Jayathirtha, C. & Fox, J. J. (1996). Home ownership and the decision to overspend: full article in HTML format

Jayathirtha, C. & Fox, J. J. (1996). Home ownership and the decision to overspend. Financial Counseling and Planning, 7, 97-106.

Copyright 1996 by AFCPE

Home Ownership And The Decision To Overspend

Chandrika Jayathirtha,(1) The Ohio State University

Jonathan J. Fox,(2) The Ohio State University

An empirical model of overspending derived from the life cycle savings model was estimated for home
owners and renters. Age, income stability indicators, family structure, marital status and race appear
to have different impacts in the models, implying that the life cycle model may not equally characterize
home owners and renters. Financial professionals need to be aware of the impact of the housing tenure
decision on overall spending and savings. Practitioners can use these results to encourage clients to
reconsider home purchases in light of expected changes in spending patterns that may impede a clients
progress toward other financial goals.

KEYWORDS: Overspending, housing tenure, life cycle savings model, consumer expenditure survey

Purchasing a home has long been considered one of a
household’s best investments. For most Americans, real
estate assets dominate other forms of wealth holdings
(U.S. Bureau of the Census, 1993, p. 487). Through the
1970s and early 1980s, U.S. home owners benefited from
10% annual increases in home values (U.S. Bureau of the
Census, 1993, p. 482). With home owners devoting a
significant portion of their budgets toward mortgage
payments (U.S. Bureau of the Census, 1993, p. 456), it
is not surprising that the asset holdings of Americans are
disproportionally weighted toward residential real estate.

Though it appears that American families have benefited
greatly from home ownership, the question remains as to
whether investing in a home makes good sense from a
financial planning perspective. Is purchasing a home a
sound investment option or simply a good consumption
decision? Are home owners forced to make better
financial decisions given the financial discipline
necessary to obtain and payoff a mortgage? Do home
owners exhibit behaviors more typical of those following
a long-term financial plan? These are the general
questions addressed in this article.

This article focuses on the overspending behavior of
renters and home owners. Overspending may be due to
any number of factors–from heavy spending on housing
and durables while establishing a household (Tobin,
1967) to fluctuating income patterns of the major wage
earners in the households (Hanna, Fan & Chang, 1995).
Although overspending may be rational at some points in
the life cycle, persistent indebtedness places households
at risk of not achieving major financial goals.

By comparing the spending behavior of families that own
their homes to those who rent, it is hoped that we can
learn more about the budgeting, investing and overall
financial planning decisions made by these individuals at
various stages in the life cycle. Analysis of the spending
behavior of home owners and renters can give new
insights as to whether overspending is part of a rational
plan to meet family goals, or whether it is mandated by
the additional burden of spending on home maintenance,
property taxes, furnishings and other expenses related to
home ownership. Financial advisors, real estate
professionals and credit counselors can use these
comparisons when working with households considering
home ownership.

The Literature

The empirical work on overspending can be categorized
as studies that focus on housing tenure, overspending,
family savings and the adequacy of emergency funds.
There are few empirical studies that focus specifically on
the impact of housing tenure on consumer spending.

Housing Tenure and Spending

Paulin (1995) provides the most extensive analysis of the
differences between the consumption decisions of home
owners and renters. Using 1989 and 1990 data from the
Consumer Expenditure Survey, Paulin found substantial
differences in consumption patterns between home
owners and renters for several aggregate commodity
categories. For both home owners and renters, income,
age and family size were related to the level of
expenditure on various aggregate commodity groups.
The relationship between income, age and family size
was not constant across owners and renters. For
example, the level of expenditure for primary housing
and related services increased with age for renters and
decreased for home owners. Paulin found that health and
personal care expenditures increased with age at a faster
rate for home owners than it did for renters. Significant
differences in reactions to income changes across tenure
status were also reported. Home owners were more
sensitive to income changes when choosing a level of
recreation and transportation expenditure but less
sensitive to income changes for food purchased for home
consumption. It is important to note that Paulin used
total expenditure as a proxy for income, with the result
being a total level of expenditure and/or income exactly
equal to the sum of the expenditures on each category.
While this is an accepted practice in demand analysis
(given the well noted problems with income under
reporting in survey data), it does not allow for the
identification of individuals whose expenditures exceed
their incomes.


Oh (1995) used reported income in an analysis of
spending patterns of renters. Oh characterized those with
a rent burden (spending more than 30% of after-tax
income on rent) using data from the 1980 to 1991
Consumer Expenditure Survey. Oh found households
with a rent burden to be more likely to spend more than
their reported income.

Bae, Hanna and Lindamood (1993) used BLS Consumer
Expenditure Data from the 1990 survey. They calculated
an income to spending ratio for each consumer unit to
identify patterns of overspending in households. They
found that 40% of American households spent more than
their take-home incomes (indicated by an income to
spending ratio above one) and 25% of the sample spent
at least 127% of their take-home income. At least 25%
of households in each family size and age group spent
more than they brought home. Bae, et al. did include
housing tenure in their model of overspending and found
home owners with mortgages to be more likely to
overspend than renters or homeowners without

Chang (1994) analyzed data from the Survey of
Consumer Finance to show that 40% of U.S. households
had negative saving between 1983 and 1986. Chang
defined saving as the increase in real net non-housing
assets between the two survey periods, and found that
40% of American families had a decrease in non-housing
wealth between 1983 and 1986. This decrease was
experienced more by younger and lower income
households than older and higher income households.
One interpretation is that younger households with lower
current income may have been expecting a higher income
in the future and thus were overspending. Chang found
that higher levels of non-housing assets in 1983 led to
less savings (declining asset levels) during the three
subsequent years, while income in 1983 was positively
related to increasing asset levels (savings) for the coming
years. Home ownership in 1983 did not impact the level
of family savings over the following three years.

Family Savings

Hefferan (1982) used the 1972-73 Consumer
Expenditure Survey to investigate the determinants of
current family savings. Hefferan found savings
(measured as the difference between current expenditure
and income) to be primarily an increasing function of
income and wealth. Home ownership appeared to be
positively related to the decision to save as well as the
level of savings. In fact, Hefferan found home owners
with mortgages to be both more likely to save as well as
saving more than families with similar educations and
family life cycle characteristics. This finding contradicts
Bae, et al. (1993) and could be a result of recent changes
in the banking industry which have made home
mortgages more accessible (The Wall Street Journal,

Davis and Schumm (1987) investigated family savings
behavior and the satisfaction with the current savings
levels of 1739 married couples in 13 rural and urban
states in 1977 and 1978. They asked respondents to
choose a category closest to the amount they had saved
or invested in the past 12 months. Davis and Schumm
(1987) identified an income threshold above which
families tended to run a surplus. For those above the
income threshold, savings was found to be a function of
income, education, household size and home ownership.
Home ownership was shown to be positively correlated
with the actual level of savings and the wife’s
satisfaction with current financial asset holdings.

Adequacy of Emergency Funds

Other studies of family spending patterns have focused
on the adequacy of emergency funds held by the family.
A family with adequate emergency funds may be more
likely and able to overspend. There is no clear uniform
recommendation for an adequate emergency fund,
however, most of the empirical studies have shown that
U.S. households have inadequate liquid assets to cover
emergency expenses for 3 months (Chang, 1995; Hanna
& Wang 1995; DeVaney, 1995; Zhou, 1995). Chang
(1995) found home owners in 1983 more likely to have
a least three months worth of emergency fund savings.
Hanna & Wang (1995), estimating liquid assets held by
households using the 1990-91 Consumer Expenditure
Survey, revealed that 70% of households did not have
enough liquid assets to cover 3 months of spending.
Those with higher levels of home equity were found to
be more likely to have liquid assets that would last 3
months if current income was discontinued. DeVaney
(1995), using the 1977 and 1989 Survey of Consumer
Finance, also found home equity levels to be positively
related to the likelihood that a family had adequate
emergency funds in 1977 and 1989. Finally, Zhou
(1995), using the 1989 Survey of Consumer Finance,
found that age, net worth and home ownership are
positively related to holding adequate emergency funds.

The empirical research directly examining the
overspending behavior of families is limited and the
findings regarding the influence of home ownership on
savings and overspending appears to be somewhat
contradictory. Therefore, the purpose of this study is to
explore the effect of home ownership on household
overspending behavior. Many household characteristics
such as household size, marital status, number of
children and employment status are added to the model
to further explain overspending behavior of households
and differentiate the spending behavior of home owners
from renters.

Theoretical Model

According to the conventional life cycle savings
hypothesis, a rational family adopts a lifetime
consumption plan that balances the utility gained from
acquiring additional investment assets against
expenditures on current consumption across all stages of
the life cyclea (Ando & Modigliani, 1963 ). However,
the consumer decision to own a home may negatively
impact families’ consumption paths given the significant
costs of buying and maintaining a home. The decision to
live in owned housing could also require the household
financial manager to be more fiscally responsible so that
the family can obtain a loan and continue making house
payments throughout the long contract period.

The life cycle savings hypothesis maintains that
individuals deliberately save and dis-save during their
lifetime, but in the case of home ownership, it may be
difficult for the family to maintain desired spending and
savings patterns. Typically, an individual’s income is
expected to be low at a young age, rise with professional
achievements, and then fall with retirement. Although
consumption may vary with time, it is assumed not to
change greatly with transitory changes in income. As a
result, to maximize satisfaction, households borrow
during the early stages of the life cycle against their
expected future earnings to offset debt and to shrink the
gap between consumption and income. The life cycle
model also suggests that households repay the debt and
accumulate wealth during middle-age, then borrow from
savings mainly to adjust for declining earned income
during retirement. In the basic life cycle model, the
household bases decisions on events which are assumed
to be known with certainty. As income is neither
constant nor certain, households may either prepare
themselves for the emergencies by accumulating assets or
borrow to adjust to unexpected income fluctuations.

A prescriptive life cycle savings model proposed by
Hanna, Fan and Chang (1995) maintained that the
percent of income to save today should depend on the
expected lifetime non-investment income pattern.
Households who are sure that their real incomes will
increase substantially in the future may rationally not
begin saving for retirement until 25 years before
retirement. However, uncertainty in future incomes and
retirement ages may make saving early appear the
rational choice (Fan, Chang & Hanna, 1992).

The life cycle savings model is expected to describe the
spending patterns of home owners and renters alike and
this is a testable hypothesis. The general hypothesis
tested in the analysis that follows is that renters and
owners exhibit similar patterns in overspending. In
particular, overspending is hypothesized to vary similarly
across housing tenure status with age, income, income
stability, education, household structure, marital status,
employment status, race and region characteristics.

The specific hypotheses based on the allocation of
consumption over the life cycle are listed below. Middle
age groups were expected to be less likely to overspend.
Higher levels of income were expected to be associated
with lower levels of overspending. The likelihood of
overspending was expected to decrease at a decreasing
rate with income, thus the natural log of income is used
as the measure of income in the empirical model. Higher
levels of financial assets may allow for greater spending
from savings and thus higher overspending rates were
expected to be associated with higher asset levels.
Collecting unemployment compensation, welfare and
income from other sources are indicators of income
instability and were expected to be associated with lower
overspending rates. The purchase of a vehicle during the
sample period was expected to be associated with higher
levels of overspending. Lower levels of education could
also imply lower future expected earnings and thus,
rationally, less overspending than would a more
educated consumer with the same income level (e.g., a
medical intern versus a factory worker). The presence of
children in larger households was expected to lead to
higher levels of overspending. Female headed
households were also considered to have less income
stability and thus lower levels of overspending were
expected for households headed by females. Marital
instability also indicates income instability and may
associate with lower overspending levels. The self
employed and unemployed were perhaps less able to
draw on future expected earnings and thus may
overspend less. Minorities may have more income
uncertainty, and thus would be expected to overspend
less than otherwise similar white non-Hispanics.
Minorities may also have less access to credit and thus
be constrained from overspending. Finally, the region of
the country may be associated with varying levels of
credit availability and overspending opportunities.

This study is similar to the Bae, et al. (1993) analysis.
However the dataset is 8.7 times larger, allowing better
estimation of effects of demographic variables such as
race on overspending. This article also analyzes
overspending separately for renters and owners, whereas
the Bae, et al. analysis only had one analysis for all



Data on expenditures and socio-demographic
characteristics were drawn from the 1990-1992
Consumer Expenditure Survey (CES) conducted by the
Bureau of the Census for the Bureau of Labor Statistics
(BLS). The CES is an ongoing study that collects data
on expenditures, income, and major socio-demographic
characteristics of a consumer unit. The CES is the most
detailed, nationally representative data set on American

Only households that completed interviews for any four
consecutive quarters during the period 1990-1992 were
included in this study so that expenditures could be
evaluated on an annual basis. Households reporting a
negative income for the year and those who did not
report income were excluded. This left a sample of 7,498
households that either lived in rented or owned housing.
All dollar values used in this study were adjusted to 1992
dollar values.

Some Details on Variable Measurement

Quarterly expenditures were summed to get each
household’s annual total expenditure. FICA and pension
contributions were subtracted from total expenditures as
most of these payments were withheld by employers.
After-tax family income was also adjusted by subtracting
FICA and pension contributions to obtain household
disposable income. This allows the income variable used
in this research to represent the amount a household can
spend on current consumption, repayment of loans, and

The spending to income ratio and the ratio threshold for
overspending used in this study are similar to the
concepts used in Bae, Hanna, & Lindamood (1993). The
spending to income ratio is defined as annual total
expenditures divided by annual disposable income. A
ratio value of greater than one is defined as
overspending, and a ratio value of one or less is defined
as not overspending. As Bae, et al. discussed, the values
for this ratio have an enormous range b. For this reason
threshold models are considered most appropriate in an
analysis of the determinants of overspending.

Household’s net financial assets were calculated by
subtracting debts from total financial assets. Total
financial assets equal the sum of checking, brokerage,
and savings account balances, money owed to the
consumer unit, market value of all stocks, bonds, mutual
funds and other such securities, and investment in own
farm or business. Credit was defined as the total amount
owed by the household as of the last set of bills.

All other variables were defined as dummy variables
where a one means the consumer unit belongs to the
group and a zero means that they do not. For example,
the dependent variable is coded as a one if the family
spending was more than income and as a zero otherwise.
The variables all appear in Table 1 where the distribution
of the sample is presented in detail.

Multivariate Analysis

Multivariate models of overspending were estimated for
both renters and home owners. Logistic regression was
used to measure the independent effects of age, income,
income stability proxies, education, family structure,
employment status, race and region on the likelihood of
overspending among renters and home owners. Logistic
regression is appropriate given the large number of
explanatory variables and the limited distribution of the
dependent variable–the decision to overspend. The
interpretation of logistic regression results is also
relatively straightforward. For example, for dummy
independent variables, the odds ratios reported in Table
2 can be interpreted as the relative likelihood of
overspending between the grouped identified by the
dummy variable and the reference group.

An empirical test for differences between renters and
home owners was conducted by estimating a full
interaction model.c In each case where the estimated
coefficient for the interaction term was significant with
90% confidence there was statistical evidence of a
difference between renters and home owners with respect
to that independent variable. In each case where a
statistical difference between models exists a check mark
appears in Table 2.


Description of the sample and the proportion of
overspending households

Table 1 provides a description of the sample and the
proportion of renters and home owners overspending.
About 71% of the sample lived in owner occupied
housing. This is somewhat higher than the U.S.
population figure of 65% of units being owner occupied
during the early 1990s (U.S. Bureau of the Census, 1993,
p. 724). The distribution of the sample across age,
education, household structure, marital status,
employment status, race and region is generally
consistent with U.S. population statistics for the sample
period (U.S. Bureau of the Census, 1993). Over 40% of
the households spent more than their take home income.
This is the same finding as Bae et al. (1993). Renters
spent more than their income in 47% of the cases, while
home owners overspent in 37% of the cases.
Overspending was most prevalent among renters in all
sub-categories defined in Table 1. Among home owners,
overspending appears to be declining with age until age
55-59. After these prime retirement saving years,
overspending appears to rise in frequency with age. The
pattern is slightly different for renters where the
frequency of overspending appears to bottom out
between 45 and 54 years of age. Very young home
owners (under 25 years old) appeared to be overspending
more than older home owners. Among renters,
households whose reference person was under 25 or over
60 years of age overspent in about half of the cases.

Home owners report significantly higher incomes and
expenditures. Home owners also report significantly
higher levels of financial assets relative to liabilities. Net
financial assets for home owners top $2,500 on average
while renters financial assets exceed liabilities by only

Welfare recipients, those purchasing a vehicle, widowed
and separated reference persons, and unemployed and
self-employed reference persons all appear to have been
members of overspending households in more than half
of the cases. Notably, about two-thirds of unemployed
and self-employed renters overspent and over half of the
Black and Hispanic renting population appeared to be

Overspending Model for Home Owners and Renters

Estimated coefficients, odds ratios and parameter
stability tests are reported in Table 2. Both models
appeared to do an adequate job of characterizing
overspending. Concordant rates above 80% and the
Pseudo R2 levels over 40% imply that a significant
amount of the variance in the likelihood of overspending
is being explained by these models.

Table 1

Distribution and Proportion Overspending

% overspending



All households 100 40.2 37.3 46.7
Under Age 25 3.5 51.0 43.3 52.3
Age 25 & < 30 9.0 38.0 33.2 41.3
Age 30 & < 35 11.4 35.7 32.0 42.4
Age 35 & < 40 11.3 37.2 32.8 47.1
Age 40 & < 45 11.3 37.6 34.2 47.6
Age 45 & < 50 8.6 36.5 34.7 43.0
Age 50 & < 55 7.4 35.0 32.9 43.4
Age 55 & < 60 6.3 34.3 30.5 48.9
Age 60 & < 65 6.9 46.9 45.1 52.8
Age 65 & < 70 7.5 48.0 47.0 53.3
Age 70 & < 75 7.1 45.9 43.7 53.3
Age 75 & < 80 5.2 48.7 47.8 51.1
Age 80 4.6 43.5 41.0 49.1
Receiving other income 9.3 44.3 43.0 49.1
Unemp. compensation 6.2 37.0 32.6 48.4
Welfare recipients 4.0 58.7 52.9 59.9
Purchased vehicle 25.5 55.1 53.3 60.7
Less than high school 23.4 48.3 45.8 51.8
High school graduate 30.9 40.2 37.7 45.6
Some college 22.1 40.2 37.1 47.3
College graduate 23.6 32.7 30.2 40.0
Size = 1 24.6 46.7 46.0 46.9
Size = 2 31.3 36.7 35.2 42.1
Size = 3 17.3 39.8 36.8 48.0
Size = 4 15.6 36.7 32.2 50.8
Size = 5 7.0 39.8 37.7 46.7
Size = 6 or more 4.2 45.0 39.9 55.0
Families with chldrn<18 yrs 28.3 38.2 33.1 51.0
Female headed household 27.1 50.7 48.4 52.8
Married 59.9 35.0 33.9 40.4
Widowed 12.7 52.6 50.4 57.5
Divorced 11.9 46.6 43.4 50.3
Separated 3.3 54.6 50.6 56.2
Never married 12.1 42.4 37.2 44.7
Employed 64.7 34.2 31.4 40.5
Not working 9.1 60.0 56.5 64.0
Self-employed 6.0 52.2 48.1 66.7
Retired 20.2 47.1 45.5 53.1
White non-Hispanic 80.3 39.2 37.4 44.7
Black non-Hispanic 10.7 44.4 37.6 50.5
Hispanic of any race 6.0 47.5 41.7 53.5
Asian 2.6 34.9 27.6 45.0
American Indian 0.4 45.6 35.3 56.2
Northeast, non-rural 19.4 39.7 35.2 48.5
Midwest, non-rural 23.3 36.9 34.6 42.5
West, non-rural 20.6 39.9 37.0 46.6
South, non-rural 25.6 41.6 38.4 47.7
Rural 11.1 45.9 44.6 52.8
Income, spending, assets($)

Disposable income

30,199 33,302 20,071
Household expenditures

26,185 29,181 18,756
Net financial assets

1,928 2,568 326

For owners and renters alike, higher income levels
(natural log) were associated with a lower likelihood of
overspending. This finding corresponds with most
empirical studies of spending patterns (Bae et al.,1993;
Davis & Schumm, 1987; Hefferan, 1982). The balance
of financial assets had little impact on the overspending
decision, with owners appearing to be slightly more
likely to overspend when net financial assets increased.
Home owners receiving unemployment compensation
were far less likely to overspend, as were all welfare
recipients. Receiving another source of income and
purchasing a vehicle were positively related to

The overspending patterns of owners and renters were
impacted similarly by education. Those with less than a
high school education were less likely to overspend than
those with higher education levels. However, renters
appear to be only half as likely to overspend when
compared with high school diploma recipients who rent,
while owners without a high school education are three
quarters as likely to overspend as their high school
educated counterparts.

Overall, renters and home owners appear to have
different determinants of overspending. Age appears to
be related to overspending for home owners more than
for renters. Relative to the oldest group (80 plus years of
age), home owners were more likely to overspend in all
age groups except for those under 25 years old. The
calculated odds ratio for home owners 45 to 50 years of
age implies that this group is three times more likely to
overspend than the oldest group when controlling for
income, assets, education, household structure,
employment, race and region differences. Age appears
to play less of a role in overspending among renters.
Only two of the younger age categories appeared to be
different than the oldest group of renters.

Household structure also appeared to have a similar
impact on overspending for renters and home owners.
Larger households were more likely to overspend and
those with children under 18 were less likely to
overspend than those without young children. However,
marital status had a very different impact on
overspending across housing tenure. For home owners,
married, divorced and separated respondents were more
likely to be in a consumer unit that out spent its
disposable income when compared to singles with similar
characteristics. Among renters, only widows appeared
more likely to overspend than single renters. Separated
home owners were nearly twice as likely to overspend
than single home owners while separated renters were
not more likely to overspend than single renters.

Table 2

Logistic Regressions for Overspending of Home Owners
and Renters

Home owners Renters
Variable Coeff.










Age (80 Age)
Age < 25 .553 1.74 .573 1.77
25 Age < 30 .972*** 2.64 .498 1.64
30 Age < 35 .865*** 2.38 .723* 2.06
35 Age < 40 .830*** 2.29 .595 1.81
40 Age < 45 1.042*** 2.83 .942** 2.56
45 Age < 50 1.103*** 3.01 .592 1.80
50 Age < 55 .897*** 2.45 .571 1.77
55 Age < 60 .617*** 1.85 .810* 2.25
60 Age < 65 .850*** 2.34 .608 1.84
65 Age < 70 .825*** 2.28 .774** 2.17
70 Age < 75 .504*** 1.66 .237 1.27
75 Age < 80 .701*** 2.01 .054 1.06 yes
Ln Disp. Inc. -3.041*** 0.05 -2.767*** 0.06
Net Fin. Assets .004* 1.00 .011 1.00
Unempl. Comp. -.373** 0.69 .358 1.43 yes
Welfare recipient -.557* 0.57 -.722*** 0.49
Other Income .444*** 1.56 .406* 1.50
1.921*** 6.83 1.610*** 5.00
Education (H. S.)
< High School -.346*** 0.71 -.609*** 0.54 yes
Some College .433*** 1.54 .317** 1.37
College Graduate .942*** 2.56 .801*** 2.23
HH Structure (Size = 2)
Size = 1 -.659*** 0.52 -.919*** 0.40 yes
Size = 3 .621*** 1.86 .479** 1.61
Size = 4 .579*** 1.78 .857*** 2.36
Size = 5 .929*** 2.53 .687*** 1.99
Size = 6 or more .747*** 2.11 1.263*** 3.54 yes
Child < 18? -.502*** 0.61 -.293* 0.75
Female Hd Hshld -.015 0.98 -.024 0.98
Marital Status (single)
Married .444** 1.56 .001 1.00
Widowed .277 1.32 .494** 1.64
Divorced .511*** 1.67 .199 1.22
Separated .676** 1.97 .019 1.02 yes
Employment (Employed)
Not working .097 1.10 .071 1.07
Self-employed .480*** 1.62 .983*** 2.67
Retired -.077 0.93 .168 1.18
Race (white non-Hispanic)
Black non-Hisp. -.676*** 0.51 -.226 0.80 yes
Hispanic -.232 0.79 .041 1.04
Asian -.090 0.91 .037 1.04
American Indian -.406 0.67 -.065 0.94
Region (South, non-rural {n-r})
Northeast,n-r .178 1.19 .276* 1.32
Midwest,n-r -.227** 0.79 -.330** 0.72
West,n-r .404*** 1.49 .412** 1.51
Rural -.314** 0.73 -.263 0.77
CONSTANT 28.223*** 25.700***

-2 Log


Model 2 2372.2***


Pseudo R2 48.7%


Concordant 86.2%


Reference groups are in parentheses Disposable income and net financial assets are in $1,000s.

*p<.1, **p<.05, ***p<.01

“yes” implies significance of the variable in full interaction model thus BHi <> BRi

Employment status had a similar impact on overspending
across housing tenure. Households where the respondent
was self-employed were more likely to overspend than
households with employed respondents.

Households with a Black non-Hispanic reference person
living in owner occupied housing were only half as likely
to overspend as households with a White non-Hispanic
reference person. Black non-Hispanic renters did not
appear to be significantly less likely to overspend when
compared to White non-Hispanic referenced households.

Renters in the Northeast and West were more likely to
overspend than those in the South. All Midwesterners
appeared to be less likely to overspend than those in the
South. Rural home owners were less likely to overspend
than non-rural home owners.

Discussion and Implications

Contrary to expectations, overspending was more
prevalent among renters in many age, income, education,
household structure, marital status, employment status,
race and region groups. In spite of the significant
expenditures which come with home ownership, it
appears as though home owners are doing a better job of
accumulating financial assets for future consumption.
Certainly the application process for mortgages and the
preparation process for home ownership leads to a
selection bias that causes home owners to appear more
fiscally responsible. In fact, it could be the application
process for a home mortgage that instills such
responsibility in some family financial resource
managers. Furthermore, recent changes in banking
regulations stemming from the Community Reinvestment
Act now require banks to ensure that credit availability
matches deposits across tightly defined geographic areas.
This is allowing many families to obtain a mortgage and
choose owner occupied housing that would have not
done so prior to the changes in regulations. This recent
change may present a significant challenge to these new
home owners who may be attaining mortgages under less
strict application guidelines (The Wall Street Journal,
1996). These new home owners may provide a unique
challenge for financial counselors who are not
accustomed to working with as many home owners in
danger of foreclosure.

Even though renters overspent more than home owners,
a significant proportion of each group overspends.
Therefore, identifying and discussing the determinants of
overspending for both groups should prove useful for
financial planners and counselors.

Overall it would appear that the home ownership process
requires a good deal of fiscal responsibility and planning.
The data for home owners appears to reflect a greater
impact of age on overspending, but the pattern of
overspending does not match that expected based on the
life cycle savings model. Younger and older households
appear less likely to overspend than middle-aged
households and all age groups appear more likely to
overspend than the oldest age group. This implies more
of a cohort effect than a decision based on life cycle
stage. Those over age 80 were the only reference
persons in the sample who were adults during the
depression and it is highly likely that their spending
patterns were permanently impacted by this experience.
Similarly, the excessive spending in the 40 to 45 year old
group could also be explained by a cohort effect.

There are other concerns for financial planners based on
the impact of age on overspending across home owners
and renters. For example, households with older
reference persons who continue to live in owned housing
may not be effectively using the wealth accumulated in
home equity. This is in evidence with 75 to 80 year old
respondents living in owned housing being more likely to
overspend than renters in the same age group. Perhaps
the recent increase in the availability and use of reverse
mortgages will change this situation.

Home owners appear to be reacting more in accordance
with expectations based on the life cycle model of
savings when it comes to income stability. Home owners
receiving unemployment or welfare are less likely to
overspend, perhaps in reference to adjusted income
expectations (Chang, 1995). Home owners, especially
those without mortgages, may be better prepared for
fluctuations in income that comes with job loss and less
willing to incur debt given lowered expectations of future

Perhaps most notable statistically, but not surprising to
financial planners or those who have worked with CES
data before, is the increase in the likelihood of
overspending if a vehicle is purchased by the consumer
unit during the sample period. Home owners who
purchased a vehicle during the sample period were
almost seven times more likely to overspend than those
who did not buy a vehicle. For renters, overspending
was five times more likely among those purchasing a
vehicle. This is ammunition that can be brought to the
table when discussing major purchases with clients.
Home ownership may lead to an appearance of credit
worthiness to other creditors which can lead to an over
accumulation of debt through several periods of

In direct accordance with the life cycle model of savings,
education appears to be positively related to the
likelihood of overspending. For many households,
continued investment in education may be the reason for
overspending. According to the life cycle model this
overspending is rational in light of higher expected future
earnings. Low likelihoods of overspending for lower
educated consumers may also reflect problems with
access to credit. Credit is given based on future
repayment ability and education has long been viewed by
credit providers as a strong indicator of the ability to

The differing impacts of marital status is one of the most
striking difference between home owners and renters
when it comes to the determinants of overspending. The
home is frequently the largest asset controlled by the
family. A newly married couple may be tempted to
overspend on housing leading to several years of deficit
spending early in married life. Impending fiscal
problems can also lead to marriage dissolution where
home owners appear to fare worst of all relative to
renters. Renters can quickly move out of lease contracts
and adjust housing expenditures upon separation or
divorce. However, home owners may not be able to
move as quickly and housing expenditures may be
difficult to adjust as income is likely to drop
significantly. This is a clear path to unanticipated
overspending that could easily derail a previously sound
financial plan. The results here can be used to coach
clients involved in a separation or divorce when it comes
to deciding whether to keep the home. As is commonly
advised, the partner who keeps the home upon separation
is often confronted with expenditures related to the home
that far outstrip the adjusted income.

Finally, the fact that Black non-Hispanic home owners
are less likely to overspend could reflect a degree of
discrimination in the mortgage application process. If
Black non-Hispanic home owners are overspending less,
than it may be a result of being held to too high of a
standard when applying for financing. However, the
appropriate data to draw these conclusions is mortgage
application and foreclosure data not necessarily data on
overspending. The fact that Black non-Hispanic home
owners are less likely to overspend bodes well for
creditors when it comes to collecting the significantly
increased amount of credit extended to this group (The
Wall Street Journal
, 1996).


As with any attempt to quantify human behavior, there
are shortcomings in this study that need to be identified.
When income data are collected from survey
respondents, it is possible that some income is not
reported or inaccurately reported given the personal
nature of the question. It is also important to note that
households interviewed between 1990 and 1992 may
have been reacting to the macroeconomic conditions that
prevailed in the early 1990s (generally considered the
tail-end of a recession) and implications for current
consumer decisions may be inappropriate.

Finally, the spending to income ratio needs to be
interpreted with care, as absolute income levels vary so
may the actual well being of families. For example, a
household having a secure “other” source of income
and/or a generous pension plan might spend well beyond
disposable income and still be making adequate progress
toward financial goals. Conversely, some lower income
families spending less than 100% of income might not be
making adequate financial progress (Bae et al., 1993).


Overall, the expectations for overspending based on the
life cycle savings hypothesis appear to be better reflected
by home owners than renters. This is not entirely
surprising given the flexibility in spending and budgeting
that comes with rented housing. Home owners receiving
unemployment compensation appeared less likely to
overspend, perhaps reacting to a change in expected
future earnings leading to a planned adjustment in
spending. Marital status was also found to impact
spending differently across housing tenure with home
owners appearing to be less able to avoid overspending
during marital disruption. While most of the hypotheses
based on the life cycle model of savings are supported by
the empirical model, age was much more important in the
decision to overspend for home owners than it was for
renters. However, the impact of age was not always
found to be as expected based on the life cycle model.
Younger and older home owners were less likely to
overspend when controlling for income, education,
household structure, marital status, employment, race and
region and the impact of age may be better interpreted as
a cohort effect than as a life cycle stage effect.

End Notes

a. For a complete mathematical treatment of the consumer’s
allocation of consumption throughout the life cycle see Chapter 1
of Deaton (1992).

The distribution of the ratio of spending to income is similar to
that reported in Bae, et al. (1993). Although the median value is
reasonable (89% for all households,) the mean values are very high
for each group, and the maximum values are extremely high
(1679300% for all households)

Distribution of the ratio of spending to income

All Owners Renters
Mean 422 136 1151
Std.dev 19919 785 37492
Maximum 1679300 439 1679300
75%tile 123 120 1320
Median 89 86 97
25%tile 68 65 74

c. In this full interaction model, a new variable was calculated to
match each independent variable by multiplying the housing
tenure dummy by each variable. These interaction terms were
added to the model of overspending and the model was estimated
using the full sample. For details on testing the stability of
regression coefficients across samples see Maddala, 1992, p. 318.


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1. Chandrika Jayathirtha, Ph.D. candidate, Consumer Sciences Department, The Ohio State University, 1787 Neil Ave., Columbus, OH
43210-1290. Phone: 614-292-4389. Fax: 614-292-7536. E-mail:

2. Jonathan J. Fox, Assistant Professor, Consumer Sciences Department, The Ohio State University, 1787 Neil Ave., Columbus, OH
43210-1290. Phone: 614-292-4389. Fax: 614-292-7536. E-mail:fox.99@osu.edu