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Rent Control Effects through the Lens of Empirical Research

DIW Roundup 139, 16 S.

Konstantin A. Kholodilin

2022

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Rent control is a highly debated social policy that has been omnipresent since World War I. Since 2010s, it has been experiencing a true renaissance, for many cities and countries facing housing shortage are desperately looking for solutions of the chronic housing shortage and direct their attention to controlling housing rents and to other restrictive policies. Is rent control useful or does it create more damage than utility? In order to answer this question, we need to know what are the effects of rent control. This study overviews a large empirical literature looking at various aspects of rent controls. We come to conclusion that rent controls are quite effective in terms of lowering housing rents or slowing down their growth, but they also lead to a wide range of adverse effects impacting both landlords and tenants.

Introduction

Rent control, as any other governmental policy, has its intended and unintended effects. The intended effect is the affordability of housing meaning that tenants face reasonable rental burden. Typically, the rental burden — defined as the share of the rental costs in the total income of the household — is considered reasonable, if it does not exceed 30%.

However, at the same time, a bunch of other effects emerge. Some of them affect other people who are not protected by rent control. Some effects work in the opposite direction damaging the protected tenants. Therefore, it is important to be conscious of the possible effects of rent control. Ideally, the policy makers should consider all possible effects with their costs and benefits. The decision on the introduction of rent control and its design must rest upon an objective cost-benefit analysis. Only when the net benefit is positive and substantial the policy will make sense. Otherwise it does more damage than utility.

Such cost-benefit analysis can draw upon the rich literature that investigates potential effects of rent control using a robust scientific methodology and reliable data. Here, we provide a comprehensive overview of this literature.infoEarlier reviews of the literature are less comprehensive and do not include the newer research results, e.g., Benjamin and Sirmans (1994), John I. Gilderbloom and Markham (1996), or Pastor, Carter, and Abood (2018). Our objective is to summarize the evidence on the effects of rent control accumulated over several decades. Although this study is very far from delivering a complete picture on the net effects of rent control, it can still provide a useful guidance for making decision on the introduction or reforming of rent control.

Country coverage

Let us first look at the country coverage of the literature. We concentrate exclusively on the empirical articles published in referred journals. A concise overview of the rent control literature is contained in Table A1 in Appendix. This is perhaps the most comprehensive review of the literature encompassing the last fifty years. The figure below depicts the distribution of rent control studies by the countries. The length of each bar is proportional to the number of studies and its color corresponds to a continent to which the respective country belongs.

Figure 1: Distribution of studies by countries and continents

Own calculations
© DIW Berlin

The number of countries for which rent control effects are investigated is rather limited — 18 compared to almost 200 countries that existed in 2021. A lion’s share of the studies — 48% of all 60 studies considered here — is devoted to the USA. One possible reason is the fact that in the United States rent control is often a regional matter, since states and even municipalities can have their own regulations. Therefore, many US studies focus on single cities. Overall, the North American continent accounts for 57% of the total number of empirical rent control studies. The share of studies on rent control in European countries is 32%. The remaining studies are distributed across Africa, Asia, and Oceania. To the best of our knowledge, there are no published studies on rent control in South America.

Potential effects

What are potential effects of rent control? Below we present different effects of rent control with the number of studies in which they are examined. Although these are probably not all the possible effects, but at least those that occurred to the researchers. Some studies analyze several effects, therefore, the sum of frequencies in this figure is not equal to the number of studies.

Figure 2: Potential effects of rent control

Own calculations
© DIW Berlin

The literature identifies 16 socioeconomic and even demographic effects of rent control. When ordered by the number of studies and, thus, by their prominence from the viewpoint of researchers, these are: controlled rents, homeownership, mobility, construction, quality, uncontrolled rents, homelessness, vacancy, misallocation, supply, segregation, value, commute times, marriage, rent discount, side payments. The effect on controlled rents is actually the intended impact. This is the main target of rent control. Most other effects are rather unintended.

The homeownership refers to the proportion of dwellings occupied by the homeowners in the total housing stock, or alternatively the share of homeowner households in the total number of households.

The residential mobility shows how much time the tenant household stays at the same place: the longer this time the lower the mobility.

The quality describes the physical state of the rental dwellings: how well are they maintained and equipped. The notion of construction in the literature can cover both the total residential construction and construction of rental dwellings in particular.

The uncontrolled rents refer to the rents paid by the tenants in the housing segment that is not subject to rent control regulations. The rental housing legislation often splits the private rental sector into two parts: those subject and those not subject to rent control. The latter are typically newly built or luxury dwellings. Sometimes, rent control is only applied to the tight housing markets. Theoretically, it can also be applied only to a specific type of landlords.

The supply refers as a rule to the existing rental housing stock. The reduction of supply can imply both its physical disappearance (when dwellings are demolished) or a change in the tenure status of the dwellings (conversion of rental dwellings into the owner-occupied ones).

The effect on homelessness means that rent control could possibly lead either to less or to more people living in the streets. In the former case, a stronger tenant protection prevents the landlords from kicking out their tenants into the street, while in the latter case, the reduction of supply of rental dwellings can result in some people having tough time in looking for an available dwelling.

The misallocation implies that by distorting price signals rent control can lead to a mismatch between the supply of and demand for rental housing. The sitting tenants in controlled dwellings may have less incentives to leave, since they are well protected and have cheap dwellings often in a good location. Even if the family situation of these people changes (for example, their grown-up children leave their nest), these people do not change their dwellings, although young families, who would need more such spacious dwellings, are struggling to obtain any dwelling. In addition, misallocation can refer to an “unfair” redistribution of resources: although rent control is designed to help the low-income households, in reality it can benefit more those with higher incomes.

The effect on vacancy means that rent control can affect the proportion of empty dwellings. For example, price control often exaggerates the already existing shortages leading to lower vacancy rates.

The value refers to the market price of the real estate. For example, rent control by creating more tenure security and limiting rent increases can make the rental properties less attractive from the point of view of potential buyers, thus, resulting a price discount.

The commute times can become longer due to a lower residential mobility: people tend to stay in the same regulated dwelling and are ready to spend more time on commuting from home to the workplace.

The marriage effect refers to the potential impact of rent control on the demographic decisions made by the people. For instance, a lack of rental housing can cause the young people to postpone their marriage, since their culture requires them to live separately from their parents.

The segregation refers to the effects of rent control on racial and social segregation of people. In some cases, rent control is thought to prevent the segregation by reducing the residential mobility.

Finally, side payments represent various unofficial payments, such as key money, that can be fostered by the introduction of rent control.

Sign and significance of effects

Figure 3 depicts the rent control effects that occupy the most prominent place in the literature. We select an effect, if more than 5 studies are devoted to it. The left (right) bar shows the number of studies that found a negative (positive) effect of rent control on the corresponding variable. The height of the bar in the middle corresponds to the number of studies that did not find any statistically significant effect of rent control on the variable.

Figure 3: Direction of the most prominent effects of rent control

Own calculations
© DIW Berlin

The most prominent effect of rent control is unsurprisingly the impact on controlled rents, that is, on rents paid by the tenants of the dwellings subject to rent control. The picture is rather unambiguous: 14 out of 18 studies point out to a statistically significant negative effect. Thus, rent control is quite effective in capping the rents.

In case of the homeownership effects, the picture is a bit less clear cut: there are more studies pointing into different directions. Nevertheless, the majority of studies predict an increase in the homeownership rate due to the rent control. This can be explained by the desire of the landlords to get rid of the properties that bring them insufficient rent revenues. Therefore, the landlords sell their dwellings or convert them into condominium ownership. By contrast, Gyourko and Linneman (1989) explain the homeownership effect from the point of view of tenants of controlled dwellings, who are less inclined to become owners, given their protected position.

The residential mobility effect seems to be clear cut: all studies find a negative effect of rent control on the mobility. Two explanations of this phenomenon can be suggested. First, the tenants occupying the controlled dwellings have little incentives to leave. This can have negative consequences for the labor market, for a lower residential mobility implies less flexible responses to the labor market shocks. If the employment situation deteriorates in their city, the tenants of controlled dwellings are less likely to move to other places where there are brighter perspectives of finding good jobs. Second, a lower residential mobility can be explained by a higher tenure stability. Rent control laws often go hand in hand with regulations protecting tenants from arbitrary evictions. Hence, tenants remain longer in the same dwellings.

The impact of rent control on the new residential construction is the most ambiguous one compared to other effects. Although more than half of the studies find negative effect, several studies find no statistically significant effect at all. This can be explained both by different design of rent control (e.g., exceptions made for the newly built housing) and by the dependent variable (rent control can affect the construction of rental dwellings, however, only data on total construction are available). Moreover, if private construction declines, the government can step in and compensate the missing construction by building the social housing. Thus, the overall number of dwelling completions can stay unchanged or even increase which can be mistakenly associated with beneficial effects of rent control.

The literature is almost unanimous with respect to the impact of rent control on the quality of housing. All studies indicate that rent control leads to the deterioration of the quality of dwellings subject to regulations. The landlords, whose revenues are eroded by rent control, have less incentives to invest in the maintenance and refurbishment letting their properties to wear out until the real value of the dwellings decreases and becomes equal to the low real rent.

According to the studies examined here, rent control leads as a rule to higher rents for uncontrolled dwellings. The imposition of rent ceilings amplifies the shortage of housing. Therefore, the waiting queues become longer and the would-be tenants must spend more time looking for a dwelling. If they are impatient or have no place to stay (e.g., in the houses of their friends or relatives), while looking for their own dwelling, they turn to the segment which is not subject to regulations. The demand for unregulated housing increases and so do the rents.

Methodological issues

The effects examined in the previous section can depend on many factors, given a large heterogeneity of the studies under inspection. They can depend on the design of rent control as well as on the data quality and on the econometric methodology. Here we point out to some specific features that can shape the effects of the regulation.

The estimated impact can vary with the degrees of rent control. A strict rent control can be more effective than a soft rent control.infoSee, for example, John I. Gilderbloom and Markham (1996). The absence of exceptions can leave less room for expansion of unregulated sectors. For example, if the newly built housing is not exempted from regulations, the housing construction is more likely to suffer from controls.

The impact can also be different depending on whether rent control is introduced in a country without antecedents of rental regulations or in a country that had a long history of rent control. In the former case, there can be a surprise effect that strengthens the impact of rent control. At first, the market participants did not elaborate yet an optimal strategy in order to react to a new challenge. In turn, the effects of deregulation must not be symmetrical but with opposite sign to those of the introduction of rent control. The structure of a market that had been regulated for decades can be different from that of a market that never knew any governmental interventions. For example, the introduction of rent control can dramatically change the tenure structure of the market — by transforming a tenant-dominated market into homeownership-dominated one. However, the removal of rent control will not necessarily lead to a quick revival of the well-functioning private rental market. The effects of partial deregulation — e.g., transition from a strict to a softer rent control — can be also different from those of a complete removal of rent control.

The enforcement of rent control regulations plays also a very important role. In some countries, rent control does not work because most market participants are simply unaware of its existence (Kholodilin 2020). Moreover, even if the market agents are well informed about existing regulations, some people can still try to avoid these regulations. The impossibility to raise rents above a legally defined “fair rent” can be compensated by requiring the tenants to make different side payments (e.g., key money). The rents can be frozen, but the principal tenants can sublet parts of dwellings to subtenants at market rates often exceeding the “fair rent” they have to pay to landlords (Mark 2013).

The econometric methodology used to estimate the rent control effect is likewise of an utmost importance. A misspecification of econometric models can lead to biased results when insignificant effects become significant and can even change their sign. While some studies reviewed here use a rigorous statistical methodology, others apply rather rudimentary descriptive analysis that can fail to account for some important omitted effects. Table 1 shows the use of different estimation techniques in the rent control analysis.

Table 1: Techniques used to estimate rent control effects

Method

Number of studies

linear regression

25

descriptive analysis

10

logit

5

panel data model

4

difference-in-differences

3

TSLS

3

simulation

2

event study

1

By far the largest group of studies — 42% — take advantage of linear regressions for cross-section data. There are also 10 studies using purely descriptive analysis. Much fewer researchers use two-stage least squares (TSLS) or difference-in-differences approach. Some studies use more “exotic” approaches, such as pooled SUR model with time-specific coefficients or spatial lag regression. In general, maybe due to the lack of the corresponding data, the possible spatial dependencies that are characteristic for the housing markets are in most cases not taken into account. A couple of studies employ time series method. However, the samples are often so short that it casts doubts on the reliability of results.

The data employed for the analysis in the rent control literature are very heterogeneous too. First, the majority of studies — 60% — use microdata (at the level of households or dwellings), while the remaining studies take advantage of the macrodata (at the level of municipalities, regions, or countries). Second, the data sources include surveys, official statistical data (for example, results of censuses), address registers, and newspaper advertisements.

Conclusion

In this study, we examined a wide range of empirical studies on rent control published in referred journals between 1972 and 2022. We conclude that, although rent control appears to be very effective in achieving its main goal — lower rents — it is resulting in a number of undesired effects. These unintended effects counteract the desired effect and, thus, diminish the net benefit of rent control. Therefore, it is not clear what is the overall impact of the rent control policy on the society. Moreover, the analysis is complicated even more by the fact that this policy is not adopted in a vacuum. Simultaneously with rent control, other housing policies — such as protection of tenants from eviction, housing rationing, housing allowances, and stimulation of residential construction (Kholodilin 2017, 2020; Kholodilin et al. 2021) — are carried out. Furthermore, banking, climate, and fiscal policies can also modify the results of rent control regulations.

Nevertheless, at least ideally, the policy makers should consider the multitude of these effects and their interactions when designing an optimal governmental policy. The researchers would readily support this by providing their expertise.

Appendix

Table A1: Articles on rent control effects in referred journals

Study

ISO alpha 3 code

Place and period

Type of data

Method

Year

Albon (1978)

AUS

Canberra and Queanbeyan, 1973-1976

macro: Rent Control Office; 1971 Census data

descriptive; simulation method

1978

Ambrosius et al. (2015)

USA

161 New Jersey communities, 2003

micro: Rent Control Survey of the New Jersey Tenants Organization and 2010 Census

linear regression

2015

Appelbaum et al. (1991)

USA

56 US cities, 1984

macro: HUD survey of homelessness in 60 metropolitan areas

linear regression

1991

Assaad, Krafft, and Rolando (2021)

EGY

Egypt, 2006 and 2012

micro: 2006 and 2012 waves of the Egypt Labor Market Panel Survey

difference-in-differences regression

2021

Ault, Jackson, and Saba (1994)

USA

New York City, 1968

micro: New York City Housing Vacancy Survey

cross-sectional regression

1994

Autor, Palmer, and Pathak (2014)

USA

Cambridge (Massachusetts), 1995

micro: parcels of land

cross-sectional regression

2014

Bailey (1999)

GBR

Aberdeen, Dundee, Edinburgh and Glasgow, 1987–1996

micro: advertisements for private rented accommodation appearing in newspapers and property guides

descriptive analysis

1999

Bettendorf and Buyst (1997)

BEL

Belgium, 1920–1939

macro: per capita expenditure data

Rotterdam demand model

1997

Block (1989)

CAN

Toronto and Vancouver, 1972-1988

macro: semiannual vacancy rates

descriptive analysis

1989

Bonneval, Goffette-Nagot, and Zhao (2021)

FRA

Lyon, 1890–1968

micro: real estate property manager’s accounting books

difference-in-differences regression for panel data

2021

Bourassa and Hoesli (2010)

CHE

Switzerland, 1998

micro: Enquête sur les revenus et la consommation

logit regression

2010

Breidenbach, Eilers, and Fries (2022)

DEU

Germany, 2013-2017

micro: object level rental price data from the RWI-GEO-RED

event study

2022

Clark and Heskin (1982)

USA

Los Angeles, 1978-1980

micro: a sample of 4,094 tenants selected using random digit-dialing techniques

contingency analysis

1982

Diamond, McQuade, and Qian (2019)

USA

San Francisco, 1990-2016

micro: entire address history of individuals from Infutor

dynamic neighborhood choice model

2019

D. W. Early (2000)

USA

New York City, 1996

micro: New York City Housing and Vacancy Survey

linear regression

2000

D. W. Early and Olsen (1998)

USA

44 US metropolitan areas, 1985–1988

macro: housing survey + micro: homelessness survey

TSLS; logit

1998

D. Early and Phelps (1999)

USA

49 US metropolitan areas, 1984-1996

micro: American Housing Survey, 1984-1996

hedonic regression

1999

Fallis and Smith (1984)

CAN

Toronto CMA, 1982

micro: random sample of 175 private buildings containing 6 or more units subject to rent control, and 140 private buildings containing 6 or more units not subject to rent control

hedonic regression

1984

Fetter (2016)

USA

51 US cities, 1940-1946

macro: monthly rent index of National Industrial Conference Board and the data on rents from intercensal housing surveys carried out by the Census Bureau and the Bureau of Labor Statistics between 1944 and 1946

linear regression

2016

Field et al. (2008)

IND

Ahmedabad, 2002

macro: riots, incidents of violence; 2,440 parts that fall within the 11 electoral jurisdictions that contain at least one mill

linear regression

2008

Gibb (1994)

GBR

Edinburgh and Glasgow, 1988 and 1992

micro: newspaper advertisements from Glasgow Herald and the Scotsman

mean-comparison; linear regression

1994

John I. Gilderbloom and Markham (1996)

USA

125 New Jersey cities, 1970-1990

macro: census data

linear regression

1996

John I. Gilderbloom and Ye (2007)

USA

76 New Jersey cities, 2003

micro: Rent Control Survey of the New Jersey Tenants Organization

linear regression

2007

Edward L. Glaeser (2003)

USA

8 cities in California and 7 cities in New Jersey, 1970 and 1990

micro: New York City Housing and Vacancy Survey; macro: US Census and 1991 HUD Report to Congress on Rent Control

linear regression

2003

Edward L. Glaeser and Luttmer (2003)

USA

New York City, 1993

American Housing Survey 1993 and New York City Housing and Vacancy Survey 1993

cross-sectional regression

2003

Goetz (1995)

USA

San Francisco, 1960–1991

macro: annual data on the number of multifamily-housing units constructed

time series analysis

1995

Grimes and Chressanthis (1997)

USA

200 US cities, 1990

macro: census data

TSLS

1997

Gyourko and Linneman (1989)

USA

New York City, 1968

micro: New York City Housing and Vacancy Survey

cross-sectional regression, logit regression

1989

Gyourko and Linneman (1990)

USA

New York City, 1968

micro: New York City Housing and Vacancy Survey

logit regression

1990

Heskin, Levine, and Garrett (2000)

USA

4 California cities (Berkeley, East Palo Alto, Santa Monica and West Hollywood), 1980 and 1990

macro: census blocks

spatial lag regression

2000

Jackson (1993)

USA

Brookline (Massachusetts), 1980-1988

macro: data on health code violations and building permits

descriptive analysis

1993

Kattenberg and Hassink (2017)

NLD

Netherlands, 2006–2008

micro: database recording all employees (SSB Banen), self-employed (SSB Zelfstandigen) and households on rent support (Raamwerk huurtoeslag of the Ministry of Internal Affairs); the WRG woonruimteregister verrijkt which contains information on both the dwelling type and the value of all houses

linear probability regression

2017

Kholodilin, Limonov, and Waltl (2021)

RUS

St. Petersburg, 1880-1917

micro: newspaper advertisements

time series analysis

2021

Krol and Svorny (2005)

USA

New Jersey, 1980, 1990, and 2000

macro: census tract data

cross-sectional regression

2005

Lauridsen, Nannerup, and Skak (2009)

DNK

Denmark, 1999–2004

macro: municipalities

pooled SUR model with time-specific coefficients

2009

Levine, Grigsby III, and Heskin (1990)

USA

Santa Monica (California), 1987

micro: Survey of Rent-Controlled Households

descriptive analysis

1990

Lind (2003)

SWE

Sweden, 1995–2001

macro: completed housing units

descriptive before–and–after comparison

2003

Malard and Poulhes (2020)

FRA

Paris, 2015-2017

micro: survey of Olap including information sur le loyer et ses déterminants (surface, nombre de pièces, adresse, époque de construction, date d’emménagement, etc.)

logit regression; hedonic linear regression

2020

Malpezzi (1998)

EGY

Cairo, 1981

micro: survey of 500 households in Cairo

hedonic linear regression; dynamic equations

1998

Marks (1984)

CAN

Vancouver, 1978

micro: 3885 apartments in the City of Vancouver (“Vancouver proper”)

hedonic regression

1984

Moon and Stotsky (1993)

USA

New York City, 1978–1987

micro: housing units

Tobit; panel data model

1993

Munch and Svarer (2002)

DNK

Denmark, 1992–1999

micro: 10% random sample of adult population

proportional hazard model

2002

Murray et al. (1991)

USA

Los Angeles, 1983-1990

macro: Housing Assistance Supply Experiment; Annual Housing Survey

simulation model

1991

Oni (2008)

NGA

Lagos State, 1997-2007

micro: survey of Estate Surveyors; property pages of newspapers and magazines in Lagos metropolis

ANOVA

2008

Oust (2018b)

NOR

Norway, 1970–2008

micro: advertisement data

panel regression

2018

Oust (2018a)

NOR

Norway, 1970–2011

micro: newspaper advertisements

linear regression

2018

Quigley (1990)

USA

50 US cities, 1984

macro: HUD survey of homelessness in 60 metropolitan areas

NA

1990

Sims (2007)

USA

Boston, 1985–1998

micro: MSA data from the American Housing Survey

difference-in-differences regression

2007

Sims (2011)

USA

Cambridge, 1985–1998

micro: demographic data from the 1990 and 2000 census records for all census tracts in Cambridge and the nearby Middlesex County communities of Somerville, Arlington, Belmont, Watertown, and Newton; city administrative records; American Housing Survey’s Boston metropolitan sample

first-difference regression

2011

Skak and Bloze (2013)

DNK

Denmark, 2004

micro: 20% sample of the rental market

hedonic regression

2013

Smith (1988)

CAN

Ontario, 1975–1986

macro: CMHC Toronto Office “Rental Apartment Vacancy Survey”

descriptive before–and–after comparison

1988

Smith and Tomlinson (1981)

CAN

Ontario, 1975–1980

macro: Teela Reports Apartment Surveys; CMHC Toronto Office “Rental Apartment Vacancy Survey”

descriptive before–and–after comparison

1981

Svarer, Rosholm, and Munch (2005)

DNK

Denmark, 1997-2000

micro: 10% random sample of the Danish adult population (large number of demographic and socioeconomic variables as well as physical characteristics)

competing risks duration model

2005

Tucker (1991)

USA

56 US cities, 1984

macro: HUD survey of homelessness in 60 metropolitan areas

linear regression

1991

Vitaliano (1985)

USA

5 counties of New York State, 1950

micro: 1950 Survey of Rents

log-linear regression

1985

Werczberger (1988)

ISR

Israel, 1957-1986

macro: various indicators from different sources

descriptive analysis

1988

Werczberger (1997)

CHE

Switzerland, 1920-1990

macro: various indicators from different sources

informal descriptive analysis

1997

Wilhelmsson, Andersson, and Klingborg (2011)

SWE

Sweden, 1994–2004

macro: municipalities

panel data model

2011

Willis, Malpezzi, and Tipple (1990)

GHA

Kumasi, 1986

micro: a random sample of 1461 households covering 6330 people (1.3% of the total population of Kumasi) and 279 landlords in 1986

linear regression

1990

References

Albon, Robert. 1978. “Rent Control, a Costly Redistributive Device? The Case of Canberra.” Economic Record 54 (3): 303–13.

Ambrosius, Joshua D, John I Gilderbloom, William J Steele, Wesley L Meares, and Dennis Keating. 2015. “Forty Years of Rent Control: Reexamining New Jersey’s Moderate Local Policies After the Great Recession.” Cities 49: 121–33.

Appelbaum, R. P., M. Dolny, P. Dreier, and J. I. Gilderbloom. 1991. “Scapegoating Rent Control: Masking the Causes of Homelessness.” Journal of the American Planning Association 57 (2): 153–64.

Assaad, Ragui, Caroline Krafft, and Dominique J Rolando. 2021. “Evaluating the Impact of Housing Market Liberalization on the Timing of Marriage: Evidence from Egypt.” Population Studies, 1–19.

Ault, Richard W, John D Jackson, and Richard P. Saba. 1994. “The Effect of Long-Term Rent Control on Tenant Mobility.” Journal of Urban Economics 35 (2): 140–58.

Autor, David H, Christopher J Palmer, and Parag A Pathak. 2014. “Housing Market Spillovers: Evidence from the End of Rent Control in Cambridge, Massachusetts.” Journal of Political Economy 122 (3): 661–717.

Bailey, Nick. 1999. “Deregulated Private Renting: A Decade of Change in Scotland.” Netherlands Journal of Housing and the Built Environment 14 (4): 363–84. https://doi.org/10.1007/BF02496763.

Benjamin, J. D., and G. S. Sirmans. 1994. “Apartment Rent: Rent Control and Other Determinants.” Journal of Property Research 11 (1): 27–50.

Bettendorf, Leon, and Erik Buyst. 1997. “Rent Control and Virtual Prices: A Case Study for Interwar Belgium.” The Journal of Economic History 57 (3): 654–73.

Block, W. 1989. “Rent Control: A Tale of Two Canadian Cities.” Mid Atlantic Journal of Business 25 (7): 85–88.

Bonneval, Loı̈c, Florence Goffette-Nagot, and Zhejin Zhao. 2021. “The Impact of Rent Control: Investigations on Historical Data in the City of Lyon.” Growth and Change.

Bourassa, Steven C, and Martin Hoesli. 2010. “Why Do the Swiss Rent?” The Journal of Real Estate Finance and Economics 40 (3): 286–309.

Breidenbach, Philipp, Lea Eilers, and Jan Fries. 2022. “Temporal Dynamics of Rent Regulations–the Case of the German Rent Control.” Regional Science and Urban Economics 92: 103737.

Clark, W. A., and A. D. Heskin. 1982. “The Impact of Rent Control on Tenure Discounts and Residential Mobility.” Land Economics 58 (1): 109–17.

Diamond, Rebecca, Tim McQuade, and Franklin Qian. 2019. “The Effects of Rent Control Expansion on Tenants, Landlords, and Inequality: Evidence from San Francisco.” American Economic Review 109 (9): 3365–94.

Early, Dirk W. 2000. “Rent Control, Rental Housing Supply, and the Distribution of Tenant Benefits.” Journal of Urban Economics 48 (2): 185–204.

Early, Dirk W, and Edgar O Olsen. 1998. “Rent Control and Homelessness.” Regional Science and Urban Economics 28 (6): 797–816.

Early, Dirk, and Jon Phelps. 1999. “Rent Regulations’ Pricing Effect in the Uncontrolled Sector: An Empirical Investigation.” Journal of Housing Research 10 (2): 267–85.

Fallis, G., and L. B. Smith. 1984. “Uncontrolled Prices in a Controlled Market — the Case of Rent Controls.” The American Economic Review 74: 193–200.

Fetter, Daniel K. 2016. “The Home Front: Rent Control and the Rapid Wartime Increase in Home Ownership.” The Journal of Economic History 76 (4): 1001–43.

Field, Erica, Matthew Levinson, Rohini Pande, and Sujata Visaria. 2008. “Segregation, Rent Control, and Riots: The Economics of Religious Conflict in an Indian City.” The American Economic Review 98 (2): 505–10.

Gibb, Kenneth. 1994. “Before and After Deregulation: Market Renting in Glasgow and Edinburgh.” Urban Studies 31 (9): 1481–95. https://doi.org/10.1080/00420989420081381.

Gilderbloom, John I., and John P. Markham. 1996. “Moderate Rent Control: Sixty Cities over 20 Years.” Journal of Urban Affairs 18 (4): 409–30. https://doi.org/10.1111/j.1467-9906.1996.tb00388.x.

Gilderbloom, John I, and Lin Ye. 2007. “Thirty Years of Rent Control: A Survey of New Jersey Cities.” Journal of Urban Affairs 29 (2): 207–20.

Glaeser, Edward L. 2003. “Does Rent Control Reduce Segregation?” Swedish Economic Policy Review 10: 179–202.

Glaeser, Edward L, and Erzo FP Luttmer. 2003. “The Misallocation of Housing Under Rent Control.” The American Economic Review 93 (4): 1027–46.

Goetz, Edward G. 1995. “A Little Pregnant: The Impact of Rent Control in San Francisco.” Urban Affairs Quarterly 30 (4): 604–12.

Grimes, Paul W, and George A Chressanthis. 1997. “Assessing the Effect of Rent Control on Homelessness.” Journal of Urban Economics 41 (1): 23–37.

Gyourko, Joseph, and Peter Linneman. 1989. “Equity and Efficiency Aspects of Rent Control: An Empirical Study of New York City.” Journal of Urban Economics 26 (1): 54–74.

Gyourko, Joseph, and Peter Linneman. 1990. “Rent Controls and Rental Housing Quality: A Note on the Effects of New York City’s Old Controls.” Journal of Urban Economics 27 (3): 398–409. https://doi.org/https://doi.org/10.1016/0094-1190(90)90009-C.

Heskin, Allan D, Ned Levine, and Mark Garrett. 2000. “The Effects of Vacancy Control: A Spatial Analysis of Four California Cities.” Journal of the American Planning Association 66 (2): 162–76.

Jackson, Raymond. 1993. “Rent Control and the Supply of Housing Services: The Brookline Massachusetts Experience.” American Journal of Economics and Sociology 52 (4): 467–75.

Kattenberg, Mark A. C., and Wolter H. J. Hassink. 2017. “Who Moves Out of Social Housing? The Effect of Rent Control on Housing Tenure Choice.” De Economist 165 (1): 43–66.

Kholodilin, Konstantin A. 2017. “Quantifying a Century of State Intervention in Rental Housing in Germany.” Urban Research and Practice 10 (3): 267–328. https://www.tandfonline.com/doi/abs/10.1080/17535069.2016.1212251

Kholodilin, Konstantin A. 2020. “Long-Term, Multicountry Perspective on Rental Market Regulations.” Housing Policy Debate 30 (6): 994–1015. https://www.tandfonline.com/doi/full/10.1080/10511482.2020.1789889.

Kholodilin, Konstantin A., Sebastian Kohl, Artem Korzhenevych, and Linus Pfeiffer. 2021. “The Hidden Homeownership Welfare State: An International Long-Term Perspective on the Tax Treatment of Homeowners.” DIW Berlin Discussion Paper 1972. https://www.diw.de/de/diw_01.c.824858.de/publikationen/diskussionspapiere/2021_1972/the_hidden_homeownership_welfare_state__an_international_long-term_perspective_on_the_tax_treatment_of_homeowners.html

Kholodilin, Konstantin A, Leonid E Limonov, and Sofie R Waltl. 2021. “Housing Rent Dynamics and Rent Regulation in St. Petersburg (1880–1917).” Explorations in Economic History, 101398. https://www.sciencedirect.com/science/article/pii/S0014498321000164

Krol, Robert, and Shirley Svorny. 2005. “The Effect of Rent Control on Commute Times.” Journal of Urban Economics 58 (3): 421–36.

Lauridsen, Jørgen, Niels Nannerup, and Morten Skak. 2009. “Geographic and Dynamic Heterogeneity of Home Ownership.” Journal of Housing and the Built Environment 24 (1): 1–17.

Levine, Ned, J Eugene Grigsby III, and Allan Heskin. 1990. “Who Benefits from Rent Control? Effects on Tenants in Santa Monica, California.” Journal of the American Planning Association 56 (2): 140–52.

Lind, Hans. 2003. “Rent Regulation and New Construction: With a Focus on Sweden 1995–2001.” Swedish Economic Policy Review 10: 135–67.

Malard, Louis, and Mathilde Poulhes. 2020. “Rent Control in Paris: Small Room Dwellings More Constrained by Rent Ceilings.” Economie Prevision, no. 1: 1–41.

Malpezzi, Stephen. 1998. “Welfare Analysis of Rent Control with Side Payments: A Natural Experiment in Cairo, Egypt.” Regional Science and Urban Economics 28 (6): 773–95.

Mark, Maya. 2013. “Just Ring Twice: Law and Society Under the Rent Control Regime in Israel, 1948–1954.” Journal of Israeli History 32 (1): 29–50.

Marks, Denton. 1984. “The Effect of Rent Control on the Price of Rental Housing: An Hedonic Approach.” Land Economics 60 (1): 81–94.

Moon, Choon-Geol, and Janet G Stotsky. 1993. “The Effect of Rent Control on Housing Quality Change: A Longitudinal Analysis.” Journal of Political Economy 101 (6): 1114–48.

Munch, Jakob Roland, and Michael Svarer. 2002. “Rent Control and Tenancy Duration.” Journal of Urban Economics 52 (3): 542–60.

Murray, Michael P, C Peter Rydell, C Lance Barnett, Carol E Hillestad, and Kevin Neels. 1991. “Analyzing Rent Control: The Case of Los Angeles.” Economic Inquiry 29 (4): 601–25.

Oni, Ayotunde Olawande. 2008. “An Empirical Study of the Lagos State Rent Edict of 1997.” Journal of the Nigerian Institution of Estate Surveyors and Valuers 31 (1): 20–32.

Oust, Are. 2018a. “The End of Oslo’s Rent Control: Impact on Rent Level.” Economics Bulletin 38 (1): 443–58.

Oust, Are. 2018b. “The Removal of Rent Control and Its Impact on Search and Mismatching Costs: Evidence from Oslo.” International Journal of Housing Policy 18 (3): 433–53.

Pastor, Manuel, Vanessa Carter, and Maya Abood. 2018. “Rent Matters: What Are the Impacts of Rent Stabilization Measures?” Los Angeles: USC Dornsife Program for Environmental and Regional Equity.

Quigley, J. M. 1990. “Does Rent Control Cause Homelessness? Taking the Claim Seriously.” Journal of Policy Analysis and Management 9 (1): 89–93.

Sims, David P. 2007. “Out of Control: What Can We Learn from the End of Massachusetts Rent Control?” Journal of Urban Economics 61 (1): 129–51.

Sims, David P. 2011. “Rent Control Rationing and Community Composition: Evidence from Massachusetts.” The BE Journal of Economic Analysis & Policy 11 (1).

Skak, Morten, and Gintautas Bloze. 2013. “Rent Control and Misallocation.” Urban Studies 50 (10): 1988–2005.

Smith, Lawrence B. 1988. “An Economic Assessment of Rent Controls: The Ontario Experience.” The Journal of Real Estate Finance and Economics 1 (3): 217–31.

Smith, Lawrence B, and Peter Tomlinson. 1981. “Rent Controls in Ontario: Roofs or Ceilings?” Real Estate Economics 9 (2): 93–114.

Svarer, Michael, Michael Rosholm, and Jakob Roland Munch. 2005. “Rent Control and Unemployment Duration.” Journal of Public Economics 89 (11-12): 2165–81.

Tucker, W. 1991. “Scapegoating Rent Control a Reply.” Journal of the American Planning Association 57 (4): 485–89.

Vitaliano, Donald F. 1985. “The Short-Run Supply of Housing Services Under Rent Control.” Urban Studies 22 (6): 535–42.

Werczberger, Elia. 1988. “The Experience with Rent Control in Israel: From Rental Housing to Condominiums.” The Journal of Real Estate Finance and Economics 1 (3): 277–93.

Werczberger, Elia. 1997. “Home Ownership and Rent Control in Switzerland.” Housing Studies 12 (3): 337–53.

Wilhelmsson, Mats, Roland Andersson, and Kerstin Klingborg. 2011. “Rent Control and Vacancies in Sweden.” International Journal of Housing Markets and Analysis 4 (2): 105–29.

Willis, Kenneth G, Stephen Malpezzi, and A Graham Tipple. 1990. “An Econometric and Cultural Analysis of Rent Control in Kumasi, Ghana.” Urban Studies 27 (2): 241–57.

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Abstract

Rent control is a highly debated social policy that has been omnipresent since World War I. Since 2010s, it has been experiencing a true renaissance, for many cities and countries facing housing shortage are desperately looking for solutions of the chronic housing shortage and direct their attention to controlling housing rents and to other restrictive policies. Is rent control useful or does it create more damage than utility? In order to answer this question, we need to know what are the effects of rent control. This study overviews a large empirical literature looking at various aspects of rent controls. We come to conclusion that rent controls are quite effective in terms of lowering housing rents or slowing down their growth, but they also lead to a wide range of adverse effects impacting both landlords and tenants

Konstantin A. Kholodilin

Research Associate in the Macroeconomics Department


Frei zugängliche Version: (econstor)
http://hdl.handle.net/10419/249152

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