Ownership Duration in the Residential Housing Market: The Influence of Structure, Tenure, Household and Neighborhood Factors

Ownership Duration in the Residential Housing
Market: The Influence of Structure, Tenure, Household
and Neighborhood Factors

Wayne R. Archer & David C. Ling & Brent C Smith
Published online: 18 July 2008
# Springer Science + Business Media, LLC 2008
Abstract Turnover rates are important as determinants of the level of activity in
housing related industries, in effecting housing market adjustments, and in revealing
prices in illiquid, highly segmented, informationally inefficient housing markets.
This study examines the relative influence of structure features, tenure, household
characteristics and neighborhood factors on ownership turnover rates. The study
exploits a Chicago database of just under 50,000 paired sales of attached housing
units, with at least one of the sales occurring between 1992 and June of 2002. Within
the framework of a Cox proportional hazard model, we focus on a number of factors
affecting turnover rates, including whether the housing unit is owner-occupied or
rented at the time of sale, price at the time of sale, unit size, age, location in a tax
increment financing district, housing density, structure size, year of sale, and
neighborhood within Chicago (by Community Area). Finding strong spatial
segmentation in turnover (hazard) rates, we further examine the capacity of four
sets of Census-derived variables to explain the spatial variation. The household
characteristics offer decidedly the strongest power in explaining the segmentation.
Results from the hazard model, combined with results from the analysis of spatial
variation suggest a household life cycle model of variation in turnover rates.
Keywords Duration . Housing . Urban . Tenure
J Real Estate Finan Econ (2010) 40:41–61
DOI 10.1007/s11146-008-9126-2
W. R. Archer : D. C. Ling
Warrington College of Business, University of Florida, Gainesville, FL 32611, USA
W. R. Archer
e-mail: wayne.archer@cba.ufl.edu
D. C. Ling
e-mail: ling@ufl.edu
B. C Smith (*)
Department of Finance, Insurance and Real Estate, School of Business,
Virginia Commonwealth University, P.O. Box 844000, Richmond, VA 23284-4000, USA
e-mail: bcsmith@vcu.edu

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Introduction
Duration of housing ownership is important because it drives the volume of activity
for a large industry of housing transaction services, because it determines the speed of
market adjustments for housing, and because it determines the completeness of price
revelation in the housing marketplace. This study conducts an empirical analysis of
factors influencing the duration of ownership for attached single-unit residences, with
attention to cross-sectional variation within a metropolitan area.
It is reasonable to expect that length of ownership of attached single-unit
residences is affected by the characteristics of the dwelling, including vintage, size,
density, and others aspects. But these features must exert their influence through
owner decisions, conditioned by the nature of the owner household attracted to the
dwelling, and conditioned by neighborhood character and dynamics. Therefore, a
major goal of this investigation is to ascertain whether knowledge of owner
household characteristics or neighborhood features can add significantly to structure
information in predicting length of ownership of a dwelling.
A unique challenge with attached residences, including townhouses, condominiums
and cluster homes, is that their market is driven by a combination of owner
occupancy demand and rental investment demand. Thus, a mixture of household
considerations and general investment return factors drives ownership termination
rates for individual multifamily residences. Because of the anticipated presence of two
clienteles for individually owned, attached housing, the study implicitly segments the
data for analysis into two subsets, owner-occupied and renter occupied housing.
Duration for all owners will be influenced by the rate of appreciation (Kiel 1994;
Henley 1998; Kim and Horner 2003), by local economic growth, and interest rates.
For owner-occupants, local employment conditions also should influence turnover
rates. The cross-sectional variation in duration to be studied here should be sensitive
to structural characteristics; and to physical neighborhood influences, including
neighborhood life cycles, reurbanization, road construction, and other influences of
local government neighborhood development policy. Finally, owner household
characteristics and neighborhood household characteristics should be factors,
including household composition and age, ethnic and racial mix, income
distribution, and employment composition.
For renter-occupied property, factors affecting investment return should dominate, including
changing vacancy rates, and factors influencing expected neighborhood price
appreciation. Especially for investors, the decision to hold or sell should be based on
incremental or marginal return criteria that compare the benefits of retaining ownership to
the benefits of an immediate sale (Brueggemen and Fisher 2001, p. 388). To fully analyze
whether a property should be sold or retained requires investigation into 1) the alternative
investments, or homes, available and the benefits those alternatives provide, and 2)
the transaction costs and tax consequences of selling one property and acquiring another.
The study employs a particularly rich dataset from the city of Chicago Assessor’s
Office for the 1992 through June of 2002 period. This database offers a useful
analytical benefit to our investigation in that it reflects a largely established stock of
housing.1 This maximizes the influence of the housing stock and neighborhood
1 In the 2000 Census, median year built was 1948 (SPA3, Table H33).

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physical features on ownership duration since the housing stock and neighborhoods
largely precede the existing population of owners rather than being shaped by them.
In any newer housing environment, we would expect the influence of the owner
household characteristics upon owner duration to be stronger since current owners
would more frequently have shaped the structure.
The analysis is structured using a proportional hazard model. The richness of
the data supports multivariate analysis of intrametroplitan duration in a number of
respects not possible with previous studies. The continuous nature of the sales
transaction data lends itself well to the use of the Cox proportional hazard model
for estimating variations in transaction frequency across a single metropolitan
area.2
In the sections of the paper that follow, we first review the literature on housing
turnover and the application of hazard models in this context. Then, we present our
use of hazard models, including a general model and a Cox proportional hazard
model. We follow with an introduction to our data, and with empirical estimation
and results. Finally, since our transaction data base does not include owner
household or neighborhood data, we construct a cross-sectional analysis over 77
Community Areas using Census data to test the potential of household and
neighborhood data to further explain residual variation in our duration estimates. We
finish with our summary and conclusions.


Literature Review

Despite the economic importance of residential ownership turnover and the
existence of myriad studies on household mobility, few researchers have derived
explicit housing turnover rates (“hazard” rates). Among the first to report such
rates were Henderson and Ioannides (1989) who fit a joint model of tenure
choice, housing consumption and occupancy duration to data from the Panel
Survey of Income Dynamics. Second were Gronberg and Reed (1985) who adapt a
hazard model to the special characteristics of the Annual Housing Survey.
Engelhardt (2003) was next to estimate (unreported) housing turnover rates using
the National Longitudinal Survey of Youth. The predominant indication of these
studies is that residential ownership turnover is slower than generally has been
thought.
A number of studies have focused on some particular aspect of housing turnover
rates. For example the effect of equity constraints under nominal price declines has
been explored by Stein (1995), and Lamont and Stein (1999). Subsequently, an
2 The choice to use the Cox proportional hazard model as the econometric framework of this study is a
matter of intuition and convenience. In fact, the study could be formulated as a logistic regression.
However, we find no less ability to derive conclusions through use of the hazard model, and the question
at hand fits quite naturally into the hazard formulation. It is generally recognized that estimation of
duration (hazard rates) is vulnerable to a heterogeneity bias in that a subpopulation with higher hazard
rates will disappear more quickly, leaving only the “slower” hazard population in the sample. Resulting
estimates of hazard rates thus tend to decline through time inappropriately. See, for example, Kiefer
(1988), pp. 673–676. Our sample enables examination of several sub-population characteristics that could
contribute to such bias, including rental vs. owner-occupied and variation by intra-metropolitan location.

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effect of nominal loss aversion upon housing turnover has been indicated and
weighed against the influence of equity constraints by Genesove and Mayer (1997,
2001), and Engelhardt (2003). The effect on housing turnover from property tax
protection through California’s Proposition 13 has been measured by Wasi and White
(2005). Another line of research focuses on duration of rental occupancy, but does
not treat duration of the underlying unit ownership (Deng et al. 2003; Gabriel and
Nothaft 2001). A focus of the Gabriel and Nothaft (2001) analysis was the effect of
duration of residential vacancies on equilibrium vacancy rates.
An especially diverse and large body of research focuses on time on the market
for housing units. However, this literature gives no attention to the length of
ownership preceding placement of the unit on the market. Examples include Belkin
et al. (1976), Zuehlke (1987), Haurin (1988), Kluger and Miller (1990), Stein
(1995), Lamont and Stein (1999), and Genesove and Mayer (1997 and 2001).3
Sternberg (1994) modeled the probability of exiting vacancy status employing a
relatively restrictive constant hazard rate framework.
Finally, another line of research focuses on holding periods for investment real
estate (Collett et al. 2003; Fisher and Young 2004). One branch of this literature
examines the effects of tax law changes, such as depreciation, amortization, or tax
rates (especially capital gain rates). Tax effect studies have typically been based on
theoretical models or numerical simulation as opposed to any empirical research.
For example, Brueggeman et al. (1981), Fisher and Stern (1982), and Hendershott
and Ling (1984) examine how such complexities as depreciation, recapture, tax
rates, the alternative minimum tax, discount rates, and inflation affect optimal ex
ante holding periods. Gau and Wang (1994) develop and empirically test a holding
period model recognizing not only taxes, but also refinancing and investor-specific
determinants. Based on a sample of over 1,000 real estate transactions with
observed holding periods, the results of their tests support the conclusion that
investors’ consumption and investment preferences and prevailing market interest
rates are more important than tax issues in determining the holding periods of real
estate investors.
A large number of the studies noted above use a duration or hazard framework.
Many of the studies focus on time on the market rather than the entire housing
ownership period. Rarely do any of the studies report hazard rates explicitly, and
none of them disaggregate below the level of central city versus suburbs, with most
being at a metropolitan level. Further, none attempts to contrast owner-occupied and
investment housing units.
Proportional Hazard Model
We first employ the Kaplan and Meier (1958) nonparametric approach to estimate
preliminary hazard rates.4 Then a proportional hazard model is specified to facilitate
3 For additional studies of factors influencing housing time on the market, with extensive bibliographical
references, see Knight (2002).
4 The Kaplan-Meier method is discussed further below. Also, see, for example, Kiefer (1988, pp. 657–661).

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more detailed analysis. The proportional hazard model, originally introduced by Cox
(1972), provides a particularly useful approach to analyze the duration of residential
ownership. Cox’s regression is a semiparametric approach to survival analysis. The
proportional hazard model is the most general of the regression models because no
assumptions concerning the nature or shape of the underlying survival distribution are
required. The model assumes that the underlying hazard rate (rather than survival
time) is a function of the independent variables (covariates). The method does not
require that a probability distribution be formally specified; however, in contrast to
nonparametric methods, Cox’s regression does use regression parameters in the same
way as generalized linear models.
Following the literature, we specify the proportional hazard of ownership
termination as:
lðx; tÞ ¼ loðtÞeXb; ð1Þ
where l(x,t) is the hazard function at time t for an attached residential unit with
covariates X. lo(t) is an unspecified base-line hazard—that proportion of the
population that would terminate ownership even under completely stationary,
homogenous conditions. eXβ is the exponential function that specifies how the
exogenous variables (x1, x2, ….xn) affect l(x,t). When analyzing ownership
termination, l(x,t) represent the probability of turnover at time t, given the
exogenous factors x1, x2, ….xn. The essential assumption of this specification is
proportionality; that is, if x1, x2, ….xn make turnover more likely at one point in
time over the owner’s holding period, they have an equiproportional effect at all
points in time. It is also implicit in Eq. 1 that the effect of x1, x2, …. xn on
termination is time-separable. Past attributes of the environment and expected
future values are assumed not to have any effect on current termination rates.
Cox regression obtains maximum-likelihood estimates of the β parameters
without, as noted above, the necessity of specifying the baseline hazard function,
lo(t). The proportional hazard model evaluates the probability of ownership
termination, conditional on ownership of the unit to that point in time. Therefore,
the model not only evaluates the determinants of turnover at the time of termination,
but also analyzes the behavior of the unit’s owner over the entire event history of
ownership.
One implementation condition of the Cox model is continuous observation of the
events over the observation period. This condition is typically not met by economic
data, which are most often gathered periodically in discrete time intervals. Thus, an
augmentation of the model, similar to that employed by Deng et al. (2003), is
required. The sales observations utilized in this analysis include the day the sale
occurred, thus; the data accommodates the requirement for continuity.
Another well-recognized problem in estimating hazard functions is right
censoring, which occurs in our study if ownership continues past the time window
of our data. Standard maximum likelihood estimation methods account for this
problem. However, a second concern present in our duration data is the possibility of
left censoring, which occurs when ownership starts prior to the time window of the
sample. Concerns regarding left censoring of sample data have been well
documented in the labor economics literature. We assume the mechanisms associated

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with the left censoring of the ownership duration data are random and therefore will
not significantly bias our estimates.5
Data and Summary Statistics
Data

The sample of attached residential units is derived from a database of closed
property sale transactions compiled by First American Real Estate Information and
Services. It is based on data from real estate transfer declarations filed with the Cook
County Assessor between 1992 and June of 2002 (see Smith 2006, forthcoming).
The use of attached residential property data is a viable alternative to studying the
traditional detached single-family housing market in Chicago because of the
extensive number of attached residential units, including connected row houses,
condominiums, and multistory rental apartments. Such housing comprises over 71
percent of the total residential housing stock in the city of Chicago (US Census
Bureau 2000).
The dataset, after cleaning and recoding for missing information, contains
49,820 property tax assessment records with actual market transaction information,
as well as locational, structural, and site information. Some cleaning of the
data was necessary to remove observations with missing variables (9 percent of
the sample) or observations that included more than one residential unit in a
single transaction (12 percent of the initial sample).6 Difference of means tests
comparing the data retained with the data deleted did not reveal selectivity bias
within the dataset.
In order to examine the impact of local market conditions on ownership duration,
each parcel is positioned in a Census defined Community Area. The Community
Area data are aggregated from STF3 Census Tract Data. In general, the first two
digits of each Census Tract in Chicago refer to the Community Area number. The 77
Community Areas are designed to represent homogenous neighborhood districts.
Community Areas are unique to Chicago and were first proposed by members of the
Social Science Research Committee at the University of Chicago during the 1920s,
and have since been used by the city government as statistical units. Except for the
addition of the O’Hare airport Community Area and the separation of a
neighborhood known as Edgewater from Uptown, the Community Areas have
remained unchanged since their inception.
We employ indicator variables to capture the fixed effects associated with a
residential unit’s location in a particular Community Area. This approach enables us
6 There were 2,872 observations deleted for missing square footage, 9 observations did not have the lot
size, and 21 observations were missing the number of units in the structure. The property tax assessor’s
records provide a single dollar value for all properties included in the sale making it impossible to
accurately allocate the total price paid when multiple properties are involved.

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to control for enumerable neighborhood differences affecting ownership duration,
while still enabling us to identify interesting variation in duration by “neighborhood.”
Although Community Areas are generally larger than urban neighborhoods,
we believe there is little information to be gained from using smaller scale location
variables defined at the census tract level. The incorporation of Community Area
dummy variables in our empirical model also allows us to recognize the variations
present in intrametropolitan markets. Goodman and Thibodeau (2003) find only
modest spatial autocorrelation when controls similar to those employed here in the
form of Community Areas are included in the hedonic regression model.
Each sale transaction is also identified by the quarter in which the most recent sale
occurred. In addition to the most recent sale transaction, information is also provided
on the date of the second most recent sale. For each residence, an ownership
duration variable is constructed based on the number of months between the two
sales. The dataset is truncated at 1975; therefore, if the second most recent sale
occurred prior to 1975, the date of this sale is set equal to 1975.7 This allows for the
construction of an event-history dataset with the ownership duration spanning from
January 1975 to June 2002, or from 1 to 330 months.
An attractive feature of the dataset is that it includes both owner-occupied and
renter-occupied units.8 This allows us to examine differences in the ownership
durations of owner- and renter-occupied units using a dataset in which the
characteristics of the units are quite similar regardless of tenure mode. Variables
used to explain the variation in observed durations include unit size and structural
elements, discrete variables indicating the year of the most recent sale, and policy
factors (whether the property is located in a designated Tax Increment Finance
district).9 The predicted sale price of the unit in the year of the most recent sale,
estimated with a standard hedonic model, is also included as an explanatory variable.
The use of the estimated price as an explanatory variable is preferred to the use of
the observed sales price because it avoids the potential endogeneity between sale
price and ownership duration. The results from the hedonic price model are provided
as an appendix.10
7 1975 was selected solely on the basis of the distribution of the observations. Very few prior sales were
present in the data that were dated before 1975 (28 or 0.06%) so that the “left censoring” problem is
minimized.
8 Units are classified as renter-occupied if the owner’s mailing address differs from the property address.
This may incorrectly classify some second homes as renter occupied. However, 2000 Census data indicate
that most second homes are classified as vacant units described as “For seasonal recreational or occasional
use.” This group constitutes 0.54 percent of the total occupied stock in the City of Chicago (Tables H7 and
H8, SF 3).
9 The TIF location variable is included because TIFs represent the principle tool used by local government
to spur economic development in Chicago’s many blighted neighborhoods, and TIFs have been identified
as influencing the appreciation rates for residential properties (Smith 2006).
10 The hedonic house price estimation results provided in the appendix are broadly consistent with
numerous prior hedonic model estimations (see, for example, Thibodeau 1995). In particular, the
regression model explains 63 percent of the variation in logged house prices. Moreover, the estimated
coefficients on the square footage of the lot and living area are positive and highly significant. Unit age is
negative and significant, as expected. However, contrary to most prior research, the square of unit age is
also positive and statistically significant. This result likely reflects the unusual age (mean equal to 84
years) of the units in our Chicago sample.

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Summary Statistics
Table 1 provides descriptive statistics on key variables used in the analysis for both
owner-occupied and renter-occupied units. The average ownership duration of
owner-occupied units in our sample is 183 months (15.25 years), while the
corresponding ownership duration for renter-occupied units is a nearly identical
186 months. The mean sale price for our sample of 32,680 owner-occupied units is
$181,410; the corresponding mean for renter-occupied units is $210,544. The higher
average sale price for renter-occupied units is, at least partially, explained by the
larger average size of renter-occupied units; 927 square feet versus 877 square feet
for owner-occupied units.
The average age of the owner-occupied attached residential housing stock in
Chicago captured in our sample is 85 years, with a range of zero to 153 years. At
84 years, the corresponding average age of the renter-occupied stock is little
different. Clearly, we are investigating a well-established housing stock. The size of
the buildings in which our renter-occupied units reside is somewhat larger. For
example, on average, renter-occupied properties are located in buildings that contain
3.67 units. The corresponding average for our owner-occupied sample is 3.0 units

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with a range of 1 to 250 units. Inspection of the corresponding standard deviations,
however, reveals significantly more variation in the number of units per building in
the renter-occupied sample. Consistent with having a larger number of units,
buildings that contain renter-occupied units are somewhat taller (2.2 floors versus
2.09 floors).
To obtain an additional measure of the intensity of development, we
constructed a variable equal to the total square footage of the building’s living
area divided by the square footage of the lot. The mean of this intensity of
development variable for renter-occupied properties is 0.818, slightly higher than the
corresponding mean for our owner-occupied sample of 0.701. As a proxy for local
government investment, eighteen percent of our renter-occupied units are located in
tax increment financing districts compared to fifteen percent of the owner-occupied
sample. Thus, local government policies designed to stimulate growth and
redevelopment activity have been used extensively in both owner- and renteroccupied
neighborhoods.
Finally, inspection of Table 1 reveals that both the owner- and renter-occupied
sales transactions in our sample are well distributed over the 1992–2002 period. For
our sample of owner-occupied sales, the percentage of sales ranges from 6.0 percent
in the first six months of 2002 to 11.8 percent in 1999. The range and pattern of sales
over the sample period is largely similar for renter-occupied units.
Preliminary Hazard Functions
A reasonable first step in the estimation of our ownership duration model is to plot and
examine a preliminary version of the hazard function by calculating the fraction of
attached residential units that are sold each month. More formally, let nt represent the
number of units that have not yet “failed” (i.e. been sold) at the beginning of time
period t. Let dt represent the number of sales that occur at time t. The Kaplan–Meier
estimator of surviving beyond time t is the product of survival probabilities in t and
the preceding periods such that:

In Fig. 1, S(t) is plotted against survival time in months. The figure contains graphs
representing the survival rates for attached owner-occupied and renter-occupied
attached residential units. Note that renter-occupied units initially sell at a faster rate
than owner occupied units. However, after approximately 100 months the owneroccupied
and renter-occupied hazard functions cross; that is, owner-occupied units
begin to have a higher conditional probability of termination. The intuition is that
rental units held for a short period of time by investors may be those properties
purchased for rehabilitation and resale (flipping).
A test for equality of the hazard functions produces a p-value for the chi-square
test of 0.009, indicating that the owner-occupied and renter-occupied hazard
functions differ significantly. Moreover, the log-rank test indicates the two subsets
have statistically distinctive survivor functions. These results provide support for
separately modeling the hazard functions of the two populations.

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