ORIGINAL PAPER

Financial Sophistication and Housing Leverage Among OlderHouseholds

Hyrum L. Smith • Michael S. Finke •

Sandra J. Huston

Published online: 14 February 2012

� Springer Science+Business Media, LLC 2012

Abstract Increasing mortgage debt among older house-

holds has been cited as evidence of financial distress

caused by low financial knowledge, poor lending practices,

and an increased appetite for debt. This paper investigates

whether housing leverage among older households is

related to financial sophistication, tax effects, and a desire

to increase portfolio allocation to risky assets. Results

indicate a time trend in low housing leverage, but no trend

in high housing leverage. While housing leverage increases

with liquidity constraints, it also increases with financial

sophistication, and tax and portfolio incentives are strongly

related to high housing leverage. The incentive to borrow

against home value created by the deductibility of mort-

gage interest appears to encourage greater housing leverage

and vulnerability to housing price shocks.

Keywords Debt � Financial sophistication � Leverage �Mortgage � Older

Introduction

Mortgage debt and housing leverage among older house-

holds have steadily increased over the last two decades. The

percent of homeowners between ages 55 and 64 who held

no mortgage debt declined from 54 to 39% between 1989

and 1998, and mean housing debt among those aged 65–74

increased sharply (Masnick et al. 2006). Households headed

by an individual 65 or older who had a mortgage increased

from 20% in 1995 to 30% in 2007 (Stafford and Gouskova

2010). Median mortgage debt secured by a personal resi-

dence (adjusted for inflation to year 2007) increased from

$50,000 in 1995 to $85,000 in 2007 for households headed

by an individual aged 55–64, from $25,700 to $69,000 for

households headed by an individual aged 65–74, and from

$15,800 to $40,000 for households headed by an individual

aged 75 and above (Federal Reserve Board 2010). There is

increasing concern, especially in light of the recent housing

crisis, that rising mortgage debt among older households is

a prelude to foreclosure or financial distress during retire-

ment (Munnell and Soto 2008; Shelton 2008). Between

1991 and 2007, the percentage change in bankruptcy peti-

tioners increased by 151 and 178% respectively among

households aged 55–64 and 65–74 (Thorne et al. 2008).

This increase in housing debt among older households is

commonly viewed as a reflection of a reduced preference

for thrift, or a ‘‘new attitude of debt tolerance that obliter-

ates budgets and savings’’ fueled by ‘‘easy credit and hard

economic times’’ (Marmon 2003 p. 1).

However, the increasing use of mortgage debt among

aging households is difficult to reconcile with cohort evi-

dence that older households, and in particular older baby

boomers, have greater wealth than previous age cohorts

and appear to invest in a more sophisticated financial

portfolio (Finke et al. 2006). If older, more risk-tolerant

households are better aware of tradeoffs among financial

assets, including the use of financial leverage and different

tax incentives, it may be rational for older households to

use mortgage debt for more sophisticated borrowing

H. L. Smith (&)

Department of Agricultural and Applied Economics, Virginia

Tech, 315 Hutcheson Hall, Blacksburg, VA 24061, USA

e-mail: hysmith@vt.edu

M. S. Finke � S. J. Huston

Department of Personal Financial Planning, Texas Tech

University, Box 41210, Lubbock, TX 79409, USA

e-mail: michael.s.finke@ttu.edu

S. J. Huston

e-mail: sandra.huston@ttu.edu

123

J Fam Econ Iss (2012) 33:315–327

DOI 10.1007/s10834-012-9293-4

strategies. For example, tax deferral on retirement accounts

has been shown to increase the expected return on invest-

ment assets (Amromin et al. 2007; Engen and Gale 1997),

leading to a higher optimal portfolio share in sheltered

accounts. This and an increased willingness to invest in

equities within these accounts led to riskier portfolios

among the more financially sophisticated and risk-averse

baby boom cohort (Calvet et al. 2009; Center for Retire-

ment Research 2009; Dammon et al. 2004; Follain and

Melamed 1998).

This study investigates whether greater housing leverage

among older households is related to financial sophistica-

tion, tax effects that reduce the cost of borrowing against

one’s home, and a desire to increase portfolio allocation

held in risky assets. We focus on the period leading up to

the mortgage crisis and test the hypothesis that housing

leverage among households with at least one individual age

55 or older is positively affected by financial sophistica-

tion, tax variables that reduce the after-tax cost of mortgage

borrowing, and larger portfolio allocations to risky assets.

Similar to other studies that have used age 55 or older as a

cutoff point to investigate financial decisions among older

households (Dammon et al. 2004; Lusardi et al. 2009;

Masnick et al. 2006; Thorne et al. 2008), we restrict our

sample to households with a head (or spouse) at least

55 years old in order to investigate determinants of housing

leverage among households who are at or near the average

age of retirement in the US.

Literature Review

Life Cycle and Other Demographic Factors

Previous literature has primarily explored the potential

problems associated with increased mortgage debt among

older households. For example, in discussing household

and mortgage debt among older households, Lee et al.

(2007) highlighted the risk of increased mortgage debt

among older households to economic well-being during

retirement years. Masnick et al. (2006) suggested that the

increasing housing debt burden would likely lead to

reduced welfare among older households, causing them to

work longer than expected or to reduce consumption dur-

ing retirement. When reporting the increase in mortgage

debt among older households, news media often focus on

the risks or negative aspects of being highly leveraged in

housing during retirement (Bayot 2004).

Life Cycle Hypothesis

The life cycle hypothesis (Ando and Modigliani 1963)

predicts that a household will transfer earnings or resources

from periods where the marginal utility from consumption

is relatively low, such as during peak working years, to

periods where the marginal utility from consumption is

relatively high, such as during retirement years. A simpli-

fied version of life cycle theory implies that households in

the middle of the life cycle save or pay down debt in

anticipation of the decline in income during retirement

years. By paying down mortgage debt, a household is able

to free up future resources that would have otherwise been

required to meet obligations, and thus reduce consumption,

in future years. In contrast, not saving sufficiently or not

paying down mortgage debt prior to retirement reduces

resources available for consumption during retirement

years and budget flexibility. Several studies have shown

that age tends be negatively related to mortgage debt and

relative measures of leverage in housing debt such as the

loan-to-value (LTV) ratio (Chen and Jensen 1985; LaCour-

Little 2007; Li 2005; Nothaft and Chang 2004; Yilmazer

and DeVaney 2005). While older households may seek to

withdraw equity from the value of their homes in an effort

to fund consumption during retirement through refinancing,

taking out a loan, using reverse mortgages, or establishing

a line of credit, older households were often less likely to

do so given the high fees and potential borrowing limita-

tions (Sinai and Souleles 2007).

In addition to age, liquidity constraints also influence a

household’s allocation of resources throughout a lifetime

(Bryant and Zick 2006). Liquidity constrained households,

who are more limited in their ability to borrow from future

earnings to smooth consumption, will be less inclined to

pay down mortgage debt since doing so would lock even

more of their assets into illiquid housing.

Other Demographic Factors

Employed households may have greater human capital

(HC) on which to borrow, and may have a greater incentive

to borrow to meet consumption needs (Lee et al. 2007).

Similarly, formal education (all else equal) increases bor-

rowing capacity given the greater HC and may indicate a

steeper pre-retirement earnings path.

The presence of children should increase optimal

housing leverage by increasing the marginal utility from

current consumption relative to the future, and therefore

the propensity to borrow or maintain outstanding debt.

While Lee et al. (2007) found that minorities and unmar-

ried households were more likely to borrow, especially

after controlling for liquidity constraints, there is little

theoretical explanation for the effect of race and marital

status on mortgage borrowing. Households with substantial

financial net worth have more resources to pay down

mortgage debt and may have less household leverage when

the income tax incentive effect is controlled.

316 J Fam Econ Iss (2012) 33:315–327

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Tax Leverage Factors

Mortgage Interest Tax Deductibility

The tax deductibility of mortgage interest could further tilt

optimal portfolios away from housing equity and toward

other investments by creating a tax shield on household debt.

The addition of a tax shield on these other investments

creates an even stronger incentive to increase housing

leverage in order to shift assets into higher yielding invest-

ments. Consistent with the use of mortgage debt to indirectly

finance investments, Engen and Gale (1997) found that

increases in 401(k) plan assets are generally offset by

increases in mortgage debt and decreases in home equity.

Follain and Melamed (1998) and Amromin et al. (2007)

described how the tax shield on mortgage debt creates an

arbitrage opportunity for certain households. Households

with a sufficiently high marginal tax rate can increase

mortgage debt, deduct the interest when itemizing, and shift

the mortgage debt to a sheltered investment to yield a net

after-tax gain. This arbitrage opportunity is greatest among

households with the highest marginal tax rates.

Allocation to Risky Assets

As households age, the optimal percentage allocated to

stocks within a portfolio has been shown to decline as HC is

exhausted (Ameriks and Zeldes 2004; Bodie et al. 1992;

McCarthy 2004). However, older households tend to benefit

from maintaining a respectable allocation to equities. For

example, using Monte Carlo simulation analysis and rea-

sonable investment return assumptions to determine opti-

mal portfolio allocation, Dammon et al. (2004) reported the

average overall proportion of equity within a portfolio for

households at least 55 years of age is predicted to be no

smaller than 53%. Lai (2008) contended that increased

appetite for equities among older households is further

justified by the desire to protect inflation and mitigate lon-

gevity risk (the risk of outliving assets).

It is possible that older households rationally choose to

increase or maintain an optimal housing LTV ratio. Inves-

tors with greater risk tolerance, more wealth, or longer

investment horizons have an optimal portfolio that includes

a relatively large share in risky assets. Home equity acts as a

bond within a household portfolio that appreciates at

roughly the rate of inflation over extended periods of time

and provides a consumption stream of housing services.

Large home equity as a proportion of total household assets

can crowd out wealth available to invest in riskier assets,

leading to a suboptimal portfolio (Coco 2005). Curcuru

(2005) reported that in 2001 approximately half of seniors’

total net assets were invested in a primary residence. Further,

the percentage of direct or indirect stock holdings as a share

of financial assets increased from 44.8% in 1995 to 54.6% in

2007 for households headed by an individual aged 55–64,

35.8 to 55.3% for households headed by an individual aged

65–74, and from 39.8 to 48.1% for households headed by an

individual aged 75 and above (Federal Reserve Board 2010).

This increasing stock participation was coupled with

increasing mortgage debt held by seniors. The size of a loan

secured by a senior’s primary residence more than tripled

between 1989 and 2001, and the portion of seniors using

their home as collateral increased by 20% (Curcuru 2005).

From 1995 to 2007 median debt secured by a primary resi-

dence (inflation adjusted to 2007) increased 70% among

households headed by an individual aged 55–64, 269%

among households headed by an individual aged 65–74, and

over two and a half times among households headed by an

individual aged 75 and above (Federal Reserve Board 2010).

Housing leverage can be used to create optimal house-

hold portfolios by shifting assets from lower- to higher-risk

investments. Several studies have shown the positive

relationship between mortgage debt and riskier assets such

as stock holdings (Coco 2005; Heaton and Lucas 2000),

while others have illustrated the benefit of increasing

mortgage debt in order to allocate a greater percentage of

household portfolio to stocks (Flavin and Yamashita 2002;

Waggle and Johnson 2009).

Financial Sophistication

Financial Sophistication: A Form of Personal Finance-

Related HC

Understanding the portfolio benefits of leveraging home

equity in order to increase risky asset share requires an

advanced knowledge of both tax law and portfolio theory.

The ability to make effective financial choices is a form of

acquired HC representing the knowledge and skills avail-

able to enhance productivity in financial decision making.

Becker (1964) described HC as a function of goods and

services, time, and current stock of HC.

In theory, a household increases its current productivity

or stock of HC through investments of time and in goods

and services. Smart (2003) further divided a household’s

current HC stock into two types—acquired and endowed.

Endowed HC represents the natural ability of a household

that cannot be increased through investment in goods or

services and time. Examples include an individual’s cog-

nitive ability or physical strength. This innate ability redu-

ces the time, t, and therefore cost (i.e., foregone wages)

associated with acquiring or attaining more HC. Acquired

HCHC represents the knowledge, skills, or enhanced pro-

ductivity an individual can achieve through investment in

time and goods or services. Similarly, personal finance-

related HC, or the degree of financial sophistication an

J Fam Econ Iss (2012) 33:315–327 317

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individual has attained, can be conceptualized as a function

of both endowed HC, such as intelligence, and acquired HC

developed through investment in time and services to

understand and take advantage of more sophisticated

financial opportunities.

Financial Sophistication and Decision Making

Financial sophistication among Americans has been shown

to be low as evidenced by results from the 2009 National

Financial Capability Study prepared for FINRA (Applied

Research and Consulting LLC 2009). According to Lusardi

et al. (2009), using data from the 2008 Health and Retirement

Study, on average Americans over the age of 55 lacked even

a basic understanding of stock and bond prices, investment

fees, risk diversification, and portfolio choice; however, there

was a variation in financial sophistication based on gender,

education, age, and race/ethnicity. Respondents who were 75

and over, women, less educated, and non-white tend to have

the least HC specific to personal finance.

Households with a greater degree of financial sophisti-

cation, i.e., stocks of personal finance-related HC, are

better equipped to navigate the complexity inherent to

financial markets and demonstrate better financial out-

comes. For example, individuals who were more finan-

cially sophisticated were more likely to plan for retirement

(Lusardi and Mitchell 2006, 2007a, b) and more likely to

invest in stocks (van Rooij et al. 2007).

Similarly, other studies have provided evidence that

more financially sophisticated households are more likely to

make better borrowing decisions. In administering a finan-

cial literacy quiz to a nationwide sample Hilgert and Ho-

garth (2002) found that 81% correctly answered questions

about mortgages and 65% correctly answered questions

about credit cards. Lusardi and Tufano (2009) reported that

the ability to understand interest compounding and other

debt related concepts was low among Americans and that

less debt literate individuals tend to incur higher fees and

high-cost borrowing. Moore (2003) similarly found that less

financially sophisticated individuals were more likely to

hold costly mortgages. Financial knowledge has been

shown to be positively correlated with more responsible

credit card use (Allgood and Walstad 2011; Robb 2011).

Campbell (2006) reported that lower-income and less edu-

cated individuals were more likely to refinance their mort-

gages at inopportune times and concluded that this was due

being less financially literate. Despite the research that has

been performed on the relationship between financial

sophistication and household debt decisions, we are not

aware of any research that has investigated the relationship

between financial sophistication and housing leverage.

Tax arbitrage and portfolio theory provide possible

explanations for increased housing debt among older

households. More financially sophisticated households are

better able to recognize benefits of using subsidized home

borrowing to increase investments in sheltered accounts

and shift their portfolio toward equity investments. The

greatest benefits from using mortgage debt as a tax shield

accrue to households with the highest taxable income. We

hypothesize that older households who recognize and

benefit from rationally increasing housing leverage will

have marginally greater housing debt and be more likely to

be highly leveraged.

Method

Data

The factors associated with housing leverage among older

Americans were tested using data from the 1995, 1998,

2001, 2004, and 2007 administrations of the surveys of

consumer finances (SCF). The SCF is a triennial cross-

sectional survey sponsored by the Federal Reserve Board

(FRB) and is one of the richest sources of household bal-

ance sheet information. The SCF contains detailed infor-

mation on mortgages and loans, value of housing, equity

and business holdings, and other household demographic

information helpful for examining the role of financial

sophistication and the use of housing leverage considering

tax advantages and portfolio choice.

The SCF is based on a dual-frame sample design of

households within the United States. The first frame is a

sample of households selected using a multi-stage area

probability approach intended to provide coverage of

characteristics, such as homeownership, that are found

among the majority of the population. The second frame is

a sample of relatively wealthy households selected dis-

proportionately from a stratified list sample of households

obtained from tax records provided by the Statistics of

Income Division of the Internal Revenue Service (SOI).

Given the oversampling of wealthy respondents, descrip-

tive data in this survey were appropriately weighted using

the SCF sampling weights provided by the FRB to generate

nationally representative statistical estimates. Further, the

SCF uses multiple imputation to estimate five values for

each missing data or response where each individual

imputation is essentially drawn from an estimated distri-

bution of the missing data conditioned on those variables

where responses are given. These imputed values are then

combined with the other variables without missing values,

which results in five data sets for each SCF year included

in this study. Given the use of multi-imputed data, the

repeated-imputation inference technique was used to

appropriately include all available data and in order to not

bias results (Montalto and Sung 1996).

318 J Fam Econ Iss (2012) 33:315–327

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Sample

The total number of households interviewed was 4299 in

1995, 4305 in 1998, 4442 in 2001, 4519 in 2004 and 4418

in 2007. Given the focus of this study, data from all five

survey years were pooled and then restricted to include

only households that owned at least one residence and had

a head (or spouse) at least 55 years of age. Restricting to

those at least 55 years of age allowed us to include a range

of older households—those approaching retirement and

those already retired—to better determine factors that

contribute to increased mortgage debt or housing leverage

among older households. The final sample included 7,458

households and all financial data were represented in terms

of 2007 dollars (by adjusting all previous year financial

amounts to the year 2007 using the Current Price Index for

all urban consumers (CPI-U-RS) and algorithm coding

provided by the FRB1).

Regression Models and Variables

To compare households with higher housing leverage to

households with no housing leverage, a multinomial logistic

regression model was used for the multivariate analysis.

Ordinal logistic regression models were also estimated

but not reported since the test for the proportional odds

assumption failed, i.e., the coefficients for the independent

variables are constant when comparing no leverage house-

holds (LTV ratio = 0%) to low leverage (0 \ LTV \ 50%)

or high leverage households (LTV [ 50%) (the v2 Score

Test rejected the null hypothesis of proportionality).

Multinomial logistic regression explores how a change

in the independent variables affects the likelihood of

belonging in one of the two higher housing leverage groups

relative to the no leverage group. Two multinomial logistic

models were included, one without tax effect variables and

one with tax effect variables. Given that this study was

primarily interested in understanding the structural rela-

tionship between independent variables and the dependent

variable of housing leverage, the multinomial regression

results were unweighted (although the descriptive statistics

displayed in this study were weighted given our primary

interest in this case of estimating descriptions of the

national population). Further discussion on the issue of

weighting regressions and descriptive statistics can be

found in the SCF codebook under the Analysis Weights

section for the most recent 2007 SCF year. Variable

measures for the dependent and independent variables were

created as follows.

Dependent Variable

The dependent variable had three nominal categories:

whether the household had no housing leverage or a home

LTV equal to 0%, a lower housing leverage or a LTV ratio

greater than 0% but less than 50%, or high housing

leverage or a LTV ratio at least 50%. For multinomial

logistic regression purposes, the baseline category (to

which the groups are compared) was the no leverage group.

In calculating LTV ratios, home loans were measured as

total housing debt including the value of mortgages, home

equity loans, home equity lines of credit, and any other

loans secured by the residence. Home values were mea-

sured as the estimated fair market value of personal resi-

dences reported by the respondent. Only the loans and

values of the primary residence and second residence with

the highest value were used to calculate the LTV ratio

since interest on debt secured by residences is only

deductible for qualified residences, which can only include

a principal and one other residence.2

A household with a LTV ratio of at least 50% was

determined to be highly leveraged since a typical house-

hold, who might enter a 30 year fixed-rate mortgage in

their early thirties, is predicted to typically have at least

50% of the mortgage paid off by the time they turn

55 years of age (assuming scheduled payments are made

and no refinancing occurs).3 While households may hold

the same mortgage for a smaller amount of time by

choosing to refinance, which occurred particularly in the

early 2000s when interest rates were low (Canner et al.

2002), the average age in which a household has their

mortgage paid off is 49 (Coulibaly and Li 2006). Thus,

using a LTV ratio of 50% for the threshold is conservative

as a measure of high housing leverage since the loan bal-

ance typically does not increase but home equity does tend

to increase over an extended period of time. Further, as a

robustness check, multinomial logistic regression results

using cutoff points of 25 and 75% for the higher leverage

group were also used and yielded similar results (direction

and statistical significance of coefficients of key variables

1 The SAS statistical program used to adjust financial amounts to the

year 2007 and create net worth and other related variables is located at

the Federal Reserve Board’s website http://www.federalreserve.gov/

pubs/oss/oss2/bulletin.macro.txt (accessed October 6, 2011).

2 Refer to the Internal Revenue Code §163(h) for further details on

the deductibility of mortgage interest related to qualified residences.3 For example, a household whose head is 30 years old when it enters

a 7.50% 30 year fixed-rate mortgage after making a 20% down

payment should have at least 50% of the original value of the home

paid off on its mortgage slightly after 19 years or turning 49 years of

age (below the 55 years of age used for restricting the sample in this

study). 7.53% was the average 30 year fixed-rate mortgage rate as

reported by Freddie Mac for the SCF years used in this study.

J Fam Econ Iss (2012) 33:315–327 319

123

were the same) as shown in this paper using the 50% cut

off point for the highly leveraged group.

Independent Variables

Life Cycle Variables Among the restricted sample of

households of homeowners with a head or spouse who is at

least 55 years of age, younger households were opera-

tionalized as those who had a head or spouse at least

55 years of age but less than 65 (coded 1), otherwise the

household was considered to be older (coded 0). A

household was considered to be liquidity constrained if it

was turned down for credit at least once during the last 5

years or was not able to obtain credit later or discouraged

from applying again.

Other Demographic Variables If either the respondent or

spouse reported working for someone else or a partnership,

being self-employed, or reported other current employ-

ment, the household was considered to be employed (coded

1) versus not employed (coded 0). If the respondent

reported being married, the household’s marital status was

considered married (coded 1); otherwise coded 0. A

household with at least one dependent child living in the

home was coded Children = 1; else coded 0. While race/

ethnicity was not the key variable of interest in this study,

race/ethnicity has been shown to influence borrowing

behavior (Lee et al. 2007; Mimura 2008; Yao et al. 2011).

Therefore, four separate binary variables were created to

capture different race/ethnicity groups: whether the

respondent representing the household considered them-

selves White (coded 1), Black/African-American (coded

1), Hispanic/Latino ethnicity (coded 1), or an Other non-

white race/ethnicity (Asian, American Indian/Alaska

Native, Native Hawaiian/Pacific Islander, other).

Net worth calculations for each household were based

on the code provided by the FRB. In order to better isolate

the effects of non-housing or financial net worth on being

highly leveraged, real estate net worth was subtracted from

total net worth to determine financial net worth. Dummy

variables were then created for each quintile of financial

net worth.

Financial Sophistication Variable The SCF does not

directly ask questions specifically designed to measure an

individual’s personal finance-related HC. To approximate

an individual’s financial sophistication this study employed

the technique described by Huston et al. (2012). First, four

variables are created using information from the SCF:

revolving balance on credit cards of more than 50% of

limit, stock ownership, investment risk aversion, and the

interviewer’s assessment of the respondent’s understanding

of questions asked during the survey. Second, factor

analysis is used to capture the underlying latent concept of

financial sophistication that is represented through these

four variables (see Table 1 for factor loadings4).

Third, factor scores are calculated for each household

and then households are categorized into financial sophis-

tication quintiles. The financial sophistication quintiles are

constructed such that households with the lowest levels of

financial sophistication are represented in the first quintile

and those with the highest levels are in the fifth quintile.

Households that indicate no revolving credit card balances,

own stock equity, are not averse to investment risk, and are

rated by interviewers as having a relatively higher under-

standing of questions asked are considered to be more

financially sophisticated than others.

Tax Leverage Variables If the household itemized

deductions on their previous year’s tax form (either jointly

or if at least spouse itemized when married filing sepa-

rately) the household was coded as itemized deduc-

tions = 1; else coded 0. The marginal tax rate of the

household was calculated using the TAXSIM coding and

included as a continuous variable in the regression model.5

An indication of risky asset holdings was included in the

model by creating quartiles based on the risky asset to net

worth ratio for each household. Total risky assets were

calculated by adding the value of all equities held (whether

in or outside a retirement account) and the value of all

business interests. This combined amount was then divided

by the household’s net worth (including the value of

housing) to calculate the percentage of risky assets in the

household portfolio.

Table 1 Factor loadings for financial sophistication factor

Variables Factor loadings

Have stock equity 0.734

Willing to take risk 0.610

Credit card balance \50% limit 0.589

Understand survey questions 0.427

4 Similar factor loadings were generated for each survey year when

factor analysis was performed using the measured variables by year

indicating that the determinants of financial sophistication were

similar across years.5 TAXSIM coding was developed by Kevin Moore from the National

Bureau of Economic Research (NBER) and represents detailed

program coding which may be used with statistical software packages

to allow researchers the ability to calculate tax liabilities and marginal

tax rates (when not reported in data sets) given available tax related

information, such as marital status, dependents, age, wages, dividend

income, IRA contributions, itemized deductions, etc. TAXSIM

coding can be accessed for all SCF years at the website

http://www.nber.org/*taxsim/to-taxsim/scf/ (accessed October 6,

2011).

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123

Results and Discussion

Descriptive Statistics

Table 2 displays the descriptive statistics of older house-

holds who own a home (but not more than two for tax

purposes) by financial sophistication quintile. On average,

more financially sophisticated households appeared to be

more highly leveraged, less liquidity constrained, less risk

averse, employed, married, have a child at home, less likely

to be of a minority, and have greater financial net worth.

Results were also consistent with previous studies that have

reported that less sophisticated households were more

likely to be older, of African-American or Hispanic race/

ethnicity, and less likely to invest in riskier assets (Lusardi

et al. 2009; van Rooij et al. 2007). As shown in Table 2,

younger households (age 55–64 compared to age 65?)

made up 63.02% of the highest quintile of financial

Table 2 Descriptive statistics of older households (who own a home) by financial sophistication quintile

Variable Financial sophistication

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5

Dependent variable

No leverage (LTV = 0%)a 72.83% 61.26% 55.91% 49.26% 47.20%

Low leverage

(0 \ LTV \ 50%)a17.52% 25.25% 27.90% 37.63% 38.49%

High leverage (50% B LTV)a 9.65% 13.49% 16.19% 13.11% 14.31%

Independent variables

Life cycle variables

Younger: age 55–64a 32.63% 34.79% 52.37% 58.85% 63.02%

Liquidity constraineda 11.16% 8.23% 8.79% 8.36% 4.43%

Other demographic variables

College degreea 7.07% 14.71% 29.33% 44.17% 58.29%

Employeda 27.22% 36.57% 52.62% 62.30% 62.89%

Marrieda 37.67% 51.93% 59.98% 63.94% 75.75%

Children at homea 17.72% 17.21% 19.07% 19.99% 16.47%

Race

Whitea 73.42% 84.57% 86.60% 89.72% 96.33%

Blacka 18.77% 9.95% 8.45% 4.74% 1.79%

Hispanica 5.83% 3.46% 3.01% 2.81% 0.51%

Other racea 1.98% 2.02% 1.94% 2.73% 1.37%

Financial net worthb Mean = $41,750 Mean = $114,495 Mean = $261,312 Mean = $781,843 Mean = $1,448,534

Median = $9,385 Median = $29,103 Median = $83,808 Median = $238,399 Median = $404,456

Tax leverage variables

Itemize deductionsa 17.68% 30.22% 47.45% 64.68% 75.20%

Marginal tax ratesb Mean = 2.92% Mean = 7.68% Mean = 13.66% Mean = 19.13% Mean = 22.27%

Median = 0% Median = 10% Median = 15% Median = 22.50% Median = 25%

% Risky assets/net worth

0–25%a 97.66% 95.31% 83.25% 56.73% 44.80%

[25–50%a 1.00% 2.00% 10.58% 26.95% 31.17%

[50–75%a 0.73% 1.38% 3.71% 11.74% 18.62%

[75%a 0.61% 1.32% 2.46% 4.58% 5.41%

Data were pooled from the 1995, 1998, 2001, 2004, and 2007 SCF years. The total sample includes all homeowners with either a head of

household or spouse who is at least 55 years of age. All statistics reported are weighted using the weighting variable x42001 provided within the

SCF. All financial variables are represented in 2007 dollars using the CPI-U-RS index as discussed in the independent variables section of the

papera All categorical variables across financial sophistication quintiles (except the children and other race variables) had v2 statistics that were

significant at least at the \0.01 levelb All continuous variable statistics within each financial sophistication quintile (except marginal tax rates of households in the third sophisti-

cation quintile), when compared to the other four groups combined, had t values that were significant at least at the \0.01 level

J Fam Econ Iss (2012) 33:315–327 321

123

sophistication (compared to 32.63% in the lowest quintile),

African-Americans made up 1.79% of the highest quintile

of financial sophistication (compared to 18.77% in the

lowest quintile), Hispanic households made up 0.50% of

the highest quintile of financial sophistication (compared to

5.83% in the lowest quintile), and 44.80% of households in

the highest quintile of sophistication held less than 25% of

risky assets (equity interests) in their portfolio (compared

to 97.66% in the lowest quintile).

Descriptive statistics in Table 2 also revealed that tax

incentives, such as itemizing or higher marginal tax rates,

and holding a greater percentage of risky assets (equities,

business interests) were positively related to financial

sophistication. 75.20% of households in the highest quintile

of sophistication itemized (compared to 17.68% in the

lowest quintile) and the average tax rate was 22.27%

among households in the highest quintile of sophistication

(compared to 2.92% in the lowest quintile).

Table 3 displays the descriptive statistics of older

households who are homeowners by no leverage (LTV =

0%), low leverage (0 \ LTV \ 50%), and high leverage

(LTV at least 50%) samples. A greater proportion of

households with no housing leverage were in the lowest

two quintiles of financial sophistication, while a greater

proportion of those with low housing leverage were in the

highest two financial sophistication quintiles. A much lar-

ger proportion of households with low and high leverage

itemize deductions, and were in higher marginal tax rates.

The proportion of those with high housing leverage whose

portfolios consisted of at least 25% equities was 34%

compared to 21% of households with no housing leverage.

A greater proportion of more highly leveraged households

were college educated, employed, married, Black, His-

panic, and had children at home. Mean and median wealth

was highest among households with low housing leverage

and lowest among households with the highest housing

leverage. A greater proportion of households with high and

low leverage were between age 55 and 64.

Multinomial Logistic Regression

Table 4 presents the multinomial logistic regression results

for the total sample for both models. Odds ratios and Wald

v2 statistics are presented for models without and including

the tax effect variables.

Model without Tax Effects

Respondents in the highest two quintiles of financial

sophistication were over 90% more likely to have low

leverage (50% B LTV) and over 140% more likely to have

high leverage (0 \ LTV \ 50%) versus no leverage than

respondents in the lowest financial sophistication quintile.

There was a time trend toward increasing likelihood of low

housing leverage during the 2000s, however there was no

trend toward high leverage. Wealthier households were

much less likely to have high housing leverage (80% less

likely in the highest wealth quintile).

Being liquidity constrained increased the likelihood of

having high leverage by 245% and the likelihood of having

low leverage versus no leverage by 71%. Being younger,

employed, having a college degree, or having children at

home were all positively related to the likelihood of having

some housing debt in older years. Older households with a

respondent who identified themselves as Black and White

were also more likely to have housing debt.

The statistically significant results (p value \0.05) from

the multinomial logistic regression model without the tax

effect variables were consistent across the two leveraged

groups and included age, liquidity constrained, employ-

ment, college degree, children, Black or Hispanic, and

financial sophistication.

Model with Tax Effects

Households that itemized deductions were more likely to be

leveraged at least somewhat in housing (either

0 \ LTV \ 50% or 50% \ LTV). Older households that

itemized were 267% more likely to choose low leverage

over no leverage than older households that did not itemize

and were 396% more likely to choose high leverage over no

leverage than non-itemizing households. In addition,

greater percentages of risky assets in net worth had statis-

tically significant increasing positive effects on the likeli-

hood of being highly leveraged versus having no leverage.

Households whose net worth was made up of 75–100% of

risky assets were 240% more likely to choose high leverage

over no leverage than households whose net worth was only

made up of 0–25% of risky assets. However, greater per-

centages of risky assets in net worth showed no statistically

significant positive effect on the likelihood of having low

leverage versus no leverage. Marginal tax rates showed no

statistically significant effect on the likelihood of having

either low or high leverage in housing.

In terms of the influence of financial sophistication in

the model with the tax effects, older households in the fifth

quintile of financial sophistication were 66% more likely to

have low leverage versus no leverage than older house-

holds in the first quintile of sophistication. Similarly,

households in the fifth quintile of financial sophistication

were 83% more likely to have high leverage versus no

leverage than households in the first quintile of sophisti-

cation. In general, older households that itemized, held a

greater percentage of net worth in risky assets, were more

financially sophisticated, and were more likely to be highly

leveraged.

322 J Fam Econ Iss (2012) 33:315–327

123

Further, adding the tax effect variables (itemizing,

marginal tax rate, and percentage of risky assets in a

portfolio) appeared to explain some of the previous influ-

ence of financial sophistication on having housing leverage

in the model without tax effects, suggesting that some of

the financially sophisticated households may have used

lower-cost mortgage debt to invest in riskier, higher

yielding investments. Households with liquidity con-

straints, low financial net worth, and children in the home

were also more likely to be highly leveraged than otherwise

similar households who were without liquidity constraints,

low financial net worth, or children at home. For instance,

Table 3 Descriptive statistics of older households (who own a home) by housing leverage category

Variable No leverage

(LTV = 0%)

(n = 4,115)

Low leverage

(0 \ LTV \ 50%)

(n = 2,473)

High leverage

(50% \ LTV)

(n = 870)

Life cycle variables

Age

Younger: age 55–64a 32.82% 68.11% 74.16%

Older: age C65a 67.18% 31.89% 25.84%

Liquidity constraineda 4.78% 8.92% 19.91%

Other demographic variables

College degreea 25.70% 40.46% 38.31%

Employeda 32.08% 67.99% 78.94%

Marrieda 51.93% 69.38% 62.76%

Children at homea 11.82% 24.57% 29.89%

Race/ethnicity

Whitea 89.14% 85.04% 79.80%

Black/African-Americana 6.97% 8.95% 12.62%

Hispanica 2.32% 3.48% 4.91%

Other racea 1.57% 2.53% 2.67%

Financial net worthb Mean = $550,642 Mean = $670,819 Mean = $338,803

Median = $89,468 Median = $140,882 Median = $51,681

Sophistication variable

Financial sophistication

1st Quintilea 21.58% 9.91% 12.06%

2nd Quintilea 23.19% 18.09% 21.39%

3rd Quintilea 19.21% 18.13% 23.45%

4th Quintilea 18.35% 26.56% 20.50%

5th Quintilea 17.67% 27.31% 22.60%

Tax leverage variables

Itemize deductionsa 34.06% 66.28% 68.61%

Marginal tax ratesb Mean = 10.26% Mean = 17.66% Mean = 17.85%

Median = 15.00% Median = 15.00% Median = 15.00%

% Risky assets/net worth

0–25%a 79.16% 68.75% 66.18%

[25–50%a 13.23% 17.95% 15.96%

[50–75%a 5.57% 10.20% 10.04%

[75%a 2.04% 3.10% 7.82%

Data were pooled from the 1995, 1998, 2001, 2004, and 2007 SCF years. The total sample includes all homeowners with either a head of

household or spouse who is at least 55 years of age. All statistics reported are weighted using the weighting variable x42001 provided within the

SCF. All financial variables are represented in 2007 dollars using the CPI-U-RS index as discussed in the independent variables section of the

papera All categorical variables across leverage groupings (except the other race variable) in this table had v2 statistics that were significant at least at

the \0.01 levelb All continuous variable statistics within each leverage group (except the financial net worth of no leverage and low leverage households), when

compared to the other two groups combined, had t values that were significant at the \0.01 level

J Fam Econ Iss (2012) 33:315–327 323

123

households with liquidity constraints were 320% more

likely to have high leverage versus no leverage than

households without liquidity constraints. Households in the

highest quintile of financial net worth were 93% less likely

to have high leverage versus no leverage than households

in the lowest quintile of financial net worth. Further,

households with children in the home were 66% more

likely to have high leverage versus no leverage than

households without children in the home. All of the life

cycle (age, liquidity constraints), demographic (employed,

Table 4 Estimated odds ratios of housing leverage among older households (multinomial logistic regression results)

Variables Model without tax effects Model with tax effects

Low leverage

0 \ LTV \ 50% group

High leverage

50% B LTV group

Low leverage

0 \ LTV \ 50% sample

High leverage

50% B LTV sample

Odds ratio Wald v2 Odds ratio Wald v2 Odds ratio Wald v2 Odds ratio Wald v2

Intercept 0.08*** 0.04*** 0.07*** 0.03***

Age 55–64 (vs. 65?) 2.31*** 181.19 2.74*** 91.82 2.30*** 174.02 2.68*** 85.36

Liquidity constrained (LIQ) 1.71*** 18.73 3.45*** 81.23 1.69*** 17.48 3.20*** 68.31

Employed 2.15*** 132.66 3.94*** 146.91 2.09*** 104.32 3.31*** 95.83

College degree 1.21** 8.93 1.34** 8.66 1.12 3.11 1.23* 3.89

Married 1.14* 3.85 0.91 0.85 1.11 2.10 0.87 2.10

Children at home 1.46*** 27.35 1.66*** 26.88 1.46*** 26.94 1.66*** 25.91

Race/ethnicity (White)

Black 1.77*** 13.91 1.91*** 12.61 1.72*** 12.07 1.88*** 11.53

Hispanic 1.52* 4.33 1.83* 5.88 1.53* 4.26 1.86* 5.71

Other 1.26 1.79 1.52 3.16 1.20 1.07 1.47 2.59

Financial NW (1st quintile)

2nd Quintile 1.14 0.75 0.90 0.357 1.05 0.12 0.81 1.43

3rd Quintile 1.03 0.03 0.55*** 11.01 0.84 1.14 0.39*** 25.32

4th Quintile 1.07 0.19 0.42*** 20.37 0.81 1.68 0.23*** 48.96

5th Quintile 0.75* 4.03 0.20*** 78.22 0.53*** 13.97 0.07*** 139.92

Financial sophistication (1st quintile)

2nd Quintile 1.65*** 12.85 1.86** 10.80 1.63*** 11.50 1.77** 8.96

3rd Quintile 1.65** 10.73 2.29*** 18.35 1.52** 7.09 2.00*** 12.06

4th Quintile 1.93*** 18.73 2.46*** 19.08 1.70*** 11.53 1.95*** 10.14

5th Quintile 1.91*** 18.16 2.47*** 19.03 1.66** 10.25 1.83*** 8.18

Year (vs. 1995)

1998 1.24* 5.37 0.98 0.01 1.27* 6.38 0.95 0.16

2001 1.20* 4.02 0.87 1.10 1.23* 4.64 0.84 1.58

2004 1.34*** 11.02 0.93 0.33 1.31** 8.81 0.86 1.23

2007 1.59*** 28.09 1.27 3.79 1.57*** 25.31 1.20 2.06

Tax leverage variables

Itemize deductions 2.67*** 142.69 3.96*** 126.05

Marginal tax rate 0.99 2.81 1.00 0.62

% Risky assets/net worth (0–25%)

25–50% 1.03 0.07 1.51** 8.57

50–75% 1.04 0.17 2.23*** 21.36

75–100% 0.75* 6.30 2.40*** 24.77

N 2,473 870 2,473 870

Sample percentage 33.2% 11.7% 33.2% 11.7%

Max-rescaled R2 0.2222 0.2597

The reference category is: no leverage households (LTV = 0%) (N = 4,115, 55.2%). All odds ratios and Wald v2 statistics were calculated using

the repeated imputed inference (RII) technique recommended by Montalto and Sung (1996)

* p \ 0.05, ** p \ 0.01, *** p \ 0.001

324 J Fam Econ Iss (2012) 33:315–327

123

college degree, children at home, financial net worth, Black

or Hispanic), year, and financial sophistication variables

remained statistically significant and the direction of the

coefficients stayed the same across both low leveraged and

highly leveraged groups when adding the tax effect

variables.

Conclusion

The increase in mortgage debt among older households has

been cited as evidence of financial distress caused by poor

lending practices and an increased appetite for debt

(American Association of Retired Persons 2004; Marmon

2003; Masnick et al. 2006). This paper estimates whether

greater housing leverage among households age 55 or older

is related to financial sophistication, tax incentives, a shift

to riskier investment portfolios, and a time trend.

There is no evidence that higher housing leverage is

related to lower financial sophistication. The proportion of

home-owning households over age 55 who have no hous-

ing leverage decreases consistently with financial sophis-

tication. Less than half of the most sophisticated

households (47%) have no mortgage debt compared to 73%

of the least financially sophisticated households. The per-

centage of households with positive LTV ratios rises with

financial sophistication, and the proportion of households

with high leverage (above 50% LTV) is highest within the

median and top sophistication quintiles. In a multivariate

analysis, households in the highest financial sophistication

quintiles are more than twice as likely to have high housing

leverage, and over 90% more likely to have low (vs. no)

housing debt. When tax effects are included, households

with the lowest financial sophistication are still much less

likely to have mortgage debt.

Multivariate and descriptive results show that tax and

portfolio incentives are consistently related to housing

leverage in the predicted direction of effect. Households

who itemize are more than twice as likely to have low

housing leverage and 300% more likely to be highly lev-

eraged. Households with a higher share of risky portfolio

assets are much more likely to have high housing leverage,

yet are not more likely to have low housing leverage.

Households whose tax status provides them with a rela-

tively lower cost of borrowing by borrowing against the

value of their home are more likely to have high housing

leverage. Likewise, households maintaining a larger share

of their portfolio in risky assets are also much more likely

to have high housing leverage.

Although the likelihood of a household having low

leverage (less than 50%) relative to no leverage increased

by 57% between 1995 and 2007, the likelihood that a

household had high leverage did not increase significantly.

Discussion

Results are consistent with the hypothesis that greater

housing leverage is related to greater financial sophistica-

tion rather than household ignorance or a lack of thrift.

While increasing leverage has often been characterized as

the result of poor spending and saving habits, these results

suggest a possible additional motive for mortgage bor-

rowing—more financially sophisticated households may

have leveraged mortgage debt to invest in riskier assets in

order to maintain an optimal household portfolio of tan-

gible and investment assets. Results were consistent with

normative household borrowing models that predict greater

borrowing when housing debt receives tax subsidies. The

tax deductibility of mortgage interest appeared to further

encourage greater housing leverage by artificially reducing

interest rates on borrowing.

Tax policy that encourages increased household lever-

age in order to take advantage of a law intended to increase

home ownership may have unintended consequences.

Deductibility of mortgage interest encourages borrowing

against housing assets, even among older households.

Financially sophisticated households who are able to more

accurately assess the costs and benefits of reduced bor-

rowing and increased expected return on investments will

rationally transform home equity into investments. Indeed,

this paper provided evidence that high leverage increases

monotonically with both financial sophistication and the

proportion of risky assets held in household portfolios. Tax

deductibility of mortgage interest and tax shielded bor-

rowing results in a welfare transfer from less financially

sophisticated to more financially sophisticated households.

This policy also increases optimal leverage for these older

households. This rational increase in leverage, although

expected utility maximizing, increases susceptibility to

income and home equity shocks—both of which occurred

after this wave of the SCF was conducted.

Although this paper provides evidence that more

sophisticated households were using housing leverage in a

way that maximizes household welfare, there is also evi-

dence that households who had been turned down for credit

or who had excessive credit card debt were also more likely

to be highly leveraged. It appears both the more sophisti-

cated and the more vulnerable were carrying greater

housing debt into retirement age. Increasing access to

borrowing among those with poor credit during the time

period of this analysis may have placed a burden of highly

leveraged homeownership on older households who will

have difficulty coping with regular debt repayment well

into retirement age. It is these households who may benefit

the most from policy that provides lender oversight to

ensure the availability of mortgage products that are suit-

able to the older household.

J Fam Econ Iss (2012) 33:315–327 325

123

References

Allgood, S., & Walstad, W. B. (2011). The effects of perceived andactual financial knowledge on credit card behavior (Networks

Financial Institute working paper No. 2011-15). Retrieved

October 5, 2011, from http://www.networksfinancialinstitute.org/

Lists/Publication%20Library/Attachments/195/2011-WP-15_

Walstad_Allgood.pdf.

American Association of Retired Persons. (2004, February 24).

Predatory lending: Are federal agencies protecting older Amer-

icans from financial heartbreak? AARP Press Center. Retrieved

October 5, 2011, from http://www.aarp.org/about-aarp/press-

center/info-2004/testimony.html.

Ameriks, J., & Zeldes, S. P. (2004). How do household portfolioshares vary with age? Unpublished manuscript. New York, NY:

Department of Finance and Economics, Columbia University.

Amromin, G., Huang, J. C., & Sialm, C. (2007). The tradeoff between

mortgage prepayments and tax-deferred retirement savings.

Journal of Public Economics, 91, 2014–2040.

Ando, A., & Modigliani, F. (1963). The ‘‘life cycle’’ hypothesis of

saving: Aggregate implications and tests. The American Eco-nomic Review, 53, 55–74.

Applied Research and Consulting LLC. (2009). Financial capabilityin the United States: Initial report of the research findings fromthe 2009 National Survey, a component of the National FinancialCapability Study. Retrieved October 5, 2011, from FINRA

Foundation website, http://www.finrafoundation.org/web/groups/

foundation/@foundation/documents/foundation/p120536.pdf.

Bayot, J. (2004, July 4). As bills mount, debts on homes rise for

elderly. The New York Times. Retrieved January 23, 2012, from

http://www.nytimes.com/2004/07/04/us/c-as-bills-mountdebts-on-

homes-rise-for-elderly-700185.html?pagewanted=all&src=pm.

Becker, G. S. (1964). Human capital: A theoretical and empiricalanalysis, with special reference to education. New York, NY:

National Bureau of Economic Research.

Bodie, Z., Merton, R. C., & Samuelson, W. F. (1992). Labor supplyflexibility and portfolio choice in a life-cycle model (NBER

working paper No. 3954). Retrieved October 5, 2011, from

http://www.nber.org/papers/w3954.pdf.

Bryant, W. K., & Zick, C. D. (2006). The economic organization ofthe household (2nd ed.). New York, NY: Cambridge University

Press.

Calvet, L. E., Campbell, J. Y., & Sodini, P. (2009). Measuring thefinancial sophistication of households (NBER working paper No.

14699). Retrieved October 5, 2011, from http://www.nber.org/

papers/w14699.

Campbell, J. (2006). Household finance. The Journal of Finance,61(4), 1553–1604.

Canner, G., Dynan, K., & Passmore, W. (2002). Mortgage refinancing

in 2001 and early 2002. Federal Reserve Bulletin, December,

461–481.

Center for Retirement Research. (2009, July). Should you carry amortgage into retirement? (Issue in Brief No. 9-15). Chestnut

Hill, MA: Anthony Webb.

Chen, A., & Jensen, H. H. (1985). Home equity use and the life cycle

hypothesis. The Journal of Consumer Affairs, 19(1), 37–56.

Coco, J. F. (2005). Portfolio choice in the presence of housing. TheReview of Financial Studies, 8(2), 535–567.

Coulibaly, B., & Li, G. (2006). Do homeowners increase consumption

after the last mortgage payment? An alternative test of the

permanent income hypothesis. Review of Economics and Statis-tics, 88(3), 10–19.

Curcuru, S. E. (2005). The changing investment behavior of U.S.seniors. Retrieved October 5, 2011, from http://ssrn.com/abstract=

875893.

Dammon, R. M., Spatt, C. S., & Zhang, H. H. (2004). Optimal asset

location and allocation with taxable and tax-deferred investing.

Journal of Finance, 59(3), 999–1037.

Engen, E. M., & Gale, W. G. (1997). Debt, taxes, and the effects of401(k) plans on household wealth accumulation. Retrieved

October 5, 2011, from http://ssrn.com/abstract=40180.

Federal Reserve Board. (2010). Table 7 showing direct and indirect

family holdings of stock and Table 13 showing family holdings

of debt by selected characteristics of families using internal data

in 1989–2007 Survey of Consumer Finances. Unpublished data.

Retrieved October 5, 2011, from http://www.federalreserve.gov/

pubs/oss/oss2/2007/scf2007home.html#.

Finke, M. S., Huston, S. J., & Sharpe, D. L. (2006). Balance sheets of

early boomers: Are they different from pre-boomers? Journal ofFamily and Economic Issues, 27(3), 542–561.

Flavin, M., & Yamashita, T. (2002). Owner-occupied housing and the

composition of the household portfolio. The American EconomicReview, 92(1), 345–362.

Follain, J. R., & Melamed, L. S. (1998). The false messiah of tax

policy: What elimination of the home mortgage interest deduc-

tion promises and a careful look at what it delivers. Journal ofHousing Research, 9(2), 179–199.

Heaton, J., & Lucas, D. (2000). Portfolio choice and asset prices: the

importance of entrepreneurial risk. The Journal of Finance,55(3), 1163–1198.

Hilgert, M., & Hogarth, J. (2002). Financial knowledge, experience

and learning preferences: Preliminary results from a new survey

on financial literacy. Consumer Interest Annual, 48(1), 1–7.

Huston, S., Finke, M. S., & Smith, H. (2012). The impact of self-

efficacy on wealth accumulation and portfolio choice. AppliedEconomics Letters, forthcoming.

LaCour-Little, M. (2007). The home purchase mortgage preferences

of low- and moderate-income households. Real Estate Econom-ics, 35(3), 265–290.

Lai, C. W. (2008). How retired households and households approach-

ing retirement handle their equity investments in the United

States. Journal of Family and Economic Issues, 29(4), 601–622.

Lee, Y. G., Lown, J. M., & Sharpe, D. L. (2007). Predictors of holding

consumer and mortgage debt among older Americans. Journal ofFamily and Economic Issues, 28, 305–320.

Li, W. (2005). Moving up: Trends in homeownership and mortgage

indebtedness. Federal Reserve Bank of Philadelphia BusinessReview, First Quarter 2005, 26–35.

Lusardi, A., & Mitchell, O. S. (2006). Financial literacy andplanning: Implications for retirement wellbeing (University of

Michigan Retirement Research Center working paper No.

2006-144). Retrieved October 5, 2011, from http://www.

mrrc.isr.umich.edu/publications/papers/pdf/wp144.pdf.

Lusardi, A., & Mitchell, O. S. (2007a). Baby boomer retirement

security: The role of planning, financial literacy, and housing

wealth. Journal of Monetary Economics, 54, 205–224.

Lusardi, A., & Mitchell, O. S. (2007b). Financial literacy andretirement planning: New evidence from the Rand American LifePanel (University of Michigan Retirement Research Center

working paper No. 2007-157). Retrieved October 5, 2011, from

http://www.mrrc.isr.umich.edu/publications/papers/pdf/wp157.

pdf.

Lusardi, A., & Tufano, P. (2009). Debt literacy, financial experience,

and overindebtedness (NBER working paper No. 14808).

Retrieved October 5, 2011, from http://www.nber.org/papers/

w14808.

Lusardi, A., Mitchell, O. S., & Curto, V. (2009). Financial literacyand financial sophistication among older Americans (NBER

working paper No. 15469). Retrieved October 5, 2011, from

http://www.nber.org/papers/w15469.pdf.

326 J Fam Econ Iss (2012) 33:315–327

123

Marmon, L. (2003, March 1). Older Americans going deeper in debt.

AARP Bulletin Today. Retrieved June 11, 2011, from http://

www-static-w4-ca.aarp.org/health/conditions/articles/olderameri

cans.html.

Masnick, G. S., Di, Z. X., & Belsky, E. S. (2006). Emerging cohort

trends in housing debt and home equity. Housing Policy Debate,17(3), 491–528.

McCarthy, D. (2004, January). Household portfolio allocation: areview of the literature. Paper presented at the international

forum organized by the Economic and Social Research Institute,

Tokyo, Japan. Retrieved October 5, 2011, from http://www.

esri.go.jp/jp/prj-rc/macro/macro15/01-2-R.pdf.

Mimura, Y. (2008). Housing cost burden, poverty status, and

economic hardship among low-income families. Journal ofFamily and Economic Issues, 29(1), 152–165.

Montalto, C. P., & Sung, J. (1996). Multiple imputation in the 1992

Survey of Consumer Finances. Financial Counseling andPlanning, 7, 133–146.

Moore, D. (2003). Survey of financial literacy in Washington State:

Knowledge, behavior, attitudes, and experiences (Tech. Rep. No.

03-39). Pullman, WA: Washington State University, Social and

Economic Sciences Research Center. Retrieved October 5, 2011,

from http://www.sesrc.wsu.edu/sesrcsite/Papers/files/dfi-techre

port-FINAL2-16-04.pdf.

Munnell, A. H., & Soto, M. (2008). The housing bubble andretirement security (Center for Retirement Research at Boston

College working paper 2008-12). Retrieved October 5, 2011,

from Center for Retirement Research at Boston College website,

http://crr.bc.edu/images/stories/Briefs/ib_8-12.pdf.

Nothaft, F. E., & Chang, Y. (2004). Refinance and the accumulationof home equity wealth (Freddie Mac working paper No. 04-02).

Retrieved October 5, 2011, from Freddie Mac website,

http://www.freddiemac.com/news/pdf/refinance_040604.pdf.

Robb, C. (2011). Financial knowledge and credit card behavior of

college students. Journal of Family and Economic Issues,. doi:

10.1007/s10834-011-9259-y.

Shelton, A. (2008). A first look at older Americans and the mortgagecrisis. Retrieved October 5, 2011, from AARP Public Policy

Research website, http://assets.aarp.org/rgcenter/econ/i9_mort

gage.pdf.

Sinai, T., & Souleles, N. (2007). Net worth and housing equity inretirement (NBER working paper No. 13693). Retrieved October

5, 2011, from http://www.nber.org/papers/w13693.pdf.

Smart, J. C. (2003). Higher education: Handbook of theory andresearch (Vol. 18). Dordrecht: Kluwer Academic Publishers.

Stafford, F. P., & Gouskova, E. (2010). Mortgage contract decisionsand mortgage distress: Family and financial life-cycle factors(University of Michigan Retirement Research Center working

paper No. 2010-225). Retrieved October 5, 2011, from http://

www.mrrc.isr.umich.edu/publications/papers/pdf/wp225.pdf.

Thorne, D., Warren, E., & Sullivan, T. A. (2008). Generations ofstruggle. Retrieved October 5, 2011, from AARP Public Policy

Research website, http://assets.aarp.org/rgcenter/consume/2008_

11_debt.pdf.

van Rooij, M., Lusardi, A., & Alessie, R. (2007). Financial literacyand stock market participation (NBER working paper No.

13565). Retrieved October 5, 2011, from http://www.nber.org/

papers/w13565.

Waggle, D., & Johnson, D. T. (2009). Homeownership and mixed-

asset portfolio allocations. The Quarterly Review of Economicsand Finance, 49, 484–500.

Yao, R., Sharpe, D. L., & Gordon, E. E. (2011). An exploratory study

of Chinese Americans’ Debt Ownership. Journal of Family andEconomic Issues,. doi:10.1007/s10834-011-9271-2.

Yilmazer, T., & DeVaney, S. A. (2005). Household debt over the life

cycle. Financial Services Review, 53, 55–74.

Author Biographies

Hyrum Smith is an Assistant Professor in the Department of

Agricultural and Applied Economics at Virginia Tech. He received

his PhD in Personal Financial Planning from Texas Tech University.

Research interests involve analysis of both quantitative and qualita-

tive factors, such as financial sophistication, using a financial planner,

or other household behaviors, that play a major role in various tax and

retirement planning decisions.

Michael Finke is an Associate Professor in the Department of

Personal Financial Planning at Texas Tech University. He received

his PhD in Family Resource Management from The Ohio State

University and PhD in Finance from the University of Missouri.

Research interests include studying household personal finance

decisions related to household investments, retirement, behavioral

finance, and household financial trends.

Sandra Huston is an Associate Professor in the Department of

Personal Financial Planning at Texas Tech University. She received

her PhD in Consumer Economics from the University of Missouri—

Columbia. Research interests involve analysis of human capital

related to personal finance including financial literacy, the impact of

using financial advice in household decision-making, and the impact

of financial sophistication on resource allocation within household

portfolios.

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