24 January 2017
A cluster analysis of pathdependent changes in the
housing systems of the 11
post-communist EU states
HOWCOME
No. 17
Changing Housing Regimes and
Trends in Social and Economic Inequality
HOWCOME Working Paper Series
Adriana Mihaela Soaita* and Caroline Dewilde*
*Department of Sociology, Tilburg University
www.tilburguniversity.edu/howcome
Funded by the
European Research Council
Grant Agreement No. 283615
1
A Cluster Analysis of Path-dependent Changes in the
Housing Systems of the 11 Post-communist EU States
Employing a long-term perspective we explore whether ideologically-rooted differences in housing
provision and quality under communism have persisted during the post-communist construction of housing
markets. Drawing on theories of path-dependent change, we hypothesize that the housing systems of the
11 post-communist EU countries cluster along past lines of divisions, namely the USSR housing model,
and the classical and reformist models of the Eastern Bloc. We operationalize housing systems in terms of
fundamentals and micro-indicators reflecting households’ experiences in order to perform Hierarchical
Cluster Analyses. Findings evidence that countries cluster along historic differences in the groups of the
Baltic States, South-East Europe and Central-East Europe, which show different patterns of housing quality
and inequality. Our study advances a valuable mid-range epistemological frame for understanding the
complex social reality of housing and helps shatter the over-generalist labelling of post-communism and
the growing view that communist housing systems were all too similar.
Keywords: housing, financialization, cluster analysis, post-communism, Eastern Europe.
1. Introduction
The Global Financial Crisis (GFC) has demonstrated the hazardous position of housing in the political
economy of financialized capitalism (Lapavitsas 2009). If financial innovations expanded mortgaged
homeownership to previously excluded groups, ‘created’ housing wealth during booms and made this
wealth more liquid to draw upon (Smith and Searle, 2010), the US sub-prime mortgage crisis and the
subsequent GFC reminded us that no one can create something from nothing. Home values crashed,
billions of newly printed money flooded the global economy, interest rates were reduced to historic
lows, and too-big-to-fail banks were bailed out, transforming a private into a public debt crisis. We
now know more about the causes and further implications of these events (Jones, Trevillion and Cowe
forthcoming; Stiglitz 2013) yet one can still observe the ubiquitous Anglo-Saxon focus of these
narratives.
The housing systems of the post-communist countries, described as ‘property without
markets’ (Zaviska, 2002), make an interesting contra-point to this dominant story. The housing booms
in these countries were amongst the highest and the busts amongst the deepest, but housing volatility
has not affected these economies since most homes are owned outright and residential mobility is
low. Although the GFC reverberated into the economies of post-communist countries to various
degrees, economic recessions were trivial compared to those of the 1990s. We thus argue that a longterm perspective is required in order to examine the extent to which current housing systems in postcommunist countries share commonalities and where important differences arise, which is the aim of
our paper.
We acknowledge that comparisons are not neutral epistemological devices: too far a distance
fades dissimilarities whereas too close-up a view enhances diversity. In this paper we opt for a midrange approach by focusing on the 11 post-communist states which have taken the complex policypath to EU membership.i We conceive housing systems in a broad fashion, as outlined in system,
2
field and regime theories, and operationalize them in terms of fundamentals and micro-indicators
reflecting the housing reality at the level of households. These indicators are then imputed in a
Hierarchical Cluster Analyses (HCA) in order to explore whether important ideologically-rooted past
differences in housing provision persisted during the post-communist construction of housing
markets.
Our exploratory analysis rests on theories of path dependency. We particularly elaborate on
the idea of path-dependent change in order to gain more insight into the ambiguous patterns of
commonalities and differences across these countries’ housing systems. Along this line, fresh debate
is emerging amongst scholars supporting ideas of (partial) convergence (Stephens 2010; Stephens,
Lux and Sunega 2015) and those recognizing emerging divergence (Lowe and Tsenkova 2003; Miao
and Maclennan 2016). However, employing Kornai’s (1992) critical distinction between the politicaleconomies of reformist and classical communist states, we argue that divergence in the nature of the
housing systems of post-communist countries can still be traced back to communist legacies of
housing provision. Hungary, Poland, Yugoslavia and Czechoslovakia were reformist states whereas
Albania, Bulgaria, GDR, Romania, and (pre-Gorbachev) USSR followed the hard Stalinist line. Not
only did these countries emerge from the communist period with dissimilar housing systems, but the
reformist states benefited from already connected economies to global economic flows (Aslund 2007;
Bradshaw and Stenning 2004). Moreover, under conditions of prolonged economic depression, pathdependency is arguably most salient in the housing field given a certain inertia embedded in the built
environment in a context of low residential mobility, few new-built homes and limited renewal of the
old stock (Kemeny 1981).
Our analysis is important in two major ways. First, discovering patterns of difference and
similarity across countries brings a valuable epistemological framework to understanding the
complex social reality of housing in order to inform policy questions, contextualize specific analyses
or appreciate why expectations and aspirations may differ. Second, we problematize the uncritical
view that communist housing systems were all too similar and bring necessary nuances to the overgeneralist labeling of ‘post-communism’ by showing that past differences have not disappeared.
The paper advances as follows. Section 2 reflects on macro-level housing concepts,
particularly those of system, field and regime. Section 3 problematizes the claimed convergence of
communist housing by distinguishing between three different models that characterized the republics
of the Eastern Bloc and the USSR while section 4 highlights continuities and differences in their
housing systems after the fall of communism, particularly after 2000. We present the methodology in
section 5 and the results in section 6, emphasizing that national housing systems still cluster along
the same historic lines of division. In section 6 we reflect at the broader implications of our analysis
and raise some questions for future research.
2. Macro-concepts: housing systems, fields and regimes
Macro-level housing concepts––particularly those of system, field and regime––remain indebted to
structural thinking (Lawson 2012) even though they have undergone a theoretical transformation,
now being understood as socially-constructed, malleable to change and continually mediated through
agents’ interactions and dispositions, acts of institutional power, and dominant ideologies (Archer
3
1995; Bourdieu 2005; Kemeny 1992). We will concisely review each in turn as a base for our
exploratory HCA.
The basic ontological assumption is that interdependent relationships between the economic,
political and cultural domains shape social phenomena, including housing. Largely used in housing
studies, the idea of housing system has been rarely defined.ii Kemeny (1988:212) was concerned
about the social construction of “housing reality” as “the interaction between structural factors and
the actions and counteractions of individuals as they struggle to make sense of their world, come to
terms with it or attempt to change it” and “by one definition of reality being imposed on other
individuals through the exercise of power in institution”. In his sociology of tenure, housing systems
may be said to represent ‘the interplay between economic organizations and political ideologies as
constrained and modified by wider social structures’ (Kemeny 1981:xiii).iii Emphasising the importance of
how housing is consumed––since most housing stock was historically built––Kemeny dismissed the alternative
‘provision thesis’ (Barlow and Duncan 1988). The latter was reinvigorated two decades later by Bourdieu’s
(2005) analysis of the housing field.
Criticizing system theory for promoting a “formal and abstract representation" of the world,
Bourdieu (2005) analyzed the social construction of housing markets as a field, i.e. a social space of
structured, socially patterned relationships, activities and practices of all the agents that inhabit it. By
documenting the interplay between the power structures of firms of production, promotion and
exchange on the supply side, and households’ tastes, dispositions and economic resources on the
demand side, Bourdieu, just as Kemeny, emphasized the dominating role of the (neoliberal) state in
the construction of both demand and supply. According to Bourdieu, states interfere in the nature of
housing demand by shifting demographic structures and changing lifestyles as much as by shaping
the economic field, including by budget decisions, infrastructure development, taxation, social
assistance, migration policies and inheritance laws. Given the immense task of accounting for myriads
of socially-embedded economic practices, scholarship must accept the sin of simplifying (Bourdieu,
2005), which we also obviously do in this exploratory analysis. Indeed, recent conceptualizations of
housing systems (Hoekstra 2010; Lawson 2003; Ronald 2008) stand for honourable simplifications
of field analyses.
Recognizing the wider societal implications of the organization of housing, scholars have
increasingly engaged with Esping-Andersen’s (1990) theory of welfare-state regimes.iv The concepts
of housing systems and welfare regimes share important theoretical foundations and definitional
similarities; nonetheless, regimes were conceived at a higher order of abstraction as Webberian ideal
types––with national welfare systems approximating them more or less closely––whereas the former
aimed to describe the actual world (Kemeny 1995). Hence, they have rarely been amalgamated into
housing regimes.v
Recently, Schwartz and Seabrooke (2009:9) conceived four different––arguably ideal types–
–“varieties of residential capitalism” as shaped by “three causal forces: the interaction of pensions
and owner-occupation, competition for investment capital, and the level of urbanization or new
settlement in the postwar period”. By connecting key arguments from the work of Kemeny and
Esping-Andersen to Hall and Soskice’s (2001) political economy thesis, they have the merit of linking
housing to both welfare and economic systems.
4
Finally, we wish to concisely reflect on explanations of social change in system/field/regime
theories, which is particularly important to our topic since Eastern Europe has experienced
paradigmatic political-economic changes. While both social constructionism and critical realism have
their own theories of change (Stones 2001), we find the basic tenets of the latter more useful.
Particularly Archer’s (1995) emphasis on the structural discontinuity between agents of the past
whose (un)intentional actions had shaped the context for subsequent interaction, has become the
hallmark of path-dependency theories.
Path-dependence may occur at the obvious level of events; correspondingly, “critical
juncture” theorists focus on identifying contingent choices that set a future trajectory, which is
difficult though not impossible to reverse. Critical juncture explanations became popular in housing
studies (Housing, Theory and Society, volume 27, issue 3) although they were criticized by a different
branch of path-dependency scholars. For instance, Malpass (2011:318) argues that change occurs
primarily incrementally through intentional actions (always restricted by essential preconditions),
non-decision making and unintended consequences, encompassing both dynamism and continuity.
Rather than identifying events, isolating the causal mechanisms that have induced path-dependent
change avoids deterministic explanations (Kemp 2015). Perceptions of policy failure, struggle over
resources, international pressures, power configurations legitimizing some agendas over others,
incremental institutional change and geographical diffusion of knowledge may all act as causal
mechanisms. We will identify in the following section some causal mechanisms which shaped
different communist and post-communist country trajectories.
Since we lack a higher-order regime theory related to the post-communist space(s)––and it is
beyond our scope to advance one in this paper––we will focus on actual housing systems rather than
regimes. We understand housing systems broadly, as encompassing the economic, political, sociocultural organization, practices and understandings of housing regulation, provision, allocation and
consumption. As other scholars, we will take on the unavoidable sin of simplifying and we
operationalize housing systems through a mix of macro-fundamentals and micro-indicators at the
level of households––each seen as interdependent parts of the whole. We make no assumption that
certain fundamentals should necessarily produce certain outcomes, which is a popular approach in
analyses empowered by regimes theories (Heijden 2013; Norris and Winston 2012).
There is too large a housing literature to review here which has identified fundamentals of
housing systems (Clapham, Clark and Gibb 2012), reflecting economic processes (levels of economic
development; economic growth and its distribution), housing market dynamics (mortgage debt to
GDP; house value volatility, residential mobility), demographic changes, housing and welfare
policies. Likewise, there is a large literature focusing on households’ housing reality from objective
measures of housing quality, conditions and affordability to subjective indicators of housing
satisfaction, aspirations and wellbeing. We will present our methodological choice in operationalizing
the housing systems of the 11 post-communist EU states in section 5, after outlining some of their
key features during communism and after.
3. Theorizing communist housing systems
To highlight key differences across the communist housing systems, we built on two major
contributions, those of Kornai’s (1992) treatise of the political economy of the socialist system and
5
Hegedus and Tosics’s (1992) conceptualization of the communist East European housing model
(EEHM) as being essentially distinct from its Soviet counterpart developed in the USSR (Bessonova
1992). Their conceptual distinctions overlap in meaningful ways (Figure 1).
Kornai (1992) differentiated between the political economy of classical versus reformist
socialist systems. The former “prevailed under Stalin, Mao Zedong, and their disciples in other
countries’; the latter ‘evolved (in chronological order) under Tito in Yugoslavia, Kadar in Hungary,
Deng Xiaoping in China, and Gorbachev in the USSR; some further countries could be named as
well” (Kornai 1990:131/132). While the reformist-socialist countries still maintained the
“fundamental attributes of a socialist system”, i.e. communist party’s undivided power, dominant role
of state-owned enterprises and centralized bureaucracy, important steps were nonetheless taken
“towards liberalization in the political sphere”; states also “decentralized the control of their stateowned sector, and allowed a somewhat larger scope for private sector”.
Such reforms were significant in Yugoslavia, Hungary and Poland where they had been
sustained since the 1950s/1960s (but remained more ambiguous in Czechoslovakia after the Prague
Spring of 1968). In these countries, the communist housing systems benefited from a quasi-market
sector of self-building and exchange, cooperative forms of ownership and provision, more complex
forms of housing finance, decentralized control in state housing, and crucially, financial support from
the state (Sillince 1990). This broad picture could be qualified by housing policy periods in each
country (Sillince 1990), but this is beyond the remit of our paper. Suffice saying that among all the
reformist states except Poland, crude housing shortages were (almost) addressed, Czechoslovakia
having had one of the most impressive performances (Michalovic 1992).
Drawing on these features of reformist housing systems, Hegedus and Tosics (1992) coined the
concept of the EEHM as characterizing all countries of the Eastern Bloc, and being essentially
different from the housing systems of the Soviet Republics of the USSR. Criticized on
epistemological and policy grounds (Kemeny and Lowe 1998), we find the concept useful if adapted
to reflect the important distinctions between the reformist and classical states of the Eastern Bloc: we
will henceforth differentiate between a reformist and a classic EEHM (Figure 1).
Figure 1. Theorizing communist housing systems
Forms of Socialist Systems (Kornai, 1992):
Classical socialism
Reformist socialism
SOVIET MODEL
CLASSIC EEHM
REFORMIST EEHM
USSR
GDR
Albania
Bulgaria
Romania
Yugoslavia
Hungary
Poland
Czechoslovakia
The Soviet housing model
The East European housing model (EEHR)
(Bessenova, 1992)
(Hegedus and Tosics, 1992)
6
As opposed to the reformist-socialist states, their classical counterparts employed to a much
larger scale the Soviet housing model (Bessonova 1992) which was established in the USSR by Stalin
and continued by Khrushchev and Brezhnev. This model best incarnates the right to free housing via
state provision. State housing was centrally planned in large, urban housing estates, whether flats
were allocated by local/central governments, state/municipal enterprises or enterprise-cooperatives–
–commonly the only alternative was overcrowding. Given Khrushchev’s and Brezhnev’s
commitment to state provision, urbanization rates increased substantially in the USSR as well as in
the GDR, which was the Eastern Bloc country that most closely followed the Soviet model (Sillince
1990).
The housing systems of the other classical states of the Eastern Bloc (Albania, Bulgaria and
Romania) were nonetheless different from both the Soviet model of the USSR and the reformist
EEHM. They also showed high degrees of difference among them. Romania and Albania had the
lowest housing investment as a percentage of total capital investment over the whole communist
period, whether the bulk of housing was produced by the state or households (Sillince 1990).
Albania’s counter-urbanization policies were unique in the Eastern Bloc, only matched by those in
Asian forms of communism. Bulgaria was unusual in that the state overwhelmingly used policies of
‘building for sale’ and shortages were filled by 1989, a unique achievement among classical-socialist
states (Sillince 1990; Soaita 2010). Given the overall states’ lack of commitment to housing (which
differed by time-periods), households had to engage in self-building. But unlike similarly widespread
practices in Yugoslavia, Slovakia and Hungary––which were supported by the state and a quasimarket private sector––Albanian, Bulgarian and Romanian households produced poorer quality
dwellings.
Table 1 indicates key differences in terms of the relative share of new housing provision by
different institutional organizations. For the last two decades of communism, it clearly indicates the
split between classical and reformist states, with state-provision ranging from 76-to-93 percent in the
former (except Albania) and accounting for only about a third in the latter. However, if we look at
the first two decades, we observe that households used to be active self-builders in both the classic
and reformist EEHMs––but never under the Soviet housing model of the USSR and GDR. Since selfbuilding was a predominantly rural phenomenon and state provision had a strong urban focus, a ruralurban divide in housing quality was created, particularly acute in the classical states. Not only are
rural houses in Albania, Bulgaria and Romania currently older on average than those in exCzechoslovakia, Hungary, Poland and ex-Yugoslavia but they are also of poorer quality for classical
states refrained from subsidizing alternative forms of provision. Increased urbanization also indicates
the extent of state provision. The last two columns in Table 1 show that urbanization increased
significantly in Bulgaria and Lithuania (by 47 and 41 percentage points, respectively); increases were
limited in Albania and Hungary (by 15 and 19 percentage points, respectively).
But state support was crucial not only for the quality but also for the quantity of housing. It
could be argued that classical states accepted higher shortages than the reformist ones. Exceptions
were reformist Poland which registered high shortages; and classical Bulgaria and the GDR which
achieved remarkable output. Consequently, the USSR, Albania, Poland and Romania entered their
post-communist transition with acute housing shortages.
7
Table 1. New housing provision during communist period in selected countries (rounded %)
19511960
19611970
19711980
19811989
State & enterprises
82
87
93
95
Households
18
13
7
5
State & enterprises
-
86
90
87
Households
-
14
10
13
State & enterprises
-
81
77
78
Households
-
19
23
22
State & cooperatives2
83
94
89
85
Households
17
6
11
15
State 1
26
50
49
54
Households
74
50
51
46
State
-
53
76
76
Households
-
47
24
24
State
12
36
80
93
Households
88
64
20
7
State & enterprises
80
40
42
29
Cooperatives
2
45
32
40
Households
18
15
26
30
State & h. associations
-
33
38
24
Households
-.
63
48
51
-
4
14
25
63
71
75
70
Households
37
29
25
30
State
42
38
36
36
Households
58
62
64
64
Form of provision:
SOVIET MODEL
Latvia SSR
Lithuania SSR
CLASSIC EEHM
GDR
Albania
Bulgaria
Romania
Czech region
REFORMIST EEHM
REFORMIST-SOCIALIST STATES
CLASSICAL–SOCIALIST STATES
Estonia SSR
Hungary
NSB 3
Poland
Yugoslavia
State & cooperatives
4
Urban
population
1950
1990
47
71
45
70
28
69
55
77
20
35
20
67
23
54
45
76
40
59
31
61
20
50
Sources: Data were estimated from the Soviet Republic of Estonia, from http://pub.stat.ee/pxweb.2001/dialog/varval.asp?ma=CO05&ti=CONSTRUCTION+OF+NEW+RESIDENTIAL+BUILDINGS&path=../I_d
atabas/Economy/05Construction/07Granted_building_permits_and_completed_buildings/&search=CO05&lang=1 (we
thank Dr Ave Hussar for indicating the source); For the Soviet Republics of Latvia and Lithuania we thank Dr
Aleksandra Burdyak who provided estimates (computed from the Statistical Yearbooks of 1969, pp. 576-9; 1971, pp.
541-4; 1976, pp.572-5; 1978, pp 414; 1991; all in Russian). For the Czech region of Czechoslovakia
from https://www.czso.cz/documents/10180/20533754/retro+tabulka+2.xlsx/3f5f6e4f-d47a-40b9-980c37c1e2714501?version=1.0 (we thank Dr Petr Sunega for indicating the source and translating the categories). For the
rest of the countries from Soaita (2010), Table 21, pp. 260 and Table 2, pp. 45 (computed from country chapters in
(Clapham et al. 1996; Sillince 1990; Turner et al. 1992).
Notes: Last column does not include the full interval for the case of: Bulgaria 1981-7; GDR, 1981-6; Hungary, 19811987; Poland, 1981-1988; Romania, 1981-1985; Yugoslavia, 1981-1986.
1
The ‘state’ heading includes units built by self-help work; however, the state also subsidized rural self-building by
households in what was the exceptional Albanian approach to extreme under-urbanization.
2
Housing cooperatives in the GDR were highly centralized and subsidized, practically no different from the state
provision (though they tended to be allocated by criteria of merit to employees rather than by need).
3
NSB stands for housing developed with mortgage loans from the National Saving Bank.
4
Housing cooperatives in Poland were genuine alternatives to state provision. Since 1960 they produced about half of all
new dwellings both for renting and building-for-sale (which required substantial down-payments). Unfortunately we
lacked disaggregated data.
8
Based on the above considerations, we formulate three hypotheses (H) in order to gauge the
path-dependent nature of housing systems in the post-communist EU countries:
•
H1: National housing systems show patterned similarities and differences, clustering along
the lines of divisions between the Stalinist model, the reformist EEHM and classical EEHM.
•
H2: We expect housing realities (Kemeny 1988) at the level of households to be on average
superior in the former reformist-socialist countries versus the classical-socialist ones.
•
H2a: Given differential state support, we expect some within-cluster differences in housing
quality, particularly Poland and Bulgaria being within-cluster outliers.
These hypotheses counteract claims of continued convergence in post-communist housing. For
instance, revisiting the nature of communist and post-communist tenure arrangements, Stephens et al
(2015) demonstrated the universalistic, unitary nature of rental and personal tenures across all
communist countries since rules of access, rights of occupancy, security of tenure and housing costs
were very similar. We support this view of convergence if restricted to tenure arrangements but not
in relation to housing quality, quantity and modes of housing provision which differed across
countries and groups of countries. However, have these ideologically-rooted differences persisted
during the post-communist construction of housing markets?
4. Mapping post-communist housing change
While economic depression during the 1990s left the communist housing legacies broadly
unchallenged (Lowe 2004), post-2000 economic growth has stimulated housing change. As the 1990s
reforms of housing privatization and restitution are by now well-documented (Clapham et al. 1996;
Lowe and Tsenkova 2003), we will focus on the changes that have taken place since 2000. Economic
growth and remittances have stimulated new housing construction throughout the region, stirring
(self-built) suburbanization (Hirt 2008; Soaita 2013; Stanilov 2007), private regeneration of
communist housing estates (Cirman, Mandic and Zoric 2013; Soaita 2012; Vranic, Vasilevska and
Haas 2015) and gentrification in some cities (Gorczynska 2016; Kovács, Wiessner and Zischner
2012).
Indeed, the economic trajectories of these countries varied widely. Figure 2 (left panel) shows
that Slovenia and the Czech Republic started from better positions and have maintained their
economic lead; the Baltic States have caught up spectacularly after 2000 whereas Bulgaria and
Romania have lagged behind (Croatia’s post-2008 economic downfall brought its GDP/capita to the
Romanian figure). The distribution of economic growth has also varied. In terms of income
distribution (Figure 2, right panel), the former reformist-socialist states have maintained a drive for
equality (including Poland, where its exceptionally high inequality has recently fallen to the
intermediate levels of Hungary and Croatia). Conversely, the formal classical-socialist countries
show some of the highest inequality levels within the EU, higher than those in the UK. Political and
welfare scholars (Adascalitei 2012) argued the choice for (in)equality stemmed from differential
economic development in 1990, which allowed enacting (or not) redistributive policies and political
autonomy from the neoliberal agenda pursuit by IMF and World Bank through borrowing
9
BG
PL
CZ
RO
EE
SK
HU
SI
LT
HR
LV
BG
CZ
EE
HR
LV
HU
PL
RO
SI
SK
LT
15,000
Gini for income (scale to 100)
25,000
40
5,000
GDP/capita, PPP (current international $)
35,000
Figure 2 The economic landscape
1995
35
30
25
20
2000
2005
2010
2015
Source: World Bank, 2016 (left) and Eurostat, 2016 (right)
conditionality. In our holistic definition of housing systems, these economic factors are not just the
context of the ‘housing reality’ but fundamentals of housing markets.
Interestingly, though perhaps not surprisingly, national patterns of economic growth have
been associated with patterns of demographic change. Population has remained about constant in the
former reformist-socialist, economically-successful Czech Republic, Poland, Slovenia and Slovakia
(and even in Croatia, after an initial 13 percent war-driven fall). However, population has decreased
significantly in the former classical-socialist, economically-laggards states though causes are
complex and include economic and nationalistic-driven migration besides falling birth rates. During
1990-2014, population fell by 15-to-25 percent in the Baltic States, Romania and Bulgaria (and by 7
percent in Hungary). Population decreases are rarely acknowledged in comparative housing studies
even though this has important implications for how housing is occupied and managed. As positive
consequences, overcrowding levels have fallen (Eurostat 2016)vi and remittances have contributed to
new-building (Soaita 2014). On the negative side, vacancy has increased and demographic aging has
accelerated. These demographic trends have challenging implications for the maintenance of housing,
particularly in the stock affected by high vacancy rates.
However, overall rates of new housing provisions have remained low. Among the EU
countries, Census 2011 (Eurostat 2016) revealed the lowest share of housing stock built since 1990
being in Latvia (10 percent), followed by Lithuania, Romania and Estonia at 3-to-4 percentage points
higher. Hungary, Slovenia and the Czech Republic showed rates of 16-to-18 percent whereas Poland
and Croatia show figures of over 21 percent, close to the EU average.vii The key implication of this
is that the communist-built housing, accounting for 59-to-75 percentage of total housing stock,
significantly influences the housing quality experienced by households.
10
Relating measures of housing quality and deprivation among homeowners, Mandic (2010)
found very high levels of unfit housing (50 percent) and severe deprivation (25-to-43 percent) among
homeowners in the former classical-socialist states, which indicates the problematic nature of
homeownership given owners’ inability to safeguard their housing through appropriate maintenance.
The situation in the former reformist-socialist states of the Czech Republic, Hungary, Slovenia and
Slovakia was better and comparable to that in the EU Mediterranean countries (but much worse than
in the rest of the EU). This ties in well with a branch of scholarship relating housing outcomes to
welfare regimes (Dewilde and Decker 2016; Stephens, Lux and Sunega 2015) and with an emerging
attention to the welfare arrangements in post-communist nations (Aidukaite 2011; Kuitto 2016).
Commonly, the latter highlights welfare-state thrift in all these countries though the more generous
provision in the Czech Republic, Slovakia and Slovenia are also recognized. Figure 3 illustrates these
arguments by showing that Slovenia stands out in terms of high total social spending as percentage
of GDP; conversely, the former classical-socialist states show extremely low figures (Poland being
just about more generous than Bulgaria). This matches somewhat the share of population at risk of
poverty/social exclusion: in Hungary, Lithuania, Romania and Bulgaria more than 30 percent of
population face this risk as opposed to 15-to-20 percent in the Czech Republic, Slovakia and Slovenia.
To enable the functioning of the new housing markets, governments have tried building up
systems of housing finance, inspired by the German savings bank system.viii However, these failed to
reach popularity. Outright homeownership––including buying with family-pooled cash––remained a
defining feature of post-communist housing systems, embedded as they are in large informal
economies. With reference to Russia, Zavisca (2012) observed a strong cultural resistance to
mortgage debt, which, she argued, was internalized as a legacy of the communist promise for free
housing, resulting in “property without markets”. Eastern Europeans’ resistance to mortgage debt was
also observed elsewhere (Soaita and Searle 2016) and justified by a refusal to pay the high servicing
costs of a mortgage; indeed, high mortgage interest rates and bank fees have made market-borrowing
unaffordable even during 2000s (Aslund 2007). Housing was commonly cast as a
Figure 3. The welfare landscape
2011
60
50
2012
2013
2014
Social spending/GDP (2013)
30
25.0
20.2
40
22.0
18.4
25
20.9
17.7
14.8
15.3
14.4
14.8
17.6
20
30
15
20
10
10
5
0
0
CZ
SK
SI
PL
Former
reformist EEHM
HR
HU
EE
LT
Former
Soviet Model
LV
RO
Total soc. expenditure
as % from GDP
% population at risk of
poverty and soc. exclusion
2010
BG
Former classic
EEHM
Source: Eurostat (2016)
11
Figure 4. Market dynamics
Mortgage-debt to GDP and residential mobility
2002
2004
2006
2008
2010
2012
2014
Mobility rate
20
15.6
40
10.9
30
10.2
7.6
7.0
10.1
7.7
3.8
3.2
1.8
20
10
5.6
0
10
0
Mobility rate (2006-11)
% mortgage debt to GDP
50
-10
HU
SI
HR
CZ
PL
SK
RO
Former reformist
EEHM
BG
LT
Former classic
EEHM
LV
EE
Former
Soviet Model
The volatility of house prices
Left: former reformist-socialist states; Right: former classical-socialist states
120
House price index (2008=100)
100
80
60
40
20
100
80
60
40
20
HU
PL
SI
SK
HR
BG
EE
LV
LT
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
CZ
2002
0
0
2001
House price index (2008=100)
120
RO
Source: Eurostat (2016) and authors’ compilation from HYPOSTAT (2005, 2007, 2011, 2013, 2015, 2016).
place of home and definitely not seen as an asset (Lux et al. 2016; Soaita 2015).
Nonetheless, mortgaged homeownership is on the increase. Figure 4 illustrates some
paradoxical market dynamics between financialization trends (indicated by mortgage debt/GDP),
house price volatility and residential mobility (over the period 2007-2012) worthy of future research.
Mortgage debt/GDP ratio increased (top panel). If in 2000 all post-communist EU states showed
figures below 10 percent except Estonia and Latvia, by 2014 only Bulgaria and Romania were below
this figure, the others ranging from 16-to-31 percent. Likewise, the share of mortgagor households
(not shown) has persistently remained very low in Romania, Bulgaria and Lithuania but it about
doubled/tripled in the remaining post-communist EU states, by 2014 reaching 18-to-19 percent in
Estonia, the Czech Republic and Hungary. While these levels are still very low within the EU, they
evidence new lines of divisions across the eastern European housing systems. Comparing trends in
12
housing financialization and residential mobility (Figure 4, top panel), Estonian housing is clearly the
most commodified and the Romanian and Bulgarian ones the least (typical “property without
markets”). Commodification may be expected to translate income into housing inequality and
diminish egalitarian legacies of housing de-commodification (Soaita 2014).
Paradoxically between 2001 and 2008, these immobile and non-financialized housing markets
saw some of the highest EU property bubbles of 3-to-8-fold increases in the former classic-socialist
states and of 2-to-2.5-fold increases in the former socialist-reformist ones (Figure 4, bottom panels).
This exuberant growth turned into some of most spectacular EU property-busts with house prices
falling commonly by 20-to-30 percent but by 50 percent in Latvia and Romania (and only 10 percent
in the Czech Republic and Poland). High volatility in quite immobile and non-financialized markets,
particularly those of Bulgaria and Romania, is a conundrum for future research (indicative of
remittance-based and largely informal economies).
Under conditions of low housing output, high house price growth (even after discounting post2008 busts) and predominant cash transactions in housing markets, family support for young people
to access independent housing––or alternatively to stay put in the parental home––has been crucial
and resulted in complex co-residence arrangements. From this point of view, the housing systems in
Eastern Europe were viewed as an extreme case of Mediterranean familism (Mandic 2008).
We have so far argued that some countries have become more affluent and unequal than
others; some housing systems have become more financialized and less under pressure than others;
and housing quality at the level of households improved faster in some countries than others.
Therefore, we wish to pair our hypothesis 1 by:
•
H1a: National housing systems no longer show patterned similarities and differences along
the lines of divisions between the three communist housing systems presented in section 3 but
we make no assumption of likely new configurations.
As we are interested in the nature of housing inequality, we explore the comparative importance of
inherited rural/urban inequality versus income-instilled inequality in housing quality at the level of
households. As there are complex interdependencies between these two forms of inequality, we
simply suggest that:
•
H3: Given the dominance of communist-built housing in the current stock, differences in
housing quality between urban/rural households are higher than those between lower/higherincome households.
•
H4: Differences in housing quality between lower-/higher-income households are higher in
the more unequal than in the more equal countries. In other words, the more equalitarian
legacies of housing de-commodification are fading faster in the former than the latter
countries.
5. Method
We explore similarities and differences in housing systems by means of HCA. This is a statistical
technique for discovering underlying similarities between cases based on a variety of quantifiable
features and it has been widely used to cluster affinities between countries (Cho 2014; Danforth 2014;
Kammer, Niehues and Peichl 2012; Mandel 2009; Mandic 2008; Minas et al. 2014). The aim is to
have the most similar countries in each cluster, which concomitantly are most dissimilar to countries
13
in other clusters. HCA starts by considering each case as a separate cluster; then, step by step, the
most similar two clusters are merged until all cases form one single cluster. In an exploratory fashion,
we compare country clusters’ averages by one-way ANOVA and Tukey tests in order to explore
where the difference lies for each variable. Given our small number of country cases and our
exploratory approach, we accept levels of statistical significance of p<.10.
As with other scholars (Mandel 2009), we opt for a wide range of indicators in order to more
finely attune our exploratory analysis to the complex nature of the social reality of housing as defined
in section 2. We opt for nine indicators measuring economic, demographic, market and policy
fundamentals that shape housing demand and supply––whether provision is achieved through
markets, family and the state––and 10 indicators reflecting the ‘objective’ housing reality at the level
of households. This approach reflects our holistic and agency-empowered definition of housing
systems, with no assumptions of a straightforward relationship between macro- and micro-indicators.
For instance, overcrowding may be a temporal household strategy to improve housing conditions by
self-building/saving and adult children may stay in the paternal home in order to opt for other lifestyle
consumption than housing. Likewise, fast raising economic growth requires time and institutions to
filter down to households’ residential practices.
Indicators are presented in Box 1. Data allowed us to quantify long-term post-communist
change in housing fundamentals (F1-F9)ix but not at the level of households. This is not problematic
as, to achieve a meaningful comparison across countries, we wish to anchor our data on housing
realities in one recent year. Hence, our single-year indicators and end-year for period-indicators refer
consistently to 2012, when Eurostat conducted the special housing module in the EU Statistics in
Income and Living Conditions (EU-SILC). Country figures are shown in Table 2.
Box 1. Our choice of indicators
HOUSING FUNDAMENTALS
The economic field:
F1
F2
Change in GDP/capita in
PPP, 1995-2012
Change in Gini coefficient
for the income
distribution since the fall
of communism
Source: World Bank (2016). 1995 is the earlier year for which data is available
for all 11 countries. PPP stands for Purchasing Power Parity.
Source: World Bank (2016) and Eurostat (2016). We use 1987-89 values from
World Bank, the only available source, and our constructed average value for the
post-2007, i.e. 2008-2012 (Eurostat, 2016). We must note a major discrepancy
between World Bank and Eurostat in post-2007 values for Romania, the former
showing Romania as belonging to the group of equal rather than unequal
countries.
Communist housing legacies:
F3
F4
% of houses built during
communism (in the 2011
housing stock)
% of flats built during
communism (in the 2011
housing stock)
Source: Census 2011 (Eurostat, 2016). This commonly includes households’
self-building (with or without financial support from the state).
Source: Census 2011 (Eurostat, 2016). A flat is a dwelling in buildings of three
or more unites, this being a close proxy for state provision in classical-socialist
states but not necessarily in the reformist ones ( for it includes cooperatives).
Housing market dynamics:
F5
% of mortgage debt to
GDP in 2012
Source: HYPOSTAT (2016). Although this measures 2012 levels, it can be seen
as measuring change since mortgage levels have started from virtually null levels
in most countries (inherited mortgage levels in Bulgaria, Hungary and
Yugoslavia having been swept by inflation in the early 1990s).
14
F6
House value growth,
2003-08 (n-fold increase)
Source: HYPOSTAT. We indexed house value growth to 2003 using annual
rates published by HYPOSTAT (2005, 2007, 2011, 2013, 2015, 2016).
Population dynamics:
F7
% of population change
1990-2012
Source: Eurostat (2016). We opt for the number of population rather than
households for the latter is mediated via household composition, relating housing
availability, affordability and strategies of migration in countless ways.
Social spending:
F8
F9
% of population at risk of
poverty & social
exclusion
% total social spending as
percentage to GDP
Source: Eurostat (2016), data for 2012.
Source: Eurostat (2016), data for 2012.
HOUSING QUALITY AT THE LEVEL OF HOUSEHOLDS
Housing conditions:
Q1
Dwelling size in m2
Q2
Housing deprivation
Q3
Difficulty to access
transport
Source: Census 2011 (Eurostat, 2016). As measures of overcrowding ignore
floor area, we include this adjacent measure of residential well/ill-being.
Source: our calculation based on Eurostat microdata for 2012. 1 Measured by %
of total population having neither a bath, nor a shower, nor indoor flushing toilet
in their household (Eurostat, 2012).
Source: our calculation based on Eurostat microdata for 2012. Measured by the
% of population reporting (very) high difficulty in accessing public transport
while having no car in their households (Eurostat, 2012).
Households’ arrangements:
Q4
Overcrowding
Q5
Extendedfam./composite
households2
Total housing cost
overburden
Q6
Source: our calculation based on Eurostat microdata for 2012. Measured by the
% of population in overcrowded homes as per Bedroom Standard (Eurostat,
2012).
Source: our calculation based on Eurostat microdata for 2012. Measured by the
% share of population living in any type of household, which is not one or two
adults,(with or without dependent children3 (Eurostat, 2012).
Source: our calculation based on Eurostat microdata for 2012. Represents % of
the population in households where total housing costs take more than 25% for
quintile 1; 30% for quintile 2; 40% for quintile 3; 50% for quintiles 4 and 5
(Eurostat, 2012).
Housing inequalities:
D1
D2
D3
D4
Distance poor
Distance rich
Distance urban
Distance rural
For each Q2-6, we construct four ‘distances from the mean’ (see Sunega and Lux,
2016) for population in quintile 1 (‘poor’) and 5 (‘rich’) of equivalised income
and for population in densely/intermediately-populated areas (‘urban’) and thinlypopulated areas (‘rural’)4. To highlight positive/negative outcomes, we discount
these values from national averages so that positive/negative figures reflect
positive/negative outcomes. We then average across each set of five, obtaining our
D1-4 indicators. This operationalisation cancels some of the difference, reflecting
common trade-offs (e.g. urbanites may suffer of overcrowding but have better
access to public transport).Source: Eurostat (2012).
Notes:
1
Eurostat microdata consist of face-to-face interviews of nationally representative samples; the number of individuals
aged 16 and over who were interviewed in 2012 ranged from 11,224 (LT) to 30,755 (PL).
2
We constructed an additional variable ‘young people access to independent housing’ measured by the % of population
living in households with adult children (i.e. aged 19-35, excluding those aged 19-26 in education). Not unexpectedly,
these were highly correlated (p=.969) hence we dropped this from the analysis.
3
4
Dependent child: aged 15 or less, or aged between 16 and 24 and economically inactive.
As this code is unavailable in Slovenia’s microdata, we used estimates (e.g. averages of ‘cities’/‘town and suburbs’ for
Q1, Q4; proportional estimates for Q5 against the all-countries average; proportional estimates for Q2, Q3, and Q6 against
15
comparative values for Slovenia from 2012 Eurostat aggregate data and Census 2011; we thank Prof Andreja Cirman for
providing data support.
16
Table 2 Country values (grouped by former housing models/geographical regions)
Former Stalinist housing
model (the Baltic States)
EE
LT
LV
Housing fundamentals
F1 GDP/capita change 1995-2012
F2 Gini change 1989-2012
F3 Communist-built houses
F4 Communist-built flats
F5 Mortgage debt to GDP, 2012
F6 House price boom 2003-2008
F7 Population change 1990-2012
F8 Population risk of poverty, 2012
F9 Social spending to GDP, 2012
Households’ housing overall quality
Q1 Dwelling size
Q2 Housing deprivation
Q3 Difficulty to access transport
Q4 Overcrowding
Q5 Extended-family
Q6 Housing cost overburden
D1 Distance Poor
D2 Distance Rich
D3 Distance Urban
D4 Distance Rural
15,162
13.85
13.8
54.9
33.1
3.5
-15.36
23.4
15.0
18,295
11.90
23.2
48.3
17.4
2.3
-18.26
32.5
16.3
66.7
6.9
6.7
63.2
13.9
8.1
14.3
19.2
13.7
-11.2
6.3
1.5
-2.3
19.7
25.3
15.4
-12.4
8.9
3.3
-2.2
62.5
14.4
8.8
36.8
31.7
16.8
-10.7
13.4
3.5
-4.3
19,569
8.63
11.2
53.5
24.0
3.2
-23.29
36.2
14.4
Former classical EEHM
(south-east Europe)
BG
RO
10,561
10.79
37.7
36.3
8.7
3.2
-18.47
13,578
10.81
37.5
35.7
6.6
7.1
-13.05
49.3
16.6
43.2
15.4
73.0
13.2
43.9
35.1
11.6
44.8
16.7
52.0
40.1
21.5
-16.4
14.6
6.8
-9.2
46.0
22.8
-18.6
7.6
2.4
-4.6
CZ
Former reformist EEHM
(central-east Europe)
HU
HR
PL
SI
14,922
5.56
19.4
39.0
13.5
13,390
1.5
1.40
15.4
20.4
1.4
-6.2
33.5
78.0
0.4
3.0
21.1
22.9
20.3
-12.4
5.9
75.6
3.8
5.8
47.3
31.0
23.8
-15.4
9.8
2.1
-3.5
0.2
-0.4
0.49
37.5
26.1
20.2
21.4
10,873
8.45
35.7
21.6
18.9
1.5
-10.21
32.6
21.1
16,161
4.41
21.6
35.1
20.6
2.0
1.73
26.7
18.9
15,255
81.6
1.9
7.7
44.7
43.4
75.2
3.3
5.3
46.9
41.6
17.7
-13.6
9.7
3.0
-4.3
80.3
13.3
-11.5
5.1
1.3
-1.4
-0.12
36.4
26.6
14.6
2.1
2.96
19.6
24.9
0.3
2.8
16.9
26.1
11.6
-8.6
3.7
-0.2
-0.3
SK
17,622
5.54
28.7
40.6
19.0
2.2
2.66
20.5
18.0
87.4
0.3
5.6
38.8
42.1
16.3
-10.2
2.6
0.6
-0.8
Note: The first/second best (or just highest) indicators in terms of housing quality across countries are shown in bold; the first/second worse (or just lowest) ones are underlined.
Acceptable levels of correlations (not shown).
17
6. Results
We perform HCA by means of Ward’s method of classification (with squared Euclidian distance) in
order to identify affinities between housing systems according to our 19 variables. As variables have
different measurement scales, we use Z-scores for standardization.
Figure 5 displays graphically the results in the form of a dendrogram, which should be read from
left to the right. It is up to the researcher to decide the cut-off point for the number of clusters that
best accommodate the theories which have informed the selection of variables. However, the
dendrogram and the agglomeration schedule (not shown) offer visual and numerical indications. We
choose the three-cluster solution as the best fit for our purpose. Countries are grouped as follows:
•
SEE: south-east Europe of the former classic EEHM (BG, RO).
•
CEE: central-east Europe of the former reformist EEHM (CZ, HR, HU, PL, SI, SK).
• BS: the Baltic States of the former Soviet housing model (EE, LV, LT).
These results confirm our H1 (rejecting the alternative H1a). Housing systems cluster neatly along
the past lines of divisions between the three communist housing models presented in section 3. This
supports the idea of path-dependent change (our fundamental variables being indeed measures of
change); claims of overall convergence across the housing systems of post-communist states should
thus be rejected. Since clustering patterns still reflect communist housing models 22 years after the
fall of communism, we could describe this model of change by the metaphor ‘running on parallel
tracks’, hence the title of this paper.
However, these results will not surprise housing scholars as they match the traditional
geographical grouping of the SEE and CEE countries observed in many analyses during the 1990s;
differences tended to be then explained by post-communist trends rather than the inherited
constitution of housing systems, which were considered to be all too similar. As the Baltic States
entered the field of comparative housing somewhat later, there was ambiguity related to their
Figure 5. Housing systems: clustering patterns (HCA on 19 variables)
BS
CEE
The visual clue is the length of the
SEE
horizontal interval between two
merging steps. The solution of three
clusters is thus obvious.
Source: SPSS software output
18
Table 3. ANOVA test for cluster means with ranking scores for housing quality
HOUSEHOLDS’ HOUSING REALITY
HOUSING FUNDAMENTALS
F
Sig.
SEE
CEE
A
17,675 B
F1
F2
F3
F4
F5
F6
F7
F8
F9
GDP/capita change 1995-2012
3.741
.071
12,069
Gini change 1989-2012
8.216
.01
10.8 B
4.1 A
11.5 B
Communist-built houses
6.429
.02
37.6 B
29.9 B
16.0 A
B
B
52.2 A
Q1
6.745
Dwelling size
10.621
Housing deprivation
15.061
Difficulty to access transport
Ranking score ( housing conditions):
2.473
Overcrowding
3.075
Extended-family
2.190
Housing cost overburden
Ranking score (household arrangements):
6.464
Distance Poor
2.040
Distance Rich
Ranking score (income inequalities):
3.879
Distance Urban
5.738
Distance Rural
Ranking score (place inequalities)
Q2
Q3
Q4
Q5
Q6
D1
D2
D3
D4
14,737
BS
O
Communist-built flats
10.737
.01
36.0
Mortgage debt to GDP, 2012
8.267
.01
7.7 A
17.8 B
24.8 B
House price boom 2003-2008
7.781
.01
5.2 A
1.8 B
3.0 B
Population change 1990-2012
14.891
.00
-15.8 B
-1.3 A
-19.0 B
Population at risk of poverty
7.342
.01
46.3 A
24.7 B
30.7 B
Social spending to GDP
9.618
.01
16.0 B
20.8 A
15.2 B
.02
58.5 1 B
24.2 1 B
14.1 1 A
3
48.41 A
43.01 A
22.11 A
3
-17.51 A
11.21 A
2
4.61 A
-6.91 A
2
79.7 3 A
1.7 3 A
5.0 3 B
9
36.02 A
34.52 O
17.22 A
6
-12.02 B
6.13 A
5
1.23 B
-1.83 B
6
64.1 2 B
13.0 2 B
7.9 2 B
6
23.63 A
25.43 B
15.33 A
9
-11.43 B
9.62 A
5
2.82
-2.92
4
10
26
24
.01
.00
.15
.10
.17
.02
.19
.07
.03
Total score for overall housing quality:
31.6
Note:
Superscript bold capital letters: these indicate results of a multiple comparisons by Tukey post-hoc HSD test for each
variable (subset for alpha = .1; p ranging from .003 to .092). As the group sizes are unequal, the harmonic mean of the
group sizes is used (n=3), type I error levels being not guaranteed. Descriptively, differences are visible to the naked eye.
Combinations:
•
A-B-C indicates that all three clusters’ means are statistically different.
•
A-B-B shows that one cluster’s mean is statistically different from the means of the other two (which are no
•
A-B-nothing indicates that only two means are statistically different (imagine a ladder with three rungs: the
extreme groups are statistically different but the one in between shows similarities with both, the upper and the
different, making so a single cluster).
lower rung).
• A-A-A shows that clusters’ means are not statistically different, making so a single cluster.
Subscript bold numbering: these indicate simple ranking scores on housing quality (from 3=best to 1=worst). The
minimum ranking sum a cluster may get is 10 and the maximum 30. As any ranking method, this remains relative for
equal ranking steps may not reflect households’ preferences; it also does not control for the actual magnitude of the
difference between indicators although for differences of less than 5 percentage point we would split the score equally.
19
comparative position though they were commonly grouped together (Hegedus and Tosics 1998;
Tosics 2003). Slightly different country grouping (Poland grouped with BS) and similarities between
the SEE and BS countries were sometimes observed (Mandic 2008; Mandic 2010; Mandic and
Cirman 2011). However, our analysis shows that the similarities among the three Baltic States are
overall larger than their similarities with any other post-communist EU states.
We will now have a more detailed look at Table 3, which shows the cluster means. We note
statistically significant differences for all nine fundamentals (at p<.05 for eight indicators) and for
seven out of 10 micro-indicators (at p<.05 for six indicators). This suggests that these clustering
patterns are stronger in fundamentals than in housing-quality indicators. On the one hand, we did not
theoretically expect a neat correspondence since we credited households’ agency. For instance, the
distribution of co-residence (and of young people in the parental home) across income quintiles
suggests complex cultural and/or consumption preferences worth of further research.x On the other
hand, the statistical clustering algorithm takes into consideration within-cluster dispersion, which is
higher in micro-indicators than in fundamentals. For instance regarding overcrowding, the lack of a
significant difference between very different cluster means may be a statistical outcome of withincluster dispersion, ranging from 14,8 (for SI) to 44.2 (for PL) in the CEE cluster and from 14.2 (EE)
to 39.4 (LV) in the BS cluster (Table 2). We will return to this within-cluster dispersion in housing
quality later.
Tables 3 also shows by superscript letters that no single variable differentiates across all three
clusters; it is rather the dynamics between each combination of two possible clusters (e.g. as in F3),
and differences between the extremes (e.g. as in F1) that determine the outcome. The superscript
letters tell us where the difference between clusters lies by variables. For instance, variables F3-&F4 in conjunction with Q1-&-Q2 and D3-&-D4 highlight the continuing legacies of the three
communist housing models discussed in section 3:
•
[SEE=CEE]≠BS on the share of communist-built houses (F3) and flats (F4) reflect key
differences between the EEHM and the Soviet housing model––Hegedus’ and Tosics’s (1992)
thesis––in that state was a less important provider in the former than in the latter; but
•
CEE≠[SEE=BS] on housing conditions (Q1-&-Q2) and rural/urban-induced housing
inequalities (D3-&-D4) reflects key differences between the classic and reformist EEHMs––
Kornai’s (1992) thesis––in that the reformist socialist-states produced higher quality housing
than their centralized counterparts.
Furthermore, interesting links between socioeconomic and housing inequality are revealed by
variables F2-&-F9 in conjunction to F8-&-D1:
•
[SEE=BS]≠CEE on Gini increase (F2) and total social spending (F9), which describe the
unique CEE drive for socioeconomic equality, yet
•
[CEE=BS]≠SEE on population at risk of poverty/social exclusion (F8) and odds of lowincome households suffering poor housing quality (D1) describes an unexpectedly good
performance of BS despite its choice for inequality. Possible explanations are the filtering
down of outstanding economic growth (F1) and better housing conditions (Q3-&-Q5) on
which BS≠SEE.
20
However, as clusters are of unequal size, the results of a Tukey post-hoc HSD should be read
descriptively only (however, these differences are commonly visible to the naked eye).
To elaborate on our H2 regarding housing quality being higher in the former reformist EEHM
(now CEE) than in the classical-socialist states (now SEE and BS), we advance a simple ranking
exercise of clusters’ means related to housing-quality indicators.xi Table 3 shos these scores in
subscripts and additional rows. Out of a min/max of 10/30 scores, the CEE cluster was ranked first
on housing quality (scoring 26), followed by the BS and SEE cluster (scoring 24 and 10, respectively).
This allows us to affirm that H2 regarding housing quality being higher in the reformist- than in the
classic-socialist states is supported by data, yet the small difference between the former-reformist
CEE and the former-classic BS clusters requires reflection.
If it is true that housing quality in the USSR was poorer than in the reformist EEHM as the
literature suggest (Sillince, 1990) and as our scoring on housing conditions maintains (‘6’ for BS and
‘9’ for CEE in Table 3)––then we witness a process of BS catching up with the CEE cluster. This
seems stirred by some fundamental post-communist developments: the highest rates for population
decrease (F7), GDP growth (F1) and housing market financialization (F5), which have improved
housing availability and access (indeed, note the higher BS versus CEE scoring on housing
arrangements, i.e. ‘9’ and ‘6’, respectively; Table 3).
Finally, we wish to concisely elaborate on H3, related to our assumption that urban/ruralinduced differences in housing quality are higher than the income-related ones. Data clearly reject
this assumption. In all clusters (Table 3 but also for each country as shown in Table 2), the sum of
the absolute values (i.e. ignoring the negative sign) for the pair of urban/rural distances is much lower
than the corresponding sum for the pair of poor/rich distances. The respective figures are 11.5 versus
28.6 for SEE; 5.7 versus 21.0 for BS; and 4.0 versus 18.1 for CEE. The fact that income-related
inequalities are higher than those stemming from urban/rural divisions is not necessarily an outcome
of the post-communist transition but it directs the attention to recognized socioeconomic inequalities
during communism (Szelenyi 1983) and to the correlation between the two.
This exercise let us also concisely elaborate on H4, related to our assumption that incomerelated housing inequalities are higher in the more unequal BS and SEE clusters than in the more
equal CEE one. Indeed SEE shows the highest figure (28.6), followed by BS (21.0) and the CEE
(18.1), supporting thus our H4. However, we wish to flag the fact that BS is closer positioned to CEE
than SEE which puts additional weight on our previous observation that high socioeconomic
inequality in the BS cluster does not straightforwardly translate into housing inequality. We also
observe that income-related housing inequality is higher at the bottom than at the top (i.e. lowerincome households are more likely to suffer negative outcomes than are higher-income households
likely to enjoy positive outcomes). This is true for all clusters (Table 3) and for each country (Table
2). We acknowledge, however, that income and place inequalities show complex interdependencies
and trades-offs and that we cannot control for endogeneity in this analysis (e.g. the phenomenon of
rural poverty).
Nuancing the results: addressing the issue of within-cluster dispersion in housing quality
We already noted the issue of within-cluster dispersion in our micro-indicators. To explore this
further, we will perform two additional HCAs, one on fundamentals and another on housing-quality
21
Figure 6. Deconstructing clustering patterns by fundamentals and housing quality
HCA on housing fundamentals, only (F1-F9)
HCA on housing quality, only (Q1-G6 and D1-D4)
CEEi
CEE
BS
SEE
Mix-EE
BS
SEEi (RO)
Source: SPSS software output
Table 4. Nuancing housing quality across four clusters (ANOVA test and ranking scores)
F
Q1
Q2
Q3
Q4
Q5
Q6
D1
D2
D3
D4
Sig.
51.814
.00
Dwelling size
18.950
.00
Housing deprivation
6.194
.02
Difficulty to access transport
Ranking score ( housing conditions):
3.255
.09
Overcrowding
1.522
.29
Extended-family
3.187
.09
Housing cost overburden
Ranking score (household arrangements):
6.818
.02
Distance Poor
7.277
.02
Distance Rich
Ranking score (income inequalities):
20.126
.00
Distance Urban
34.909
.00
Distance Rural
Ranking score (place inequalities)
Total score for overall housing quality:
SEEi
CEEi
BS
Mix-EE
43.9 1
35.1 1
16.7 1
3
52.0 1
40.1 1
21.5 1.5
3.5
-16.4 1
14.6 1
2
6.8 1
-9.2 1
2
81.8 4
0.7 4
4.8 4
12
30.4 3
33.6 3
15.4 3.5
9.5
-10.7 4
4.3 4
8
0.5 4
-0.7 4
8
64.1 2
13.0 2
7.9 2.5
6.5
23.6 4
25.4 4
15.3 3.5
11.5
-11.4 3
9.6 2.5
5.5
2.8 2.5
-2.9 3
5.5
74.6 3
6.8 3
7.6 2.5
8.5
46.3 2
39.5 2
21.4 1.5
5.5
-15.9 2
9.1 2.5
4.5
2.5 2.5
-4.1 2
4.5
10.5
37.5
29
23
Note:
Subscript bold numbering: these indicate simple ranking scores (from 4=best to 1=worst). The minimum ranking sum a
cluster may get is 10 and the maximum 40. As any ranking method, this remains relative for equal ranking steps may not
reflect households’ preferences; it also does not control for the actual magnitude of the difference between indicators
although for differences of less than 5 percentage point we split the score equally (e.g. R3, R6, R8 and R9). Having a onecountry cluster, we cannot explore multiple comparisons by Tukey post-hoc HSD.
22
indicators. Figure 6 displays the two dendrograms, showing our choice for the cut-off point. We note
the same three-cluster solution on fundamentals (BS, CEE and SEE) but a four-cluster solution on
micro-indicators. We will further reflect exclusively on the latter. Countries are now grouped in the
following clusters:
•
Incomplete SEE, henceforth SEEi (RO; without BG).
•
Incomplete CEE, henceforth CEEi (CZ, HR, SI, SK; without HU, PL).
•
New, mixed SEE and CEE, henceforth Mix-EE (BG, HU, PL).
•
Unchanged BS (EE, LV, LT).
We find these results particularly revealing because the Mix-EE cluster includes the worst performer
in the reformist EEHM and the best one in the classic EEHM, i.e. Poland and Bulgaria. We expected
these countries to induce within-cluster dispersion (H2a), which our analysis indeed evidences (please
consider that their overall housing systems cluster in accordance to their historic belonging, as shown
in Figure 5). Housing scholarship has offered us no grounds to deduce Hungary’s intermediate
position but country data in Table 2 clearly expose it: in terms of worst/second-worst ranks within
the CEE, Hungary gets nine (with Poland getting seven, Croatia two and the Czech Republic one).
By interpreting the ranking scores on housing quality shown in Table 4, Figure 7 offers an easy way
to comparatively characterize each cluster in terms of overall and specific dimensions of housing
quality. It clearly shows Romania performing the worst, CEEi the best, and the intermediate positions
of the Mix-EE and BS.
Figure 7 Assessment of housing quality across four clusters (scale 10 to 40)
RO (SEE i)
Mix-EE
BS
CEE i
Low
High
quality
(10.5)
Extremely poor
housing conditions
High
urban/rural-related
housing inequality
(23.0)
(29.0)
(37.5)
quality
Intermediate housing conditions
Best housing
conditions
Low urban/rural-related housing inequality
Very low
urban/rural-related
housing inequality
Extremely difficult households’
housing (affordability) arrangements
Least difficult households’
housing (affordability) arrangements
High income-related housing inequality
Low income-related housing inequality
23
7. Concluding Discussion
In this article, we chose a long-term perspective to examine the extent to which current housing
systems in post-communist countries share commonalities and where important differences arise.
More specifically, we aimed to explore whether important ideologically-rooted past differences in
housing provision have persisted during the post-communist construction of housing markets. We
conceived housing systems in a broad fashion and operationalize them in terms of fundamentals and
micro-indicators reflecting the housing reality at the level of households. These indicators were
imputed in a HCA.
Our analysis evidenced that the post-communist EU countries’ housing systems still cluster
along the same historic lines of division––the three communist housing models presented in section
3––in the groups of the Baltic States, central-east Europe and south-east Europe (supporting our
hypothesis 1). We described this model of change under the metaphor of ‘running on parallel tracks’,
i.e. past legacies of difference were carried through the post-communist transformation whether
through continuities (i.e. persistent features of the built environment) or discontinuities (i.e. the choice
for equality/inequality). Conceptualizing this model of change is one of our key contributions to the
current debates regarding continued convergence versus emerging divergence across post-communist
housing systems (Miao and Maclennan 2016; Stephens, Lux and Sunega 2015). As common in cluster
analyses of country systems/regimes, we faced the issue of statistical power. Rather than reducing the
number of variables to the sample size (for 11 countries, conventional wisdom suggests the use of
two to three variables), we preferred to nuance our analysis by using a broader set of indicators; this
however means that findings remain descriptive and exploratory. Under this reservation, we wish to
qualify our findings by the following statements.
Housing quality at the level of households (hypothesis 2 and 2a)
Our findings supported the assumption that housing quality was higher in the reformist-socialist states
(now CEE) than in the classical-socialist ones (now BS and SEE). However, as we hypothesized some
degree of heterogeneity induced by different country performance during communism, we have
nuanced these findings by means of HCA on micro-indicators (addressing thus the observed withincluster means’ dispersion). Results were revealing in that they differentiated a new cluster of mix
eastern European countries, comprising the laggards in the best-performing cluster (Hungary and
Poland) and the leader in the worst-performing one (Bulgaria). Our analysis showed that households’
housing quality is extremely poor in Romania; poor-to-intermediate in the mix group of Bulgaria,
Hungary and Poland; intermediate-to-good in the Baltic States, and higher in Croatia, the Czech
Republic, Slovenia and Slovakia. Evidencing these further differences in housing quality, particularly
singling out Romania from Bulgaria and highlighting the second-best position of the Baltic States is
a key contribution of our study to comparative housing systems, nuancing previous findings (Mandic
and Cirman 2011).
Socioeconomic versus housing inequalities (hypotheses 3 and 4)
We observed a pattern of low versus high socioeconomic inequality between the former reformistsocialist and classical-socialist states as measured by the Gini coefficient for income and by total
24
social expenditure. Yet this pattern does not consistently filter down to households across the three
clusters, which is an intriguing finding of our analysis. While socioeconomic inequality is associated
with housing inequalities in SEE, this is clearly not the case in the BS cluster. Despite both clusters
sharing increases of about 11 percentage points in the Gini coefficient for income over the postcommunist period, SEE is different from BS in terms of patterns of housing inequalities. Table 3
showed that, on average, SEE’s lower and higher income households (D1/D2) are farther away from
the national mean than they are in the other two clusters (in fact lower income households are on
average better off in BS than the more equal CEE countries but not in the CEEi). In our view, housing
legacies, in conjunction with demographic and economic changes, have greatly advantaged the BS
versus SEE housing systems. SEE’s states and households produced poorer quality housing than their
Soviet counterparts, qualitatively if not quantitatively (e.g. smaller dwellings; lack of utility
provision). While overcrowding affected all these countries, higher population falls in BS improved
quantitative deficits (including by means of residential mobility supported by their more financialised
housing markets).
The social welfare role of outright homeownership
An argument can be made that legacies of housing de-commodification––whether through command
economies, self-building, co-habitation and inheritance––still temper the translation of income into
housing inequality, particularly so in the Baltic States. First and foremost, this seems supported by
many micro-indicators being not (much) different across countries of high (BS) and low (CEEi)
income inequality, as shown in Table 4: housing cost overburden, overcrowding, extendedfamily/composite households and the disadvantage suffered by lower-income households (D1
variable) showing comparable levels. While this is true at our level of aggregation and in our
methodological construction of housing inequality––in which we allowed for some trade-offs by
summing up negative and positive values––we believe comparative cross-country analysis on
relevant subgroups of population would be welcome. A particularly interesting focus would be
identifying vulnerable social groups, for instance those who suffer both overcrowding and housing
cost overburden.
Future change: continuities and discontinuities
We highlighted that no single variable differentiates between all three clusters, which theoretically
means that clusters may be more amenable to change than otherwise constituted. While notionally
any trajectory of change could be imagined––full or partial convergence, countries switching between
existing clusters, new clusters to emerge or current patterns to be reinforced–––some trajectories may
be more likely than others. We wish to highlight some likely continuities and discontinuities and their
implications to the nature of housing systems.
Kemeny’s (1981) thesis on the importance of historic housing legacies to current housing
systems was clearly supported in the context of post-communist states. This was a key point in the
debate between the consumption and provision thesis, which we mentioned in section 2. Kemeny
argued that new-built housing constitutes a small part of the housing stock, hence the larger structural
effect of historic housing policies to households’ options and choices. The built legacies of
communism are likely to persist until new-built housing becomes quantitatively more significant in
25
the housing stock and qualitatively superior. However, legacies of de-commodification through selfbuilding and state-provision, and their associated housing conditions will necessarily fade away,
albeit slowly. New housing construction may become a mechanism of discontinuity, but not
necessarily so. For instance, the structure of poor housing quality in south-east Europe, particularly
Romania, has been reproduced through a persistent share of poorly new-built housing (Soaita 2017).
While the SEE cluster seems thus likely to persist, we do not have to overplay the similarities
between the CEE and BS clusters for they have so far opted for a very different political-economic
path, that of social-democratic versus neoliberal politics (expressed in our analysis by Ginicoefficients and levels of total social expenditure). If these countries continue following splitting
political paths, then inequality in income distribution will increasingly determine inequality in
housing outcomes. The fast financialization of BS housing markets is a clear factor of discontinuity.
The growing share of mortgaged homeownership––in conjunction with increasing affluence as shown
by the significantly higher growth of GDP/capita––warn us against endorsing an over-deterministic
path-dependency lens and raises interesting questions regarding its implications to housing
inequalities. We showed that the relationship between income and housing inequality is far from
straightforward given legacies of housing de-commodification. Our study thus invites theoretical
reflection of the mechanisms through which post-communist housing continuities are being
maintained and emerging discontinuities constructed across countries, but particularly across social
groups. Given the inherent contribution of mass outright homeownership to welfare in the postcommunist space (Stephens, Lux and Sunega 2015), we suggest that scholarship on post-communist
welfare regimes would greatly benefit from a serious engagement with the differential nature of
housing systems.
Finally, our study advances a valuable middle-range epistemological frame (Kemeny and
Lowe 1998) for understanding the complex social reality of housing at both the micro and macro
level. This may inform specific policy questions, contextualize more focused analyses or help explain
differential practices, aspirations or expectations across (groups of) countries. Our study helps shatter
the over-generalist labeling of ‘post-communism’ (Tuvikene 2016) and problematizes the growing
view that communist housing systems were all too similar; they indeed were not.
Acknowledgements
We thank Dr Aleksandra Burdyak (Institute for Social Analysis and Forecasting, Moscow), Prof Andreja
Cirman (University of Ljubljana), Dr Ave Hussar (University of Tartu) and Dr Petr Sunega (the Czech
Academy of Sciences, Prague) for their help with data in Table 1 and 3; to Prof Mark Stephens (Heriot-Watt
University, Edinburgh) and the three peer reviewers for their supportive comments; and to our colleagues in
the Department of Sociology, Tilburg University, for enjoyable and productive discussions. This research was
funded by ERC Starting Grant Agreement No. 283615, project “HOWCOME: Changing Housing Regimes
and Trends in Social and Economic Inequality”, PI Dr Caroline Dewilde, Tilburg University. EU-SILC data
were provided by Tilburg University.
References
Adascalitei, Dragos. 2012. "Welfare State Development in Central and Eastern Europe: a State of the Art Literature
Review." Studies of Transition States and Societies 4(2):59-70.
26
Aidukaite, Jolanta. 2011. "Welfare Reforms and Socio-economic Trends in the 10 New EU Member States of Central
and Eastern Europe." Communist and Post-Communist Studies 44(3):211-19.
Allen, Judith. 2006. "Welfare Regimes, Welfare Systems and Housing in Southern Europe " European Journal of Housing
Policy 6(3):251-77.
Archer, Margaret. 1995. Realist Social Theory: The Morphogenetic Approach. Cambridge: Cambridge University Press.
Aslund, Anders. 2007. How Capitalism Was Built: The Transformation of Central and Eastern Europe, Russia and
Central Asia. New York: Cambridge University Press.
Barlow, James, and Simon Duncan. 1988. "The Use and Abuse of Housing Tenure." Housing Studies 3(4):219-31.
Bessonova, Olga. 1992. "The Reform of the Soviet Housing Model: The Search for a Concept." in The Reform of Housing
in Eastern Europe and Soviet Union, 276-89, edited by B. Turner, J. Hegedus, and I. Tosics. London: Routledge.
Bourdieu, Pierre. 2005. The Social Structures of the Economy. Cambridge: Polity Press.
Bradshaw, Michael, and Alison Stenning eds. 2004. East Central Europe and the Former Soviet Union: The Post-Socialist
States. Harlow: Pearson Education Ltd.
Cho, E. Yin-Nei. 2014. "Defamilization Typology Re-examined: Re-measuring the Economic Independence of Women
in Welfare States." Journal of European Social Policy 24(5):442-54.
Cirman, Andreja, Srna Mandic, and Jelena Zoric. 2013. "Decisions to Renovate: Identifying Key Determinants in Central
and Eastern European Post-socialist Countries." Urban Studies 50(16):3378-93.
Clapham, David F, William A V Clark, and Kenneth Gibb. eds. 2012. The SAGE Handbook of Housing Studies London:
SAGE.
Clapham, David, Jozsef Hegedus, Keith Kindrea, Ivan Tosics, and Helen Kay. eds. 1996. Housing Privatization in
Eastern Europe. London: Greenwood Press.
Danforth, Benjamin. 2014. "Worlds of Welfare in Time: A Historical Reassessment of the Three-World Typology."
Journal of European Social Policy 24(2):164-82.
Decker, Pascal De, and Caroline Dewilde. 2010. "Home-ownership and Asset-based Welfare: the Case of Belgium."
Journal of Housing and the Built Environment 25(2):243-62.
Delfani, Neda, Johan De Deken, and Caroline Dewilde. 2014. "Home-ownership and Pensions: Negative Correlation, but
no Trade-off." Housing Studies 29(5):657-76.
Delfani, Neda, Johan De Deken, and Caroline Dewilde. 2015. "Poor because of Low Pensions or Expensive Housing?
The Combined Impact of Pension and Housing Systems on Poverty among the Elderly." International Journal
of Housing Policy 15(3):260-84.
Dewilde, Caroline, and Pascal De Decker. 2016. "Changing Inequalities in Housing Outcomes across Western Europe."
Housing, Theory and Society 33(2):121-61.
Esping-Andersen, Gøsta. 1990. The Three Worlds of Welfare Capitalism. Cambridge: Polity Press.
Eurostat. 2012. Microdata of EU Statistics in Income and Living Conditions (EU-SILC).. Held by the European
Comission. Provided by Tilburg University, the Neherlands.
Eurostat. 2016. Your key to European statistics (aggregate, publicly available database owned by the European Comission,
Luxemburg; accessed December, 1, 2016). Luxembourg:. http://ec.europa.eu/eurostat/data/database
Gorczynska, Magdalena. 2016. "The Property Restitution in Warsaw: Renaissance or Decline of Pre-war Buildings?"
Journal of Housing and the Built Environment 31:367–86.
Hall, Peter A, and David Soskice. eds. 2001. Varieties of Capitalism: The Institutional Foundations of Comparative
Advantage. Oxford: Oxford University Press.
Hegedus, Jozsef, and Ivan Tosics. 1992. "Conclusions: Past Tendencies and Recent Problems of the East European
Housing Model." in The Reform of Housing in Eastern Europe and Soviet Union, 318-34, edited by B. Turner,
J. Hegedus, and I. Tosics. London: Routledge.
Hegedus, Jozsef, and Ivan Tosics. 1998. "Rent Reform-issues for the Countries of Eastern Europe and the Newly
Independent States." Housing Studies 13(5):657-78.
Heijden, Harry van der. 2013. West European Housing Systems in a Comparative Perspective. Delft: OTB.
Hirt, Sonia. 2008. "Landscapes of Post-modernity: Changes in the Built Fabric of Belgrade and Sofia since the End of
Socialism." Urban Geography 29(8):785-809.
Hoekstra, Joris. 2010. “Divergence in European welfare and housing systems.” PhD diss., Delft University.
27
Hussar, Ave. 2012. National Report for Estonia. TENLAW: Tenancy Law and Housing Policy in Multi-level Europe.
Available at www.tenlaw.uni-bremen.de/reports/EstoniaReport_18062014.pdf.
HYPOSTAT. 2005, 2007, 2011, 2013, 2015, 2016. A Review of Europe’s Mortgage and Housing Markets: Annual Report
Brussels: European Mortgage Federation.
Jones, Colin A, Edward Trevillion, and Stewart Cowe. forthcoming. Property Boom and Banking Bust: London: Wiley.
Kammer, Andreas, Judith Niehues, and Andreas Peichl. 2012. "Welfare Regimes and Welfare State Outcomes in Europe."
Journal of European Social Policy 22(5):455-71.
Kemeny, Jim. 1981. The Myth of Home Ownership: Private versus Public Choices in Housing Tenure. London:
Routledge.
Kemeny, Jim. 1988. "Defining Housing Reality: Ideological Hegemony and Power in Housing Research." Housing
Studies 3(4):205-18.
Kemeny, Jim. 1992. Housing and Social Theory. New York: Routledge.
Kemeny, Jim. 1995. "Theories of Power in the Three Worlds of Welfare Capitalism." Journal of European Social Policy
5(2):87-96.
Kemeny, Jim, and Stuart Lowe. 1998. "Schools of Comparative Housing Research: From Convergence to Divergence."
Housing Studies 13(2):161-76.
Kemp, Peter A. 2015. "Private Renting after the Global Financial Crisis." Housing Studies 30(4):601-20.
Kornai, Janos. 1990. "The Affinity between Ownership and Forms and Coordination Mechanisms." Journal of Economic
Perspective 4(3):131-47.
Kornai, Janos. 1992. The Socialist System: The Political Economy of Communism. Oxford: Oxford University Press.
Kovács, Zoltán, Reinhard Wiessner, and Romy Zischner. 2012. "Urban Renewal in the Inner City of Budapest:
Gentrification from a Post-socialist Perspective." Urban Studies 50(1):22-38.
Kuitto, Kati. 2016. Post-Communist Welfare States in European Context Patterns of Welfare Policies in Central and
Eastern Europe. Cheltenham: Edwaer Elgar.
Lapavitsas, Costas. 2009. "Financialised Capitalism: Crisis and Financial Expropriation" Historical Materialism
17(2):114-48.
Lawson, Julie. 2003. "Critical realism and housing studies: An explanation for diverging housing solutions." PhD diss.,
Amsterdam University.
Lawson, Julie. 2012. "A Review of Structurally Inspired Approaches in Housing Studies - Concepts, Contributions and
Future Perspectives." in The SAGE Handbook of Housing Studies, 188-205, edited by David F Clapham, William
A-V Clark, and Kenneth Gibb. London: SAGE.
Lersch, Philipp M., and Caroline Dewilde. 2015. "Employment Insecurity and First-time Homeownership: Evidence from
Twenty-two European Countries." Environment and Planning A 47(3):607-24.
Lowe, Stuart. 2004. "Too Poor to Move, too Poor to Stay." in Too Poor to Move, Too Poor to Stay, 13-22, edited by
James Fearn. Budapest: Open Society Institute.
Lowe, Stuart, and Sasha Tsenkova. eds. 2003. Housing Change in East and Central Europe: Integration or
Fragmentation? Ashgate: Aldershot.
Lux, Martin, Petr Gibas, Irena Boumová, Martin Hájek, and Petr Sunega. 2016. "Reasoning behind Choices: Rationality
and Social Norms in the Housing Market Behaviour of First-time Buyers in the Czech Republic." Housing
Studies:1-23.
Malpass, Peter. 2011. "Path Dependence and the Measurement of Change in Housing Policy." Housing, Theory and
Society 28(4):305-19.
Mandel, Hadas. 2009. "Configurations of Gender Inequality: the Consequences of Ideology and Public Policy." The
British Journal of Sociology 60(4):693-719.
Mandic, Srna. 2008. "Home-leaving and its Structural Determinants in Western and Eastern Europe: an Exploratory
Study." Housing Studies 23(4):615-37.
Mandic, Srna. 2010. "The Changing Role of Housing Assets in Post-socialist Countries." Journal of Housing and the
Built Environment 25(2):213-26.
Mandic, Srna, and Andreja Cirman. 2011. "Housing Conditions and their Structural Determinants: Comparisons within
the Enlarged EU." Urban Studies 49(4):777-93.
28
Miao, Julie Tian, and Duncan Maclennan. 2016. "Exploring the ‘Middle Ground’ between State and Market: the Example
of China." Housing Studies 32(1):73-94.
Michalovic, Peter. 1992. "Housing in Czechoslovakia: Past and Present Problems." in The Reform of Housing in Eastern
Europe and Soviet Union, 46-61, edited by B. Turner, J. Hegedus, and I. Tosics. London: Routledge.
Minas, Christos, David Jacobson, Efi Antoniou, and Caroline McMullan. 2014. "Welfare Regime, Welfare pillar and
Southern Europe." Journal of European Social Policy 24(2):135-49.
Norris, Michelle, and Nessa Winston. 2012. "Home-ownership, Housing regimes and Income Inequalities in Western
Europe." International Journal of Social Welfare 21(2):127-38.
Pollard, Julie. 2009. "Political Framing in National Housing Systems: Lessons from Real Estate Developers in France
and Spain." in The Politics of Housing Booms and Busts, 170-187, edited by Herman M Schwartz and Leonard
Seabrooke. London: Palgrave Macmillan.
Ronald, Richard. 2008. The Ideology of Home Ownership: Homeowner Societies and the Role of Housing Basingstoke:
Palgrave Macmillan.
Schwartz, Herman M, and Leonard Seabrooke. eds. 2009. The Politics of Housing Booms and Busts. London: Palgrave
Macmillan.
Sillince, John. ed. 1990. Housing Policies in Eastern Europe and Soviet Union. New York: Routledge.
Soaita, Adriana M, and Beverley A Searle. 2016. "Debt Amnesia: Homeowners’ Discourses on the Financial Costs and
Gains of Homebuying." Environment and Planning A 48 (6):1087–106.
Soaita, Adriana M. 2010. "Unregulated Housing Privatism: A Comparative Analysis of the Nature and Extent of Housing
Problems and Resident Responses in Two Forms of Romanian Urban Housing." PhD diss., King's College
London.
Soaita, Adriana M. 2012. "Strategies for In-situ Home Improvement in Romanian Large Housing Estates." Housing
Studies 27(7):1008-30.
Soaita, Adriana M. 2013. "Romanian Suburban Housing: Home Improvement through Owner-building." Urban Studies
50(10):2084-101.
Soaita, Adriana M. 2014. "Overcrowding and ‘Under-occupancy’ in Romania: a Case Study of Housing Inequality."
Environment and Planning A 46(1):203-21.
Soaita, Adriana M. 2015. "The Meaning of Home in Romania: Views from Urban Owner-Occupiers." Journal of Housing
and the Built Environment 30(1):69-85.
Soaita, Adriana M. 2017. "The Changing Nature of Outright Home Ownership in Romania: Housing Wealth and Housing
Inequality " in Housing Wealth and Welfare, 236-257, edited by Caroline Dewilde and Richard Ronald.
Cheltenham: Edward Elgar.
Stanilov, Kiril. ed. 2007. The Post-Socialist City. Urban Form and Space Transformations in Central and Eastern Europe
after Socialism. Dordrecht: Springer.
Stephens, Mark. 2010. "Locating Chinese Urban Housing Policy in an International Context." Urban Studies
47(14):2965-82.
Stephens, Mark, and Suzanne Fitzpatrick. 2007. "Welfare Regimes, Housing Systems and Homelessness: How are They
Linked?" European Journal of Homelessness 1(1).
Stephens, Mark, Martin Lux, and Petr Sunega. 2015. "Post-socialist Housing Systems in Europe: Housing Welfare
Regimes by Default?" Housing Studies 30(8):1210-34.
Stiglitz, Joseph. 2013. The Price of Inequality: The Avoidable Causes and Invisible Costs of Inequality New York: Norton.
Stones, Rob. 2001. "Refusing the Realism-structuration Divide." European Journal of Social Theory 4(2):177-97.
Sunega, Petr, and Martin Lux. 2016. "Subjective Perception versus Objective Indicators of Overcrowding and Housing
Affordability." Journal of Housing and the Built Environment.
Szelenyi, Ivan. 1983. Urban Inequalities under State Socialism. Oxford: Oxford University Press.
Tosics, Ivan. 2003. "Comparative perspectives on urban housing conditions." in Housing Change in East and Central
Europe: Integration or Fragmentation?, 73-80, edited by Stuart Lowe and Sasha Tsenkova. Ashgate: Aldershot.
Tuvikene, Tauri. 2016. "Strategies for Comparative Urbanism: Post-socialism as a De-territorialized Concept."
International Journal of Urban and Regional Research 40(1):132-46.
29
Vranic, Petar, Ljiljana Vasilevska, and Tigran Haas. 2015. "Hybrid Spatialities: Multi-storey Extensions of Socialist
Blocks of Flats under Post-socialist Transition in Serbia, the Case of Nis." Urban Studies 53(6):1261-77.
World Bank. 2016. World DataBank (Aggregate, publicly available database; accessed December, 1, 2016).
http://databank.worldbank.org/data/home.aspx.
Zavisca, Jane R. 2012. Housing the New Russia: Cornell University Press.
ii
Bulgaria (BG), Croatia (HR), the Czech Republic (CZ), Estonia (EE), Hungary (HU), Latvia (LV), Lithuania (LT),
Poland (PL), Romania (RO), Slovakia (SK) and Slovenia (SE). During communism, some were part of the Eastern Bloc
(BG, Czechoslovakia, HU, PL, RO and Yugoslavia) while others were Soviet Republics of the USSR (EE, LV and LT).
ii
Exceptionally, van der Heijden (2013) dedicated a one-page review, tracing conceptual definitions back to Bournes
(1981:12)–“an imprecise, but nevertheless convenient expression encompassing the full range of interrelationships
between all the actors (individual and corporate), housing units, and institutions involved in the production, consumption,
and regulation of housing”–and to Priemus (1983)–“the complex of actors, including their many relationships and
interactions, that are involved in housing”’, including “housing broader context”. From a consumption perspective, Allen
(2006:272) defines it as “the complex of activities and practices which shape how people access housing”. Other
definitions appear in footnotes such as Hoekstra’s (2010:1) citation of Bournes (1981); Pollard’s (2009:169) citation of
Allen (2006); and Stephens and Fitzpatrick (2007:205) narrower definition “the housing market and housing policies”.
iii
He defined, however, the sociology of tenure as “the economic, social and political organization of housing
consumption” (1981:3). Kemeny’s call for a critical re-conceptualization of basic housing concepts (e.g. dwelling,
household and residence) did not include the concept of housing system which he used widely without ever defining it.
iv
Esping-Andersen (1990:80) defined a welfare regime as “the institutional arrangements, rules and understandings that
guide and shape concurrent social-policy decisions, expenditure developments, problem definition, and even the response
and demand structure of citizens and welfare consumers”.
v
Exceptions include Dewilde’s and colleagues’ work of validating housing regimes through patterns of housing outcomes
along the axes of de-commodification, stratification and the division of welfare responsibility between state, markets and
families (Delfani et al 2014, 2015; Dewilde and De Decker, 2016; Lersch and Dewilde 2015).
vi
If in 2005 only CZ and BG had levels below 50 percent, the situation has improved by 2014, only RO showing levels
above that figure. Overcrowding rate fell slightly (2-to-10 percent) in SK, HU, BG, PL and RO and significantly (14-to32 percent) in the Baltic States, CZ and SI.
vii
For comparison, only Denmark, United Kingdom and Belgium showed some similarly low figures (12%, 14% and
17%, respectively). Highest figures registered in Spain (32%), Portugal (35%), Ireland (43%) and Cyprus (52%).
viii
These were implemented in the Czech and Slovak Republics in 1993; Hungary in 1997; Croatia in 1998; Romania in
2003; Bulgaria in 2004; whereas the US-inspired securitization system was only implemented in Russia and Ukraine
(Stephens et al. 2015).
ix
We wish to quantify the different starting positions of these countries on their post-communist transitions by sets of
paired 1990/2015 variables, but data availability requires separate analytical decisions for each indicator.
x
In all countries, quintiles 3-and-4 show higher levels of co-residence than quintiles 1-and-2 while quintile 5 never show
in the lowest figures as one would expect. This indicates that households’ agency matters, and suggests several causal
mechanisms at play, e.g. income constraints for quintiles 1-and-2, strategies of consumption, savings and family welfare
for quintiles 3-and-4, and cultural preferences across the board.
xi
We interpret ‘better housing quality’ in terms of larger dwellings and lower levels of each: housing deprivation,
difficulty to access public transport, overcrowding, co-residence in extended-family/composite households, cost
overburden and inequality differences (variables Q1-Q6 and D1-D4).
30