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Condo rents and apartment rents
N. Edward Coulson and Lynn M. Fisher
Highly preliminary, please do not cite.
Abstract: Coulson and Fisher (2014) note various incentive issues in the formation of ownership of multiunit buildings. The flow of housing services, particularly the maintenance of the building is a function of that
ownership structure. We find weak evidence that rents in condo buildings are higher than rents in
comparable apartment buildings, even accounting for unobservable quality differences in the two types of
buildings. This premium generally increases with the number of units in the building, highlighting the
incentive problems discussed in Coulson and Fisher (2014).
Affiliations: Coulson: Department of Economics and Lied Institute for Real Estate Studies, University of
Nevada, Las Vegas, [email protected]. Fisher: Mortgage Bankers Association, [email protected]
Acknowledgements: We thank Herman Li for computational assistance.
1. Introduction
In the United States there are two dominant forms of housing tenure: owner-occupation and renting, the
latter of which encompasses any situation where the occupying household enjoys the use of the unit but has
no right to income or resale, and pays rent to a landlord. In the US, tenure type is strongly associated with
structural characteristics. In particular, owner-occupiers largely occupy single family detached units, while
renters largely occupy units in multi-family buildings. The converses are also true, multi-family structures are
largely rental units, while single family structures are mostly owner occupied. Table 1 displays all of these
facts using data from the 2010 census.
Within the class of multifamily structures, there are generically two ownership forms, which we refer as
apartments and condos. Apartments are units within a multifamily structure, where the entire structure is
owned by a single ownership group. This is usually an individual investor, or a group of partners. The
occupying units all pay rent to the owner(s) who we call the landlord. There are also condos which are units in
multifamily structures in which ownership is decentralized; each unit is (potentially) owned by a distinct
entity, and notionally that owner also occupies the unit.
In the 2011 American Housing Survey, Coulson and Fisher (2014) found that condos units are roughly 15 %
of the multifamily housing stock, as noted in Table 2 (discussed below in more detail). Perhaps surprisingly,
about half of the surveyed condo units are in fact not owner-occupied but rented to another household.
Thus there are in fact two different kinds of rentals, depending on the ownership structure of the multifamily
building.
In this paper, we conduct, to our knowledge for the first time, a hedonic comparison of these two markets.
That is, we wish to estimate a hedonic regression in which one of the characteristics is the ownership
structure--- a condo indicator. There are two motivations that suggest that for otherwise physically identical
units the flow of services from these two types of housing units. The first is suggested by the long literature
(based on single family structures) on homeowner externalities, the idea that homeowners are better
neighbors than renters. Motivated by both their longer expected residency spell and the fact that greater
investment in the social capital of the neighborhood has a payoff in higher utility flows (and resale prices)
homeowners more often engage in actions that benefit the neighborhood as a whole. Such actions include
better maintenance and gardening, greater participation in public and civic affairs, better child outcomes, and
the like. DiPasquale and Glaeser (1999), Haurin et al (2002), Harding et al (2000), Green and White (1997) ,
Galster (1983) among others have contributed to this literature. This literature is not uncontroversial, and
contrary evidence is provided in Englehardt et al (2010), Barker and Miller (2009), Holupka and Newman
(2012), Gatzlaff et al (1998) and others. However, the net impact of homeowners is perhaps best measured
by the impact of neighborhood ownership rates on single family home prices. Coulson and Li (2013)
demonstrate that there is a plausibly causal link from neighborhood ownership rates to home values based on
the cluster samples of the American Housing Surveys from 1985 through 1993. Similarly, since condos are at
least in part occupied by owners, we hypothesize that their investment in neighborhood (that is to say,
housing complex) social capital will cause condo rental rates to be greater than apartment rentals for
otherwise comparable units.
Our second motivation arises from consideration of the correlation between structure type and tenure. In
observing this correlation Glaeser and Shapiro (2003; see also Glaeser 2011a, 2011b) note that in the single
family context, the inability of landlords and tenants to base contracts on the tenant’s utilization rate leads to
overutilization by the tenant. This is dubbed the renter externality in the early work on this issue, particularly
Henderson and Ioannides (1982). As such, owner-occupation becomes the dominant ownership form.
In multi-family structures another force exists. Building-level utilization and maintenance (of the HVAC
system shared by all units, the commonly shared lobby, swimming pool, etc) as opposed to unit-level
utilization is a common pool resource and therefore subject to free-riding by the occupants of the individual
units. Centralized ownership under a landlord would seem to be superior organizational form, since under
the decentralized ownership structures of condos, the decisions about maintenance are subject to sometimes
intense coordination costs, which would increase as the size of the complex increases. Free-riding should
also increase in a parallel fashion (Cornes and Sandler, 1984).
Both of these would seem to indicate that condo organization should decrease in the size of the complex, but
the evidence presented in Coulson and Fisher (2014) shows that the opposite in fact occurs. Condo
organization is more prevalent the larger the complex size. This is presumably because the problems adduced
above in condo buildings can be overcome by assigning maintenance to an external manager, but that such a
step might only be feasible for larger buildings. Buildings with a smaller number of units, what we will
occasionally call the unit count, will not have sufficient scale to take such a step1.
The high prevalence of condo rentals presented in Table 2 is in any case something of a puzzle, since in such
an ownership structure the unit is subject to both the rental externality and the difficulties of decentralized
1
In larger apartment structures the ownership form is less likely to be a single owner/landlord/investor and more likely to be
(passive) limited partners who themselves hire a managing general partner (who in turn also hires a building management firm.) The
resulting incentive problem (to induce efficient effort by the general partner and manager) exist at whatever building size engenders
passive partners but otherwise are probably not as sensitive to building size as exists in condos.
ownership which can only be overcome if sufficient scale is reached. Thus we hypothesize both that the
rental rate for condos is higher than for comparable apartments, but that the condo premium varies with
complex size, and will be smaller for smaller complexes.
In performing this analysis our primary empirical challenge is the possibility that unobserved quality
differences between condos and apartments that would lead us to incorrectly assign the higher rents in
condos to the condo organization rather than those differences. Given, for example, the special tax
treatment of owner-occupied property, we would expect condos to have both higher observable quality
(Hansen, 2013) and higher unobserved quality than apartment buildings, and we must proceed as if selection
into condo organization by the developer of the complex is not random. Furthermore condo rents are only
going to be observable if the condo unit is rented out, and again, perhaps because of the tax treatment of
owner-occupation, the owner’s decision to live in, or rent out, the unit will not be random. We use a double
selection model based originally on Poirier (1980) to deal with these selection problems; we calculate twodimensional Mills ratios (as in Hotchkiss and Pitts, 2005) that are added to the hedonic regressions in order to
consistently estimate the parameters of the hedonic function, including the parameter that estimates the
condo premium.
To preview our results, there is weak evidence for a condo premium of about 8-9 percent of rent, with or
without accounting for selection. This premium increases for larger units over a large range of unit counts.
On the assumption that the ownership externality is less sensitive to building size, this is consistent with a
theory the quality of maintenance in condos increases with the size of the building, presumably due to the
increased prevalence of outside management2. Interestingly, the condo premium declines in building age,
especially for larger buildings. One interpretation of this is that even though maintenance problems are
overcome in condos with sufficient scale, the level of this maintenance over time is in fact, relative to
apartments, still low, and lower yet in larger buildings.
2. Data
Our data source is the 2011 American Housing Survey national sample. The AHS is a biennial survey of
housing units and occupants conducting by the US Department of Housing and Urban Development. Table
2 outlines some initial facts about the survey. There are 186,448 housing units surveyed of which 110,132
are usable in the sense of being records of permanent inhabitable structures with more or less complete
2
See Coulson and Fisher (2014) for evidence on the increased utilization of management as the number of units rises.
information. Of these 27,000 are in multifamily buildings, and 4,900 of those are condominium units. The
table also shows, as noted in the introduction, that only about half of the condos are owner-occupied.
Table 3 lists, for units in multifamily structures, means and standard deviations of unit characteristics,
stratified by three main tenure-ownership groups: apartment (i.e. landlord-owned) buildings, owner-occupied
condos and rented condos3. The important point to note is that as suggested earlier, the quality measures for
both types of condos are higher than those of apartments, and those of the owner-occupied condos are
higher than of the rented condos. This is due, at least in part, to the effect of the tax preferences to real
estate investment, and of owner-occupied housing on the extensive margin of housing purchases. Hanson
(2013) has presented strong evidence that more favorable tax treatment of owner-occupied housing (as
manifested in the deductibility of mortgage interest and the combined effective state and federal tax rate)
increases the quantity and quality of housing purchased. This reinforces the idea expressed above that there
are quite possibly unobserved quality differences between these three groups.
Note that rents are higher in condo buildings, by about 26%. Condos are in larger buildings (as measured by
nunits, the number of units in the structure) and note that owner-occupiers are in slightly larger buildings .
The percentage of renters that have a pool in their complex is less than in condos, another indicator of quality
differences, but this time referencing the complex itself rather than an individual unit. All of the physical
quality indicators (in the table, from baths to unitsf (square feet of the unit)) are lower for apartments than for
condos, and lower for renter-occupied condos than owner-occupied condos.
Note in particular that apartments are older than condos (although the ages of the two types of condos are
the same). This is attributable at least in part to the fact that condo organization was for all practical purposes
legally impossible in almost every state before the early 1960s. This fact will aid our identification strategy,
discussed below.
Finally, we have two categorical measures of maintenance quality, that for the building, and for the grounds.
Both of these are measured according to categories good, bad, or nonexistent. As can be seen from the table,
only 5% of apartment respondents claim that maintenance (of either type) does not exist, and 65-70% claim
that maintenance is good. For condos, those figures drop to 1.4% for the no-maintenance category, and
increase to 74-78% claiming good maintenance, thus indicating that upkeep is superior for condo buildings.
These questions are not asked of owner-occupiers, but this will not hinder our econometric specification.
This table reflects our regression sample, which is a bit reduced from the samples reported in Table 2, due to missing
pieces of information.
3
3. Econometric model
Our key equation relates a housing unit’s monthly rent to a binary variable indicating whether the unit is
located is an apartment (i.e. in a landlord-owned complex) or in a condo. So the basic structure of the model
is:
𝑅𝑒𝑛𝑡𝑖 = 𝑋𝑖 𝛽 + 𝐶𝑖 𝛾 + 𝐶𝑖 𝑓(𝑁𝑖 ) + 𝑣𝑖
(1)
Note that the sample for equation 1 consists only of housing units in multifamily structures that are occupied
by someone other than the owner who pays rent to said owner. There are two classes of these, which are
specified by the binary variable Ci which equals one if the unit is in a condo building (but is occupied by a
renter), and equals zero if the unit is in an apartment building. The parameter γ is the baseline value of the
condo premium. Note again that condos that are owner-occupied are not included in this sample. We
discuss the selection issues adhering to this shortly. X is a vector of structural characteristics, β their
associated coefficients. Ni is the unit count of the building; f(Ni) is a flexible function of the unit count.
The nonlinearity of building value and other variables in unit count was a notable feature of the data
presented in Coulson and Fisher (2014). For reasons discussed in the previous section, we interact f(Ni) with
Ci to allow the condo premium to vary with scale of the building.
The sign of γ presumably does not depend on the nature of the rental externality as it pertains to the
individual units. In this case we are simply comparing renters of condos, and renters of apartments, and there
is no a priori reason (controlling for X and N) that one type of renter should utilize more intensely than
another4.
In estimating the parameters of (1) we fact two related empirical challenges. The first is that even though the
dimension of observable X variables is large, there may be unobservable quality differences between units in
condo and those in apartment buildings. As noted, the existence of observable quality differences, implies
On the other hand, the rental externality as it pertains to the common area will theoretically be less in a
condo building than an apartment building for the simple reason that everyone in the apartment has the
incentive to utilize heavily, whereas the owner-occupiers in a condo will utilize less and the utility derived
from the common area will be higher, leading to a condo premium.
4
that there may be unobservable quality differences between condos and apartments and condos that are
owner-occupied and those that are rented out. As in Coulson and Fisher (2014) we attempt to overcome
these challenges by estimating a bivariate probit model of the developer’s choice of tenure and, given that the
choice is condo, the owner’s choice to occupy vs. rent. The developer’s choice is modeled as a net random
utility model of the form:
𝑌1𝑖 = 𝑍1𝑖 𝛽1 + 𝑒1𝑖
where Y1 represents the unobserved net utility from condo development, Z1 a set of building characteristics
and e1 represents the effect of unobserved housing characteristics on the net utility. We observe I1 = 1 if this
net utility is positive, and therefore:
𝑃(𝐼1𝑖 = 1) = 𝑃(𝑒1𝑖 > −𝑍1𝑖 𝛽1 )
The ith observation in our sample is included in regression (1) if I1i = 0 because we observe the rent in any
(occupied) apartment. However we only include those observations of I1i=1 if that apartment is not owneroccupied. Conditional on I1=1 we then posit a net random utility model for the condo owner’s choice to rent
out the unit. The net utility of this choice is Y2, and using obvious notation, we posit:
𝑌2𝑖 = 𝑍2𝑖 𝛽2 + 𝑒2
The conditional probability of condo rental is therefore:
𝑃(𝐶𝑖 = 1) = 𝑃(𝑒2𝑖 > −𝑍2𝑖 𝛽2 𝑎𝑛𝑑 𝑒1𝑖 > −𝑍1𝑖 𝛽1 )
The conditioning of one choice on the other assumes that e1 and e2 are correlated, which is likely to be the
case if both choices depend on quality. The two parameter vectors are jointly estimated using a bivariate
probit.
Note therefore that the treatment variable in (1), Ci, is correlated with vi if it the latter is correlated with e1 and
e2. This is likely again to be the case, since rent will also depend on unobserved quality. We rewrite (1) in
expectations as
𝐸(𝑅𝑒𝑛𝑡𝑖 ) = 𝑋𝑖 𝛽 + 𝐶𝑖 𝛾 + 𝐶𝑖 𝑓(𝑁𝑖 ) + 𝐸(𝑣𝑖 |𝑒1𝑖 < −𝑍1𝑖 𝛽1 𝑜𝑟[ 𝑒1𝑖 > −𝑍1𝑖 𝛽1 𝑎𝑛𝑑 𝑒2𝑖 > −𝑍2𝑖 𝛽2 ])
If v, e1 and e2 are trivariate normal the expectation can be written as
𝐸(𝑅𝑒𝑛𝑡𝑖 ) = 𝑋𝑖 𝛽 + 𝐶𝑖 𝛾 + 𝐶𝑖 𝑓(𝑁𝑖 ) + (1 − 𝐼1𝑖 )𝜎𝑣𝑒1 𝜆𝑅 + 𝐶𝑖 𝜎𝑣𝑒1 𝜆𝐶1 + 𝐶𝑖 𝜎𝑣𝑒2 𝜆𝐶2
(2)
where the σ’s are the covariances of the subscripted random variables and the λ’s are the appropriate Mills
ratios that come out of the bivariate probit model (see Hotchkiss and Pitts (2005), Poirier (1980), Coulson
and Li (2013)
The estimation takes place in two steps, with the bivariate probit yielding estimates of the β parameters as
well as 𝜎𝑒1 𝑒2 . The λ’s are then constructed and the regression implied by (2) is estimated via OLS.
4. Empirical specification and results
We turn now to the specification of Z1, Z2 and X. As noted in Table 3 there are distinct quality differences in
the three ownership modes, and particularly between apartments and condos generally. Therefore, we use all
of the physical characteristics of the unit displayed in Table 3 (pool, climb, floors, baths, halfb, etc.). Given the
theoretical discussion in the section above we include in Z1 and Z2 the cubic of the number of units. The
choice of condo vs. apartment organization depends somewhat on the legal and tax environment at the time
and place (i.e. state) of construction, so we include state dummy variables, and three binary variables that
describe the vintage of the building. The first is builtbefore1960, which equals one if the building is built before
1960. The second is built19601980 which describes the era between condo legalization and the Economic
Recovery Tax Act of 19825. The third is built19801985, which characterizes the era between the Economic
Recovery Tax Act which enacted lower tax rates, more generous depreciation of real estate, and new rules on
passive investments, and the Tax Reform Act of 1986, which saw even lower marginal tax rates, but withdrew
many tax preferences enacted by previous legislation. The default category is for buildings built roughly after
the TRA1986.
The only difference between Z1 and Z2 is that in Z2 we replace the vintage categories with
the age of the building and its square (although using Z2 = Z1 does not alter the results in any meaningful
way). The difference between Z2 and X, the variables characterizing the hedonic rent function, are that the
state dummies are replaced with CMSA dummies, in the belief that the flow of housing services are
determined by local amenity and economic characteristics, which are better characterized by the metropolitan
area than the state. 6
The results of the bivariate probit are contained in Table 4, where the cell contents are the marginal change in
the probability of condo organization due to a unit change in the indicated variable, and then, conditional on
being condo, the marginal change in the probability of owner-occupation. Turning first to the probability
that a building is condo, we see first of all that observable quality measures have mostly strong correlation
with that probability. An additional bath or half-bath raises the condo probability by 9-10 percentage points,
and these probabilities are precisely measured. Also fairly precisely measured are the marginal increase in
condo probability arising from the inclusion of a porch (3 percentage point), garage (8 percentage points) and
air conditioning (4 percentage points). Larger units are also more likely to be condo, although the effect is
statistically significant, it is a bit smaller than expected; an increase in the size of the unit by 1000 square feet,
in effect doubling its size, raises the probability by 1.5 percentage points. The number of floors in the
building also has a small effect, about 1.1 percentage points, although this does not appear to be correlated
5
The variable built is categorical, and finer gradations that would allow state-specific characterization of exact date that condoenabling legislation was put into place, or the exact date of tax legislation, is not possible.
6 Pennington-Cross employs similar reasoning to identify the effect of state-level legal environments using metropolitan areas that
cross state lines.
with better views, since the floor of the sampled unit itself (as measured by the number of climbed floors
needed to access the unit) has only one-tenth of that effect, and this effect is not precisely measured.
The marginal probability calculations of the effect of the vintage variables are mostly in accord with our
expectations. Buildings built in the years 1960 through 1985 have a higher probability of being condo. One
unexpected result is that pre-1960 vintage buildings have the same probability of being condo as those built
after 1985. This may be due to conversion of older buildings or a relative paucity of newer condos in the
sample7. As expected, the cubic in unit count provides basically a rising probability of condo organization
although it does not exactly correspond to the L-shaped curve found in Coulson and Fisher (2014).
The results on owner-occupation are much less robust. The first thing to note is that the correlation of the
two relative utility shocks e1 and e2 is strong (-0.68) and statistically significant (z=-4.15). Conditioning on
this choice in the two stage probit model evidently explains whatever can be explained about the choice to
owner-occupy. The marginal probability calculations yield estimates that are relatively small, and not
precisely estimated. For example, an additional bath raises the probability by 2 percentage points, and the zratio is only about 1. Similarly the t-ratios for number of units, half-baths, fireplace, floor, and porch are all
less than one. Curiously, air conditioning is negatively related (and significantly so) to owner-occupation.
Although metro binaries are included in this specification, this may be related to rental condos being located
in nice, warm, places where rentals are more common. On the other hand, the most basic measures of
quality do seem to matter. Increases in floor space have a bigger impact (almost 3 percentage points) on the
probability of owner-occupation than they did on condo organization itself. This effect is relatively precisely
measured. Finally, age has a weak, though statistically significant (in the quadratic) effect on this probability.
Older units are more likely to be owner-occupied.
The estimation of the hedonic rent model is contained in Table 5. Not included in the table are the CMSA
dummies and those binaries that describe the location of the unit within the metro area. There are six slightly
different specifications, but we are able to discuss the coefficient estimates for the various background
variables without reference to these differences, because they are largely the same in each model.
To summarize, a pool adds 11-12% to rent, and additional bath adds 26-27%, an additional half-bath 9%, a
working fireplace 13-14%, a porch or balcony 5-6 %, and a garage 23-24%. As before, the existence of air
conditioning subtracts from value, but this (again) may be due to the regional distribution of units with air
conditioners. In models (1) through (3) the depreciation rate (the decline in rents per year of age, at least) is
7
We are presently looking at those buildings that are matched in AHS waves in the 1990s to investigate this question.
about 1% a year at young ages, but this decreases with vintage, according to all five models. The estimated
depreciation declines with the addition of interaction terms in models (5) and (6) (see below). These
estimates are largely in line with previous estimates of hedonic coefficients. Rents increase pretty linearly with
the number of units; the quadratic term is very small and insignificant in all specifications.
We include in this specification four binary measures of maintenance. Recall that the variables ggmaint and
gbmaint are equal to one if grounds and building maintenance (respectively) are rated as good. The variables
nogmaint and nobmaint similarly are equal to one if ground and building maintenance are not provided by the
owner or the management company. The omitted category for each is that maintenance is supplied, but is
not rated as “good”. The coefficients on these variables are always of the expected sign: positive for “good”
maintenance and negative for absent maintenance. In the former case both good grounds and good building
maintenance add about 2.3 to 2.4 percent to the rental rate for the unit. Note also that these coefficients are
estimated fairly precisely. The variables measuring absence of building maintenance have coefficients of 1.2-1.5%, while those measuring absence of grounds maintenance are around -10%. These are not all that
precisely measured, but this is perhaps due to the relative paucity of buildings for which this is true, as noted
in Table 3.
In model (1) the effect of condo organization is characterized by a simple binary, conditional on the
observables of the unit, but without accounting for selection. The effect is estimated to be around 8.1% (with
a t-ratio of about 5.5). Appropriately adding the Mills ratios as described around equation (2) actually raises
the premium to about 9.3%, although the standard error rises even more. The coefficient is statistically
significant in the usual sense only under mildly generous probabilities of Type I error (in the one-sided case,
around 10%). Given the increase in the coefficient, we do not strictly attribute the condo premium to
unobservable quality variables, but recognize that the non-random selection makes it a bit more difficult to
pin down the difference.
Recall from the introduction that theory suggests that condos will be different from apartments both because
of the presence of owner-occupiers (and the accompanying externalities) and the differing patterns of
maintenance, and that these maintenance patterns would be related to the number of units in the building.
For that reason, in model (3) we interact the condo binary with the number of units and the number of units
squared (condoxu and condoxu2). These interaction terms again have standard errors that are a bit higher than
the usual values for statistical significance (so that the t-ratios are 1.4 and 1.24 respectively). The premium
rises by about half a percent for every 10 units but that rate declines as the number of units increases. The
blue plot in Figure 1 displays the condo premium as estimated by this model and as can be seen, it rises until
it reaches the maximum of about 12% at just under 300 units. The interpretation is that (congruent with the
evidence presented in Coulson and Fisher (2014)) is that larger building sizes enable the condo owners to
overcome the fixed cost of hiring managers and in turn largely solve their free rider and coordination
problems. This increases the maintenance flow in condo units relative to both smaller condos, and apartment
buildings.
In model (4) we remove the four maintenance variables, in order to estimate how much of the premium is
due to the average differences in observable maintenance across condos and apartments. The table, and
Figure 1, show that while the curvature of the premium remains almost identical, its level drops by about 2.4
percentage points. The maintenance issues, on this account, are only a small part of the condo premium.
Even more interestingly, these crude measures of maintenance are only slightly correlated with building size.
In model (5) we return the maintenance variables to the specification, and add the interaction of the condo
binary and the age of the building. This interaction coefficient is statistically significant by the usual
standards, and indicates that the condo premium declines by about 1 percent for every 10 years of age. In the
final model (6) we add a triple interaction term that multiplies the condo, age and the number of units. This
interaction term also has a negative coefficient (with a t-ratio of around 7.1) , suggesting that as buildings age,
the condo premium declines faster in larger buildings. One interpretation of this, of course, is that even
though maintenance problems are overcome in condos with sufficient scale, the level of this maintenance
over time is still low still low, and lower yet in larger buildings. The bulk of the condo premium is due to the
positive ownership externalities that are inherent in condos and by definition absent in apartment buildings.
5. Conclusion
There are two fundamental reasons why condos and apartments should exhibit different flows of housing
services, for otherwise physically identical units. The first is the ownership externality, the idea that owneroccupiers create better environments—social capital—in their neighborhoods. The second is that the
incentive problems in providing maintenance in multi-unit buildings are different under these differing
ownership structures. We provide here, for the first time to our knowledge, comparisons of rents in
apartments and condos. We find weak evidence for a condo premium. This premium is somewhat related to
the number of units in the building, which we take to be related to the relative incentive problems in the
building management. The premium rises as unit count rises, indicating an increased ability of condos to
engage in more efficient maintenance, presumably due to the use of outside management. Nevertheless,
these considerations are somewhat second-order relative to the estimated baseline value, leading us to the
conclusion that externalities that are created by the owner-occupiers are the source of the condo premium.
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Effects or Self-Selection?” Real Estate Economics, forthcoming.
Hotchkiss, Julie L., and M. Melinda Pitts. 2005. "Female labour force intermittency
and current earnings: switching regression model with unknown sample selection." Applied
Economics, 37: 545-560.
Poirier, Dale J. 1980. "Partial observability in bivariate probit models." Journal of Econometrics, 12: 51-69.
Owner Households
Renter households
ownership rate %
Single family detached
(millions)
58.256
8.532
80.58
Other (millions)
13.561
27.132
23.92
%in sfd
83.83
31.45
Table 1: Tabulations from 2010 Census
Table 2: Tabulations from 2011 American Housing Survey
Apartments
Variable
Obs
Mean
Std. Dev.
Renter-Occupied
Condos
Owner-occupied
Condos
Mean
Mean
Std. Dev.
Std. Dev.
rent
nunits
pool
climb
floors
849.1183
28.50718
0.392898
1.20046
2.812946
727.827
64.49448
0.488406
1.757428
2.335635
1074.424
38.58127
0.498567
1.450345
3.215241
910.3417
84.38318
0.5001
2.495955
3.343337
42.94084
0.489424
1.85415
3.996713
89.0782
0.499992
3.259765
4.366858
baths
halfb
fplwk
porch
airsys
1.191974
0.106617
0.116819
0.668909
0.523833
0.429813
0.345746
0.321211
0.470617
0.499443
1.354276
0.191326
0.212809
0.760843
0.645318
0.509585
0.452843
0.409377
0.426655
0.478514
1.594084
0.292112
0.353328
0.836894
0.66516
0.58441
0.507773
0.478102
0.369538
0.472031
garage
unitsf
built
ggmaint
gbmaint
0.330144
917.1884
1965.84
0.69527
0.673874
0.476379
841.2416
25.21849
0.460304
0.468805
0.467369
999.5732
1974.461
0.776652
0.741792
0.554464
765.4368
20.93156
0.416574
0.437738
0.681183
1349.091
1974.711
0.466114
1175.99
22.5661
nobmaint
nogmaint
N
0.053399
0.053309
22154
0.224833
0.224653
0.014187
0.014187
2467
0.118286
0.118286
2434
Table 3: The table entries are means and standard deviations of the indicated variables in each of the three
ownership categories.
Prob (Condo)
Prob (Owner-occ.)
Marginal
Marginal
Variable
Prob.
Z-ration Probability Z-ratio
nunits
0.000452
3.79
6.88E-06
0.03
-1.78Enunitsq
06
-3.71
1.40E-08
0.01
nunitcu
1.49E-09
3.25
-1.60E-10
-0.18
baths
0.097887
20.76
0.022134
1.02
halfb
0.08652
16.68
0.001765
0.11
fplwk
0.085391
14.37
0.014728
0.73
floors
0.011139
9.13
0.000654
0.24
climb
0.001056
0.73
-0.00049
-0.19
porch
0.028068
5.09
0.025931
1.55
airsys
0.044151
8.1
-0.03933
-3.52
garage
0.081586
16.38
0.065881
2.2
unitsf
1.45E-05
6.42
2.69E-05
2.99
age
4.76E-05
0.07
age2
1.66E-05
2.28
builtpre1960
0.000134
0.02
built6080
0.038384
6.14
built8085
0.024038
2.97
Table 4: The table entries are the marginal effects (and the z-ratios) of the indicated variable on the
probability that a building is organized as a condo, and conditional on condo organization, the probability
that it is owner-occupied. State dummies are included in the first specification and CMSA dummies in the
second. Binaries describing the metro location (center city, suburb, etc) are included in both.
Model
nunits
nunitsq
xcondo
pool
baths
halfb
fplwk
climb
porch
airsys
garage
unitsf
age
age2
ggmaint
gbmaint
nobmaint
nogmaint
selrent1
selrent2a
(1)
.0007552***
(0.000)
-3.39E-07
(0.000)
.080687***
(0.015)
.1175902***
(0.011)
.2618159***
(0.011)
.0946618***
(0.013)
.1345905***
(0.014)
0.001589
(0.003)
.0555565***
(0.010)
-.159827***
(0.011)
.2270305***
(0.010)
.000037***
(0.000)
-.008152***
(0.001)
.0000817***
(0.000)
.0247692*
(0.012)
0.023059
(0.012)
-0.01594
(0.094)
-0.10328
(0.094)
(2)
.0007519***
(0.000)
-3.45E-07
(0.000)
0.0927695
(0.072)
.1176467***
(0.011)
.260169***
(0.015)
.0918588***
(0.016)
.1325719***
(0.017)
0.0012826
(0.003)
.0559046***
(0.011)
-.162345***
(0.012)
.2273568***
(0.013)
.0000375***
(0.000)
-.008112***
(0.001)
.0000818***
(0.000)
.024515*
(0.012)
0.0234429
(0.012)
-0.0128115
(0.094)
-0.1047809
(0.094)
-0.0107961
(0.059)
-.1218539*
(0.060)
(3)
.0006813***
(0.000)
-2.12E-07
(0.000)
0.0738421
(0.073)
.1176148***
(0.011)
.260629***
(0.015)
.0920332***
(0.016)
.1331928***
(0.017)
0.0011551
(0.003)
.0554459***
(0.011)
-.162377***
(0.012)
.2270938***
(0.013)
.0000375***
(0.000)
-.00812***
(0.001)
.0000817***
(0.000)
.0246958*
(0.012)
0.0233133
(0.012)
-0.0120897
(0.094)
-0.1049574
(0.094)
-0.0118814
(0.059)
-0.1134229
(0.061)
(4)
.0006964***
(0.000)
-2.25E-07
(0.000)
0.0776729
(0.073)
.1176714***
(0.011)
.2604645***
(0.015)
.0916407***
(0.016)
.1332652***
(0.017)
0.0011821
(0.003)
.0553525***
(0.011)
-.161852***
(0.012)
.2288031***
(0.013)
.0000367***
(0.000)
-.00831***
(0.001)
.0000833***
(0.000)
(5)
.0006762***
(0.000)
-2.05E-07
(0.000)
0.0403904
(0.075)
.1170608***
(0.011)
.2605178***
(0.015)
.0908966***
(0.016)
.1325597***
(0.017)
0.001016
(0.003)
.055587***
(0.011)
-.163528***
(0.012)
.2280212***
(0.013)
.0000379***
(0.000)
-.005796***
(0.001)
.0000834***
(0.000)
.0244419*
(0.012)
0.0232928
(0.012)
-0.013405
(0.094)
-0.1035853
(0.094)
-0.0157242
-0.0095874
(0.059)
(0.059)
-0.1150575 -.1949179**
(0.061)
(0.072)
(6)
.0015836***
(0.000)
-4.93E-07
(0.000)
0.07724
(0.075)
.1221691***
(0.011)
.2606595***
(0.015)
.0887927***
(0.016)
.1326002***
(0.017)
0.001908
(0.003)
.0510838***
(0.011)
-.163516***
(0.012)
.2218092***
(0.013)
.0000369***
(0.000)
-.00479***
(0.001)
.0000735***
(0.000)
.024416*
(0.012)
0.0232297
(0.012)
-0.0151434
(0.094)
-0.1052715
(0.094)
-0.0295282
(0.059)
-.1969927**
(0.072)
selrent2b
-.1978765*
(0.082)
-.1886595*
(0.082)
0.0005517
(0.000)
-9.24E-07
(0.000)
-.1907458*
(0.083)
0.0005565
(0.000)
-9.41E-07
(0.000)
-.3006451**
(0.099)
0.0005456
(0.000)
-9.57E-07
(0.000)
-.0008647*
(0.000)
6.561312***
(0.067)
6.562687***
(0.067)
6.597894***
(0.066)
6.57154***
(0.067)
condoxu
condoxu2
condoxage
condoxagexu
_cons
6.560856***
(0.066)
-.2963797**
(0.099)
-0.0001513
(0.000)
-5.41E-07
(0.000)
-0.0006545
(0.000)
-8.16e-06***
(0.000)
6.51959***
(0.067)
The table entries are coefficients and their standard errors in a regression of log(rent) on the indicated vector
of variables. * indicates prob-value<.05, ** prob-value<.01, and *** prob-value<.001. CMSA dummies are
included throughout as well as binaries describing the metro location (center city, suburb, etc).
.04
.06
.08
.1
.12
Figure 1
0
100
Condo premium
200
Number of units
300
400
Condo premium, including maintenance