03

/201

0

1/08

/201

0

1/01

/201

1

1/06

/201

1

1/11

/201

1

1/04

/201

2

1/09

/201

2

1/02

/201

3

1/07

/201

3

1/12

/201

3

1/05

/201

4

1/10

/201

4

Western Europe

Austria Belgium Switzerland Germany France Netherlands

73

Appendix 2: Banking sector data series

Figure 15: Histogram of ratio of deposits over total assets

This graph shows the histogram of the ratio of deposits over total assets. The x-axis provides the upper value of each

of the bins and is set up using five percent intervals. The information that is used to set up this graph is obtained via

the Bankscope database (Bureau van Dijk, 2015).

Figure 16: Histogram of ratio of equity over total assets

This graph shows the histogram of the ratio of equity over total assets. The x-axis provides the upper value of each

of the bins and is set up using five percent intervals. The information that is used to set up this graph is obtained via

the Bankscope database (Bureau van Dijk, 2015).

0

10

20

30

40

50

60

5 15 25 35 45 55 65 75 85 95 More

Fre

qu

en

cy

Bin

0

20

40

60

80

100

120

5 15 25 35 45 55 65 75 85 95 More

Fre

qu

ency

Bin

74 Table 15: List of all included banking organizations

This table contains the descriptive information of each of the data sets that are used to form one banking sector index per country. It is ordered alphabetically based on main

domestic country and ISIN code. The table is constructed using information that is available via the Bankscope database (Bureau van Dijk, 2015). The following information is

provided: Bureau van Dijk ID number, organization name, specialization, primary business line, whether the company is listed or delisted, the delisting date if the company is delisted

as well as a delisting explanation, the total assets of the last available year in million euros, the ratio of equity over total assets of the last available year in percentages, and the ratio

of deposits and short term funding over total assets of the last available year in percentages.

Main domestic country

BvD ID Number ISIN Number Bank Name Specialization Primary business line Listed/delisted Delisted date

Delisted text

Total Assets mil EUR Last avail. yr

Equity / Total Assets % Last avail. yr

Deposits / Total Assets % Last avail. yr

Austria AT40494 AT0000606306 Raiffeisen Bank International AG

Commercial Banks

Operates as a holding company that, through its subsidiaries, provides various banking services

Listed 130 640 7.93 73.90

AT44316 AT0000625108 Oberbank AG Commercial Banks

Operates as a national commercial bank involved in the provision of a range of financial products and services

Listed 17 571 8.09 77.27

AT43887 AT0000625504 Bank für Tirol und Vorarlberg AG-BTV (3 Banken Gruppe)

Commercial Banks

Engaged in the provision of financial products and services

Listed 9 589 9.52 76.01

AT46146 AT0000652011 Erste Group Bank AG Bank Holding & Holding Companies

Engaged in the management activities of a holding company

Listed 199 876 7.40 71.18

AT47752 AT0000698402 Vorarlberger Landes-und Hypothekenbank AG

Real Estate & Mortgage Bank


Delisted 1/01/2008 This company has been delisted since 20080000 (company request)

14 145 5.99 39.93

AT44482 AT0000755665 Oesterreichische Volksbanken AG

Cooperative Banks

Engaged in the provision of financial services in Austria and internationally

Listed 20 904 5.84 54.67

AT37108 AT0000824701 Volksbank Vorarlberg e.Gen.

Cooperative Banks

An Austrian cooperative bank that offers a wide spectrum of financial services to private and corporate customers

Listed 2 219 5.82 82.44

AT46588 AT0000995006 UniCredit Bank Austria AG-Bank Austria

Commercial Banks

Engaged in the activities of a commercial bank

Delisted 21/05/2008 This company has been delisted since 20080521 (takeover by UniCredit SpA)

196 210 7.67 69.29

Belgium BE0403227515 BE0003565737 KBC Groep NV/ KBC Groupe SA-KBC Group

Bank Holding & Holding Companies

Operates as a national commercial bank that provides financial services

Listed 241 306 6.02 58.48

75

BE0458548296 BE0003796134 Dexia Bank Holding & Holding Companies

Operates as a commercial bank that provides retail banking services to its customers

Listed 222 936 1.78 48.17

Denmark DK64865218 DK0010015072 Nordfyns Bank Commercial Banks

Financial firm primarily engaged in the provision of banking services

Listed 326 10.85 82.38

DK37760412 DK0010017367 Salling Bank A/S Commercial Banks

Engaged in the provision of banking services

Listed 374 8.38 84.46

DK57928913 DK0010017607 Oestjydsk Bank A/S Commercial Banks

Operates as a commercial bank in Denmark

Listed 718 6.37 79.68

DK13255504 DK0010150523 Sparekassen Faaborg A/S

Commercial Banks

Providing a range of financial products and services

Delisted 11/10/2013 This company has been delisted since 20131011

848 8.33 74.15

DK13538530 DK0010201532 Laan & Spar Bank A/S Commercial Banks

Engaged in the provision of financial services and solutions

Listed 1 681 7.41 88.33

DKGL39070 DK0010230630 Bank of Greenland-Gronlandsbanken A/S

Commercial Banks


Listed 677 17.33 79.98

DK16339016 DK0010253764 Kreditbanken A/S Commercial Banks

Operates as a commercial bank Listed 321 17.12 81.32

DK61126228 DK0010274414 Danske Bank A/S Commercial Banks

Operates as a commercial bank Listed 432 305 4.51 38.42

DK45801012 DK0010295922 Skjern Bank Commercial Banks


DK24431118 DK0010302488 DK Company A/S Commercial Banks


DK34631328 DK0010304500 Vestjysk Bank A/S Commercial Banks

Engaged in providing a range of financial products and services in Denmark

Listed 4 388 3.03 63.26

DK17616617 DK0010307958 Jyske Bank A/S (Group)

Commercial Banks


Listed 35 099 6.67 75.60

DK12626509 DK0010311471 Sydbank A/S Commercial Banks


Listed 19 812 6.92 77.04

DK36684828 DK0060000107 Lollands Bank Commercial Banks


Listed 371 12.82 83.30

DK37536814 DK0060032068 Ringkjoebing Landbobank

Commercial Banks

Bank that is engaged in providing a range of financial products and services

Listed 2 623 14.82 81.05

DK30828712 DK0060034353 Nordjyske Bank A/S Commercial Banks

Engaged in providing a range of financial products and services

Listed 1 194 15.14 68.20

DK13737584 DK0060036564 Spar Nord Bank Commercial Banks


Listed 9 994 8.76 67.30

DK28299494 DK0060050045 Jutlander Bank A/S Savings Bank Operates a commercial bank Listed 1 254 15.54 78.15

DK50020010 DK0060056083 Selskabet af 1. september 2008 A/S

Commercial Banks

Commercial bank Delisted 09/03/2009 This company has been delisted since 20090309 (in receivership)

1 176 29.46 50.40

76 DK32127711 DK0060076941 DiBa Bank A/S Commercial

Banks Operates as a commercial bank in Denmark

Delisted 15/01/2014 This company has been delisted since 20140115 (takeover by Sydbank A/S)

703 7.85 81.51

DK10349818 DK0060082758 Totalbanken A/S Commercial Banks

Commercial bank Listed 321 7.98 81.78

DK65746018 DK0060133841 Moens Bank A/S Commercial Banks


Listed 257 12.58 82.48

DK34790515 DK0060135549 Noerresundby Bank A/S

Commercial Banks

Engaged in providing a range of financial products and services

Listed 1 240 15.49 82.26

DK64855417 DK0060135978 Hvidbjerg Bank Aktieselskab

Commercial Banks


Listed 122 7.51 89.46

DK40713816 DK0060136273 Djurslands Bank A/S Commercial Banks

Financial institution that is engaged in providing a full range of financial products and services

Listed 881 11.92 85.77

DK31843219 DK0060299063 Danske Andelskassers Bank A/S

Commercial Banks

Engaged in the provision of a range of financial products and services

Listed 1 517 7.42 83.61

DK25802888 DK0060520377 Fynske Bank A/S Commercial Banks


DKFO10 FO0000000088 BankNordik P/F Commercial Banks

Engaged in the operation of a commercial bank

Listed 2 289 12.62 78.92

DKFO3293 FO0005702340 Eik Banki P/F Savings Bank Provides a range of financial products and services


978 17.74 79.84

Finland FI01450193 FI0009001127 Alandsbanken Abp-Bank of Aland Plc

Commercial Banks

Engaged in the provision of commercial banking services

Listed 3 887 4.74 82.04

FI01999207 FI0009003222 Pohjola Bank plc-Pohjola Pankki Oyj

Commercial Banks

Engaged in the provision of banking, investment and non-life insurance services

Delisted 01/10/2014 This company has been delisted since 20141001 (takeover by OP-Pohjola Group)

43 720 6.96 56.22

FI21817028 FI4000058870 Aktia Bank Plc Commercial Banks

Engaged as a commercial bank in Finland

Listed 10 934 5.87 53.48

France FR784608416 FR0000045072 Crédit Agricole S.A. Cooperative Banks

Engaged in the provision of retail banking, financial, asset management, insurance, private banking, and corporate and investment banking products and services

Listed 1 536 873 3.12 51.68

FR568501282 FR0000064560 Crédit Foncier et Communal d'Alsace et de Lorraine (Banque)-CFCAL Banque


Engaged in mortgage banking Delisted 09/07/2012 This company has been delisted since 20120709 (takeover by CREDIT MUTUEL ARKEA)

1 148 14.70 36.48

FR754500551 FR0000065526 Banque Tarneaud Commercial Banks

Engaged in the provision of banking products and services

Delisted 11/01/2013 This company has been delisted since 20130111 (takeover by CREDIT DU NORD)

3 156 5.73 87.64

77

FR542044524 FR0000120685 Natixis Commercial Banks

Engaged in commercial banking activities

Listed 510 131 3.52 53.73

FR552120222 FR0000130809 Société Générale Commercial Banks


Listed 1 235 262 4.38 43.75

FR662042449 FR0000131104 BNP Paribas Commercial Banks


Listed 1 800 139 5.06 51.75

FR542016381 FR0005025004 Crédit Industriel et Commercial - CIC

Commercial Banks

Operates as a bank holding company

Listed 232 920 4.82 71.83

Germany DE43696 DE0005072300 DAB Bank AG Commercial Banks

Engaged in the provision of financial and brokerage services to mutual fund brokers, investment advisors, banks and asset managers in Germany and Austria

Listed 5 328 4.64 93.81

DE47958 DE0005088108 Baader Bank AG Commercial Banks

Engaged in securities trading operations

Listed 624 23.46 73.13

DE13216 DE0005140008 Deutsche Bank AG Commercial Banks

Engaged in the provision of banking and related products and services in Germany

Listed 1 611 400 3.41 41.99

DE13222 DE0005408116 Aareal Bank AG Real Estate & Mortgage Bank

Engaged in the provision of financial and management advisory services in Germany

Listed 42 981 5.70 29.35

DE25570 DE0005479307 Varengold Bank AG Cooperative Banks

Engaged in three areas of activity: Asset management, derivative brokerage and managed futures solutions

Listed 53 17.93 75.85

DE46603 DE0005570808 UmweltBank AG Commercial Banks

Engaged in the provision of commercial banking services

Listed 2 513 4.74 92.26

DE14133 DE0008001009 Deutsche Postbank AG

Commercial Banks

A consumer bank that provides various financial products and services to private, business and corporate customers in Germany

Listed 161 506 3.85 87.68

DE46802 DE0008022005 UniCredit Bank AG Commercial Banks

Engaged in the provision of a comprehensive range of banking and financial products and services

Delisted 15/09/2008 This company has been delisted since 20080915 (takeover by UniCredit SpA)

290 018 7.24 56.44

DE15517 DE0008023227 Landesbank Berlin Holding AG-LBB Holding AG


Operates as a financial holding firm for a group that offers a range of financial services nationally an internationally

Delisted 02/08/2012 This company has been delisted since 20120802 (takeover by ERWERBSGESELLSCHAFT DER S-FINANZGRUPPE MBH & CO. KG)

102 437 2.29 61.85

78 DE16697 DE0008027707 Hypo Real Estate

Holding AG Bank Holding & Holding Companies

Holds various firms engaged in real estate financing, public sector and infrastructure financing, and capital market and asset management business activities

Delisted 14/10/2009 This company has been delisted since 20091014 (takeover by SOFFIN)

122 454 5.18 24.96

DE45186 DE0008029000 Berlin-Hannoversche Hypothekenbank AG-Berlin Hyp


A real estate financing bank that facilitates the lending of funds or issuance of credit by engaging in such activities as mortgage and loan brokerage, clearinghouse and reserve services, and check cashing services

Delisted 25/01/2011 This company has been delisted since 20110125 (takeover by Landesbank Berlin Holding AG - LBB Holding AG)

33 367 2.59 34.37

DE13230 DE0008042003 Deutsche Hypothekenbank (Actien-Gesellschaft)


Engaged in the provision of financing and consultancy services associated with real estate

Delisted 31/10/2008 This company has been delisted since 20081031 (takeover by NORDDEUTSCHE LANDESBANK GIROZENTRALE NORD/LB)

31 247 2.97 50.99

DE18830 DE0008051004 Wüstenrot & Württembergische


Serves an investment holding for a group that offers various financial services and products in Germany and Eastern Europe

Listed 75 043 4.34 42.99

DE13319 DE0008063306 IKB Deutsche Industriebank AG

Investment Banks

Engaged in the provision of corporate financing to small and medium-sized enterprises, international enterprises, and private-equity funds

Listed 24 045 6.47 82.34

DE13588 DE0008076001 Hypothekenbank Frankfurt AG


Involved in mortgage, business banking, government loans as well as securities trading and management

Delisted 29/07/2008 This company has been delisted since 20080729 (takeover by Commerzbank AG)

130 453 3.33 56.68

DE13554 DE0008148206 Merkur-Bank KGaA Commercial Banks

A commercial bank engaged in providing a wide spectrum of banking and financial solutions to both individual and institutional clients

Listed 866 5.36 90.23

DE13190 DE000CBK1001 Commerzbank AG Commercial Banks

Operates as a parent bank of a group that provides financial services for private and business customers as well as for small to medium-sized companies (Mittelstand) and major corporations and multinationals in Germany and abroad

Listed 549 661 4.90 68.22

Ireland IE024173 IE0000197834 Allied Irish Banks plc Commercial Banks

Commercial bank that provides financial services

Listed 117 734 8.91 75.48

79

GBRC000206 IE0030606259 Bank of Ireland-Governor and Company of the Bank of Ireland

Commercial Banks

Engaged in the provision of banking and other financial services to small and medium-sized commercial and industrial companies in Ireland and internationally

Listed 132 137 5.96 65.26

IE348819 IE0072559994 Depfa Bank Plc Real Estate & Mortgage Bank

Engaged in the provision of financial services

Delisted 03/10/2007 This company has been delisted since 20071003 (takeover by Hypo Real Estate Holding AG)

49 126 4.32 20.79

Italy ITMI0343508 IT0000062957 Mediobanca SpA-MEDIOBANCA - Banca di Credito Finanziario Società per Azioni

Investment Banks

Engaged in the provision of lending and investment banking services

Listed 70 464 11.27 39.64

ITMI0004450 IT0000064482 Banca Popolare di Milano SCaRL

Cooperative Banks


Listed 49 353 7.39 65.52

ITSO0002313 IT0000064516 Credito Valtellinese Soc Coop

Cooperative Banks

Engaged in monetary intermediation activities

Listed 27 199 7.04 71.62

ITMO0222528 IT0000066123 Banca popolare dell'Emilia Romagna

Cooperative Banks


Listed 61 758 7.63 67.20

ITMI1598155 IT0000072170 FinecoBank Banca FinEco SpA-Banca FinEco SpA

Commercial Banks


Listed 17 682 2.37 81.33

ITTO0947156 IT0000072618 Intesa Sanpaolo Commercial Banks

Engaged in the provision of a broad array of banking services

Listed 626 283 7.20 44.89

ITRM0444286 IT0000088853 Banca Finnat Euramerica SpA

Commercial Banks


Listed 1 135 16.35 81.92

ITMI0775664 IT0000226503 Banca Italease SpA Finance Companies (Credit Card, Factoring & Leasing)

Engaged in the provision of financial and leasing services

Delisted 08/04/2010 This company has been delisted since 20100408 (takeover by Banco Popolare - Societa Cooperativa - Banco Popolare)

8 319 13.99 70.98

ITSO0003167 IT0000784196 Banca Popolare di Sondrio Societa Cooperativa per Azioni

Cooperative Banks

Cooperative company that provides financial services such as loans, guarantees and deposits in Italy

Listed 32 770 6.14 81.72

ITCA0160586 IT0001005070 Banco di Sardegna SpA

Commercial Banks

Commercial bank that is involved in the provision of a range of financial products and services

Listed 12 877 9.61 69.14

ITPG0170173 IT0001007209 Banca Popolare di Spoleto SpA

Commercial Banks

Operates as a commercial bank that offers a full line of banking and financial solutions including savings accounts, loans, credit cards and debit cards

Listed 3 776 4.58 59.25

80 ITTS0103698 IT0001031084 Banca Generali SpA-

Generbanca Commercial Banks


Listed 6 603 7.11 88.14

ITMB0129094 IT0001041000 Banco di Desio e della Brianza SpA-Banco Desio

Commercial Banks

Operates as a commercial bank that provides various banking and financial intermediation services to private individuals and small and medium-sized businesses

Listed 9 270 8.83 63.94

ITMI1272549 IT0001073045 Banca Profilo SpA Commercial Banks

Engaged in the provision of private banking, investment banking, and capital market services

Listed 1 890 8.73 68.92

ITRE0219769 IT0003121677 Credito Emiliano SpA-CREDEM

Commercial Banks


Listed 31 531 6.84 64.69

ITVE0247118 IT0003188064 Banca Ifis SpA Commercial Banks

Primarily engaged in providing factoring and other related services

Listed 11 338 3.35 95.65

ITGE0331717 IT0003211601 Banca Carige SpA Commercial Banks

Engaged in banking, insurance, trustee, instrumental, and financial businesses

Listed 42 156 3.90 54.51

ITMI1374109 IT0003477673 IW Bank SpA Commercial Banks

Operates as a commercial bank in Italy

Delisted 19/04/2011 This company has been delisted since 20110419 (takeover by Unione di Banche Italiane Scpa - UBI Banca)

3 794 3.28 94.73

ITBG0345283 IT0003487029 Unione di Banche Italiane Scpa-UBI Banca

Cooperative Banks

An Italian co-operative credit bank

Listed 124 242 9.00 52.90

ITFI0444267 IT0004194970 Cassa di Risparmio di Firenze SpA-Banca CR Firenze SpA

Savings Bank Engaged in the provision of financial products and services

Delisted 15/04/2008 This company has been delisted since 20080415 (takeover by Intesa Sanpaolo)

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ITRM1179152 IT0004781412 UniCredit SpA Commercial Banks

Engaged in the provision of banking and financial services

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ITAR0094544 IT0004919327 Banca popolare dell'Etruria e del Lazio Soc. coop.

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ITVR0358122 IT0005002883 Banco Popolare - Società Cooperativa-Banco Popolare

Cooperative Banks

A cooperative bank engaged in the provision of financial products and services

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Commercial Banks

Engaged in the activities of a commercial bank

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Lithuania LT112029270 LT0000100174 AB DNB Bankas Commercial Banks

Engaged in the provision of a wide range of banking and other financial services


3 460 11.97 87.40

LT112025973 LT0000101925 Bankas Snoras Commercial Banks

Engaged in the provision of a wide range of banking and other financial services in Lithuania and abroad


3 172 6.02 91.35

LT112025254 LT0000102253 Siauliu Bankas Commercial Banks

Engaged in the provision of banking and other financial services

Listed 1 541 6.08 89.87

Malta MTC2833 MT0000020116 Bank of Valletta Plc Investment Banks

National Bank Listed 8 297 7.41 86.85

MTC3177 MT0000030107 HSBC Bank Malta Plc Commercial Banks

Operates as a commercial bank engaged in the provision of banking and financial related products and services in the country

Listed 5 722 7.39 79.69

MTC1607 MT0000040106 Lombard Bank (Malta) Plc

Commercial Banks

Licensed as a credit institution by the Central Bank of Malta and for investment business by the Malta Financial Services Authority

Listed 610 13.84 81.44

MTC17003 MT0000180100 FIMBank Plc Finance Companies (Credit Card, Factoring & Leasing)


Listed 896 12.04 83.76

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Engaged as a bank holding firm Listed 17 670 7.58 67.52

NL33231073 NL0000303600 ING Groep NV Bank Holding & Holding Companies

Engaged in the provision of banking, investment, life insurance, and retirement services

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NL33162223 NL0000335578 BinckBank NV Commercial Banks

Online bank for investors Listed 3 209 13.45 73.24

NL16062627 NL0000390706 SNS Reaal NV Bank Holding & Holding Companies

Engaged in the provision of a full array banking and insurance services for small and medium-sized enterprises

Delisted 27/03/2013 This company has been delisted since 20130327 (takeover by STAAT DER NEDERLANDEN)

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Norway NO961095026 NO0003021909 ABG Sundal Collier Holding ASA

Investment Banks

Operates as an investment company

Listed 406 35.16 53.39

NO817244742 NO0003025009 Voss Veksel-og Landmandsbank ASA

Finance Companies (Credit Card, Factoring & Leasing)

Provision of banking products and services

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82 NO944521836 NO0006000207 SpareBank1 BV Savings Bank Provision of financial products

and services to individuals and businesses

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NO837897912 NO0006000603 Indre Sogn Sparebank Savings Bank Operates as a commercial bank Listed 413 7.36 68.99

NO952706365 NO0006000801 Sparebank 1 Nord-Norge

Savings Bank Savings bank providing financial products and services to private and corporate customers

Listed 9 245 10.97 65.54

NO832554332 NO0006000900 Sparebanken Vest Savings Bank Retail bank, which focuses it activities on the market area

Listed 16 028 6.05 48.54

NO915691161 NO0006001007 Sandnes Sparebank Savings Bank Engaged in the provision of banking services

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NO937887787 NO0006001205 Totens Sparebank Savings Bank Engaged in the provision of banking and financial services

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NO937894538 NO0006001502 Sparebanken Sor Savings Bank Engaged in the provision of financial products and services

Listed 5 457 6.80 50.90

NO937885644 NO0006001601 Aurskog Sparebank Savings Bank Operates as a commercial bank Listed 873 8.71 55.47

NO837889812 NO0006001809 Skue Sparebank Savings Bank Engaged in the provision of financial products and services

Listed 907 8.01 73.52

NO937901291 NO0006001908 Melhus Sparebank-MelhusBanken

Savings Bank Engaged as a federally chartered savings bank that provides commercial banking, securities management and other financial services, as well as the management of real estates

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NO937888937 NO0006222009 Sparebanken Ost Savings Bank Engaged in the provision of financial products and services

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NO937899319 NO0006390004 Sparebanken More Savings Bank Engaged in the provision of retail and corporate banking services, securities trading, real estate operations and other related banking services

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NO937901003 NO0006390301 SpareBank 1 SMN Savings Bank Operates as a bank Listed 13 758 9.75 62.51

NO937889275 NO0006390400 SpareBank 1 Ringerike Hadeland

Savings Bank Commercial bank which is engaged in the provision of banking services

Listed 2 151 13.61 64.25

NO937885822 NO0010012636 Hoeland og Setskog Sparebank

Savings Bank Operates as a savings bank Listed 555 7.39 71.98

NO937904029 NO0010029804 Helgeland Sparebank Savings Bank Engaged in the provision of traditional banking products and services

Listed 3 099 7.65 52.48

NO981276957 NO0010031479 DnB ASA Bank Holding & Holding Companies


Listed 284 964 5.95 53.81

83

NO837884942 NO0010285562 Sparebank 1 Ostfold Akershus

Savings Bank Engaged in the provision of financial products and services

Listed 2 165 9.23 63.22

NO937895976 NO0010359433 Klepp Sparebank Savings Bank Engaged as a regional bank that provides financial products and services

Listed 726 7.98 59.06

NO937890362 NO0010391295 SpareBank 1 Notteroy - Tonsberg

Savings Bank Operates as a bank that is engaged in providing a range of banking and financial products and services to business owners and individuals

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NO937895321 NO0010631567 SpareBank 1 SR-Bank Savings Bank Provides financial products and services to private and corporate clients

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Slovakia SK31320155 SK1110001437 Vseobecna Uverova Banka a.s.

Commercial Banks

Operates as a commercial bank in Slovakia

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SK31318916 SK1110001452 OTP Banka Slovensko, as

Commercial Banks

Operates as a commercial bank in Bratislava, Slovakia

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Sweden SE5020329081 SE0000148884 Skandinaviska Enskilda Banken AB

Commercial Banks

Provides a full range of banking services to corporate customers, institutions and private individuals

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SE5020177753 SE0000242455 Swedbank AB Savings Bank Provides a full range of financial services for private individuals, corporates and organisations in Sweden and internationally

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SE5164060120 SE0000427361 Nordea Bank AB (publ)


Provides a wide range of financial services

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Commercial Banks

Provides a wide range of banking services for private and corporate clients

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Listed 4 482 7.37 78.02

CHCHE105779532 CH0001341608 Hypothekarbank Lenzburg AG


Engaged in the provision of mortgage and other banking services

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CHCHE106824247 CH0003977193 Neue Aargauer Bank AG

Commercial Banks

Engaged in the provision of a wide range of banking and financial services

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CHCHE105884494 CH0012138530 Credit Suisse Group AG


A management holding company of a group that provides financial services

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CHCHE101390939 CH0018116472 Bank Coop AG Commercial Banks

Engaged in the provision of banking services in Switzerland

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84 CHCHE110261749 CH0030730391 ICB Financial Group

Holdings Bank Holding & Holding Companies

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CHCHE114934412 CH0102484968 Julius Baer Group Ltd Bank Holding & Holding Companies

Engaged in private banking activities primarily in Switzerland, Europe, and Asia

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GB03938288 GB0002228152 Bradford & Bingley Plc Commercial Banks

Operates as a national commercial bank principally in the United Kingdom


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GB00966425 GB0004082847 Standard Chartered Plc


Engaged in the business of banking and the provision of other financial services

Listed 488 993 6.95 71.71

GB00617987 GB0005405286 HSBC Holdings Plc Bank Holding & Holding Companies

Operates as a holding firm for a group of subsidiaries engaged in various business activities

Listed 1 936 973 7.00 63.79

GBSC095000 GB0008706128 Lloyds Banking Group Plc


Provides a range of banking and financial services in the United Kingdom and in certain locations overseas

Listed 1 011 498 4.64 56.83

GBSC218813 GB0030587504 HBOS Plc Bank Holding & Holding Companies

Firm that, through its subsidiaries, provides banking and insurance services

Delisted 19/01/2009 This company has been delisted since 20090119 (takeover by Lloyds Banking Group Plc)

665 983 3.92 83.83

GB00048839 GB0031348658 Barclays Plc Bank Holding & Holding Companies

Holding firm for a group that provides a wide range of financial services

Listed 1 567 070 4.87 55.88

GBSC045551 GB00B7T77214 Royal Bank of Scotland Group Plc (The)


UK-based holding company for a group that provides a wide range of banking, insurance and finance-related activities in the United Kingdom and worldwide

Listed 1 227 461 5.76 53.65

GB07312896 GB00BM7S7K96 OneSavings Bank Plc Savings Bank Engaged as a savings bank Listed 4 495 4.12 86.49

85

Figure 17: Banking sector indices (Northern and Eastern Europe)

These graphs provide the banking sector index evolution over the sample period for the countries of Northern and

Eastern Europe. The indices are normalized to 100 units at 2005Q1. The input data series for the graphs are obtained

from the Datastream Professional database (Thomson Reuters, 2015b). The two vertical red lines in the graphs

represent the beginning of the 2008 financial crisis and the beginning of the subsequent sovereign crisis.

0

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Eastern Europe

Slovak Republic

86

Figure 18: Banking sector indices (Southern and Western Europe)

These graphs provide the banking sector index evolution over the sample period for the countries of Southern and

Western Europe. The indices are normalized to 100 units at 2005Q1. The input data series for the graphs are

obtained from the Datastream Professional database (Thomson Reuters, 2015b). The two vertical red lines in the

graphs represent the beginning of the 2008 financial crisis and the beginning of the subsequent sovereign crisis.

0

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100

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Italy Malta

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4

Western Europe

Austria Belgium Switzerland Germany France Netherlands

87

Appendix 3: Trend reversals in the real estate data series

Figure 19: Real estate indices including trend reversal

These graphs show the trend reversal identification based on the methodology described under 3.4.5 Hypothesis 4

that is applied to the real estate indices of all countries during the sample period of this dissertation. The countries

for which no trend reversal is identified are not portrayed.

020406080

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France

88

Figure 19: Real estate indices including trend reversal (continued)

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89


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90


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Slovak Republic

91

Appendix 4: Rolling regression graphs of the market factor

Hypothesis 1

Figure 20: Rolling regression estimates of market factor - Hypothesis 1

The graph shows the estimates of the market factor coefficient for the rolling regression that is based on regression

1 (solid line). It furthermore provides the 95 % confidence interval for each estimate (dotted lines). The portrayed

estimates are reported at the end of each individual regression sample period.

Hypothesis 2


The graphs show the estimates of market factor coefficient for the rolling regression that is based on regression 4

(solid line), which is a regression that extends the base model by including a lagged real estate factor. It furthermore

provides the 95 % confidence interval for each estimate (dotted lines). The portrayed estimates are reported at the

end of each individual regression sample period.

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1/0

8/20

13

1/0

1/20

14

1/0

6/20

14

1/1

1/20

14

Market factor

92

Hypothesis 3


The figure shows the estimates of the market factor coefficient for the rolling regression that is based on regression

7 (solid line), yet leaving out the category with medium mortgage activities. It furthermore provides the 95 %

confidence interval for each estimate (dotted lines). The first graph provides the rolling regression regarding the

difference between the most and least mortgage-intensive banking sectors, whereas the latter two graphs show the

rolling regressions based on only the least mortgage-intensive banking sectors (middle) and the most mortgage-

intensive banking sectors (bottom). The portrayed estimates are reported at the end of each individual regression

sample period.

-5,0

-4,0

-3,0

-2,0

-1,0

0,0

1,0

2,0

1/03

/200

7

1/08

/200

7

1/01

/200

8

1/06

/200

8

1/11

/200

8

1/04

/200

9

1/09

/200

9

1/02

/201

0

1/07

/201

0

1/12

/201

0

1/05

/201

1

1/10

/201

1

1/03

/201

2

1/08

/201

2

1/01

/201

3

1/06

/201

3

1/11

/201

3

1/04

/201

4

1/09

/201

4

Market factor (difference)

-1,0

0,0

1,0

2,0

3,0

4,0

5,0

1/03

/200

7

1/08

/200

7

1/01

/200

8

1/06

/200

8

1/11

/200

8

1/04

/200

9

1/09

/200

9

1/02

/201

0

1/07

/201

0

1/12

/201

0

1/05

/201

1

1/10

/201

1

1/03

/201

2

1/08

/201

2

1/01

/201

3

1/06

/201

3

1/11

/201

3

1/04

/201

4

1/09

/201

4

Market factor (low mortgage activity)

-1,5-1,0-0,50,00,51,01,52,02,53,03,5

1/03

/200

7

1/0

8/20

07

1/0

1/20

08

1/0

6/20

08

1/1

1/20

08

1/0

4/20

09

1/0

9/20

09

1/0

2/20

10

1/0

7/20

10

1/1

2/20

10

1/05

/201

1

1/1

0/20

11

1/0

3/20

12

1/0

8/20

12

1/0

1/20

13

1/0

6/20

13

1/1

1/20

13

1/0

4/20

14

1/0

9/20

14

Market factor (high mortgage activity)

93

Hypothesis 4


The figure shows the estimates of the market factor coefficient for the rolling regression that is founded on the base

model and that considers the differences between the countries that have experienced the highest real estate

downturns and those that were not confronted with any real estate downturn (solid line). It furthermore provides

the 95 % confidence interval for each estimate (dotted lines). The first graph deals with the difference between the

two groups while the latter two graphs individually focus on the countries that experienced no downtrends (middle)

and those that experienced the most severe downtrends (bottom). The portrayed estimates are reported at the end

of each individual regression sample period.

-3,0

-2,0

-1,0

0,0

1,0

2,0

3,0

4,0

1/03

/200

7

1/08

/200

7

1/01

/200

8

1/06

/200

8

1/11

/200

8

1/04

/200

9

1/09

/200

9

1/02

/201

0

1/07

/201

0

1/12

/201

0

1/05

/201

1

1/10

/201

1

1/03

/201

2

1/08

/201

2

1/01

/201

3

1/06

/201

3

1/11

/201

3

1/04

/201

4

1/09

/201

4


-1,5-1,0-0,50,00,51,01,52,02,53,03,5

1/03

/200

7

1/08

/200

7

1/01

/200

8

1/06

/200

8

1/11

/200

8

1/04

/200

9

1/09

/200

9

1/02

/201

0

1/07

/201

0

1/12

/201

0

1/05

/201

1

1/10

/201

1

1/03

/201

2

1/08

/201

2

1/01

/201

3

1/06

/201

3

1/11

/201

3

1/04

/201

4

1/09

/201

4

Market factor (no downtrend)

-2,0

-1,0

0,0

1,0

2,0

3,0

4,0

5,0

1/0

3/20

07

1/0

8/20

07

1/0

1/20

08

1/06

/200

8

1/1

1/20

08

1/0

4/20

09

1/0

9/20

09

1/0

2/20

10

1/0

7/20

10

1/1

2/20

10

1/0

5/20

11

1/1

0/20

11

1/0

3/20

12

1/08

/201

2

1/0

1/20

13

1/0

6/20

13

1/11

/201

3

1/0

4/20

14

1/0

9/20

14

Market factor (severe downtrend)

94

Hypothesis 5


The figure shows the estimates of the market factor coefficient for the rolling regression that is based on regression

10 (solid line). It furthermore provides the 95 % confidence interval for each estimate (dotted lines). The first graph

considers the difference between the countries with overvalued real estate markets in 2007 and those without

overvalued real estate markets, whereas the latter two graphs individually focus on the countries without

overvalued real estate markets (middle) and with overvalued real estate markets (bottom). The portrayed estimates

are reported at the end of each individual regression sample period.

-2,5-2,0-1,5-1,0-0,50,00,51,01,52,02,53,0

1/03

/200

7

1/08

/200

7

1/01

/200

8

1/06

/200

8

1/11

/200

8

1/04

/200

9

1/09

/200

9

1/02

/201

0

1/07

/201

0

1/12

/201

0

1/05

/201

1

1/10

/201

1

1/03

/201

2

1/08

/201

2

1/01

/201

3

1/06

/201

3

1/11

/201

3

1/04

/201

4

1/09

/201

4


-1,0

-0,5

0,0

0,5

1,0

1,5

2,0

2,5

3,0

1/03

/200

7

1/08

/200

7

1/01

/200

8

1/06

/200

8

1/11

/200

8

1/04

/200

9

1/09

/200

9

1/02

/201

0

1/07

/201

0

1/12

/201

0

1/05

/201

1

1/10

/201

1

1/03

/201

2

1/08

/201

2

1/01

/201

3

1/06

/201

3

1/11

/201

3

1/04

/201

4

1/09

/201

4

Market factor (not overvalued)

-1,5-1,0-0,50,00,51,01,52,02,53,03,5

1/03

/200

7

1/08

/200

7

1/01

/200

8

1/06

/200

8

1/11

/200

8

1/04

/200

9

1/09

/200

9

1/02

/201

0

1/07

/201

0

1/12

/201

0

1/05

/201

1

1/10

/201

1

1/03

/201

2

1/08

/201

2

1/01

/201

3

1/06

/201

3

1/11

/201

3

1/04

/201

4

1/09

/201

4

Market factor (overvalued)

real estate prices and bank...

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