1. Microeconomic factors influencing housing tenure choice Differences between European countries Analysis based on CHER database (Consortium of Household Panels for European Socio-Economic Research) Monika Bazyl Warsaw School of Economics

2. 2 Different proportions of owners and tenants Source: CHER 2000, HBS 2006 81%

3. 3 In case of tenants: different proportions of landlords Source: CHER 2000

4. 4 Data used for analysis CHER micro database Consortium of Household Panels for European Socio-Economic Research Established in 2000 7 National Panels + ECHP dataset (18 countries) Designed for comparative research Project funded by European Commission HBS 2006 (Household Budget Survey) Carried out by Central Statistical Office

5. 5 Literature Wide literature on impact of different factors on housing tenure choice; may be classified according to a subset of factors studied: Households’ status (socio-economic, race, marital etc) Previous dwelling (characteristics) Housing market circumstances (price, mortgage interest etc.) (W. A. V. Clark, M. C. Deurloo and F. M. Dieleman, Entry to Home-ownership in Germany: Some Comparisons with the United States, Urban Studies, Vol. 34, No. 1, 7± 19, 1997) Psychological factors (Danny Ben-Shahar, Tenure Choice in the Housing Market: Psychological Versus Economic Factors, Environment and Behavior 2007; 39; 841) Location (Iwarere, L.J, Williams, J.E., A Micro-Market Analysis of Tenure Choice Using The Logit Model, The Journal of Real Estate Research, 1991)

6. 6 Binary logistic regressions Regression run for each country compare coefficients Regressions run with two types of dependent variable (Own): 1. Owner = 1 Tenant = 0 2. Owner = 1 Tenant with private landlord = 0 3. Owner = 1 Tenant with private landlord = 2 Tenant with public landlord = 3 (as a multinomial logistic regression for Poland 2006) Regressions run on two samples: 1. All households in the sample 2. Recent movers (moved to current dwelling in 1995 or later) Due to missing data or absence of certain variables in some countries the comparison will cover each time a different subsample of countries.

7. 7 Explanatory variables Log = β 0 + β 1 X 1 + … + β k X k Prob(Own) 1-Prob(Own) Demographic: Age of the household’s breadwinner (in four subgroups 16-29, 30-39, 40-59 and 60 plus) + Ownership rate should increase with age VariablesExpected influence Cross – sectional analysis:

8. 8 Explanatory variables

9. 9

10. 10 Explanatory variables Marital status (married1=1 else=0) Partnership or legally married (two binary variables: married=1, partnership=1, single=0) + Marriage is an incentive to buy a house Partnership status might give less incentive to buy a house than marriage but still more than in case of a single person VariableExpected influence

11. 11 Explanatory variables VariableExpected influence Country of citizenship (national=1 not a national=0) + Not-nationals tend to rent more often

12. 12 Explanatory variables VariableExpected influence Urban/rural indicator (urban=1 rural=0) - In urban area rental market is usually more developed Income (ln_inc) Logarithm of yearly net disposable income of a household + Higher income is expected to increase the probability of owning

13. 13 Explanatory variables Housing quality Number of rooms per person in previous dwelling Difference between income burden in current and previous dwelling (rent to income or mortgage payment to income ratio) VariableExpected influence - Worse conditions in previous dwelling (room stress) should encourage to change from rental accomodation to own a house Households should seek to lower burden on their income, on the other hand they might be ready to decide to increase the burden if only it will give them a possibility to own a dwelling

14. 14 Exp(B) – effect of explanatory variable on the odds ( ) Exp(B)>1 positive effect Exp(B)<1 negative effect Prob(Own) Prob(Rent) Dependent variable: owner=1 tenant=0 Sample: All Exp(B)Germ Italy Lux Neth Switz UK Aust Denm age_30_401.8 *** 1.0 1.4 ** 1.9 *** 1.5 ** 2.0 *** 1.0 1.8 *** age_40_602.6 *** 1.5 *** 2.7 *** 1.7 *** 4.2 *** 3.1 *** 1.3 * 2.7 *** age_60_plus3.5 *** 1.9 *** 4.1 ***.9 6.6 *** 3.5 *** 1.2 2.7 *** married12.2 *** 1.2 *** 1.8 *** 2.2 *** 3.1 *** 2.3 *** 2.1 *** 3.2 *** national4.0 *** 9.7 *** 7.9 *** 2.1 ** 3.1 *** 1.9 ** 6.4 *** 1.3 ln_inc2.5 *** 1.6 *** 2.6 *** 5.9 *** 2.3 *** 3.0 *** 1.4 *** 2.7 *** Constant.0 ***.0 ***.0 ***.0 ***.0 ***.0 ***.0 ***.0 *** -2 Log likelihood 7690.1 4966.3 2077.6 5221.5 4110.6 3835.4 2909.4 2371.0 Nagelkerke R Square.23.04.36.34.29.26.10.30 N 6579 5448 2314 4905. 3672 3981 2407 2251 Model 1

15. 15 Model 1 Exp(B)Finl Fran Gree Irel Port Spain Pol age_30_402.9 *** 3.7 *** 1.7 *** 1.5 * 1.2 1.4 **.6 ** age_40_606.2 *** 7.8 *** 3.0 *** 4.1 *** 1.3 ** 2.0 ***.6 *** age_60_plus23.9 *** 16.3 *** 7.3 *** 21.0 *** 1.4 *** 2.9 ***.7 ** married11.8 *** 2.5 *** 1.4 *** 1.9 *** 1.4 *** 1.8 *** 1.4 *** national2.8 * 2.7 *** 2.0 3.2 * 1.4 4.2 *** ln_inc4.3 *** 2.0 *** 1.0 3.0 *** 1.1 1.3 ***.9 Constant.0 ***.0 ***.5.0 ***.7.0 *** 3.5 ** -2 Log likelihood 2556.7 5284.4 2621.0 1056.3 3952.1 3003.7 4041.1 Nagelkerke R Square.47.32.09.24.01.06.01 N 3039 5034 3593 1868 3989 4763 2751 Pol 2006 2.2*** 4.1*** 6.8*** 1.6*** 1.6***.0*** 32398.01 36950 Exp(B)>1 positive effect Exp(B)<1 negative effect

16. 16 Model 1 Comments: The odds of homeownership is increasing with age almost in all countries. The exception is Netherlands where the odds of homeownership at the age 60+ are not significantly different from the odds of homeownership at the age 16-29. Netherlands have quite big rental market (41%) out of which 89% is public, so it is probable that older households sell their houses and move to public rental market. Another exception is Poland in 2000 where results show that all groups of households aged 30-60+ have lower odds of homeownership than households aged 16-29. This might be explained by the fact that Polish housing market was still going through a transition period. In 2006 results were similar to other western European countries.

17. 17 Model 1 Comments: As expected marriage in each country is a significant incentive to buy a house. The odds of homeownership for married couples are 1.2 – 3.2 higher compared to single and partnerships. Nationality plays in many countries even more important role in explaining tenure choice than marriage. In Germany, Italy Luxembourg, Austria or Spain people with national citizenship have 4 – 7.9 higher odds of being a homeowner. Income as expected has a positive influence on the odds of owning a home. The exceptions are Greece and Portugal where income seems insignificant, but this is a result of not controlling whether a household lives in urban or rural area. Incomes in rural area are much lower but homeownership rates are much higher there.

18. 18 Model 2 Exp(B)Swit UK Aus t Denm Fra n Gre Irel Port Pol age_30_401.5 ** 2.0 *** 1.3 1.8 *** 4.0 *** 1.7 *** 1.5 * 1.4 **.9 age_40_604.4 *** 3.2 *** 1.8 *** 3.1 *** 8.9 *** 3.0 *** 4.0 *** 1.4 **.9 age_60_plus7.5 *** 3.6 *** 2.2 *** 2.8 *** 18.6 *** 6.8 *** 18.2 *** 1.4 ** 1.1 married2.5 *** 2.4 *** 1.6 *** 3.8 *** 1.8 *** 1.3 * 1.8 *** 1.3 *** partnership.4 *** 1.0.6 ** 1.6 ***.7 *** 1.2.7.5 *** national2.8 *** 1.8 * 7.2 *** 1.4 1.9 *** 2.0 3.6 ** 1.2 ln_inc2.7 *** 2.9 *** 1.6 *** 2.2 *** 2.6 *** 1.1 * 3.1 *** 1.3 *** 1.1 urban.3 ***.9.1 ***.2 ***.2 ***.2 ***.3 ***.3 ***.1 *** Constant.0 ***.0 ***.0 ***.0 ***.0 ***.5.0 ***.2 ** 2.5 -2 Log likelihood 3937. 183 a 3338. 310 b 2274.840 c 2215.0 39 d 4704. 796 e 2420. 269 f 997. 967 g 3440.738 h 2896.978 i Nagelkerke R Square.33.27.39.36.40.17.29.09.44 N 3667 3457 2398 2236 4894 3585 1865 3754 798 Dependent variable: owner=1 tenant=0 Sample: All Accounting for urban/rural area and partnership/married versus single status Exp(B)>1 positive effect Exp(B)<1 negative effect Pol 2006 1.9*** 3.63.6 6.16.1 1.2*** 0.6*** 1.9***.2.2.0*** 30327.19 36950

19. 19 Model 2 Comments: When controlling for urban/rural indicator income in Greece and Portugal becomes significant in explaining tenure choice. In case of Poland, impact of income on the odds of homeownership rose from 1.6 to 1.9. In all presented countries (except for UK) the odds of ownership is much lower in urban area. In most of the countries cohabitating couples are more likely to rent a dwelling than a single person. Only in Denmark cohabitating status has significantly higher odds of homeownership compared to single people (however still twice lower compared to marriage). To some extent this might be explained by the popularity of cohabitating status in a given country. In Denmark there is one of the highest percentage of partnerships.

20. 20 Model 3 Dependent variable: owner=1 tenant with private landlord=0 Sample: All GermItaly Lux Net UK Aust DenmFinl Fr Gree Irel SpainPol age_30_40.9 2.5 ** 3.5 *** 3.0 *** 1.0 2.2 *** 3.5 *** 4.5 *** 1.5 ** 1.8 * 1.3 *.8 age_40_601.6 *** 1.6 *** 2.8 *** 5.2 *** 4.6 *** 1.6 ** 4.5 *** 9.6 *** 11.3 *** 2.7 *** 10.2 *** 2.2 *** 2.1 age_60_plus2.3 *** 2.3 *** 4.3 *** 3.4 *** 6.9 *** 1.4 * 5.9 *** 42.5 *** 23.3 *** 6.2 *** 29.2 *** 2.9 *** 9.7 *** married11.2 ** 1.2 ** 1.3 8.0 *** 3.1 *** 2.7 *** 3.8 *** 1.8 *** 2.5 *** 1.4 *** 5.9 *** 2.0 *** 1.7 national8.6 *** 8.6 *** 2.8 *** 2.6 3.1 *** 13.9 *** 1.3.0 1.8 ** 2.2 * 2.5 4.6 *** ln_inc1.5 *** 1.5 *** 6.6 *** 2.8 *** 2.4 *** 1.4 *** 2.4 *** 4.8 *** 1.7 ***.9 2.1 *** 1.3 *** 1.3 * Constant.0 ***.0 ***.0 ***.0 ***.0 ***.0 ***.0 *** 2.1.0 *** 2.8.0 ***.0 *** 1.0 -2 Log likelihood 5933. 199 a 3786. 419 a 417.5 08 a 1115. 468 a 2129. 842 a 1738. 047 a 1405. 874 a 1408. 772 a 3553. 755 a 2519. 233 a 420.4 13 a 2619. 716 a 386.6 17 a Nagelkerke R Square.249.049.187.357.266.145.322.513.346.087.291.069.066 N 5056 5134 1722 3086 3363 1966 1818 2530 4209 3564 1729 4683 1491 Exp(B)>1 positive effect Exp(B)<1 negative effect

21. 21 Model 3a Dependent variable: owner=1 tenant with private landlord=2 tenant with public landlord=3 Sample: All Owner vs tenant with private landlord Tenant with public landlord versus tenant with public landlord age_30_40 2,5 *** 1,6 *** age_40_60 8,3 *** 3,3 *** age_60_plus 13,8 *** 3,3 *** married 1,2 *** 1,0aaaa partnership 0,5 *** 0,8 *** urban 0,5 *** 2,9 **a ln_inc 1,9 *** 1,0 *** -2 Log likelihood 34578 McFadden R Square 0,12 N 36829 Exp(B)>1 positive effect Exp(B)<1 negative effect Multinomial logistic regression for Poland 2006

22. 22 Model 3 Comments: When excluding from the sample public rental market it occurs that nationality in some countries plays even more significant role in defining tenure choice (in Germany and Austria the impact of nationality on the odds of homeownership rose twice, in Netherlands the coefficient gained significance). This indicates that not nationals live mainly in private rental market. On the other hand in Finland the impact of nationality has lost significance which indicates that many not-national households are entitled to live in public rental accommodation.

23. 23 Model 4 Dependent variable: owner=1 tenant=0 Sample: Recent movers (moved to current dwelling in 1995 or later) Exp(B)Germ Neth UK Denm Finl Fran Spain dIncBurd15.3 *** 34.4 *** 1.0 1.3 1.5 6.4 *** 9.7 *** Household size1.1 * 1.0.9 * 1.1 ln_inc7.3 *** 9.0 *** 6.3 *** 10.4 *** 7.0 *** 4.2 *** 1.7 *** previous_rooms _per_pers 1.2*1.1 1.5***1.2 1.3*.9.7* age_30_401.4 1.5 1.9 *** 1.3 1.9 *** 3.4 *** 1.5 age_40_601.5 1.2 1.6 *** 1.0 1.3 3.8 ***.9 age_60_plus.5 **.1 ***.3 ***.6 1.8 1.4.5 married13.2 *** 3.1 *** 3.3 *** 1.8 ** 1.7 ** 2.1 *** 1.8 * national3.4 *** 3.6 1.7 1.9 1.7.9 5.5 ** Constant.0 ***.0 ***.0 ***.0 ***.0 ***.0 ***.0 *** -2 Log likelihood 1267.7 720.6 1547.2 677.3 694.2 1167.8 340.6 Nagelkerke R Square.391.530.467.413.371.317.216 N 1541 841 1902 668 693 1083 319 Exp(B)>1 positive effect Exp(B)<1 negative effect

24. 24 Model 4 Comments: Positive sign by IncBurd variable means that all households are ready to increase burden on their income in order to become a homeowner. The impact of income on the odds of homeownership in case of recent movers is much higher compared to models built on the whole sample of households. The so called ‘room stress’ effect is only valid in case of Spain. The lower size of the housing (lower number of rooms per person) in previous dwelling the higher probability of turning to or remaining in ownership. In Germany, Netherlands and UK households aged 60 and more have significantly higher odds of being a tenant compared to young households.

25. 25 Conclusion Differences in homeownership rates among European countries arise mainly from different approaches of states toward housing (more or less developed public housing) and from different numbers of not-national households living in particular countries. Also the extent to which cohabitating status is accepted in each country influences the size of the rental market. In certain countries there is a substantial movement of 60+ households from ownership into rental market