An Examination of the Impact of Rent Control on Mobile
Home Prices in California
By
Diehang Zheng, Yongheng Deng, Peter Gordon, and David Dale-Johnson†
Lusk Center for Real Estate
University of Southern California
Los Angeles, CA 90089-0626
September, 2006
Abstract
This study examines the impact of rent control of mobile home parks in seven counties of
California between 1983 and 2003. We assembled an extensive and timely data set and, thus,
were able to test more carefully specified econometric models than had been employed in prior
studies of California mobile-home rent control. We find that the nature of the rent control regime
differentially impacts mobile home prices: the imposition of rigid rent control, rent control
without vacancy decontrol, leads to higher growth rates in resale prices. While a flexible regime,
or rent control with vacancy decontrol, results in lower growth rates in resale prices. This is
consistent with economic theory, suggesting that the imposition of rigid rent control will lead to
the capitalization of future rent savings when a coach is sold. That is, the buyer will not only pay
for the coach but also for the net present value of the expected savings associated with the future
legally constrained pad rent obligations to the landlord.
†
The authors are grateful for helpful comments from John Quigley, and participants at the
International Conference for Real Estates and the Macroeconomy at Beijing, Hoyt Fellows
Research Seminar at the Homer Hoyt Institute, and the Western Regional Science Association
Annual Conference at Santa Fe. The authors also gratefully acknowledge the Pacific Legal
Foundation for providing the mobile home data and the financial support from the Lusk Center
for Real Estate at USC for this study.
1. Introduction
This study uses a unique micro-level mobile home transactions data set to analyze the impact
of mobile home rent control in California. Is so doing, we hope to advance our understanding of
the impact of price controls.
The social and economic impacts as well as the legal implications of price controls in the
rental housing market have been debated extensively among economists and legal scholars. There
appears to be widespread agreement on a number of points. Most economists agree that “a ceiling
on rents reduces the quantity and quality of housing available” (Alston, Kearl, and Vaughan,
1992).1 While some renters benefit from lower rents, there are also costs (Olsen, 1972).2 Severe
rent controls can cause undersupply in the apartment rental markets, and increase the search costs
for tenants (Arnott and Igarashi, 2000). Below-market prices, if enforced, create opportunities for
non-price rationing. Potential renters are obliged to invest time and effort chasing down access to
the limited supply. These resource expenditures do not accrue to any sellers and cannot be
converted to product and thus become a “deadweight” efficiency loss.
Also, maintenance of the existing housing stock and investment in new stock are expected to
decline. In fact, abandonment of housing stock by owners has been observed. Housing
conversions and demolitions have also been attributed to rent controls, an outcome further
reducing the housing stock. Additional efficiency losses come from renters failing to adjust their
housing consumption even though their circumstances change so they can hold on to their rent
controlled units. (See Gyourko and Linneman, 1988, and Glaeser and Luttmer, 2003, for a
thorough discussion of such effects for the case of New York city.) In addition, higher-income
households are likely have an advantage in tracking valuable information on available units.
Thus, equity gains from rent controls have seldom been realized by those most in need such
benefit, suggesting that rent control has not been a policy useful for lower-income households
1
Alston, Kearl, and Vaughan (1992) reported that from their survey of a stratified random sample of 1,350 economists
employed in the United States, more than three-quarters of the respondents agreed with the above statement.
2
One seminal study of rent control in New York City estimated that the costs to property owners are two times the
benefits to renters (Olsen, 1972).
1
that seek affordable housing (Quigley, 2002). For many reasons, then, rent controls preclude land
and housing markets from doing what they are supposed to do, namely allocate scarce resources
to their highest and best uses in light of ever changing wants, opportunities and constraints. Some
existing studies found that a well-designed rent control regime could improve the unrestricted
equilibrium of an imperfect market (Arnott, 1995). Rent control is preferred when the market
distortion is the unavailability of insurance against a sharp, unanticipated rent rise. However,
economists disagree on the net social effect of rent controls, for example, whether rent controls
might affect increased homelessness by reducing the supply of rental units (Tucker, 1989,
Quigley, 1990, HUD, 1991, Early and Olsen, 1998).
Rent control policies have caught the attention of legal scholars and have been extensively
litigated. Legal scholars have debated whether some rent control regulations have violated the
Fifth Amendment’s Takings Clause. In Yee v. City of Escondido, Cal. (90-1947), 503 U.S. 519
(1992), the Supreme Court affirmed a Superior Court decision rejecting the argument that the
ordinance effected a physical taking by depriving park owners of all use and occupancy of their
property and granting to their tenants, and their tenants' successors, the right to physically
permanently occupy and use the property. The Supreme Court ruled that the argument that rent
control ordinances benefit current but not future property owners has “nothing to do with whether
it causes a physical taking.” Since then, some legal scholars have nevertheless argued that certain
rent controls constitute a regulatory taking because the regulation has unfairly singled out some
property owners to bear a burden that should be borne by the public at-large. (See Rubinfeld,
1992, and Radford, 2004, for a discussion.)
A unique aspect of the mobile home industry is the separate ownership of the coach and the
pad where the coach is located; i.e., the mobile homeowner owns only the coach unit and rents a
pad in a mobile home park on which she parks her (not-so-mobile) home. If the rent of the pad is
constrained to be below market rents, coach owners are hypothesized to be able to sell the coach
for more than it would be worth without rent control, thereby capitalizing the rent savings arising
2
from rent control. The framework of separate ownership of coach unit and pad provides a unique
opportunity to explicitly test the economic impact of rent control policy.
Several studies by Hirsch (1988), Hirsch et al (1988) and Hirsch and Rufolo (1999) have
analyzed capitalization of the effects of rent control on coach unit values. Using straightforward
hedonic modeling techniques and relatively small samples, the authors demonstrated that the
savings from pad rent controls are capitalized.
In this study, we propose an extended economic model using a large sample of about
200,000 mobile home transaction records from January 1983 to May 2003 from seven counties in
California.3 The coach transaction records include information on each coach’s quality (brand,
length, and width), age, address, original sale price, last sale price, last sale date, etc.
The purpose of this study is twofold. First, we intend to provide a more explicit and
comprehensive measure of the economic impacts of rent controls through an analysis of micro
transactions data. Second, we seek to provide evidence on the extent to which mobile home rent
control constitutes a regulatory taking.
The remainder of this paper is organized as follows: Section two summarizes the unique
characteristics of the mobile home market and rent controls in such markets. Section three
describes our methodology for measuring rent control capitalization. Section four discusses the
data; Section five presents our empirical results. The last section offers concluding remarks.
2. The Mobile Home Industry
In 2003, almost 9 million of the nation's 121 million housing units were mobile homes. This
was 7.4 percent of the housing stock, up from 6.5 percent of the housing stock in 1987. Of the
more than 338 thousand mobile homes added to the U.S. housing stock in 1999, more than 10
percent were added in the western states. As the number of Americans living in mobile homes
increases, more attention is being paid to mobile home parks, their management and associated
3
The data are maintained by the California Department of Housing and Community Development (HCD).
3
public policies. Among the issues of great interest are the effects of various types of mobile home
rent control.
The imposition of rent controls in many California jurisdictions has been shown to explain
declining shipments of mobile homes to California (Hirsch and Rufolo, 1999). More up-to-date
time series transactions data for California also show that from 1983-2003 (through the month of
May), the number of mobile homes subject to rent controls increased. Figures 1a through 1c show
the transaction data in our sample by various mobile home descriptors including Total Traded
Square Feet, Total Traded Value (constant dollars), and the Number of Transactions.
Rent control policies and by-laws vary in scope and severity. Flexible rent control regimes
allow vacancy decontrol while more rigid regimes permit rent increases tied to one of a variety of
cost-of-living indices. Rent control regulations in many cities have gone through cycles of control
and decontrol, in some cases resulting in various vintages of the stock being grandfathered. A
visible result is that some cities have neighborhoods with well-kept free-market rental housing
adjacent to poorly maintained rent controlled housing.
Rent control systems are most well known for having been imposed on traditional
multi-family rental housing units where the landlord owns the land and the building and rents
individual units to tenants using formal or informal leasing arrangements. Legal systems have
evolved to define the property rights of landlords and tenants. Rent controlled systems often
“piggy-back” on these systems as security of tenure is deemed critical if landlords are permitted
to increase rents when a unit is vacated.4
In contrast, in mobile home parks, the site (“pad”) is rented to the tenant who either acquires
the mobile home from a prior tenant or buys a new mobile home which is then assembled on-site.
The curious distinction from the more common rent control of multi-family apartment units is
that in a mobile home park the landlord owns the land (the pad) and the tenant owns the
improvements (the coach). If there is no vacancy decontrol (ability on the part of the landlord to
4
In more rigid rent control systems, controls are tied to the unit rather than the tenant.
4
adjust the pad rent to the market rent when the coach is sold), the net present value of the
anticipated future rent savings should be capitalized into the sales price of the coach if it stays in
place. And usually it does as mobile homes are seldom moved once they are located on a pad.
This unique aspect of rent control for mobile home parks has been the subject of some empirical
analysis. Rent control systems for mobile home parks provide an opportunity to investigate the
economic impact of the rent control policy on the tenants of mobile parks (i.e., the mobile coach
owners).
Rapidly rising housing and land prices in California explain rising pad rents in many of the
state’s mobile home parks. As a consequence, renters in many jurisdictions responded by
launching efforts to have rent controls enacted into law. In 2003, ninety seven California cities
and eight counties had some sort of mobile home rent control.
In a series of papers published in the late 1980s, Hirsch and his colleagues (Hirsch, 1988,
and Hirsch and Hirsch 1988) examined the impacts of rent controls on mobile homes in
California. As alluded to previously, their analysis rests on the insight that ownership of mobile
home living space is divided between the owner of the coach and the owner of the land, which is
then leased to coach owners. Therefore, a coach atop a rent-controlled pad can be expected to sell
at a premium. Hirsch et al’s studies contain estimates of these values. Basing their analysis on a
relatively small sample of observed transactions from the mid-1980s, they estimated that sales
prices were boosted by 32 percent because of rent controls, other things equal.
An updated analysis by Hirsch and Rufolo (1999) focused on a single mobile home park in
Oceanside, California. A hedonic regression based on a sample of 90 mobile home sales over the
period 1986-1992 found that, other things equal, rent controls explain a price premium of eight
percent.
More recently, Quigley (2002) presented an economic analysis of mobile home rent control
based on a single mobile home park in San Rafael, California. The study documented the
arms-length sales of 40 mobile homes in that park during a three-year period. The study estimated
5
the average value of the rent control premium varied from $16 to $160,000. Price premiums for
coaches enjoying rent control benefits in this study averaged an impressive 366 percent.
3. The Model
This section lays out the methodology adopted to model the capitalization of rent control.
The approach to measuring housing price appreciation has advanced as a consequence of work by
Bailey, Muth and Nourse (1963), Case and Shiller (1989), Case and Quigley (1992) and Harding,
Rosenthal and Sirmans (2005) among others. In this paper, we employ repeat sales price indexes
in the mobile home market in California to examine the impact of variation in rent control
policies among jurisdictions, while controlling the socio-economics of the neighborhoods in
question and of the physical attributes of the coaches themselves.
As discussed in previous sections, due to the separate ownership of the mobile home coach
and the pad where the coach parks, coach owners in a jurisdiction with rigid rent control can
capitalize the anticipated savings from controlled rents when they sell their units. Our model
extends the work by Bailey, Muth and Nourse (1963) and Case and Shiller (1989) by
decomposing the mobile home repeated sale house price differential into mobile home property
value appreciation and the capitalization of the mobile home park rent control premium.
5
Consider a mobile home coach which is observed to have been purchased and sold in periods
t and t + τ , respectively. The purchase price is
Pt = A∏ x j ,t j e yt β e ztδ eγ t ,
α
'
'
(1)
j
where A is the parameter to capture the common factors’ effect on all the units in the study;
x j ,t ( j = 1,..., q ) and yt are a set of property’s hedonic characteristics observed at time t; zt
is a vector of binary regulatory features indicating different rent control regimes; α , β and δ
5
Our decomposition process is similar to the model proposed by Harding, Rosenthal and Sirmans (2005) in which they
modify the repeated sales house price index model by decomposing the housing price appreciation into the components
of capital depreciation and impact of housing maintenances.
6
are shadow prices for hedonic characteristics, and regulatory features respectively; γ t is a vector
of parameter for price index capturing aggregate price appreciation.
Let the price vary over time, from equation (1) the selling price at time t + τ will be
Pt +τ = A∏ x j ,t +τ j e yt +τ β e zt +τ δ eγ t +τ .
α
'
'
(2)
j
From equations (1) and (2), the price ratio and the log price ratio, respectively, between
t + τ and t can be expressed as follows:
⎛ x j ,t +τ
Pt +τ
= ∏⎜
⎜
Pt
j ⎝ x j ,t
⎞ ( yt+τ − yt )' β ( zt +τ − zt )' δ (γ t +τ −γ t )
e
e
,
⎟⎟ e
⎠
αj
(3)
and
⎛P
ln ⎜ t +τ
⎝ Pt
(
)
⎞
'
'
⎟ = ∑ α j ln ( x j ,t +τ ) − ln ( x j ,t ) + ( yt +τ − yt ) β + ( zt +τ − zt ) δ + (γ t +τ − γ t ) .
j
⎠
(4)
If we assume the hedonic characteristics and regulatory attributes remain constant between
the two transactions, i.e. x j ,t +τ = x j ,t ,
yt +τ = yt , and zt +τ = zt , then equation (4) can be
reduced to
( γ t +τ −γ t )
Pt +τ = Pe
,
t
(5)
or the price change reflexes only the appreciation of market value of a constant quality property.
This is exactly the repeat house price index model developed by Bailey, Muth and Nourse (1963)
and Case and Shiller (1989).
Suppose the binary regulatory attributes between t and t + τ change from z to z * , as a
consequence of the adoption of a rent control ordinance during the time between the two
transactions. The transacted price growth can be decomposed into constant quality property price
appreciation and the impact of change in regulatory attributes.
( γ t +τ − γ t ) ( z − z ) δ
Pt +τ = Pe
e
.
t
*
7
'
(6)
Let’s assume the property price follows a log normal distribution6. We can test the above
specifications by taking logarithms and rearranging equations (5) and (6), such that,
ln(
Pt +τ
) = γ t +τ − γ t + ε (τ ) ,
Pt
(7)
and
ln(
Pt +τ
) = γ t +τ − γ t + ( z * − z )' δ + υ (τ ) ,
Pt
(8)
respectively, where ε (τ ) and υ (τ ) are random error terms following a normal distribution with
a diffusion variance which increases with the time span, τ , between the two transactions.
For the case of mobile homes, provided all the other characteristics except for the rent
control policy are time-invariant, the problem of selecting the proper specification of functional
form or how qualitative and quantitative characteristics determine housing price growth has been
eliminated. This elimination enhances the reliability of the estimates for the price index, γ .
For a sample of mobile homes that are transacted at various time periods, we obtain
ln(
Pt +τ
) = D 'γ + ( RCt +τ − RCt )' δ + υ (τ ) ,
Pt
(9)
where D is a vector of indicator variables taking values of -1, 0, or 1, determined by both
purchase and sale of each property7; γ is price index vector; RC is a vector of indicator variables
for various rent control regimes,8 and the modified repeated sales house price index model
expressed in equation (9) allows us to decompose the capitalization of any rent control premium
from property value appreciation.
Yet, Redfearn (2005) has pointed out an additional source of significant bias in aggregate
indexes is the ignorance of local dynamics. Case and Quigley (1992) acknowledged that it is
6
The same assumption was implied by Bailey, Muth and Nourse (1963), Case and Shiller (1989), and Case and
Quigley (1992).
7
The indicator variable takes value of -1 if the first transaction of the property occurs in period t; and takes value of 1
if the second transaction of the property occurs in period t + τ ; and takes value of 0 for all the other periods.
8
1 indicates the property is under rent control; 0 means no rent control policy adopted.
8
appropriate to control statistically for the varying characteristics of properties in inferring price
trends. Clapham et al. (2004) compared the stability of the repeat sales index and the hedonic
index and found that character-based hedonic indexes appear to be substantially more stable than
repeat-sales indexes. Hence we can modify equation (9) such that
⎛P
ln ⎜ t +τ
⎝ Pt
(
)
⎞
'
'
⎟ = D ' γ + ∑ α j ln ( x j ,t +τ ) − ln ( x j ,t ) + ( yt +τ − yt ) β + ( RCt +τ − RCt ) δ + μ (τ )
j
⎠
= D ' γ + Z 'θ + ( RCt +τ − RCt ) δ + μ (τ ) ,
'
(10)
where θ is a vector of parameters which contains both α and β ; Z (contains x and y) is a
vector of control variables capturing the location effect (measured by county), as well as hedonic
effects (coach structure such as single-, double-, or triple-width, size, and neighborhood quality,
etc.). This additional vector measures the proportional effects of location and the coach’s
amenities on the price growth rate.
4. The Data
In this research, twenty years of transactions data for mobile homes in seven counties of
California (Los Angeles, Orange, Riverside, San Bernardino, San Diego, Santa Clara and
Ventura) were made available to us. After geo-coding and matching with census tracts, there were
201,228 records from the period beginning of January 1983 and ending in May 2003. As we
wished to create hybrid repeat-sale indexes, our focus is on sales of coaches for which there are
two transactions, the first purchase and one resale, thus records for single sales were removed
from the data set9. Other records were eliminated because of incomplete city or mobile home park
(Park ID) information. Outliers were also removed. 10 These modifications left a 20-year
population of 137,221 sales in the data set.
9
For those properties with more than two transactions, only one resale record is included in the analysis.
Records with incomplete geographic information and the top and bottom 1 percent of records based on the variables
Original Sales Price (Constant 1996 dollar), Resale Price (Constant 1996 dollar), Size and Average Annual Growth
10
9
Each record in the original data set is a mobile home transaction and includes the county,
city, address, mobile home park name and ID, the original sale price for the new structure and
resale price, the dates of sale (year, month, and day), and measures of other attributes of the unit
such as model year, manufacturer, width, length, and type (single, double, or triple) and so on.
These data were combined with census data by census tract, including median household income,
changes in median household income, vacancy rate, proportion of the elderly (≥ 65 years old),
unemployment rate, and proportion of households with public assistance income, etc. We assume
the census tract descriptors apply to the population living in mobile home parks. Finally,
city-specific mobile home park rent control information was incorporated in the merged data set,
including an indicator of the nature of the rent control regime.11 Table 1 defines each of the
variables included in the data set.
There are 201 municipalities in the seven counties included in this study, among which 49
cities have a rent control ordinance. As many as 48,664 transactions in the data set (35.5 percent)
were actually in jurisdictions where there was rent control. Among those, around half were under
rigid rent control. From Figures 1a through 1c, we can see that the share of transactions as
measured by square footage, value and number has increased significantly over the twenty-year
period under study. Table 2 reveals in the right-hand column that the share of rent controlled units
among the total mobile home transactions in the seven counties grew from almost thirteen percent
in the early 1980’s to more than forty percent in 2003; moreover, the growth of those under rigid
rent control outpaces the growth of transactions of units under flexible rent control since the
middle 1990s. The change in the share of the square footage traded and the value traded
attributable to rent-controlled units is of the same order. Table 3 presents the annual growth rates
in rent-controlled units among the transactions as measured by square footage, value and number.
Rate were removed from the data set. We also explored the impact of focusing on properties where 0.5 < (Constant
Resale Price/Constant Original Sales Price) < 3.0. We found that the results were robust to the imposition of this
constraint. See Table 1 for descriptions of the variables.
11
For cities with rent control, dummy variables were including starting in the quarter in which rent control was
implemented. We also included a dummy variable that indicates whether or not there is vacancy decontrol. No vacancy
decontrol reflects a more rigid rent control regime.
10
The growth rates were 13.02, 13.19 and 13.17 percent, respectively. Clearly, rent control policies
in the mobile home marketplace in California are taking on increasing importance.
Table 4 provides descriptive statistics for the continuous covariates for the complete data set
as well as for each county. The original sale price varies from an average of $36,940 (San
Bernardino) to $46,410 (Santa Clara); while the average resale price varies from $22,085 (San
Bernardino) to $54,441 (Santa Clara). The average original sale price and resale price of the
seven counties are $40,775 and $34,118 respectively. Mobile homes are less likely to keep up
with rapid residential land market appreciation. Declines in the values of mobile homes are more
common than for “stick-built” houses because the land component is often not part of the selling
price.12
Census tract variables including Median Household Income, Proportion of Households with
Public Assistance Income, and Proportion of Persons ≥ 65 Years Old were collected and also
reported in Table 4. Other census tract-level variables, including Changes in Median Household
Income, Vacancy Rate, and Unemployment Rate, were collected but were ultimately not of
significance in the analysis so their summary statistics are not reported here.
Riverside county had the lowest median household income ($33,149) accompanied by the
highest elderly population (24.4 percent). Santa Clara county was at the other extreme with the
highest median household income ($52,492) and was tied with two other counties for the second
lowest proportion of elderly population (11.4 percent). Perhaps surprisingly, Los Angeles county
had the lowest proportion of elderly at 10.3 percent. San Bernardino and Riverside counties had
the highest proportion of households benefiting from public assistance (9.0 and 8.6 percent,
respectively) while Ventura and Orange counties had the lowest (5.0 and 5.2 percent,
respectively).
We ultimately used the census information to segment the data set. Specifically, high- (or
low-) income is defined by whether the census tract median household income is above (or
12
A more suitable comparable for mobile home sales would be sales of stick-built houses on leased land.
11
below) the median census tract household income in the corresponding county. High- (or low-)
elderly population proportion is defined by whether the census tract older population proportion
is above (or below) the median census tract elderly population proportion in the corresponding
county.
The smallest mobile homes are in San Bernardino and Los Angeles counties as measured by
Unit’s Size (1,093 and 1,094 square feet) while the largest are in Orange county (1,199 square
feet). Ventura and San Diego counties have the oldest units (20.85 and 20.75 years, respectively)
while Riverside county has the youngest (16.14 years).
Table 5 provides descriptive statistics for the sample’s categorical covariates. Double-width
mobile homes are the most common as indicated by a market share of almost 70 percent for the
type “Double”. The second biggest share is for single-width or the “Single” category at a market
share of 26 percent; triple-width or the “Triple” category represent the luxury high-end mobile
homes and have a five percent share of the market. The market share of double-width in Orange
county is about 15 percentage points higher than in Los Angeles, while the market share of
single-width is about 15 percentage points lower perhaps reflecting stage of the evolution of the
mobile home market as well as the role of mobile homes as a housing choice when the homes
were originally sold and the mobile home parks developed.
As noted earlier, a number of qualitative (“dummy”) variables were created which are
critical to the analysis. First, communities are identified in which rent control of mobile home
parks was present. Second, the policy is classified as rigid (by-laws which do not permit vacancy
decontrol) or flexible (by-laws which permit vacancy decontrol), depending on whether or not
vacancy decontrol is permitted. Then transactions within these jurisdictions were identified
accordingly. Third, the first transaction after the adoption of a rent control policy is also
indicated. The results of these classifications for the aggregate data set also appear in Table 5.
The prevalence of rent control ordinances varies dramatically among the seven counties.
While in the aggregate, less than five percent of the transactions in Orange county were in
12
rent-controlled parks, 82.7 percent of the transactions in Ventura county were in rent-controlled
parks. As noted earlier, there was significant variation in these percentages between 1983 and
2003 with an increasing number and share of mobile home units being regulated by rent control
policies over time. Of the 35.5 percent of mobile home transactions in our data set that are within
rent controlled jurisdictions, roughly half of them are in jurisdictions with flexible rent control
regimes and the rest are in jurisdictions with rigid rent control regimes.13
5. Empirical Results
We had a much larger and more comprehensive sample available to us than was employed in
Hirsch (1988), Hirsch et al (1988, 1999) or Quigley (2002). As noted, we focused on 137,221
observations collected from over twenty years of mobile home transactions between 1983 and the
early part of 2003. The ultimate transaction data base included only repeat- or multiple-sales
along with descriptive information about the coach. Each sale was geo-coded permitting the
census tract variables including Median Household Income (constant 1996 dollar), Proportion of
Households with Public Assistance Income and Proportion of Persons ≥ 65 Years Old to be
appended to each record. The first two are proxies for local amenity values as well as demand,
while the third is a proxy for one of the components of demand for mobile home units -- as many
older households choose mobile homes as a cost-effective housing choice in retirement.
Our working hypothesis was that because rigid rent control policies allow coach owners to
pass on future pad rent savings to subsequent owners of the coach, prices of coaches in these
communities will increase more rapidly or decrease less rapidly than the prices of coaches in
communities without rent control or flexible rent control. To be sure, this is actually a net effect.
Buyers value protections from rent increases but also may be wary of owning property in cities
13
Rent control regimes may become less rigid or more rigid through time as policies evolve due to the changing
economic and political environment. We were able to identify regimes which are currently rigid (no vacancy decontrol
permitted) or flexible (vacancy decontrol permitted). In order to ascertain whether today’s regime accurately reflected
the nature of the regime since 1983, we surveyed every city in our data set (201). We received 55 responses in total
with 20 of them from cities with rent control in place. The results of the survey supported the approach taken. We
inferred that the results of the survey could be extrapolated to the full sample.
13
with governments that have shown themselves to be ready to place limits on property rights.
This suggests that any weak form of rent control could be associated with especially ambiguous
expectations. It is not clear which effect would dominate.
Thus, if rent control or the rigidity of the rent control regime influences the rate of change of
coach prices, the relevant estimated coefficient will be positive and significant. Moreover, the
biggest beneficiary is the first generation of mobile home owners when the rent control policy
is/was adopted. For the standard case, the following coach owners are not able to realize a
comparable benefit because they have paid for most of the premium for rent control upfront.
Nevertheless, in cases when future appreciation had been underestimated, there would be
additional rents to be further capitalized in the later transactions.
Table 6 presents the results of GLS estimation of equations (9) and (10).14 In model (1),
mobile homes values are positively influenced if rigid rent control policies are adopted but those
will decrease when flexible rent control policies are adopted. This set of relationships still holds
when the proportional effects of other hedonic factors have been considered in model (2).
Riverside and San Bernardino have the lowest growth rates, while Ventura outperforms all the
other counties. The larger luxury units experience higher appreciation than smaller ones. The
coaches in the neighborhoods with a higher proportion of households with public assistance
suffer from lower growth rates. For rent control policies, the first generation mobile home owners
under rent control benefit from the adoption of rigid rent control ordinances (rent control without
vacancy decontrol), but they experience a net loss, ceteris paribus, from flexible rent control (rent
control with vacancy decontrol), as suggested may be possible in our theoretical discussion.
14
We adopt GLS estimation approach to address the problem of heterogeneity due to the diffusion process of the error
terms specified in equations (9) and (10). Following Case and Shiller (1989) and Deng, Quigley and Van Order (2000),
we first estimate equations (9) and (10) by OLS. In the second stage, we estimate a diffusion process of the variance by
regressing the square term of residuals, ε , collected from the first stage OLS regression on a quadratic function of
time span between the two sales, such that ε (τ ) = β1τ + β 2τ 2 + ω , where τ is the duration between original sale and
2
2
resale (measured in quarters), and ω is a normally distributed error term. In the third stage we re-estimate the mobile
home price index model using a GLS estimation approach weighted by the square root of the estimated diffusion
variance, εˆ (τ ) , obtained from the second stage.
14
Our results also show that for the following generations of tenants, rigid rent control policy
is no longer a significant factor in determining the resale price, while a flexible rent control
regime may continue contributing to lower resale price.
Because rigid rent control practically freezes real rents paid, in a market where demand
increases in the long run such as in the market for mobile homes, the longer this restriction is in
force, the bigger would be the price gap, between the controlled units and the uncontrolled units.
Model (3) replaces the simple dummy variable “adoption of rent control without vacancy
decontrol” by the interactive variable “adoption of rent control without vacancy decontrol ×
number of years under rent control at time of transaction”. The interactive variable is significant
and keeps the same sign as its simple dummy counterpart in model (2). It shows that the adoption
of rigid rent control will push up the mobile home value by 1.5 percent per year till the first
transaction after the introduction of the policy. Also, because of different rent control adoption
schedules, San Diego and Santa Clara have different signs for their dummy variables in this
model compared to model (2).
Figure 2a provides the plot of dependent variable, Log (resale price/original price) or
Log-Ratio, for different counties. Santa Clara has the highest growth rate and the lowest variance;
Riverside has the lowest growth rate with the greatest variance. Hence, it is inappropriate to treat
the seven counties equally at the aggregate level.
Similarly, Figure 2b and 2c give the plots of Log-Ratio for different structure groups (single,
double, and triple), and for different rent control groups. Due to the difference among the
subgroups a proportional model as in Equation (10) is adopted.
From a policy perspective it might be valuable to know which income-age group can benefit
the most from a rent control policy, and also, who might suffer the most. Figure 3a and 3b present
the plots of Log-Ratio by different median household income census tracts and different
proportion of elderly population census tracts. The differences between higher and lower income
communities, as well as older and younger communities are visible. Hence, we refine the full
15
sample and re-estimate the index with proportional location and hedonic factors for each
income/age group, in order to estimate rent control’s impact under different scenarios.
The re-estimated results for different median household income census tract groups are
presented in Table 7a. Adoption of rigid rent control leads to higher increase in resale price for
coaches in wealthier communities, but homeowners in poorer communities suffer less from the
adoption of flexible rent control. Moreover, the future homeowners in wealthier neighborhoods
can still enjoy the benefit from rigid rent control to a lesser extent.
Table 7b provides the estimates for census tracts with higher and lower proportions of
elderly population. Mobile home owners in neighborhoods with younger populations benefit from
the adoption of rigid rent control ordinances more than in the neighborhoods with elderly
populations, while the latter suffer less from new flexible rent control policies. Clearly, younger
mobile home owners have a longer anticipated benefit period. The following generations of
mobile home owners in older neighborhoods are relatively better off from both rigid and flexible
rent control ordinances and slightly better off under the former.
In Table 7c, we show the results for further refined subsamples by both median household
income and proportion of elderly population at the census tract level. When we consider income
and age at the same time, age is a higher order factor than income: both younger communities,
with higher or lower income, enjoy greater benefit from the adoption of rigid rent control than the
older communities. Homeowners in younger communities with higher median household income
are the biggest beneficiaries, while the ones in older communities with lower median household
income experience the lowest benefits. For the newly adopted flexible rent control policies,
homeowners in older communities with higher median household income might be the only
beneficiaries, and all the rest will suffer, especially the ones in younger communities with higher
median income. The future homeowners in the former community will continue benefiting from
rigid rent control but to a lesser extent.
16
Finally, Figure 4b presents the simulated comparison of indexes between units in the
jurisdictions without rent control and units in hypothetical jurisdictions which adopted rigid rent
control in the beginning of 1993. The price index of the latter diverges from the former soon after
the adoption of the policy. In Tables 9a, b, c and d, we report results for an average mobile home
in Los Angeles, we find that if the unit was under a flexible rent control regime before the
original purchase, its real growth rate in price would be 1.7 percentage points less than
comparable units in a market without rent control or units under a rigid rent control regime. In
contrast with an unchanged rent control policy environment, the adoption of rent control between
the first and second transactions leads to a greater and more significant change in prices. More
rigid rent control leads to a growth rate of 13.6 percentage points more than the units in a market
without rent control, or an increase in coach value of $8,081. In contrast, flexible rent control
results in a growth rate 8.8 percentage points lower than the coaches in a market without rent
control, or a loss in value of $1,088.
6. Conclusions
Mobile homes are usually owned by households that pay a periodic rent for use of the land in
a mobile home park (a pad) on which the coach is located. Because mobile homes tend not to be
mobile but rather fixed on the pad on which they are initially located, and since the pads are
rented, economic theory suggests that the imposition of rent control will lead to the capitalization
of future rent savings when a coach is sold. That is, the buyer will not only pay for the coach but
also for the net present value of the expected savings associated with the constrained future pad
rent obligations to the landlord.
Our results support the hypothesis, based on a more extensive and timely data set than had
been employed in similar prior studies in California. Results from our extensive analysis suggest
that higher income groups receive a relatively larger first time rigid rent control premium
(adoption of rigid rent control increases the coach value by 18.6 percent for higher income groups
17
and 12.5 percent for lower income groups, respectively), but also have to face greater losses from
the adoption of flexible rent control (coach value decrease by 11 percent for higher income
groups and 5 percent for lower income groups). Younger owners on average can get about a 24.8
percent premium for adoption of rigid rent control while elderly owners on average benefit from
an 8.6 percent premium which is 18 percentage points lower than the former. On the other hand,
the elderly owners are able to gain from the adoption of flexible rent control policies through a
6.6 percent increase in their coach value, while the younger owners suffer a 3.4 percent decrease.
When we combine the two factors of income and age, higher income younger owners are the
biggest winners from the adoption of rigid rent control which brings them a roughly 34 percent
increase in coach value. In contrast, lower income elderly owners on average benefit the least
from rigid rent control with a 7 percent increase in coach value. Higher income elderly owners
also benefit from rigid rent control with a 16 percent increase in coach values.
We hope to have contributed to the rent control discussion by estimating the size and
incidence of its impacts as well as how they vary with the strictness of the controls. This is a
controversial policy area and we hope that our estimated effects based on a much larger sample
than had heretofore been available as well as the use of more rigorous and detailed methods
enhance the quality of the debate.
18
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20
Table 1. Definitions of Variables
ADDRESS
CITY
COUNTY
PARKNAME
PARKID
MDLYR
MFG
TRADE
W1/L1
W2/L2
W3/L3
LEGAL
SOLD
ORIG
CONORIG
RESALE
CONRESALE
TYPE
SIZE
WELFARE
MDHHY
OLD
RENT CONTROL
DECON
NODEC
Address
Located city
Located county
Mobile home park name
Mobile home park ID
Model Year
Manufacturer
Trade
W1/L1 single wide/length
W1W2/L1L2 double wide/length
W1W2W3/L1L2L3 triple wide/length
Legal owner
Sold date (year, month, day)
Original sale price (current dollar)
Original sale price (constant 1996 dollar)
Resale price (current dollar)
Resale price (constant 1996 dollar)
Type of the unit (single, double, triple)
Total size of the unit (square footage)
Proportion of households with public assistance income, census tract
Median household income (current dollar) census tract
Proportion of persons ≥ 65 years old, census tract
Dummy variable (1: with RC; 0: without RC)
Dummy variable (1: RC with vacancy decontrol)
Dummy variable (1: RC without vacancy decontrol)
Table 2a. Time Varying Mobile Home Transaction Statistics
Year
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003*
All
3780.61
4692.13
5338.81
5660.14
6159.30
6908.11
7668.03
6747.48
5545.80
5588.80
6533.61
7749.34
8438.82
7859.52
8421.09
9880.72
10559.00
10742.12
11350.12
11713.24
3330.66
Rent
control
group
549.43
793.32
1017.03
1169.70
1327.07
1708.47
2259.03
2230.17
1980.36
2027.33
2840.45
3650.92
4036.33
3427.39
3610.09
4042.02
4098.66
4444.57
4675.10
4629.11
1339.89
Total units traded (square footage, in thousands)
Rent
Rent control Percentage Percentage of rent
control with
without
of rent
control with
vacancy
vacancy
control
vacancy decontrol
decontrol
decontrol
group (%)
group (%)
235.73
313.69
14.53
6.24
403.75
389.57
16.91
8.60
537.97
479.05
19.05
10.08
659.36
510.34
20.67
11.65
755.21
571.87
21.55
12.26
955.21
753.26
24.73
13.83
1360.25
898.78
29.46
17.74
1353.02
877.15
33.05
20.05
1212.42
767.93
35.71
21.86
1204.14
823.18
36.27
21.55
1554.55
1285.91
43.47
23.79
1948.21
1702.72
47.11
25.14
2013.67
2022.66
47.83
23.86
1484.17
1943.22
43.61
18.88
1557.90
2052.20
42.87
18.50
1668.37
2373.65
40.91
16.89
1846.78
2251.88
38.82
17.49
1856.43
2588.13
41.38
17.28
2134.15
2540.96
41.19
18.80
1959.71
2669.40
39.52
16.73
645.71
694.19
40.23
19.39
* First five months of year 2003.
22
Percentage of rent
control without
vacancy decontrol
group (%)
8.30
8.30
8.97
9.02
9.28
10.90
11.72
13.00
13.85
14.73
19.68
21.97
23.97
24.72
24.37
24.02
21.33
24.09
22.39
22.79
20.84
Table 2b. Time Varying Mobile Home Transaction Statistics (Con.)
Year
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003*
All
149.15
187.08
214.95
228.32
248.29
282.15
316.37
263.64
187.68
156.79
141.68
143.31
146.08
144.17
172.45
226.68
264.58
330.46
358.56
402.18
117.17
Rent
control
group
20.47
30.60
38.27
44.81
51.68
65.51
87.19
81.85
62.05
52.20
60.83
70.03
71.54
65.65
80.86
98.28
105.92
148.97
153.78
166.20
48.09
Total traded value (constant 1996 dollar, in millions)
Rent control
Percentage Percentage of rent
Rent control
without
of rent
control with
vacancy
control
vacancy decontrol
with vacancy
decontrol
group (%)
group (%)
decontrol
8.25
12.22
13.73
5.53
14.57
16.03
16.36
7.79
18.81
19.47
17.81
8.75
23.35
21.46
19.63
10.23
27.23
24.45
20.82
10.97
33.84
31.67
23.22
11.99
48.03
39.16
27.56
15.18
46.54
35.30
31.05
17.65
35.87
26.18
33.06
19.11
29.36
22.84
33.30
18.73
31.11
29.73
42.94
21.96
32.69
37.34
48.87
22.81
30.04
41.50
48.97
20.56
24.06
41.59
45.54
16.69
29.87
50.99
46.89
17.32
33.82
64.46
43.36
14.92
41.79
64.13
40.03
15.79
46.57
102.40
45.08
14.09
56.01
97.77
42.89
15.62
58.98
107.22
41.32
14.67
20.51
27.58
41.04
17.51
* First five months of year 2003.
23
Percentage of rent
control without
vacancy decontrol
group (%)
8.19
8.57
9.06
9.40
9.85
11.22
12.38
13.39
13.95
14.57
20.98
26.06
28.41
28.85
29.57
28.44
24.24
30.99
27.27
26.66
23.54
Table 2c. Time Varying Mobile Home Transaction Statistics (Con.)
Year
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003*
All
3452
4311
4883
5167
5619
6199
6763
6050
5057
5150
5929
6918
7517
6974
7481
8702
9195
9225
9815
10013
2801
Rent
control
group
455
671
848
974
1128
1460
1918
1929
1760
1862
2562
3221
3558
3016
3190
3583
3584
3802
4036
3974
1133
Rent control
with vacancy
decontrol
198
351
458
568
652
828
1197
1192
1081
1113
1419
1738
1801
1329
1412
1509
1663
1657
1889
1732
554
Number of units transacted
Rent control
Percentage
Percentage of rent
without
of rent
control with
vacancy
control
vacancy decontrol
decontrol
group (%)
group (%)
257
13.18
5.74
320
15.56
8.14
390
17.37
9.38
406
18.85
10.99
476
20.07
11.60
632
23.55
13.36
721
28.36
17.70
737
31.88
19.70
679
34.80
21.38
749
36.16
21.61
1143
43.21
23.93
1483
46.56
25.12
1757
47.33
23.96
1687
43.25
19.06
1778
42.64
18.87
2074
41.17
17.34
1921
38.98
18.09
2145
41.21
17.96
2147
41.12
19.25
2242
39.69
17.30
579
40.45
19.78
* First five months of year 2003.
24
Percentage of rent
control without
vacancy decontrol
group (%)
7.44
7.42
7.99
7.86
8.47
10.20
10.66
12.18
13.43
14.54
19.28
21.44
23.37
24.19
23.77
23.83
20.89
23.25
21.87
22.39
20.67
Table 3. Annual Growth Rates (1984-2002)
Year
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
Average
annual
growth
rate
All
24.11%
13.78%
6.02%
8.82%
12.16%
11.00%
-12.01%
-17.81%
0.78%
16.91%
18.61%
8.90%
-6.86%
7.15%
17.33%
6.86%
1.73%
5.66%
3.20%
6.65%
Total units traded (square footage)
Rent control
Rent
Rent control
without
control
with vacancy
vacancy
group
decontrol
decontrol
44.39%
71.27%
24.19%
28.20%
33.25%
22.97%
15.01%
22.56%
6.53%
13.45%
14.54%
12.06%
28.74%
26.48%
31.72%
32.23%
42.40%
19.32%
-1.28%
-0.53%
-2.41%
-11.20%
-10.39%
-12.45%
2.37%
-0.68%
7.20%
40.11%
29.10%
56.21%
28.53%
25.32%
32.41%
10.56%
3.36%
18.79%
-15.09%
-26.30%
-3.93%
5.33%
4.97%
5.61%
11.96%
7.09%
15.66%
1.40%
10.69%
-5.13%
8.44%
0.52%
14.93%
5.19%
14.96%
-1.82%
-0.98%
-8.17%
5.05%
13.02%
13.71%
13.00%
Total traded value (constant 1996 dollar)
Rent control
Rent
Rent control
without
control
with vacancy
vacancy
group
decontrol
All
decontrol
25.43%
49.46%
76.55%
31.17%
14.89%
25.09%
29.07%
21.47%
6.22%
17.08%
24.15%
10.25%
8.75%
15.34%
16.62%
13.95%
13.64%
26.75%
24.29%
29.50%
12.13%
33.10%
41.92%
23.66%
-16.67%
-6.13%
-3.09%
-9.85%
-28.81% -24.19%
-22.93%
-25.84%
-16.46% -15.87%
-18.15%
-12.74%
-9.64%
16.53%
5.94%
30.13%
1.15%
15.13%
5.10%
25.61%
1.93%
2.15%
-8.12%
11.14%
-1.31%
-8.23%
-19.90%
0.22%
19.61%
23.17%
24.15%
22.60%
31.45%
21.54%
13.19%
26.43%
16.72%
7.77%
23.57%
-0.52%
24.90%
40.64%
11.44%
59.67%
8.51%
3.23%
20.28%
-4.52%
12.16%
8.07%
5.30%
9.66%
6.56%
13.19%
25
13.13%
13.79%
Number of units transacted
All
24.88%
13.27%
5.82%
8.75%
10.32%
9.10%
-10.54%
-16.41%
1.84%
15.13%
16.68%
8.66%
-7.22%
7.27%
16.32%
5.67%
0.33%
6.40%
2.02%
Rent
control
group
47.47%
26.38%
14.86%
15.81%
29.43%
31.37%
0.57%
-8.76%
5.80%
37.59%
25.72%
10.46%
-15.23%
5.77%
12.32%
0.03%
6.08%
6.15%
-1.54%
Rent control
with vacancy
decontrol
77.27%
30.48%
24.02%
14.79%
26.99%
44.57%
-0.42%
-9.31%
2.96%
27.49%
22.48%
3.62%
-26.21%
6.25%
6.87%
10.21%
-0.36%
14.00%
-8.31%
Rent control
without
vacancy
decontrol
24.51%
21.88%
4.10%
17.24%
32.77%
14.08%
2.22%
-7.87%
10.31%
52.60%
29.75%
18.48%
-3.98%
5.39%
16.65%
-7.38%
11.66%
0.09%
4.42%
6.22%
13.17%
14.07%
13.00%
Figure 1a. Total Traded Square Footage
(in thousands)
14,000
12,000
10,000
8,000
6,000
4,000
2,000
0
1983
1984
1985
1986
1987
1988
1989
Without rent control
1990
1991
1992
1993
1994
Rent control with vacancy decontrol
26
1995
1996
1997
1998
1999
Rent control w/o vacancy decontrol
2000
2001
2002
Figure 1b. Total Traded Value (constant $, base=1996)
(in millions)
450
400
350
300
250
200
150
100
50
0
1983
1984
1985
1986
1987
1988
1989
Without rent control
1990
1991
1992
1993
1994
Rent control with vacancy decontrol
27
1995
1996
1997
1998
1999
Rent control w/o vacancy decontrol
2000
2001
2002
Figure 1c. Number of Transactions
12,000
10,000
8,000
6,000
4,000
2,000
0
1983
1984
1985
1986
1987
1988
1989
Without rent control
1990
1991
1992
1993
1994
Rent control with vacancy decontrol
28
1995
1996
1997
1998
1999
Rent control w/o vacancy decontrol
2000
2001
2002
Table 4. Descriptive Statistics – means and standard deviations of continuous covariates
Original price (constant 1996 dollar)
Resale price (constant 1996 dollar)
Median household income (constant 1996 dollar)
Proportion of households with public assistance income
Proportion of persons ≥ 65 years old
Unit's size (sq. ft.)
Unit's age
Sample size
All
40,775
(21150)
34,118
(26201)
43,274
(13524)
0.067
(0.047)
0.132
(0.094)
1,127
(396)
19.23
(9.15)
137,221
Los
Angeles
40,696
(22183)
33,207
(24331)
46,923
(14011)
0.070
(0.046)
0.103
(0.055)
1,094
(411)
19.10
(9.39)
31,404
Note:
Standard deviations are in parenthesis.
29
Orange
45,362
(21313)
38,982
(26981)
47,193
(12135)
0.052
(0.039)
0.117
(0.088)
1,199
(370)
19.28
(9.15)
23,934
Riverside
39,691
(20029)
28,567
(25116)
33,149
(9862)
0.086
(0.040)
0.244
(0.160)
1,160
(422)
16.01
(8.51)
12,912
San
Bernardino
36,940
(18324)
22,085
(17834)
36,650
(11097)
0.090
(0.053)
0.115
(0.067)
1,093
(383)
19.09
(9.24)
20,827
San
Diego
38,102
(19407)
30,925
(22767)
39,991
(10462)
0.059
(0.045)
0.151
(0.083)
1,103
(389)
20.75
(9.07)
28,551
Santa
Clara
46,410
(25154)
54,441
(31546)
52,492
(15100)
0.057
(0.044)
0.114
(0.079)
1,158
(406)
18.82
(8.55)
13,423
Ventura
38,699
(18691)
42,687
(28392)
48,194
(11262)
0.050
(0.033)
0.122
(0.052)
1,113
(376)
20.85
(8.61)
6,170
Table 5. Descriptive Statistics – frequencies of categorical covariates
Single
Double
Triple
Rent Control
Rent control with vacancy decontrol
Rent control without vacancy decontrol
Adoption of rent control with vacancy
decontrol
Adoption of rent control without
vacancy decontrol
Sample size
All
35,787
(26.1)
94,110
(68.6)
7,324
(5.3)
48,664
(35.5)
24,341
(17.7)
24,323
(17.7)
20,980
(15.3)
21,723
(15.8)
137,221
Los
Angeles
9,157
(29.2)
20,394
(64.9)
1,853
(5.9)
8,828
(28.1)
7,035
(22.4)
1,793
(5.7)
5,926
(18.9)
1,415
(4.5)
31,404
Orange
3,532
(14.8)
18,997
(79.4)
1,405
(5.9)
1,092
(4.6)
1
(0.0)
1,091
(4.6)
1
(0.0)
1,041
(4.3)
23,934
Note:
Percentages to corresponding full or county samples are in parenthesis.
30
Riverside
3,513
(27.2)
8,338
(64.6)
1,061
(8.2)
7,865
(60.9)
5,720
(44.3)
2,145
(16.6)
4,862
(37.7)
1,942
(15.0)
12,912
San
Bernardino
6,741
(32.4)
13,265
(63.7)
821
(3.9)
8,959
(43.0)
3,401
(16.3)
5,558
(26.7)
2,833
(13.6)
5,148
(24.7)
20,827
San Diego
7,820
(27.4)
19,457
(68.1)
1,274
(4.5)
10,871
(38.1)
4,745
(16.6)
6,126
(21.5)
4,399
(15.4)
5,445
(19.1)
28,551
Santa
Clara
3,325
(24.8)
9,412
(70.1)
686
(5.1)
5,948
(44.3)
252
(1.9)
5,696
(42.4)
228
(1.7)
5,063
(37.7)
13,423
Ventura
1,699
(27.5)
4,247
(68.8)
224
(3.6)
5,101
(82.7)
3,187
(51.7)
1,914
(31.0)
2,731
(44.3)
1,669
(27.1)
6,170
Figure 2. Logarithm of Price Growth Rate by Sub-Groups
These figures present the box plot of the logarithm of price growth rates by various sub-groups. The boxes contain
the middle 50% of the data. The upper edges of the boxes indicate the 75th percentile of the data set, and the lower
edges indicate the 25th percentile. The lines in the boxes indicate the median value of the data. The ends of the
vertical lines indicate the minimum and maximum data values. The logarithm of price growth rate is defined as:
ln(
Pt +τ
Pt
),
where ln ( i ) is natural logarithm function, Pt +τ is the resale price after τ periods since the original sale
at t , Pt is the original sales price at t .
Figure 2a. Logarithm of Price Growth Rate by County
31
Figure 2b. Logarithm of Price Growth Rate by Coach Structure
Figure 2c. Logarithm of Price Growth Rate by Rent Control Regime
32
Table 6. GLS Estimates for Rent Control Impact on Mobile Home Price
The table reports rent control impacts on mobile home prices. We estimate the rent control impact by decomposing the capitalization of
rent control premium from a repeated-sales mobile home price index model specified in equations (9) and (10) using a three-stage GLS
estimation approach. In the first stage, we use OLS to estimate the following mobile home price index model which decomposes the rent
control premium capitalization from the mobile home price appreciation: ln( Pt +τ ) = D ' γ + Z 'θ + ( RC − RC )' δ + μ (τ ) , where ln ( i ) is
Pt
t +τ
t
natural logarithm function, Pt +τ is the resale price after τ periods since the original sale at t , Pt is the original sales price at t ,
D is a repeated-sales transaction vector composed of -1, 0 and 1; RC is a vector of indicator variables to capture the rent control premium
capitalization; Z is a vector of hedonic characteristics capturing the location effect (measured by county), and coach structure
characteristics, such as single, double, or triple-width, size, and neighborhood quality, etc., and μ is the normally distributed error term
with a diffusion variance varying by the time span between two transactions. In the second stage, we estimate a diffusion process of the
mobile home price index by regressing the square term of residuals, ε , collected from the first stage OLS regression on a quadratic
2
function of time span between the two sales, such that, ε (τ )2 = β τ + β τ 2 + ω , where τ is the duration between original sale and
resale (measured in quarters), and ω is a normally distributed error term. In the third stage we re-estimate the mobile home price index
model using a GLS estimation approach weighted by the square root of the estimated diffusion variance, εˆ (τ ) , obtained from the
second stage. Fore readability, the estimated mobile home price indices are not reported in the table.
1
2
Impact of Rent Control
(1)
Orange County
Riverside
San Bernardino
San Diego
Santa Clara
Ventura
Double
Triple
Size
Under rent control without vacancy decontrol
Under rent control with vacancy decontrol
Adoption of rent control without vacancy decontrol
Adoption of rent control without vacancy decontrol
× number of months years under rent control
Adoption of rent control with vacancy decontrol
R square
Sample size
0.15
(28.77)
-0.11
(-20.70)
0.412
137,221
33
Impact of Rent Control together with Other Effects
(3)
(2)
-0.04
-0.04
(-8.79)
(-8.38)
-0.28
-0.27
(-45.92)
(-45.91)
-0.30
-0.29
(-55.88)
(-55.03)
0.31
-0.03
(50.24)
(-6.47)
-0.03
0.33
(-6.17)
(53.87)
0.37
0.36
(42.13)
(41.59)
0.19
0.19
(30.34)
(30.39)
0.26
0.27
(21.25)
(21.46)
1.63E-05
1.67E-05
(2.47)
(2.54)
-1.39
-1.34
(-39.57)
(-38.18)
0.01
0.01
(1.44)
(1.10)
-0.02
-0.02
(-1.85)
(-1.88)
0.12
(23.22)
0.015
(30.06)
-0.09
-0.08
(-16.57)
(-15.76)
0.496
0.498
137,221
137,221
Figure 3. Logarithm of Price Growth Rate by Income and Age Sub-Groups
These figures present the box plot of the logarithm of price growth rates by various sub-groups. The boxes contain the middle 50
percent of the data. The upper edges of the boxes indicate the 75th percentile of the data set, and the lower edges indicate the 25th
percentile. The lines in the boxes indicate the median value of the data. The ends of the vertical lines indicate the minimum and
maximum data values. The logarithm of price growth rate is defined as: ln( Pt +τ ) , where ln ( i ) is natural logarithm function, Pt +τ
Pt
is the resale price after τ periods since the original sale at t , Pt is the original sales price at t .
Figure 3a. Logarithm of Price Growth Rate by Income Group
Figure 3b. Logarithm of Price Growth Rate by Age Group
34
Table 7. GLS Estimates for Logarithm of Price Growth Rate, by Subgroups
The table reports rent control impacts on mobile home prices. We estimate the rent control impact by decomposing the
capitalization of rent control premium from a repeated-sales mobile home price index model specified in equations (9) and
(10) using a three-stage GLS estimation approach. In the first stage, we use OLS to estimate the following mobile home
price index model which decomposes the rent control premium capitalization from the mobile home price appreciation:
P
ln( t +τ ) = D ' γ + Z 'θ + ( RC − RC )' δ + μ (τ ) , where ln ( i ) is natural logarithm function, Pt +τ is the resale price after τ
Pt
t +τ
t
periods since the original sale at t , Pt is the original sales price at t , D is a repeated-sales transaction vector composed
of -1, 0 and 1; RC is a vector of indicator variables to capture the rent control premium capitalization; Z is a vector of
hedonic characteristics capturing the location effect (measured by county), and coach structure characteristics, such as
single, double, or triple-width, size, and neighborhood quality, etc., and μ is the normally distributed error term with a
diffusion variance varying by the time span between two transactions. In the second stage, we estimate a diffusion process
of the mobile home price index by regressing the square term of residuals, ε , collected from the first stage OLS
regression on a quadratic function of time span between the two sales, such that, ε (τ )2 = β1τ + β 2τ 2 + ω , where τ is the
duration between original sale and resale (measured in quarters), and ω is a normally distributed error term. In the third
stage we re-estimate the mobile home price index model using a GLS estimation approach weighted by the square root of
the estimated diffusion variance, εˆ (τ ) , obtained from the second stage. Fore readability, the estimated mobile home price
indices are not reported in the table.
2
35
Table 7a. GLS Estimates for Logarithm of Price Growth Rate, by Census Tract Household Income
Orange County
Riverside
San Bernardino
San Diego
Santa Clara
Ventura
Double
Triple
Size
Proportion of households with public assistance income
Under rent control without vacancy decontrol
Under rent control with vacancy decontrol
Adoption of rent control without vacancy decontrol
Adoption of rent control with vacancy decontrol
R square
Sample size
Higher Median Household
Income
0.04
(4.49)
-0.17
(-18.21)
-0.23
(-29.81)
0.17
(13.57)
-0.061
(-9.10)
0.37
(22.29)
0.17
(16.65)
0.25
(13.41)
-1.22E-05
(-1.23)
-2.10
(-26.47)
0.070
(4.90)
-0.02
(-1.95)
0.16
(18.40)
-0.11
(-14.12)
0.44
52,921
36
Lower Median Household
Income
-0.07
(-9.53)
-0.34
(-39.15)
-0.35
(-44.49)
0.36
(42.96)
-0.004
(-0.58)
0.37
(33.58)
0.20
(24.68)
0.27
(15.55)
4.10E-05
(4.46)
-1.00
(-22.94)
-0.014
(-1.04)
0.01
(0.44)
0.12
(18.21)
-0.05
(-7.40)
0.54
84,300
Table 7b. GLS Estimates for Logarithm of Price Growth Rate, by Census Tract Proportion of Elderly Population
Orange County
Riverside
San Bernardino
San Diego
Santa Clara
Ventura
Double
Triple
Size
Proportion of households with public assistance income
Under rent control without vacancy decontrol
Under rent control with vacancy decontrol
Adoption of rent control without vacancy decontrol
Adoption of rent control with vacancy decontrol
R square
Sample size
Higher Proportion of Elderly
Population
-0.05
(-6.50)
-0.36
(-40.69)
-0.41
(-54.09)
0.32
(36.98)
-0.08
(-11.78)
0.33
(26.63)
0.17
(18.40)
0.23
(13.26)
4.99E-05
(5.21)
-1.13
(-19.42)
0.06
(4.29)
0.02
(1.73)
0.08
(11.50)
-0.02
(-2.90)
0.48
69,547
37
Lower Proportion of Elderly
Population
-0.03
(-4.75)
-0.21
(-25.65)
-0.19
(-25.75)
0.31
(33.88)
0.03
(3.94)
0.41
(33.51)
0.21
(24.56)
0.26
(14.78)
-2.63E-05
(-2.96)
-1.21
(-27.02)
-0.03
(-2.51)
-0.03
(-2.54)
0.21
(24.80)
-0.14
(-17.16)
0.52
67,674
Table 7c. GLS Estimates for Logarithm of Price Growth Rate, by Census Tract Median Household Income and Proportion of
Elderly Population
Orange County
Riverside
San Bernardino
San Diego
Santa Clara
Ventura
Double
Triple
Size
Proportion of households
with public assistance
income
Under rent control without
vacancy decontrol
Under rent control with
vacancy decontrol
Adoption of rent control
without vacancy decontrol
Adoption of rent control with
vacancy decontrol
R square
Sample size
High Income & High
Proportion of Elderly
0.08
(6.96)
-0.21
(-9.93)
-0.47
(-33.53)
0.33
(15.24)
-0.10
(-9.92)
0.34
(10.94)
0.16
(9.70)
0.26
(9.18)
-1.58E-05
(-1.01)
-0.99
High Income & Low
Proportion of Elderly
0.01
(1.01)
-0.11
(-10.58)
-0.11
(-11.69)
0.15
(9.68)
-0.02
(-1.91)
0.40
(20.03)
0.17
(13.29)
0.18
(7.68)
-1.24E-05
(-0.99)
-2.09
Low Income & High
Proportion of Elderly
-0.02
(-1.98)
-0.32
(-27.73)
-0.35
(-32.54)
0.40
(34.83)
0.01
(0.78)
0.42
(28.25)
0.19
(16.54)
0.26
(11.60)
4.04E-05
(3.19)
-0.88
Low Income & Low
Proportion of Elderly
-0.10
(-10.25)
-0.35
(-27.11)
-0.34
(-27.66)
0.31
(24.44)
0.01
(1.14)
0.33
(18.97)
0.22
(18.68)
0.25
(9.51)
2.85E-05
(2.12)
-0.97
(-6.62)
0.15
(6.64)
-0.011
(-0.43)
0.11
(8.49)
0.031
(2.53)
0.41
24,826
(-22.27)
-0.01
(-0.34)
-0.003
(-0.20)
0.26
(19.93)
-0.148
(-14.08)
0.49
28,095
(-12.72)
-0.04
(-1.98)
0.022
(1.30)
0.06
(7.82)
-0.03
(-2.89)
0.53
44,721
(-16.14)
0.02
(1.11)
0.009
(0.57)
0.22
(19.23)
-0.07
(-6.06)
0.55
39,579
38
Table 8. Repeat Sale Price Index (1983=100)
Quarter
1983-1
1983-2
1983-3
1983-4
1984-1
1984-2
1984-3
1984-4
1985-1
1985-2
1985-3
1985-4
1986-1
1986-2
1986-3
1986-4
1987-1
1987-2
1987-3
1987-4
1988-1
1988-2
1988-3
1988-4
1989-1
1989-2
1989-3
1989-4
1990-1
1990-2
1990-3
1990-4
1991-1
1991-2
1991-3
1991-4
1992-1
1992-2
1992-3
1992-4
1993-1
Index
100.00
110.70
115.93
116.74
105.00
116.80
118.28
119.37
100.65
115.81
118.67
116.06
97.77
115.64
117.35
116.39
100.26
113.32
115.11
111.95
101.38
111.78
114.57
113.21
96.10
110.82
114.37
111.61
93.16
108.98
107.49
99.47
80.86
89.73
88.68
80.06
71.82
72.18
67.98
61.27
54.38
Quarter
1993-2
1993-3
1993-4
1994-1
1994-2
1994-3
1994-4
1995-1
1995-2
1995-3
1995-4
1996-1
1996-2
1996-3
1996-4
1997-1
1997-2
1997-3
1997-4
1998-1
1998-2
1998-3
1998-4
1999-1
1999-2
1999-3
1999-4
2000-1
2000-2
2000-3
2000-4
2001-1
2001-2
2001-3
2001-4
2002-1
2002-2
2002-3
2002-4
2003-1
2003-2
39
Index
52.75
50.64
44.60
47.65
42.06
40.84
39.79
43.23
37.77
37.31
37.47
41.33
35.19
36.88
35.23
44.32
34.84
36.62
39.27
48.01
40.16
41.21
41.63
48.82
43.78
42.99
44.06
50.04
46.18
49.03
51.35
52.00
50.78
52.30
51.92
51.85
52.18
55.11
55.70
52.07
56.63
40
20
03
20
02
20
01
20
01
20
00
19
99
19
98
19
98
19
97
19
96
19
95
19
95
19
94
19
93
19
92
19
92
19
91
19
90
19
89
19
89
19
88
19
87
19
86
19
86
19
85
19
84
19
83
19
83
02
03
04
01
02
03
04
01
02
03
04
01
02
03
04
01
02
03
04
01
02
03
04
01
02
03
04
01
Figure 4a. Full Sample Index
140
120
100
80
60
40
20
0
Figure 4b. Comparison in Indexes: No Rent Control vs. Change in Rent Control Policy (Adoption of
Rent Control without Decontrol)
75
70
65
60
Adoption of rent control without decontrol
55
50
45
40
35
19
92
01
19
92
03
19
93
01
19
93
03
19
94
01
19
94
03
19
95
01
19
95
03
19
96
01
19
96
03
19
97
01
19
97
03
19
98
01
19
98
03
19
99
01
19
99
03
20
00
01
20
00
03
20
01
01
20
01
03
20
02
01
20
02
03
20
03
01
30
Quarter
Adoption of rent control without decontrol
41
No rent control
Table 9. Resale Price Comparison for an Average 19 years old Structure in Los Angeles
The following Tables present the estimated resale prices for the average 19 years old mobile home units
in each subgroup in Los Angeles County, under different rent control regimes. The average unit is
defined as the one with average structure (double, triple), in census tract with average proportion of
households with public assistance income, etc. By using the average units' characteristics and the
parameters estimated in Table 7, we can simulate the resale prices as presented here.
Table 9a. Resale Price Comparison for an Average 19 years old Structure in Los Angeles
Rent Control Regime
Under rent
Adoption of
control with
rent control
vacancy
without
decontrol
vacancy
decontrol
Resale price
comparison for an
average 19 years old
structure
No rent
control
Under rent
control
without
vacancy
decontrol
Resale price
$43,263
$43,263
$42,588
$48,824
$39,656
Price increase
Growth rate
(percentage)
Growth out from rent
control (percentage)
$2,519
$2,519
$1,845
$8,081
-$1,088
6.18
6.18
4.53
19.83
-2.67
-
0
-1.66
13.65
-8.85
42
Adoption of
rent control
with vacancy
decontrol
Table 9b. Resale Price Comparison for an Average 19 years old Structure in Los Angeles, Different Income Communities
High Median Household Income
Under rent
Under rent
Adoption of
control
control with
rent control
without
vacancy
without
vacancy
decontrol
vacancy
decontrol
decontrol
Resale price
comparison for an
average 19 years old
structure
No rent
control
Resale price
$43,500
$46,760
$42,427
$366
$3,625
0.85
-
Price increase
Growth rate
(percentage)
Growth out from rent
control (percentage)
Low Median Household Income
Under rent
Under rent
Adoption of
control
control with
rent control
without
vacancy
without
vacancy
decontrol
vacancy
decontrol
decontrol
Adoption of
rent control
with vacancy
decontrol
Adoption of
rent control
with vacancy
decontrol
No rent
control
$51,538
$38,731
$38,237
$38,237
$38,418
$42,664
$36,406
-$707
$8,403
-$4,403
$2,744
$2,744
$2,926
$7,172
$914
8.40
-1.64
19.48
-10.21
7.73
7.73
8.24
20.21
2.57
7.56
-2.49
18.63
-11.05
-
0
0.51
12.47
-5.16
Table 9c. Resale Price Comparison for an Average 19 years old Structure in Los Angeles, Different Age Communities
High Proportion of Elderly Population
Under rent
Under rent
Adoption of
Adoption of
control
control with
rent control
rent control
vacancy
without
with vacancy
without
decontrol
vacancy
decontrol
vacancy
decontrol
decontrol
Low Proportion of Elderly Population
Under rent
Under rent
Adoption of Adoption of
control
control with rent control
rent control
without
vacancy
without
with vacancy
vacancy
decontrol
vacancy
decontrol
decontrol
decontrol
Resale price
comparison for an
average 19 years old
structure
No rent
control
Resale price
$47,383
$50,199
$48,503
$51,030
$46,435
$39,892
$38,574
$38,796
$49,560
$34,617
Price increase
Growth rate
(percentage)
Growth out from rent
control (percentage)
$4,903
$7,720
$6,024
$8,550
$3,956
$938
-$381
-$159
$10,605
-$4,338
11.54
18.17
14.18
20.13
9.31
2.41
-0.98
-0.41
27.22
-11.14
-
6.63
2.64
8.59
-2.23
-
-3.38
-2.82
24.82
-13.54
43
No rent
control
Table 9d. Resale Price Comparison for an Average 19 years old Structure in Los Angeles, Different Income and Age Communities
High Income & High Proportion of Elderly
Under rent
Under rent
Adoption of Adoption of
control
control with rent control
rent control
without
vacancy
without
with vacancy
vacancy
decontrol
vacancy
decontrol
decontrol
decontrol
High Income & Low Proportion of Elderly
Under rent
Adoption of Adoption of
Under rent
control
control with rent control
rent control
without
vacancy
without
with vacancy
vacancy
decontrol
vacancy
decontrol
decontrol
decontrol
Resale price
comparison for an
average 19 years old
structure
No rent
control
Resale price
$47,132
$54,326
$47,132
$52,407
$48,534
$41,229
$41,229
$41,229
$55,365
$34,640
Price increase
Growth rate
(percentage)
Growth out from rent
control (percentage)
$3,069
$10,263
$3,069
$8,344
$4,471
-$799
-$799
-$799
$13,337
-$7,388
6.97
23.29
6.97
18.94
10.15
-1.90
-1.90
-1.90
31.73
-17.58
-
16.33
0
11.97
3.18
-
0
0
33.63
-15.68
Resale price
comparison for an
average 19 years old
structure
No rent
control
Resale price
$41,807
$40,373
$41,807
$44,425
$40,751
$35,746
$35,746
$35,746
$43,491
$33,459
Price increase
Growth rate
(percentage)
Growth out from rent
control (percentage)
$3,743
$2,309
$3,743
$6,362
$2,687
$2,180
$2,180
$2,180
$9,924
-$107
9.83
6.07
9.83
16.71
7.06
6.49
6.49
6.49
29.57
-0.32
-
-3.77
0
6.88
-2.77
-
0
0
23.07
-6.81
Low Income & High Proportion of Elderly
Under rent
Under rent
Adoption of Adoption of
control
control with rent control
rent control
vacancy
without
with vacancy
without
decontrol
vacancy
decontrol
vacancy
decontrol
decontrol
44
No rent
control
No rent
control
Low Income & Low Proportion of Elderly
Under rent
Under rent
Adoption of Adoption of
control
control with rent control
rent control
without
vacancy
without
with vacancy
vacancy
decontrol
vacancy
decontrol
decontrol
decontrol
Figure 5. Simulation Results: Resale Price Comparison for Los Angeles County
The following figures present the estimated resale prices for the average 19 years old mobile home units in each subgroup in Los Angeles County, under different rent control
regimes. The average unit is defined as the one with average structure (double, triple), in census tract with average proportion of households with public assistance income,
etc.
Figure 5a. Resale Price Comparison
$50,000
$48,000
$46,000
$44,000
$42,000
$40,000
$38,000
$36,000
$34,000
$32,000
$30,000
No rent control
Under rent control without
vacancy decontrol
Under rent control with
vacancy decontrol
45
Adoption of rent control Adoption of rent control with
without vacancy decontrol
vacancy decontrol
Figure 5b. Resale Price Comparison-Median Household Income
$55,000
$50,000
$45,000
$40,000
$35,000
$30,000
No rent
control
Under rent
control
without
vacancy
decontrol
No rent
control
Under rent Adoption of Adoption of
control with rent control rent control
without with vacancy
vacancy
decontrol
vacancy
decontrol
decontrol
High Median Household Income
Under rent
control
without
vacancy
decontrol
Under rent Adoption of Adoption of
control with rent control rent control
without with vacancy
vacancy
decontrol
vacancy
decontrol
decontrol
Low Median Household Income
46
Figure 5c. Resale Price Comparison-Age Community
$55,000
$50,000
$45,000
$40,000
$35,000
$30,000
No rent
control
Under rent
control
without
vacancy
decontrol
No rent
control
Under rent Adoption of Adoption of
control with rent control rent control
without with vacancy
vacancy
decontrol
vacancy
decontrol
decontrol
High Proportion of Elderly Population
Under rent
control
without
vacancy
decontrol
Under rent Adoption of Adoption of
control with rent control rent control
without with vacancy
vacancy
decontrol
vacancy
decontrol
decontrol
Low Proportion of Elderly Population
47
$60,000
$55,000
$50,000
$45,000
$40,000
$35,000
$30,000
Under rent control without vacancy decontrol
Under rent control with vacancy decontrol
Adoption of rent control without vacancy
decontrol
Adoption of rent control with vacancy decontrol
48
High Income & Low Proportion of
Elderly
No rent control
Under rent control without vacancy decontrol
Under rent control with vacancy decontrol
Adoption of rent control without vacancy
decontrol
Adoption of rent control with vacancy decontrol
Low Income & High Proportion of
Elderly
No rent control
Under rent control without vacancy decontrol
Under rent control with vacancy decontrol
Adoption of rent control without vacancy
decontrol
Adoption of rent control with vacancy decontrol
Low Income & Low Proportion
of Elderly
No rent control
Under rent control without vacancy decontrol
Under rent control with vacancy decontrol
Adoption of rent control without vacancy
decontrol
Adoption of rent control with vacancy decontrol
Figure 5d. Resale Price Comparison-Median Household Income and Elderly Proportion
High Income & High Proportion of
Elderly
No rent control