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Who Moves Out of Social Housing? The Effect of Rent Control on Housing Tenure Choice

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Abstract

Rent control provides substantial in-kind benefits to tenants of social housing. In the Netherlands these benefits equal almost 40% of the market rent on average. We show that rent control benefits for the 10% tenants with highest income are 5% points higher than the benefits for the 10% with lowest incomes. Next we provide evidence that rent control influences the housing tenure choice decision. We find that on average rent control reduces transitions within the social housing sector, but not transitions from the social housing sector. Only the 20% tenants with highest incomes postpone moves out of social housing in response to rent control. This suggests that the inequitable distribution of rent control benefits is prolonged by the reduction in transition rates out of social housing. It also suggests that recent policy in the Netherlands that reduces rent control benefits for high income households can increase the mobility of those affected.

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Source: Tweede Kamer (2011)

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Notes

  1. In Austria, Denmark, England, France, the Netherlands and Sweden at least 17% of the housing stock is social housing, see Scanlon and Whitehead (2007).

  2. Donders et al. (2010) conclude that the benefits of rent control add up to about 8.5 billion in 2005. Even in the United States about 0.6% of GDP is redistributed using various forms of housing assistance, which is typically more than expenditures on social assistance (Diamond and McQuade 2016).

  3. Also, a reduced transition rate from social housing to the owner-occupied sector reflects the presence of potential spillover effects of rent control. Naturally, it might have labor market consequences as housing mobility influences labor market mobility.

  4. They conclude this based on various models including linear, logistic and duration models. The one that is econometrically the most advanced (Munch and Svarer 2002) uses a duration model that allows for a random household specific component in the error term. None of the models correct for unobserved household specific fixed effects as we do.

  5. For the Netherlands Van Ommeren and Koopman (2011) and Van Ommeren and Graaf-de Zijl (2013) analyze tenant mobility to study demand for rent controlled housing. Their data covers a research period which is closer to ours (i.e. the period 1995–2001 and the year 2002), but they do not relate the mobility of tenants directly to rent control benefits.

  6. See for instance Arnott (1995), Arnott and Igarashi (2000). Bulow and Klemperer (2012) develop a model where rent control reduces welfare. Lind (2007) compares the design of several theoretical articles on rent control.

  7. See Scanlon and Whitehead (2007).

  8. Van Ommeren and Koopman (2011), Van Ommeren and Graaf-de Zijl (2013) use tenant mobility, whereas Van Ommeren and Van der Vlist (2016) analyse the length of waiting lists for social housing to study this topic.

  9. This result does not carry over to countries rent-controlled housing is privately provided like the United States. See for instance Early (2000) or Sims (2007).

  10. Svarer et al. (2005).

  11. See Van Ommeren and Graaf-de Zijl (2013).

  12. This is true for our sample period. Since July 2013 the maximum allowed rent increase is based on inflation and income.

  13. Rents of social housing often increase with less than the maximum allowed rent increase. Landlords renting out social housing do not publicly compete on this.

  14. ROS is an acronym for Region Of Scarcity, as the ROS-municipalities are called schaarstegebieden in Dutch.

  15. As the private rental market is small we only consider these transitions.

  16. Tenants who rent from private agents are omitted from the analysis. There are few tenants who do so. We include tenants of social housing if they rent from a housing agency and the value of the house is less than 300 thousand euro. About 3% of the total housing stock managed by housing agencies is non-controlled housing (CSED 2010).

  17. The average transition rate is approximately equal to the sum of the within transition rate and the between transition rate. In general, it does not equal this sum exactly, because the transitions within and from the social housing sector are competing risks. Once tenants in controlled social housing have moved to non-controlled housing, they drop out of the sample of rent-controlled households and the households become right censored (before the observation period has ended). As a result, the denominators in the overall transition rate, the transition rate within the social housing sector and transition rate out of social housing are not equal. The absolute number of transitions (without direction) equals the sum of the number of transitions within and out of social housing.

  18. In the Netherlands the value of the house is assessed annually for both owner-occupied and rental housing. The assessment is based on the characteristics of the house and the selling price of comparable housing in the neighborhood that has been sold in the months prior to the month of assessment. Owners of housing pay taxes over the value of the house, and can comply if they think the value of the house is wrongly assessed.

  19. On average about 75% of the observed controlled rents within a neighborhood are within 20% of the median value.

  20. This has been confirmed by specialists on the social housing market working at the Ministry of the Interior and Kingdom Relations.

  21. A list of included variables is found in Table 1. Many of these characteristics vary very little over the sample period. As a result, coefficients on these regressors are identified by a small group of households who switch status. For those that do not switch status, the effect ends up in the individual fixed effect \(\alpha _\mathrm{i}^{\hbox {z}}\). Therefore, the fact that the almost time-invariant variables are identified for a small group of households does not influence the parameter estimates. In particular, as our measure of rent-control benefits is continuous, its coefficient is identified for all households in the sample.

  22. As we have ten estimated parameters in which b appears, we test whether \(\zeta =\hbox {z}\left( {\beta ^{\hbox {z}}} \right) =\mathop \sum \nolimits ^{\hbox {k}} \beta _{\hbox {k}}^{\hbox {z}} /10=0\).

  23. Linear Probability Models may be vulnerable to heteroscedasticity, which will be removed by the clustered standard errors. Furthermore, it removes some of the intertemporal correlation of the error term.

  24. This estimator is not available in the remote access environment of Statistics Netherlands.

  25. See Van Ommeren and Van der Vlist (2016).

  26. Following Smith and Todd (2005) the indicator living in ROS-municipalities throughout the sample period is explained using the differences in variables such that household specific fixed effects are removed.

  27. We choose nearest neighbor matching and use the Stata program teffects that takes into account that the propensity score itself is estimated, see Abadie and Imbens (2016).

  28. Naturally, this difference can only be interpret as a causal effect if the change in variables in the selection equation is not influenced by living in ROS-municipality. Most likely this condition does not hold.

  29. Results are available upon request.

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Authors and Affiliations

  1. Netherlands Bureau for Economic Policy Analysis, PO Box 80510, 2508 GM, The Hague, The Netherlands

    Mark A. C. Kattenberg

  2. Utrecht University School of Economics (U.S.E.) and IZA, Kriekenpitplein 21-22, 3584 EC, Utrecht, The Netherlands

    Wolter H. J. Hassink

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  1. Mark A. C. Kattenberg
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Correspondence to Mark A. C. Kattenberg.

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Kattenberg, M.A.C., Hassink, W.H.J. Who Moves Out of Social Housing? The Effect of Rent Control on Housing Tenure Choice. De Economist 165, 43–66 (2017). https://doi.org/10.1007/s10645-016-9286-z

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