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Rent control: redistributive and economic effects

Guillaume Chapelle, Gabrielle Fack
May 2026
This note performs an evaluation of the costs, benefits and risks associated with the roll-out of the rent control pilot scheme.
Reference IPP Policy Brief No. 125

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Chapelle, Guillaume and Fack, Gabrielle, ‘Rent control: what are the redistributive effects?’, IPP Policy Brief, no. 125, Institut des politiques publiques (IPP), May 2026
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Presentation

The rent control scheme, initially introduced by the Alur Act in 2014 and reintroduced on a trial basis under Article 140 of the Élan Act in 2018, aims to moderate the cost of housing in areas where there is significant pressure on the housing market.

This mechanism applies mainly to unfurnished or furnished properties let as a main residence and is based on the annual setting, by prefectural order, an ‘increased reference rent’, corresponding to 120 per cent of the observed or estimated median rent for each category of accommodation, defined by location and certain characteristics such as the number of rooms, the period of construction and the type of tenancy (furnished or unfurnished). In practice, the basic rent may not exceed this increased ceiling, unless a ‘rent supplement’ is applied to reflect exceptional amenities.

Implementation of the scheme is a voluntary initiative by the relevant local authorities or public inter-municipal cooperation bodies (EPCI), which must demonstrate significant housing pressure. Between 2019 and 2025, this policy was thus implemented in the central municipalities of several major conurbations (Paris, Lille, Lyon, Bordeaux, Montpellier, Grenoble), as well as in certain municipalities in Seine-Saint-Denis and tourist regions such as the Basque Country.

Key Results

  • Rent control has effectively moderated the rate of rent increases. Compared with towns and cities with similar property markets, rent increases in towns and cities subject to rent control were approximately 2–4 per cent lower in the two years following the introduction of rent control, and up to 5 per cent lower by the end of the period.
  • These rent reductions represent a transfer of income to tenants estimated at around €600 million, two-thirds of which comes from landlords – the majority of whom are well-off – whilst one-third is borne by the State and the social security system via reduced tax revenue.
  • The redistributive impact is highly uneven: the scheme benefits low-income households in Seine-Saint-Denis, but is of greater benefit to relatively well-off tenants in Paris.
  • The scheme’s effectiveness is hampered by fragmented institutional management and an incomplete statistical framework, making it difficult to monitor compliance with the rent caps.
  • A decline in the number of rental listings has been observed in certain areas, raising questions about the long-term impact on the supply of rental accommodation.

Method and Data

Method

The estimation method used takes advantage of the phased introduction of Rent control across different regions, which constitutes a generalisation of the ‘double differences’ method (Chaisemartin and D’Haultfoeuille, 2024). Each local authority where rent control has been introduced is compared with other similar local authorities where it has not been introduced. Rather than simply comparing rents at a given point in time, the authors examine how they change over time, both before and after the scheme was introduced. This method makes it possible to distinguish between the effects of the policy itself and those attributable to general trends in the property market.

However, it is based on a key assumption: in the absence of the reform, rents would have evolved in a comparable manner in the areas under study and in the comparison areas. In other words, the differences observed after the scheme was introduced can be attributed to the policy if the trends were similar prior to its introduction. To take account of the heterogeneity of the treatment areas, the local authorities are divided into three groups: city centres in conurbations outside Île-de-France, Plaine Commune and Est-Ensemble (local authorities in Île-de-France excluding Paris), and central Paris. The municipalities in the control group are selected from similar municipalities also located in the centres of urban areas with a property market considered to be under pressure. The analysis does not include municipalities in the Basque Country and the Grenoble metropolitan area, which introduced the regulatory framework too recently for the analysis to be carried out in this study.

The estimates incorporate control variables measured at municipal level in 2015 to account for initial differences in density, rent, sale prices and the proportion of dwellings classified as energy-inefficient. In the regression analyses, each municipality is weighted by the size of its private rental stock in 2015.

The main variables of interest are rents and the volume of rental advertisements.

Data

The analyses are based on indicators calculated from adverts published by the main property websites and collected by Yanport, a company that aggregates information on rents by carrying out several data collections per day from these sites. This enables a representative sample to be established for each municipality or borough over the period 2015–2024, based on 30 million unique property listings.

Analysing the redistributive effects

To assess the redistributive impact, the authors carry out simulations based on estimates of the effect of rent control on rents, and on information regarding household incomes. The aim is to measure how the economic benefits generated by rent control are actually distributed amongst tenants, landlords and the State. The analysis is based on a comparison between rents actually paid (drawn from the ‘Gérer mes biens immobiliers’ database) and a ‘Counterfactual’ scenario in which, in the absence of regulation, rents would be on average 5 per cent higher.

In this context, every euro reduction in rent represents an immediate gain in purchasing power for the tenant, but a loss of gross income for the landlord. However, this loss is in fact shared with the public purse: as the landlord receives less income, they also pay less tax and social security contributions. To quantify this mechanism precisely, the authors use national tax data (the POTE database) to apply to each landlord the marginal tax rate associated with their income, to which social security contributions (17.2 per cent) are added. The tax data also make it possible to identify the incomes of tenant households (except in certain specific cases, such as students who are still fiscally dependent on their parents, or foreign nationals who do not file a tax return in France, who are therefore excluded from the analysis). This methodology enables a detailed analysis of how the financial burden of the regulation is distributed according to households’ wealth levels and what proportion is ultimately borne by the State budget in the form of reduced tax revenue.

Last modified: July 21, 2026