[Skip to main content](/content/review/b3e64110-83ee-4fd7-87d8-65a6fe06010b/source/df0ee5df3e3240bba80bdd28b814101f#main-content/index.html) Effects of Rent Control on Housing Dynamics (public) Research reportView only Create alertChat September 12, 2025 # What are the effects of rent control policies on housing market dynamics and tenant stability? ## Rent control policies yield mixed results, decreasing rents in controlled units by 4-47% and extending tenant stays by up to six years, while simultaneously reducing housing supply and increasing eviction filings, with effects varying by policy design and local market conditions. # Abstract Rent control policies yield mixed effects on housing market dynamics and tenant stability. In regulated markets, studies report rent declines ranging from 4% to 47% in controlled units while some analyses note rent increases in uncontrolled or post‐decontrol sectors. In one San Francisco study, rents rose concurrently with a 15% reduction in housing supply and a 20% drop in tenant mobility. Other research documents tenants staying an extra six years under tighter regulation, although reports of 83–127% higher eviction filings suggest displacement risks in certain settings. Separate analyses show shifts in market composition. For example, one study cited annual rent subsidies of about US$2440 (roughly 27% of income), and another observed reduced racial disparities accompanied by increased minority presence. These findings, based on quasi‐experimental and econometric approaches across diverse jurisdictions, indicate that the impacts of rent control vary with policy design and local market conditions. Methods We analyzed 40 sources from an initial pool of 989, using 8 screening criteria. Each paper was reviewed for 5 key aspects that mattered most to the research question. More on methods Papers identified with Elicit search n = 989 Papers screened using: Intervention Type, Outcome Measures, Study Design, Geographic Context, Public/Social Housing Focus, Housing Voucher/Assistance Focus, Empirical Data, Comparison Groups n = 989 Papers screened out n = 949 Papers included for extraction n = 40 Press enter or space to select a node.You can then use the arrow keys to move the node around. Press delete to remove it and escape to cancel. Press enter or space to select an edge. You can then press delete to remove it or escape to cancel. ## Paper search Using your research question “What are the effects of rent control policies on housing market dynamics and tenant stability?”, we [searched](https://support.elicit.com/en/articles/553025) across over 126 million academic papers from the [Semantic Scholar](https://www.semanticscholar.org/) corpus. We retrieved the 989 papers most relevant to the query. ## Screening We screened in sources based on their abstracts that met these criteria: - **Intervention Type**: Does this study examine rent control, rent stabilization, or rent regulation policies? - **Outcome Measures**: Does this study measure at least one of the following: housing market outcomes (rental supply, housing prices, construction rates, property maintenance, market competition) OR tenant stability outcomes (residential mobility, displacement rates, tenure length, eviction rates, housing security)? - **Study Design**: Is this study one of the following: randomized controlled trial, quasi-experimental design, natural experiment, longitudinal study, cross-sectional study with comparison groups, systematic review, or meta-analysis? - **Geographic Context**: Was this study conducted in urban or metropolitan areas with rental housing markets? - **Public/Social Housing Focus**: Is this study NOT focused solely on public housing or social housing programs without rent control components? - **Housing Voucher/Assistance Focus**: Is this study NOT examining only housing voucher or rental assistance programs without rent regulation? - **Empirical Data**: Is this study NOT purely theoretical or modeling without empirical data? - **Comparison Groups**: Is this study NOT a case study or descriptive study without comparison groups or control conditions? We considered all screening questions together and made a holistic judgement about whether to screen in each paper. ## Data extraction We asked a large language model to extract each data column below from each paper. We gave the model the extraction instructions shown below for each column. - **Study Design Type**: Identify the primary research design used in the study. Look in the methods section for explicit description of the study design. Possible design types include: - Difference-in-differences - Quasi-experimental - Empirical analysis - Econometric study - Cross-sectional analysis If multiple design elements are used (e.g., difference-in-differences with discontinuity design), list all relevant approaches. Be precise about the specific methodological approach. If the design type is not clearly stated, review the analytical methods and statistical techniques used to infer the most appropriate design classification. - **Geographic Setting and Context**: Extract the specific geographic location(s) where the study was conducted. Include: - Country - City/metropolitan area - Specific neighborhoods or regions (if applicable) If multiple geographic areas are studied, list all of them. Note any specific contextual details about the housing market that are relevant to understanding the rent control policy (e.g., housing density, urban vs. suburban, economic conditions). If geographic context is not explicitly stated, check methods, data sources, and results sections for implicit geographic information. - **Rent Control Policy Details**: Extract comprehensive details about the rent control policy: - Year of policy implementation - Specific type of rent control (first-generation, second-generation, etc.) - Key policy mechanisms (e.g., price caps, exemptions) - Scope of application (which housing units are covered) Look in introduction, methods, and policy description sections. If multiple policy aspects are discussed, provide a comprehensive summary. If policy details are complex, create a bulleted list to capture nuanced information. Note any explicit exceptions or special conditions in the policy. - **Policy Impact Outcomes**: Identify and extract the specific outcomes measured related to rent control policy: - Rental prices (regulated and unregulated sectors) - Housing supply - Land values - Maintenance investments - Tenant stability - Demolition rates - Housing market dynamics Extract quantitative values where available. If multiple outcomes are measured, list all with their specific measurements and statistical significance. Prioritize outcomes directly addressing rental prices, housing supply, and market dynamics. Look in results and discussion sections for comprehensive outcome reporting. - **Key Quantitative Findings**: Extract the primary quantitative results, focusing on: - Magnitude of effects - Statistical significance - Confidence intervals or standard errors (if provided) - Directional impact of rent control policy Prioritize results that directly answer the research question about housing market dynamics and tenant stability. If multiple statistical models or analyses are presented, extract results from the primary or most comprehensive analysis. Include specific numeric values and percentage changes where available. Ensure extracted findings are directly quoted or paraphrased from the results section, maintaining the original statistical context. # Results ## Characteristics of Included Studies Study Study Location/Period Rent Control Policy Type Primary Outcomes Measured Methodology Full text retrieved Diamond et al., 2019a San Francisco, USA; 1994 onward Not explicitly labeled; limits rent increases, multi-family 2-4 units built 1900-1990 Rental prices, housing supply, tenant stability, maintenance Quasi-experimental Yes Jofre-Monseny et al., 2023 Catalonia, Spain; 2020 Not specified; price caps, high coverage Rental prices, housing supply Difference-in-differences, event-study, quasi-experimental No Munch & Svarer, “Rent Control and Tenancy Duration” Denmark Not specified in abstract Tenancy duration, mobility Econometric study No Mense et al., 2017 Germany; 2015 Second-generation; exception for new units Rental prices, land values, supply, maintenance Difference-in-differences with discontinuity, quasi-experimental No Breidenbach et al., 2021 Germany; 2015 Not specified in abstract Rental prices, quality Quasi-experimental (triple difference event study) No Mense et al., 2019 Germany; 2015 Second-generation; rent ceiling, multiple exceptions Rental prices, supply, land values, maintenance, mobility Quasi-experimental (difference-in-differences with discontinuity) Yes Gyourko & Linneman, 1989 New York City, USA; 1943 onward First-generation; rents frozen, later exemptions Rental prices, supply, tenant stability, maintenance Econometric, cross-sectional Yes Autor et al., 2012 Cambridge, MA, USA; 1970-1995 First-generation; strict caps, no vacancy decontrol Rental prices, supply, land values, maintenance, turnover Quasi-experimental, difference-in-differences, econometric Yes Early & Phelps, 1999 USA; 1984-1996 Not specified; new construction exempt Rental prices (uncontrolled sector), supply Econometric study No Fallis & Smith, 1985 Toronto, Canada Not specified; exemptions Rental prices (controlled/uncontrolled) Econometric, cross-sectional No toof Pageof View 30 more rows Across 40 studies of rent control policies, the following patterns were observed: Primary Outcomes Measured: - Rental prices:Measured in 25 studies. - Housing supply (including new construction and conversions):Measured in 20 studies. - Tenant stability (including tenure duration, mobility, turnover, and displacement):Measured in 16 studies. - Maintenance, quality, investment, or renovations:Measured in 9 studies. - Evictions or eviction filings:Measured in 3 studies. - Land values, property values, or wealth transfer:Measured in 5 studies. - Vacancy rates:Measured in 2 studies. - Market dynamics (including spillovers, misallocation, search/matching costs, and disequilibrium):Measured in 6 studies. - Rent burden, rent discounts, or rent subsidy:Measured in 4 studies. - Community composition or segregation:Measured in 2 studies. - Owner/tenant knowledge, queue time, and income distribution:Each measured in 1 study. - Other outcomes (such as distribution, race, tenant welfare, and consumption):Found in 5 studies. Rent Control Policy Type: - First-generation rent control:Studied in 7 studies. - Second-generation rent control:Studied in 10 studies. - Third-generation rent control:Studied in 1 study. - Rent stabilization:Explicitly labeled in 3 studies. - No clear specification of policy type:21 studies. - User value system (other):1 study. Methodology: - Quasi-experimental methods (including difference-in-differences, event study, regression discontinuity, interrupted time series, and triple difference):Used in 24 studies. - Econometric methods (including cross-sectional and empirical econometric analysis):Used in 21 studies. - Empirical analysis (not otherwise specified):Used in 11 studies. We did not find mention of some outcomes or policy types for several studies, either due to lack of reporting or lack of access to the full text. * * * ## Effects ### Effects on Housing Market Dynamics Study Outcome Measure Effect Direction Effect Size Statistical Significance Diamond et al., 2019a Market rents, supply Increase in rents, decrease in supply 15% supply reduction, 20% decrease in mobility No mention found Jofre-Monseny et al., 2023 Average rents Decrease in rents 4–6% reduction No mention found Munch & Svarer Tenancy duration Increase in duration +6 years (most vs. least regulated) No mention found Mense et al., 2017 Rents (regulated/unregulated), land values Increase in unregulated, decrease in regulated, increase in land values No mention found No mention found Breidenbach et al., 2021 Rental prices Decrease in rents (short-term) Up to 5% (all), 9% (some types) Significant, effect vanishes after 1 year Mense et al., 2019 Regulated/free-market rents Decrease in regulated, increase in free-market No mention found No mention found Gyourko & Linneman, 1989 Rent subsidy Increase in subsidy $2440/year (27.2% income) 95% confidence interval: $2367–$2511 Autor et al., 2012 Rents, property values Increase in rents post-decontrol 40% (controlled), 13% (never-controlled) No mention found Early & Phelps, 1999 Uncontrolled rents Increase in rents Positive, significant Yes Fallis & Smith, 1985 Rent difference Attributable to controls ~50% of difference No mention found toof Pageof View 30 more rows Across 40 studies, the following patterns were observed in effect direction and statistical significance: - Decrease in rents:Reported in 11 studies. - Increase in rents:Reported in 10 studies. Some studies reported both increases and decreases, depending on market segment (for example, regulated versus unregulated units). - Decrease in supply or construction:Found in 4 studies. - Increase in supply or construction:Found in 3 studies. - Increase in tenant stability or tenure:Found in 2 studies. - Decrease in tenant stability or tenure:Found in 1 study. - Increase in evictions or displacement:Found in 3 studies. - Decrease in evictions or displacement:Found in 1 study. - Increase in property or land values:Found in 1 study. - Decrease in property or land values:Found in 1 study. - Increase in vacancy:Found in 2 studies. We did not find studies reporting a decrease in vacancy. - Increase in rent burden, savings, discounts, or subsidy:Found in 5 studies. - Mixed, minimal, or no effect:Reported in 5 studies. - Other outcomes (queue times, demographic changes, misallocation costs):Reported in 2–3 studies. Statistical significance: - Explicit statements of statistical significance:Found in 9 studies, based on available full texts or abstracts. - Explicit statements of non-significance:Found in 2 studies. - No mention of statistical significance:In the available full texts or abstracts for the remaining 29 studies. The most common outcome measures were rents or rental prices, supply or construction, tenant stability, tenure or mobility, evictions or displacement, property or land values, and rent burden, savings, or subsidy. We did not find mention of outcome measure information in the available full texts or abstracts for some studies, and some studies reported multiple outcomes. * * * ### Effects on Tenant Stability and Demographics ### Tenant Composition and Income Distribution Study Outcome Measure Effect Direction Effect Size Statistical Significance Gyourko & Linneman, 1989 Rent subsidy, tenure Increase in subsidy, increase in tenure $2440/year, significant tenure increase 95% confidence interval: $2367–$2511 Donner & Kopsch, 2023 Subsidy, queue, income Increase in subsidy for high-income, increase in queue 21 vs. 10 years (high vs. low subsidy) No mention found Chen et al., 2023 Rent discount, race Decrease in racial disparity, increase in discount $410/month, $4–5.4B/year No mention found Sims, 2011 Minority/poor presence Increase in minority, decrease in poor No mention found Robust Zapatka & Galvao, 2022 Rent burden, composition Decrease in burden, increase in Hispanic/foreign-born No mention found No mention found Ahern & Giacoletti, 2022 Wealth transfer Regressive 2–8% by income Significant toof Pageof Across these 6 studies: - Outcome measures: - Rent subsidy or discount: 2 studies. - Regressive or high-income benefit effects: 2 studies. - Minority or racial/ethnic composition or disparity: 2 studies. - Rent burden: 1 study. - Wealth transfer: 1 study. - Some studies covered multiple topics. - Effect direction: - Regressive effects or greater benefits for high-income groups: 2 studies. - Positive effect of subsidy on tenure: 1 study. - Reduced racial disparity: 1 study. - Increased minority presence and decreased poor presence: 1 study. - Reduced rent burden with increased Hispanic/foreign-born presence: 1 study. - Statistical significance: - Statistical significance or robustness was reported in 3 studies, based on available full texts or abstracts. - We did not find mention of statistical significance in the available full texts or abstracts for the other 3 studies. * * * ### Residential Mobility and Tenure Duration Study Outcome Measure Effect Direction Effect Size Statistical Significance Diamond et al., 2019a Mobility Decrease in mobility 20% reduction No mention found Munch & Svarer Tenancy duration Increase in duration +6 years No mention found Mense et al., 2019 Mobility Decrease (high-income), increase (low-income, short distance) No mention found No mention found Karpestam, 2022 Mobility Decrease (older units), increase (newer units) 5.7% decrease with 10% increase in income No mention found Nagy, 1997 Tenure duration Increase in duration (stabilized) No mention found No mention found Chapple et al., 2022 Outmigration Decrease (low socioeconomic status, stabilization), increase (market-rate) 1–2% increase in outmigration No mention found toof Pageof Across these 6 studies: - Outcome measures: - Mobility: 3 studies. - Tenancy or tenure duration: 2 studies. - Outmigration: 1 study. - Effect direction: - Decrease only (in mobility): 1 study. - Increase only (in tenancy/tenure duration): 2 studies. - Mixed effects, with both increases and decreases depending on subgroup or context (for example, by income, unit age, or stabilization status): 3 studies. - Effect sizes: - 20% reduction in mobility: 1 study. - 5.7% reduction in mobility with a 10% increase in income: 1 study. - Increase of 6 years in tenancy duration: 1 study. - 1–2% increase in outmigration: 1 study. - Statistical significance: - We did not find mention of statistical significance in the available full texts or abstracts for any of the 6 studies. * * * ### Neighborhood Segregation and Community Composition Study Outcome Measure Effect Direction Effect Size Statistical Significance Sims, 2011 Segregation Increase in segregation No mention found Robust Chapple et al., 2022 Exclusionary impact Increase in exclusion No mention found No mention found Chen et al., 2023 Racial disparities Decrease in disparities No mention found No mention found toof Pageof Across these three studies: - Outcome measures: - Segregation: 1 study. - Exclusionary impact: 1 study. - Racial disparities: 1 study. - Effect direction: - Increase in the measured outcome (segregation or exclusion): 2 studies. - Decrease in disparities: 1 study. - Statistical significance: - Statistical significance was described as “robust” in 1 study. - We did not find mention of statistical significance in the available full texts or abstracts for the other 2 studies. ## References [Jordi Jofre-Monseny, Rodrigo Martínez-Mazza, M. 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Wheaton\\ (1981).Housing policies and urban “markets” in developing countries: The Egyptian experience☆](/content/review/b3e64110-83ee-4fd7-87d8-65a6fe06010b/source/b364869c6e9c4c50a99a074678b31e12/index.html) [Max Gardner, Brian Asquith\\ (2024).The Effect of Rent Control Status on Eviction Filing Rates: Causal Evidence From San Francisco. Housing Policy Debate](/content/review/b3e64110-83ee-4fd7-87d8-65a6fe06010b/source/6e82dc30fc5d477ea02340611911336e/index.html) [Yeon Hur, Hanming Gu, Kabsung Kim\\ (2025).Do the Right to Lease Renewal and the Rent Cap Curb Apartment Rent Increases?: Dissecting Rent Dynamics in a Regulated Housing Market. Korean Association for Housing Policy Studies](/content/review/b3e64110-83ee-4fd7-87d8-65a6fe06010b/source/711fc196bb364342abd54d341ca150a2/index.html) Download BIBDownload RISDownload TXT ## Report Status Gather sources 989 sources found Details Screen sources 40 sources included Details Extract data 200 data points extracted Details Generate report Save PDF BIBLaTeX, ZoteroRISZotero, MendeleyTXTAPA BibliographyPDFPDFDOCXMicrosoft Word Chat Got some follow-up questions? [Sign up](/content/users/auth?redirectToPath=%2Freview%2Fb3e64110-83ee-4fd7-87d8-65a6fe06010b%2Fsource%2Fdf0ee5df3e3240bba80bdd28b814101f&show=signup/index.html) or [sign in](/content/users/auth?redirectToPath=%2Freview%2Fb3e64110-83ee-4fd7-87d8-65a6fe06010b%2Fsource%2Fdf0ee5df3e3240bba80bdd28b814101f/index.html) to chat with this report. Back ## The impact of rent control: Investigations on historical data in the city of Lyon Loïc Bonneval, F. Goffette-Nagot, Zhejin Zhao Growth and Change· 2015· 7 citations SourceDOI Plain textPDFFigures Searching for PDF Unable to find PDF from source Back Study Design Type Difference-in-differences Geographic Setting and Context \- Country: France \- City/metropolitan area: Lyon \- Specific neighborhoods or regions: Central area of Lyon Rent Control Policy Details \- Year of policy implementation: 1914 \- Specific type of rent control: \- 1914-1929: Second-generation \- 1930-1948: First-generation \- 1949-1968: Second-generation \- Key policy mechanisms: \- Price caps (e.g., rents could not exceed twice their 1914 level by 1926) \- Exemptions for rents above certain thresholds (e.g., above 250 Fr in 1942) \- Regulated biannual rent increases to reach a reference rent by 1955 \- Scope of application: Residential flats, excluding commercial-use flats like shops or workshops Policy Impact Outcomes \- Rental prices: \- Regulated sector: 11% reduction between 1914 and 1929, 47% reduction by 1949-1968. \- Unregulated sector: No increase in rents. \- Housing supply: Not directly measured, but implied reduction due to rent control. \- Land values: Not mentioned. \- Maintenance investments: Not mentioned. \- Tenant stability: Decreased mobility due to subcontracting arrangements. \- Demolition rates: Not mentioned. \- Housing market dynamics: Rent control deepened over time, affecting rent levels and tenant mobility. Key Quantitative Findings \- The impact of rent control deepened over time, with an initial reduction of 11% in rents between 1914 and 1929, increasing to a 47% reduction in the regulated rental market during the 1949-1968 period. \- The rent control rules implemented starting in 1914 implied a decrease in rents by 24%. \- The estimated impact on flats observed for longer was around 36%. \- The rent reduction between 1930 and 1948 was between 9% and 17%. \- The policy imposed by the 1948 law did not significantly alter rent evolutions. \- Restricting the control group intensified the negative effect of the 1930-1948 rent control rules on rents. \- Dropping flats from the control group once they were released from control amplified the estimated effect of rent control. This paper reexamines the debated issue of the effects of rent control policy on the rental market. We investigate the impact on rents of three different forms of rent regulation in Lyon over a 78-year period. We use an original historical data set which allows us to track regulation changes, rent paid, and tenant moves for a long-run panel of flats. Using a difference- in-differences method, we estimate the impact of regulation on rents depending on the type of rent control over different economic periods. Our results show that the impact of rent control deep-ened over time. Starting with an 11% reduction in rents between 1914 and 1929, it reached a decrease by 47% in the regulated rental market in the 1949– 1968 period. We do not find any increase in rents in the unregulated segment of the rental market, which could be a result of a reduction in housing investment in the long run. 1.Introduction Several countries experienced various degree of rent control in the past, including Canada, Germany, the United Kingdom and the United States. As to the recent period, a rent control law was passed in France in February 2014 and implemented in Paris since August 1, 2015. 1 This law states that the rent of a dwelling cannot be higher than a cap defined as 20% above the median rent observed for dwellings in the same neighborhood, with the same number of rooms and construction period. 2 In the context of rising housing prices, this law aims to constrain rent increases when a landlord lets a property for the first time or relets it, as rent increases within the course of a tenancy are already regulated. Capping rents or their increase rate seems to directly benefit consumers and especially low-income households. Yet, the full economic consequences of such an intervention on the housing market need to be carefully assessed. There are, broadly speaking, two types of rent control policies. In the first case, rents are controlled both over the course of a tenancy and when the tenant changes. This is conventionally referred to as a first-generation rent control and it is the stricter form of rent control. In the second case, rent control is released when the tenant changes, which means rents are regulated only within a tenancy. This kind of rent control is commonly called a second-generation rent control policy. There is some consensus among economists about the potential negative consequences of rent control. On one hand, this regulation is able to cap increases in rents immediately, but it may also have unexpected consequences. In a nutshell, a rent control policy might result in an increase of rents, both in the regulated and in the unregulated segments of the rental market. Firstly, the diminution of rental returns may reduce housing investment and thus housing supply, inducing a rent increase in both the regulated and the unregulated rental markets (Early, 2000;Diamond, 2019). Secondly, a second-generation rent control could lead landlords to increase rents upon new leases to compensate for the interdiction of rent increases during the lease, resulting in higher rents even in the regulated rental market as argued by Nagy (1997). The long-run impact on rents of a rent control policy is therefore questionable. Empirical evidence on the impacts of rent control is based on only a few cases and the magnitude of estimated effects varies across locations and over time, thus empirical analyses in different contexts are still required to lead to a better understanding of the equilibrium impacts of a rent control policy. This paper attempts to contribute to this literature by considering a rent control policy over a long period of time, within a changing economic context in France, where rent control was used over the majority of the 20th century. Using historical data in the city of Lyon over a 78-years period, our study quantifies the effects of two types of rent regulation using a difference-in-differences identification strategy. Our data is taken from a property manager's account books covering the 1890-1968 period. This unique data allows us to track regulation changes, rent paid and tenant moves at the flat level. It gives us an original point of view on a period over which rent control was largely implemented in France but almost never evaluated using policy impact methods due to a lack of data. Our study shows that the impact of rent control became larger over time. Starting with a 11% reduction in rents between 1914 and 1929, it reached a decrease by 47% in the regulated rental market during the period 1949-1968. The remainder of this paper is organized as follows. The next section details the literature review. Section 3 depicts the rent control history in Lyon and section 4 presents the data. Section 5 introduces the empirical strategy. Section 6 reports the results and section 7 concludes. 2.Literature review Regulatory intervention on housing markets is broad and deep. Housing markets are governed by planning processes, zoning, land use regulations, financial regulations and numerous other rules, in which rent control is the most important regulation historically (Turner and Malpezzi, 2003;Gyourko and Glaeser, 2008). The impact of rent control on equilibrium rent levels is controversial, as several mechanisms might hinder the expected rent stabilization. The first mechanism relates to the supply reduction that might be induced by rent control. Early (2000) argues that the likely effect of rent control is to lower the returns on investments in the controlled sector and decrease housing supply, thus the long-run, rents may be higher even in the controlled segments of the rental housing market. He uses New York City data in 1996 to test this hypothesis. The results suggest that tenants lost 44 dollars per month for households in rent stabilized apartments and 4 dollars per month for households in more strictly rentcontrolled housing. Tenants in the controlled sector would have been better off had rent control never been implemented in New York City. Sims (2007) uses a difference-indifferences strategy to estimate the impact of rent decontrol in Massachusetts, and finds that rent control encourages owners to shift housing from rental status to owner occupancy. In a recent paper, Diamond et al. (2019) show that rent control in San Francisco increased the probability of renters staying at their address by 20%, reduced the supply of rental housing by 15%, and led to a rent increase in the long run. They conclude that the rent control policy caused a substantial welfare loss. Another mechanism is involved in the case of second-generation rent control. Nagy (1997) argues that landlords can set a higher price than the rents in an uncontrolled market at the beginning of a new lease in order to compensate for the fact that the rents will have to remain unchanged until the tenant changes. As the time goes, the rents paid by tenants in the controlled sector increase. As a consequence, the regulation may change nothing except alter the timing of payment. Nagy uses data from New York City during the period 1978-1987 to test this hypothesis and finds that new tenants paid higher rents in the controlled sector compared to those who occupied similar apartments in the uncontrolled sector. Still, tenants in the controlled sector paid less provided they stayed longer in their flat. More sophisticated hypotheses can be made, however, under which a second-generation rent control policy produces the expected rent reduction. Raess and von Ungern-Sternberg ( 2002) propose a theoretical model of the rental market with search costs, switching costs and price discrimination caused by product heterogeneity, and show that with these features, a second-generation rent control leads indeed to lower equilibrium rents. Likewise, Basu and Emerson (2003) consider the effect of a second-generation rent control on rents and argue that its impact is similar to that of a first-generation rent control policy. Because of inflation and information asymmetry, landlords prefer short-staying to longstaying tenants, but they cannot distinguish which type a tenant is. Long-staying tenants have the incentive to conceal this information to prospective landlords. Considering this point, monopolistic landlords hold prices down to attract a "better quality" tenant (i.e. a short-staying tenant). Therefore, a second-generation rent control can reduce rental levels in a way that mimics first-generation rent control policies. Not only is the impact of rent control in the controlled sector questionable, but rent regulation is also suspected to increase rents in the uncontrolled sector due to spillovers or general equilibrium effects. Indeed, if rent control reduces the supply of housing, the induced shortage in the whole housing market is likely to increase the rent in the uncontrolled segment of the housing market. Fallis and Smith (1984), using data for Los Angeles during the 1969-1978 period, find that rent control effectively raised rents in the uncontrolled segments of the markets. Caudill (1993) estimates the effect of New York City's rent controls in 1968 and finds that rents in the free sector would be lower by 22% to 25% if rent control did not exist. Hubert (1993) argues that rents in the uncontrolled sector can rise or fall after a rent control policy is implemented in a part of the housing market. If tenants can satisfy their needs in the controlled sector, they will reduce their demand in the free market, which results in a fall of rents in the free market. However, if tenants are unable or unwilling to obtain housing in the controlled sector, they have to go to the unregulated market, which may result in a rise of rents in this market. Based on a spatial equilibrium model, Heffley (1998) shows that tenants in both the controlled and uncontrolled zones can benefit from rent control, at the expense of landlords and the public sector. This result depends on the model specification and parameter values, but nonetheless highlights that the external effect of rent control depends on tenants economic and location decisions. Early and Phelps (1999) also claim that the effect of rent control on the uncontrolled sector is ambiguous, but using 1984-1986 data from the American Housing Survey, they find that the price in the uncontrolled sector increased since the introduction of rent control. Their interpretation is that rent control reduced the supply of rental housing. However, these effects decline through time and may disappear after several years. Some other authors concluded that rent control leads to higher rents in the uncontrolled sector (Navarro, 1985;Ho, 1992). For example, Early (2000) shows that the fraction of rental units under rent control is positive correlated with the pricing of rental housing in the uncontrolled sector. Autor et al. (2014) find that the unanticipated elimination of rent control in Cambridge, Massachusetts in 1995 raised housing values of both decontrolled and never-controlled residential properties, due to spillover effects. Many other consequences of rent control policies have been studied, regarding housing supply, housing maintenance, residential mobility or benefits to renters. 3 3. History of the French rent control policy in the 20th century Before 1914, there was no public regulation of rents in France. In 1914, due to the burden of World War I, renters were allowed not to pay their rent for periods up to 90 days if the rent was below a ceiling set to 600 Francs (Fr) in Lyon. The associated temporary eviction moratorium lasted throughout the World War I period. Between World Wars I and II, a special regime was put in place, in a context where the shortage of housing and the economic situation required the protection of renters. This was an explicit rent control policy, with a regulation applying to rent increase rates, with the 1914 level as the reference level. In fact, a complex system of accumulating successive rules was implemented. In a nutshell, it mainly consisted in two rules: first, the regulation of nominal rent increase rates within tenancies; second a ceiling, determined by the law and adjusted over time, above which rents were released from the regulation. These two rules are represented on Figure 1 , with allowed rent levels compared to the 1914 rent level and ceiling adopted in different years. For example, in Lyon in 1926, nominal rents could not exceed twice their 1914 level, meaning the allowed rent increase rate was actually less than the inflation rate. The nominal rent ceilings decreased and changed seven times in total over the period. For instance, rents above 9000 Fr were not controlled anymore in 1928. The last change happened in 1942: all rents above 250 Fr were not controlled anymore. A significant change occurred in 1930, when the control started applying even when the tenant changed, which was not the case before. Apart from this change, the regulation was similar as during the previous period. We can thus consider the rent control policy implemented in Lyon during the 1914-1929 period as a second-generation rent control, followed by a first-generation rent control from 1930 to 1948. A new law was passed in 1948, which aimed to end the special rent regime and increase the return of housing properties in order to favor housing construction and maintenance. A reference rent was computed based on the flat characteristics such as location, maintenance and quality. Biannual increases were then applied for the rent to reach this reference rent by 1955. Continued leases ensured a capped rent increase, but flats were released from this regulation upon tenant change. The 1949-1968 period can therefore be considered as a rent control of second-generation type. It is worth noting that the incentive for tenants to stay in the apartment or to subcontract was high, in a context of rising rents following the strong contraction of rents imposed by the rent regulation of the previous period. A summary of these different phases of the rent control policy is presented in Table 1 . Importantly, the rent regulation was meant to protect low-income renters and therefore did not apply to flats for commercial-use like shops or craftsmen workshops. This policy was applied at the national level, and Lyon, as one French major city, is a relevant study case. 4.1.Data source This study uses data collected from a real estate property manager's accounting books covering the 1890-1968 period. These books were used to register, for each building managed by the company, all the rents paid by tenants, and all the expenditures at the building level. These information were collected and processed by Bonneval and Robert (2009) (see Bonneval and Robert, 2009 for a detailed description of the original data). The company managed upper middle class real estate in the 19th and 20th centuries, mainly in the central area of Lyon, which explains the average observed rent being generally higher than that in the whole of Lyon. 4 Nonetheless, the observed sample is sufficiently heterogenous to offer an acceptable representation of buildings in the central area of Lyon as shown in Figure 2 . Some sample attrition occurs because low quality buildings were more likely to be demolished during the urbanization process in the period under study. This data allows us to compare controlled and uncontrolled rents over a long historical period; there is no other data source in France which allows for this to our knowledge. This data provides flat-level information including whether the flat is used for housing or commercial use, the tenant name, the rent paid, the number of rooms, floor area, an indicator of quality category and rent control status. Comparing tenant name in subsequent periods allows detection of tenant changes. The construction period and construction type, number of floors, total built surface area, and geographical coordinates are registered at the building level. The original sample is an unbalanced panel of about 500 flats. It has 32,745 records, each of them corresponding to a rent payment by a given tenant in a given period (year or semester). Some observations are dropped from the original sample due to missing information on flat use (66 observations) and floor area or number of rooms (1,438 observations). Information about tenant change is imputed for 43 observations for which it is missing. We assume there were no tenant changes in these instances. Rents registered at the semester level are summed to get yearly rents. Regarding control status, if one flat is controlled at least one semester in a year, we assume it was controlled for the whole year. All monetary amounts are transformed in 1999 Francs using inflation coefficients taken from Friggit (2002) 4.2.Descriptive statistics Table 2 presents the yearly average of the number of flats in the sample, by subperiods and control status. The number of flats managed by the property manager varied roughly speaking between 220 and 290 depending on period. About a quarter of them were flats in commercial use, which were actually shops or workshops located at the ground floor of the managed buildings. Almost all residential flats had regulated rents between 1914 and 1929. Only a few were uncontrolled because their initial rent exceeded the ceiling for regulation. During the subsequent period (1930) (1931) (1932) (1933) (1934) (1935) (1936) (1937) (1938) (1939) (1940) (1941) (1942) (1943) (1944) (1945) (1946) (1947) (1948) , about one-third of residential flats were regulated, although this figure varied over time, from 94% at the start of the period to 0% by the end, as a consequence of flats being progressively released from control (see Figure 3 ). The average share of controlled rents over the 1949-1968 period was higher with 65% of controlled residential flats, and still 40% of controlled flats in 1968. As previously stated, rent control was released upon a tenant move, which gave high incentives for tenants to stay in their flat or to subcontract (which was a largely adopted practice), therefore exit from the regulated status was limited. Figure 4 shows the observation period and rent control changes for each flat in the sample. One important feature of our dataset is the observation of flats for long periods of time, which allows us to monitor rent changes while controlling for flat quality. As explained in the next section, our estimation strategy exploits variations in control status for each flat to estimate the impact of the different types of rent control policies implemented during the period. Figure 5 presents the rent changes in constant Francs disaggregated by flat use and control status, and Figure 6 presents the same changes in current Francs in logarithm. Overall, this period was characterized by large rent variations, both in constant and nominal Francs. Rents of both commercial and residential flats reached their lowest levels in constant Francs (Figure 5 ) just after World War I and World War II. It is worth noting that flats in commercial use experienced much larger rent variation than residential flats. Before 1914, rents of housing units were steadily increasing. The introduction of rent regulation was followed by a sharp decrease, which continued for flats under regulation until 1948. Flats which were released from control had, by definition, higher rents. Still, they experienced rent decreases over time, both in constant Francs and in nominal values. Although one observes on Figure 5 that these rent decreases occurred while flats in commercial use experienced rent increases (1926-1935 period) , the low number of uncontrolled residential flats during this period (there were less than 30 controlled residential flats before 1935) makes this comparison less meaningful. Table 3 presents descriptive statistics by time period and rent control status. On average, for all periods, flats subject to rent control were smaller than residential uncontrolled flats. There is however variability within each group, as shown by large standard errors, so these differences are not significant. Flats under regulation were also more frequently in lower quality buildings, with a higher share in ancient buildings as opposed to buildings constructed during the Haussmann period. However, all buildings in the sample include flats which have been subject to rent control. The relatively lower quality of controlled flats is more apparent at the floor level, with segregation occurring vertically at this time due to the absence of elevators. The distribution of flats by floor level indeed shows that controlled housing units were more frequently on the highest floor levels. There is no significant difference in location between controlled and uncontrolled residential flats, apart from the location of shops being predominantly in the two central districts. As will be explained in the next section, our identification allows to control for differences in the characteristics of controlled and uncontrolled flats, by exploiting control status changes for each flat. 5.Empirical strategy Our aim is to estimate the impact of the rent control policy on the rental housing market over time. More precisely, our goal is twofold. Firstly, we want to evaluate the stringency of the different forms of rent control in Lyon over the whole period when rent control was ongoing. Specifically, the complex and evolving rules regarding the rent ceilings and caps on rent increase rates do not allow for precise identification of which rent reductions were induced by policy. By comparing the evolution of rents for flats on which control was applied to that of others, we will be able to give an account of the real impact of the rent control policy. Secondly, we want to attempt to evaluate a possible impact of rent control on uncontrolled housing units, that is, the externalities of the policy on the uncontrolled segment of the housing market. To do so, we exploit rent control status variations across time periods and flats and use a difference-in-differences (DiD hereafter) identification strategy based on a classic double fixed effects estimation. Generally speaking, the DiD strategy consists of estimating the impact of a treatment (here, the rent control policy) on a group of individuals, by comparing their evolution to that of a control group. The control group has to be defined such that it can represent the evolution of housing prices that would have occurred, would the rent control not have been introduced. Considering the three different phases in the rent control policy implemented in Lyon during the observation period, the difference-in-differences method can be implemented by estimating the following equation: where ln y it represents the logarithm of rent of flat i in year t, α i is a flat fixed effect, S i is a dummy taking value 1 if the flat is in residential use, and λ t are year dummies that control for the general evolution of real estate prices, and µ t are year dummies that are interacted with flat use in order to allow housing units to have specific trends relatively to shops. C 1it is a dummy taking value 1 if the rent of flat i was controlled during the 1914-1929 period (when a second-generation rent control policy was implemented), C 2it is a dummy taking value 1 if the rent of flat i was controlled during the 1930-1948 period (when the rent control policy changed to a first-generation type), C 3it is a dummy taking value 1 if the rent of flat i was controlled during the 1949-1968 period (when again a second-generation rent control policy was implemented), and ε it is a classical error term. In the baseline estimations, the standard errors are clustered at the flat level, and then at the building level as a robustness check. The flat fixed effects capture time-constant flat-specific factors affecting the rents. They allow control for differences in unobserved quality depending on control status. With these flat fixed effects, the impact of the policy is identified through changes in the control status for each flat. The year dummies control for the general evolution of rents in the observed period. Interactions of flat use with year dummies allow for specific trends for housing units relatively to shops, consistently with the observation of differing evolutions. The main coefficients of interest are β 1 , β 2 and β 3 , which in such a difference-in-differences setting can be interpreted as the causal effect of the three types of rent control on rents. They are based on the comparison of the evolution of rents of flats subject to rent control to that of flats belonging to the control group. The control group comprises two types of observations: (1) flats which were never controlled in the observation period; as will be clearer later, all of them are actually flats in commercial use, to which rent control did not apply; (2) flats which are not controlled in each year. 5 The first type of control observations is what is commonly used in a differencein-differences strategy. The second type can be used here as we have a staggered adoption design, and more specifically a non-monotonic treatment, meaning that the policy does not apply at the same date for all flats, as explained previously (see also Figure 4 ). Uncontrolled housing units are the most natural control group for estimating the impact of the rent control rules. However, they are likely to be subject to externalities from the controlled segment due to substitution effects between the two segments. As a robustness check, we will consider alternative definitions of the control group, especially by restricting it to commercial flats. Our data is an unbalanced panel of rental units, as shown on Figure 4 . However, the DiD method requires the observation of treated individuals before the treatment starts. Furthermore, to avoid uncontrolled composition effects, it is desirable for the control group to remain the same over the estimation period. Thus, we restrict the estimation sample to units which are observed before rent control began, and are still observed during the treatment period. 6 The selection of a quasi-balanced panel of flats over the 1890-1968 period is restrictive, as A crucial assumption for the DiD method to yield valid results is the parallel trend assumption, according to which the evolution of the outcome for the treated and control groups, absent the treatment, would be the same. We test for this assumption by comparing the evolution of rents before rent control, for the group of flats that are later controlled, and the group which remains untreated. To do so, we run the following regression over the estimation samples used for the main results: where t < 1914, C 0it is a dummy taking value 1 if the flat is controlled at least once during the rent control period, and ν t is a year dummy. The parallel trend assumption requires that the coefficients β 0 are all non-significantly different from zero. This equation is estimated on subsamples defined following the same rules as for the main model: flats present during the first two, first three and first four subperiods, and whole sample. We also perform robustness checks by changing the control group. We consider the baseline model (1) over the whole period (1890-1968) but apply two restrictions successively. First, we want to deal with the fact that flats which have been subject to rent control can leave it, especially following a tenant move. In our baseline estimation, these flats are included in the control group when they leave treatment. To check if the presence of these previously controlled flats in the control group impact our results, we run an estimation in which they are discarded from the sample once they are released from rent control. The control group then only includes flats before they start being controlled and shops. Second, we go a step further by keeping only shops in the control group. However, a remaining limit of this empirical design is that tenant moves, which determine the release from rent regulation for each flat, could be caused by rent evolution. We will keep this in mind when commenting on our results. 6.Results In the following, we present regression results for Equation (1), in which separate coefficients for the rent control policy applied in different subperiods are estimated, so as to evaluate the impact of the different rent control regimes. As a reminder, between 1914 and 1929, the rent control was of second-generation type, then turned to first-generation between 1930 and 1948, and turned back to a second-generation type with regulated capped rent increases during leases in 1949. Before presenting these results, we first show the results of the common trend assumption test. This test is based on the estimation of equation ( 2) and is conducted on four different samples corresponding to the samples used to estimate the main model. Estimated coefficients for year dummies specific to flats which enter rent control at some point during the 1914-1968 period are plotted in Figure 7 . Given the logarithm form of the explained variable, these coefficients represent the rent variation in percentage for housing units subject to rent regulation as compared to never-controlled flats. For all subsamples considered, none of these coefficients are significantly different from 0 at the 5% level, except for year 1898. This means that, after controlling for time-constant flat heterogeneity, the rate of change in rents for residential flats (which will all be controlled at least for some time starting in 1914) is similar to that of commercial flats. This justifies using commercial flats only, or commercial flats together with residential flats as control observations. Table 4 shows the main regression results. Column (1) focuses on the impact of rent control during the first phase of the policy, from 1914 to 1929, Column (2) on the first and second phases, between 1914 and 1948 and Column (3) on the whole period. In each case, the largest possible quasi-balanced panel is selected. In Column ( 4), all available observations are kept in the sample, which means that 72 flats not selected in the three previous samples are added in the estimation sample. According to Column (1), the rent control rules implemented starting in 1914 implied a decrease in rents by 24%. This impact is estimated over a sample of 306 flats. The same impact estimated on a subsample of flats observed over a longer period (274 flats observed before 1914 and beyond 1930, Column (2)) is very similar, and the difference between the two estimated coefficients is not statistically significant. The estimated impact on flats observed for longer are even stronger, around 36%, but given the associated standard errors, equality with the coefficient in Column (2) cannot be ruled out. However, this increase in the coefficient means that flats which disappeared after 1929 were not those which were the most impacted by rent control. The result of the estimation based on the whole sample shown in Column (4) is in line with the previous one. This observation implies that there are little differences in the impact of rent control between flats which started to be managed by the property manager after 1914 and others. Note that although the sample size decreases significantly when considering larger time periods (Columns ( 2) and ( 3)), standard errors are overall not much affected. As a robustness check, we also estimated the model with standard errors clustered at the building level (standard errors in squared brackets in Table 4 and 5 ), which only slightly changes the coefficients significance. For the rent control rule which started in 1930, results in Columns (2) to (4) show that the impact of rent control was smaller than in the previous period. The rent reduction is indeed between 9% and 17% depending on the estimation sample. This lower impact of the policy is at odds with the idea that the rent control policy became more stringent in this period, when rent increases were capped even upon tenant changes. However, it is worth recalling that the 1930's were a period of economic crisis. As shown on Figure 6 , rents of uncontrolled flats stagnated in current Francs and even decreased in constant Francs (Figure 5 ). As rent change caps were applied on nominal rents, this can explain the moderate impact of the policy in constant currency. Here, as in the case of the first rent control policy, comparing Columns ( 3) and ( 4) with Column (2) shows that considering flats that stayed longer in the sample amplifies the estimated impact. Finally, estimated coefficients for the impact of the rent control policy imposed by the 1948 law suggest that this policy did not significantly alter rent evolutions relatively to the control group. This result can be surprising given that the 1948 law is viewed in France as having had large impacts on the rental market. Two points can possibly explain our result. First, the 1948 law allowed regulated rent increases for controlled housing units. Its goal was indeed to dampen the depreciation impacts of the previous rent control policy while still keeping rent growth reasonable. Second, although landlords could and did increase the rent when starting a new lease, they were probably less induced to do so during a continuing lease. As shown on Figures 5 and 6 , flats subject to regulation, which are defined as flats with a continuous lease starting in 1948 or earlier, indeed experienced rent augmentations. The annual increase rate in this group was 7.9%. By comparison, rent increase rates within a lease for flats released from rent control, were much lower, with 0.8% on average. In actuality, rent increases occurred between leases, with rent increase rates equal to 8.1% on average. Observing associated standard deviations (27.1%, 71.6%, and 38.8% respectively) shows that there is, however, a large variability within each of these groups. It is important to keep in mind one limit of the analysis, which is that tenant moves, and hence treatment exit, are likely to be caused by rent changes and therefore be endogenous. The observation that rent increases remained low for flats released from control could be viewed as an externality of rent control on the uncontrolled segment of the rental market. It is worth noting that the share of controlled residential flats decreased steadily over time as the consequence of tenant mobility, but remained above 40% for practically the whole period. This is explained by the decrease in residential mobility, which can be viewed as a result of this policy, with an average annual mobility rate of 6.6%, against 11.4% before 1914 , 10.5% from 1914 to 1929 , and 7.5% between 1930 and 1948. (See Figure 8) This apparent decrease in mobility was also the consequence of subcontracting arrangements that were a common practice during this period. This practice made finding a flat under control a possible option on the rental market, giving incentives to landlords to limit rent increases, even once the flat was not subject to control anymore. The externalities from the rent control policy on unregulated housing units can be further investigated by looking at different definitions of the control group. Table 5 shows estimated coefficients for the baseline control group, and for two variants, in which firstly, flats that have been released from rent control are dropped from the control group (Column (2)) and then, only flats in commercial use (hence never controlled) are included in the control group (Column (3)). Note that only few not yet treated housing units are used in the control group in Column (2), so that the two control groups are in fact quite similar. As shown on Figure 3 , the share of controlled housing units varies widely across years, and so does also the composition of the control group in the baseline estimation. This composition effect is avoided when the control group includes only commercial flats. (Column (3) in Table 5 ). The estimated impact of rent control during the 1914-1929 period is lower when only commercial flats are taken as control group, although the large standard error of the previously estimated coefficient is such that the difference is not statistically significant. This higher impact is consistent with the rule according to which flats with rent higher than a threshold were released from control. There is thus a selection of high rent flats in the group under control, which is not the case when only shops are included in the control group. Restricting the control group intensifies, on the contrary, the negative effect of the 1930-1948 rent control rules on rents. This higher impact is consistent with the observation that the share of flats released from rent control increases over time during this period, such that there is then a significant proportion of residential flats in the baseline control group (see Figure 3 ). As these housing units are likely to be subject to externalities from the regulated segment, their price is likely to decrease over time following the rent evolution of the controlled segment, hence reducing the estimated impact of rent control. This does not happen when shops only are included in the control group. The same observation can be made for the impact of rent control after 1948. Dropping flats from the control group once they have been released from control strongly amplifies the estimated effect of rent control, which becomes then statistically significant, consistent with the average evolution of rents presented on Figure 5 . This can be interpreted as showing a strong externality effect of rent control on non-regulated flats during this period. One can discuss the use of flats in commercial use in the control group. On one hand, these flats are located in the same buildings as residential flats in our sample, and are therefore subject to the same real estate market evolutions. On the other hand, they are not perfect substitutes to housing units and might be subject to specific evolution. Nonetheless, we think they are a good control group, especially as they are less subject to externalities from the rent control policy. As a last comment on our results, it is worth attempting to interpret the difference between the three rent control policies that were implemented over this period. According to estimated coefficients in Column (3) of Table 5 , the impact of rent control on rents deepened over time. Starting with a 11% reduction in rents between 1914 and 1929, it reached a decrease by 47% in the regulated rental market in the 1949-1968 period. These differences should be considered relative to the general evolution of the real estate market. In the first subperiod, rents were still appreciating in nominal terms, flat with rents reaching thresholds were released from control, and rent increases were allowed upon tenant moves, all three factors which might explain the moderate impact of rent regulation. In the following period, the introduction of a first-generation rent control implied stronger effects on rents, both in the regulated segment and the unregulated segment, as a result of externalities of the former on the latter. During the last period, the rent regulation policy constrained rents even more intensely, even if capped rent increases were allowed for controlled housing units. Only tenant moves allowed for the release of control, and subcontract arrangements reduced such occurrences. Additionally, the large share of flats with controlled rents created a heavy externality of rent control on uncontrolled housing units. 7.Conclusion Few studies of rent control policies in Europe have been carried out. This paper attempts to contribute to this literature by considering a rent control policy over a long period of time, within a changing economic context in France. Using historical data in the city of Lyon over a 78-years period, our study quantifies the effects of three rent regulation policies using a difference-in-differences identification strategy. Our unique data allows us to track regulation changes, tenant moves and rent paid at the flat level for an extended panel data, giving us an original point of view on a period with almost no existing datasets. Because flats for commercial use were not subject to rent control, they are included in the control group in the difference-in-differences estimation, together with housing units which are not subject to rent control in a given year. We checked based on the pre-regulation period that flats in commercial use and residential flats have comparable rent change rate. We also use two variants of the control group, firstly excluding residential flats having been released from control, and secondly keeping only flats in commercial use. Our results show that the rent control imposed during the 1914-1929 period had the strongest depreciation impact on rent levels if we compare controlled flats with uncontrolled residential flats. However, this impact dependent on the specific rule according to which flats whose nominal rent reached a threshold were released from control. Using commercial flats only as a control group strongly reduces the estimated impact. The impact of rent control on rents deepened in the subsequent two periods. Starting with a 11% reduction in rents between 1914 and 1929, it reached a decrease by 47% in the regulated rental market in the 1949-1968 period. These differences should be placed in perspective relative to the general evolution of the real estate market. In the first subperiod, rents were still appreciating in nominal terms, rents reaching thresholds were released from control, and rent increases were allowed upon tenant moves. In the following period, the introduction of a first-generation rent control implied stronger effects on rents. During the last period, the rent regulation policy constrained rents even more intensely, even if capped rent increases were allowed for controlled housing units and the large share of flats with controlled rents created a heavy externality of rent control on uncontrolled housing units. 1894 1899 1904 1909 1913 1894 1899 1904 1909 1913 1894 1899 1904 1909 1913 1894 1899 1904 1909 1913 Note subperiod : 1890-1913, 2nd subperiod: 1914-1929, 3rd subperiod: 1930-1948, 4th subperiod: 1949-1968 . Note: Currency amounts are expressed in 1999 Fr. Standard deviations between parentheses. Within subperiods, the composition of each subsample evolves over years as some residential flats are released from control. Acknowledgements This study thus highlights the varying impacts of rent control, depending on precise mechanisms of regulation and economic context.More important, it points to the absence of any unexpected increase in rents due to the rent control policy, neither in the regulated nor in the unregulated segments of the rental housing market, contrary to predictions of some theoretical models. Failed to load PDF: StripeM-Inner Paper sources Abstract screening pilot Abstract screening results Extraction pilot Extraction results Research report * * * Modify setup