A Critical-Realist View of Housing Quality within the Post-Communist EU States: Progressing towards a Middle-Range Explanation

Figures & data

Figure 1. A critical-realist approach to housing quality.

Figure 2. Theorizing communist housing systems.

Table 1. New housing provision during communist period in selected countries (rounded %).

			Form of provision	1951–1960	1961–1970	1971–1980	1981–1989	Urban population
								1950	1990
Classical state-socialism	Soviet model	Estonian SSR	State & enterprises	82	87	93	95	47	71
			Households	18	13	7	5
		Latvian SSR	State & enterprises	–	86	90	87	45	70
			Households	–	14	10	13
		Lithuanian SSR	State & enterprises	–	81	77	78	28	69
			Households	–	19	23	22
		GDR	State & cooperatives^b	83	94	89	85	55	77
			Households	17	6	11	15
	Classic EEHM	Albania	State ^a	26	50	49	54	20	35
			Households	74	50	51	46
		Bulgaria	State	–	53	76	76	20	67
			Households	–	47	24	24
		Romania	State	12	36	80	93	23	54
			Households	88	64	20	7
Reformist state-socialism	Reformist EEHM	Czech region	State & enterprises	80	40	42	29	45	76
			Cooperatives	2	45	32	40
			Households	18	15	26	30
		Hungary	State & h. associations	–	33	38	24	40	59
			Households	–	63	48	51
			NSB^c	–	4	14	25
		Poland	State & cooperatives^d	63	71	75	70	31	61
			Households	37	29	25	30
		Yugoslavia	State	42	38	36	36	20	50
			Households	58	62	64	64

Notes: Last column does not include the full interval for the case of: Bulgaria 1981–1987; GDR, 1981–1986; Hungary, 1981–1987; Poland, 1981–1988; Romania, 1981–1985; Yugoslavia, 1981–1986.

Sources: Data were estimated from the Soviet Republic of Estonia, from http://pub.stat.ee/px-web.2001/I_Databas/Economy/05Construction/07Granted_building_permits_and_completed_buildings/07Granted_building_permits_and_completed_buildings.asp (Last accessed November 2016; we are grateful to Dr Ave Hussar for indicating the source; see also Hussar 2012); For the Soviet Republics of Latvia and Lithuania we are grateful to Dr Aleksandra Burdyak who provided estimates (computed from the Statistical Yearbooks of 1969, 576–579; 1971, 541–544; 1976, 572–575; 1978, 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-40b9-980c-37c1e2714501?version=1.0 (we are grateful to Dr Petr Sunega for indicating the source and translating the categories). For the rest of the countries from Soaita (2010, 260, Table 21, and 45, Table 2) (computed from country chapters in Clapham et al. 1996; Sillince 1990; Turner, Hegedus, and Tosics 1992).

^a 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.

^b 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).

^c NSB stands for housing developed with mortgage loans from the National Saving Bank.

^d 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.

Table 2. Country values (grouped by former housing models/geographical regions).

		Former Stalinist housing model (the Baltic States)			Former classical EEHM (south-east Europe)		Former reformist EEHM (central-east Europe)
		EE	LT	LV	BG	RO	CZ	HU	HR	PL	SI	SK
Micro-indicators (households’ housing quality)
Q1	Dwelling size (m^2)	66.7	63.2	62.5	73.0	43.9	78.0	75.6	81.6	75.2	80.3	87.4
Q2	Housing deprivation	6.9	13.9	14.4	13.2	35.1	0.4	3.8	1.9	3.3	0.3	0.3
Q3	Difficulty to access transport	6.7	8.1	8.8	11.6	16.7	3.0	5.8	7.7	5.3	2.8	5.6
Q4	Overcrowding	14.3	19.7	36.8	44.8	52.0	21.1	47.3	44.7	46.9	16.9	38.8
Q5	Extended-fam./composite hlds	19.2	25.3	31.7	46.0	40.1	22.9	31.0	43.4	41.6	26.1	42.1
Q6	Total housing cost overburden	13.7	15.4	16.8	22.8	21.5	20.3	23.8	13.3	17.7	11.6	16.3
D1	Distance poor	−11.2	−12.4	−10.7	−18.6	−16.4	−12.4	−15.4	−11.5	−13.6	−8.6	−10.2
D2	Distance rich	6.3	8.9	13.4	7.6	14.6	5.9	9.8	5.1	9.7	3.7	2.6
D3	Distance Urban	1.5	3.3	3.5	2.4	6.8	0.2	2.1	1.3	3.0	−0.2	0.6
D4	Distance rural	−2.3	−2.2	−4.3	−4.6	−9.2	−0.4	−3.5	−1.4	−4.3	−0.3	−0.8
Macro-indicators (underlying structures)
S1	GDP/capita change 1995–2012	19,569	18,295	15,162	10,561	13,578	14,922	13,390	10,873	16,161	15,255	17,622
S2	Gini change 1989-average 2008–2012	8.63	11.90	13.85	10.79	10.81	5.56	0.49	8.45	4.41	−0.12	5.54
S3	Communist-built houses	11.2	23.2	13.8	37.7	37.5	19.4	37.5	35.7	21.6	36.4	28.7
S4	Communist-built flats	54.9	48.3	53.5	36.3	35.7	39.0	26.1	21.6	35.1	26.6	40.6
S5	Mortgage debt to GDP, 2012	33.1	17.4	24.0	8.7	6.6	13.5	20.2	18.9	20.6	14.6	19.0
S6	House price boom 2003–2008 (n-fold)	3.5	2.3	3.2	3.2	7.1	1.5	1.4	1.5	2.0	2.1	2.2
S7	Population change 1990–2012	−15.36	−18.26	−23.29	−18.47	−13.05	1.40	−6.2	−10.21	1.73	2.96	2.66
S8	Pop. risk poverty/exclusion, 2012	23.4	32.5	36.2	49.3	43.2	15.4	33.5	32.6	26.7	19.6	20.5
S9	Total social spending, % GDP, 2012	15.0	16.3	14.4	16.6	15.4	20.4	21.4	21.1	18.9	24.9	18.0

Notes: The best/second best (or just highest) indicators in terms of housing quality across countries are shown in bold; the worst/second worst (or just lowest) ones are underlined. Tables A2 and A3 in the online annex show Pearson bivariate correlation levels (two-tailed). With Mooi and Sarstedt 2011, 242), we consider levels below absolute values of 0.9 as being acceptable for cluster analysis. All correlation levels are acceptable between macro-indicators, but we note four statistically problematic correlations in our micro-indicators: D3–D4 (−0.943**); Q2–Q1 (−0.942**); Q2–D3 (0.922**); and Q2–Q3 (0.911**). A possible solution is the elimination of variable Q2 from cluster analysis (so that a shared underlying factor is not double-weighted in the computation of country clusters). However, the results of cluster analyses remain unchanged whether we include or exclude variable Q2 (see Figure A1 in the online annex). Given Q2’s non-impact on clustering patterns but its major importance to households’ wellbeing, we decided to maintain it for our further assessments of households’ housing quality.

Table 3. ANOVA test for cluster means with ranking scores for housing quality.

			F	Sig.	SEE	CEE	BS
	Q1	Dwelling size	6.745	0.019	58.51^B	79.73^A	64.12^B
	Q2	Housing deprivation	10.621	0.006	24.21^B	1.73^A	13.02^B
	Q3	Difficult access to transport	15.061	0.002	14.1 1^A	5.0 3^B	7.92^B
		Housing conditions: score			3	9	6
	Q4	Overcrowding	2.473	0.146	48.41^A	36.02^A	23.63^A
	Q5	Extended-fam./composite hlds	3.075	0.102	43.01^A	34.52	25.43^B
	Q6	Housing cost overburden	2.190	0.174	22.11^A	17.22^A	15.33^A
		Household arrangements: score			3	6	9
	D1	Distance poor	6.464	0.021	−17.51^A	−12.02^B	−11.43^B
	D2	Distance rich	2.040	0.192	11.21^A	6.13^A	9.62^A
		Housing inequality/income: score			2	5	5
	D3	Distance urban	3.879	0.066	4.61^A	1.2 3^B	2.82
	D4	Distance rural	5.738	0.028	−6.91^A	−1.8 3^B	−2.92
		Housing inequality/place: score			2	6	4
	Overall score: housing quality				10	26	24
	S1	GDP/capita change 1995–2012	3.741	0.071	12,069^A	14,737	17,675^B
	S2	Gini change 1989–2013	8.216	0.011	10.8^B	4.1^A	11.5^B
	S3	Communist-built houses	6.429	0.022	37.6^B	29.9^B	16.0^A
	S4	Communist-built flats	10.737	0.005	36.0^B	31.6^B	52.2^A
	S5	Mortgage debt to GDP, 2015	8.267	0.011	7.7^A	17.8^B	24.8^B
	S6	House price boom 2003–2008	7.781	0.013	5.2^A	1.8^B	3.0^B
	S7	Population change 1990–2014	14.891	0.002	−15.8^B	−1.3^A	−19.0^B
	S8	Population at risk of poverty	7.342	0.015	46.3^A	24.7^B	30.7^B
	S9	Total social spending % of GDP	9.618	0.007	16.0^B	20.8^A	15.2^B

Notes:

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.

Superscript bold capital letters: these indicate results of a multiple comparisons by Tukey post hoc HSD test for each variable (subset for alpha = 0.1; p ranging from 0.003 to 0.092). As the group sizes are unequal, the harmonic mean of the group sizes is used (n = 3), type I error levels being not guaranteed; nonetheless, descriptively, these differences are also 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 different, making so a single cluster).

• 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 lower rung).

• A–A–A shows that clusters’ means are not statistically different, making so a single cluster.

Table 4. Nuancing housing quality across four clusters (ANOVA test and ranking scores).

		F	Sig.	SEEi	CEEi	BS	Mix-EE
				RO	CZ, HR, SI, SK	EE, LV, LT	BG, HU, PL
Q1	Dwelling size	51.814	0.000	43.91	81.84	64.12	74.63
Q2	Housing deprivation	18.950	0.001	35.11	0.74	13.0 2	6.83
Q3	Difficult access to transport	6.194	0.022	16.7 1	4.8 4	7.9 2.5	7.62.5
	Housing conditions: score			3	12	6.5	8.5
Q4	Overcrowding	3.255	0.090	52.01	30.43	23.6 4	46.32
Q5	Extended-fam./composite hlds	1.522	0.291	40.11	33.63	25.4 4	39.52
Q6	Housing cost overburden	3.187	0.094	21.51.5	15.43.5	15.3 3.5	21.41.5
	Household arrangements: score			3.5	9.5	11.5	5.5
D1	Distance poor	6.818	0.017	−16.41	−10.74	−11.4 3	−15.92
D2	Distance rich	7.277	0.015	14.61	4.34	9.6 2.5	9.12.5
	Housing inequality/income: score			2	8	5.5	4.5
D3	Distance urban	20.126	0.001	6.8 1	0.5 4	2.8 2.5	2.52.5
D4	Distance rural	34.909	0.000	−9.21	−0.7 4	−2.9 3	−4.12
	Housing inequality/place: score			2	8	5.5	4.5
Overall score on: housing quality				10.5	37.5	29	23

Notes: 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 one-country cluster, we cannot explore multiple comparisons by Tukey post hoc HSD.

Figure 3. Market dynamics. Source: Authors’ compilation from HYPOSTAT (2005, 2007, 2011, 2013, 2015, 2016) for all except Eurostat’s (2016) mobility rates available at https://appsso.eurostat.ec.europa.eu/nui/show.do?data-set=ilc_hcmp05&lang=en.

Box 1. Our choice of indicators.

Micro-indicators (housing quality at the level of households)
Housing conditions
Q1	Dwelling size in m^2	Source: Census 2011 (Eurostat 2016). As measures of overcrowding ignore floor area, we include this adjacent measure of residential well/ill-being
Q2	Housing deprivation	Source: our calculation based on Eurostat microdata for 2012.^a Measured by % of total population having neither a bath, nor a shower, nor indoor flushing toilet in their household (Eurostat 2012)
Q3	Difficulty to access transport	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’ (affordability) arrangements
Q4	Overcrowding	Source: our calculation based on Eurostat microdata for 2012. Measured by the % of population in overcrowded homes as per Bedroom Standard (Eurostat 2012)
Q5	Extended-fam./composite households	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 children^b (Eurostat 2012)
Q6	Total housing cost overburden	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	Distance poor	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 thinly-populated areas (“rural”).^c 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 transport) (Eurostat 2012)
D2	Distance rich
D3	Distance urban
D4	Distance rural
Macro-indicators (underlying structures)
The economic field
S1	Change in GDP/capita in PPP, 1995–2012	Source: World Bank (2016). 1995 is the earlier year for which data is available for all 11 countries. PPP stands for Purchasing Power Parity
S2	Change in Gini coefficient for the income distribution since the fall of communism	Source: World Bank (2016) and Eurostat (2016). We use 1987–1989 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^d
S3	% of houses 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)
S4	% of flats built during communism (in the 2011 housing stock)	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
S5	% 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)
S6	House value growth, 2003–2008 (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
S7	% 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
S8	% of population at risk of poverty & social exclusion	Source: Eurostat (2016), data for 2012
S9	% total social spending as percentage to GDP	Source: Eurostat (2016), data for 2012

^a 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).

^b Dependent child: aged 15 or less, or aged between 16 and 24 and economically inactive. We considered 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), which we dropped given the high correlation between these variables (p = 0.969) and their theoretical overlap.

^c 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 comparative values for Slovenia from 2012 Eurostat aggregate data and Census 2011; we are grateful to Prof. Andreja Cirman for providing data support.

^d The share of communist-built houses and flats in the 2011 housing stock is both a proxy for communist forms of housing provision by households and state/cooperatives, respectively – measuring thus the legacies of the three communist housing models by 2011 – as they are indicators of change since the housing stock is continually transformed through new housing construction, demolition and change of use (we will check the robustness of our results by inclusion of five more macro-indicators, including the share of post-communist build housing).

Figure 4. Clustering patterns (HCA on 19 variables). Source: SPSS software output. Notes: The visual clue is the length of the horizontal interval between two merging steps. The solution of three clusters is thus obvious. The three clusters remain stable upon the elimination of variable Q2 (see Figure A1 in the online annex) and/or the inclusion of five additional macro-indicators (i.e. GDP/capita in 1995; post-communist built stock; % homeoweners in total population; 2007–2012 residential mobility rates; % change in household numbers, see Figure A2 in the online annex).

Figure 5. Deconstructing clustering patterns by macro- and micro-indicators. Source: SPSS software output. Note: The three cluster solution (left side) and four cluster solution (right side) remain stable upon the elimination of variable Q2 and/or the inclusion of additional macro-indicators (i.e. GDP/capita in 1995; post-communist built stock; % homeoweners in total population; 2007–2012 residential mobility rates; % change in household numbers, see Figure A1 and A2 in the online annex).

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