Arbeitspapiere der FOM · Arbeitspapiere der FOM, Nr. 57: Residential trade and industry PREFACE...

Arb

eits

papi

ere

der

FOM

Nr. 57

Residential trade and industry

European market analysis, future trends and influencing factors

~Roberto Cervelló-Royo / Francisco Guijarro Martínez /

Thomas Pfahler / Marion Preuss

Roberto Cervelló-Royo / Francisco Guijarro Martínez /

Thomas Pfahler / Marion Preuss

Residential trade and industry European market analysis, future trends and influencing factors

Arbeitspapiere der FOM, Nr. 57 Essen 2015

ISSN 1865-5610

© 2015 by

MA Akademie Verlags- und Druck-Gesellschaft mbH Leimkugelstraße 6, 45141 Essen Tel. 0201 81004-351 Fax 0201 81004-610

Das Werk einschließlich seiner Teile ist urheberrechtlich geschützt. Jede Verwertung außerhalb der engen Grenzen des Urhebergeset-zes ist ohne Zustimmung der MA Akademie Verlags- und Druck-Gesellschaft mbH unzulässig und strafbar. Das gilt insbesondere für Vervielfältigungen, Übersetzungen, Mikroverfilmungen und die Ein- speicherung und Verarbeitung in elektronischen Systemen.

Die Wiedergabe von Gebrauchs-namen, Handelsnamen, Warenbe-zeichnungen usw. in diesem Werk berechtigt auch ohne besondere Kennzeichnung nicht zu der Annah-me, dass solche Namen im Sinne der Warenzeichen- und Marken-schutz-Gesetzgebung als frei zu betrachten wären und daher von jedermann benutzt werden dürfen. Oft handelt es sich um gesetzlich geschützte eingetragene Waren- zeichen, auch wenn sie nicht als solche gekennzeichnet sind.

Arbeitspapiere der FOM, Nr. 57: Residential trade and industry

PREFACE

Residential trade and industry assets are a basic need of individuals with the

result of assessing housing as a right in several nations all over the world. Though

there is a strong focus on the relationship between populations and housing

demands, there is no direct interaction between them for two reasons: it is mainly

households and not individuals that require real estate and, secondly, the needs

of households change over time.

Therefore, various correlations can be identified in order to clarify the movements

detected in housing asset stock on a foundation of household characteristics as

well as population and society performance structures. The analysis of housing

stock in terms of demographic development is important for gaining an in-depth

understanding of housing tendencies, general characteristics and underlying

economic factors.1

In the present volume of the FOM Arbeitspapiere the authors2 establish a market

and trend analysis of the European member countries on the basis of specified

criteria. Thus they identify numerous streams as well as analogical tendencies.

1 Cp. Leal, J. (2007), p. 22 ff. 2 Corresponding author: Marion Preuss


II

The identified results could be on the one hand useful for the corresponding

decision-makers to minimise risks, uncertainties and tilts in the medium-term. On

the other hand the latter could support the long-term development of real estate

markets in order to manage public and private investments in this sector with the

target of stabilising and advancing future real estate assets.

Hamburg, August 2015

Dr. Sabine Quirrenbach

Geschäftsleitung FOM Hochschulzentrum Hamburg

University of Applied Sciences


III

INDEX

LIST OF ABBREVIATIONS ................................................................................ IV

LIST OF FIGURES .............................................................................................. V

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

2 Market analysis of the residential trade and industry in the European Union ............................................................................................................. 3

2.1 The progress in the European Union 27 ............................................. 3

2.2 Demographic progress ........................................................................ 8

2.3 Space progress ................................................................................. 46

2.4 Environmental social progress .......................................................... 57

3 Trends of the different European Union countries ...................................... 81

4 Conclusion and outlook ............................................................................... 95

LIST OF LITERATURE ....................................................................................... 97

APPENDIX........................................................................................................ 101


IV

LIST OF ABBREVIATIONS

A.D. Anno Domini

B.C. Before Christ

EEC European Economic Community

FOM Hochschule für Oekonomie &

Management

USA United States of America

USD United States Dollar

USSR Union of Soviet Socialist Republics

UVP Universidad Politécnica de Valencia


V

LIST OF FIGURES

Figure 1: Development of the population in Bulgaria .......................................... 8

Figure 2: Formation of the ageing indicators in Bulgaria ................................... 10

Figure 3: Trend of the number of households in Bulgaria ................................. 11

Figure 4: Development of the population in Estonia ......................................... 12

Figure 5: Formation of the ageing indicators in Estonia .................................... 13

Figure 6: Trend of the number of households in Estonia .................................. 15

Figure 7: Development of the population in Germany ....................................... 16

Figure 8: Formation of the ageing indicators in Germany ................................. 17

Figure 9: Trend of the number of households in Germany ............................... 19

Figure 10: Development of the population in Hungary ...................................... 20

Figure 11: Formation of the ageing indicators in Hungary ................................ 21

Figure 12: Trend of the number of households in Hungary ............................... 23

Figure 13: Development of the population in Latvia .......................................... 24

Figure 14: Formation of the ageing indicators in Latvia .................................... 25

Figure 15: Trend of the number of households in Latvia ................................... 26

Figure 16: Development of the population in Lithuania ..................................... 27

Figure 17: Formation of the ageing indicators in Lithuania ............................... 29

Figure 18: Average number of people per household in Lithuania ................... 30

Figure 19: Development of the population in Poland ........................................ 31

Figure 20: Formation of the ageing indicators in Poland ................................... 33

Figure 21: Clusters of households in Poland..................................................... 34

Figure 22: Development of the population in Romania ..................................... 35

Figure 23: Formation of the ageing indicators in Romania ............................... 37

Figure 24: Trend of the number of households in Romania .............................. 38

Figure 25: Development of the population in Slovakia ...................................... 39

Figure 26: Formation of the ageing indicators in Slovakia ................................ 40

Figure 27: Trend of the number of households in Slovakia ............................... 42

Figure 28: Development of the population in Spain .......................................... 43

Figure 29: Formation of the ageing indicators in Spain ..................................... 44

Figure 30: Formation of vacant dwellings in Spain ........................................... 45


VI

Figure 31: Build quality inside the housing in Bulgaria ...................................... 46

Figure 32: Trend of the age distribution of housing stock in Estonia ................ 47

Figure 33: Trend of the age distribution of housing stock in Germany .............. 48

Figure 34: Trend of the age distribution of housing stock in Hungary ............... 50

Figure 35: Trend of the age distribution of housing stock in Latvia ................... 51

Figure 36: Trend of the age distribution of housing stock in Lithuania .............. 52

Figure 37: Trend of the age distribution of housing stock in Poland ................. 53

Figure 38: Trend of the age distribution of housing stock in Romania .............. 54

Figure 39: Trend of the age distribution of housing stock in Slovakia ............... 55

Figure 40: Trend of the age distribution of housing stock in Spain ................... 56

Figure 41: The movement of the income level in Bulgaria ................................ 58

Figure 42: Economic conditions in Bulgaria ...................................................... 59

Figure 43: Economic conditions in Estonia........................................................ 61

Figure 44: Economic conditions in Germany ..................................................... 64

Figure 45: Economic conditions in Hungary ...................................................... 66

Figure 46: Economic conditions in Latvia .......................................................... 69

Figure 47: Economic conditions in Lithuania ..................................................... 71

Figure 48: Economic conditions in Poland ........................................................ 73

Figure 49: Economic conditions in Romania ..................................................... 75

Figure 50: Economic conditions in Slovakia ...................................................... 78

Figure 51: Economic conditions in Spain .......................................................... 80

Figure 52: Trends of the population development ............................................. 81

Figure 53: Tendencies of the median age of the populations ........................... 82

Figure 54: Development of the formation of the number of household ............. 83

Figure 55: The growing of the smaller 1- and 2-person households ................. 84

Figure 56: Shrinkage of the average number of people per household ............ 85

Figure 57: Movement to high-density and urban clusters ................................. 86

Figure 58: Formation of owner-occupied tenure status ..................................... 87

Figure 59: Exposure of total housing costs in purchasing power standards ..... 88

Figure 60: Equation of the construction cost indexes ........................................ 89

Figure 61: Trend of the income per capita ......................................................... 90


VII

Figure 62: Tendency of the GDP per capita ...................................................... 90

Figure 63: Formation of the population at risk of poverty .................................. 91

Figure 64: The tendencies of vacant conventional dwellings ............................ 92

Figure 65: Trends of the age distribution of housing stock ............................... 93


1

1 Introduction

Major demographic developments in recent decades have caused economies to

fluctuate, effecting supply and demand in the residential trade and industry. The

industry has had to react in order to stabilise, expand and avert shrinkage of its

assets. Rational decision-making is a must when dealing with this situation. There

is a high necessity for sense making of managerial decisions and strategies to

protect asset values. To successfully manage the situation, it is necessary to

focus on various fields such as demographic, space and environmental social

areas in order to gain an overview of the diverse movements and developments

in the different countries of the EU (European Union).

The aim of this paper is to realise a market analysis and identify the different

variables in order to differentiate between several streams and analogical

tendencies. In the focus of the present market analyses are the 27 countries of

the EU3 with decreasing populations, especially Bulgaria, Estonia, Germany,

Hungary, Latvia, Lithuania, Poland, Romania and Slovakia. Furthermore, there

will be a comparison with Spain whose population is expected to increase.

In Chapter 2, this paper begins with a brief description of the EU and following

the 10 in this study analysed countries, listing major historic developments and

also substantial data pertaining to them in order to gain an impression of the

protagonists at play. Then various variables relating to demographics, space and

environmental social progress are mentioned and analysed to identify the areas

of concern for the residential trade and industry.

To focus on the common ground and dissimilarities of the different countries in

these areas, important trends and shifts are highlighted in Chapter 3 to carving

out possible decision-making strategies. In Chapter 4, the paper ends with a

summary.

3 Although Croatia has been the 28th member state of the EU since July 2013, it is not

part of this analysis as a result of missing data.


3

2 Market analysis of the residential trade and industry in the European Union

A market analysis is essential for detecting different movements in the residential

trade and industry of Europe. Therefore, this chapter begins with a short

explanation of the afore-mentioned ten EU countries including their basic historic

progresses. Then several data of demographics, space and environmental social

developments are declared and analysed in order to detect the significant fields

of interest for the real estate segment. Consequently various base years of

databases from, e.g., The European Commission, The EU, Eurostat (European

Statistical Office), The United Nations and many more are explained in this

chapter with base years from around 1950 until 2050.4 Nevertheless, some data

are not available and therefore not mentioned in the analyses of this market

research.

2.1 The progress in the European Union 27

The EU is an economic and political alliance established in 1992 after ratification

of the Maastricht Treaty by members of the European Community. This alliance

includes 27 member states and expanded the political coverage of the European

Economic Community, mainly in the field of foreign and security affairs. The EU

advocated the establishment of an European bank and the implementation of a

common valuta, named the euro.5

In 2012 the GDP (Gross Domestic Product) of the EU economy amounted to

12,945,402 million euros. With around 7.0 % of the world’s population, the EU

trades about 20.0 % of all global exports and imports. Approximately 67.0 % of

the EU states trade with other EU countries. The EU was the largest importer with

a share of 16.4 % in 2011, followed by the United States with 15.5 % and China

with 11.9 %. The EU was also the most important exporter with 15.4 % of all

4 Cp. Appendix 1-8 of this analysis, pp. 78-102. 5 Cp. European Union (w.y.a), w. p., (date of demand: 15.07.2015) and European

Union (w.y.b), w. p., (date of demand: 15.07.2015).


4

exports, followed by China with 13.4 % and the United States with 10.5 %.6

Therefore, this alliance has strong macro- and micro-economic power with a high

potential for the EU member states.

There now brief descriptions of the analysed EU states will follow covering their

historical and economic developments of the last few decades:

Bulgaria is a country located in the southeast of Europe. It was first populated in

the 6th century A.D. From the 14th century it was occupied by the Ottoman Empire,

referred to sometimes as the Turkish Empire, and became independent in 1908.

After invading Bulgaria in 1944, the Soviet Union founded the People’s Republic

of Bulgaria, after which the country was under a communist government until

1989. The state established a demographic constitution in 1991. Bulgaria’s

capital is Sofia.7 It gained admission to the EU in 2007.8

Estonia is a state located in Northern Central Europe west of Russia. The country

was settled before the 1st century A.D. In the period between the 13th and 18th

centuries Estonia was occupied by the states Denmark, Germany, Sweden and

Russia. It became independent in 1918. In 1940 it was annexed by the USSR. In

1941 it was occupied by Germany, but in 1944 it reverted to being a Soviet state

named the Estonian Soviet Socialist Republic. Complete independence was

established in 1991. The capital of Estonia is Tallinn.9 Its EU entry was in 2004.10

Germany is a state in Northern Central Europe. In 500 B.C. it was annexed by

Germanic tribes. In the 6th century A.D. Germany became part of the Frankish

empire. Afterwards it became a federation of different princedoms and the core

of the Holy Roman Empire. In 1806 this imperial state was finished by Napoleon.

After 1815 Germany became an alliance and from 1871 to 1918 it was an empire

located around Prussia. After defeat in the First World War, it was restructured

6 Cp. European Union (w.y.c), w. p., (date of demand: 20.07.2015). 7 Cp. The American Heritage (2013a), w. p., (date of demand: 17.08.2015). 8 Cp. European Union (2014w.y.b), w. p., (date of demand: 20.07.2015). 9 Cp. The American Heritage (2013b), w. p., (date of demand: 17.08.2015). 10 Cp. European Union (w.y.c), w. p., (date of demand: 20.07.2015).


5

as the Weimar Republic, which came to an end when Adolf Hitler formed the

Third Reich. With Germany's defeat in 1945 at the end of the Second World War

the country was divided into four occupation zones controlled by the Allied

powers. In 1949 the zones of the allies USA, France and Britain formed West

Germany, while the Soviet zone became East Germany. These two parts of

Germany were reunified in 1990 after the collapse of the East German

Communist regime. The capital of Germany is Berlin.11 Germany was one of the

founding members of the EU and prior to this it was a member of the European

Economic Community from 1952.12

Hungary is a state in central Europe. Before the late 9th century Hungary was

under the authority of the Roman, Hunnish, Gothic and Slavic federations.

Foreign control ended when the country was captured by Magyars. In 997 St.

Stephen founded the first Hungarian state. After 1526, Hungary was ruled by the

Ottoman Turks. It later fell under Habsburg control, during which time in 1867 it

became part of Austria-Hungary. In 1918 Hungary became independent again. A

communist regime was installed in 1949, which lasted until 1989 when the

country became democratic. Budapest is the capital of Hungary.13 It entered the

EU in 2004.14

Latvia is a state in northern central Europe. In the 1200s the ancestral inhabitants,

the Letts, were captured and Christianised by German knights, named the

Livonian Brothers of the Sword, who controlled the area until 1561 when Latvia

was passed to Poland. From the 18th century, the country was under Russian

control. After the First World War, Latvia became independent. In 1940 the state

was annexed by the USSR and named the Latvian Soviet Socialist Republic. In

11 Cp. The American Heritage (2013c), w. p., (date of demand: 17.08.2015). 12 Cp. European Union (w.y.c), w. p., (date of demand: 20.07.2015). 13 Cp. The American Heritage (2013d), w. p., (date of demand: 17.08.2015). 14 Cp. European Union (w.y.c), w. p., (date of demand: 20.07.2015).


6

1990 Latvia declared its independence. Riga is its capital city.15 It entered the EU

in 2004.16

Lithuania is a state in northern central Europe. In the 1200s its different regions

were first brought together, whereby it became one of the largest countries in

medieval Europe. In 1569 Lithuania merged with Poland, but was annexed into

three Russian parts of Poland in 1772, 1793 and 1795. Lithuania was an

independent country from 1918 to 1940 when it became a constituent regime of

the USSR. From 1941 until 1944 it was occupied by Germany, but after the

Second World War it reverted to Soviet rule as the Lithuanian Soviet Socialist

Republic. In 1991 Lithuania again achieved independence. Vilnius is its capital.17

The country has belonged to the EU since 2004.18

Poland is a state in central Europe. In the 10th century it was unified as a kingdom,

and was established under the Jagiello Dynasty in the period from 1386 until

1572. It was a major power in the 15th and 16th centuries. In 1697 Poland lost its

independence and became fragmented into three parts in 1772, 1793 and 1795.

In 1918 Poland was reconstituted as a republic. Its current borders were fixed at

the end of the Second World War. Warsaw is the capital of Poland.19 Its EU entry

was in 2004.20

Romania is a country in southeast Europe. From the 3rd to the 12th century, the

state was annexed by a succession of invaders including the Goths, Huns,

Magyars and Mongols. In the 13th century the princedoms Moldavia and

Wallachia emerged within the Ottoman Empire and the Russian protectorates. In

1862 the princedoms were united then became an independent state in 1878. As

a result of a growing fascist system in the 1930s the monarchy regime changed

to a dictatorship in 1940. During the Second World War, Romania surrendered to

15 Cp. The American Heritage (2013e), w. p., (date of demand: 17.08.2015). 16 Cp. European Union (w.y.c), w. p., (date of demand: 20.07.2015). 17 Cp. The American Heritage (2013f), w. p., (date of demand: 17.08.2015). 18 Cp. European Union (w.y.c), w. p., (date of demand: 20.07.2015). 19 Cp. The American Heritage (2013g), w. p., (date of demand: 17.08.2015). 20 Cp. European Union (w.y.c), w. p., (date of demand: 20.07.2015).


7

the USSR and the state was declared a communist regime in 1947. In 1989 the

regime was overthrown with military-backed revolts. The capital of Romania is

Bucharest.21 Its entry to the EU was in 2007.22

Slovakia is a landlocked state in central Europe. In the 6th century A.D. it was

settled by Slavic peoples. In the early 10th century the area was conquered by the

Magyars. It later became part of the Hungarian regime, which lasted until 1918.

Afterwards it became part of the state of Czechoslovakia. In 1945, Slovakia was

annexed by the Soviets as a result of the Second World War and was again made

part of Czechoslovakia, which became a communist regime in 1948. With the end

of the communist regime in 1989, the country was split into two independent

republics. On 1st January 1993, the Republic of Slovakia came into its existence.

Bratislava is the capital.23 Slovakia has been a member of the EU since 2004.24

Spain is a country in southwest Europe. The area was colonised by the

Phoenicians and the Greeks and ruled after 201 B.C. by Carthage and Rome. In

409 A.D. the Barbarians penetrated Spain and were eliminated by the Moors from

North Africa during the period from 711 until 719. The Moors were displaced by

Christian countries and ousted from their last fortress in Granada in 1492.

Ferdinand of Aragon and Isabella of Castile then became lords of Spain. In the

18th and 19th centuries the empire was lost and Spain experienced social and

economic turbulence as a result of the Spanish Civil War from 1936 until 1939

and the reign of Francisco Franco. After the death of Franco, the monarchy was

rebuilt in 1975 under King Juan Carlos, who created a parliamentary democracy.

Madrid is the capital of Spain.25 Prior to the advent of the EU Spain had been a

member of the EEC (European Economic Community) from 1986.26

21 Cp. The American Heritage (2013h), w. p., (date of demand: 17.08.2015). 22 Cp. European Union (w.y.c), w. p., (date of demand: 20.07.2015). 23 Cp. The American Heritage (2013i), w. p., (date of demand: 17.08.2015). 24 Cp. European Union (w.y.c), w. p., (date of demand: 20.07.2015). 25 Cp. The American Heritage (2013j), w. p., (date of demand: 17.08.2015). 26 Cp. European Union (w.y.c), w. p., (date of demand: 20.07.2015).


8

2.2 Demographic progress

Bulgaria (BG)

Bulgaria has a population of around 7.5 million,27 which is in decline. In 1970 it

was 8.5 million28 representing an 11.8 % reduction compared to the recorded

figure for 2010. In 2050 the population is predicted to be 5.9 million,29 which will

mean a fall of 21.5 % (see figure 1). This reduction would be much greater without

the increase of the net migration ratio from minus 9.9 thousand in 2010 to plus

3.8 thousand people predicted for 2050,30 which would balance the population

shrinkage in a positive manner. The reason for the population fall is a low fertility

rate, which fell from 2.2 in 197031 to 1.6 in 2010.32 Nevertheless, the fertility rate

is one of the highest for the analysed countries. Forecasts indicate that this rate

will stabilise at this low insufficient level.33

Figure 1: Development of the population in Bulgaria

Source: based on European Commission (2012), p. 297, Eurostat (2010a), p. 163.

27 Cp. European Commission (2012), p. 297. 28 Cp. Eurostat (2010a), p. 163. 29 Cp. European Commission (2012), p. 297. 30 Cp. European Commission (2012), p. 296. 31 Cp. Eurostat (2011), p. 112 ff. 32 Cp. European Commission (2012), p. 294. 33 Cp. European Commission (2012), p. 294.

1970 2010 2050

Population (millions) 8,50 7,50 5,90

0,0

2,0

4,0

6,0

8,0

10,0

Po

pu

lati

on

(m

illio

ns)


9

The age structures have shown a significant shift in Bulgaria. As mentioned

before, the fertility rate is low with the consequence of a decline of younger

generations. On the other hand, life expectancy is increasing. From a male life

expectancy of 69.1 and female of 73.5 in the base year 1970,34 the figures had

risen to 70.3 and 77.5, respectively, by 2010.35 In 2050 it is predicted that the

figures will be 79.7 and 85.0, respectively.36 This shows a growth trend of around

15.4 % for males and 15.7 % for females from 1970 to 2050. As a result, the

ageing indicators are changing rapidly. In 1970 children aged 0 to 14 represented

22.8 % of the population.37 The working-age population aged 15 to 64

represented 67.5 % and the elderly population aged 65 years and older 9.6%.38

Today with the latest base year 2010 children have shown a shift of nearly minus

40 %, while for the elderly population it is plus 83 %. The working population is

on the whole stable at 68.7 %.39 In 2050 a marginal difference of the children’s

population of 13.5 % in total is expected, a significant shift of minus 19.5 % in the

working population, and one of 177.5 % in the elderly population,40 which

demonstrates the demographic trends in a clear manner (see figure 2). The

median age will rise from 43.0 currently to 48.1 in 2050.41 The current median

age is the second highest after Germany; nevertheless, it will stabilise by 2050 to

a mid-table position among the researched states.

34 Cp. Eurostat (2011), p. 112 ff. 35 Cp. European Commission (2012), p. 294 f. 36 Cp. European Commission (2012), p. 294 f. 37 Cp. United Nations (2013), p. 336. 38 Cp. United Nations (2013), p. 336. 39 Cp. European Commission (2012), p. 298 ff. 40 Cp. European Commission (2012), p. 298 ff. 41 Cp. United Nations (2013), p. 70 ff.


10

Figure 2: Formation of the ageing indicators in Bulgaria

Source: based on European Commission (2012), p. 298 ff., United Nations (2013), p. 336.

The trend in the residential trade and industry has been the opposite. The number

of households in 1965 was 2,542,48042 compared to 2,900,80043 in 2009; by 2030

it is predicted to be 3,236,000 after a period of continuous growth (see figure 3).44

This tendency demonstrates an increase of 27.3 % compared to 1965. The

reason is the development of the household clusters: In 1965 the approximate

share of 1-person households was 17.0 %, 2-person households 20.7 %, 3-

42 Cp. United Nations (1974), p. 38 ff. 43 Cp. CECODHAS (2012), p. 38 ff. 44 Cp. United Nations (2001), p. 246.

1970 2010 2050

Children population (0-14 years)

22,82% 13,70% 13,50%

Working agepopulation (15-64

years)67,54% 68,70% 55,30%

Elderly population (age65+)

9,64% 17,60% 31,20%

0%

10%

20%

30%

40%

50%

60%

70%

80%

Age

ing

ind

icat

ors

(%

of

tota

l po

pu

lati

on

)


11

person households 21.6 % and 4-and-more-person households 40.6 %45 with an

average number of people per household of 3.2.46 In 2009 the average number

of households was 2.4.47 Although the percentage of clusters of households is

not available for this base year, it represents a change from 3-and-more-person

households to smaller 1- and 2-person households with the result of an increase

of households in total.

Figure 3: Trend of the number of households in Bulgaria

Source: based on CECODHAS Housing Europe (2012), p. 38 ff., United Nations (1974), p. 38 ff., United Nations (2001), p. 246.

Estonia (EE)

In 2010 the population of Estonia was around 1.3 million,48 which is also in

decline; in 1970 it was 1.449. In 2050 population is predicted to be 1.2 million50,

which is also a decrease of around 15 % from 1970 to 2050 (see figure 4). Like

Bulgaria this trend would be more significant without the development of the net

migration from minus 0.5 thousand in 2010 to plus 0.8 thousand people predicted

45 Cp. United Nations (1974), p. 38 ff. 46 Cp. United Nations (1974), p. 56 ff. 47 Cp. CECODHAS (2012), p. 38 ff. 48 Cp. European Commission (2012), p. 297. 49 Cp. Eurostat (2010a), p. 163. 50 Cp. European Commission (2012), p. 297.

1965 2009 2030

Number of households(millions)

2,54 2,90 3,24

- 0,50 1,00 1,50 2,00 2,50 3,00 3,50

Ho

use

ho

ld in

dic

ato

rs

(mill

ion

s)


12

for 2050.51 Again like Bulgaria, this population fall is a result of a low fertility rate,

which was 1.6 in 2010,52 representing the highest fertility level of the researched

countries together with Bulgaria. By 2050 it is predicted to have risen to 1.7

children per woman,53 which will also be among the highest of the analysed

states.

Figure 4: Development of the population in Estonia


Furthermore, there is movement in the age indicators in Estonia. The age clusters

for the base year 1970 are not available54; nevertheless, in 2010 the life

expectancies were 69.8 for males and 80.1 for females.55 In 2050 the forecast is

79.6 and 86.6, respectively.56 This trend represents a development of plus 9.8

years for males and plus 6.5 years for females from 2010 to 2050. Consequently

the ageing indicators will change. In 1970 the cluster of the children’s population

aged 0 to 14 represented 22.0 % of the total population.57 The working-age

51 Cp. European Commission (2012), p. 296. 52 Cp. European Commission (2012), p. 294. 53 Cp. European Commission (2012), p. 294. 54 Cp. Eurostat (2011), p. 112 ff. 55 Cp. European Commission (2012), p. 294 f. 56 Cp. European Commission (2012), p. 294 f. 57 Cp. United Nations (2013), p. 336.

1970 2010 2050


1,10

1,15

1,20

1,25

1,30

1,35

1,40

1,45

Po

pu

lati

on

(m

illio

ns)


13

population aged 15 to 64 represented 66.2 % and the elderly population aged 65

and older nearly 12 %.58

Today the children’s population has fallen to 15.2 % and the elderly population

raised to 17.0 %.59 The working population has increased to 67.7 %.60 In 2050

forecasts indicate that the children’s population will represent 15.0 % of the total,

the working-age population 57.1 % and the elderly population 27.9 % (see figure

5).61 This is a significant shift of minus 31.8 % for children, minus 13.7 % for the

working population and a higher one of plus 132.5 % for the elderly from 1970 to

2050. The result is an increase of the median age in years from 40.9 in 2013 to

44.4 in 2050,62 which falls in the middle of the analysed states today, but will

develop to become one of the youngest median ages in the future.

Figure 5: Formation of the ageing indicators in Estonia


58 Cp. United Nations (2013), p. 336. 59 Cp. European Commission (2012), p. 298 ff. 60 Cp. European Commission (2012), p. 298 ff. 61 Cp. European Commission (2012), p. 298 ff. 62 Cp. United Nations (2013), p. 70 ff.

1970 2010 2050

Children population(0-14 years)

22,00% 15,20% 15,00%


years)66,23% 67,70% 57,10%

Elderly population(age 65+)

11,77% 17,00% 27,90%

0%10%20%30%40%50%60%70%80%

Age

ing

ind

icat

ors

(%

of

tota

l p

op

ula

tio

n)


14

The tendency in the residential trade and industry is the opposite. As a result of

the annexation by the USSR for the base year 1965 there is just the total number

of USSR households available, 50,333,000; the number for Estonia is not

available.63 In 2009 the number for Estonia was 548,500.64 An increasing trend

is forecast until 2030 when the number of households will reach 607,000,65 which

is a development of approximately 10.6 % over a period of 21 years (see figure

6). In 2008 1-person households represented 33.0 % of the total, 2-person

households 30.0 %, 3-person households 20.0 % and 4-and-more-person

households 17.0 %.66 Despite the lack of available base-year data, it can still be

determined that Estonia has experienced an increase in smaller 1- and 2-person

households, because the average number of people per household was 3.7 in

196567 compared to 2.4 in 2009.68 The shift in age structures is clearly visible in

the context of the household compositions: The share of the single adults under

65 is 18.3 %; single adults aged 65 and over 15.4 %; couples both under 65

11.1 %; couples with at least one aged 65 and older 7.8 %; others, no under 18s

19.1 %; single adults with children 4.2 %; and two and more adults with children

24.2 %.69 This demonstrates that senior households represent a minimum of

23.2 % of the total, which is a significant share held by senior households. 8 % of

dwellings were vacant in 2009,70 which is high in comparison to the other

analysed EU states.

63 Cp. United Nations (1974), p. 38 ff. 64 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 65 Cp. United Nations (2001), p. 246. 66 Cp. Ministry of the Interior and Kingdom Relations et al. (2010), p. 30. 67 Cp. United Nations (1974), p. 56 ff. 68 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 69 Cp. Eurostat (2010b), p. 84. 70 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 63.


15

Figure 6: Trend of the number of households in Estonia

Source: based on CECODHAS Housing Europe (2012), p. 38 ff., United Nations (2001), p. 246.

Germany (DE)

In 2010 Germany’s population was 81.7 million people,71 an increase of 4.3 %

from 78.3 million in 1970.72 Nevertheless, it is predicted that the population will

have fallen to 70.6 million people by 2050,73 which is again a negative trend of

around minus 9.8 % from 1970 to 2050 (see figure 7). Also, in Germany net

migration will prevent a higher decline; from 41.0 thousand migrants coming to

Germany in 2010, the figure is predicted to have risen to 87.7 thousand by 2050.74

Like in the afore-mentioned countries, Germany’s fertility rate was quite low at

1.4 children per woman in 2010.75 This figure is predicted to have risen to 1.5 by

2050,76 which is also low and the average for the EU.

71 Cp. European Commission (2012), p. 297. 72 Cp. Eurostat (2010a), p. 163. 73 Cp. European Commission (2012), p. 297. 74 Cp. European Commission (2012), p. 296. 75 Cp. European Commission (2012), p. 294. 76 Cp. European Commission (2012), p. 294.

2009 2030

Number ofhouseholds (millions)

0,55 0,61

0,50

0,52

0,54

0,56

0,58

0,60

0,62

Ho

use

ho

ld in

dic

ato

rs


16

Figure 7: Development of the population in Germany


In Germany age indicators demonstrate strong demographic development. While

in 1970 life expectancies were 67.5 for males and 73.6 for females,77 in 2010 the

figures are 77.6 and 82.7, respectively, with the expectation that they will continue

to rise until 2050 and reach 83.6 and 87.8, respectively.78 This demonstrates a

high rise of plus 16.1 years for males and plus 14.2 years for females from 1970

to 2050. Consequently ageing indicators will change significantly in this country.

In 1970 the children’s population aged 0 to 14 represented 23.3 % of the total

population, the working-age population aged 15 to 64 63.1 %, and the elderly

aged 65 and older 13.6 %.79 Today the children’s population has displayed a

negative shift to 13.4 %, the elderly an increase to 20.6 %, the working population

a marginal decrease to 66.0 %.80 In the forecast year 2050 it is estimated the

children’s population will represent 12.1 %, the working-age population 55.6 %

and the elderly 32.3 % including a cluster of the very elderly aged 80 years and

older of 14.5 %.81 82 This is a significant deviance of minus 48.1 % in the children’s

77 Cp. Eurostat (2011), p. 112 ff. 78 Cp. European Commission (2012), p. 294 f. 79 Cp. United Nations (2013), p. 336. 80 Cp. European Commission (2012), p. 298 ff. 81 Cp. European Commission (2012), p. 298 ff. 82 For 1970 no additional data are available.

1970 2010 2050


65,00

70,00

75,00

80,00

85,00

Po

pu

lati

on

(m

illio

ns)


17

population and plus 138.2 % of the elderly population from 1970 to 2050.

Therefore, the median age in years is predicted to grow from 45.5 in 2013 to 51.5

in 2050.83 These median ages for today and the future are the highest among the

analysed countries, meaning that Germany’s population is and will be in future

much older than in other countries with shrinking populations in the EU. The

relevant figures are summarized in the following figure:

Figure 8: Formation of the ageing indicators in Germany


83 Cp. United Nations (2013), p. 70 ff.

1970 2010 2050


23,30% 13,40% 12,10%

Working age population(15-64 years)

63,14% 66,00% 55,60%


13,56% 20,60% 32,30%

Very elderly population(age 80+)

- 5,10% 14,50%

0,00%

10,00%

20,00%

30,00%

40,00%

50,00%

60,00%

70,00%

Age

ing

ind

icat

ors

(%

of

tota

l po

pu

lati

on

)


18

The residential trade and industry in Germany has shown a decline as pictured

in figure 9. The number of households was 25,008,191 in the base year 1961 for

the Federal Republic of Germany and 1964 for the Democratic Republic of

Germany;84 in 2009 the number was 40,188,000.85 The forecast for 2030 is a fall

to 38,815,000 households,86 which is a development of approximately minus

3.4 % over the period from 2009 to 2030. In 1961/1964 1-person households

represented 22.2 % of total households, 2-person households 28.1 %, 3-person

households 22.1 % and 4-and-more-person households 27.6 %;87 in 2008 the

figures were 1-person households 39.0 % and 2-person households 34.0 %; 3-

person households had fallen to 13.0 % and 4-and-more-person households to

14 %.88 Therefore, the average number of people per household was 2.8 in

1961/197189, which had fallen to 2.0 in 2009.90 This is the lowest for the

researched states. The movement of the age structures stands out sharply in the

household compositions of Germany: The share of single adults under 65 is

24.4 % of the total population; single adults aged 65 and older 14.0 %; couples

both under 65 14.7 %; couples where at least one is aged 65 and older 14.2 %;

others, no under 18s 11.5 %; single adults with children 3.1 %; and two and more

adults with children 18.1 %.91 This demonstrates a high proportion of senior

households at a minimum of 28.2 %. The vacant conventional dwelling quote as

a percentage of the total dwelling stock counted in 1968 in the Federal Republic

as well as 1971 in the Democratic Republic was 1.7 %92 rising to 8.0 % in 2006,93

which demonstrates a fall in the number of real estate assets without a custom

function. The relevant figures are summarized in the following figure:

84 Cp. United Nations (1974), p. 38 ff. 85 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 86 Cp. United Nations (2001), p. 246. 87 Cp. United Nations (1974), p. 38 ff. 88 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 30. 89 Cp. United Nations (1974), p. 56 ff. 90 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 91 Cp. Eurostat (2010b), p. 84. 92 Cp. United Nations (1974), p. 56 ff. 93 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 63.


19

Figure 9: Trend of the number of households in Germany

Source: based on CECODHAS Housing Europe (2012), p. 38 ff., United Nations (1974), p. 38 ff. as well as United Nations (2001), p. 246.

Hungary (HU)

Hungary’s population in 2010 was around 10.0 million,94 which represents a 2.9 %

fall over the period of 1970 to 2010 from 10.3 million in 1970.95 The 2050

population is predicted to be 9.2 million,96 which will be again a declining trend of

around minus 8.0 % from 2010 to 2050. It is predicted that Hungary’s net

migration will increase; 22.5 thousand migrants came to Hungary in 2010 and this

is expected to rise to 22.0 thousand in 2050.97 This will be one of the most

important migrations in the analysed EU countries. The fertility rate is low: in 1970

the rate was below the average of balanced populations at 2.0 children per

woman98 with a significant drop to 1.3 in 2010.99 This current fertility rate ties with

Latvia as the lowest of the researched states. For 2050 the estimation is

marginally more positive at 1.5 children per woman.100 Nevertheless, this is the

94 Cp. European Commission (2012), p. 297. 95 Cp. Eurostat (2010a), p. 163. 96 Cp. European Commission (2012), p. 297. 97 Cp. European Commission (2012), p. 296. 98 Cp. Eurostat (2011), p. 112 ff. 99 Cp. European Commission (2012), p. 294. 100 Cp. European Commission (2012), p. 294.

1961/64 2009 2030


25,01 40,19 38,82

-

10,00

20,00

30,00

40,00

50,00H

ou

seh

old

ind

icat

ors


20

lowest rate across the decreasing populations of the EU 27. The relevant figures

are summarized in the following figure:

Figure 10: Development of the population in Hungary


The shift of the age indicators points to a tendency towards a more senior-

focussed population. In 1970 the average life expectancy was 66.3 for males and

72.1 for females,101 which had risen to 70.4 and 78.4, respectively, in 2010. The

predicted figures for 2050 are 80.0 and 85.9, respectively102, representing an

increase of 20.7 % for males and 19.1 % for females across the period 1970 to

2050. Also the ageing indicators will change as a result. In 1970 the children’s

population aged 0 to 14 represented 20.9 % of the total population, the working-

age population aged 15 to 64 67.5 % and the elderly population aged 65 and

older 11.6 %.103 In 2010 the children’s population had dropped to 14.7 %, the

elderly population increased to 16.7 %, and the working population fallen to

68.6%.104 In 2050 it is predicted that the children’s population will be 12.5%, the

working-age population 58.1% and the elderly population 29.4% (see figure

101 Cp. Eurostat (2011), p. 112 f. 102 Cp. European Commission (2012), p. 294 f. 103 Cp. United Nations (2013), p. 336. 104 Cp. European Commission (2012), p. 298 ff.

1970 2010 2050


8,50

9,00

9,50

10,00

10,50

Po

pu

lati

on

(m

illio

ns)


21

11).105 This is a deviance of minus 40.5% in the children’s population and plus

145.0 % in the elderly population from 1970 to 2050. The median age in years

will change from the current 40.6 to 46.1 in 2050.106 These ages fall in the mid

range of the analysed countries.

Figure 11: Formation of the ageing indicators in Hungary

Source: based on European Commission (2012), p. 298, cp. United Nations (2013), p. 336.

The residential trade and industry has shown an increase. In 1970 the number of

households was 2,720,500;107 by 2009 this had risen to 3,790,600.108 Forecasts

indicate that by 2030 it will have risen further to 3,946,000 (see figure 12),109

which represents a strong shift of approximately 45.0 % over the period 1970 to

105 Cp. European Commission (2012), p. 298 ff. 106 Cp. United Nations (2013), p. 70 ff. 107 Cp. United Nations (1974), p. 38 ff. 108 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 109 Cp. United Nations (2001), p. 246.

1970 2010 2050


20,89% 14,70% 12,50%


years)67,54% 68,60% 58,10%


11,57% 16,70% 29,40%

0%

10%

20%

30%

40%

50%

60%

70%

80%

Age

ing

ind

icat

ors

(%

of

tota

l po

pu

lati

on

)


22

2030. The household clusters have shifted accordingly. In 1970 1-person

households represented 0.0 % of the total, 2-person households 29.6 %, 3-

person households 29.3 % and 4-and-more-person households 41.2 %.110 2004

saw a tendency towards smaller households: 1-person households 29.0 %, 2-

person households 30.0 %, 3-person households 19.0 % and 4-and-more-person

households 23.0 %.111 The average number of people per household of 3.0 in

1970112 had fallen to 2.6 by 2009.113 The movement of the age structures is

apparent from Hungary’s household compositions: In 2007 single adults under

65 represented 11.5 % of the total population; single adults aged 65 and older

12.8 %; couples both under 65 12.8 %; couples where at least one is aged 65

and older 8.6 %; others, no under 18s 22.6 %; single adults with children 3.2 %;

and two and more adults with children 28.6.114 This means senior households

represent a minimum of 21.4 % of the total; nevertheless, this is relatively low in

comparison to the other countries. In 1970 3.4 % of dwellings were vacant,115

which had risen to 5.6 % by 2005.116 This is more balanced and lower than for

the other analysed countries.

110 Cp. United Nations (1974), p. 38 ff. 111 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 30. 112 Cp. United Nations (1974), p. 56 ff. 113 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 114 Cp. Eurostat (2010), p. 84. 115 Cp. United Nations (1974), p. 56 ff. 116 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 63.


23

Figure 12: Trend of the number of households in Hungary

Source: based on CECODHAS Housing Europe (2012), p. 38 ff., cp. United Nations (1974), p. 38 ff. as well as United Nations (2001), p. 246.

Latvia (LV)

Latvia’s population in 2010 as around 2.2 million,117 which is in decline; in 1970 it

was 2.4 million118 with 1.8 million forecast for 2050 (see figure 13).119 This

represents a high reduction of 25 % from 1970 to 2050. Also in Latvia, net

migration is expected to take a positive swing: while net migration in 2010 was

minus 3.4 thousand, it is predicted to be 1.9 thousand in 2050.120 While the past

fertility rate is not available, the figure for 2010 was 1.3 children per woman.121

Along with Hungary this is the lowest among the researched EU countries. For

2050 it is predicted to have risen marginally to 1.5.122 Although this trend is low

in which the demographic characteristics will continue over the future time.


1970 2009 2030


2,72 3,79 3,95

-

1,00

2,00

3,00

4,00

5,00H

ou

seh

old

ind

icat

ors


24

Figure 13: Development of the population in Latvia

Source: based on European Commission (2012), p. 297 and Eurostat (2010a), p. 163.

The life expectancies are developing in a mostly analogical manner compared to

the afore-mentioned countries: While the figures for 1970 are not available, in

2010 they were 68.3 for males and 78.0 for females.123 In 2050 they are predicted

to have risen to 78.9 and 85.6, respectively124, representing a significant increase

of 15.5 % for males and 9.7 % for females from the base year 1970 to 2050. In

1970 the children’s population aged 0 to 14 represented 21.6 % of the total

population, the working-age population aged 15 to 64 66.4 % and the elderly

population aged 65 and older 12.0 %.125

Today with an actual base year of 2010 the children’s population has a negative

development with a quotation of 13.8 %, the elderly population increases to a

share of 17.3 %; the working population will marginally increase to 68.9 %.126 In

2050 it is estimated that the children’s population be 12.3 % in total, the working-

age population will be 56.6 % and the elderly 31.2 % (summarized in the following

figure). The figure for the very elderly generation aged a minimum of 80 years will

123 Cp. European Commission (2012), p. 294 f. 124 Cp. European Commission (2012), p. 294 f. 125 Cp. United Nations (2013), p. 336. 126 Cp. European Commission (2012), p. 298 ff.

1970 2010 2050


0,00

0,50

1,00

1,50

2,00

2,50

3,00

Po

pu

lati

on

(m

illio

ns)


25

be 10.7 %,127 which is the 3rd highest of the population. This is a crucial difference

of minus 43.1 % in the children’s population and plus 160.0 % in the elderly

population between 1970 and 2050. The median age in years will also increase

from 41.5 per person to 42.7 in 2050.128

Figure 14: Formation of the ageing indicators in Latvia

Source: based on European Commission (2012), p. 298 ff. and United Nations (2013), p. 336.

The residential trade and industry of Latvia is on a downward trend in contrast to

most of the afore-mentioned countries (see figure 15). In 1970 the total number

of households was only analysed for the whole of the USSR.129 Nevertheless, in

2009 the figure was 863,400.130 In 2030 it is forecast to have fallen to 839,000,131

which demonstrates a negative trend of minus 2.8 % over the period 2009 to

2030. While the data for clusters of households are not available for 1970 and

127 Cp. European Commission (2012), p. 298 ff. 128 Cp. United Nations (2013), p. 70 ff. 129 Cp. United Nations (1974), p. 38 ff. 130 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 131 Cp. United Nations (2001), 246.

1970 2010 2050


21,61% 13,80% 12,30%


years)66,38% 68,90% 56,60%


12,01% 17,30% 31,20%

0,00%10,00%20,00%30,00%40,00%50,00%60,00%70,00%80,00%

Age

ing

ind

icat

ors

(%

of

tota

l p

op

ula

tio

n)


26

2030, in 2004 each had the following share: 1-person households 24.0 %, 2-

person households 30.0 %, 3-person households 23.0 % and 4-and-more-person

households 23.0 %.132 In 1965 the average number of people per household in

the former USSR was 3.7;133 the 2009 figure for Latvia was just 2.5.134 The 2007

share of age structures are as follows: single adults under 65 represent 12.8 %

of the total population; single adults aged 65 and older 12.4 %; couples both

under 65 8.6 %; couples where at least one is aged 65 and older 6.5 %; others,

no under 18s 25.7 %; single adults with children 4.0 % and two and more adults

with children 30.1.135 This is a relatively low level of senior households with a

minimum of 18.9 % with the result of a more balanced household structure in

comparison to other EU states. In the base year of 2008 8.6 % of dwellings were

vacant,136 which can be viewed as high.

Figure 15: Trend of the number of households in Latvia

Source: based on CECODHAS Housing Europe (2012), p. 38 ff., United Nations (1974), p. 38 ff.as well as United Nations (2001), p. 246.

132 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 30. 133 Cp. United Nations (1974), p. 56 ff. 134 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 135 Cp. Eurostat (2010b), p. 84. 136 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 63.

2009 2030


0,86 0,84

0,83 0,83 0,84 0,84 0,85 0,85 0,86 0,86 0,87 0,87

Ho

use

ho

ld in

dic

ato

rs


27

Lithuania (LT)

Lithuania’s population in the base year 1970 was 3.1 million,137 and had grown to

around 3.3 million by 2010.138 Nevertheless, in 2050 the population is predicted

to have fallen to 2.8 million139 (see figure 16), which represents a shift of minus

9.7% across this whole timeframe from 1970 to 2050. The high net migration in

2010 caused a negative change of minus 13.0 thousand people, but it is predicted

to have risen to plus 2.2 thousand by 2050.140 This positive trend will prevent

greater population shrinkage. Lithuania had a positive fertility rate in 1970 of 2.4

children per woman,141 but this had fallen to a low of 1.5 in 2010.142

Forecasts indicate that it will remain more or less stable until 2050 when it will be

1.6 children, which is also low.143

Figure 16: Development of the population in Lithuania


137 Cp. Eurostat (2010a), p. 163. 138 Cp. European Commission (2012), p. 297. 139 Cp. European Commission (2012), p. 297. 140 Cp. European Commission (2012), p. 296. 141 Cp. Eurostat (2011), p. 112 ff. 142 Cp. European Commission (2012), p. 294. 143 Cp. European Commission (2012), p. 294.

1970 2010 2050


2,40

2,60

2,80

3,00

3,20

3,40

Po

pu

lati

on

(m

illio

ns)


28

In Lithuania the age indicators demonstrate a demographic shift. While in 2010

the average life expectancy was 67.7 years for males and 78.7 for females, it is

expected to have risen by 2050 to 78.5 and 85.6, respectively.144 This

demonstrates an increase of 10.8 years for males and 6.9 years for females from

2010 to 2050. As a consequence the ageing indicators will rapidly change. In

1970 the cluster of the children’s population aged 0 to 14 represented 27.0 % of

the total population, the working-age population aged 15 to 64 had a percentage

of 62.8 % and the elderly population aged 65 and older had a share of nearly

10.1 %.145 Today the children’s population has shown a negative shift to a

quotation of 15.0 %, the elderly population has risen to a share of 16.1%; and the

working population increased to 68.9 %.146 In the forecast year 2050 it is

estimated that the children’s population will represent 14.0% of the total, the

working-age population 58.2 % and the elderly population 27.8 %, which includes

a cluster of a very elderly population aged 80 years and older of 10.2 % (see

figure 17).147 This is a significant deviance of minus 48.1% in the children’s

population and plus 175.2 % of the elderly population between 1970 and 2050.

Therefore, the median age in years is estimated with a growing shift from an

average age of 39.3 per person in 2013 to a median age of 44.2 in 2050.148 This

medium age is one of the lowest ages in the analysed countries.

144 Cp. European Commission (2012), p. 294 f. 145 Cp. United Nations (2013), p. 336. 146 Cp. European Commission (2012), p. 298 ff. 147 Cp. European Commission (2012), p. 298 ff. 148 Cp. United Nations (2013), p. 70 ff.


29

Figure 17: Formation of the ageing indicators in Lithuania


The formation in the residential trade and industry in Lithuania is showing a

positive growth development. The number of households in 2009 was

1,392,700;149 by 2030 forecasts indicate to have increased to 1,528,000

households,150 which demonstrate a development of approximately 9.7 % over

the period 2009 until 2030. The household cluster formations for the current and

past decades are not available. Nevertheless, the average number of people per

149 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 150 Cp. United Nations (2001), 246.

1970 2010 2050


27,03% 15,00% 14,00%


years)62,83% 68,90% 58,20%


10,14% 16,10% 27,80%

0,00%

10,00%

20,00%

30,00%

40,00%

50,00%

60,00%

70,00%

80,00%A

gein

g in

dic

ato

rs (

% o

f to

tal p

op

ula

tio

n)


30

household of 3.7 in 1965151 and 2.4 in 2009152 demonstrates a trend towards

smaller households (see figure 18). In 2007 a shift in age structures is visible from

the household compositions in Lithuania: The share of the single adults under 65

number 12.1 % of the total population; single adults aged 65 and older 14.9 %;

couples both under 65 9.6 %, couples where at least one is aged 65 and older

7.9 %; others, no under 18s 21.9 %; single adults with children 3.8 % and two

and more adults with children 29.8 %.153 This demonstrates senior households

with a proportion of a minimum of 22.8 % in total, which is a relatively balanced

composition level. Total vacant dwelling stock amounted to 3.7 % in 2001,154

which is the lowest level of the analysed countries. Therefore, it is estimated that

there is a basic realisation of custom-made dwellings.

Figure 18: Average number of people per household in Lithuania

Source: based on CECODHAS Housing Europe (2012), p. 38 ff., United Nations (1974); p. 56 ff.

151 Cp. United Nations (1974), p. 56 ff. 152 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 153 Cp. Eurostat (2010b), p. 84. 154 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 63.

1965 2009

Average number ofpersons perhouseholds

3,70 2,40

- 0,50 1,00 1,50 2,00 2,50 3,00 3,50 4,00

Ho

use

ho

ld in

dic

ato

rs


31

Poland (PL)

Poland’s population in 2010 was around 38.2 million (see the following figure),155

which is an increase on the 1970 figure of 32.7 million.156 By 2050 it will have

fallen to 34.5 million157, which is also a decreasing trend of around 9.7 % over the

period 2010 to 2050. Like the other analysed countries, this trend would be more

important without the increasing net migration from 11.7 thousand in 2010 to the

34.2 thousand calculated for 2050.158 The population shrinkage like in the other

states is also generated as a result of a too-low fertility rate of 1.4 in 2010.159 For

2050 it is predicted to have increased marginally to 1.5 children per woman.160

Figure 19: Development of the population in Poland

Source: Own representation based on European Commission (2012), p. 297 and Eurostat (2010a), p. 163.


1970 2010 2050


28,00

30,00

32,00

34,00

36,00

38,00

40,00

Po

pu

lati

on

(m

illio

ns)


32

Age indicators are a crucial demographic aspect in Poland. The age clusters in

the base year 1970 are not available.161 In 2010 the life expectancies were fixed

at an average of 71.7 for males and 80.1 for females.162 In 2050 it is forecast to

be 80.6 for males and 86.6 for females.163 This trend represents a development

of plus 8.9 years for males and plus 6.5 years for females from 2010 to 2050.

These age tendencies point to a high ageing process of the population in contrast

to most of the other analysed countries. Consequently, the ageing indicators will

also change. In 1970 the cluster of the children’s population aged 0 to 14

represented 26.9 % of the total population, the working-age population aged 15

to 64 64.9 %, and the elderly aged 65 and older nearly 8.3 %.164 Today in the

base year 2010 the children’s population has seen a negative shift to 15.1 %,

while the elderly have risen to 13.5 %. The working population has increased to

71.3 %.165 In 2050 it is predicted that the children’s population will represent

12.5 % of the total population, the working-age population 56.9 % and the elderly

30.6 %.166 This is a significant shift of minus 53.5 % for children, minus 12.3 %

for the working population and a more significant shift of plus 270.9 % for the

elderly from 1970 to 2050. This development is one of the most important in the

researched EU areas and, therefore, represents a major challenge for the real

estate sector. The result is an increase of the median age from 38.8 in 2013 to

48.9 in 2050.167 The relevant indicators are summarized in the following figure:

161 Cp. Eurostat (2011), p. 112 ff. 162 Cp. European Commission (2012), p. 294 f. 163 Cp. European Commission (2012), p. 294 f. 164 Cp. United Nations (2013), p. 336. 165 Cp. European Commission (2012), p. 298 ff. 166 Cp. European Commission (2012), p. 298 ff. 167 Cp. United Nations (2013), p. 70 ff.


33

Figure 20: Formation of the ageing indicators in Poland


There has been a tendency towards growth in the residential trade and industry

in Poland. In 2009 the total number of households was 13,319,200.168 By 2030 it

is predicted to have risen to 14,362,000169, which is approximately a 7.8 %

increase over a period of 21 years. The current household cluster figures for 2004

showed a balance: 25.0 % 1-person households, 23.0 % 2-person households,

20.0 % 3-person households and 32.0 % 4-and-more-person households (see

figure 21).170 Although only the data for 2009 are available, it can still be

determined that Poland is shifting towards smaller 1- and 2-person households

as the average number of people per household was 3.5 in 1970,171 but only 2.8

168 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 169 Cp. United Nations (2001), 246. 170 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 30. 171 Cp. United Nations (1974), p. 56 ff.

1970 2010 2050


26,89% 15,10% 12,50%


64,86% 71,30% 56,90%


8,25% 13,50% 30,60%

0,00%

10,00%

20,00%

30,00%

40,00%

50,00%

60,00%

70,00%

80,00%

Age

ing

ind

icat

ors

(%

of

tota

l po

pu

lati

on

)


34

in 2009.172 The movement of the age structures is apparent from the household

compositions: In 2007 the share of single adults under 65 amounted to 11.3 % of

the total population; single adults aged 65 and older 13.4 %; couples both under

65 10.0 %; couples with at least one partner aged 65 and older 6.6 %; others, no

under 18s 24.6 %; single adults with children 1.8 %; and two and more adults

with children 32.4 %.173 This demonstrates that senior households represent a

minimum of 20.0% of the total, which today points strongly to a balanced age

structure. The amount of vacant conventional dwellings has changed over the

years: in 1970 the figure was 2.6 %174 and in 2002 5.3 %.175 Nevertheless, this

represents the 2nd smallest growth after Lithuania among the countries analysed,

and it can be concluded that the country consists mainly of stable residential trade

and industry portfolio assets because most are in use by the population.

Figure 21: Clusters of households in Poland

Source: based on Ministry of the Interior and Kingdom Relations (2010), p. 30.

172 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 173 Cp. Eurostat (2010b), p. 84. 174 Cp. United Nations (1974), p. 56 ff. 175 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 63.

25%

23%20%

32%

Clusters of households 2004

1-person-households in %



4-person-households in %and more


35

Romania (RO)

Romania’s population in 2010 was around 21.4 million176, which represents a

decline; in 1970 it was 20.1 million177 with 18.4 million forecast for 2050 (see

figure 22).178 This points to an 8.5 % drop between 1970 and 2050. Also in

Romania the net migration has shown a positive development: while the current

net migration figures for 2010 showed a negative trend of about minus 0.2

thousand, migration for 2050 is predicted to be plus 16.8 thousand.179 While the

past fertility rate is not available, the 2010 figure of 1.4 children per woman in

2010 is one of the lowest among the analysed countries.180 For 2050 the forecast

is marginally more positive at 1.5,181 but remains low.

Figure 22: Development of the population in Romania

Source: European Commission (2012), p. 297, Eurostat (2010a), p. 163.


1970 2010 2050


16,00

17,00

18,00

19,00

20,00

21,00

22,00

Po

pu

lati

on

(m

illio

ns)


36

Life expectancy develops in an analogical way to the afore-mentioned

demographic characteristics of the country.182 While in 1970 the average life

expectancy was 65.8 for males and 70.4 for females,183 in 2010 the figures had

risen to 70.0 and 77.5, respectively.184 By 2050 they are expected to have risen

further to 79.8 and 85.1, respectively.185 This demonstrates the significant

increase of 21.3 % for males and 20.9 % for females from the base year 1970 to

2050. In 1970 the children’s population aged 0 to 14 represented 25.9 % of the

total population, the working-age population aged 15 to 64 66.6 % and the elderly

aged 65 and older nearly 8.4 %.186 Today with the current base year of 2010 the

children’s population has fallen to 15.2 %, the elderly population increased to 14.9

% and the working population increased to 69.9 %.187 In 2050 it is estimated that

the children’s population will have fallen further to 11.9 %, the working-age

population to 57.0 % and the elderly population increased to 31.1 %. The very

elderly aged at least 80 years will represent 9.6 %,188 which is high for this

population (see figure 23). This is a crucial difference of minus 54.1 % in the

children’s population and plus 267.6 % of the elderly population from 1970 to

2050, which is one of the most crucial shifts in the EU with shrinking populations.

The median age in years will also increase from 39.4 in 2013 to a much higher

48.8 in 2050.189

182 No additional data available. 183 Cp. Eurostat (2011), p. 112 ff. 184 Cp. European Commission (2012), p. 294 f. 185 Cp. European Commission (2012), p. 294 f. 186 Cp. United Nations (2013), p. 336. 187 Cp. European Commission (2012), p. 298 ff. 188 Cp. European Commission (2012), p. 298 ff. 189 Cp. United Nations (2013), p. 70 ff.


37

Figure 23: Formation of the ageing indicators in Romania


The residential trade and industry of Romania has growing structures (see figure

24). In 1966 the sum of households was analysed with 5,954,555.190 In 2009 the

number was 7,395,700.191 In 2030 the formation will increase with a total number

of 8,288,000 households,192 which demonstrates a significant growth trend of

about 39.2 % over the period 1966 to 2030. In 1966, 1-person households

represented 14.2 % of the household share, for 2-person households 23.4 %, 3-

person households 23.4 % and 4-and-more-person households 39.1 %;193 in

2008 the figures were 18.0 % for 1-person households, 27.0 % 2-person

households, 23.0 % 3-person households and 33.0 % 4-and-more-person

190 Cp. United Nations (1974), p. 38 ff. 191 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 192 Cp. United Nations (2001), 246. 193 Cp. United Nations (1974), p. 38 ff.

1970 2010 2050


25,91% 15,20% 11,90%


65,62% 69,90% 57,00%


8,46% 14,90% 31,10%


- 3,20% 9,60%

0%10%20%30%40%50%60%70%80%

Age

ing

ind

icat

ors

(%

of

tota

l po

pu

lati

on

)


38

households.194 The average number of people per household was 3.2 in 1966,195

which had fallen to 2.9 by 2009.196

Figure 24: Trend of the number of households in Romania

Source: based on CECODHAS Housing Europe (2012), p. 38 ff., United Nations (1974), p. 38 ff., United Nations (2001), 246.

Slovakia (SK)

In 2010 Slovakia had around 5.4 million inhabitants,197 which is an important

growth tendency of 20.0 % in the period 1970 to 2010 as a result of the population

having been 4.5 million in 1970.198 The future population for 2050 is forecast at

5.3 million (see figure 25)199, which is a tendency towards a marginal declining

trend of around minus 1.9 % in this time frame from 2010 to 2050. The net

migration of Slovakia has a positive formation; the net sum of migrants in 2010

was 10.6 thousand and is predicted to remain relatively stable and be at a level

of 9.9 thousand in 2050.200 The fertility quote of Slovakia is low: In 1970 the rate

194 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 30. 195 Cp. United Nations (1974), p. 56 ff. 196 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 197 Cp. European Commission (2012), p. 297. 198 Cp. Eurostat (2010a), p. 163. 199 Cp. European Commission (2012), p. 297. 200 Cp. European Commission (2012), p. 296.

1966 2009 2030


5,95 7,40 8,29

- 1,00 2,00 3,00 4,00 5,00 6,00 7,00 8,00 9,00

Ho

use

ho

ld in

dic

ato

rs


39

was a balanced growth level with 2.4 children per woman201 with the result of an

increasing population structure. This fell sharply to 1.4 children per woman in

2010.202 For 2050 the estimated rate is marginally more positive at 1.5 children

per woman.203

Figure 25: Development of the population in Slovakia


The shift of the age indicators shows a strong demographic tendency (see the

following figure). In 1970 the average life expectancies were 66.8 for males and

73.0 for females,204 and in 2010 71.6 for males and 79.1 for females.205 This trend

is predicted to develop strongly until 2050 with a life expectancy of 80.3 for males

and 86.0 for females,206 representing an increase of about 20.2 % for males and

17.8 % for females in the period 1970 to 2050. Consequently the ageing

indicators will also change significantly. In 1970 the cluster of the children’s

population aged 0 to 14 represented 27.4 % of the total population, the working-

age population aged 15 to 64 63.5 %, and the elderly aged 65 and older nearly

201 Cp. Eurostat (2011), p. 112 ff. 202 Cp. European Commission (2012), p. 294. 203 Cp. European Commission (2012), p. 294. 204 Cp. Eurostat (2011), p. 112 ff. 205 Cp. European Commission (2012), p. 294 f. 206 Cp. European Commission (2012), p. 294 f.

1970 2010 2050


0,00

1,00

2,00

3,00

4,00

5,00

6,00

Po

pu

lati

on

(m

illio

ns)


40

9.1 %.207 By 2010 the children’s population had shown a negative shift down to

15.3 %, the elderly population an increase to 12.3 % and the working population

a marginal decrease to 72.4 %.208 In 2050 forecasts indicate that the children’s

population will be 12.7 %, the working-age population 57.4 % and the elderly

29.9 %.209 This is a significant deviance of minus 53.6 % in the children’s

population and plus 232.2 % in the elderly population from 1970 to 2050, which

shows a strong shift mainly in the ageing indicators of the senior generation. The

median age in years will increase significantly from the current 38.2 to 48.2 in

2050,210 which is a significant increase of 10 years across this timeframe.

Figure 26: Formation of the ageing indicators in Slovakia


207 Cp. United Nations (2013), p. 336. 208 Cp. European Commission (2012), p. 298 ff. 209 Cp. European Commission (2012), p. 298 ff. 210 Cp. United Nations (2013), p. 70 ff.

1970 2010 2050


27,43% 15,30% 12,70%


63,50% 72,40% 57,40%


9,07% 12,30% 29,90%

0,00%

10,00%

20,00%

30,00%

40,00%

50,00%

60,00%

70,00%

80,00%

Age

ing

ind

icat

ors

(%

of

tota

l po

pu

lati

on

)


41

Slovakia has positive residential trade and industry formations (see figure 27). In

2009 it had 1,756,500 households,211 which are predicted to have risen to

2,396,000 by 2030,212 which is an increase of approximately 36.4 % over the

period 2009 to 2030. The developments in regard to household clusters are

relevant here. In 1970 in Czechoslovakia 1-person households represented

14.2 % of the total, 2-person households 26.8 %, 3-person households 22.1 %

and 4-and-more-person households 36.9 %.213 By 2004 there had been a major

change in 1-person households rising to 26.0 %. 2-person households stagnated

at 22.0 %, 3-person households declined at 18.0 % and 4-and-more-person

households decreased at 35.0 %.214 The average number of people per

household in 1961 in Czechoslovakia was 3.1,215 which had fallen to 2.8 by

2009.216 The changes of the age structure are tied to the household compositions

in Slovakia: In 2007 single adults under 65 represented 11.4 % of the total

population; single adults aged 65 and older 13.1 %; couples with both partners

under 65 8.0 %; couples where at least one is aged 65 and older 7.9%; others,

no under 18s 30.1 %; single adults with children 1.3 %; and two and more adults

with children 28.2 %.217 This means that senior households represent a minimum

of 21.0 %. In 1961 0.2 % of dwellings were vacant218 compared to 11.1 % in

2008.219 This current vacancy level is the 2nd highest after Spain, which shows

the significantly high non-use of real estate assets in Slovakia.

211 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 212 Cp. United Nations (2001), 246. 213 Cp. United Nations (1974), p. 38 ff. 214 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 30. 215 Cp. United Nations (1974), p. 56 ff. 216 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 217 Cp. Eurostat (2010b), p. 84. 218 Cp. United Nations (1974), p. 56 ff. 219 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 63.


42

Figure 27: Trend of the number of households in Slovakia

Source: based on CECODHAS Housing Europe (2012), p. 38 ff., United Nations (1974), p. 38 ff., United Nations (2001), p. 246.

Spain (ES)

In 2010 Spain had a population of about 46.1 million220, which is an increase of

37.2 % as in 1970 it was 33.6 million (see figure 28).221 In 2050 it is predicted to

have risen to 52.7 million222, which is again a significant growth trend of around

41.7 % across the whole time frame from 1970 to 2050. The net migration of

Spain points to a strong population formation; the migration figure for 2010 was

79.1 thousand, which is expected to have increased to 209.7 thousand by

2050.223 However, also Spain has low fertility rates: 1.4 children per woman in

2010 and a predicted 1.5 for 2050224, which is also low.

220 Cp. European Commission (2012), p. 297. 221 Cp. Eurostat (2010a), p. 163. 222 Cp. European Commission (2012), p. 297. 223 Cp. European Commission (2012), p. 297. 224 Cp. European Commission (2012), p. 294.

2009 2030


1,76 2,40

-

0,50

1,00

1,50

2,00

2,50

3,00

Ho

use

ho

ld in

dic

ato

rs


43

Figure 28: Development of the population in Spain


In Spain the age indicators demonstrate a shift towards a more senior-focused

society (see figure 29). The average life expectancies in the base year 2010 were

78.6 for males and 84.7 for females.225 These are predicted to have risen to 84.2

and 89.0, respectively, by 2050.226 These are the highest for the analysed

countries in the EU, and represents a large increase plus 5.6 years for males and

plus 4.3 years for females from 2010 to 2050.

As a consequence the ageing indicators will change rapidly. In 1970 the cluster

of the children’s population aged 0 to 14 represented 27.9 % of the total

population, the working-age population aged 15 to 64 62.5 %, and the elderly

aged 65 and older nearly 9.7 %.227 Today the children’s population has seen a

negative shift to 15.0 %, the elderly population increase to 17.0 %, and the

working population grow to 68.0 %.228 In 2050 forecasts indicate that the

children’s population will be 13.1 %, the working-age population 55.3 % and the

225 Cp. European Commission (2012), p. 294 f. 226 Cp. European Commission (2012), p. 294 f. 227 Cp. United Nations (2013), p. 336. 228 Cp. European Commission (2012), p. 298 ff.

1970 2010 2050


0,00

10,00

20,00

30,00

40,00

50,00

60,00P

op

ula

tio

n (

mill

ion

s)


44

elderly population 31.6 %, which means a very elderly population aged 80 years

and older of 11.5 %.229 This is a significant deviance of minus 53.0 % in the

children’s population and plus 227.1 % in the elderly population from 1970 to

2050, which also shows a significant large difference between the former and

future years.230 Therefore, the median age in years is estimated to increase from

41.4 in 2013 to 50.4 in 2050.231 This predicted median age for 2050 is the 2nd

highest after Germany.

Figure 29: Formation of the ageing indicators in Spain


The formation in the residential trade and industry in Spain is declining (see figure

30). In 2009 the total number of households was 17,076,300.232 By 2030 it is

229 Cp. European Commission (2012), p. 298 ff. 230 For 1970 no additional data available. 231 Cp. United Nations (2013), p. 70 ff. 232 Cp. CECODHAS Housing Europe (2012), p. 38 ff.

1970 2010 2050


27,89% 15,00% 13,10%


years)62,45% 68,00% 55,30%


9,66% 17,00% 31,60%


- 5,00% 11,50%

0%10%20%30%40%50%60%70%80%

Age

ing

ind

icat

ors

(%

of

tota

l po

pu

lati

on

)


45

predicted to have fallen to 12,713,000,233 which is a significant decrease of

approximately 25.6 % over the period 2009 to 2030. In 2008 the development of

household clusters was balanced: 1-person households 18.0 %, 2-person

households 29.0 %, 3-person households 26.0 % and 4-and-more-person

households 26 %.234 The average number of people per household was 4.0 in

1960,235 which had shown a marked decrease to 2.1 by 2009.236 The movement

of the age structures is defined within the household compositions of Spain: The

share of single adults under 65 represents 8.6 % of the total population; single

adults aged 65 and older 8.7 %; couples where both partners are under 65

12.2 %; couples where at least one partner is aged 65 and older 10.0 %; others,

no under 18s 29.2 %; single adults with children 1.1 %; and two and more adults

with children 30.2 %.237 This demonstrates a balance among senior households

with a minimum of 18.7 %. In 1950 1.2 % of dwellings were vacant,238 with a

crucial shift to 21.9 % in 2004.239 This is the highest among the researched EU

countries, with the conclusion that the real estate sector is in a sharp decline

today.

Figure 30: Formation of vacant dwellings in Spain

Source: based on Eurostat (2010b), p. 84, Ministry of the Interior and Kingdom Relations (2010), p. 63.

233 Cp. United Nations (2001), 246. 234 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 30. 235 Cp. United Nations (1974), p. 56 ff. 236 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 237 Cp. Eurostat (2010b), p. 84. 238 Cp. United Nations (1974), p. 56 ff. 239 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 63.

1950 2004

Vacant dwellings (%of total dwelling

stock)1,20% 21,90%

0,00%10,00%20,00%30,00%

Ho

usi

ng

ind

icat

ors


46

2.3 Space progress

Bulgaria (BG)

Because the age distribution of the housing stock for this country is not available,

an interpretation of the age distribution for the future is not possible. However,

the housing amenities were recorded from 1965 to 2001: Housing with flush

toilets increased from 11.8 %240 to 66.2 %241; residences with a fixed bath or

shower rose from 8.7 %242 to 77.9 %243 which demonstrates a significant

movement towards custom-fit real estate assets as shown in the following figure:

Figure 31: Build quality inside the housing in Bulgaria

Source: based on United Nations (1974), p. 182 ff., United Nations (2012), Table 6, p. 1 ff., United Nations (2012), Table 7, p. 1 ff.

240 Cp. United Nations (1974), p. 182 ff. 241 Cp. United Nations (2012), Table 6, p. 1 ff. 242 Cp. United Nations (1974), p. 182 ff. 243 Cp. United Nations (2012), Table 7, p. 1 ff.

1965 2001

Toilet flush 11,80% 66,20%

Fixed bath or shower 8,70% 77,90%

0,00%

10,00%

20,00%

30,00%

40,00%

50,00%

60,00%

70,00%

80,00%

90,00%

Bu

ild Q

ual

ity

( in

% in

dw

elli

ngs

)


47

Estonia (EE)

The age composition of housing stock of Estonia in 2009 is remarkable (see

figure 32): a relatively high share of 9.4 % of the housing stock had a construction

year older than 1919; 14.2 % were constructed between 1919 and 1945, 30.0 %

between 1946 and 1970, 21.5 % between 1971 and 1980, 19.6 % between 1981

and 1990, 2.0 % between 1990 and 2000 and 3.3 % have been relatively newly

built since 2000.244 The result is a housing stock, of which 53.6 % is older than

45 years with the earliest construction of at least 96 years. Consequently a need

will arise for refurbishments, new constructions and modernisations in the coming

years and decades. The average number of rooms per dwelling is 3.3,245 which

is in contrast to the demographic shift of the state.

Figure 32: Trend of the age distribution of housing stock in Estonia

Source: based on Ministry of the Interior and Kingdom Relations (2010).

244 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 54. 245 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 52.

9%

14%

30%22%

20%

2% 3%

Age distribution of housing stock 2009 in %

< 1919

1919-1945

1946-1970

1971-1980

1981-1990

1990-2000

> 2000


48

Germany (DE)

In Germany the age formation of housing stock in 2006 had a strong and

unbalanced tendency (as shown in figure 33): a high share of 14.4 % was built

prior to 1919, 13.6 % between 1919 and 1945, a significant 46.3 % between 1946

and 1970, 0.0 % between 1971 and 1980, 13.2 % between 1981 and 1990, just

9.2 % between 1990 and 2000 and a low rate of 3.3% since 2000.246

Consequently 74.3 % of housing stock is older than 45 years, which

demonstrates a high rate of old residential trade and industry assets with the need

of advancement and further development. The average number of rooms per

dwelling also has an adverse balance and was 4.4 in 2008.247 This points to a

tendency that runs counter to the demographic development of household sizes

in Germany.

Figure 33: Trend of the age distribution of housing stock in Germany

Source: based on Ministry of the Interior and Kingdom Relations (2010), p. 54


15%

14%

46%

0%

13%

9%

3%


< 1919

1919-1945

1946-1970

1971-1980

1981-1990

1990-2000

> 2000


49

Hungary (HU)

The age composite of housing stock of Hungary in 2005 was as follows: 0.0 % of

the housing stock had a construction year prior to 1919, 20.8 % had one between

1919 and 1945, a significant 27.2 % between 1946 and 1970, 23.1 % between

1971 and 1980, 17.8 % between 1981 and 1990, 7.9 % between 1990 and 2000,

and just 3.2 % new constructions built after 2000 (see figure 34).248 As a result

the share of housing stock built earlier than 1970 is 48.0 %, which demonstrates

a more balanced rate of old residential trade and industry assets in contrast to

the afore-mentioned analysed states. The average number of rooms per dwelling

was 2.6 in 2010,249 which is low and in line with the trend of smaller households

in future. The housing amenities were recorded from 1970 to 2001: housing with

a flush toilet increased from 32.7 %250 to 86.5 %251; residences with a fixed bath

or shower rose from 32.2 %252 to 88.8 %,253 which is a significant development.

248 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 54. 249 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 52. 250 Cp. United Nations (1974), p. 182 ff. 251 Cp. United Nations (2012), Table 6, p. 1 ff. 252 Cp. United Nations (1974), p. 182 ff. 253 Cp. United Nations (2012), Table 7, p. 1 ff.


50

Figure 34: Trend of the age distribution of housing stock in Hungary


Latvia (LV)

The age consistency of housing stock of Latvia in 2008 was at a high percentage

of 13.8 % for buildings built before 1919; 13.1 % between 1919 and 1945; a

significant 22.1 % between 1946 and 1970; 19.4 % between 1971 and 1980;

20.2 % between 1981 and 1990; 7.0 % between 1990 and 2000 and just 4.4 %

after 2000 (see figure 35).254 49.0 % of housing stock was constructed earlier

than 1970, which points to a more balanced rate of newer residential trade and

industry assets in contrast to other countries mentioned before. In 2008 the

average number of rooms per dwelling was 2.5.255 These shifts could be useful

for the movement to smaller households in future. Furthermore, the interior quality

of housing stock is improving: in 2000 79.7 % of the total dwelling stock had a


0%

21%

27%

23%

18%

8%

3%

Age distribution of housing stock 2005 in % - rounded

< 1919

1919-1945

1946-1970

1971-1980

1981-1990

1990-2000

> 2000


51

piped water system,256 72.8 % a flush toilet,257 98.6 % electric lighting,258 and

67.3 % a fixed bath or shower.259

Figure 35: Trend of the age distribution of housing stock in Latvia


256 Cp. United Nations (2012), Table 5, p. 1 ff. 257 Cp. United Nations (2012), Table 6, p. 1 ff. 258 Cp. United Nations (2012), Table 10, p. 1 ff. 259 Cp. United Nations (2012), Table 7, p. 1 ff.

14%

13%

22%20%

20%

7% 4%


< 1919

1919-1945

1946-1970

1971-1980

1981-1990

1990-2000

> 2000


52

Lithuania (LT)

In 2002 in Lithuania the age mixture of housing stock was at an unbalanced level:

6.2 % of the housing stock was built prior to 1919, a significant 23.3 % between

1919 and 1945, 33.1 % between 1946 and 1970, 17.6 % between 1971 and 1980,

13.5 % between 1981 and 1990, 6.3 % between 1990 and 2000, and 0.0 % after

2000.260 (The figures are shown in figure 36) As a result the share of housing

stock older than 45 years was 62.6 %, which demonstrates a significant rate of

old residential trade and industry assets and a zero percentage of new buildings

built after 2000. The average number of rooms per dwelling was 2.5 in 2003.261

This figure makes concessions to the demographic tendency in this country,

which is an advantage for the residential trade and industry in this field.262

Figure 36: Trend of the age distribution of housing stock in Lithuania


260 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 54. 261 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 52. 262 Detailed information e.g. concerning sanitary facilities are not available.

6%

23%

33%

18%

14%6%

0%


< 1919

1919-1945

1946-1970

1971-1980

1981-1990

1990-2000

> 2000


53

Poland (PL)

In Poland the age of housing stock in 2002 comprised 10.1 % of the buildings

built prior to 1919, 13.1 % between 1919 and 1945, again 26.9 % between 1946

and 1970, 18.3 % between 1971 and 1980, 18.7 % between 1981 and 1990,

12.9 % between 1990 and 2000, and 0.0 % after 2000 (see figure 37).263 50.1 %

of the housing stock is at least 45 years old; therefore, there will be a need for

asset development such as in regard to refurbishments, new constructions and

modernisations in the coming years and decades. The average number of rooms

per dwelling was 3.7 in 2008.264 With a view to the country’s demographic

development tending towards non-custom-fit dwellings this share is high.

Figure 37: Trend of the age distribution of housing stock in Poland



10%

13%

27%18%

19%

13%

0%


< 1919

1919-1945

1946-1970

1971-1980

1981-1990

1990-2000

> 2000


54

Romania (RO)

Romania had the following age distribution of housing stock in 2002: just 3.9 %

was constructed later than 1919, 11.5 % between 1919 and 1945, a significant

37.3 % between 1946 and 1970, 23.8 % between 1971 and 1980, 14.8 %

between 1981 and 1990, 7.3 % between 1990 and 2000, and just 1.4 % after

2000 (see following figure).265 52.7 % of housing stock was constructed earlier

than 1970, which points to a more balanced rate of newer residential trade and

industry assets in contrast to other countries mentioned earlier. In 2008 the

average number of rooms per dwelling was 2.6.266 This represents a shift in a

direction that could be useful for the movement to smaller households in future.

Figure 38: Trend of the age distribution of housing stock in Romania



4%

12%

37%24%

15%

7%

1%


< 1919

1919-1945

1946-1970

1971-1980

1981-1990

1990-2000

> 2000


55

Slovakia (SK)

Slovakia has a more balanced rate of old real estate assets. The age distribution

of real estate assets in 2001 was as follows: 3.4 % had a construction year prior

to 1919, a low 6.6 % between 1919 and 1945, the major portion of 35.1 %

between 1946 and 1970, 25.6 % between 1971 and 1980, 21.0 % between 1981

and 1990, just 6.2 % between 1990 and 2000, and a marginal 0.6 % are newer

buildings built after 2000 as shown in figure 39.267 Therefore, the share of housing

stock constructed in 1970 and earlier was 45.1 %, which is a low and balanced

rate of old residential trade and industry assets. The average number of rooms

per dwelling was 3.2 in 2001.268 This share is high in front of the demographic

development of the country.

Figure 39: Trend of the age distribution of housing stock in Slovakia



3%

7%

36%

26%

21%

6%

1%


< 1919

1919-1945

1946-1970

1971-1980

1981-1990

1990-2000

> 2000


56

Spain (ES)

The age mixture of housing assets of Spain in 2001 was notable: 8.9 % of the

housing stock was built prior to 1919, 4.2 % between 1919 and 1945, a significant

33.5 % between 1946 and 1970, again 24.1 % between 1971 and 1980, 13.6 %

between 1981 and 1990, 15.7 % between 1990 and 2000, and a remarkable

0.0 % after 2000 as shown in figure 40.269 Therefore, 46.6 % of housing were

older stock constructed before 1970; these rates demonstrate an equated

proportion of age distributions. The average number of rooms per dwelling was

5.1 in 2008,270 which is very high and stands in significant contrast to the

demographic tendencies of the country.

Figure 40: Trend of the age distribution of housing stock in Spain



9%4%

33%

24%

14%

16%

0%


< 1919

1919-1945

1946-1970

1971-1980

1981-1990

1990-2000

> 2000


57

2.4 Environmental social progress

Bulgaria (BG)

Bulgaria covers an area of 110,912 km2.271 In 1970 the population density per

km2 was 77 people.272 The urban cluster was 50.6 % of the total population, while

for rural areas the figure was 49.4 %.273 The tenure status in the base year 1965

had a high level of owner-occupiers representing 71.0 % of the total; the second

cluster of private renters was 17.1 %.274 The additional clusters are not available.

In the current base year 2007 the population density per km2 had fallen to 69

people.275 The population was heading in a more urban direction until 2006: a

high-density cluster of 35 %, an urban cluster of 26 % and a rural cluster of

39 %.276 Also tenure status changed significantly: In 2008 the share of owner-

occupied housing was 95.6 %277 with an increase of 34.6 % in contrast to 1965.

These processes will continue in the future. In 2050 the forecast for population

per km2 is predicted to be much lower at 46.0 people278 and a high urban cluster

of 83.41 %.279 A forecast of how tenure status will develop in the future is not

available.

Also the economic conditions are important to focus on. The 2010 per-capita

income level in comparison to the other countries with shrinking populations in

the EU 27 is low at 2,542 USD.280 The share of housing costs in disposable

income is 18.4 %.281 This is a relatively low and, therefore, positive level in

comparison to other analysed countries in the EU. The number of dependent

people in Bulgaria is 333,000.282 This is around 4.4 % of the population, which is

271 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 19. 272 Cp. United Nations (1971), p. 110 ff. 273 Cp. United Nations (1971), p. 148 ff. 274 Cp. United Nations (1974), p. 56 ff. 275 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 19. 276 Cp. Eurostat (2012a), p. 199. 277 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 278 Cp. Geohive (w.y.b), w. p. (date of demand: 10.07.2015). 279 Cp. Geohive (w.y.a), w. p. (date of demand: 10.07.2015). 280 Cp. HSBC Global Research (2012), p. 4 f. 281 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 282 Cp. European Commission (2012), p. 358.


58

at a relatively stable level. For 2050 there is a higher income of 13,154 USD per

capita forecasted (see figure 41) 283, which is less significant than in other states

in the EU 27 the number of dependent people will increase by about 6.3 % of the

population equating to 370,000.284

Figure 41: The movement of the income level in Bulgaria

Source: based on HSBC Global Research (2012), p. 4 f.

The total housing costs in purchasing power standards to express the volume of

economic aggregates is 165.6285 based on 2009, which represents a lower

standard than most of the other countries mentioned earlier. The construction

cost index as an indicator of the average cost movement over time of a fixed

basket of representative goods and services related to the construction industry

with a basis of 2005 equal to 100 % is 139.9,286 which is one of the highest and,

therefore, more adverse levels of the evaluated countries with shrinking

populations. Estimations for the future are not available. Nevertheless, these

economic conditions demonstrate contrasting relationships and could have

consequences for the portfolio management. The unemployment rate of the

283 Cp. HSBC Global Research (2012), p. 4 f. 284 Cp. European Commission (2012), p. 358. 285 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 286 Cp. CECODHAS Housing Europe (2012), p. 38 ff.

2010 2050

Income per capita(USD)

2.542,00 13.154,00

-

2.000,00

4.000,00

6.000,00

8.000,00

10.000,00

12.000,00

14.000,00

Inco

me

leve

l


59

working-age population was 10.5 % in 2010.287 For 2060 it is estimated to be

7.3 %,288 which could be an indication of the demographic developments in

Bulgaria. The share of the population at risk of poverty, which is the percentage

of people with an equalised available income under the risk-of-poverty level of

60 % of the national median spendable income, is at 46.2 %289, which is the

highest of the analysed countries. Future estimates are not available. This could

become a critical area in the future economy. The GDP rate is 1.9 % per capita

and is expected to have fallen by 2050 to 1.4 %.290 The potential GDP growth

rate in 2010 was 1.8 %, and is expected to have fallen by 1% to 0.8% in 2050,291

which represents a reduction of the economic potential for the industry. The

economic conditions are summarized in the following figure:

Figure 42: Economic conditions in Bulgaria

Source: based on European Commission (2012), p. 85, European Commission (2012), p. 301 ff.

287 Cp. European Commission (2012), p. 85. 288 Cp. European Commission (2012), p. 85. 289 Cp. Eurostat (2012b), p. 272. 290 Cp. European Commission (2012), p. 301 ff. 291 Cp. European Commission (2012), p. 301 ff.

2010 2050/60

Unemployment rate(%, 15-64))

10,50% 7,30%

GDP per capita 1,90% 1,40%

Potential GDP (growthrate)

1,80% 0,80%

0%

2%

4%

6%

8%

10%

12%

Eco

no

mic

co

nd

itio

ns


60

Estonia (EE)

Estonia covers an area of 45,227 km2.292 In 1970 the population density per km2

in the USSR was low at 11 people293 with a balanced urban cluster of 56.3 % of

the total population, and 43.7 % for rural areas.294 In 2007 the average population

density per km2 had risen to 30.9 people.295 The tenure status of the base year

1965 is not available. The population density per km2 in 2006 showed an increase

of the high-density cluster to 32.0 %, urban cluster 29.0 %, and a decrease of the

rural cluster to 39.0 %.296 Tenure data for 2008 demonstrated a high and crucial

share of owner-occupied housing with 96.0 %.297 In 2050 the population density

per km2 is predicted to have fallen to 25.0 people298 with the urban cluster having

increased to 80.0 %.299

The proportion of housing costs from disposable income was 15.5 % in 2009.300

This is the lowest share of the analysed countries in the EU and shows a positive

economic tendency in the residential trade and industry. Dependent people

number 95,000,301 which equates to approximately 7.3 % and is relatively high

for this country. In 2050 it is predicted that the amount of dependent people will

have risen to 9.4 % equating to 113,000.302 The total housing costs in purchasing

power standards is 179.0303 for the base year 2009; this standard is located in

the middle of the analysed states. The construction cost index with a basis of

2005 equal to 100 % is 115.3.304 Therefore, the prices for new buildings are

mainly stable in the economy. The high unemployment rate of the working-age

292 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 19. 293 Cp. United Nations (1971), p. 110 ff. 294 Cp. United Nations (1971), p. 148 ff. 295 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 19. 296 Cp. Eurostat (2012a), p. 199. 297 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 298 Cp. Geohive (w.y.b), w. p. (date of demand: 10.07.2015). 299 Cp. Geohive (w.y.a), w. p. (date of demand: 10.07.2015). 300 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 301 Cp. European Commission (2012), p. 358. 302 Cp. European Commission (2012), p. 358. 303 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 304 Cp. CECODHAS Housing Europe (2012), p. 38 ff.


61

population was 17.2 % in 2010.305 For 2060 it is estimated to have fallen to

7.3 %306 as a result of an upturn in the employment market in Estonia. 23.4 % of

the population is at risk of poverty,307 which is one of the lowest levels among the

selected states. Future estimates are not available. The GDP rate in 2010 was

minus 0.8 % per capita, but by 2050 is expected to have increased to plus

1.1 %.308 The potential GDP growth rate was also low in 2010 at minus 0.8 %,

but is forecast to have increased to 0.9 % by 2050.309 These figures show

Estonia’s poor economic situation (see figure 43), both today and in future, which

will also have consequences for developments in the real estate sector.

Figure 43: Economic conditions in Estonia


305 Cp. European Commission (2012), p. 85. 306 Cp. European Commission (2012), p. 85. 307 Cp. Eurostat (2012b), p. 272. 308 Cp. European Commission (2012), p. 301 ff. 309 Cp. European Commission (2012), p. 301 ff.

2010 2050/60


17,20% 7,30%

GDP per capita -0,80% 1,10%


-0,80% 0,90%

-2%0%2%4%6%8%

10%12%14%16%18%20%

Eco

no

mic

co

nd

itio

ns


62

Germany (DE)

Germany has an area of 357,031 km2.310 In 1970 the population density per km2

was 212.73 people311 with a balanced urban cluster of 43.3 % of the total

population in 1968/1969; for rural areas it was 56.7 %.312 In the actual base year

2007 the appropriate population rate per km2 was calculated at 229.9 people.313

The population density per km2 in 2006 reveals an important movement towards

a high-density cluster of 31.0 %, an urban cluster of 41.0 %, and a low rural cluster

of 28.0 %.314 In comparison to other researched countries the tenure level in 2008

showed a relatively low rate of owner-occupied housing at 42.0 %, private

housing at 53.0 % and social housing at 5.0 %.315 In 2050 forecasts indicate that

the population density per km2 will have fallen to 203.0 people,316 with an

increased urban cluster of 83.8 % and a rural cluster of 16.2 %.317

The income level of 2010 is in comparison to the other countries with shrinking

populations in the EU 27 on a high level with a per-capita income of 25,083.00

USD predicted to be 52,683.00 USD in 2050.318 The amount of housing costs

from disposable income was calculated to be high and, therefore, an adverse

share of 31.0 % in 2009 was noticed.319 In 2009 the free market rent was available

to the amount of 4,900 euros per year and up to an area of 71.0 m2 per

habitation.320 There are 8,408.0 thousand dependent people in the country,321

representing more than 10 %, which is deemed highly. By 2050 it is predicted to

have increased further to 13.9 % equating to 9,810.0 thousand people.322 As this

310 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 19. 311 Cp. European Commission (2012), p. 110 ff. 312 Cp. United Nations (1971), p. 148 ff. 313 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 19. 314 Cp. Eurostat (2012a), p. 199. 315 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 316 Cp. Geohive (w.y.b), w. p. (date of demand: 10.07.2015). 317 Cp. Geohive (w.y.a), w. p. (date of demand: 10.07.2015). 318 Cp. HSBC Global Research (2012), p. 4 f. 319 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 320 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 90. 321 Cp. European Commission (2012), p. 358. 322 Cp. European Commission (2012), p. 358.


63

defines people dependent on financial support, this places the real estate sector

and the economy on a rather downward slope. The total housing costs in

purchasing power standards was 771.5 in 2009.323 This is by far the highest of

the analysed countries and limits the additional spending capacity of the

population. The construction cost index in 2005 equal to 100 % was at a balanced

level of 111.5.324 The unemployment rate of the working-age population was the

lowest of the evaluated states at 7.2 % in 2010.325 For 2060 it is expected to have

fallen to 6.1 %.326 20.0 % of the population is deemed at risk of poverty,327 which

is one of the lowest levels of the analysed states. The GDP rate in 2010 was

1.2 % per capita, but by 2050 is expected to have increased to 1.4 %.328 In 2010

the size of the economy was calculated at 2,058 billion USD, and is predicted to

have risen to 3,714 USD by 2050.329 Nevertheless, these mentioned economic

conditions (summarized in the following figure) point to a stable and increasing

tendency of Germany.

323 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 324 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 325 Cp. European Commission (2012), p. 85. 326 Cp. European Commission (2012), p. 85. 327 Cp. Eurostat (2012b), p. 272. 328 Cp. European Commission (2012), p. 301 ff. 329 Cp. HSBC Global Research (2012), p. 4.


64

Figure 44: Economic conditions in Germany


Hungary (HU)

Hungary covers an area of 93,030 km2.330 In 1970 its population density per km2

was 111.0 people.331 In 1969 its urban cluster represented 44.4 % of the total

population and the remaining part of 55.6% is represented by the rural areas.332

In 2007 the average population rate per km2 was calculated to have fallen to

108.1 people.333 The population density per km2 fixed in the last base year 2006

was again at a balanced level of a high-density cluster with 24.0 %, an urban

cluster of 33.0 % and a rural cluster of 43.0 %.334 By 2050 the population per km2

is expected to have fallen to 96.0 people335 with a significant share of an urban

cluster of 82.1 %.336 This tendency demonstrates a high development towards

330 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 19. 331 Cp. United Nations (1971), p. 110 ff. 332 Cp. United Nations (1971), p. 148 ff. 333 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 19. 334 Cp. Eurostat (2012a), p. 199. 335 Cp. Geohive (w.y.b), w. p., (date of demand: 10.07.2015). 336 Cp. Geohive (w.y.a), w. p., (date of demand: 10.07.2015).

2010 2050/60


7,20% 6,10%



1,20% 0,80%

0%1%2%3%4%5%6%7%8%

Eco

no

mic

co

nd

itio

ns


65

more urban areas. The tenure level in 1970 demonstrated a high proportion of

owner-occupied housing with 62.9 % and private housing with 29.6 %.337 In 2008

the tenure status of owner-occupied housing shifted to a crucial level of 92.0 %.338

There have been important changes regarding economic conditions. The Per-

capita income was 5,833 USD in 2010 and is predicted to be 31,966 USD in

2050.339 The proportion of housing costs from disposable income is calculated

with a balanced quote of 23.2 % in 2009.340 The free market rent is analysed with

1,700 euros per year and a size of 51.0 m2 per habitation in 2009; the level of

rent in regulated markets was much lower at 400 euros per year and an average

size dwelling of 48 m2 in 2009.341 8.1 % of the population were described as being

dependent on others, equating to 805.0 thousand people.342 By 2050 this number

is expected to have increased to 1,002.0 thousand.343 This figure of 10.9 % is

significant for the economic situation due to a reduction in the spending power of

the inhabitants of Hungary.

The total housing costs in purchasing power standards were 244.60 euros in

2009.344 This lies in the middle of the researched states of the EU. The 2005

construction cost index equal to 100 % was relatively high in comparison to the

other countries and came to 123.1.345 The unemployment rate of the working-age

population was 11.3 % in 2010. By 2060 it is expected to have fallen to 7.3 %.346

29.6 % of the population was deemed at risk of poverty in 2009, which is relatively

high compared to the other countries analysed.347 The gross domestic product

rate in 2010 was negative at minus 1.5 % per capita but by 2050 is expected to

337 Cp. United Nations (1974), p. 56 ff. 338 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 339 Cp. HSBC Global Research (2012), p. 4 f. 340 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 341 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 90. 342 Cp. European Commission (2012), p. 358. 343 Cp. European Commission (2012), p. 358. 344 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 345 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 346 Cp. European Commission (2012), p. 85. 347 Cp. Eurostat (2012b), p. 272.


66

have increased to plus 1.2 %.348 The size of the economy in 2010 was 58.0 billion

USD and is expected to have increased to 295.0 billion USD by 2050.349

Therefore, this economic tendency will move in a more positive direction in the

future as shown as follows:

Figure 45: Economic conditions in Hungary


348 Cp. European Commission (2012), p. 304. 349 Cp. HSBC Global Research (2012), p. 4 f.

2010 2050/60


11,30% 7,30%



0,20% 0,90%

-4%-2%0%2%4%6%8%

10%12%

Eco

no

mic

co

nd

itio

ns


67

Latvia (LV)

Latvia covers an area of 64,589 km2.350 In 1970 under the USSR regime the

population density per km2 was 11.0 people351 with an urban cluster of 56.3 % of

the total population; rural areas according to the available base year 1959

represented 43.7 % of the population.352 By 2007 the average population rate per

km2 had increased to 36.5 people.353 The population density per km2 in the last

base year 2006 included a balanced share of a high-density cluster of 35.0 %, an

urban cluster of 25.0 % and a rural cluster of 40.0 %.354 In 2050 the population

per km2 is again expected to have fallen to 26.0 people355, with the urban cluster

rising crucially to 78.1 % and the rural group falling to 21.9 %.356 Like the countries

analysed earlier this tendency again demonstrates a high development to urban

areas. In 2010 the high share of owner-occupied housing was calculated at

84.9 %, private housing at 14.7 % and a small amount of social housing at

0.4 %.357 The income per capita was calculated at 4,973 USD in 2010 and is

estimated to have reached a more balanced level of 27,143 USD by 2050.358 The

proportion of housing costs from disposable income was found to be the 3rd

lowest of the countries analysed at 18.0 % in 2009.359 6.2 % of the population

was calculated to be dependent people equating to 137.0 thousand.360 In 2050 it

is expected to have increased to 157.0 thousand361 or 8.7 %, which will be on a

balanced level in comparison to the other analysed countries. The total housing

costs in purchasing power standards had fallen to be among the middle of the

other ranked states with 186.2 in 2009.362 The 2005 construction cost index equal

350 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 19. 351 Cp. United Nations (1971), p. 110 ff. 352 Cp. United Nations (1971), p. 148 ff. 353 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 19. 354 Cp. Eurostat (2012a), p. 199. 355 Cp. Geohive (w.y.b), w. p., (date of demand: 10.07.2015). 356 Cp. Geohive (w.y.a), w. p., (date of demand: 10.07.2015). 357 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 358 Cp. HSBC Global Research (2012), p. 4 f. 359 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 360 Cp. European Commission (2012), p. 358. 361 Cp. European Commission (2012), p. 358. 362 Cp. CECODHAS Housing Europe (2012), p. 38 ff.


68

to 100 % was 152.5.363 This is the highest of the researched countries with the

result of higher prices in the new building sector. The unemployment rate of the

working-age population was the 2nd highest after Spain with 19.0 % in 2010; for

2060 it is expected to have fallen sharply to 7.3 %.364 The share of the population

at risk of poverty in 2009 was established as the 3rd highest with 37.4 %.365 The

GDP rate in 2010 was negative at minus 0.7 % per capita and will have increased

marginally by 2050 to plus 1.0 %.366 The size of the economy was fixed in 2010

at 11.0 billion USD and is predicted to be 52.0 billion USD for 2050.367 In

consequence the economy focuses on difficult conditions, but will exhibit a

tendency towards a more positive development in the future (see figure 46). The

average annual rent for rental dwellings of the free market was fixed in 2009 at

85,000 euros and an average dwelling size of 48 m2, while the rent of regulated

markets was much lower at 14,000 euros per year and 51 m2 the average size

for the rental dwellings,368 which reveals a strong difference between free and

regulated markets. The average price for an existing dwelling was 24,000 euros

in 2009,369 which is on a relatively low level in contrast to the rents of the free

market.

363 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 364 Cp. European Commission (2012), p. 85. 365 Cp. Eurostat (2012b), p. 272. 366 Cp. European Commission (2012), p. 304. 367 Cp. HSBC Global Research (2012), p. 4. 368 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 90. 369 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 89.


69

Figure 46: Economic conditions in Latvia


Lithuania (LT)

Lithuania covers an area of 65,300 km2.370 In 1970 the population density in the

former USSR was 11 people per km2 371 with an urban cluster of 56.3 % of the

total population; in 1959 rural areas represented 43.7 %.372 In the actual base

year 2007 the population rate per km2 for Lithuania itself was calculated to be a

higher 53.9 people.373 The population density per km2 in 2006 was stable with a

high-density cluster of 32.0 %, urban cluster of 12.0 % and rural cluster of

56.0 %.374 Therefore, Lithuania includes the highest tendency towards rural living

compared to the afore-mentioned countries. In 2050 the population per km2 is

370 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 19. 371 Cp. United Nations (1971), p. 110 ff. 372 Cp. United Nations (1971), p. 148 ff. 373 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 19. 374 Cp. Eurostat (2012a), p. 199.

2010 2050/60


19,00% 7,30%



-1,90% 0,30%

-5%

0%

5%

10%

15%

20%

25%Ec

on

om

ic c

on

dit

ion

s


70

expected to have fallen to 39.0 people375, with an urban cluster of 78.7 % and

rural cluster of 21.3 % of the population,376 which points to an assimilated

formation in contrast to previous decades in this country. The tenure level in 2008

demonstrated a high rate of owner-occupied housing with 91.0 %, private housing

4.0 % and social housing 3.0 % with about 2.0 % being other housing

constellations.377

The income level of 2010 was low with a per-capita income of 5,154 USD which

will be increased to 20,955 USD by 2050 according to forecasts.378 The

proportion of housing costs coming out of disposable income was 15.9 % in

2009.379 This is one of the lowest shares of the evaluated countries in the EU 27,

which allows the possibility of growth for the real estate sector in the future. The

free market rent was available at 1,100 euros per year and a size of 61.0 m2 per

habitation in 2008; the average rent of the regulated market was around 10 % of

the free market rents, which equates to 100 euros per year with a dwelling size

of 44 m2.380 8.5 % of the population is deemed dependent on others equating to

280 thousand people.381 In 2050 this is expected to have increased to 327

thousand382 or 11.7 %, which is high. The total housing costs in purchasing power

standards were 168.2 in 2009.383 This is the 3rd lowest of the researched countries

and should have a positive influence on the real estate sector. The 2005

construction cost index equal to 100 % came to 116.1.384 Also this trend is stable

and ranks in the middle of the analysed countries. In 2010 the unemployment rate

of the working-age population was very high at 18.1 % compared to the other

states; for 2060 it is forecast to have fallen sharply to 7.3 %.385 The percentage

375 Cp. Geohive (w.y.b), w. p. (date of demand: 10.07.2015). 376 Cp. Geohive (w.y.a), w. p. (date of demand: 10.07.2015). 377 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 378 Cp. HSBC Global Research (2012), p. 4 f. 379 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 380 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 90. 381 Cp. European Commission (2012), p. 358. 382 Cp. European Commission (2012), p. 358. 383 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 384 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 385 Cp. European Commission (2012), p. 85.


71

of the population at risk of poverty is relatively high at 29.5 %.386 The GDP rate in

2010 was low at 0.8 % per capita but will increase marginally until 2050 to 1.2 %

(as shown in the following figure).387 The size of the economy in 2010 was 17.0

billion USD and is expected to be 59.0 USD in 2050.388

Figure 47: Economic conditions in Lithuania


Poland (PL)

Poland covers an area of 312,685 km2.389 In 1970 the population density per km2

was 105 people390 with an urban cluster of 52.0 % of the total population; rural

areas represented 48.0 %.391 By 2000 the average population per km2 had

increased to 122.4 people.392 By 2006 the population density per km2 was

386 Cp. Eurostat (2012b), p. 272. 387 Cp. European Commission (2012), p. 304 ff. 388 Cp. HSBC Global Research (2012), p. 4. 389 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 19. 390 Cp. United Nations (1971), p. 110 ff. 391 Cp. United Nations (1971), p. 148 ff. 392 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 19.

2010 2050/60


18,10% 7,30%



-0,30% 0,70%

-5%

0%

5%

10%

15%

20%

Eco

no

mic

co

nd

itio

ns


72

exhibiting a stable and balanced tendency towards urban living with a high-

density cluster of 28.0 %, an urban cluster of again 28.0 % and a rural cluster of

44.0%.393 The tenure standard in 2008 demonstrated a more balanced share in

comparison to the other analysed states with 62.4 % of owner-occupied housing,

8.0 % private rent, 10.0 % social rent and other habitations with 19.6 %.394 By

2050 the population per km2 is forecast to have fallen to a level similar to 1970

with 109.0 people395, but with an urban cluster of 73.6 % of the population.396 This

tendency towards less rural living is exhibited by a significant reduction to 40.0 %

in comparison to the actual base year 2006.

The proportion of housing costs taken from disposable income ranks in the middle

of the analysed states at 21.1 % in 2009.397 6.3 % of the population was deemed

dependent on others in 2010 equating to 2,424 thousand people.398 By 2050 it is

forecast to have grown to 9.7 % or 3,349 thousand.399 The total housing costs in

purchasing power standards were 250.1400 for the base year 2009 and, therefore,

one of the highest of the researched countries with the result of a reduction in the

spending power of inhabitants. Nevertheless, there is a relatively low construction

cost index with a basis of 2005 equal to 100 % is 115.8.401 Consequently new

buildings are more affordable in Poland than in other states of the EU. In 2010

the unemployment rate of the working-age population was comparatively low at

9.8 %.402 By 2060 it is expected to be 7.3 %403 with the result of a boost to

Poland’s employment market. In 2009 the percentage of the population at risk of

poverty at 27.8 %404 ranked it in the middle of the analysed countries. The GDP

393 Cp. Eurostat (2012a), p. 199. 394 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 395 Cp. Geohive (w.y.b), w. p. (date of demand: 10.07.2015). 396 Cp. Geohive (w.y.a), w. p. (date of demand: 10.07.2015). 397 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 398 Cp. European Commission (2012), p. 358. 399 Cp. European Commission (2012), p. 358. 400 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 401 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 402 Cp. European Commission (2012), p. 85. 403 Cp. European Commission (2012), p. 85. 404 Cp. Eurostat (2012b), p. 272.


73

rate was low at 1.9 % per capita in 2010 and will decrease until 2050 to an

estimated plus of 1.0 %.405 The potential GDP growth rate of 4.3 % in 2010 was

the highest basis, but will have fallen to 0.5 % by 2050 according to the

forecasts.406 These databases point to a negative level and development of the

economy of Poland in the future. Nevertheless, the size of the economy in 2010

was 250.0 billion USD and is expected to have risen to 786.0 USD by 2050407,

which is the 3rd highest of the researched EU states. Also the income level of

2010 will see a shift of per-capita income from 6,563 USD to 24,547 USD in

2050,408 which will support spending in the real estate sector. The economic

conditions are summarized in the following figure.

Figure 48: Economic conditions in Poland


405 Cp. European Commission (2012), p. 304. 406 Cp. European Commission (2012), p. 301. 407 Cp. HSBC Global Research (2012), p. 4 f. 408 Cp. HSBC Global Research (2012), p. 4 f.

2010 2050/60


9,80% 7,30%



4,30% 0,50%

0%

2%

4%

6%

8%

10%

12%

Eco

no

mic

co

nd

itio

ns


74

Romania (RO)

Romania covers an area of 238,391 km2.409 In 1970 the population density per

km2 was low at 85.0 people410 with an urban cluster of 40.8 % of the total

population, with rural areas representing 59.2 % in the base year 1970.411 In 2007

the average population rate per km2 had risen to 93.7 people.412 The population

density per km2 included in the last base year 2006 showed a high-density cluster

of 30.0 %, an urban cluster of 21.0 % and again a balanced rural cluster of

49.0 %.413 By 2050 the population per km2 is again expected to fall to 75.0

people414 with a high urban cluster with 77.3 % and a minor rural one of 22.6 %.415

In total this demonstrates a very high tendency towards urban clusters. In 2008

the share of owner-occupied housing was high at 96.0 %, private housing was

0.7 %, social housing 2.3 %, and 1.0 % for other housing.416

Economic conditions will also change in Romania (see figure 49). The income

per capita was the 2nd lowest after Bulgaria and was calculated at 2,596 USD in

2010; for the future it is again estimated to exhibit comparatively low movement

before reaching 20,357 USD in 2050.417 The proportion of housing costs coming

from disposable income was 25.3 % in 2009.418 This is the 2nd highest after

Germany and retards the public’s buying power in the residential trade and

industry. 1,317.0 thousand people are deemed dependent on others.419 This is in

line with the normal standard of 6.2 % in comparison to the afore-mentioned

countries. By 2050 it is expected to have increased to 9.4 % equating to 1,728.0

thousand,420 again points to a stable formation. The total housing costs in

409 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 19. 410 Cp. United Nations (1974), p. 110 ff. 411 Cp. United Nations (1971), p. 148 ff. 412 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 19. 413 Cp. Eurostat (2012a), p. 199. 414 Cp. Geohive (w.y.b), w. p. (date of demand: 10.07.2015). 415 Cp. Geohive (w.y.a), w. p. (date of demand: 10.07.2015). 416 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 417 Cp. HSBC Global Research (2012), p. 4 f. 418 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 419 Cp. European Commission (2012), p. 358. 420 Cp. European Commission (2012), p. 358.


75

purchasing power standards were 138.4 in 2009.421 This is the lowest level of the

researched countries, which offers a competitive advantage in comparison to the

other countries. The 2005 construction cost index equal to 100 % was 148.2,422

which is the 2nd highest after Latvia and will have a negative effect on the new

building sector. The unemployment rate of the working-age population was low

at 7.6 % in 2010; by 2060 this is expected to have fallen slightly to 7.0 %.423 The

share of the population at risk of poverty in 2009 was one of the highest at

43.1 %.424 The GDP rate in 2010 was positive at 2.2 % per capita, but will

decrease until 2050 to 1.1 %.425 The size of the economy in 2010 was 56.0 billion

USD and is expected to have risen to 377.0 billion USD by 2050.426 In

consequence the economy will have a tendency towards a more positive

development in the future.

Figure 49: Economic conditions in Romania


421 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 422 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 423 Cp. European Commission (2012), p. 85. 424 Cp. Eurostat (2012b), p. 272. 425 Cp. European Commission (2012), p. 304. 426 Cp. HSBC Global Research (2012); p. 4.

2010 2050/60


7,60% 7,00%


Potential GDP(growth rate)

2,00% 0,50%

0%1%2%3%4%5%6%7%8%

Eco

no

mic

co

nd

itio

ns


76

Slovakia (SK)

Slovakia covers an area of 49,035 km2.427 In 1970 the population density in

Czechoslovakia per km2 was 113.0 people428 with an urban cluster of 47.6 % of

the total population; rural areas represented 52.4 %.429 In 2007 the average

population rate per km2 had fallen to 110.1.430 The population density per km2 in

the last base year 2006 showed a high-density cluster of 17.0 %, an urban cluster

of 35.0 % and a rural cluster of 48.0.431 This rural formation is more balanced and

stable in comparison to the other researched states in the EU. By 2050 the

population per km2 is estimated to have fallen to 102.0 people,432 but with a

growing urban cluster of 69.3 %.433 This demonstrates a high tendency towards

more urban areas. Nevertheless, in comparison to the afore-mentioned countries

it is at a relatively stable level. The tenure level in 1961 in Czechoslovakia

demonstrated something of a balance between owner-occupied housing at

50.4 % and private housing at 42.0 %.434 In 2008 the tenure status of owner-

occupied housing shifted to a high and significant formation of 92.0 %.435

The income per capita was the 3rd highest after Germany and Spain at 8,042 USD

in 2010 and is expected to have risen to 27,639 USD by 2050.436 The proportion

of housing costs taken from disposable income was calculated at 22.0 % in

2009437 and, therefore, ranks in the middle of the other analysed countries of the

EU. The size of a habitation in the free market was analysed with a high sum of

124.1 m2 on average; the average size of dwellings in regulated markets is in

comparison to the free market sizes at a low level of 59.8 m2 on average in

427 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 19. 428 Cp. United Nations (1971), p. 110 ff. 429 Cp. United Nations (1971), p. 148 ff. 430 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 19. 431 Cp. Eurostat (2012a), p. 199. 432 Cp. Geohive (w.y.b), w. p. (date of demand: 10.07.2015). 433 Cp. Geohive (w.y.a), w. p. (date of demand: 10.07.2015). 434 Cp. United Nations (1974), p. 56 ff. 435 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 436 Cp. HSBC Global Research (2012), p. 4 f. 437 Cp. CECODHAS Housing Europe (2012), p. 38 ff.


77

2009.438 People dependent on others number a high share of 508.0 thousand439

or 9.4 % of the total population; in 2050 this is expected to have risen to 797.0

thousand440 which is a high level of 15.0 % and could illustrate decreasing

spending power of inhabitants in the real estate sector. The total housing costs

in purchasing power standards was 310.3 in 2009441, which is the 3rd highest

standard after Germany and Spain with an economic disadvantage in contrast to

other countries. The 2005 construction cost index equal to 100 % comes to

116.8.442 The unemployment rate of the working-age population amounted to

14.4 % in 2010; by 2060 it is estimated to have fallen to 7.3 %.443 In 2009 19.6 %

of the population was deemed at risk of poverty,444 which is the lowest among

and represents an advantage over the other afore-mentioned states. The GDP

rate in 2010 was about 3.0 % per capita, but will have fallen to 0.9 % by 2050

according to forecasts.445 In 2010 the size of the economy was 44.0 billion USD

and it is predicted to be 145.0 USD in 2050,446 which is a positive formation in

this economic field. The economic conditions are summarized in the following

figure.

438 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 90. 439 Cp. European Commission (2012), p. 358. 440 Cp. European Commission (2012), p. 358. 441 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 442 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 443 Cp. European Commission (2012), p. 85. 444 Cp. Eurostat (2012b), p. 272. 445 Cp. European Commission (2012), p. 304. 446 Cp. HSBC Global Research (2012), p. 4.


78

Figure 50: Economic conditions in Slovakia


Spain (ES)

Spain covers an area of 505,124 km2.447 In 1970 the population density per km2

was a low 66.0 people448 with an urban cluster of 42.9 % of the total population;

rural areas represented 57.1 %.449 In the actual base year 2000 the population

rate per km2 was calculated to have grown to 79.6.450 The population density per

km2 in 2006 revealed a high-density cluster of 43.0 %, urban cluster of 25.0 %

and rural cluster of 32 %451, which is balanced. The tenure level shifted from

45.9 % with owner-occupied status in 1950,452 to a high 85.0 % in the base year

447 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 19. 448 Cp. United Nations (1971), p. 110 ff. 449 Cp. United Nations (1971), p. 148 ff. 450 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 19. 451 Cp. Eurostat (2012a), p. 199. 452 Cp. United Nations (1974), p. 56 ff.

2010 2050/60


14,40% 7,30%


Potential GDP(growth rate)

3,50% 0,60%

0%

2%

4%

6%

8%

10%

12%

14%

16%

Eco

no

mic

co

nd

itio

ns


79

2008.453 By 2050 the population per km2 is again expected to have risen to 96.0

people454 with an urban cluster of 86.5 % and a rural cluster of 13.5 % of the

population,455 which demonstrates a more unbalanced trend and a significant

shift towards urban living. Economic conditions are important for Spain. The 2010

income level was high with a per-capita income of 15,699.0 USD.456 This is

expected to have risen to 38,111.0 USD by 2050,457 which after Germany is the

2nd highest among the analysed countries. In 2009 the proportion of housing costs

taken from disposable income was relatively low at 18.6 %.458 The free market

rent is available at 5,100 euros per year and a size of 74.8 m2 per habitation, the

regulated market rent was 1,600 euros a year with a similar average size in

comparison to the free market level of 74.9 m2 in 2009.459 2,485 thousand people

were deemed dependent on others,460 which is relatively high. By 2050 this is

expected to have risen to 7.8 % or 4,093 thousand.461 The total housing costs in

purchasing power standards were 363.3, based on 2009462 and are the 2nd

highest after Germany and represents an economic disadvantage in the

residential trade and industry. The 2005 construction cost index equal to 100 %

was 122.3463 and ranks in the middle of the other researched countries. The

unemployment rate of the working-age population was the highest of the

evaluated states at 20.2 % in 2010; by 2060 it is expected to have fallen sharply

to 7.3 %.464 The proportion of the population at risk of poverty in 2009 was

23.4 %.465 The GDP-rate in 2010 was 0.0 % per capita, but by 2050 is expected

453 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 454 Cp. Geohive (w.y.b), w. p. (date of demand: 10.07.2015). 455 Cp. Geohive (w.y.a), w. p. (date of demand: 10.07.2015). 456 Cp. HSBC Global Research (2012), p. 4 f. 457 Cp. HSBC Global Research (2012), p. 4 f. 458 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 459 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 90. 460 Cp. European Commission (2012), p. 358. 461 Cp. European Commission (2012), p. 358. 462 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 463 Cp. CECODHAS Housing Europe (2012), p. 38 ff. 464 Cp. European Commission (2012), p. 85. 465 Cp. Eurostat (2012b), p. 272.


80

to have increased to 1.2 %.466 The high size of the economy in 2010 was 711

billion USD and is expected to be 1,954 billion USD in 2050.467 The economic

conditions are summarized in the following figure:

Figure 51: Economic conditions in Spain


466 Cp. European Commission (2012), p. 304. 467 Cp. HSBC Global Research (2012), p. 4.

2010 2050/60


20,20% 7,30%



0,70% 1,20%

0%

5%

10%

15%

20%

25%

Eco

no

mic

co

nd

itio

ns


81

3 Trends of the different European Union countries

First this study focuses on the common ground and dissimilarities between the

different countries in these areas, after which central tendencies and moves are

highlighted.

The afore-mentioned databases demonstrate significant trends in the EU 27. As

a result of the demographic developments, the populations in 9 countries of the

EU exhibit shrinking tendencies. While half of these states – Germany, Lithuania,

Poland, Romania and Slovakia – had growing formations from 1970 to 2010, the

others – Bulgaria, Estonia, Hungary and Latvia – decreased across the whole

period. In contrast to these states, Spain’s population increased by 56.8 % (see

the following figure). Nevertheless, these tendencies are not solely responsible

for the future shifts in the residential trade and industry as analysed in this

chapter.

Figure 52: Trends of the population development


This development has a strong effect on the adjustment to the age structures,

which have shown a decrease in young generations and an increase in older

formations as analysed in Chapter 2. A crucial aspect here is that the median age

has been changing and will continue to do so in future. While the range today lies

BG EE DE HU LV LT PL RO SK ES

1970 8,50 1,40 78,30 10,30 2,40 3,10 32,70 20,10 4,50 33,60

2010 7,50 1,30 81,70 10,00 2,20 3,30 38,20 21,40 5,40 46,10

2050 5,90 1,20 70,60 9,20 1,80 2,80 34,50 18,40 5,30 52,70

0,0010,0020,0030,0040,0050,0060,0070,0080,0090,00

Population (millions)


82

between 38.2 in Slovakia and 45.5 in Germany, the age level for 2050 will be

between 42.7 in Latvia and 51.5 in Germany, which represents the oldest

inhabitants currently and in the future. Consequently there will be movements in

the median ages with a maximum of around plus 10 years in the next 37 years

with the highest developments in Poland and Slovakia. The most balanced level

will be in Latvia with an increase of 1.2 years of the median age.468 These

tendencies of high median ages in future will change the demands of inhabitants

in reference to habitations, which will have to be constructed in a more senior-

compatible manner in future years as illustrated in the next figure.

Figure 53: Tendencies of the median age of the populations

Source: based on United Nations (2013), p. 70 ff.

Nevertheless, the tendencies in the residential trade and industry will shift mainly

in the reverse direction until 2030. With the exceptions of Germany, Latvia and

Spain, there is a growing development of households in the other countries469

468 Cp. United Nations (2013), p. 70 ff. 469 Cp. CECODHAS Housing Europe (2012), p. 38 ff. as well as United Nations (2001),

246.


2013 43,00 40,90 45,50 40,60 41,50 39,30 38,80 39,40 38,20 41,40

2050 48,10 44,40 51,50 46,10 42,70 44,20 48,90 48,80 48,20 50,40

0,00

10,00

20,00

30,00

40,00

50,00

60,00Median age


83

and, therefore, an increasing tendency towards a higher demand for real estate

assets as shown in the following figure:

Figure 54: Development of the formation of the number of household

Source: based on CECODHAS Housing Europe (2012), p. 38 ff., United Nations (2001), p. 246.

As a consequence of the strong demographic movements and changing age

structures, household sizes are also changing, which is an important factor for

the real estate sector. While in the past decades there was a predominant share

of 3-and-more-person households, today there is a trend towards smaller 1- and

2-person households (see figure 55) ranging from 45.0 % in Romania to 73.0 %

in Germany470, 471 which represents an increased demand for such habitations

that will continue to grow in the future.

470 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 30 and United Nations

(1974), p. 38 ff. 471 For Lithuania no data are available.


2009 2,90 0,55 40,19 3,79 0,86 1,39 13,32 7,40 1,76 17,08

2030 3,24 0,61 38,82 3,95 0,84 1,53 14,36 8,29 2,40 12,71

-

5,00

10,00

15,00

20,00

25,00

30,00

35,00

40,00

45,00

Number of households (millions)


84

Figure 55: The growing of the smaller 1- and 2-person households

Source: based on Ministry of the Interior and Kingdom Relations (2010), p. 30 and United Nations (1974), p. 38 ff.

Therefore, also the average number of people per household is changing (see

figure 56). In the past base years from 1961 to 1971 the highest number of people

per household was to be found in Spain with 4.0 people; the lowest was detected

in Germany with 2.8. Today In the base year 2009 there was an upward shift to

a high of 2.9 people per household in Romania and a low of 2.0 in Germany,

which demonstrates significant trends in this field.472 Consequently there is

mainly a need for higher numbers of housing and smaller dwellings in real estate

assets, which has to be responded to in each country’s portfolio management.

472 Cp. CECODHAS Housing Europe (2012), p. 38 ff. and United Nations (1974), p. 56 ff.


1961-70 37,70% - 50,29% 29,60% - - - 37,60% 41,00% -

2004-08 - 63,00% 73,00% 59,00% 54,00% - 48,00% 45,00% 48,00% 47,00%

0%

10%

20%

30%

40%

50%

60%

70%

80%1- and 2-person-households


85

Figure 56: Shrinkage of the average number of people per household

Source: based on CECODHAS Housing Europe (2012), p. 38 ff. and United Nations (1974), p. 56 ff.

Another area of the change in demands of population is the location of the

residential trade and industry portfolios. In the past decades 1960/1970 there was

mainly stability between urban and rural living. This changed with the

consequence of a major percentage now living in high-density or urban clusters,

ranging from 51.0 % in Romania to 72.0 % in Germany. In Lithuania there was

the opposite tendency from 1960/1970 to 2006, but this will also change and

increase until 2050 (see the following figure). In 2050 forecasts indicate that the

figures will range from 69.3 % in Slovakia to 86.5 % in Spain473 with the

consequence of a focus on real estate portfolios located in more urban areas.

473 Cp. Eurostat (2012a), p. 199, Geohive (w.y.a), w. p. (date of demand: 10.07.2015) as

well as United Nations (1971), p. 148 ff.


1960-71 3,20 3,70 2,81 3,00 3,70 3,70 3,50 3,20 3,10 4,00

2009 2,40 2,40 2,00 2,60 2,50 2,40 2,80 2,90 2,80 2,10

-

0,50

1,00

1,50

2,00

2,50

3,00

3,50

4,00

4,50

Average number of people per household


86

Figure 57: Movement to high-density and urban clusters

Source: based on Eurostat (2012a), p. 199, Geohive (w.y.a), w. p. (date of demand: 10.07.2015) as well as United Nations (1971), p. 148 ff.

The owner-occupied tenure also indicates a tendency towards a change in the

residential trade and industry (see figure 58).474 While the available databases

covering the past decades demonstrate a percentage of owner-occupied

habitations representing 45.9 % of the total in Spain and 71.0 % in Bulgaria, there

is currently a significant demand for own housing ranging from 42.0 % in

Germany to 96.0 % in Estonia and Romania. Germany is by far the lowest in this

area, followed by Poland with 62.4 %; nevertheless, the other researched

countries lie between 84.9 % and 96.0 %.475 Therefore, in most of these countries

apartment buildings play a minor role so that the tenures are responsible for the

realisation of adequate housing.

474 For some countries (see Figure 58) there are no additional data available. 475 Cp. CECODHAS Housing Europe (2012), p. 38 ff., cp. United Nations (1974), p. 56 ff.


1970 50,60% 56,30% 43,30% 44,40% 56,30% 56,30% 52,00% 40,80% 47,60% 42,90%

2006 61,00% 61,00% 72,00% 57,00% 60,00% 44,00% 56,00% 51,00% 52,00% 68,00%

2050 83,41% 80,01% 83,81% 82,14% 78,08% 78,73% 73,62% 77,37% 69,32% 86,47%

0%

20%

40%

60%

80%

100%High density and urban clusters (% of population)


87

Figure 58: Formation of owner-occupied tenure status

Source: based on CECODHAS Housing Europe (2012), p. 38 ff. and United Nations (1974), p. 56 ff.

The total housing costs in purchasing power standards, needed to realise an

effective comparability between the countries, are high in Germany at 771.5. The

other states of the EU 27 are valued from a relatively low of 138.4 in Romania to

a high of 363.3 in Spain.476 Because the housing costs in some countries are

comparably high (see figure 59), it could be estimated that additional expenditure

in the future for, e.g., modernisations or new buildings will be limited.

476 Cp. CECODHAS Housing Europe (2012), p. 38 ff.


1950-70 71,00% - - 62,90% - - - - 50,40% 45,90%

2008-10 95,60% 96,00% 42,00% 92,00% 84,90% 91,00% 62,40% 96,00% 92,00% 85,00%

0%

20%

40%

60%

80%

100%

120%

Owner occupied tenure status (% of housing)


88

Figure 59: Exposure of total housing costs in purchasing power standards

Source: based on CECODHAS Housing Europe (2012), p. 38 ff.

Also the construction cost index is at relatively high levels in the analysed states

(see figure 60). Germany had a stable index of 111.5 in 2005 that pointed to a

development of 11.5 % over a period of 5 years.477 Nevertheless, Bulgaria, Latvia,

Romania and Spain realised high movements with the consequence of an

establishment of significant prices in the new building sector. In these countries

these dimensions could inhibit the realisation of custom-made housing through

new constructions.

477 Cp. CECODHAS Housing Europe (2012), p. 38 ff.


2009 165,6 179,0 771,5 244,6 186,2 168,2 250,1 138,4 310,3 363,3

-

100,00

200,00

300,00

400,00

500,00

600,00

700,00

800,00

900,00

Total housing costs in Purchasing Power Standard


89

Figure 60: Equation of the construction cost indexes

Source: based on CECODHAS Housing Europe (2012), p. 38 ff.

Furthermore, the economic conditions differ between the researched countries,

although they all include a growth tendency of per-capita income (see the

following figure).478 The most important growth is in Romania with a projected

increase of 784.2 % from 2010 to 2050. The lowest is Germany with 210.0 %

predicted for 2050.479 For the residential trade and industry this trend could be an

impulse for the growth of real estate assets if consumers invest their higher

budgets in real estate assets.

478 For Estonia no data are available. 479 Cp. HSBC Global Research (2012), p. 4 f.


2010 139,90 115,30 111,50 123,10 152,50 116,10 115,80 148,20 116,80 122,30

-

20,00

40,00

60,00

80,00

100,00

120,00

140,00

160,00

180,00

Construction cost index (2005=100)


90

Figure 61: Trend of the income per capita

Source: based on HSBC Global Research (2012), p. 4 f.

The GDP per capita develops in two different ways (see figure 62). In Estonia,

Germany, Hungary, Latvia, Lithuania and Spain the movement will be positive

until 2050. Consequently in these states economic growth is predicted, which can

also increase growth in the residential trade and industry. The states Bulgaria,

Poland, Romania and Slovakia will realise a negative economic shift.480 This

could be disadvantageous for the fulfilment of custom-fit real estate assets.

Figure 62: Tendency of the GDP per capita

Source: based on European Commission (2012), p. 304.

480 Cp. European Commission (2012), p. 304.


2010 $2.54 - $25.0 $5.83 $4.97 $5.15 $6.56 $2.59 $8.04 $15.6

2050 $13.1 - $52.6 $31.9 $27.1 $20.9 $24.5 $20.3 $27.6 $38.1

$-

$10.000,00

$20.000,00

$30.000,00

$40.000,00

$50.000,00

$60.000,00Income per capita


2010 1,90% -0,80% 1,20% -1,50% -0,70% 0,80% 1,90% 2,20% 3,00% 0,00%

2050 1,40% 1,10% 1,40% 1,20% 1,00% 1,20% 1,00% 1,10% 0,90% 1,20%

-2,00%

-1,00%

0,00%

1,00%

2,00%

3,00%

4,00% GDP per capita


91

The percentage of the population at risk of poverty is a crucial area in the

countries (see figure 63). Slovakia has the lowest at 19.6 %; Bulgaria the highest

at 46.2 %, followed by Romania with 43.1%.481 Consequently the self-dependent

fund of consumer demand habitation assets will be a challenge in countries with

high levels in this economic area, which stands in contrast to the other positive

economic conditions mentioned above.

Source: based on Eurostat (2012b), p. 272.

Figure 63: Formation of the population at risk of poverty

In a nutshell it can be stated that there is a higher demand for residential trade

and industry assets in most of the analysed countries as a consequence of a shift

towards smaller household sizes in the EU. Consequently a focus on smaller

dwelling sizes is necessary.482 Additionally as a result of a strong increase of

older generations and the change of the median ages across the states, real

estate assets also have to respond to the needs of these growing generation

clusters and focus on a higher share of senior-compatible habitations. For the

claim of infrastructural surroundings as a conclusion of the shift to older

481 Cp. Eurostat (2012b), p. 272. 482 For some countries no additional data are available.


2009 46,20% 23,40% 20,00% 29,60% 37,40% 29,50% 27,80% 43,10% 19,60% 23,40%

0,00%

5,00%

10,00%

15,00%

20,00%

25,00%

30,00%

35,00%

40,00%

45,00%

50,00%

Population at-risk-of-poverty (% of population)


92

generations, urban areas embrace a growing trend. Nevertheless, the high

numbers of owner-occupied habitations and negative economic conditions in

some fields will mean a limited willingness to pay for additional configurations of

real estate assets.

However, research has revealed that today real estate assets are not responding

to the demands of the populations. While the amount of vacant habitations ranged

in 1950/1971 from 0.2 % in Slovakia to 3.4 % in Hungary, there has been a major

shift to 3.7 % in Lithuania and 21.9 % in Spain483, which is a strong indicator of

real estate assets not fitting the demands of the current population (see figure

64).

Figure 64: The tendencies of vacant conventional dwellings

Source: based on Ministry of the Interior and Kingdom Relations (2010), p. 63 as well as United Nations (1974), p. 56 ff.484

Certainly one of the challenges to offer custom-made residences is the high age

distribution of the housing stocks in each country (see figure 65; however for

Bulgaria unfortunately there are no data available). Also in this field there is a

483 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 63 and United Nations

(1974), p. 56 ff. 484 For some countries no additional data are available.


1950-71 2,80% - 1,72% 3,40% - - 2,60% 2,40% 0,20% 1,20%

2001-09 - 8,00% 8,00% 5,60% 8,60% 3,70% 5,30% - 11,10% 21,90%

0%

5%

10%

15%

20%

25%

Vacant conventional dwellings


93

clear trend towards mainly high-aged real estate assets. With the base years

2001 to 2009 the databases showed that the construction ages of the habitations

falling in the period 1970 and earlier represent a high share of asset portfolios.

The most significant real estate portfolios aged 45 years and older are in

Germany with 74.3 %; nevertheless, the lowest of 45.1 % in Slovakia

demonstrates an unbalanced level of age distributions.485

Figure 65: Trends of the age distribution of housing stock


485 Cp. Ministry of the Interior and Kingdom Relations (2010), p. 54.


2001-09 - 53,60% 74,30% 48,00% 49,00% 62,60% 50,10% 52,70% 45,10% 46,60%

-

0,10

0,20

0,30

0,40

0,50

0,60

0,70

0,80

Age distribution of housing stock - 45 years and older (%)


95

4 Conclusion and outlook

As analysed in the previous chapters, there is a strong tendency towards deciding

demographic trends in the EU 27. On the one hand, consumers are mainly

demanding a higher number of smaller-sized dwellings as a result of changes in

the age clusters in the different countries. Because the highest increasing group

of inhabitants are the elderly, there is a necessity to realise more senior-

compatible dwellings. Furthermore, the demand to live in urban locations is

growing disproportionately. On the other hand, the current real estate market

offers high-aged buildings with an age of a minimum of 45 years that mostly hold

a tenure status. Although economic instability is evident in some areas, current

housing costs remain high and the price of newly constructed habitations is also

at an important level.

Hence, there is a strong shift in the demographic development of the countries in

the EU 27. Therefore, demands are changing and will do so in future. To stabilise

the real estate portfolios in these states, it is necessary to offer customised

residential trade and industry assets. Nevertheless, although countries comprise

analogical tendencies, it is crucial to establish real estate portfolios that conform

to the demographic, space, and environmental social economic characteristics of

each country. This additional study of real estate portfolio strategies in countries

with shrinking populations is a following research that will be published soon by

the authors.


97

LIST OF LITERATURE

Cecodhas Housing Europe (pub.) (2011): Housing Europe Review 2012 – The

nuts and bolts of European social housing systems. Editorial: Cecodhas

Housing Europe. Brussels, Belgium.

Cecodhas Housing Europe (pub.) (2012): Preparing the Future - Affordable

Housing and the Challenge of an Ageing Population in Europe – Success

Stories. European Year for Active Ageing and Solidarity between

Generations 2012. Editorial: Cecodhas Housing Europe. Brussels,

Belgium.

European Commission (pub.) (2012): The 2012 Ageing Report – Economic and

Budgetary projections for the 27 EU Member States (2010-2060). Editorial:

European Commission. Brussels, Belgium.

European Union (pub.) (w.y.a): EU member countries, under:

http://europa.eu/about-eu/countries/member-countries/index_en.htm, (Date

of demand: 15.07.2015).

European Union (pub.) (w.y.b): About the European Union, under:

http://europa.eu/about-eu/basic-information/about/index_en.htm, (Date of

demand: 15.07.2015).

European Union (pub.) (w.y.c): How the EU works. Brussels, Belgium, under:

http://europa.eu/about-eu/index_en.htm, (Date of demand: 20.07.2015).

Eurostat (pub.) (2010a): Europa in Zahlen - Eurostat Jahrbuch 2010. Editorial:

Eurostat. Brussels, Belgium.

Eurostat (pub.) (2010b): Income and living conditions in Europe. Editorial:


Eurostat (pub.) (2011): Demography Report 2010 - Older, more numerous and

diverse Europeans. Editorial: Eurostat. Brussels, Belgium.

Eurostat (pub.) (2012a): Focus on territorial typologies. Editorial: Eurostat.

Brussels, Belgium.

Eurostat (pub.) (2012b): Europe in figures - Eurostat yearbook 2012. Editorial:


Geohive (pub.) (w.y.a): Urban/ rural division of countries for 2010. Brussels,

Belgium, under: http://www.geohive.com/earth/pop_urban2.aspx, (Date of

demand: 10.07.2015).

http://www.geohive.com/earth/pop_urban2.aspx


98

Geohive (pub.) (w.y.b): Future Population Density. Brussels, Belgium, under:

http://www.geohive.com/earth/pop_density.aspx, (Date of demand:

10.07.2015).

HSBC Global Research (pub.) (2012): The World in 2050. Editorial: HSBC

Global Research. London, UK.

Kalusche, W. (2004): Technische Lebensdauer von Bauteilen und

wirtschaftliche Nutzungsdauer eines Gebäudes. Editorial:

Brandenburgische Technische Universität Cottbus. Cottbus, Germany.

Leal, J. (2007): Social and demographic change: Issues for housing provision

from a Spanish perspective, in: Cecodhas Social Housing (pub.): Welfare

Transformation and Demographic Change in Europe: Challenges for the

Social Housing Sector. Editorial: Cecodhas Social Housing. Brussels,

Belgium, pp. 22-35.

Ministry of the Interior and Kingdom Relations (pub.) (2010): Housing Statistics

in the European Union 2010. Editorial: Ministry of the Interior and Kingdom

Relations. Delft, Netherlands.

The American Heritage (pub.) (2013a): Dictionary of the English Language:

Bulgaria. Boston, USA, under: http://www.yourdictionary.com/bulgaria,

(Date of demand: 17.08.2015).

The American Heritage (pub.) (2013b): Dictionary of the English Language:

Estonia. Boston, USA, under: http://www.yourdictionary.com/estonia, (Date

of demand: 17.08.2015).

The American Heritage (pub.) (2013c): Dictionary of the English Language:

Germany. Boston, USA, under: http://www.yourdictionary.com/germany,


The American Heritage (pub.) (2013d): Dictionary of the English Language:

Hungary. Boston, USA, under: http://www.yourdictionary.com/hungary,


The American Heritage (pub.) (2013e): Dictionary of the English Language:

Latvia. Boston, USA, under: http://www.yourdictionary.com/latvia, (Date of

demand: 17.08.2015).

The American Heritage (pub.) (2013f): Dictionary of the English Language:

Lithuania. Boston, USA, under: http://www.yourdictionary.com/lithuania,



99

The American Heritage (pub.) (2013g): Dictionary of the English Language:

Poland. Boston, USA, under: http://www.yourdictionary.com/poland, (Date

of demand: 17.08.2015).

The American Heritage (pub.) (2013h): Dictionary of the English Language:

Romania. Boston, USA, under: http://www.yourdictionary.com/romania,


The American Heritage (pub.) (2013i): Dictionary of the English Language:

Slovakia. Boston, USA, under: http://www.yourdictionary.com/slovakia,


The American Heritage (pub.) (2013j): Dictionary of the English Language:

Spain. Boston, USA, under: http://www.yourdictionary.com/spain, (Date of

demand: 17.08.2015).

United Nations (pub.) (1971): Demographic Yearbook 1970. Editorial: United

Nations. New York, USA.

United Nations (pub.) (1974): Compendium of Housing Statistics 1971. Editorial:

United Nations. New York, USA.

United Nations (pub.) (2001): Compendium of Human Settlements Statistics 2001.

Editorial: United Nations. New York, USA.

United Nations (pub.) (2012): Compendium of Housing Statistics 2011. Editorial:

United Nations. New York, USA.

United Nations (pub.) (2013): World Population Prospects – The 2012 Revision

- Volume I: Comprehensive Tables. Editorial: United Nations. New York,

USA.


101

APPENDIX

Appendix 1 Databases demographic characteristics, past basic years ........... 102

Appendix 2 Databases demographic characteristics, actual basic years ........ 106

Appendix 3 Databases demographic characteristics, future basic years ......... 110

Appendix 4 Databases space characteristics, past basic years ...................... 115

Appendix 5 Databases space characteristics, actual basic years.................... 116

Appendix 6 Databases environmental social characteristics,

past basic years ............................................................................ 118


actual basic years ......................................................................... 123


future basic years .......................................................................... 130


102

Appendix 1: Databases demographic characteristics, past basic years


103


104


105


106

Appendix 2: Databases demographic characteristics, actual basic years


107


108


109


110

Appendix 3: Databases demographic characteristics, future basic years


111


112


113


114


115


116

Appendix 4: Databases space characteristics, past basic years


117


118

Appendix 5 Databases space characteristics, actual basic years


119


120

Appendix 6: Databases environmental social characteristics, past basic years


121


122


123


124


125


126

Appendix 7: Databases environmental social characteristics, actual basic years


127


128


129


130


131


132

Appendix 8: Databases environmental social characteristics, future basic years


133


134


135

Folgende Bände sind bisher in dieser Reihe erschienen: Band 1 (2005) Hermeier, Burghard / Frère, Eric / Heuermann, Marina Ergebnisse und Effekte des Modellprojektes „Fit machen fürs Rating...“ ISSN 1865-5610 Band 2 (2006) Hermeier, Burghard / Platzköster, Charlotte Ergebnisse der ersten bundesweiten FOM-Marktstudie „Industrie-Dienstleistungen“ ISSN 1865-5610 Band 3 (2006) Kern, Uwe / Pankow, Michael Die Stärkung des traditionellen 3-stufigen Vertriebswegs im Sanitärmarkt durch den Einsatz neuer Medien ISSN 1865-5610 Band 4 (2006) Kürble, Peter Die unternehmensinterne Wertschöpfungskette bei Dienstleistungen am Beispiel der TV-Programmveranstalter ISSN 1865-5610 Band 5 (2007) Klumpp, Matthias Begriff und Konzept Berufswertigkeit ISSN 1865-5610 Band 6 (2007) Klumpp, Matthias / Jasper, Anke Efficient Consumer Response (ECR) in der Logistikpraxis des Handels ISSN 1865-5610 Band 7 (2007) Klumpp, Matthias / Koppers, Laura Kooperationsanforderungen im Supply Chain Management (SCM) ISSN 1865-5610

Band 8 (2008) Klumpp, Matthias Das deutsche System der Berufsbildung im europäischen und internationalen Qualifikationsrahmen ISSN 1865-5610 Band 9 (2008) Göke, Michael Homo oeconomicus im Hörsaal – Die Rationalität studentischer Nebengespräche in Lehrveranstaltungen ISSN 1865-5610 Band 10 (2008) Klumpp, Matthias / Rybnikova, Irma Internationaler Vergleich und Forschungsthesen zu Studienformen in Deutschland ISSN 1865-5610 Band 11 (2008) Kratzsch, Uwe Eine ökonomische Analyse einer Ausweitung des Arbeitnehmer-Entsendegesetzes ISSN 1865-5610 Band 12 (2009) Friedrich, Klaus Organisationsentwicklung – Lernprozesse im Unternehmen durch Mitarbeiterbefragungen ISSN 1865-5610 Band 13 (2009) Chaudhuri, Arun Die Outsourcing/Offshoring Option aus der Perspektive der Neuen Institutionenökonomie ISSN 1865-5610 Band 14 (2009) Seng, Anja / Fleddermann, Nicole / Klumpp, Matthias Der Bologna-Prozess Hintergründe – Zielsetzung – Anforderungen ISSN 1865-5610

Band 15 (2009) Jäschke, Thomas Qualitätssteigerung bei gleichzeitigen Einsparungen – Widerspruch oder Zukunft in der hausärztlichen Versorgung? ISSN 1865-5610 Band 16 (2010) Schütte, Michael Beiträge zur Gesundheitsökonomie ISSN 1865-5610 Band 17 (2010) Bode, Olaf H. / Brimmen, Frank / Redeker, Ute Die Einführung eines Mindestlohns in Deutschland – Eine Makroökonomische Analyse Introduction of a Minimum Wage in Germany – A Macroeconomic Analysis ISSN 1865-5610 Band 18 (2011) Nietsch, Cornelia / Weiffenbach, Hermann Wirtschaftsethik – Einflussfaktoren ethischen Verhaltens in Unternehmen ISSN 1865-5610 Band 19 (2011) Frère, Eric / Schyra, Andreas Ausgewählte steuerliche Einflussfaktoren der Unternehmensbewertung ISSN 1865-5610 Band 20 (2011) Schulenburg, Nils / Jesgarzewski, Tim Das Direktionsrecht des Arbeitgebers – Einsatzmöglichkeiten und Grenzen ISSN 1865-5610 Band 21 (2011) Fichtner-Rosada, Sabine Interaktive Hochschuldidaktik als Erfolgsfaktor im Studium für Berufstätige – Herausforderung und kompetenzorientierte Umsetzung ISSN 1865-5610

Band 22 (2011) Kern, Uwe / Negri, Michael, Whyte, Ligia Needs of the Internet Industry ISSN 1865-5610 Band 23 (2011) Schütte, Michael Management in ambulanten Sektor des Gesundheitswesens ISSN 1865-5610 Band 24 (2011) Holtfort, Thomas Intuition, Risikowahrnehmung und Investmententscheidungen – Behaviorale Einflussfaktoren auf das Risikoverhalten privater Anleger ISSN 1865-5610 Band 25 (2012) Heinemann, Stefan / Hüsgen, Thomas / Seemann, Volker Die Mindestliquiditätsquote – Konkrete Auswirkungen auf den Wertpapier-Eigenbestand der Sparkassen ISSN 1865-5610 Band 26 (2012) Hose, Christian / Lübke, Karsten / Nolte, Thomas / Obermeier, Thomas Rating und Risikomanagement – Chancen und Risiken der Architektur des Ratingprozesses für die Validität der Ratingergebnisse ISSN 1865-5610 Band 27 (2012) Serfas, Sebastian Illustrating the distortive impact of cognitive biases on knowledge generation, focusing on unconscious availability-induced distortions and SMEs ISSN 1865-5610 Band 28 (2012) Wollenweber, Leif-Erik Customer Relationship Management im Mittelstand ISSN 1865-5610

Band 29 (2012) Nentwig, Holger / Obermeier, Thomas / Scholl, Guido Ökonomische Fitness ISSN 1865-5610 Band 30 (2012) Büser, Tobias / Stein, Holger / von Königsmarck, Imke Führungspraxis und Motivation – Empirische 360-Grad-Analyse auf Grundlage des MoKoCha-Führungsmodells und des Team Management Systems (TMS) ISSN 1865-5610 Band 31 (2012) Schulenburg, Nils / Knauer, Stefan Altersgerechte Personalentwicklung – Bewertung von Instrumenten vor dem Hintergrund des demografischen Wandels ISSN 1865-5610 Band 32 (2013) Kinne, Peter Balanced Governance – Komplexitätsbewältigung durch ausgewogenes Managen im Spannungsfeld erfolgskritischer Polaritäten ISSN 1865-5610 Band 33 (2013) Holtfort, Thomas Beiträge zur Verhaltensökonomie: Einfluss von Priming-Effekten auf rationale vs. intuitive Entscheidungen bei komplexen Sachverhalten ISSN 1865-5610 Band 34: (2013) Mahood, Ed / Kameas, Achilles / Negri, Michael Labelisation and Certification of e-Jobs – Theoretical considerations and practical approaches to foster employability in a dynamic industry ISSN 1865-5610 Band 35 (2013) Gondek, Christian / Heinemann, Stefan An insight into Drivers of Customer Satisfaction – An empirical Study of a global automotive brand ISSN 1865-5610

Band 36 (2013) Rödder, Sascha / Schütte, Michael Medizinische Versorgungszentren – Chancen und Risiken der Implementierung im ambulanten Sektor des Gesundheitswesens ISSN 1865-5610 Band 37 (2013) Abele, Thomas / Ecke, Astrid Erfolgsfaktoren von Innovationen in reifen Märkten ISSN 1865-5610 Band 38 (2013) Vatanparast, Mir Farid Betriebswirtschaftliche Elemente im Social Entrepreneurship ISSN 1865-5610 Band 39 (2013) Seidel, Marcel Die Anwendung heuristischer Regeln – Eine Übersicht am Beispiel von Fusionen ISSN 1865-5610 Band 40 (2013) Coburger, Dieter Vertragsabschlüsse auf Internetplattformen – Rechtliche Risiken und Gestaltungsmöglichkeiten am Beispiel der Internetplattform eBay ISSN: 1865-5610 Band 41 (2013) Kraus, Hans Big Data − Einsatzfelder und Herausforderungen ISSN: 1865-5610 Band 42 (2013) Schmitz, Elmar Textsammlung zur deutsch-chinesischen Wissenschaftsdialog ISSN: 1865-5610

Band 43 (2014) Bruns, Kerstin Führungskraft und Frau – manchmal ein Teufelskreis ISSN 1865-5610 Band 44 (2014) Deeken, Michael Merkmale zukunftsfähiger Unternehmen – Erkenntnisse am Beispiel der Vermögensverwaltungsbranche ISSN 1865-5610 Band 45 (2014) Holzkämper. Hilko Reformoptionen der Pflegeversicherung – Eine ordnungstheoretische Analyse ISSN 1865-5610 Band 46 (2014) Kiefer, Markus Neue Potenziale für die Krisenkommunikation von Unternehmen – Social Media und die Kommunikation von großen Infrastrukturprojekten ISSN 1865-5610 Band 47 (2014) Hose, Christian / Lübke, Carsten / Nolte, Thomas / Obermeier, Thomas Nachhaltigkeit als betriebswirtschaftlicher Wettbewerbsfaktor – Eine Propensity Score Analyse Deutscher Aktiengesellschaften ISSN 1865-5610 Band 48 (2014) Chiwitt, Ulrich Ratingagenturen – Fluch oder Segen? Eine kritische Bestandsaufnahme ISSN 1865-5610 Band 49 (2014) Kipp, Volker Aktuelle Entwicklungen in der Finanzierung mittelständischer Unternehmen ISSN 1865-5610

Band 50 (2014) Nastansky, Andreas Systemisches Risiko und systemrelevante Finanzinstitute ISSN 1865-5610 Band 51 (2014) Schat, Hans-Dieter Direkte Beteiligung von Beschäftigten – Historische Entwicklung und aktuelle Umsetzung ISSN 1865-5610 Band 52 (2014) Sosa, Fabian Anwaltskanzleien und Exportversicherungen – Konfliktlösungen für internationale Handelsgeschäfte ISSN 1865-5610 Band 53 (2014) Hose, Christian / Lübke, Karsten / Nolte, Thomas / Obermeier, Thomas Einführung von Elektromobilität in Deutschland – Eine Bestandsaufnahme von Barrieren und Lösungsansätzen ISSN 1865-5610 Band 54 (2015) Klukas, Jörg Trend Empfehlungsmarketing in der Personalbeschaffung – Einordnung und empirische Analyse ISSN 1865-5610 Band 55 (2015) Wohlmann, Monika Finanzmarktintegration in Mittelosteuropa: Eine empirische Analyse der integrativen Wirkung des Euro ISSN 1865-5610 Band 56 (2015) Rudolph, Elke Crossmedia-Kommunikation, Komponenten, Planung, Implementierung und Prozesskontrolle- illustriert mit Beispielen aus der Entertainmentbranche ISSN 1865-5610

The authors

Prof. Dr. Roberto Cervelló-Royo

holds a PhD in Economics from the UPV - Universidad

Politécnica de Valencia (Spain) and a M.Sc. in Design

Engineering. He is Assistant Professor at the Economics

and Social Sciences Department, Universidad Politécnica

de Valencia. He has been a Research Fellow in the

Department of Real Estate and Planning, University of

Reading (UK).

He has been published in the Journal of the Operational

Research Society, the Journal of Cultural Heritage

Management and Sustainable Development, Expert

Systems with Applications (an international journal),

Mathematical and Computer Modelling (an international

journal), Spanish Journal of Agricultural Research, the

Journal of Intellectual Capital and The European Journal of

Economics, and Finance and Administrative Sciences,

among others.

His research interests include international and real estate

finances, regional and urban economics, sustainable urban

development, real estate and property, residential tourism,

cultural heritage management.

Prof. Dr. Francisco Guijarro Martínez

is a Lecturer of Finance at the Business Management

School of the UPV - Universidad Politécnica de Valencia

(Spain). He has published several refereed papers in the

European Journal of Operational Research, Journal of the

Operational Research Society, Annals of Operations

Research, Computers & Operations Research,

International Journal of Business Performance and Supply

Chain Modelling, Journal of Business Economics and

Management, Service Business, Expert Systems with

Applications, etc. His research interests focus on firm

valuation, portfolio management, trading rules

development and risk analysis.

Prof. Dr. Thomas Pfahler

studied Business Administration and Economics at the

University of Bayreuth and graduated in 1990 as Diplom-

Kaufmann (Master in Business Administration equivalent)

and Diplom-Volkswirt (Master in Economics). Before he

was awarded his doctorate in Economics he worked as an

assistant professor at the Chair of Economics II at the

University of Bayreuth, where he also gained his

habilitation in 1999.

Having been appointed senior lecturer in the same year he

accepted a professorship at the HAW - University of

Applied Sciences in Hamburg in 2004. There until today he

has been teaching Public Finance and Economics and

conducting research in the fields of institutional and

educational economics as well as human capital theory.

Furthermore, he is deputy chairman of the examination

board and a member of the research board of the Faculty

of Economy and Social Affairs.

Since 2006 he teaches Financial Management and

Economics at the FOM University of Applied Sciences in

Hamburg. With his students he develops conceptual

procedures to measure processes in the cost and activity

accounting with a focus on the following business areas:

analysing weaknesses, optimising the business process,

refining of controlling instruments, implementing an

organisational and personnel development process,

promoting the potentials of managers with the help of

orientation centres, supporting and accompanying spin-

offs and privatisations, creating general and municipal

marketing concepts. He has published numerous

monographs and articles in economic journals.

Corresponding author

Marion Preuss, MBA

holds a Bachelor of Business Administration and Master of Business Administration degree. At present she is working in an international doctoral program in cooperation between the UPV – Universidad Poli- técnica de Valencia (Spain) and the HAW – University of Applied Sciences, Hamburg (Germany).

She possesses a great deal of experience in the field of demographic development in the residential trade and industry – firstly from having gained wide-ranging practical management experience in the real estate sector. She also completed a real estate education and underwent additional advanced training, and has worked in the sector for 28 years. Her business involvements range from the operational level, such as administration and sales, to the strategic level in demographic development and financial management. Nowadays she is a Lecturer of Business Management at the FOM – University of Applied Sciences, Hamburg (Germany).

[email protected]

ISSN 1865-5610

KCC KompetenzCentrum für Corporate Social Responsibilityder FOM Hochschule für Oekonomie & Management

FOM – eine Hochschule, viele Möglichkeiten.

Die mit bundesweit über 32.700 Studierenden größte private Hochschule Deutschlands führt seit 1993 Studiengänge für Berufstätige durch, die einen staatlich und international anerkannten Hochschulabschluss (Bachelor/Master) erlangen wollen.

Die FOM ist der anwendungsorientierten Forschung verpflichtet und verfolgt das Ziel, adaptionsfähige Lösungen für betriebliche bzw. wirtschaftsnahe oder gesellschaftliche Problemstellungen zu generieren. Dabei spielt die Verzahnung von Forschung und Lehre eine große Rolle: Kongruent zu den Masterprogrammen sind Institute und KompetenzCentren gegründet worden. Sie geben der Hochschule ein fachliches Profil und eröffnen sowohl Wissen- schaftlerinnen und Wissenschaftlern als auch engagierten Studierenden die Gelegenheit, sich aktiv in den Forschungsdiskurs einzubringen.

Weitere Informationen finden Sie unter fom.de

FOM Hochschule

Unter dem Titel »FOM forscht« gewähren Hochschullehrende der FOM Einblick in ihre Projekte.Besuchen Sie den Blog unter fom-blog.de

Arbeitspapiere der FOM · Arbeitspapiere der FOM, Nr. 57: Residential trade and industry PREFACE...

Documents

Transcript of Arbeitspapiere der FOM · Arbeitspapiere der FOM, Nr. 57: Residential trade and industry PREFACE...