Rent Regulations` Pricing Effect in the

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Rent Regulations` Pricing Effect in the
Journal of Housing Research • Volume 10, Issue 2
Mae Foundation 1999. All Rights Reserved.
267
q Fannie
Rent Regulations’ Pricing Effect in the Uncontrolled
Sector: An Empirical Investigation
Dirk W. Early and Jon T. Phelps*
Abstract
Rent controls, designed to lower the cost of housing for renters, may have the perverse effect of increasing rents for tenants in the unregulated sector. Although new construction is exempt from current
rent control laws, a reduction in supply of rental housing will occur if investors are wary of future
controls affecting their units. Also, if controls reduce landlord maintenance, total housing supply in a
market will fall. Using 1984 to 1996 data from the American Housing Survey, this study examines the
role of rent controls in determining the variations in prices of uncontrolled rental housing across
metropolitan areas.
The results suggest a positive and statistically significant relationship between the introduction of
rent control and price in the uncontrolled sector. However, the link between controls and prices declines
through time and may completely disappear after 20 to 30 years with no new construction subject to
controls.
Keywords: Rent control; Housing price indices; Housing markets
Introduction
Rent controls are ordinances that regulate the rate at which nominal rents for housing units
are allowed to rise. New York, the city with the longest continuous history of rent controls
in the United States, enacted controls in November 1943 on authority of the U.S. Emergency
Price Act of 1942 to protect against excess rent increases brought on by World War II
(Gyourko and Linneman 1989). Several other major cities also imposed controls at that time.
These first-generation rent controls were very restrictive and typically froze nominal rents.
All jurisdictions eliminated controls during the 1950s and 1960s except New York City, which
retained first-generation style rent controls on units built before 1947 and occupied since
1971 (New York State Division of Housing and Community Renewal 1997). During the inflationary 1970s, less restrictive second-generation rent controls were enacted across several
urban areas, including New York City, to protect tenants from rapid rent increases. Instead
of freezing nominal rents, these controls allow for annual increases in rents, exempt new
construction from regulation, and permit rent increases in response to improvements by
*Dirk W. Early is an Assistant Professor of Economics at Southwestern University, Georgetown, TX. Jon T. Phelps
is Controller at CyBerCorp.com, Austin, TX.
The authors are grateful for the comments and suggestions from Edgar Olsen and two anonymous referees. Sara
Barnes offered incredible research assistance during the preliminary drafts of this study. This research was initially
funded through the Mundy Faculty Fellowship Fund at Southwestern University. The generous support of the
Brown Foundation made the final verson of this article possible. Any errors are the sole responsibility of the authors.
268
Dirk W. Early and Jon T. Phelps
landlords (Arnott 1995). Though rent controls exist in some 200 communities in six states,
major metropolitan areas, such as Boston,1 Los Angeles, San Francisco, New York City,
Newark, and San Jose, hold the vast majority of rent controlled housing units (U.S. Department of Housing and Urban Development 1991).
Rent controls are controversial because of the possible adverse effects they can have on the
housing market. Lower maintenance by owners, longer tenancy rates, mismatching of tenant
needs with unit characteristics, and higher prices in the uncontrolled sector are some distortions that rent controls purportedly cause. The last effect is the focus of this study.
Examining the role rent control plays in determining the price of housing in the uncontrolled
sector is crucially important. Studies of the benefits and costs of rent control to tenants and
owners of controlled units should take into account any distortions that rent controls cause
in the uncontrolled market. However, all empirical studies of the benefits of rent control fail
to control for possible changes in the price of rental housing service in the uncontrolled sector
(Ault and Saba 1990; Gyourko and Linneman 1989; Linneman 1987; Olsen 1972). If rent
controls lead to an increase in rents in the uncontrolled sector, past measures of the benefits
are exaggerated and ignore the cost to tenants in uncontrolled housing.
This study examines the effects of rent controls on rental housing prices in the uncontrolled
sector. Previous research examining the link between rent control and the price of rental
housing is described in the next section. This is followed by a discussion of the hedonic model
used to construct a price index for uncontrolled rental housing. The results section describes
the estimation of a reduced-form equation describing the price of uncontrolled rental housing
as a function of—among other things—the existence of rent controls in the urban area. The
article concludes with a brief summary of the findings and suggestions for further research.
Previous Studies
Rent control can influence the price of housing in the uncontrolled sector by altering the
supply of and demand for rental housing service in a metropolitan area. Hubert (1993)
examines the role of rationing in the controlled sector and its effect on prices in the free
sector. Tenants unable to obtain controlled housing must find housing in the free sector. If
the free sector has higher than average demands for housing, prices in that sector will rise.
Hubert (1993) concludes that prices in the free sector vary according to the method used to
ration units of controlled housing.
Landlords may also reduce the maintenance of their controlled units if unable to offset fully
their costs by raising rents or if the administrative costs of approving rent increases are
prohibitively expensive. Kutty (1996) develops a dynamic infinite horizon model to derive
the optimal landlord maintenance path under various rent control ordinances. She shows
that under most regulations landlord maintenance will fall, although building codes, tenant
maintenance, and certain second-generation features can mitigate some of the deleterious
impacts of rent control on housing quality. If a reduction in maintenance occurs, the quality
1
Massachusetts eliminated rent controls in 1994.
Rent Regulations’ Pricing Effect in the Uncontrolled Sector: An Empirical Investigation
269
of controlled units falls, and tenants with the strongest tastes for housing may move to the
free sector, pushing up rents in the uncontrolled sector. However, as Olsen (1988) points out,
tenant maintenance may offset some reduction in landlord maintenance, lessening this effect. Using panel data from New York City from 1978 to 1987, Moon and Stotsky (1993)
estimate the conditional probability that a sound unit becomes substandard. They find that
the probability of a unit deteriorating in quality increases with the size of the implied subsidy
from rent control. In a similar study, Gyourko and Linneman (1990) examine the probability
that a unit of housing in New York City is substandard as a function of its rent control
status. Their results suggest that, holding all other factors constant, controlling the rent of
a unit increases the probability that the unit will become dilapidated.
Controls also can influence the supply of housing in the free sector. Although new construction is typically exempt from current rent control regulations, potential investors in rental
housing may be wary of future controls affecting their units. This concern may be greater
in metropolitan areas with a history of controlling rents. If this is true, investors in rental
housing in areas with rent control ordinances will require a risk premium to compensate for
this uncertainty, pushing up rents in the uncontrolled sector (Olsen 1972). Furthermore,
owners of rental housing may convert their properties to alternative uses if controls are
established or if they fear future controls. Hohm (1983) surveyed landlords in San Diego
County when a ballot measure was being debated that would have controlled rents in the
area. He found that nearly 70 percent of landlords were thinking of lowering their investment in rental housing because of the threat of implementing rent controls.
Although higher rent in the uncontrolled sector is a commonly mentioned cost of rent regulations, few empirical studies have examined the role of rent control in determining rent
in the unregulated sector. The first empirical estimates of the effect of controls on prices in
the uncontrolled sector were from Fallis and Smith (1984), who examined the price of rental
housing in the controlled and uncontrolled sectors using a rent-forecasting model. Analyzing
data from 1969 to 1978, they find that rent controls in Los Angeles effectively lowered the
rate at which rents rose in the controlled sector and increased the rate at which rents rose
in the uncontrolled sector. Caudill (1993) examined the role of controls on New York City
housing prices in 1968. His estimates suggest that if controls were removed, rents in the
uncontrolled sector of New York City would fall by 22 to 25 percent (Caudill 1993). More
recently, Early and Olsen (1998) estimate a simultaneous-equations model explaining
housing consumption, vacancy rates, and price per unit of housing service, using data across
44 urban areas. They find that controls increase the price of rental housing service in the
uncontrolled sector. Malpezzi (1996), using data across 54 large metropolitan areas, finds a
positive and statistically significant relationship between the existence of rent control and
median contract rent. However, he does not restrict his examination to rents in the uncontrolled sector.
A casual examination of the data used in this study shows that the price of uncontrolled
rental housing is higher in metropolitan areas with rent controls. In 1996 dollars, the mean
rent of a standard unit of housing in metropolitan areas without rent regulations is roughly
$450. In areas with rent controls the mean rent of a similar unit in the uncontrolled sector
is more than $700. The goal of this article is to determine whether rent controls are responsible for these higher prices. In the next section, we describe the model used to estimate the
causes of variations in prices of uncontrolled rental housing.
270
Dirk W. Early and Jon T. Phelps
Method of Estimation
A Simple Model of the Urban Housing Market
The model developed here draws from the methodology presented by Malpezzi, Chun, and
Green (1998); Malpezzi (1996); and Ozanne and Thibodeau (1983). Consider an urban housing market made up of six subsectors: the demand for rental housing service in each of the
uncontrolled and controlled sectors, the supply for each of those sectors, the demand for
owner-occupied housing, and the supply of owner-occupied housing. These subsectors are
linked through the rationing of controlled units and the decision of each household either
to rent or purchase housing.
Because prices are not free to adjust in the controlled sector, no market clearing equilibrium
for controlled housing will occur. Instead, the features of that sector are determined by which
units are controlled and the mechanism used to allocate the short supply of controlled housing. Of interest is whether rent controls affect the price of housing in the uncontrolled sector.
Therefore, a simple model of the market for unregulated housing is developed that, in its
reduced form, will allow for an estimate of the role played by rent controls in determining
prices in the free sector.
Let the following equations represent the demand for uncontrolled rental housing and
owner-occupied housing, respectively:
QRD 4 D R (PHR ,P X ,Y R ,D R ,N R ,T,eRD)
(1)
O
,P X ,Y O ,D O ,N O ,T,eOD)
QOD 4 D O (PH
(2)
R
where QD
R represents the quantity of rental housing service demanded, and PH its price, in
the uncontrolled sector; PX, the price of nonhousing goods; YR, the mean income, and DR,
the demographic characteristics of renters in that sector; NR, the number of renters, and
T, the property tax rate. The equation explaining the demand for owner-occupied housing
is similarly represented. Demographic characteristics are included to control for differences
in housing tastes across groups. Both demand equations also include an assumed normally
distributed mean zero error term, eRD, and eOD, capturing unobserved characteristics and the
heterogeneity of preferences across urban areas.
Supply of uncontrolled rental and owner-occupied housing depends on the corresponding
price of housing (PHR or PHO), a measure of construction costs (C), whether the metropolitan
area is adjacent to a large body of water (G) that is an impediment to growth and an urban
amenity, interest rates (i), whether rent controls have been established in the metropolitan
area (RC), and the property tax rate. The equations describing the supply of rental and
owner-occupied housing are:
QRS 4 S R (PHR ,C,G,i,RC,T,eRS)
(3)
and
S
O
4 S O (PH
,C,G,i,T,eOS).
QO
(4)
Rent Regulations’ Pricing Effect in the Uncontrolled Sector: An Empirical Investigation
271
As with the demand equations, error terms are included to capture unobserved characteristics and the heterogeneity across metropolitan areas.
The uncontrolled rental and owner-occupied markets are linked by the choice between owning and renting, conditional on not entering the controlled sector. The fraction of households
in the metropolitan area that rent uncontrolled housing would depend on the income in the
metropolitan area (Y), metropolitan area demographics (D), the price of rental and owneroccupied housing, the interest rate, the property tax rate, and the existence of rent control.
Since the burden of property taxes passed on to the consumer may be different for renters
and owners, the property tax rate is included. The tenure choice relationship is given by:
O
,i,T,RC,e N ),
N R 3 N 4 N(Y,D,PHR ,PH
(5)
where NR is the number of uncontrolled renter households and N is the total number of
households in the metropolitan area.
The Reduced-Form Equation
Because the above equations are simultaneously determined, several variables are endogenous. Most obvious are the prices, quantities, and fraction renting. The tenure choice decision means that the sector-specific demographic variables are also endogenous. Deriving
the reduced-form solution for the price of rental housing in the uncontrolled sector—the
focus of this article—yields:
PHR 4 fn(Y,D,P X ,N,i,T,C,RC,e P ).
(6)
It is expected that larger incomes and higher prices of other goods will lead to increased
prices of rental housing in the uncontrolled sector. Cities with larger populations are expected to have higher prices. The interest rate, tax rate, and construction cost index are
expected to be directly related to the price of uncontrolled rental housing, since they measure
the cost of producing housing. As discussed earlier, the existence of rent control is expected
to increase the price of housing in the uncontrolled sector. The metropolitan area demographic variables measure the fraction of households headed by blacks and Hispanics, the
fraction between ages 25 and 34, and average household size. Demographic characteristics
are included to capture variation in tastes across household types. These characteristics also
might alter prices other than through their influence on the demand for housing. The inclusion of race and ethnic composition may pick up the effects of discrimination in housing.
Rent increases in unregulated markets are typically less for sitting tenants than when a
unit becomes vacant. These tenure discounts will be less prevalent for the most mobile
groups in a housing market. Since young people move more frequently, they face higher
rents. Hence, a larger fraction of the population between the ages of 25 and 34 is expected
to put upward pressure on prices in the uncontrolled sector. Larger households may have to
pay a higher price per unit of housing service to compensate landlords for higher rates of
depreciation. Furthermore, large households may have stronger tastes for housing. Therefore, a larger average household size is expected to lead to higher prices of rental housing.
The dependent variable used in reduced-form equation 6 is the price of uncontrolled rental
housing. Existing measures of the price of rental housing across urban areas are not re-
272
Dirk W. Early and Jon T. Phelps
stricted to uncontrolled housing and, therefore, could not be used in this study. The next
subsection describes the data and the hedonic model we use to estimate a price index for
uncontrolled rental housing.
The Data and Hedonic Estimation
The primary data sources are the metropolitan area files of the American Housing Surveys
(AHS) for the years 1984 to 1996 (U.S. Department of Commerce 1985–97). The AHS collected data across 49 areas. Generally, each area is surveyed every four years, with 7 to 13
areas surveyed in any one year. Nearly all of the metropolitan statistical areas (MSAs) were
surveyed more than once over the 13 years covered by this study. Starting in 1993, the AHS
reduced the number of areas surveyed in their metropolitan area files and offset this by
including large samples for a few major MSAs in their national sample. In 1995, the survey
information for five MSAs was taken from the national file, including two areas with rent
control, New York and Los Angeles (U.S. Department of Commerce 1996). Combining the
areas from the MSA and national AHS files yields a total of 141 observations across 49
MSAs. Table 1 lists the MSAs used in this study, the years in which they were surveyed,
and their rent control status.
All published rental housing price indices use information on units in the controlled and
uncontrolled sectors and, therefore, are unsuitable for this study. Estimates of the price of
rental housing service in the uncontrolled sector are estimated using data from the AHS.
The procedure follows closely the hedonic specification developed by Thibodeau (1992, 1995)
in his calculation of price indices—except that the sample used here is restricted to uncontrolled rental housing. The natural log of the rent of a unit is assumed to be a function of
the following characteristics: number of bathrooms, bedrooms, and other rooms; number of
units in the building; dwelling age; type of heating equipment; the existence of air conditioning; unit and neighborhood quality; race of the household head; persons per room; length
of tenure; utilities included in the rent; and month of the interview. Race of the head of the
household is included as a proxy for neighborhood characteristics, such as segregation by
race, that are not captured by the measures of neighborhood quality included above. Because
higher density can lead to higher rates of unit depreciation, it seems reasonable that landlords would charge higher rents to larger households. The number of persons per room is
included to capture this effect. Table A.1 in the appendix describes the explanatory variables
used in the hedonic regressions.
Hedonic regressions, using unsubsidized, unregulated units, are estimated to predict the
rent of a unit as a function of these characteristics. Price indices are found by estimating
the mean rent of a constant quality unit across metropolitan areas. The estimated rent of a
unit of housing with the average characteristics of rental housing units in 1995 is used as
the price per unit of uncontrolled rental housing service in this study. These prices are given
in the last column of table A.2.2
2
Although not used in this study, consistent with Thibodeau (1995), the price of rental housing service is also found
for three distinct qualities of housing: substandard units, standard housing (housing at least three years old and
not substandard), and new construction (housing less than three years old and not substandard). These price indices
are reported in table A.2 because they may be of interest to others studying market-determined prices of rental
housing.
Rent Regulations’ Pricing Effect in the Uncontrolled Sector: An Empirical Investigation
273
Table 1. Metropolitan Areas, Survey Years, and Rent Control Status
Metropolitan Statistical Area
Anaheim–Santa Ana, CA
Atlanta, GA
Baltimore, MD
Birmingham, AL
Boston, MA
Buffalo, NY
Charlotte–Gastonia–Rock Hill, NC–SC
Chicago, IL
Cincinnati, OH–KY–IN
Cleveland, OH
Columbus, OH
Dallas, TX
Denver, CO
Detroit, MI
Fort Worth–Arlington, TX
Hartford, CT
Houston, TX
Indianapolis, IN
Kansas City, MO–KS
Los Angeles–Long Beach, CA
Memphis, TN
Miami–Fort Lauderdale, FL
Milwaukee, WI
Minneapolis–St. Paul, MN–WI
New Orleans, LA
New York–Nassau–Suffolk, NY
Newark–Northeastern New Jersey, NJ
Newport News–Hampton, VA
Norfolk–Virginia Beach–Newport News, VA
Oklahoma City, OK
Paterson–Clifton–Passaic, NJ
Philadelphia, PA
Phoenix, AZ
Pittsburgh, PA
Portland, OR–WA
Providence, RI
Rochester, NY
Sacramento, CA
Salt Lake City, UT
San Antonio, TX
San Bernardino–Riverside–Ontario, CA
San Diego, CA
San Francisco, CA
San Jose, CA
Seattle–Everett, WA
St. Louis, MO–IL
Tacoma, WA
Tampa–St. Petersburg, FL
Washington, DC–MD–VA
Survey Years
Rent Control
1986, 1990, 1994
1987, 1991, 1996
1987, 1991
1984, 1988, 1992
1985, 1989, 1993
1984, 1988, 1994
1995
1987, 1991, 1995b
1986, 1990
1984, 1988, 1992, 1996
1987, 1991, 1995
1985, 1989, 1994
1986, 1990, 1995
1985, 1989, 1993, 1995b
1985, 1989, 1994
1987, 1991, 1996
1987, 1991
1984, 1988, 1992, 1996
1986, 1990, 1995
1985, 1989, 1995b
1984, 1988, 1992, 1996
1986, 1990, 1995
1984, 1988, 1994
1985, 1989, 1993
1986, 1990, 1995
1987, 1991, 1995b
1987, 1991
1984, 1988, 1992
1984, 1988, 1992
1984, 1988, 1992, 1996
1987, 1991
1985, 1989, 1995b
1985, 1989, 1994
1986, 1990, 1995
1986, 1990, 1995
1984, 1988, 1992
1986, 1990
1996
1984, 1988, 1992
1986, 1990, 1995
1986, 1990, 1994
1987, 1991, 1994
1985, 1989, 1993
1984, 1988, 1993
1987, 1991, 1996
1987, 1991, 1996
1987, 1991
1985, 1989, 1993
1985, 1989, 1993
no
no
no
no
yes
yesa
no
no
no
no
no
no
no
no
no
no
no
no
no
yes
no
no
no
no
no
yes
yes
no
no
no
yes
no
no
no
no
no
no
no
no
no
no
no
yes
yes
no
no
no
no
yes
Source: U.S. Department of Housing and Urban Development, 1991.
a
Because rent controls in Buffalo only apply to the 1.4 percent of units built before 1947, estimates were also
produced considering Buffalo free of controls. The difference between those estimates and the estimates presented
in this study were trivial.
b
From the 1995 national American Housing Survey.
274
Dirk W. Early and Jon T. Phelps
Next, we turn to the estimation of the reduced-form regressions to test whether the existence
of controls affects the price in the uncontrolled sector. The dependent variable, determined
by the estimation of the hedonic model described above, is the price of uncontrolled rental
housing. Nearly all of the independent variables were constructed using data from the AHS.
Ideally permanent income would be used in a study of the housing decision of a household.
Unfortunately, only measures of current income are available. To control for downturns in
the economy, which may prevent current income from being a good proxy for permanent
income, a dummy variable indicating the data were collected during a recession year, 1990
or 1991, was included in the regression using information across all 13 years (National
Bureau of Economic Research 1999).
Three additional variables were derived using information from sources other than the AHS:
the price of nonhousing goods and services, the construction cost index, and whether the
metropolitan area borders a large body of water. The price of other goods is constructed
using the American Chamber of Commerce Research Association’s (ACCRA) Cost of Living
Index across urban areas (ACCRA 1984 to 1996) and the Bureau of Labor Statistics’ (BLS)
Cost of Living Index across time (BLS 1998). Each year ACCRA produces a price index that
measures the cost of a bundle of goods in each metropolitan area relative to the average cost
of that bundle across all metropolitan areas in that year. A price index for all goods other
than housing was derived using the price index for all goods, the weight-assigned housing
expenditure in its calculation, and the price index for housing. Because the average price
index constructed by ACCRA is 100 every year, they are not comparable across time. Therefore, the consumer price index from the BLS is used to control for changes in average prices
across time. The cost of providing housing is the Boeckh building cost index (E. H. Boeckh
1984 to 1996). A map was used to determine whether a large body of water bordered the
metropolitan area.
Several variables were transformed to their natural logarithms, namely the uncontrolled
rental housing price index, the price of nonhousing goods, the number of households, average
household size, and the construction cost index. The aggregate measure of income is the
mean of the natural logarithm of income. Table 2 reports the means and standard deviations
of all variables used in the estimation procedure.
Results
The reduced-form equation for the price of rental housing in the uncontrolled sector is estimated using data from the AHS across 13 years, 1984 to 1996. Using the entire sample, a
random-effects regression on the panel of data was estimated. These results are presented
in the second column of table 3. To test whether the effects of rent control have changed
through time, regressions were run separately for three periods: 1984 to 1987, 1988 to 1992,
and 1993 to 1996. Because nearly all of the MSAs were sampled only once within each period,
ordinary least squares methodology was employed instead of relying on panel data estimates. The results of these three regressions are also reported in table 3.
The results suggest that the introduction of rent controls increased the price of uncontrolled
rental housing. The panel estimates imply that, holding all other factors constant, the existence of rent control increases rents in the uncontrolled sector by more than 13 percent.
It appears, however, that this effect has diminished through time. The coefficient on the
Rent Regulations’ Pricing Effect in the Uncontrolled Sector: An Empirical Investigation
275
Table 2. Means, Standard Deviations, and Descriptions of the Variables
Variable
Rental housing
price index
Log of rental housing
price indexa
Mean income
Mean of the log of
incomeb
Mean
(Standard
Deviation)
$402.49
(137.27)
5.94
(0.33)
$38,153.14
(7,493.07)
Description
Price index for rental housing in the uncontrolled
sector
Natural logarithm of the price of rental housing
service in the uncontrolled sector
Average annual household income
10.21
(0.21)
Mean of the natural logarithm of household income
Price of nonhousing
goods
85.59
(15.32)
Constructed from the ACCRA Cost of Living Index
and the BLS Consumer Price Index for All Urban
Consumers
Log of the price of
nonhousing goodsb
4.43
(0.17)
Natural logarithm of the price of nonhousing goods
Number of households
Log of the number of
householdsb
Black or Hispanicb
911,540.6
(723,598.7)
13.50
(0.65)
19.43
(10.69)
Number of households
Natural logarithm of the number of households
Fraction of households headed by Blacks or Hispanics
Youngb
22.65
(3.07)
Fraction of households headed by persons ages 25
to 34
Household size
2.65
(0.12)
Average number of persons in a household
Log of household sizeb
0.97
(0.05)
Natural logarithm of the average number of persons
in a household
Property tax rateb
1.37
(0.67)
Average of property tax paid divided by property
value
Mortgage interestb
9.11
(0.65)
Average interest rate on 30-year, fixed-rate mortgages
issued in the last two years
Rent controlb
0.18
(0.38)
Existence of rent control in the metropolitan area
Construction cost index
1,500.49
(291.63)
Boeckh building cost index for apartments, hotels, and
office buildings
Log of the construction
cost indexb
7.30
(0.19)
Natural logarithm of the Boeckh building cost index
for apartments, hotels, and office buildings
Geographic restrictionb
0.55
(0.50)
Dummy variable for the MSA bordering a large lake,
bay, or ocean (1 4 yes, 0 4 otherwise)
Economic downturnb
0.17
(0.38)
Dummy variable for surveyed in recession years, 1991
or 1992 (1 4 yes, 0 4 otherwise)
Note: Unless otherwise mentioned, variables were found using American Housing Survey data.
a
Dependent variable.
b
Included explanatory variables.
276
Dirk W. Early and Jon T. Phelps
Table 3. Reduced-Form Regression Results
Coefficient (Standard Error)
Variable
Intercept
Mean of the log of income
Log of the price of
nonhousing goods
Log of the number of
households
Black or Hispanic
Young
Log of household size
Tax rate
Interest rate
Geographic boundary/
urban amenity
Log of construction
cost index
Economic downturn
Existence of rent
controls
N
adj-R2
Panel
Estimation
1984–96
16.397***
(1.126)
0.578***
(0.087)
0.172
(0.131)
0.015
(0.032)
0.003
(0.002)
0.010*
(0.005)
0.157
(0.340)
10.013
(0.023)
0.018
(0.024)
0.190***
(0.040)
0.654***
(0.124)
10.022
(0.027)
0.131**
(0.053)
141
0.859
X2 4 713.15
OLS
Estimation
1984–87
17.177***
(2.327)
0.579**
(0.165)
0.372
(0.348)
10.007
(0.047)
10.001
(0.003)
0.006
(0.009)
0.356
(0.556)
10.007
(0.039)
0.064
(0.062)
0.147***
(0.050)
0.618**
(0.257)
—
—
0.148**
(0.063)
47
0.758
F 4 14.09
OLS
Estimation
1988–92
18.666***
(1.495)
0.797***
(0.185)
0.202
(0.220)
0.010
(0.039)
0.002
(0.002)
10.002
(0.009)
0.274
(0.453)
10.036
(0.029)
0.040
(0.108)
0.194***
(0.041)
0.648***
(0.167)
—
—
0.109*
(0.064)
56
0.854
F 4 30.24
OLS
Estimation
1993–96
17.137**
(3.471)
0.675***
(0.218)
0.310
(0.457)
0.008
(0.061)
0.006
(0.003)
0.025
(0.015)
10.816
(0.729)
10.001
(0.037)
10.050
(0.121)
0.202***
(0.061)
0.691**
(0.251)
—
—
0.084
(0.087)
38
0.779
F 4 12.83
Note: Dependent variable 4 log of the price of rental housing in the uncontrolled sector.
*p < 0.10. **p < 0.05. ***p < 0.01.
existence of rent control using data from 1984 to 1987 is statistically significant and suggests
that controls increase the price in the uncontrolled rental market by nearly 15 percent.
Results using data from 1988 to 1992 and from 1993 to 1996 suggest a positive relationship
between rent control and prices in the uncontrolled sector; however, the effect is smaller.
Furthermore, the hypothesis that rent controls did not alter the price of rental housing in
the 1993 to 1996 sample cannot be rejected.
The reduction in the importance of rent control over time fits well with the notion that the
supply of uncontrolled housing falls when investors are concerned that future ordinances
will control the rents of new construction. It seems reasonable that the probability of new
controls being implemented would decrease as the number of years since implementation
increases. If investors become less wary of future controls, they will be more willing to supply
housing. This may explain the lack of a statistically significant relationship between controls
Rent Regulations’ Pricing Effect in the Uncontrolled Sector: An Empirical Investigation
277
and prices in the free sector in the late 1990s, nearly 30 years since rents on existing units
were controlled in most areas.
One concern in the interpretation of these results is the relationship between the price of
rental housing and a city’s decision to implement rent controls. If areas with high growth
rates of housing costs are more likely to introduce controls, the existence of rent control
variables is correlated with the error terms in the above equations. The possible endogeneity
of rent control casts some doubt on the reliability of the above results that suggest rent
controls increase the price in the uncontrolled sector. However, if the high general inflation
of the 1970s—experienced by all the urban areas examined in this study—combined with a
stronger propensity of some areas to regulate prices led to controls, the concern over rent
control being endogenous is not as great.
The results suggest that the price of rental housing in the uncontrolled sector is positively
and statistically significantly related to mean income, the presence of a geographic boundary,
and construction costs. Surprisingly, the coefficients measuring the effects of other measures
of the cost of supplying housing, namely tax and interest rates, were insignificant under all
specifications. Demographic characteristics did not show a consistent relationship across
model specifications and were rarely significant.
To measure the magnitude of price change in uncontrolled rental housing, the increase in
rent for a standard unit of housing attributable to the existence of rent control is estimated
for each MSA with a controlled sector. The results of the panel regression allow for an
estimate of the price of rental housing in the uncontrolled sector if rent controls are removed.
Comparing this with the predicted price of controlled rental housing gives a dollar estimate
of the magnitude of the effect of rent controls on rents in the uncontrolled sector. The estimated rents in the uncontrolled sector, with and without rent control, and the monthly
increases in rents due to controls are presented in table 4. All pecuniary values are in 1996
dollars. The results suggest that, on average, the monthly rent of a typical uncontrolled unit
is roughly $85 higher because of the existence of rent controls.
Since the price of rental housing is not expected to adjust immediately to the removal of
rent controls, these estimates should be considered the long-term pricing change in rental
housing if controls are lifted. If prospective investors in rental housing were hesitant to
invest in areas where rent controls existed, they may be equally hesitant to invest shortly
after their removal, anticipating the possibility of future ordinances. It may take many years
for investors to believe that future controls are unlikely and stop demanding a risk premium
to invest in certain areas.
Conclusion
This study uses data from the American Housing Survey from 1984 to 1996 to estimate the
effects of rent control on the price of housing in the uncontrolled sector. The results suggest
that the introduction of rent controls increases the price per unit of rental housing service
in the uncontrolled sector. However, this effect is more prominent shortly after the implementation of controls and may completely disappear once sufficient time has passed without
new construction being subject to controls. An analysis of housing markets, roughly a decade
after they were established, found a positive and statistically significant relationship be-
278
Dirk W. Early and Jon T. Phelps
Table 4. The Effect of Rent Controls on Rental Housing Prices in the Uncontrolled Sector
Survey
Year
Price with
Rent Controls
Price without
Rent Controls
Buffalo, NY
1984
454.98
399.16
55.81
San Jose, CA
1984
831.61
729.60
102.01
Boston, MA
1985
622.73
546.34
76.39
Metropolitan Area
Monthly Rent
Increase
Los Angeles–Long Beach, CA
1985
687.40
603.07
84.32
San Francisco, CA
1985
738.10
647.56
90.54
Washington, DC–MD–VA
1985
703.11
616.86
86.25
New York–Nassau–Suffolk, NY
1987
711.13
623.90
87.23
Newark–Northeastern New Jersey, NJ
1987
668.11
586.15
81.96
Paterson–Clifton–Passaic, NJ
1987
698.88
613.15
85.73
Buffalo, NY
1988
493.74
433.18
60.57
San Jose, CA
1988
807.13
708.12
99.01
Boston, MA
1989
696.29
610.87
85.41
Los Angeles–Long Beach, CA
1989
784.41
688.19
96.22
San Francisco, CA
1989
795.30
697.74
97.56
Washington, DC–MD–VA
1989
739.42
648.71
90.70
New York–Nassau–Suffolk, NY
1991
791.32
694.25
97.07
Newark–Northeastern New Jersey, NJ
1991
690.65
605.93
84.72
Paterson–Clifton–Passaic, NJ
1991
716.81
628.88
87.93
Boston, MA
1993*
705.41
618.88
86.53
San Francisco, CA
1993*
759.43
666.27
93.16
San Jose, CA
1993*
790.72
693.73
97.00
Washington, DC–MD–VA
1993*
714.26
626.64
87.62
Buffalo, NY
1994*
537.69
471.73
65.96
Los Angeles–Long Beach, CA
1995*
705.28
618.76
86.52
New York–Nassau–Suffolk, NY
1995*
741.03
650.13
90.90
Note: All dollar values are in 1996 dollars.
*The hypothesis that the existence of rent control does not alter the price in the uncontrolled sector cannot be
rejected for these years.
tween prices in the uncontrolled sector and the existence of controls. Furthermore, the coefficient estimates suggest a substantial increase of nearly 15 percent in prices. However, it
is not possible to reject the hypothesis that controls have no effect on prices in the
uncontrolled sector, once 20 to 30 years have passed without regulating the rent of new
construction.
These results suggest that the introduction of new controls would increase the price of uncontrolled housing. However, policy makers concerned with the second-generation controls
Rent Regulations’ Pricing Effect in the Uncontrolled Sector: An Empirical Investigation
279
that are currently in existence can look to these results as an indication that the detrimental
effects on the price of uncontrolled housing have passed. According to our findings, the
elimination of current controls would not be expected to alter the price of uncontrolled
housing.
One concern with the above analysis is the possible endogeneity of rent controls in a model
explaining variations in prices of uncontrolled rental housing. It could be argued that high
growth rates of housing costs led to cities implementing rent controls. If this is true, the
coefficients on the existence of controls reported above must be interpreted with care. Future
research needs to investigate the direction of causality between higher prices in the uncontrolled sector and the existence of rent controls.
A second concern with the finding that rent controls alter rental prices in the uncontrolled
sector is the lack of a comparative analysis of the characteristics of controlled and uncontrolled units. If controlled units are substantially different from uncontrolled units, they
may represent different submarkets, with different demands for rental housing, reducing
the link between the two subsectors. Future research is needed to explore this possibility.
Two extensions of this research should be considered. First, the method used to ration controlled housing was not modeled in this study. A careful analysis of the mechanisms employed by landlords to allocate regulated units would increase our understanding of the role
played by rent controls in determining prices in the uncontrolled sector. Analysis of the
rationing of units would also yield important measures of the distribution of the benefits of
rent control. Second, an examination of the specifics of each rent control regulation would
allow for a more detailed analysis to determine which restrictions led to the largest price
changes in the uncontrolled sector. This type of analysis would help determine which specific
restrictions of second-generation controls are less detrimental than the restrictions found in
first-generation, or hard, controls.
280
Dirk W. Early and Jon T. Phelps
Appendix
Table A.1. Explanatory Variables Used in the Hedonic Regression
One and one-half bathrooms
Two or more full baths
No bedrooms
One bedroom
Three bedrooms
0.25 times the number of bedrooms, if number of bedrooms is greater than 4
One other room
Three other rooms
0.25 times the number of other rooms, if number of other rooms is greater than 3
Single-family detached
Single-family attached or duplex
3- or 4-unit multifamily
10- to 19-unit multifamily
20-unit or larger multifamily
Age of the building
Age of the building, squared
Age of the building, cubed
Built before 1940: 1 4 yes, 0 4 no
Central electric heat
Built-in electric units
Central oil heat
Other heating system not specified above
No air conditioning
At least one room air conditioner but no central air conditioning
Building problems: 1 4 the unit has two or more of the following problems: basement leaks, roof leaks,
open cracks or holes in walls or ceilings, holes in floor, or broken plaster or peeling paint over an area
exceeding one square foot; 0 4 otherwise
Public hallway problems: 1 4 unit is in a multifamily building and has at least two of the following
problems: absence of light fixtures in public halls, hazardous steps on common stairs, or stair railings
not firmly attached; 0 4 otherwise
Lack important features: 1 4 unit has any of the following deficiencies: lacks complete plumbing; lacks
complete kitchen facilities; sewer system is a chemical toilet, privy, outhouse, facilities in another
structure, or some sewage/toilet facilities; wiring in house not concealed; or some rooms lack working
electrical outlets; 0 4 otherwise
Multiple equipment breakdowns: 1 4 unit has any of the following equipment breakdowns: two or more
water breakdowns lasting six hours or more, two or more flush toilet breakdowns lasting six hours or
more, two or more public sewer breakdowns lasting six hours or more, or fuses or circuit breakers blew
two or more times within the last 90 days; 0 4 otherwise
Tenant rates neighborhood quality as excellent: 1 4 yes, 0 4 otherwise
Tenant rates neighborhood quality as good: 1 4 yes, 0 4 otherwise
Tenant observed signs of rats in last 90 days: 1 4 yes, 0 4 otherwise
Census enumerator observed abandoned buildings on the street: 1 4 yes, 0 4 otherwise
Tenants are disturbed by trash, litter, or junk in the streets, on empty lots, or on properties in the
neighborhood: 1 4 yes, 0 4 otherwise
Crime is a problem: 1 4 yes, 0 4 otherwise
Noise is a problem: 1 4 yes, 0 4 otherwise
Race of the head of the household: 1 4 African American, 0 4 otherwise
Ethnicity of the head of the household: 1 4 Hispanic, 0 4 otherwise
Number of persons per room
Length of tenure in years
Length of tenure, squared
Electric heat is included in rent: 1 4 payment for electric heat is included, 0 4 otherwise
Electricity is included in rent: 1 4 yes, 0 4 otherwise
Gas heat is included in rent: 1 4 yes, 0 4 otherwise
Oil heat is included in rent: 1 4 yes, 0 4 otherwise
Other utilities included in rent: 1 4 yes, 0 4 otherwise
Surveyed in August: 1 4 yes, 0 4 otherwise
Surveyed in September: 1 4 yes, 0 4 otherwise
Surveyed in November: 1 4 yes, 0 4 otherwise
Surveyed in December: 1 4 yes, 0 4 otherwise
Note: The definitions of the variables used in this study follow Thibodeau’s methodology (1995, table 1, 444–48).
Rent Regulations’ Pricing Effect in the Uncontrolled Sector: An Empirical Investigation
281
Table A.2. Price of Rental Housing Service in the Uncontrolled Sector
Metropolitan Statistical Area
Survey Substandard Standard
New
Metropolitan
Year
Housinga Housingb Constructionc
Aread
Birmingham, AL
Buffalo, NY
Cleveland, OH
Indianapolis, IN
Memphis, TN
Milwaukee, WI
Newport News–Hampton, VA
Norfolk–Virginia Beach–
Newport News, VA
Oklahoma City, OK
Providence, RI
Salt Lake City, UT
San Jose, CA
1984
1984
1984
1984
1984
1984
1984
1984
165.99
230.05
236.96
191.14
169.79
295.63
250.64
238.08
197.36
289.04
297.14
246.46
215.32
364.15
276.60
287.05
293.02
378.65
400.84
395.60
296.09
539.25
387.74
383.43
196.10
284.58
292.84
243.38
212.04
360.43
276.54
283.95
1984
1984
1984
1984
207.25
291.71
258.23
435.97
257.69
364.19
301.76
536.58
412.18
444.01
428.83
665.82
255.34
358.09
299.96
528.52
Boston, MA
Dallas, TX
Detroit, MI
Fort Worth, TX
Los Angeles–Long Beach, CA
Minneapolis–St. Paul, MN–WI
Philadelphia, PA
Phoenix, AZ
San Francisco, CA
Tampa–St. Petersburg, FL
Washington, DC–MD–VA
1985
1985
1985
1985
1985
1985
1985
1985
1985
1985
1985
454.94
297.51
247.90
239.68
369.22
337.21
285.99
220.75
444.63
173.45
363.07
505.46
342.73
310.22
260.14
446.92
380.92
352.22
279.60
506.32
227.76
417.91
594.15
466.53
452.30
382.53
665.52
483.47
492.16
417.06
634.00
323.41
538.11
502.45
340.78
306.45
260.85
443.24
378.73
348.06
276.18
502.77
223.78
414.89
Anaheim–Santa Ana, CA
Cincinnati, OH–KY–IN
Denver, CO
Kansas City, MO–KS
Miami–Fort Lauderdale, FL
New Orleans, LA
Pittsburgh, PA
Portland, OR–WA
Rochester, NY
San Antonio, TX
San Bernardino–Riverside–Ontario, CA
1986
1986
1986
1986
1986
1986
1986
1986
1986
1986
1986
484.59
258.51
296.65
176.94
255.52
255.35
175.34
251.86
264.06
223.07
258.43
520.98
310.42
353.85
234.05
317.37
299.49
233.89
329.69
319.28
270.00
320.75
739.30
392.64
507.93
362.01
424.15
390.62
323.48
494.86
405.96
381.78
486.29
522.33
306.69
351.08
230.35
312.96
296.89
229.17
324.47
315.24
267.43
317.53
Atlanta, GA
Baltimore, MD
Chicago, IL
Columbus, OH
Hartford, CT
Houston, TX
New York–Nassau–Suffolk, NJ
Newark–Northeastern New Jersey, NJ
Paterson–Clifton–Passaic, NJ
St. Louis, MO–IL
San Diego, CA
Seattle–Everett, WA
Tacoma, WA
1987
1987
1987
1987
1987
1987
1987
1987
1987
1987
1987
1987
1987
245.76
297.85
290.09
207.31
409.78
201.63
472.57
446.83
439.43
211.69
451.82
375.21
283.20
323.79
362.88
355.34
249.51
452.32
245.66
538.09
491.41
495.90
265.46
499.94
429.86
338.80
465.17
533.24
501.84
382.59
610.11
343.42
735.87
669.84
735.15
414.37
692.98
549.77
430.59
318.01
359.46
351.48
247.79
451.63
243.06
535.76
491.05
495.72
262.79
499.39
426.89
335.11
Birmingham, AL
Buffalo, NY
1988
1988
168.23
287.39
233.07
337.32
319.82
456.02
227.48
334.76
282
Dirk W. Early and Jon T. Phelps
Table A.2. Price of Rental Housing Service in the Uncontrolled Sector (continued)
Metropolitan Statistical Area
Survey Substandard Standard
New
Metropolitan
Year
Housinga Housingb Constructionc
Aread
Cleveland, OH
Indianapolis, IN
Memphis, TN
Milwaukee, WI
Newport News–Hampton, VA
Norfolk–Virginia Beach–
Newport News, VA
Oklahoma City, OK
Providence, RI
Salt Lake City, UT
San Jose, CA
1988
1988
1988
1988
1988
1988
279.95
213.33
206.96
341.18
304.95
304.58
353.97
273.62
254.05
389.91
347.12
353.38
503.02
407.59
402.62
585.69
465.03
482.68
348.96
269.78
252.03
389.05
345.47
351.10
1988
1988
1988
1988
167.74
392.52
232.21
599.82
204.29
448.78
265.82
677.08
326.92
596.94
374.04
849.32
202.96
446.25
264.72
673.11
Boston, MA
Dallas, TX
Detroit, MI
Fort Worth, TX
Los Angeles–Long Beach, CA
Minneapolis–St. Paul, MN–WI
Philadelphia, PA
Phoenix, AZ
San Francisco, CA
Tampa–St. Petersburg, FL
Washington, DC–MD–VA
1989
1989
1989
1989
1989
1989
1989
1989
1989
1989
1989
583.98
275.22
337.85
242.48
557.30
392.46
399.28
270.95
571.47
242.64
420.71
667.73
319.65
405.57
264.97
634.39
427.45
472.20
320.46
665.77
283.02
463.05
676.48
520.04
548.17
418.09
781.76
552.10
655.37
452.90
827.40
412.46
590.01
658.90
319.21
401.47
266.01
629.73
426.85
468.49
318.15
659.60
281.63
461.58
Anaheim–Santa Ana, CA
Cincinnati, OH–KY–IN
Denver, CO
Kansas City, MO–KS
Miami–Fort Lauderdale, FL
New Orleans, LA
Pittsburgh, PA
Portland, OR–WA
Rochester, NY
San Antonio, TX
San Bernardino–Riverside–Ontario, CA
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
648.64
274.08
298.60
197.26
383.56
273.99
232.72
244.54
330.75
233.98
373.53
693.48
317.11
340.77
258.95
448.67
309.11
293.65
302.48
377.95
282.18
429.10
889.72
440.22
511.66
390.84
637.46
403.22
377.58
470.06
433.40
426.01
604.30
693.68
315.32
340.05
254.81
445.90
307.64
288.80
299.67
374.27
280.12
427.15
Atlanta, GA
Baltimore, MD
Chicago, IL
Columbus, OH
Hartford, CT
Houston, TX
New York–Nassau–Suffolk, NJ
Newark–Northeastern New Jersey, NJ
Paterson–Clifton–Passaic, NJ
St. Louis, MO–IL
San Diego, CA
Seattle–Everett, WA
Tacoma, WA
1991
1991
1991
1991
1991
1991
1991
1991
1991
1991
1991
1991
1991
271.19
365.89
417.94
250.82
539.35
268.07
610.66
584.96
635.85
251.37
521.21
465.74
421.83
342.55
421.65
500.31
300.66
589.15
317.76
704.58
614.06
730.66
284.24
574.73
541.56
466.83
496.36
590.77
758.18
457.39
723.26
420.77
1004.36
693.85
741.37
427.10
771.23
734.44
723.03
338.05
419.53
497.14
298.68
587.30
314.85
701.37
613.25
721.09
283.98
573.70
537.93
467.94
Birmingham, AL
Cleveland, OH
Indianapolis, IN
Memphis, TN
1992
1992
1992
1992
200.89
293.08
297.54
200.28
248.42
369.31
345.59
253.55
354.11
549.62
524.21
392.57
245.52
364.77
344.38
250.62
Rent Regulations’ Pricing Effect in the Uncontrolled Sector: An Empirical Investigation
283
Table A.2. Price of Rental Housing Service in the Uncontrolled Sector (continued)
Metropolitan Statistical Area
Survey Substandard Standard
New
Metropolitan
Year
Housinga Housingb Constructionc
Aread
Newport News–Hampton, VA
Norfolk–Virginia Beach–
Newport News, VA
Oklahoma City, OK
Providence, RI
Salt Lake City, UT
1992
1992
340.29
300.54
356.22
353.93
522.42
491.43
358.43
351.32
1992
1992
1992
194.53
445.20
300.34
245.65
515.49
339.27
370.44
650.58
487.67
242.65
511.15
338.58
Boston, MA
Detroit, MI
Minneapolis–St. Paul, MN–WI
San Francisco, CA
San Jose, CA
Tampa–St. Petersburg, FL
Washington, DC–MD–VA
1993
1993
1993
1993
1993
1993
1993
637.69
393.97
433.17
550.70
697.87
290.99
550.13
696.34
475.88
492.05
639.73
764.26
323.41
652.33
826.16
698.03
699.65
808.90
958.99
474.49
777.32
693.45
471.85
490.63
634.20
762.05
323.44
644.36
Anaheim–Santa Ana, CA
Buffalo, NY
Dallas, TX
Fort Worth–Arlington, TX
Milwaukee, WI
Phoenix, AZ
San Bernardino–Riverside–Ontario, CA
San Diego, CA
1994
1994
1994
1994
1994
1994
1994
1994
647.25
346.01
321.60
275.93
452.58
342.09
410.29
564.03
692.42
399.34
373.03
314.54
530.55
373.83
481.97
622.47
893.94
466.85
593.63
486.96
745.58
573.97
616.79
835.70
692.56
395.30
372.35
314.27
527.01
375.03
477.45
621.42
Charlotte–Gastonia–Rock Hill, NC–SC
Columbus, OH
Denver, CO
Kansas City, MO–KS
Miami–Fort Lauderdale, FL
New Orleans, LA
Pittsburgh, PA
Portland, OR–WA
San Antonio, TX
Chicago, IL
Detroit, MI
Los Angeles–Long Beach, CA
New York–Nassau–Suffolk, NY
Philadelphia, PA
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
274.92
300.43
426.65
252.78
456.80
310.63
287.24
474.72
308.70
439.90
352.49
626.12
743.31
409.88
331.91
368.10
496.01
333.33
533.72
359.10
378.21
554.76
366.65
524.25
444.90
690.23
810.31
511.64
443.91
549.41
832.94
510.50
748.34
531.42
463.46
811.07
571.22
743.12
621.54
850.31
953.25
527.80
328.34
364.66
495.70
328.02
530.31
357.80
369.94
551.91
364.83
520.22
438.53
687.41
807.26
500.87
Atlanta, GA
Cleveland, OH
Hartford, CT
Indianapolis, IN
Memphis, TN
Oklahoma City, OK
Sacramento, CA
St. Louis, MO–IL
Seattle–Everett, WA
1996
1996
1996
1996
1996
1996
1996
1996
1996
343.66
361.83
536.11
353.13
258.87
235.17
402.19
316.92
546.57
404.23
422.45
588.34
391.05
343.60
281.22
433.25
359.69
637.54
530.28
562.86
732.21
597.72
574.30
427.67
547.30
456.66
895.48
400.68
419.24
586.43
391.63
338.77
279.47
432.88
357.49
633.69
Source: Authors’ calculations using the American Housing Survey.
a
Substandard units, as defined by the U.S. Department of Housing and Urban Development, include units that are lacking
amenities common to most units (e.g., a working toilet, complete kitchen facilities, or heating equipment) or have
substantial maintenance problems (e.g., exposed wiring, missing steps in common hallways, holes in floors, or a leaky roof).
b
Standard housing (housing not less than three years old or substandard).
c
New construction (housing less than three years old and not substandard).
d
The dependent variable used in this study, all rental units in the metropolitan area.
284
Dirk W. Early and Jon T. Phelps
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