# RECORD d3cc2b5ffbf39645fc47562257a85c240474e3d7
# TITLE: Investment incentives of rent controls and gentrification: Evidence from German micro data
# YEAR: 2024.0  VENUE: Real Estate Economics  DOI: 10.1111/1540-6229.12478
# AUTHORS: Vera Baye; Valeriya Dinger
# PAGES: 42  CANDIDATE TABLE PAGES (1-based): [8, 12, 21, 22, 23, 25, 33, 34, 35, 36, 37, 38, 39, 40, 41]
# ABSTRACT: We empirically document that the effectiveness of the German rent control introduced in 2015 in achieving rental housing affordability is limited. Exploring the reasons for this limited effectiveness, we focus on the impact of the rent control on the yield on rental housing investments proxied by rent‐price ratios, which we derive by predicting sale prices to rental objects based on a hedonic model using micro‐level quotes on rental and sale listing. Exploiting the temporal, regional, and object‐specific variation generated by the design of the rent control, we identify a causal negative effect of the rent control on the yield of rental objects subject to the regulation. Furthermore, we zoom into the spillovers across regulated objects and objects in the affected markets that were exempt from the regulation and find rising yields for the exempted objects, suggesting that the regulation contributed to gentrification via a shift of rental housing supply away from the regulated segment.


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DOI: 10.1111/1540-6229.12478
O R I G I NA L A RT I C L E
Investment incentives of rent controls and
gentrification: Evidence from German micro
data
Vera Baye1
Valeriya Dinger1,2
1Institute of Empirical Economic
Research, School of Business
Administration and Economics,
University of Osnabrueck, Osnabrueck,
Germany
2Accounting and Finance Department,
Leeds University Business School, Leeds,
England
Correspondence
Vera Baye, University of Osnabrueck,
Osnabrueck, Germany.
Email: vera.baye@uni-osnabrueck.de
Abstract
We empirically document that the effectiveness of the
German rent control introduced in 2015 in achieving
rental housing affordability is limited. Exploring the
reasons for this limited effectiveness, we focus on the
impact of the rent control on the yield on rental hous-
ing investments proxied by rent-price ratios, which we
derive by predicting sale prices to rental objects based
on a hedonic model using micro-level quotes on rental
and sale listing. Exploiting the temporal, regional, and
object-specific variation generated by the design of the
rent control, we identify a causal negative effect of
the rent control on the yield of rental objects subject to
the regulation. Furthermore, we zoom into the spillovers
across regulated objects and objects in the affected mar-
kets that were exempt from the regulation and find rising
yields for the exempted objects, suggesting that the reg-
ulation contributed to gentrification via a shift of rental
housing supply away from the regulated segment.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and
reproduction in any medium, provided the original work is properly cited.
© 2024 The Authors. Real Estate Economics published by Wiley Periodicals LLC on behalf of American Real Estate and Urban Economics
Association.
Real Estate Econ. 2024;52:843–884.
wileyonlinelibrary.com/journal/reec
843


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850
BAYE and DINGER
TA B L E 1
Descriptive statistics of rental and sale objects.
Variable
Mean
Std. Dev.
Min
p25
p50
p75
Max
N
A. Rental objects
Year of construction
1971
34.80
1800
1957
1975
1997
2020
5,662,358
Living space
76.72
31.578
10
56.16
72
91
1000
5,662,358
Number of rooms
2.68
0.99
1
2
3
3
10
5,662,358
Rent per square meter
8.42
3.03
3.66
6.10
7.80
10
20.45
5,662,358
B. Sale objects
Year of construction
1978
33.55
1800
1965
1984
2003
2020
5,609,255
Living space
85.37
38.38
10
61
79
101
1000
5,609,255
Number of rooms
2.94
1.11
1
2
3
3.5
10
5,609,255
Price per square meter
2269
1284
396
1316
1970
2909
7715
5,609,255
Note: This table shows descriptive statistics of the quotes on rental objects and on objects for sale from the micro-level housing
dataset based on German residential real estate advertisements. The data are a systematic collection of all objects that were offered
for rent on the internet platform ImmobilienScout24 with a monthly frequency covering the period from January 2010 to December
2019.
For the empirical analysis, we use data on apartments for sale and apartments for rent. The
raw data provide a high number of observations. To ensure the quality of the analyzed dataset,
incomplete advertisements that do not contain a net rent could not be included in the analysis.
Moreover, we only consider objects with a listed postcode area,7 that were built in 1800 or later,
with a minimum number of rooms of one, reported living space of at least ten square meters, and
that do not belong to the cheapest or most expensive 1% in terms of price per square meter. The
average apartment for rent in our dataset is located in a building that was built in 1971, has three
rooms, and a living space of 76.72 square meters. The monthly net rent of this object would be
645.98 Euros, as the rent per square meter is 8.42 Euros (Table 1, Panel A). The average apartment
for sale has three rooms as well, is slightly newer, larger, and has an average sale price per square
meter of 2269 Euros (Table 1, Panel B).
To ensure the representativeness of our dataset, we compare the distribution of the key property
characteristics living space and year of construction in the latest available German census data
(German Federal Statistical Office, 2023) from the year 2011 to our rental and sale data from the
same year. Figure A1 shows that our data compare to the census data, thus, indicating that our
dataset includes a representative sample of the German housing market.8 Recent studies based on
these data were, for example, published by Breidenbach, Eilers et al. (2022) analyzing the temporal
dynamics of rent prices due to the rent control, Breidenbach, Cohen et al. (2022), Pommeranz and
7 The German postcode system is a pure number system consisting of five digits. By only considering objects in five-digit
postcode areas, the quality of the observations considered should be ensured. Studies using the same data set use similar
procedures for quality assurance (Breidenbach, Eilers, et al., 2022; Deschermeier et al., 2016; Eilers, 2017).
8 One deviation appears in the share of newer dwellings built after 2006 whose share is larger in the ImmobilienScout24
dataset than in the census data. These deviations could be expected as the census depicts the characteristics of the stock of
existing housing, while the ImmobilienScout24’s data are representative of the dwellings currently offered for sale or rent.
As newer and more standard properties are more likely to be sold and standard properties more likely to be rented out,
the fact that we observe these differences relative to the census data is not surprising. In addition, the ImmobilienScout24
dataset does not perfectly mirror the stock of housing units in very small municipalities where only a few dwellings are
offered for rent or sale. This applies especially to larger apartments.
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BAYE and DINGER
851
Steininger (2021), Klick and Schaffner (2019), Eilers (2017), and Deschermeier et al. (2016) who
focus on recent developments in the housing market for rentals and sales.
We are aware that the asking prices might deviate from actual transaction prices, but as Dinkel
and Kurzrock (2012), Kholodilin et al. (2016), and Lyons (2013) emphasize, asking price data show
reliable price trends. Especially for the advertised rent prices, significant deviations from the trans-
actions do not need to be assumed because, as Zhu (2005) emphasizes, bargaining over rent prices
is relatively rare, especially in regions with a demand overhang. This is particularly true for Ger-
many, where it is not common to negotiate apartment rental prices (Breidenbach, Eilers et al.,
2022), and landlords generally obtain their asking prices (Deschermeier et al., 2016). Of course,
although we use a large and representative data set on micro-level, we might still miss some rental
agreements that may be used to bypass the regulation, as for example shadow rental agreements
and subletting.
We match the micro-level real estate data to self-collected data from the federal state’s laws on
the application of rent controls, which were introduced on the municipality level at various points
in time by the federal states. Further data on regional characteristics, local economic activity, and
socioeconomic variables at the municipality and district level9 are collected from the “Genesis”
regional data platform maintained by the German Federal Statistical Institute (Statistisches Bun-
desamt) and the German Federal Institute for Research on Building, Urban Affairs and Spatial
Development which offers indicators of spatial and urban development (INKAR). Broad sum-
mary statistics for regulated and unregulated municipalities of those variables used as controls in
our empirical analyses are provided in Table 2.
4
THE EFFECTS OF RENT CONTROL ON RENTAL HOUSING
AFFORDABILITY
As a point of departure, we first evaluate the effectiveness of the German rent control by analyzing
the effect of the introduction of this policy instrument on the rent-income ratio, which we use to
proxy the affordability of rental housing. We explicitly do not focus on the development of the rent
prices but rather on the rent-income ratio, because we intend to rule out an increased demand for
higher living space and housing standards which might occur due to growing income and focus
on the intended target of improving housing affordability. The variable displays the proportion of
the household income that is spent on rental payments proxied by the mean of the yearly net rent
of the newly offered flats per district.10 Our data show that despite the implementation of the rent
9 Municipalities are cities or towns with an own local government, they define the lowest level of territorial division in
Germany. In Germany exists more than 11,000 municipalities in 401 districts. The German districts are at an intermedi-
ate level of administration between the German federal states and the municipality governments. Cities with more than
100,000 inhabitants do not usually belong to a district, but form their own district.
10 As described above, this indicator is also used to identify tight housing markets (Simons et al., 2020).When using the
rent-income ratio in our analysis, we are aware of the concerns of Favilukis et al. (2023) who underline that this ratio
must be interpreted carefully because rent-income ratios reflect equilibrium rents and the income of those people who
have sorted themselves into each area in the spatial equilibrium. In their dynamic stochastic spatial equilibrium model, an
increasing rent-income ratio can be a sign of an effective regulation as misallocation decreases and low-income households
can move to more expensive areas, like city centers, where they could not get an apartment before a specific regulation
was implemented. However, there are various reasons why the use of the rent-income ratio is still reasonable in our anal-
ysis. First, the rent control in Germany is not targeted and does not include an income qualification of tenants, which is
crucial in the framework of Favilukis et al. (2023). Second, the rent control is introduced in tense housing markets with
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854
BAYE and DINGER
TA B L E 3
The effect of rent control on the rent-income.
(1)
(2)
Rent-income ratio
Rent-income ratio
ATT
0.0122***
0.0515***
(0.0028)
(0.0075)
Treatment variable
Discrete
Continuous
Note: This table presents the results of the staggered difference-in-differences analyses to estimate the effect of the introduction
of rent controls on the rent-income ratio. The dependent variable is the rent-income ratio, which displays the proportion of the
household income that is spent on rental payments proxied by the yearly mean net rent of the newly offered flats per district.
The included covariates cover region-specific dummy variables on urbanization and location in Western Germany, as well as the
population density, the number of students, and the number of building completions. The overall treatment effect in Column 1
is estimated using the doubly robust difference-in-differences estimator based on inverse probability and weighted least squares
estimator from Sant’Anna and Zhao (2020). In Column 2, we extend the staggered design from Column 1 with the treatment
intensity, which is modeled by the share of regulated objects per district. The sample covers the observation period from 2010
to 2019. Standard errors are displayed in parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% level,
respectively.
F I G U R E 1
Dynamic effects of rent control on rent-income ratio. [Color figure can be viewed at
wileyonlinelibrary.com]
level of income spent on rental payments, the estimator for the ATT will be significantly negative
or insignificant.
In Table 3, Column 1, the overall treatment effect of the introduction of rent control on the
rent-income ratio is reported, which is calculated as the average of all ATTs weighted by group
size. It shows that after the introduction of rent control, the rent-income ratio in regulated areas
increases on average by 1.22 percentage points. Thus, rental payments seem to increase faster
than household incomes in these tight markets after the implementation of the rent control and
housing affordability is not improved.
To test for the conditional parallel trends, we estimate Equation (1) including the covariates
captured in 𝑋across all cohorts for each period relative to the period first treated. Figure 1 shows
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BAYE and DINGER
855
the estimated ATTs. The coefficients suggest that in some pretreatment periods, the rent-income
ratio already lies above the level in the never-treated municipalities, which can be explained by
the fact that rent control was introduced in tense housing markets. However, these deviations are
of small scale, compared to the growing difference in rent-income ratios that occurs 1 year after
the implementation of the regulation and then increases from period to period. Thus, although
the preexisting differences reflect the fact that the treatment is not introduced randomly, after the
implementation of the treatment we see an increase in these differences in the rent-income ratios
of treatment and control districts.
In addition to the standard staggered difference-in-differences estimation, we modify the esti-
mation from Equation (1) and estimate the effect of the treatment intensity proxied by the
rent-controlled share of apartments in treated districts. The results presented in Table 3, Column
2, reveal that the increase of the rent-income ratio in treated districts increases with the share of
rent-controlled apartments in the district. We next zoom into the potential reasons for this failure
of the regulation and exploit the micro dimension of our dataset to investigate how rent control
affects the supply side of the housing market by affecting the yields on rental housing.
5
RENT CONTROL AND THE RENT-PRICE RATIO
Our results so far indicate that the effectiveness of rent control in terms of achieving affordabil-
ity of rental housing is limited. In this section, we explore the granularity of our real estate data
and identify a channel through which rent control might affect the supply side of the housing
market: the reduction of the yield of investments in regulated rental housing. As the provision
of affordable housing naturally depends on the supply of living space, we put the yield on rental
housing, proxied by the rent-price ratio, in the center of the analysis. As it indicates the attractive-
ness of investing in rental housing, the rent-price ratio reflects the investment incentives which
are needed to address supply shortages in the long term. Using this ratio allows us a more holistic
view of the dynamics of the incentives to supply rental housing relative to a simple focus on rent
levels. This is particularly the case as we look at periods when not only rents but also house price
levels are changing substantially.
5.1
Variables and descriptive analysis
5.1.1
The rent-price ratio
The rental housing yield is measured by the rent-price ratio. This is calculated for each rental
object from the reported yearly net rent and the potential sale price which we derive from esti-
mating a hedonic model since each dwelling in our dataset is advertised either as a rental or as a
sale object:
𝑝𝑟𝑖𝑐𝑒𝑖= 𝛽𝑋𝑖+ 𝛿
(
𝑅𝐶𝑖× 𝐴𝑑× 𝐵𝑦
)
+ 𝜀𝑖.
(2)
Here, 𝑝𝑟𝑖𝑐𝑒𝑖is log of the purchase price per square meter of object 𝑖and 𝑋includes a rich set of
property characteristics for each object in district 𝑑in year 𝑦. Specifically, we include the log of the
dwellings’ year of construction, the log of the number of rooms, a vector of dummy variables that
indicate the object condition, and if a garden or balcony belongs to the apartment. 𝛿estimates the
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BAYE and DINGER
863
TA B L E 5
The effect of the rent control on the rent-price ratio.
(1)
(2)
(3)
(4)
Variables
Rent-price
ratio
Rent-price
ratio
Rent-price
ratio
Rent-price
ratio
Treatment municipality
0.0260***
(0.0093)
Regulated objects in regulated mun
–0.1330***
–0.2788***
–0.1944***
–0.1471***
(𝑚𝑢𝑛𝑖𝑐𝑖𝑝_𝑟𝑒𝑔_𝑎𝑝𝑝𝑙𝑖𝑒𝑑𝑚𝑞× 𝑜𝑏𝑗𝑒𝑐𝑡_𝑟𝑒𝑔𝑖)
(0.0192)
(0.0424)
(0.0378)
(0.0228)
Unregulated objects in regulated mun
0.2522***
(𝑚𝑢𝑛𝑖𝑐𝑖𝑝_𝑟𝑒𝑔_𝑎𝑝𝑝𝑙𝑖𝑒𝑑𝑚𝑞× 𝑜𝑏𝑗𝑒𝑐𝑡_𝑢𝑛𝑟𝑒𝑔𝑖) (0.0288)
Object-specific variables:
Base yield
0.9551***
0.9754***
0.9809***
0.9768***
(0.0217)
(0.0282)
(0.0269)
(0.0162)
Year of construction
–0.0003
–0.0008***
–0.0014***
–0.0012***
(0.0002)
(0.0001)
(0.0002)
(0.0002)
Living space
0.0001
0.0015*
0.0017*
–0.0012
(0.0009)
(0.0008)
(0.0009)
(0.0009)
Number of rooms
–0.2310***
–0.2333***
–0.2424***
–0.2210***
(0.0053)
(0.0073)
(0.0063)
(0.0052)
Basement (dummy)
–0.0066*** –0.0082***
–0.0084***
–0.0059***
(0.0013)
(0.0015)
(0.0016)
(0.0015)
Balcony (dummy)
–0.1203***
–0.1327***
–0.1337***
–0.1107***
(0.0104)
(0.0169)
(0.0142)
(0.0097)
Object condition
0.1536***
0.1491***
0.1549***
0.1024***
(1 = new; 10 = demolition)
(0.0046)
(0.0080)
(0.0070)
(0.0052)
Region-specific variables:
Urban area (dummy)
0.0097
0.0148
0.0169
–0.0052
(Regional centers)
(0.0141)
(0.0370)
(0.0398)
(0.0139)
City/metropolitan area (dummy)
–0.0278
–0.0665***
–0.0660***
–0.0324
(0.0180)
(0.0230)
(0.0237)
(0.0199)
West/East Germany (dummy)
0.0657**
–0.0873
–0.1180**
–0.4779***
(1 = West, 0 = East)
(0.0305)
(0.0584)
(0.0547)
(0.1544)
Socioeconomic variables:
Population density
0.0014
0.0001
0.0058
0.0088
(0.0089)
(0.0117)
(0.0121)
(0.0094)
Population growth
0.3231***
0.1679
0.1256
0.1858**
(0.0746)
(0.1734)
(0.1692)
(0.0788)
Primary income per capita
–0.0063**
–0.0055
–0.0088***
–0.0082***
(0.0025)
(0.0035)
(0.0033)
(0.0026)
Students
0.4240
0.5223
0.6020
1.2369**
(0.3168)
(0.5629)
(0.5244)
(0.4975)
(Continues)
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864
BAYE and DINGER
TA B L E 5
(Continued)
(1)
(2)
(3)
(4)
Variables
Rent-price
ratio
Rent-price
ratio
Rent-price
ratio
Rent-price
ratio
Unemployment rate
3.0265**
3.7454*
4.8997**
2.2513
(1.4089)
(2.0504)
(1.9070)
(1.7935)
Construction completions
0.0682*
0.1633**
0.0489
0.1118
(0.0365)
(0.0633)
(0.0593)
(0.0682)
Social assistance recipients
–0.0017***
–0.0012
–0.0022**
–0.0020**
(0.0006)
(0.0012)
(0.0011)
(0.0009)
Constant
0.4678
1.3856***
2.4277***
3.1244***
(0.3272)
(0.3565)
(0.3428)
(0.4064)
Observations
4,619,727
2,406,160
2,247,501
3,739,728
R2
0.6192
0.5875
0.5786
0.6228
Year FE
Yes
Yes
Yes
Yes
District FE
Yes
Yes
Yes
Yes
Sample
Full
Regulated
municipalities
Regulated
municipalities
w/o newbuilds
Regulated
municipalities
w/o renovated
Note: This table presents the results of the OLS regression modeling the determinants of housing yield described in Equation (3)
using micro data. The dependent variable is the rent-price ratio, that is, the yearly net rent divided by the objects’ potential sale
price. The first explanatory variable is the dummy variable (𝑚𝑢𝑛𝑖𝑐𝑖𝑝_𝑟𝑒𝑔𝑚), which divides the sample into a treatment and a con-
trol group depending on the application of the rent control. The interaction terms of the dummy variable 𝑚𝑢𝑛𝑖𝑐𝑖𝑝_𝑟𝑒𝑔_𝑎𝑝𝑝𝑙𝑖𝑒𝑑𝑚𝑞,
which equals 1 if the rent control applies in the municipality at the point of time of the observation, and the dummy variables
𝑜𝑏𝑗𝑒𝑐𝑡_𝑟𝑒𝑔𝑖and 𝑜𝑏𝑗𝑒𝑐𝑡_𝑢𝑛𝑟𝑒𝑔𝑖, which indicate if the object itself is regulated due to its year of construction and condition, dis-
play the effects of the introduction of the rent control on the rent-price ratio depending on the individual objects’ regulation
status.
In the estimation of Column 1, regulated and unregulated municipalities are considered. In Columns 2, 3, and 4, we conduct
within-municipality analyses; that is, we only include regulated municipalities in our estimation. In Column 3, objects that are
categorized as first occupancy are excluded from the sample. In Column 4, objects that are categorized as first occupancy after
reconstruction and like new are excluded.
The control variables can be categorized as object-specific, region-specific, and socioeconomic variables. The object-specific con-
trol variables include the base yield, that is, the quarterly mean of the rent-price ratio in the object’s municipality, the object’s year
of construction, the living space, the number of rooms, dummy variables considering if the object has a basement and a balcony
or terrace and the object condition which can vary between 1 = new and 10 = demolition.
The region-specific variables indicate if an object is located in an urban area, a city, or a metropolitan area and in West or East
Germany. The considered socioeconomic variables include population density, population growth, primary income per capita,
the number of students, the unemployment rate, construction completions, and the number of social assistance recipients. Fixed
effects for years and districts are included.
The sample covers the observation period from 2010 to 2019. Robust standard errors clustered for districts are displayed in
parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% level, respectively.
We review the results in several robustness analyses. Specifically, we show that the direction
and significance of the results are robust to including the year of construction and the object
condition as categorical variables (Table A2). Furthermore, we show that the estimates are robust
to an extension of the observation period from 2010–2019 to 2008–2019 (Table A3, A4, Column 2).
We also ensure that the results are not driven by a specific subsample, for example, in the largest
cities or housing markets with high construction activity.
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BAYE and DINGER
865
TA B L E 6
The effect of the rent control on the rental and sale prices.
(1)
(2)
(3)
(4)
Variables
Rent price
per sqm (log)
Rent price per
sqm (log)
Sale price
per sqm
(log)
Sale price per
sqm (log)
Treatment municipality
0.0632***
0.0850***
(0.0140)
(0.0245)
Regulated objects in regulated mun
0.0236***
–0.0064
0.0987*** 0.0735***
(𝑚𝑢𝑛𝑖𝑐𝑖𝑝_𝑟𝑒𝑔_𝑎𝑝𝑝𝑙𝑖𝑒𝑑𝑚𝑞× 𝑜𝑏𝑗𝑒𝑐𝑡_𝑟𝑒𝑔𝑖)
(0.0055)
(0.0066)
(0.0099)
(0.0206)
Unregulated objects in regulated mun
0.0426***
–0.0093
(𝑚𝑢𝑛𝑖𝑐𝑖𝑝_𝑟𝑒𝑔_𝑎𝑝𝑝𝑙𝑖𝑒𝑑𝑚𝑞× 𝑜𝑏𝑗𝑒𝑐𝑡_𝑢𝑛𝑟𝑒𝑔𝑖)
(0.0126)
(0.0082)
Year of construction (log)
–0.4313
–1.0019***
0.5455
–0.5379*
(0.2871)
(0.1922)
(0.7097)
(0.3098)
Living space (log)
–0.0013
0.0209
0.2474***
0.2512***
(0.0311)
(0.0362)
(0.0269)
(0.0289)
Number of rooms (log)
–0.0650***
–0.0703***
–0.1438*** –0.1403***
(0.0138)
(0.0171)
(0.0144)
(0.0151)
Basement (dummy)
–0.0018***
–0.0017***
0.0009
0.0016
(0.0004)
(0.0005)
(0.0014)
(0.0014)
Balcony (dummy)
0.0200***
0.0169***
0.0329***
0.0262***
(0.0035)
(0.0052)
(0.0039)
(0.0037)
Urban area (dummy)
0.0442*
0.1112***
0.0454
0.1922**
(Regional centers)
(0.0246)
(0.0229)
(0.0465)
(0.0908)
City/metropolitan area (dummy)
–0.0090
–0.1104***
–0.0113
–0.1282***
(0.0325)
(0.0089)
(0.0442)
(0.0344)
West/East Germany (dummy)
0.0991
0.2437***
0.0982*
0.1968***
(1 = West, 0 = East)
(0.0645)
(0.0508)
(0.0557)
(0.0381)
Population density (log)
0.0441***
0.0213***
0.0495***
0.0040
(0.0068)
(0.0079)
(0.0114)
(0.0137)
Population growth (log)
0.0014
0.0037
–0.0069*** –0.0036
(0.0022)
(0.0049)
(0.0025)
(0.0054)
Primary income per capita (log)
0.2254***
0.1638*
0.6727***
0.5359***
(0.0845)
(0.0882)
(0.1092)
(0.0998)
Students (log)
–0.0092
–0.0071
–0.0123
0.0007
(0.0107)
(0.0152)
(0.0159)
(0.0129)
Unemployment rate (log)
–0.2109*
–0.3313**
–0.2579*** –0.3812***
(0.1154)
(0.1417)
(0.0826)
(0.0744)
Construction completions (log)
0.0157***
0.0113**
0.0272***
0.0164**
(0.0029)
(0.0055)
(0.0043)
(0.0079)
Social assistance recipients (log)
–0.0234
–0.0577
0.1073*
0.1747*
(0.0527)
(0.0805)
(0.0625)
(0.0902)
Constant
0.9231
6.6185***
–11.8200**
–2.7222
(3.0588)
(2.4338)
(5.1400)
(2.7286)
(Continues)
 15406229, 2024, 3, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/1540-6229.12478 by University Of Leeds The Brotherton Library, Wiley Online Library on [22/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License


===== PAGE 24 (score 29, numeric-paren 0, table-words 5, var-words 19) =====

866
BAYE and DINGER
TA B L E 6
(Continued)
(1)
(2)
(3)
(4)
Variables
Rent price
per sqm (log)
Rent price per
sqm (log)
Sale price
per sqm
(log)
Sale price per
sqm (log)
Observations
3,064,152
2,017,686
1,716,317
1,210,342
R2
0.6618
0.5514
0.6895
0.6125
Year FE
Yes
Yes
Yes
Yes
District FE
Yes
Yes
Yes
Yes
Sample
Full
Regulated
municipalities
Full
Regulated
municipalities
Note: This table presents the results of the OLS regression modeling the determinants of rental and sale prices described in Equa-
tion (4) using micro data. The dependent variable in Columns 1 and 2 is the log of the rent price per square meter and in Columns 3
and 4; it is the log of the sale price per square meter. The variable “Treatment municipality” is a dummy variable which divides the
sample into a treatment and a control group depending on the application of the rent control. The second and third reported esti-
mators belong to the interaction terms of the dummy variable 𝑚𝑢𝑛𝑖𝑐𝑖𝑝_𝑟𝑒𝑔_𝑎𝑝𝑝𝑙𝑖𝑒𝑑𝑚𝑞, which equals 1 if the rent control applies
in the municipality at the point of time of the observation, and the dummy variables 𝑜𝑏𝑗𝑒𝑐𝑡_𝑟𝑒𝑔𝑖and 𝑜𝑏𝑗𝑒𝑐𝑡_𝑢𝑛𝑟𝑒𝑔𝑖, which indi-
cate if the object itself is regulated due to its year of construction and condition. They display the effects of the introduction of rent
control on the rent-price ratio depending on the individual objects’ regulation status.
In the estimation of Columns 1 and 3, regulated and unregulated municipalities are considered. In Columns 2 and 4, we conduct
within-municipality analyses; that is, we only include regulated municipalities in our estimation. Thus, we cannot estimate coef-
ficients for “Treatment municipality” and for “Unregulated objects in regulated mun” as the unregulated objects are the control
group.
The control variables can be categorized as object-specific, region-specific, and socioeconomic variables. The object-specific con-
trol variables include the log of the object’s year of construction, the log of the living space, the log of the number of rooms,
dummy variables considering if the object has a basement and a balcony or terrace, and for the different object condition categories
(unreported).
The region-specific variables indicate if an object is located in an urban area, a city, or a metropolitan area and in West or East
Germany. The considered socioeconomic variables include population density, population growth, primary income per capita,
the number of students, the unemployment rate, construction completions, and the number of social assistance recipients. Fixed
effects for years and districts are included.
The sample covers the observation period from 2010 to 2019. Robust standard errors clustered for districts are displayed in
parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% level, respectively.
5.2.3
Respective effects on rental and sale prices
In sum, our micro-level results so far are consistent with the district-level results reported in Sec-
tion 4 and suggest a supply-driven within-market shift toward high-priced newbuilds or renovated
objects in tense, regulated housing markets resulting in no improvement (at least in the short run)
of rental housing affordability. To examine if the effect on the rent-price ratio is driven by the rental
or sale prices, we separately estimate the effects on rents and sale prices with the simple model:
𝑝𝑖= 𝛼+ 𝛾𝑚𝑢𝑛𝑖𝑐𝑖𝑝_𝑟𝑒𝑔𝑚+ 𝛿1 𝑚𝑢𝑛𝑖𝑐𝑖𝑝_𝑟𝑒𝑔_𝑎𝑝𝑝𝑙𝑖𝑒𝑑𝑚𝑞× 𝑜𝑏𝑗𝑒𝑐𝑡_𝑟𝑒𝑔𝑖
+ 𝛿2 𝑚𝑢𝑛𝑖𝑐𝑖𝑝_𝑟𝑒𝑔_𝑎𝑝𝑝𝑙𝑖𝑒𝑑𝑚𝑞× 𝑜𝑏𝑗𝑒𝑐𝑡_𝑢𝑛𝑟𝑒𝑔𝑖+ 𝛽𝑋𝑖𝑚𝑑𝑦+ 𝐴𝑑+ 𝐵𝑦+ 𝜀𝑖,
(4)
where 𝑝𝑖is either the log of the rental or sale price of object 𝑖, 𝛾accounts for general differences
between the treated and the untreated municipalities 𝑚as 𝑚𝑢𝑛𝑖𝑐𝑖𝑝_𝑟𝑒𝑔𝑚is the treatment group
indicator. The coefficient 𝛿1 (𝛿2) displays the effect of the introduction of the rent control on the
price variable of (un)regulated objects (in regulated municipalities). 𝑋contains several object-
 15406229, 2024, 3, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/1540-6229.12478 by University Of Leeds The Brotherton Library, Wiley Online Library on [22/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License


===== PAGE 25 (score 54, numeric-paren 35, table-words 0, var-words 16) =====

BAYE and DINGER
867
TA B L E 7
Spillover effects of rent controls in regulated areas to regulated and excluded objects.
(1)
Variables
Rent-price ratio
Direct treatment effect
–0.6154***
𝑜𝑏𝑗𝑒𝑐𝑡_𝑟𝑒𝑔𝑖
(0.0118)
Spillover effects to treated units
0.4010***
𝑜𝑏𝑗𝑒𝑐𝑡_𝑟𝑒𝑔1 × 𝑜𝑏𝑗𝑒𝑐𝑡_𝑟𝑒𝑔𝑚𝑦
(0.0174)
Spillover effects to control units
0.1370***
(1 −𝑜𝑏𝑗𝑒𝑐𝑡_𝑟𝑒𝑔1) × 𝑜𝑏𝑗𝑒𝑐𝑡_𝑟𝑒𝑔𝑚𝑦
(0.0052)
Object-specific variables:
Year of construction
–0.0038***
(0.0000)
Living space
0.0058***
(0.0000)
Number of rooms
–0.3487***
(0.0012)
Basement (dummy)
–0.0056***
(0.0004)
Balcony (dummy)
–0.1473***
(0.0015)
Object condition
0.1485***
(1 = new; 10 = demolition)
(0.0003)
Region-specific variables:
Urban area (dummy)
0.4525***
(Regional centers)
(0.0083)
City/metropolitan area (dummy)
–0.5006***
(0.0095)
West/East Germany (dummy)
–0.0739***
(1 = West, 0 = East)
(0.0154)
Socioeconomic variables:
Population density
0.1041***
(0.0026)
Population growth
–0.0007
(0.0749)
Primary income per capita
–0.0262***
(0.0007)
Students
–3.4645***
(0.1163)
Unemployment rate
1.9816***
(0.3575)
Construction completions
0.6494***
(0.0107)
(Continues)
 15406229, 2024, 3, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/1540-6229.12478 by University Of Leeds The Brotherton Library, Wiley Online Library on [22/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License


===== PAGE 26 (score 49, numeric-paren 2, table-words 13, var-words 21) =====

868
BAYE and DINGER
TA B L E 7
(Continued)
(1)
Variables
Rent-price ratio
Constant
13.3005***
(0.0443)
Observations
2,406,210
R2
0.4266
Year FE
Yes
District FE
Yes
Note: This table presents the results of the spillover analysis based on Equation (5) using micro data. The dependent variable
is the rent-price ratio, that is, the yearly net rent divided by the objects’ predicted sale price. The dummy variable 𝑜𝑏𝑗𝑒𝑐𝑡_𝑟𝑒𝑔𝑖
displays the direct treatment effect. The variable 𝑜𝑏𝑗𝑒𝑐𝑡_𝑟𝑒𝑔𝑚𝑞is the group-level average treatment intensity, that is, the proportion
of apartments in a municipality that is regulated, thus, not new or renovated. The interaction term with the treatment effect
reveals the spillover effects of rent control on regulated objects. To extract the spillover effects to the control units, we interact the
unregulated objects (1 −𝑜𝑏𝑗𝑒𝑐𝑡_𝑟𝑒𝑔𝑖) in the regulated area with the group-level average treatment intensity 𝑜𝑏𝑗𝑒𝑐𝑡_𝑟𝑒𝑔𝑚𝑞.
The control variables can be categorized as object-specific, region-specific, and socioeconomic variables. The object-specific con-
trol variables include the base yield, that is, the quarterly mean of the rent-price ratio in the objects’ municipality, the object’s year
of construction, the living space, the number of rooms, dummy variables considering if the object has a basement and a balcony
or terrace and the object condition which can vary between 1 = new and 10 = demolition.
The region-specific variables indicate if an object is located in an urban area, a city, or a metropolitan area and in West or East
Germany. The considered socioeconomic variables include population density, population growth, primary income per capita, the
number of students, the unemployment rate, construction completions, and the number of social assistance recipients. Year and
district fixed effects are included.
The sample covers the observation period from 2010 to 2019. Only observations from regulated municipalities are included. Robust
standard errors are displayed in parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% level, respectively.
specific and region-specific control variables20 and we include district and year fixed effects in
𝐴𝑑and 𝐵𝑦. We also estimate the effect that occurs within the regulated municipalities (Table 6,
Columns 2 and 4).
The results reveal that the rent price per square meter of regulated objects is 2.36% higher in
regulated municipalities than the rent price per square meter of comparable objects in unregulated
municipalities (Table 6, Column 1). This shows that the difference between the rents in regulated
and unregulated municipalities increased after the introduction of the regulation. However, as
the sale prices for regulated objects are 9.87% higher after the introduction of rent control in these
municipalities (Table 6, Column 3), the rent-price ratio of regulated objects is smaller after the
application of the regulation. The analysis from the previous section shows that the rent-price
ratio of unregulated, new objects in regulated municipalities increased after the introduction of
rent control. Since rents are 4.26% higher (Table 6, Column 1) and sale prices are not affected
significantly (Table 6, Column 3), this coefficient can be explained by the higher rental prices for
unregulated newbuilds and renovated objects in tense housing markets.
If we conduct the analysis only in treatment municipalities where rent control is introduced
during the observation period, we can confirm that compared to unregulated objects, the rent-
price ratio of regulated objects is smaller (Table 5) because of increases in sale prices for these
objects (Table 6, Column 4). If the regulation was successful, we would expect a significant
20 Included control variables are the logs of the year of construction, living space, and number of rooms, dummy variables
for a basement, balcony, or terrace, the location in an urban area, city, and Western Germany, the logs of the socioeconomic
factors population growth, primary income per capita, number of students, unemployment rate, and social assistance
recipients in each district 𝑑and year 𝑦, and construction completions in each municipality 𝑚and year 𝑦.
 15406229, 2024, 3, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/1540-6229.12478 by University Of Leeds The Brotherton Library, Wiley Online Library on [22/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License


===== PAGE 33 (score 57, numeric-paren 39, table-words 3, var-words 8) =====

BAYE and DINGER
875
How to cite this article: Baye, V., & Dinger, V. (2024). Investment incentives of rent
controls and gentrification: Evidence from German micro data. Real Estate Economics, 52,
843–884. https://doi.org/10.1111/1540-6229.12478
APPENDIX
TA B L E A 1
Number of building completions after introduction of rent control.
(1)
(2)
(3)
Variables
Lag of
implementation = 1
Lag of
implementation = 2
Lag of
implementation = 3
y = number of building completions
(L = 1)
(L = 2)
(L = 3)
𝑚𝑢𝑛𝑖𝑐𝑖𝑝_𝑟𝑒𝑔𝑚
22.3212**
27.7601***
31.8351***
(Treatment municipality)
(9.1955)
(7.8060)
(6.5848)
𝑚𝑢𝑛𝑖𝑐𝑖𝑝_𝑟𝑒𝑔𝑚× 𝑝𝑒𝑟𝑖𝑜𝑑_𝑟𝑒𝑔𝑡−𝐿
57.6797**
55.9226*
63.7477*
(implementation of rent control)
(27.4879)
(30.2519)
(35.9447)
Inhabitants
0.0031***
0.0031***
0.0031***
(0.0003)
(0.0003)
(0.0003)
Urban area (dummy)
15.8539
15.4476
15.1274
(regional centers)
(12.3264)
(12.2953)
(12.2514)
City/metropolitan area
−199.5527***
−198.9124***
−198.5782***
(dummy)
(60.9356)
(61.1287)
(61.2912)
Western/Eastern GER
7.1835**
7.2029**
7.1657**
(1 = West, 0 = East)
(2.8580)
(2.8597)
(2.8476)
Constant
−16.1400***
−16.6407***
−16.9681***
(2.9808)
(3.1131)
(3.2374)
Observations
45,905
45,905
45,905
Number of municipalities
7201
7201
7201
Year FE
Yes
Yes
Yes
Municipality-type FE
Yes
Yes
Yes
Note: This table presents the results of the OLS regression modeling the determinants of the number of building completions
described by the equation 𝑏𝑢𝑖𝑙𝑑𝑖𝑛𝑔𝑐𝑜𝑚𝑝𝑙𝑒𝑡𝑖𝑜𝑛𝑠𝑚𝑦= 𝑚𝑢𝑛𝑖𝑐𝑖𝑝_𝑟𝑒𝑔𝑚+ 𝑚𝑢𝑛𝑖𝑐𝑖𝑝_𝑟𝑒𝑔𝑚× 𝑝𝑒𝑟𝑖𝑜𝑑_𝑟𝑒𝑔𝑦−𝐿+ 𝑋𝑚𝑦+ 𝐵𝑦+ 𝜀.
Building completions show the number of building completions per municipality 𝑚in year 𝑦. The dummy variable 𝑚𝑢𝑛𝑖𝑐𝑖𝑝_𝑟𝑒𝑔𝑚
divides the sample into a treatment (regulated) and a control group (unregulated) depending on the application of the rent control.
The dummy variable 𝑝𝑒𝑟𝑖𝑜𝑑_𝑟𝑒𝑔𝑦−𝐿indicates the treatment period when the rent control was implemented in a specific munici-
pality. The columns of the table vary by the considered lags (one, two, and three years) of the rent control implementation. Thus,
the interaction term displays the effects of the introduction of the rent control on the number of building completions 1, 2, and 3
years after the application of the regulation.
We control for inhabitants per municipality and region-specific variables which indicate if an object is located in an urban area, a
city, or a metropolitan area and in West or East Germany. Year fixed effects and municipality-type fixed effects (city, large town,
small town, mid-sized, rural municipality) are included in all specifications.
The sample covers the observation period from 2010 to 2019. Robust standard errors clustered for districts are displayed in
parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% level, respectively.
 15406229, 2024, 3, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/1540-6229.12478 by University Of Leeds The Brotherton Library, Wiley Online Library on [22/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License


===== PAGE 34 (score 73, numeric-paren 52, table-words 0, var-words 15) =====

876
BAYE and DINGER
TA B L E A 2
Robustness test with categorical variables.
(1)
(2)
Variables
Categorical variables for
object condition
Categorical variables for
object condition and year
of construction
y = rent-price ratio
Treatment municipality
0.0297***
0.0382***
(0.0097)
(0.0101)
Regulated objects in regulated mun
−0.1910***
−0.1870***
(𝑚𝑢𝑛𝑖𝑐𝑖𝑝_𝑟𝑒𝑔_𝑎𝑝𝑝𝑙𝑖𝑒𝑑𝑚𝑞× 𝑜𝑏𝑗𝑒𝑐𝑡_𝑟𝑒𝑔𝑖)
(0.0186)
(0.0190)
Unregulated objects in regulated mun
0.3545***
0.3280***
(𝑚𝑢𝑛𝑖𝑐𝑖𝑝_𝑟𝑒𝑔_𝑎𝑝𝑝𝑙𝑖𝑒𝑑𝑚𝑞× 𝑜𝑏𝑗𝑒𝑐𝑡_𝑢𝑛𝑟𝑒𝑔𝑖)
(0.0262)
(0.0274)
Object-specific variables:
Base yield
0.9525***
0.9355***
(0.0220)
(0.0216)
Year of construction
−0.0001
(0.0003)
Living space
0.0004
−0.0008
(0.0009)
(0.0010)
Number of rooms
−0.2395***
−0.2203***
(0.0052)
(0.0057)
Basement (dummy)
−0.0060***
−0.0063***
(0.0013)
(0.0013)
Balcony (dummy)
−0.1212***
−0.1159***
(0.0102)
(0.0105)
Region-specific variables:
Urban area (dummy)
0.0049
0.0044
(Regional centers)
(0.0149)
(0.0143)
City/metropolitan area (dummy)
−0.0258
−0.0336*
(0.0170)
(0.0201)
West/East Germany (dummy)
0.0339
−1.0584***
(1 = West, 0 = East)
(0.0298)
(0.0498)
Socioeconomic variables:
Population density
−0.0000
0.0163*
(0.0091)
(0.0088)
Population growth
0.3439***
0.2823***
(0.0784)
(0.0809)
Primary income per capita
−0.0076***
−0.0119***
(0.0026)
(0.0035)
Students
0.4909
0.4376
(0.3353)
(0.3646)
(Continues)
 15406229, 2024, 3, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/1540-6229.12478 by University Of Leeds The Brotherton Library, Wiley Online Library on [22/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License


===== PAGE 35 (score 43, numeric-paren 14, table-words 4, var-words 19) =====

BAYE and DINGER
877
TA B L E A 2
(Continued)
(1)
(2)
Variables
Categorical variables for
object condition
Categorical variables for
object condition and year
of construction
Unemployment rate
2.7590**
2.2974*
(1.3092)
(1.2633)
Construction completions
0.0699*
0.0665
(0.0421)
(0.0434)
Social assistance recipients
−0.0016**
−0.0015**
(0.0007)
(0.0006)
Constant
0.2661
1.4412***
(0.5183)
(0.1291)
Observations
4,619,727
4,619,724
R2
0.6352
0.6492
Object condition
Categorical
Categorical
Year of construction
Categorical
Year FE
Yes
Yes
District FE
Yes
Yes
Note: This table presents the results of the OLS regression modeling the determinants of housing yield described in Equation (3)
using micro data. The dependent variable is the rent-price ratio, that is, the yearly net rent divided by the objects’ potential sale
price. The first explanatory variable is the dummy variable (𝑚𝑢𝑛𝑖𝑐𝑖𝑝_𝑟𝑒𝑔𝑚), which divides the sample into a treatment and a con-
trol group depending on the application of the rent control. The interaction terms of the dummy variable 𝑚𝑢𝑛𝑖𝑐𝑖𝑝_𝑟𝑒𝑔_𝑎𝑝𝑝𝑙𝑖𝑒𝑑𝑚𝑞,
which equals 1 if the rent control applies in the municipality at the point of time of the observation, and the dummy variables
𝑜𝑏𝑗𝑒𝑐𝑡_𝑟𝑒𝑔𝑖and 𝑜𝑏𝑗𝑒𝑐𝑡_𝑢𝑛𝑟𝑒𝑔𝑖, which indicate if the object itself is regulated due to its year of construction and condition, dis-
play the effects of the introduction of the rent control on the rent-price ratio depending on the individual objects’ regulation
status.
The control variables can be categorized as object-specific, region-specific, and socioeconomic variables. The object-specific con-
trol variables include the base yield, that is, the quarterly mean of the rent-price ratio in the objects’ municipality, the object’s
year of construction, the living space, the number of rooms, and dummy variables considering if the object has a basement and
a balcony or terrace. In this specification, we include the object condition, which can vary in ten categories between new and
demolition, as a categorical variable. Moreover, in Column 1, the year of construction is included as a continuous variable and in
Column 2 as a categorical variable (grouped by ten years, i.e., 1800–1810).
The region-specific variables indicate if an object is located in an urban area, a city, or a metropolitan area and in West or East
Germany. The considered socioeconomic variables include population density, population growth, primary income per capita, the
number of students, the unemployment rate, construction completions, and the number of social assistance recipients.
Year fixed effects are included in all specifications.
The sample covers the observation period from 2010 to 2019. Robust standard errors are displayed in parentheses. *, **, and ***
indicate statistical significance at the 10%, 5%, and 1% level, respectively.
 15406229, 2024, 3, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/1540-6229.12478 by University Of Leeds The Brotherton Library, Wiley Online Library on [22/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License


===== PAGE 36 (score 112, numeric-paren 92, table-words 0, var-words 10) =====

878
BAYE and DINGER
TA B L E A 3
Robustness analysis with different subsamples.
(1)
(2)
(3)
(4)
(5)
Variables
Baseline
Baseline
Top 7 cities
Little
construction
completions
Many
construction
completions
y = rent-price ratio
Treatment municipality
0.0260***
0.0260***
0.0346**
0.0364***
(0.0093)
(0.0094)
(0.0141)
(0.0106)
Regulated objects in regulated mun
−0.1330***
−0.1365***
−0.0893***
−0.1096***
−0.1871***
(𝑚𝑢𝑛𝑖𝑐𝑖𝑝_𝑟𝑒𝑔_𝑎𝑝𝑝𝑙𝑖𝑒𝑑𝑚𝑞× 𝑜𝑏𝑗𝑒𝑐𝑡_𝑟𝑒𝑔𝑖)
(0.0192)
(0.0188)
(0.0227)
(0.0185)
(0.0243)
Unregulated objects in regulated mun
0.2522***
0.2620***
0.2275***
0.2224***
0.2709***
(𝑚𝑢𝑛𝑖𝑐𝑖𝑝_𝑟𝑒𝑔_𝑎𝑝𝑝𝑙𝑖𝑒𝑑𝑚𝑞× 𝑜𝑏𝑗𝑒𝑐𝑡_𝑢𝑛𝑟𝑒𝑔𝑖)
(0.0288)
(0.0299)
(0.0469)
(0.0299)
(0.0427)
Object-specific variables:
Base yield
0.9551***
0.9547***
0.9919***
0.9664***
0.8980***
(0.0217)
(0.0226)
(0.0238)
(0.0246)
(0.0097)
Year of construction
−0.0003
−0.0003
−0.0009***
−0.0002
−0.0005**
(0.0002)
(0.0002)
(0.0002)
(0.0002)
(0.0002)
Living space
0.0001
0.0002
0.0031***
0.0006
−0.0016*
(0.0009)
(0.0009)
(0.0005)
(0.0011)
(0.0009)
Number of rooms
−0.2310***
−0.2320***
−0.2172***
−0.2359***
−0.2004***
(0.0053)
(0.0057)
(0.0130)
(0.0050)
(0.0089)
Basement (dummy)
−0.0066***
−0.0043***
−0.0105**
−0.0065***
−0.0050**
(0.0013)
(0.0012)
(0.0030)
(0.0015)
(0.0021)
Balcony (dummy)
−0.1203***
−0.1159***
−0.1100***
−0.1039***
−0.1507***
(0.0104)
(0.0104)
(0.0179)
(0.0102)
(0.0070)
Object condition
0.1536***
0.1572***
0.1393***
0.1474***
0.1544***
(1 = new; 10 = demolition)
(0.0046)
(0.0049)
(0.0130)
(0.0055)
(0.0034)
(Continues)
 15406229, 2024, 3, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/1540-6229.12478 by University Of Leeds The Brotherton Library, Wiley Online Library on [22/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License


===== PAGE 37 (score 96, numeric-paren 76, table-words 0, var-words 11) =====

BAYE and DINGER
879
TA B L E A 3
(Continued)
(1)
(2)
(3)
(4)
(5)
Variables
Baseline
Baseline
Top 7 cities
Little
construction
completions
Many
construction
completions
Region-specific variables:
Urban area (dummy)
0.0097
0.0091
0.0164
0.0031
(Regional centers)
(0.0141)
(0.0139)
(0.0150)
(0.0167)
City/metropolitan area (dummy)
−0.0278
−0.0289*
−0.0228
−0.0331
(0.0180)
(0.0172)
(0.0166)
(0.0322)
West/East Germany (dummy)
0.0657**
−0.6544***
−0.0081
0.0958***
1.9781***
(1 = West, 0 = East)
(0.0305)
(0.0399)
(0.3333)
(0.0103)
(0.0716)
Socioeconomic variables:
Population density
0.0014
−0.0013
−0.4700**
−0.0096
0.0353***
(0.0089)
(0.0090)
(0.1392)
(0.0099)
(0.0120)
Population growth
0.3231***
0.3134***
−0.4874*
0.1940***
0.4364***
(0.0746)
(0.0816)
(0.2443)
(0.0611)
(0.1336)
Primary income per capita
−0.0063**
−0.0078***
−0.0412***
−0.0076**
−0.0036
(0.0025)
(0.0019)
(0.0102)
(0.0031)
(0.0027)
Students
0.4240
0.3999
0.7087
0.7587**
−0.2503
(0.3168)
(0.2858)
(0.7883)
(0.3555)
(0.5304)
Unemployment rate
3.0265**
2.3026**
−10.8256**
2.0086
6.8074***
(1.4089)
(1.0735)
(3.2604)
(1.5577)
(1.0968)
Construction completions
0.0682*
0.2007***
−0.0958*
0.1005**
0.1010
(0.0365)
(0.0355)
(0.0398)
(0.0418)
(0.0823)
Social assistance recipients
−0.0017***
0.0017*
−0.0010**
−0.0038***
(0.0006)
(0.0008)
(0.0005)
(0.0008)
(Continues)
 15406229, 2024, 3, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/1540-6229.12478 by University Of Leeds The Brotherton Library, Wiley Online Library on [22/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License


===== PAGE 38 (score 25, numeric-paren 8, table-words 4, var-words 8) =====

880
BAYE and DINGER
TA B L E A 3
(Continued)
(1)
(2)
(3)
(4)
(5)
Variables
Baseline
Baseline
Top 7 cities
Little
construction
completions
Many
construction
completions
Constant
0.4678
1.1814***
3.7102***
0.2707
−0.8682**
(0.3272)
(0.3233)
(0.8117)
(0.3574)
(0.3663)
Observations
4,619,727
5,111,963
1,140,284
3,244,786
1,767,476
R2
0.6192
0.6155
0.5431
0.6209
0.6464
Year FE
Yes
Yes
Yes
Yes
Yes
District FE
Yes
Yes
Yes
Yes
Yes
Note: This table presents the results of the OLS regression modeling the determinants of housing yields described in Equation (3) using micro data. The dependent variable is the rent-price ratio.
Column 1 shows the baseline specification as described in Section 5.2. Column 2 displays the results of the estimation with an extended observation period, already beginning in 2008. Column 3
shows the subsample analysis of the seven biggest cities (Hamburg, Berlin, Duesseldorf, Frankfurt, Stuttgart Cologne, and Munich). Because of the reduction of the sample, all municipalities are
subject to rent control; thus, the dummy to differentiate between treatment and control groups cannot be included. The variables that determine if an object is located in an urban area, a city, or
a metropolitan area are not included as well. For the results in Columns 4 and 5, the sample is split by the median of the yearly construction completions measured by square meters of newbuild
living space per capita on municipality level.
Year and district fixed effects are included in all specifications. The sample covers the observation period from 2010 (2008) to 2019. Robust standard errors clustered for districts are displayed in
parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% level, respectively.
 15406229, 2024, 3, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/1540-6229.12478 by University Of Leeds The Brotherton Library, Wiley Online Library on [22/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License


===== PAGE 39 (score 133, numeric-paren 111, table-words 0, var-words 10) =====

BAYE and DINGER
881
TA B L E A 4
Robustness analysis with subsamples divided by building permits.
(1)
(2)
(3)
(4)
(5)
(6)
Variables
Little building
permits
Many building
permits
Little building
permits
Many building
permits
Little building
permits
Many building
permits
y = rent-price ratio
Lag = 1 year
Lag = 1 year
Lag = 2 years
Lag = 2 years
Treatment municipality
0.0274**
0.0341***
0.0277**
0.0340***
0.0282**
0.0336***
(0.0109)
(0.0122)
(0.0110)
(0.0121)
(0.0110)
(0.0121)
Regulated objects in regulated mun
−0.0897***
−0.1705***
−0.0886***
−0.1717***
−0.0878***
−0.1731***
(𝑚𝑢𝑛𝑖𝑐𝑖𝑝_𝑟𝑒𝑔_𝑎𝑝𝑝𝑙𝑖𝑒𝑑𝑚𝑞× 𝑜𝑏𝑗𝑒𝑐𝑡_𝑟𝑒𝑔𝑖)
(0.0235)
(0.0246)
(0.0235)
(0.0244)
(0.0237)
(0.0244)
Unregulated objects in regulated mun
0.1948***
0.2764***
0.1945***
0.2774***
0.1939***
0.2773***
(𝑚𝑢𝑛𝑖𝑐𝑖𝑝_𝑟𝑒𝑔_𝑎𝑝𝑝𝑙𝑖𝑒𝑑𝑚𝑞× 𝑜𝑏𝑗𝑒𝑐𝑡_𝑢𝑛𝑟𝑒𝑔𝑖)
(0.0395)
(0.0344)
(0.0396)
(0.0347)
(0.0397)
(0.0350)
Object-specific variables:
Base yield
0.9574***
0.9222***
0.9576***
0.9225***
0.9574***
0.9225***
(0.0214)
(0.0226)
(0.0214)
(0.0226)
(0.0214)
(0.0225)
Year of construction
−0.0002
−0.0006***
−0.0002
−0.0006***
−0.0002
−0.0006***
(0.0003)
(0.0002)
(0.0003)
(0.0002)
(0.0003)
(0.0002)
Living space
0.0002
−0.0007
0.0002
−0.0007
0.0002
−0.0007
(0.0011)
(0.0008)
(0.0011)
(0.0008)
(0.0011)
(0.0008)
Number of rooms
−0.2409***
−0.1989***
−0.2410***
−0.1990***
−0.2410***
−0.1989***
(0.0096)
(0.0070)
(0.0096)
(0.0070)
(0.0096)
(0.0069)
Basement (dummy)
−0.0063***
−0.0059***
−0.0063***
−0.0058***
−0.0062***
−0.0057***
(0.0016)
(0.0018)
(0.0016)
(0.0018)
(0.0016)
(0.0018)
Balcony (dummy)
−0.1078***
−0.1358***
−0.1080***
−0.1357***
−0.1080***
−0.1358***
(0.0109)
(0.0111)
(0.0109)
(0.0111)
(0.0109)
(0.0111)
Object condition
0.1514***
0.1483***
0.1515***
0.1487***
0.1516***
0.1489***
(1 = new; 10 = demolition)
(0.0031)
(0.0066)
(0.0031)
(0.0066)
(0.0031)
(0.0066)
(Continues)
 15406229, 2024, 3, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/1540-6229.12478 by University Of Leeds The Brotherton Library, Wiley Online Library on [22/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License


===== PAGE 40 (score 111, numeric-paren 88, table-words 0, var-words 11) =====

882
BAYE and DINGER
TA B L E A 4
(Continued)
(1)
(2)
(3)
(4)
(5)
(6)
Variables
Little building
permits
Many building
permits
Little building
permits
Many building
permits
Little building
permits
Many building
permits
Region-specific variables:
Urban area (dummy)
0.0238
−0.0060
0.0252
−0.0073
0.0247
−0.0068
(Regional centers)
(0.0160)
(0.0133)
(0.0159)
(0.0133)
(0.0160)
(0.0132)
City/metropolitan area (dummy)
−0.0256
−0.0315
−0.0266
−0.0311
−0.0268
−0.0320
(0.0179)
(0.0273)
(0.0180)
(0.0264)
(0.0181)
(0.0264)
West/East Germany (dummy)
−0.0229
0.1362***
−5.4607***
2.1477***
0.4122***
−0.0488
(1 = West, 0 = East)
(0.0150)
(0.0329)
(0.3978)
(0.0676)
(0.0699)
(0.0421)
Socioeconomic variables:
Population density
−0.0019
0.0310**
−0.0021
0.0316**
−0.0019
0.0316**
(0.0085)
(0.0126)
(0.0084)
(0.0126)
(0.0084)
(0.0127)
Population growth
0.2555***
0.3645***
0.2438***
0.3666***
0.2345***
0.3640***
(0.0704)
(0.1122)
(0.0684)
(0.1138)
(0.0669)
(0.1101)
Primary income per capita
−0.0065**
−0.0058**
−0.0065**
−0.0059**
−0.0065**
−0.0060**
(0.0031)
(0.0027)
(0.0031)
(0.0027)
(0.0031)
(0.0026)
Students
0.7227**
−0.2937
0.7196**
−0.2802
0.7255**
−0.2848
(0.3258)
(0.5549)
(0.3253)
(0.5532)
(0.3239)
(0.5482)
Unemployment rate
2.6516
5.4674***
2.6814
5.4439***
2.6481
5.6040***
(1.6867)
(1.1005)
(1.6872)
(1.0817)
(1.6814)
(1.0707)
Construction completions
−0.0469
0.0550
−0.0421
0.0586
−0.0371
0.0652
(0.0795)
(0.0651)
(0.0801)
(0.0645)
(0.0788)
(0.0645)
Social assistance recipients
−0.0027***
−0.0006
−0.0027***
−0.0006
−0.0028***
−0.0007
(0.0007)
(0.0006)
(0.0007)
(0.0006)
(0.0007)
(0.0006)
(Continues)
 15406229, 2024, 3, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/1540-6229.12478 by University Of Leeds The Brotherton Library, Wiley Online Library on [22/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License


===== PAGE 41 (score 26, numeric-paren 10, table-words 4, var-words 6) =====

BAYE and DINGER
883
TA B L E A 4
(Continued)
(1)
(2)
(3)
(4)
(5)
(6)
Variables
Little building
permits
Many building
permits
Little building
permits
Many building
permits
Little building
permits
Many building
permits
Constant
0.3281
0.9773***
5.7616***
−1.0371***
−0.1063
1.1421***
(0.4163)
(0.3150)
(0.4908)
(0.3030)
(0.3929)
(0.3229)
Observations
3,005,476
2,006,786
3,005,988
2,007,398
3,007,339
2,008,881
R2
0.5990
0.6513
0.5985
0.6516
0.5983
0.6522
Year FE
Yes
Yes
Yes
Yes
Yes
Yes
District FE
Yes
Yes
Yes
Yes
Yes
Yes
Note: This table presents the results of the OLS regression modeling the determinants of housing yields described in Equation (3) using micro data. The dependent variable is the rent-price ratio.
The sample is divided into different subsamples depending on the median of building permits, measured by authorized living space (thousand square meters) per capita on municipality level,
with no lag (Columns 1 and 2), 1-year lag (Columns 3 and 4), and a 2-year lag (Columns 5 and 6).
Year and district fixed effects are included in all specifications. The sample covers the observation period from 2010 (2008) to 2019. Robust standard errors clustered for districts are displayed in
parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% level, respectively.
 15406229, 2024, 3, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/1540-6229.12478 by University Of Leeds The Brotherton Library, Wiley Online Library on [22/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License


===== PAGE 42 (score 5, numeric-paren 0, table-words 0, var-words 5) =====

884
BAYE and DINGER
0
5
10
15
Proportion (in %)
< 30 sqm
30 - 39 sqm
40 - 49 sqm
50 - 59 sqm
60 - 69 sqm
70 - 79 sqm
80 - 89 sqm
90 - 99 sqm
100 - 109 sqm
110 - 119 sqm
120 - 129 sqm
130 - 139 sqm
140 - 149 sqm
150 - 159 sqm
160 - 169 sqm
170 - 179 sqm
> 180 sqm
0
5
10 15 20
Proportion (in %)
until 1919
1919 - 1949
1950 - 1959
1960 - 1969
1970 - 1979
1980 - 1989
1990 - 1999
2000 - 2005
2006 and later
Census data
Rentals ImmobilienScout24
Sales ImmobilienScout24
F I G U R E A 1
Distribution of apartments over size and year of construction. [Color figure can be viewed at
wileyonlinelibrary.com]
 15406229, 2024, 3, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/1540-6229.12478 by University Of Leeds The Brotherton Library, Wiley Online Library on [22/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License