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Key Factors Affecting Valuation for Senior Apartments
JONATHAN A. WILEY Department of Real Estate
Robinson College of Business
Georgia State University
35 Broad St., 14th Floor
Atlanta, GA 30303
DAVID WYMAN Arthur M. Spiro Institute for Entrepreneurial Leadership
College of Business & Behavioral Science
Clemson University, Clemson, SC 29631
Abstract
The value of senior apartments is estimated relative to traditional apartments in 34 US markets.
In some markets, senior apartments transact at higher prices than predicted; in others, a discount.
Market differences in the valuation of senior apartments are examined, and several attributes are
found to have a significant impact and become capitalized into differential values for senior
apartments including: the supply of apartments per senior resident, housing prices, market size,
education, and life expectancy. Other variables appear to have no effect, including rent and
income, suggesting that the price impact is symmetrical for senior and traditional apartments.
Keywords: Apartments; Senior housing; Valuation
1
1. Introduction
Investors in the senior housing market have markedly different experiences, depending on where
senior housing is located. Senior housing is an industry positioned for considerable growth with
projections by the US Census Bureau for the 55 and older age group to increase by nearly 30
percent over the next decade, significantly more than for any other age group (US Census
Bureau, 2009b). The senior housing industry is full of complexity, due in part to the fact that the
industry can be divided into at least five sectors with specialized business models (Lynn and
Wang, 2008), yet these sectors are not mutually exclusive.1 The fastest growing sector is that of
senior apartments, accounting for 48 percent of all new construction for senior housing in 2006
(NIC/ASHA, 2006).2 Senior apartments are one point of entry to the senior rental housing
continuum. Further along, the level of service offered to each resident increases corresponding
with assistance required in activities of daily living. For example, a senior may begin by living
in an age-restricted senior apartment property. Later in life, that tenant may move to an
independent-living facility including a meal plan in the common dining area, along with
housekeeping and transportation services. A resident with a greater need for services may select
assisted-living facilities for assistance with activities of daily living. Another option is in the
skilled nursing facilities for seniors requiring regular medical care. A Continuing Care
Retirement Community (CCRC) has at least three of these in one facility. There are also hybrid
communities evolving that overlap sectors in the senior housing industry, increasing uncertainty
for investors as expected cash flows become less predictable.
Investment in senior apartments is often classified as a real estate allocation by institutional
investors due to its operational transparency. As a result, investment in senior apartments
2
competes for funding with other real estate sectors, including hotels and traditional multifamily
(Worzala, Karofsky and Davis, 2009). For properties with higher levels of hospitality and
healthcare services, rents are a smaller component of cash flow. High-service amenities (e.g.,
assisted-living and skilled nursing facilities) result in classification as a healthcare investment,
which is associated with the perception of increased risk relative to traditional real estate
investments (Lynn and Wang, 2008).
Whether valuation of senior apartments should mirror other multifamily properties is an
important topic due to certain factors that impact the risk of investing in senior apartments.
Rents from senior apartments tend to produce more stable income streams than other more
traditional apartment rentals because senior incomes are often supplemented by Social Security,
and in many cases Social Security serves as the only source of income for seniors.3 Another issue
is that operating costs are often lower because seniors have reduced turnover rates and cause less
wear and tear in the absence of children (Rubin and Rosen, 2003). Insurance can be higher with
an increased likelihood of age-related accidents. Finally, potential tenants for senior apartments
are scarce in certain markets increasing the investment risk for age-restricted housing.
A recent survey of PREA members reveals investor attitudes about the attractiveness of investing
in age-restricted property compared to alternative real estate investments (Worzala, Karofsky and
Davis, 2009). The survey recognizes the scarcity of institutional investors who are currently
invested in age-restricted apartments. Many respondents perceive senior housing as higher risk
and have little familiarity with the scope of potential investment opportunities in the senior
housing sector. Lack of familiarity and risk perception limits investment and contributes to cap
3
rate spreads of 150–175 basis points higher for senior vs. conventional apartments (Lynn and
Wang, 2008).
In this study, we examine whether predicted senior apartment values are divergent from
traditional apartments values and are able to identify several explanations for these differences
across markets.4 Our sample includes 25,346 multifamily transactions in 34 markets collected
from the CoStar COMPS® database. Based on hedonic estimations, senior apartments appear to
sell at a premium to traditional apartments in some markets, but prices are discounted in others.
Differences between actual transaction price for senior apartments and predicted values are
collected to evaluate whether market factors contribute to differential pricing. Our empirical
model examines the following factors for each market: median condo prices, educational
attainment (for the metropolitan population age 25 and older), average life expectancy at birth
for current residents, apartment rents, apartment vacancy rates, income per capita for the market
population, and inventory of apartments per senior resident. The impact from each of these
market factors is estimated and detailed.
Background on the senior housing industry and related literature are included in Section 1.
Section 2 outlines the data and methods implemented in this study. Section 3 covers the
empirical findings from the hedonic estimations across the 34 markets, and subsequent analysis
of market factors contributing to differences in senior apartment values. Section 4 summarizes
the findings and provides conclusions that can be drawn from this research.
4
2. Background
2.1. Demand for Senior Apartments
Senior apartments are one of several age-qualified housing options where occupancy may be
restricted to individuals who are 55 and older with protection under the Fair Housing Act of 1968
and the Housing for Older Persons Act of 1995. In a 2007 NIC survey, 28 percent of
respondents living in age-qualified housing are in senior apartments. The percentage of seniors
planning to move to an apartment in the future is highest for respondents ages 55–64, compared
to those 65 and older (Moschis, Bellenger and Curasi, 2005). Population in the 55–64 year old
age bracket is projected to increase by more than 18 percent over the next decade to include
more than 43 million Americans (US Census Bureau, 2009b). For individuals who consider the
menu of senior housing options, the decision to relocate to a senior apartment is influenced by a
motivation for moving, awareness, and perceptions about senior housing product types and
affordability.
The primary reasons most commonly cited in the NIC (2007) study for relocating seniors include
dissatisfaction with current housing (37%), geographic preferences (21%), affordability (9%),
and health (8%). Seniors register high levels of contentment with over 95 percent of respondents
reporting that they are either satisfied or very satisfied with their current residence (NIC, 2007).5
Eighty percent of seniors elect to “age in place” and remain in their homes as long as possible
(Gibler, Lumpkin and Moschis, 1998). A challenge faced by many seniors is to avoid selling
their home in a down market with home equity contributing to a significant portion of their
wealth. Seventy percent of senior homeowners reported less than $200,000 of equity in their
home (NIC, 2007). Nevertheless, more than 3 million individuals who were 55 or older
5
relocated in the last year alone (US Census Bureau, 2009a), and 40 percent of those in the 55–64
years old age group indicate that they plan to move in the future (NIC, 2007). From the seniors
who relocated during 2009, more than 60 percent stayed in the same county and less than 20
percent moved to a different state (US Census Bureau, 2009a), suggesting that demand for senior
apartments may be drawn predominantly from the local population.
Gibler, Moschis and Lee (1998, p.292) note that elderly movers “appear to be both pushed and
pulled into moves” with younger, more affluent seniors leaning toward long-distance moves into
amenity-rich retirement communities, while older and less healthy seniors are pushed into more
supportive housing situations. James (2008) finds that attitudes toward the rental housing option
are more positive in older age groups. Owner-occupants under 40 years of age are more satisfied
with their housing option compared to renters, but this satisfaction gap between owner-occupiers
and tenants is much smaller for owners in their 50s and 60s. Renter-occupants in their 70s
registered a higher level of housing satisfaction compared to owner-occupants of the same age,
citing increased dissatisfaction with lawn and maintenance requirements for detached property
(James, 2008).
The decision to move, whether to rent or own, and the appropriate senior housing option is
influenced by consumer awareness and perceptions, which vary according to income and
education. In the NIC (2007) study, 53 percent of respondents with income less than $25,000
were aware of senior apartments in their area, compared to 67 percent of those earning more than
$75,000. Forty-five percent of respondents aged 60 and older considered market rate apartments
as either “desirable” or “very desirable” (NIC, 2007). Households that are more educated are
6
more likely to consider assisted living, CCRCs, and rent-subsidized apartments as undesirable
(NIC, 2007).
Demand for senior apartments could intensify if the rate of market penetration increases relative
to other senior housing options. Two studies for Minneapolis/St. Paul report more than a
doubling in market share for unsubsidized senior housing from 1990–2002 (Maxfield, 2003;
Maxfield, 2008).6 Another study conducted in Florida during the late 1990s indicates that the
no-care, renter-occupied sector is projected to be the fastest growing sector of the senior housing
market including nearly 14 percent of Florida’s seniors by 2025 (Macpherson and Sirmans,
1999).
Finally, a lack of affordability for other senior housing options may attract low-income seniors to
rent-subsidized senior apartments (McGovern and Whiting, 2007). In the Current Population
Survey (2008), Social Security contributed to more than half of the income for nearly 69 percent
of the seniors. The net worth of the average senior renter is only 10 percent of the same amount
for the average senior property owner (Gibler, 2003). Subsidized senior apartments are often the
most affordable senior housing option available and have the lowest level of services.
2.2. Investment in Senior Housing
The attractiveness of senior housing to institutional investors has been the subject of several
studies that have focused on the Real Estate Investment Trusts (REITs) that invest in senior
housing. Mueller and Anikeeff (2001) examine the risk and return performance for six
alternative REITs, finding that volatility increases as REITs have proportionately more
7
operational income (hotels and retail) compared to rental income (industrial and office).7
Mueller and Anikeeff (2001) explain that supply limitations for senior housing during the early
1990s positively affected their returns.
Laposa and Singer (1999) compare the performance of the senior housing industry to the
multifamily and lodging REIT sectors. Based on size, operating, and financial performance
variables, they conclude that the senior housing industry compares favorably with the alternative
investments and deserves increased attention from institutional investors. For senior apartments,
the level of services provided is slightly greater than the level of services in traditional
apartments which provides additional opportunities for non-real estate income (e.g., laundry,
cleaning).
The potential attractiveness of investment in senior housing is given increased attention
following evidence of premiums in age-qualified communities for owner-occupied property. For
example, Allen (1997) finds evidence of significant premiums, around 10–14 percent, for condo
prices in age-qualified neighborhoods (age 55+) of southeast Florida. In a subsequent study
using recent data, Allen, Carter, Lin and Haloupek (2010) find that the presence of age
restrictions is highly sensitive to an economic downturn with senior housing discounted 17 to 23
percent to comparable property. Guntermann and Moon (2002) document premiums around 17
percent for manufactured housing in age-qualified subdivisions of Mesa, Arizona during 1983–
2000. Guntermann and Moon suggest that these premiums may be due to deed restrictions that
reduce uncertainty about the future character of the neighborhood. In a later study, Guntermann
and Thomas (2004) examine the impact of revocation of an age-qualified ordinance.
8
Youngtown, Arizona was established as the first master-planned retirement community in the US
in 1954 requiring a minimum age of 50 for at least one member per household. The age-
qualified ordinance was ruled invalid in 1997 and the housing market was opened to younger
families. Guntermann and Thomas (2004) find that the community premium of 18 percent for
house prices disappeared within 12–18 months of the ordinance being lifted. They conclude that
age restriction is a valuable amenity that can be capitalized similar to physical and locational
attributes. Each of these studies considers premiums for ownership property, and the real estate
literature lacks evidence to support whether such premiums exist for the rental market; a primary
motivation behind the current research study.
The only known study to directly compare the performance of senior apartment rentals to the
more traditional multifamily apartment rentals was conducted in Tampere, Finland. Tyvimaa
and Gibler (2009) compare 93 senior apartments and 99 traditional apartments operated by one
owner in six buildings. They find that the senior apartments generate similar returns to ordinary
apartments, but that both categories are operated inefficiently. Little, if any, attention has been
devoted to the understanding of differences in performance for senior apartments relative to
traditional multifamily properties. The contribution of this study is to estimate price differentials
for senior apartment properties across a large number of markets and to identify market-level
factors as well as regional effects that impact the pricing of senior apartments compared to the
more traditional apartment investments. The data and methodology for this study are described
in the next section.
3. Data and Methodology
9
3.1. CoStar Data
Data used in this study is collected from the CoStar COMPS® database, which provides detailed
information on commercial and multifamily property transactions. Our focus is on the properties
that are classified as multifamily; investments with a health care classification are excluded. Our
intention is to restrict the analysis of the senior housing sector to age-restricted, senior
apartments due to considerable variation in living standards and amenities found in the
alternative senior housing options that are more service based. For instance, assisted-living,
congregate senior housing, and continuing care retirement centers often overlap and are
dissimilar to non-senior apartment properties, increasing the challenge of creating an accurate
benchmark comparison.8 Independent-living facilities are similar to traditional apartments, but
often include a common dining area and rents cover additional services including meals,
housekeeping, and transportation so they are not included in the analysis.
The evaluation process begins with a collection of all available multifamily transactions reported
in the CoStar database. The transaction dates range from December, 1997 to January, 2010.
Properties were removed from the sample when there was insufficient data available for
variables like the sale price, number of units, average unit size, lot size, and property age. In
addition, we excluded any markets where there were no observations for senior apartments with
adequate data coverage, or when the number of observations within a market was smaller than
the number of independent variables (including submarket and quarterly indicator variables).
Adequate data was available for 34 markets, and a total of 25,346 observations were analyzed.9
Due to inconsistent data reporting, there were severe outliers when the price per square foot
values were analyzed so the sample was trimmed to exclude the 10 percent observation tails of
10
the price per square foot distribution on both ends for each market. The remaining sample
included 20,277 observations; the property-level variables of interest are described in Table 1.
Summary statistics are provided in Table 2. The variable of interest in this study is Senior,
which takes on the value of one if the property is described as “Apartment Units—Senior” and
zero otherwise. Comparing the sample of senior apartments to the remainder of the market, it is
evident that senior apartments are significantly newer properties containing a higher number of
total units with smaller average unit sizes; in addition, on average, this property type is selling at
a lower average price per unit. There are exceptions to this, including in Milwaukee and
Minneapolis where the average price per unit is higher than that of traditional apartments. These
statistics provide a descriptive overview of the sample, and empirical techniques are needed to
accurately compare differences in prices while controlling for uniqueness in physical, locational,
and market timing characteristics for the transaction. As an example, senior properties sell for
about $21,000 less on a price per unit basis; however, nearly 82 percent of those units are one-
bedroom and studio apartments compared to 59 percent for the non-senior sample. On a price
per square foot basis, the average traditional apartment sells for $122 per square foot, while
senior apartments average $105 per square foot.
3.2. Hedonic Specification
Early discussions surrounding the multifamily housing market focus on the determinants of
apartment rents. Hedonic approaches consider that rents at the property level are impacted by a
set of locational and physical attributes, including property size and age. The depreciating
behavior of housing rents is documented by Malpezzi, Ozanne and Thibodeau (1987), who find
11
that rents decrease with property age at an estimated constant rate that varies across markets.
Subsequent studies include a variable for property age in the rent equation and support this
finding, including Guntermann and Norrbin (1987) and Sirmans, Sirmans and Benjamin (1989,
1990). The contribution of property size to market rent has been measured by the number of
units (Sirmans, Sirmans and Benjamin, 1989) and square foot per unit (Guntermann and Norrbin,
1987; Sirmans, Sirmans and Benjamin, 1990). A common treatment for the impact of locational
attributes on rent is to use indicator variables to control for differences across submarkets, as in
Sirmans, Sirmans and Benjamin (1989, 1990) and Benjamin and Sirmans (1996).
As reliable data for multifamily property transactions has been made available, more recent
studies consider that the set of physical and locational characteristics affecting rents should have
a similar and consistent impact on apartment prices. For property age, newer properties are
found to sell for a premium in Frew and Jud (2003), Lambson, McQueen and Slade (2004),
Benjamin, Chinloy, Hardin and Wu (2008), and Sirmans and Slade (2010). For property size,
the relevant metrics found to impact apartment values include the number of units (Frew and Jud,
2003; Lambson, McQueen and Slade, 2004; Benjamin, Chinloy, Hardin and Wu, 2008), square
footage (Frew and Jud, 2003; Lambson, McQueen and Slade, 2004; Sirmans and Slade, 2010),
and acreage (Asabere and Huffmann, 1996; Frew and Jud, 2003; Lambson, McQueen and Slade,
2004; Benjamin, Chinloy, Hardin and Wu, 2008; Sirmans and Slade, 2010). Indicator variables
are used to control for locational factors that impact apartment prices by Lambson, McQueen and
Slade (2004) and Sirmans and Slade (2010).
12
Our empirical approach is motivated by previous studies that consider that apartment property
values are a function of physical and locational characteristics. The set of physical
characteristics includes Age, which measures the property age in years. We anticipate that
apartment property values will depreciate with age. Units measures the number of units in the
apartment complex, SF measures the average unit size, and Lot measures the acreage per unit.
We expect that property values will be increasing with all metrics for size. This set of physical
attributes is similar to the hedonic specification of Lambson, McQueen and Slade (2004) who
also make use of the multifamily transaction data from CoStar.10 In Lambson, McQueen and
Slade (2004), a sequential search model is developed with unique preferences for investors based
on the quantity of units they desire to purchase. From this assumption, they consider the impact
on property values with the log of price per unit, Price, as the dependent variable. In addition,
our model includes indicator variables to control for differences across submarkets and over
time, and for unique sale conditions. We create D_Submarket indicator variables for each
submarket within each market as defined by CoStar, D_Quarter variables to identify transactions
within each quarter of data available, and D_Condition variables to identify unique sale
conditions. The operational model we use to examine the impact of Senior on Price is provided
as:
Price = β0 + β1·Age + β2·Age2 + β3·Units + β4·Units
2 + β5·SF + β6·SF
2 + β7·Lot + β8·Lot
2
+ ∑=
m
i
i
9
β ·D_Submarketi + ∑+=
n
mj
j
1
β ·D_Quarterj + ∑+=
p
nk
k
1
β ·D_Conditionk
+ βp+1·Senior + ε. (1)
Rather than consider the differences for senior apartments at the national level with market
control variables, our approach is to estimate equation (1) individually for the 7 markets with the
13
largest number of senior apartment observations.11 Summary statistics for these markets are
listed in Table 2.
Individual estimations for equation (1) provide the opportunity to explore these potential
differences across markets. Due to the considerable variance of individual property
characteristics (noted in Table 2), a concern is that the error term of the regression model does
not satisfy the homoscedasticity assumption for the covariance matrix. Heteroscedasticity for the
error term does not bias the parameter estimates, although it does misstate the standard errors and
corresponding test statistics. To resolve this concern, we incorporate a heteroscedasticity-
consistent estimator for the covariance matrix introduced by White (1980). The
heteroscedasticity-consistent standard errors are the square root of the diagonal for the
covariance matrix and the reported χ2 test statistics are based on these standard errors.
To analyze differences between actual and predicted prices for senior apartments, we modify
equation (1) by removing the Senior variable and storing the standardized residuals (SRES). The
residuals are collected from the estimation of equation (2).
Price = β0 + β1·Age + β2·Age2 + β3·Units + β4·Units
2 + β5·SF + β6·SF
2 + β7·Lot + β8·Lot
2
+ ∑=
m
i
i
9
β ·D_Submarketi + ∑+=
n
mj
j
1
β ·D_Quarterj + ∑+=
p
nk
k
1
β ·D_Conditionk + ε. (2)
Standardized residuals for observations where Senior takes on a value of one are merged with
market variables to evaluate factors that might explain differences in relative values for senior
apartments across markets.
3.3. Market Variables & Residual Analysis
14
In real estate market analysis, demand for senior housing is treated as a market sizing exercise.
Total demand for senior housing is based on the population size within a particular age bracket,
the ability to pay, and the need for assistance (e.g., Doctrow, Mueller and Craig, 1999). For a
given market, the estimated total demand for space is compared to the inventory of existing
space plus expected deliveries to determine whether the market is over- or under-supplied with
senior housing product. The ability of new projects to capture existing and future demand is
highly dependent on the locational, physical, and service amenities available.
In the market for apartments, services delivered include the use of space and amenities for a
price. Quantity demand is measured by the share of the local population who elect rental over
homeownership, responding to changes in income, employment and population growth. Supply
is present at the beginning of each period, but there are rigidities in adjustment from one period
to the next due to supply constraints. With senior apartments in the age-qualified housing sector,
a segmented market exists which is related to the broader apartment market with a demand
constraint––at least one household member must be 55 or older. No similar constraint exists for
supply leading to a reduced pool of potential tenants who are free to choose traditional
apartments or any other housing option over senior apartments.
Our empirical approach considers that the variance between senior and traditional apartment
values may be related to fundamental differences in apartment supply and demand across these
same markets. In each specification, there is a vacancy variable, a proxy to measure the “price”
of real estate services, and either relative supply or its components. Vacancy measures the
percent of unoccupied rental units in a market for 2007, reported by the US Census Bureau. The
15
price of real estate services is proxied by Condo prices, Rent or Income (each considered
separately). The relative supply of apartments, Inventory, is measured as the total number of
apartment units divided by the population age 55 and older. The total number of apartment units
is from the National Multifamily Housing Council (NMHC) for the 25 largest MSAs in 2007.
Metropolitan area population counts are from the US Census Bureau for 2006.
Condo prices measure the median condominium price in 2007 for each market reported by the
National Association of Realtors® (NAR). Condo prices are more relevant than single-family
prices due to similar physical features and amenities attracting seniors who seek the transition to
a low-maintenance lifestyle. High condo prices cause residents making the tenure choice
decision to realize that ownership is cost prohibitive and seniors who live on fixed incomes may
be more likely to reside in senior apartments.12 As of 2007, the highest condo prices are in
coastal California and in the large cities of the Northeast (New York, Boston, Washington, DC).
Alternative measures for the price of real estate include Income and Rent. Income is the metro
area per capita income for 2009 reported by the Bureau of Economic Analysis (BEA).13 Rent is
the median fair market rent from the Department of Housing and Urban Development (HUD) for
2009 divided by Income.
An alternative measure for market size is to use the log of total MSA population, Population.
Larger markets sustain higher levels of economic activity that contributes to a broader and more
diverse selection of dining, shopping, and entertainment options. Large markets also provide
greater access to public goods, including parks, recreation, and community centers as a
consequence of the larger tax base. However, large cities are burdened with negative
16
externalities including pollution and crime, and it may be difficult to access the local amenities
when there is an excessive volume of pedestrian and automobile traffic. The percent of the
population age 55 and older is captured by the 55&Older variable as a measure of fraction of
total housing demand by seniors in the local market.
Another factor that should make a market more amenable to investment in senior apartments is
Life expectancy, estimated for each state in 2005 by the US Census Bureau.14 Increased life
expectancy extends the number of years that a senior tenant will remain relatively healthy and
mobile; this increases the probability that the average tenant of a senior apartment will renew
their lease (Anikeef, 1999). Life expectancy is varied across markets with California,
Connecticut, Massachusetts, and Minnesota having high life expectancies, while Alabama,
Georgia, Oklahoma, and Washington DC are among the lowest. In addition, the results of the
NIC (2007) study reveal that preferences for senior housing options vary by educational
attainment. Education is measured as the percentage of the population in each state over age 25
who have attained a Bachelor’s degree or higher, estimated by the US Census Bureau for 2006.15
The market variables described above and in Table 1 are evaluated as factors that influence
relative values for senior apartments. An issue is that some of the senior apartments may
actually be classified as low rent, which can alter the stability and overall level of cash flows
from the property. This information is difficult to verify in CoStar as the data is only
sporadically mentioned in a few transaction notes. To resolve the issue of unreliable data for
affordable housing reported by CoStar, we conduct a comprehensive search of the affordable
apartment database maintained by the Department of Housing and Urban Development (HUD).
17
To identify senior apartments in the low rent category we search the HUD database for elderly
apartments based on the city listed for each property address in our sample. We are able to
verify a match for 38 senior apartments listed. HUD property takes on a value of one when the
senior apartment is confirmed in the HUD database, and zero otherwise. The HUD property
variable is included with the market variables in the estimation of factors that influence
differences in valuation across markets.
To analyze the impact of market variables on the SRES variable generated from equation (2), we
consider correlations among regressors. A basic model includes the Vacancy, HUD property,
and Inventory variables, and examines the remaining market variables in three separate
estimations. The first estimation considers the impact of Condo prices.
SRES = β0 + β1·Vacancy + β2·HUD property + β3·Inventory + β4· Condo prices + ε. (3)
Equation (4) considers the impact of Rent on SRES, omitting Condo prices.
SRES = β0 + β1·Vacancy + β2·HUD property + β3·Inventory + β4· Rent + ε. (4)
Equation (5) considers the impact of Income, Education, and Life expectancy on SRES. Condo
prices and Rent are suppressed in equation (5).
SRES = β0 + β1·Vacancy + β2·HUD property + β3·Inventory + β4·Income + β5·Education
+ β6·Life expectancy + ε. (5)
An alternative to equations (3), (4), and (5) is to substitute the Population and 55&Older
variables for the Inventory variable. Together, Population and 55&Older measure market size
18
along with the senior population, while Inventory measures the supply of apartments per senior.
This substitution increases sample size because the Inventory variable is only available for the
largest 25 markets, while Population and 55&Older are available for all. Equation (6) is the
alternative to (3).
SRES = β0 + β1·Vacancy + β2·HUD property + β3·Population + β4·55&Older
+ β5· Condo prices + ε. (6)
Equation (7) is the alternative to (4), with Population and 55&Older variables substituted for the
Inventory variable.
SRES = β0 + β1·Vacancy + β2·HUD property + β3·Population + β4·55&Older
+ β5· Rent + ε. (7)
Equation (8) is the alternative to (5), with Population and 55&Older variables substituted for the
Inventory variable.
SRES = β0 + β1·Vacancy + β2·HUD property + β3·Population + β4·55&Older
+ β5·Income + β6·Education + β7·Life expectancy + ε. (8)
Results from the estimation of equation (1) are reported for the 7 markets with the largest
number of senior apartment observations available. The dependent variable in equations (3)–(8)
is based on the standardized residuals from equation (2) estimated for 34 markets. The empirical
results are discussed in the next section.
19
4. Empirical Results
The hedonic model estimates the impact of age restrictions on the logged unit price of
apartments, controlling for differences across submarkets, over time, and for physical
characteristics, including property age, number of units, average unit size, and lot size. Results
from hedonic estimates of equation (1) for the 7 markets with the largest number of observations
for senior apartments available are reported in Table 3. Equation (1) is developed from the
works of previous studies who find that apartment unit values are decreasing in age and unit
quantity, and increasing in value with the average unit size and lot size. Second-order
coefficients in the quadratic equation have the opposite sign, which implies convexity for Age
and Units (that are decreasing) and concavity for SF and Lot. For instance, higher values for SF
and Lot typically increase Price but at a decreasing rate. Estimates are largely consistent with
expectation.
Estimates for the coefficient and significance of the Senior variable reported in Table 3 reveal
several differences in the valuation of senior apartments across the 7 markets. Senior apartments
sell at a significant estimated premium in San Diego and Minneapolis; and at a significant
discount to traditional apartments in Los Angeles, Northern New Jersey, and Portland. There
appears to be no significant difference in senior apartment values for Milwaukee and the Inland
Empire (CA).
While the coefficients for the Senior variable reported in Table 3 have limited implications,
useful information is contained in the standardized residuals for the senior apartment
observations. This is true for the residuals from the estimation of equation (2), where the Senior
20
variable is suppressed.16 Results from the estimation of equation (2) for the 34 markets are not
reported, although the coefficients for all other variables and goodness-of-fit measures
experience minimal change when the Senior variable is omitted. Residuals from all estimations
of equation (2) are collected for observations where Senior equals 1 and standardized to create
the SRES variable. SRES measures the number of standard deviations away from the estimated
Price for senior apartments, based on the local market hedonics.
Average values for SRES are reported for each of the 34 markets in Table 4. Fifteen of 34
markets have positive values for SRES, with Minneapolis and Boston topping the list. Markets
where senior apartments are the most heavily discounted include Houston, Chicago, Long Island
(NY), and Northern New Jersey. Our focus is on understanding these differences in senior
apartment valuations across markets.
An explanation lies in the factors that influence supply and demand in the segmented market for
senior apartments. Factors considered in this study include differences in market size, home
prices, vacancy rates, rents, income, education, and life expectancy. Discrepancies in senior
apartment values across markets may also be the result of a biased sample if transactions for
some markets are primarily for properties serving low-income elderly residents, marketed in the
HUD database. Table 5 reports the results of the six estimations that consider the impact of
market factors on senior apartment values, controlling for low-income tenantry. The HUD
property variable turns out to have zero impact on apartment values in all six models.
21
The first model in Table 5 reports the estimates from equation (3), which evaluates vacancy
rates, inventory, and condo prices for 34 markets. Inventory is negative and significant in
estimations for equations (3), (4), and (5) with coefficients ranging between -2.38 and -3.58. An
interpretation is that a lower quantity of apartments per senior resident results in higher valuation
for senior apartments relative to traditional apartments. Average values for Inventory takes on
low values of .207 in San Diego and .242 in Minneapolis, compared to high values of .621 in Los
Angeles, .517 in Atlanta, and .489 in Long Island. An increase in the supply of apartments per
senior by 0.1 reduces senior apartment values by a standard deviation of .224 to .326 relative to
traditional apartment values. The coefficient for the Condo prices variable is positive and
significant in the estimation of equations (3) reported in Table 5. Historically, home prices are
associated with overall cost of living metrics for each market. Extreme home prices cause
potential homebuyers to be renters. Thus, the substitution effect, along with any cost of living
adjustments associated with high home prices, appears to be capitalized into senior apartment
values.
The second estimation reported in Table 5 is for equation (4), which substitutes Rent for Condo
prices. The coefficient for Rent is insignificant from zero—similar to the coefficient for
Vacancy in all models. This result is due to the fact that SRES measures the relative pricing of
senior apartments compared to traditional apartments, and the estimations used to create the
SRES variable are carried out separately for each market. While rent and vacancy commonly
impact the average pricing of apartments within a market, when traditional tenants and seniors
are impacted symmetrically then differential pricing will not exist in equilibrium.
22
In equation (5), the effects of income, education, and life expectancy are considered. The
coefficient for Income is zero, indicating that income effects are similar for the senior and
traditional apartment values. Education is positive and significant with a coefficient of 0.091.
An increase in educational attainment by 1 percent of the population increases senior apartment
values by a standard deviation of 0.091 relative to traditional apartment values. From NIC
(2007), preferences and awareness for senior housing options are known to vary by education.
Cincinnati, Cleveland, and Columbus are in a state that has one of the lowest values for
educational attainment out of the 34 markets, at 23.3 percent for Ohio in 2006. Boston in
Massachusetts has the highest level of educational attainment for any state in the US at 40.4
percent. Boston and the Ohio markets are at opposite ends of the spectrum for senior apartment
valuations (SRES values reported in Table 4).
Life expectancy has a positive and significant impact on values of senior apartments, with a
coefficient estimated at 0.274 in equation (3) reported in Table 5. A market that has a higher life
expectancy by two years values senior apartments at more than one-half standard deviation
greater than the price of traditional apartments. Life expectancies in Atlanta are among the
lowest for the markets considered at 75.3 years. At the top of the list are Minneapolis and
Boston with life expectancy over 78 years. Senior apartments serve a demographic window
spanning from early retirement until the point that tenants require significant assistance in
activities of daily living. Extended years of health and mobility are directly capitalized in senior
apartment values through higher occupancies and reduced tenant turnover. This evidence
supports arguments of a relationship between senior housing demand and life expectancy by
Anikeeff (1999). Life expectancy is often related to quality-of-life factors that are difficult to
23
quantify, including health care quality, local agriculture, environmental hazards, and the
influence of community on diet and exercise.
Equations (6), (7), and (8) reconsider the impact of the market variables on the valuation of
senior apartments, with Population and 55&Older substituted for Inventory in all three
equations. Coefficients for Population are negative and significant in all three models. Potential
benefits in large markets include those related to higher levels of economic activity and broader
support for public goods. Concerns are related to pollution, congestion, and crime. The
disadvantages associated with larger markets appear to outweigh the benefits for seniors.
Evidence for the relevance of condo prices, education, and life expectancy in equations (6), (7),
and (8) are largely consistent with results obtained from equations (3), (4), and (5).
For robustness, we consider an alternative to equation (2) to generate the standardized residual
variable. The dependent variable is ln(Sale price), which is the natural log of the actual sale
price, rather than the price per unit. The hedonic variables Age, Units, SF, and Lot are all logged,
rather than in quadratic form.
ln(Sale price) = β0 + β1·ln(Age) + β2·ln(Units) + β3·ln(SF) + β4·ln(Lot)
+ ∑=
m
i
i
5
β ·D_Submarketi + ∑+=
n
mj
j
1
β ·D_Quarterj + ∑+=
p
nk
k
1
β ·D_Conditionk + ε. (9)
Standardized residuals from the estimation of equation (9) for observations where Senior equals
1 are stored to create the SRES2 variable. It is noteworthy that equation (9) provides a
considerably better fit for the data used in this study. For the estimation of equation (2) for each
of the 34 markets, the average R2 is 68% and the minimum is 40.3%. For the estimation of
equation (9) for each of the 34 markets, the average R2 is 95.4% and the minimum is 87.2%.
24
The SRES2 variable from estimations of equation (9) for the 34 markets is substituted for the
dependent variable in equations (3)–(8) and the analysis is reexamined with a different set of
residuals. The results are reported in Table 6, and are largely consistent with the results in Table
5. Inventory is negative and significant in equations (3), (4), and (5) with similar coefficients.
Education, Life expectancy, and Condo prices are all positive and significant. Vacancy, Rent,
and Income coefficients are insignificant from zero in Table 6.
5. Conclusions
Demographic trends foretell a steady increase in the general demand for senior housing. The
lure of a low-maintenance lifestyle and affordability are attracting residents to senior apartments,
while perceptions about existing product, reluctance to move and real estate downturns are
limiting opportunities for this product. Senior apartments have been increasing market share in
the senior housing industry and this trend is expected to continue. Investors remain uncertain
and have higher perceived risk due to a lack of familiarity with the senior apartment product.
However, several factors may impact the risk of investment in senior apartments compared to
traditional apartments. Senior apartments are often associated with lower rates of tenant turnover
and experience limited wear and tear on the individual units. Senior apartments are in a
segmented market with age restrictions and the market demand for this product is limited by
those restrictions. Whether the segmented demand is a net benefit to those in the senior
apartment industry ultimately depends on the relative supply of senior apartments in the local
market, as well as competition from traditional apartments.
25
The findings of this study reveal that senior apartments transact at prices divergent from
traditional apartment prices. Hedonic estimations for the impact of age restrictions on the value
of apartments are provided for several markets with considerable variation in the estimated
coefficients across markets. In some markets, senior apartments are sold at significant
premiums; in others, these properties are discounted. A metric is developed that considers the
difference between the actual transaction price for senior apartments and the predicted price
based on estimates for traditional apartments, standardized by the measurement error. This
standardized residual variable is used to evaluate market factors that contribute to differences in
senior apartment values across the various markets.
Several market-level factors are examined to determine whether any of these factors have an
impact on the valuation of senior apartments. The list of market variables includes
measurements for local apartment rents and vacancy rates which characteristics the supply and
demand equilibrium of the metropolitan apartment market. Variables for population and income
relate to the market size and capacity to pay for real estate services. Within each local apartment
market is a subset of senior apartments with age restrictions. Potential demand for senior
apartments is measured by the percent of the local population age 55 and older. From the
demand pool of senior residents, the senior apartment submarket competes with traditional
apartments. Accordingly, our measure of relative supply includes the inventory of all local
apartments scaled by the senior population. Life expectancies also vary geographically. Markets
with longer life expectancies are expected to have increased demand for senior housing products
resulting from reduced tenant turnover and lengthier tenure for the average senior housing
26
resident. Condominium prices are used to evaluate possible substitution effects between the
rental and ownership multifamily products. Geographic differences in educational attainment are
used a proxy for differences in perception for the attractiveness of various senior housing
options, following evidence from a recent survey of potential senior residents (NIC, 2007).
Several market variables are found to be capitalized into the transaction prices for senior
apartments including the inventory of apartments per senior resident, condo prices, market size,
education, and life expectancy. Each of these factors has a greater impact on the pricing of
senior apartments than on the pricing of the traditional apartments in the same markets. Due to
the presence of age restrictions, demand for senior apartments is constrained, while demand for
traditional apartments is not. We find that senior apartment values are relatively higher in
markets where the total supply of apartments per senior resident is low. In addition, the price of
residential ownership influences the decision to rent for income-constrained seniors. We find
that high condo prices are associated with significantly higher values for senior apartments. The
level of education is associated with a perception bias for other senior housing options, including
a less favorable perception of assisted-living by educated households (NIC, 2007). A less
favorable perception for alternative senior housing products should lead to increased demand for
senior apartments in markets where educational attainment is higher. We document that a
market with high educational attainment experiences relatively higher values for senior
apartments. Finally, greater life expectancies create increased demand for the senior housing
product during the later years. We find that senior apartment values are relatively higher than
traditional apartment values in markets with high life expectancy.
27
Endnotes
1 The senior rental housing sector includes senior apartments, independent living, assisted living, skilled
nursing facilities, and continuing care retirement communities (CCRCs).
2 NIC defines senior apartments as multifamily residential rental properties that are age-restricted (or age-
qualified) to adults aged 55 years and older.
3 The Current Population Survey (2008) by the US Census Bureau estimates that Social Security
contributed more than 90 percent of household income for nearly 41 percent of senior beneficiaries.
While this statistic indicates steady income, it also suggests that a significant cohort of the current
senior population is entirely dependent on Social Security, which is a restrictive source of income.
Discussion for the subsidized, low-income housing sector of senior apartments is provided later in this
article.
4 In this study, predicted market values are estimated in a hedonic regression model which includes
physical, time and locational variables that compare the average pricing of attributes in each market.
This is in contrast to valuation concepts in appraisal which are traditionally not based on regression
estimates and instead rely on individual sales comparison, cost, and income approaches to determine an
estimate for market value. The term “valuation” is used throughout this study in reference to the
hedonic regression approach.
5 Respondents in the NIC (2007) study include households living in single-family detached, semi-
detached, multi-unit buildings, and manufactured/mobile homes. Respondents include the general
population and are not limited to only those already living in senior housing.
6 Only 6.4% of senior households resided in such units in 1990, this more than doubled to 13.3% in 2002
and was projected to reach almost 18% of senior households by 2010.
7 Healthcare REITs are anomalous in that they earn above market rents with lower return volatility despite
sizeable income from healthcare operations.
8 As an example, these facilities often include common dining and recreation areas not found in the
traditional apartment complex. In the CoStar database, many of these facilities list only the number of
28
beds and rentable building area that would be a more common unit of measurement for hospitals or
nursing homes (rather than the number of units and square feet per unit that are typical for considering
the value of apartment investments).
9 The list of 34 markets examined in this study is comprised of Atlanta, Baltimore, Boston, Charlotte,
Chicago, Cincinnati, Cleveland, Columbus, Dallas, Denver, Detroit, Fresno, Hartford, Houston, Inland
Empire (CA), Kansas City, Las Vegas, Long Island (NY), Los Angeles, Milwaukee, Minneapolis,
Northern New Jersey, Orlando, Phoenix, Portland, Sacramento, San Diego, Seattle, South Florida,
Tampa, Tucson, Tulsa, Washington DC, and West Michigan.
10 Lambson, McQueen and Slade (2004) also propose that parking, laundry, tennis, and clubhouse
amenities are relevant; however, data for these potential variables is not consistently available in the
CoStar database so using them would excessively reduce the sample size.
11 Estimation of equation (1) reveals that in some markets, senior apartments sell at a significant premium
compared to apartments in some markets, a discount in others, while in some cases, the behavior is no
different than traditional apartments. In Table 3, the coefficient for Senior is positive and significant in
two markets, negative and significant in three, and insignificant from zero in the two remaining
markets. Properties marketed as senior apartments make up a relatively small portion of the
transactions in each market. Results for the remaining markets (that have fewer senior apartment
transactions) yield similar results with senior apartments earning premiums in some and discounts in
others.
12 The tenure choice decision refers to the decision to rent or purchase a home, introduced by Henderson
and Ioannides (1983).
13 Household income is another possible option for measuring income. At the national level, household
income and income per capita are highly correlated over the period 1980-2002 with a correlation
coefficient of 0.99. Income per capita is used here because population is the proxy for market size
used in equation (8).
29
14 Reliable data on life expectancies are not available by metropolitan area at the time of this study.
While differences in life expectancy would be expected between rural and urban areas, the state level
data is used as a proxy for differences across metropolitan areas. In the year 2000, it is estimated that
more than 79 percent of the U.S. population lived in urban areas (US Census Bureau, 2000).
15 The most recent data for educational attainment at the level of metropolitan area is from the 2000
Census. Educational attainment at the state level is available, reported as recent as 2006. The
Education variable considered in this study is measured based on the more recent, state-level data.
16 When Senior is included in the model, the residuals are reported relative to other senior apartments.
When Senior is omitted, residuals are relative to all other apartments.
30
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36
Table 1. Variable Legend
Variable Description
Property variables [Source: CoStar]
Age Property age (in years)
Lot Average lot size per unit
Price Total sale price divided by Units
SF Average square feet per unit
Units Number of apartment units
Senior Equals 1 if property is identified as a senior apartment complex
SRES Standardized residual for observations where Senior = 1 based on the estimation of equation (2) for each market
HUD property Equals 1 if property is registered as affordable housing for elderly tenants [Source: HUD]
Market variables:
55&Older Percent of population age 55 and older per metropolitan area, 2007 [Source: Census]
Condo prices Log of median condo prices per metropolitan area, 2007 [Source: NAR]
Education Educational attainment levels by state for population aged 25 and over: Bachelor's degree or higher, 2006 [Source: Census]
Income Log of annual income per capita by metropolitan area, 2009 [Source: BEA]
Inventory Inventory of apartments per metropolitan area divided by the senior population (Population * 55&Older) [Source: Census]
Life expectancy Life expectancy at birth per state, 2005 [Source: Census]
Population Log of total population per metropolitan area, 2007 [Source: Census]
Rent Fair market rents (monthly) divided by metropolitan area income per capita [Sources: HUD, BEA]
Vacancy Apartment rental vacancy rates per metropolitan area, 2007 [Source: Census]
37
Table 2. Summary Statistics
Panel A. Full Sample: 20,277 Apartments (153 Senior Apartments)
Standard Mean t-test Variable Mean Deviation (Senior=1) of difference
Age 53.1 28.2 27.8*** (-15.39)
Lot 8,518.0 632,656.6 1,790.4 (-1.40)
Price $101,793 $94,949 $80,886*** (-4.69)
SF 863.6 573.9 798.6** (-2.37)
Units 51.9 102.5 108.0*** (7.72)
Senior 0.007 0.086
Panel B. Summary of Markets
Mean Mean Mean Mean Mean
Market Senior Age Lot Price SF Units Observations
Inland Empire (CA)
0 36.7 2,737.9 $92,554 858.0 38.3 338
1 24.0 1,597.3 $81,744 726.0 105.0 13
Los Angeles 0 50.8 1,156.1 $144,273 810.6 16.3 4,101
1 32.5 1,100.1 $127,644 794.8 102.8 21
Milwaukee 0 50.5 2,308.0 $57,248 942.7 24.8 523
1 23.3 2,648.2 $67,057 914.3 59.1 8
Minneapolis 0 58.6 1,838.5 $67,055 992.8 35.0 424
1 9.8 2,079.2 $108,889 1,316.5 104.8 9
Northern New Jersey
0 68.6 5,735.0 $92,064 812.4 41.1 216
1 22.2 5,223.9 $75,777 818.7 145.6 9
Portland 0 38.4 1,894.2 $71,523 856.5 45.8 503
1 35.2 1,470.5 $51,069 716.0 55.2 8
San Diego 0 41.1 1,525.4 $134,888 772.7 29.1 673
1 25.5 939.4 $123,278 604.3 59.5 14
Notes: Panel A of this table reports summary statistics for the trimmed sample of 20,277 observations for apartment transactions extracted from the CoStar COMPS® database, considering multi-family property types only. The sample includes 34 markets where sufficient data is available for property described as senior apartments. Transaction dates range from July 12, 1993 to May 26, 2010. All variables are described in Table 1. The second-to-last column reports the sample mean for the subset of senior apartment transactions. The final column reports t-statistics in parentheses for the difference between the mean where Senior = 1 compared to the remainder of the sample. *** and ** designate a statistically significant difference from the remainder of the sample at the 1 and 5 percent levels, respectively, based on the corresponding t-test. The summary statistics reported in Panel A include 153 observations where Senior = 1. Panel B reports the summary statistics for senior and non-senior apartments for the 7 markets where the coefficient for Senior is estimated and reported.
38
Table 3. Hedonic Estimations [Dependent variable = Price]
Inland Empire (CA) Los Angeles Milwaukee Minneapolis
Northern New Jersey Portland San Diego
Constant 9.75*** 10.79*** 10.14*** 9.74*** 10.70*** 10.37*** 10.49***
(2442.3) (19143.7) (4473.4) (2828.5) (3177.1) (2866.9) (6268.2)
Age -0.006 -0.005*** -0.005** -0.006** -0.007** -0.007*** -0.002
(2.2) (49.7) (5.8) (6.0) (6.0) (28.0) (0.8)
Age2 5.9E-5 1.6E-5*** 3.6E-5** 4.0E-5** 5.0E-5** 6.2E-5*** 3.1E-5*
(1.6) (8.0) (5.2) (5.9) (5.0) (26.1) (3.0)
Units 1.4E-3** -1.3E-3*** -2.4E-3*** -1.3E-3** 1.0E-4 1.5E-4 -6.6E-4*
(5.9) (23.6) (18.6) (4.8) (0.0) (0.2) (3.2)
Units2 -3.7E-6** 3.6E-6*** 4.0E-6*** 3.0E-6** 3.5E-7 1.8E-7 2.0E-6**
(4.6) (15.8) (13.3) (3.9) (0.2) (0.0) (4.3)
SF 2.6E-3*** 1.5E-3*** 1.0E-3*** 1.3E-3*** 1.6E-3*** 9.5E-4*** 1.4E-3***
(83.1) (376.7) (81.0) (51.8) (14.6) (19.4) (180.7)
SF2 -8.5E-7*** -3.2E-7*** -1.2E-7*** -2.9E-7*** -4.8E-7** -6.1E-8 -2.3E-7***
(38.1) (62.9) (9.5) (17.7) (4.9) (0.3) (33.5)
Lot 1.4E-6 7.5E-5*** 3.1E-5*** 9.7E-5*** 5.6E-6 -6.2E-5* 2.0E-5
(0.0) (72.1) (8.1) (11.9) (0.2) (3.5) (2.4)
Lot2 -7.0E-10 -2.1E-9*** -6.1E-10** -7.7E-9*** -9.2E-12 1.1E-8 -3.4E-10
(1.0) (60.1) (5.3) (8.4) (0.2) (2.7) (0.5)
Senior 0.089 -0.123* 0.071 0.456*** -0.443*** -0.115** 0.142*
(1.3) (2.8) (0.6) (9.2) (17.4) (4.3) (3.6)
Fixed 20 qtrs 25 qtrs 24 qtrs 22 qtrs 20 qtrs 21 qtrs 18 qtrs effects: 1 sbmkt 13 sbmkts 14 sbmkts 18 sbmkts 22 sbmkts 15 sbmkts 13 sbmkts
R2: 62.0% 62.5% 59.0% 62.6% 62.2% 62.2% 60.8%
N: 351 4122 531 433 225 511 687
Notes: This table reports results from hedonic estimations of equation (1) for 7 individual markets. These 7 markets have the highest number of observations for senior apartments in the sample. The number of senior observations for each market is detailed in Panel B of Table 2. The dependent variable in each model is Price, which is the natural log of price per unit. Physical, time and locational regressors are based generally on the approach of Lambson, McQueen and Slade (2004). All variables are described in Table 1. The fixed effects rows report the total number of submarkets and quarters of available data for each market. In both cases, indicator variables are included for each submarket and quarter with one variable suppressed (coefficients for these variables are not reported but available from the authors upon request). Sale condition control variables are also included in all estimations. Coefficients for physical attributes are reported on each row for the corresponding independent variable. χ2 test statistics are reported in parentheses beneath each coefficient. χ2 test statistics are calculated based on the heteroscedasticity-consistent estimator of the covariance matrix introduced by White (1980). ***, **, and * designate a statistically significant coefficient based on the χ2 test at the 1, 5, and 10 percent levels, respectively.
39
Table 4. Average Standardized Residuals for Senior Apartments
Senior Average Senior Average Market observations SRES Market observations SRES
Minneapolis 9 1.398 Charlotte 2 -0.004
Boston 1 1.013 Tucson 2 -0.008
Tulsa 1 0.911 Las Vegas 2 -0.125
Seattle 5 0.885 Fresno 3 -0.261
Dallas 2 0.763 Baltimore 3 -0.291
San Diego 14 0.720 Hartford 1 -0.333
Denver 3 0.695 South Florida 3 -0.373
Orlando 1 0.614 Sacramento 2 -0.377
West Michigan 2 0.368 Phoenix 6 -0.462
Detroit 1 0.365 Atlanta 4 -0.463
Washington DC 5 0.324 Portland 8 -0.533
Milwaukee 8 0.244 Los Angeles 21 -0.542
Kansas City 1 0.178 Columbus 3 -0.844
Inland Empire (CA) 13 0.038 Cleveland 3 -0.862
Tampa 7 0.008 Cincinnati 2 -0.912
Northern New Jersey 9 -1.084
Long Island (NY) 2 -1.308
Chicago 2 -1.552
Houston 2 -2.231
Notes: This table reports the average standardized residual for senior apartment observations for each of the 34 markets based on individual hedonic estimations of equation (2). The Senior
observations column reports the total number of senior apartment transactions used to calculate the Average SRES for each market. This table is sorted descending by the size of the average standardized residual with market having positive values on the left-hand side and negative values on the right-hand side.
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Table 5. Market Determinants of Senior Apartment Values [Dependent variable = SRES]
Variable Eq. (3) Eq. (4) Eq. (5) Eq. (6) Eq. (7) Eq. (8)
Constant -4.45* 0.756 -17.73 3.51 4.79* -24.19**
(2.8) (1.1) (1.7) (1.0) (3.0) (5.1)
Vacancy 2.28 -3.72 1.28 3.00 -0.91 3.59
(0.4) (1.3) (0.1) (0.8) (0.1) (1.7)
HUD property 0.026 0.240 0.096 -0.050 0.087 0.010
(0.0) (0.6) (0.1) (0.0) (0.1) (0.0)
Inventory -2.59** -3.58*** -2.38**
(5.9) (10.3) (5.3)
Population -0.691*** -0.321* -0.449**
(7.2) (2.9) (5.7)
55&Older -0.025 -0.024 -0.045
(0.4) (0.4) (1.3)
Condo prices 0.916** 1.28**
(4.0) (6.3)
Rent 29.99 18.76
(2.3) (0.9)
Income -0.522 0.833
(0.5) (1.1)
Education 0.091*** 0.072***
(7.3) (8.6)
Life expectancy 0.274** 0.265***
(5.8) (11.2)
R2: 12.1% 12.6% 16.8% 11.8% 3.0% 14.1% N: 75 88 88 91 113 113
Notes: This table reports the from the estimation of equations (3)–(8) which consider the impact of market-level variables on the dependent variable, SRES, for only the sample of senior apartments (where Senior = 1). Not all variables are included in each model due to high correlations among regressors. SRES is the standardized residual from the hedonic estimation of equation (2). All market variables are described in Table 1. The market variables are not available for all markets. Inventory is only available for the 25 largest MSAs, and Condo prices is only available for 19 of those markets. In this case, equation (4) can be estimated with only 75 observations for senior apartments, which is the smallest number of observations used in the analysis. The largest number is 113 observations for equation (6). Coefficients for market variables are reported on each row for the corresponding independent variable. χ2 test statistics are reported in parentheses beneath each coefficient. χ2 test statistics are calculated based on the heteroscedasticity-consistent estimator of the covariance matrix introduced by White (1980). ***, **, and * designate a statistically significant coefficient based on the χ2 test at the 1, 5, and 10 percent levels, respectively.
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Table 6. Robustness: Market Determinants [Dependent variable = SRES2]
Variable Eq. (3) Eq. (4) Eq. (5) Eq. (6) Eq. (7) Eq. (8)
Constant -4.00 1.09 -18.54 3.64 5.08* -21.39**
(2.2) (2.6) (1.9) (1.1) (3.7) (3.9)
Vacancy 2.51 -3.20 1.63 3.72 -0.025 3.70
(0.5) (1.2) (0.2) (1.2) (0.0) (1.7)
HUD property 0.091 0.232 0.098 -0.031 0.079 0.005
(0.1) (0.5) (0.1) (0.0) (0.1) (0.0)
Inventory -2.97*** -3.77*** -2.72***
(8.6) (12.8) (7.2)
Population -0.673*** -0.324* -0.510***
(7.6) (3.5) (7.4)
55&Older -0.022 -0.022 -0.044
(0.3) (0.4) (1.3)
Condo prices 0.861* 1.19**
(3.5) (5.4)
Rent 20.66 7.82
(1.3) (0.2)
Income -0.097 1.22
(0.0) (2.4)
Education 0.077** 0.054**
(5.2) (4.5)
Life expectancy 0.233** 0.195**
(4.4) (6.1)
R2: 14.7% 14.9% 18.7% 10.9% 2.9% 12.4% N: 75 88 88 91 113 113
Notes: This table reports the from the estimation of equations (3)–(8) which consider the impact of market-level variables on the dependent variable, SRES2, for only the sample of senior apartments (where Senior = 1). Not all variables are included in each model due to high correlations among regressors. SRES2 is the standardized residual from the hedonic estimation of equation (9). All market variables are described in Table 1. Coefficients for market variables are reported on each row for the corresponding independent variable. χ2 test statistics are reported in parentheses beneath each coefficient. χ2 test statistics are calculated based on the heteroscedasticity-consistent estimator of the covariance matrix introduced by White (1980). ***, **, and * designate a statistically significant coefficient based on the χ2 test at the 1, 5, and 10 percent levels, respectively.
