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Microeconomic factors influencing housing tenure choice Differences between European countries

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. Different proportions of owners and tenants. 81%.

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Microeconomic factors influencing housing tenure choice Differences between European countries

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

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

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

  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. 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 VersusEconomic 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. 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. Explanatory variables Cross – sectional analysis: Prob(Own) Log = β0 + β1X1 + … + βkXk 1-Prob(Own) Variables Expected influence • + Ownership rate should increase with age • Demographic: • Age of the household’s breadwinner (in four subgroups 16-29, 30-39, 40-59 and 60 plus)

  8. Explanatory variables

  9. Explanatory variables

  10. Explanatory variables Variable Expected influence • 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

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

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

  13. Explanatory variables Variable Expected influence • 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) • - 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. Model 1 Prob(Own) Exp(B) – effect of explanatory variable on the odds ( ) Exp(B)>1 positive effect Exp(B)<1 negative effect Prob(Rent) Dependent variable: owner=1 tenant=0 Sample: All

  15. Model 1 Exp(B)>1 positive effect Exp(B)<1 negative effect

  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. 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. Model 2 Accounting for urban/rural area and partnership/married versus single status Exp(B)>1 positive effect Exp(B)<1 negative effect Dependent variable: owner=1 tenant=0 Sample: All

  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. Model 3 Exp(B)>1 positive effect Exp(B)<1 negative effect Dependent variable: owner=1 tenant with private landlord=0 Sample: All

  21. Model 3a Multinomial logistic regression for Poland 2006 Exp(B)>1 positive effect Exp(B)<1 negative effect Dependent variable: owner=1tenant with private landlord=2 tenant with public landlord=3 Sample: All

  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. Model 4 Exp(B)>1 positive effect Exp(B)<1 negative effect Dependent variable: owner=1tenant=0 Sample: Recent movers (moved to current dwelling in 1995 or later)

  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. 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

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