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Consumer Mortgage Prepayment Behavior within the U.S.

Economy

Tingting Ji.

Manager, Financial Risk Management, Risk Advisory Services, KPMG LLP

E-mail：tji@kpmg.com

ABSTRACT

This essay uses Bivariate-Choice Regression Method to explain consumer mortgage prepayment behavior

within the U.S. Economy. The paper uses economic survey data from Panel Study of Income Dynamics

from Michigan University, which traced the mortgage prepayment behavior and income dynamics of more

than 6000 representative U.S. households over a 12-year period. To estimate the effects of dynamic housing

value on mortgage prepayment, the author innovatively computed a covariance measure in the Bivariate-

Choice Regression Method, which can incorporates the interaction effect between dynamic house value and

household income. The results show that homeownership has a diversification effect on overall consumer

portfolio risk even with the presence of mortgage.

Keywords：mortgage prepayment forecast，bivariate-choice regression method

1. Introduction

After checking and savings accounts, owner-occupied housing is the largest and the most important

component in household portfolios. Bertaut and Starr-McCluer (2000) use household sector assets and

liabilities data from the Federal Reserve Board’s Flow of Funds accounts to show that residential property

accounted for 20-30% of total household assets during 1983-1998. A similar calculation using Survey of

Consumer Finance weighted data shows that primary residence comprises 32% of the average household’s

total assets. The random sample I constructed from Panel Study of Income Dynamics shows that at least

60% of American households own a house, and the mean value of the house increased from $65,000 to

$100,000 during the 10-year period from 1984 to 1994.

Researchers have recognized the fact that housing investment can significantly affect household portfolio

structure due to the dual roles of housing, which is both a durable consumption good and an investment

vehicle. Economists have begun to link the issue of housing investment to the well-known stock market

nonparticipation problem. Brueckner (1997) suggests that portfolio inefficiency results from the

homeowner’s rational balancing of the consumption benefits and portfolio distortion associated with

housing investment. Cocco (2000) shows that investment in housing reduces equity market participation for

younger and poorer investors due to their limited wealth. House prices also crowd out stock-holding, and

this effect is larger for those with less net worth. Flavin and Yamashita (1998) use the mean-variance

frontier to show that the consumption demand for housing can generate a life-cycle pattern in the portfolio

shares of stocks and bonds.

Despite the wide recognition of the importance of housing investments, housing investments and their

interaction with either labor income or other financial assets remain unexplored in the academic literature.

In this paper, I show that the interaction of uninsurable labor income and dynamic house value stabilize

overall portfolio and decrease equity investment. This is because the available financial resources after

saving for down payment or mortgage of the house are usually limited, especially for younger and poorer

households. Further, if a large residential investment comes together with a riskier labor income flow, the

household will feel even more reluctant to bear additional equity risks in the stock market.

A similar covariance term between housing investment return and stock return shows that negative

covariance can boost stockholding. Goetzmann (1993) uses a mean-variance framework to show that

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residential property ownership is relatively stable from the investment point of view since it can reduce the

overall portfolio risk. Yao and Zhang (2002) use a dynamic programming model to show theoretically that

housing investments can diversify stock market risks for homeowners.

2. Data

I use data from the Panel Study of Income Dynamics for household demographics and self-reported house

value. The stock market participation information is collected in the wealth supplement, which is

administered every five years in PSID. In each survey, the respondent is asked for the present value of

his/her house, i.e. how much it would bring if the respondent sells it today. Self-reported house value no

doubt is an imperfect measure of transaction value. Skinner(1994) finds, however, that the house value

series derived from PSID resembles the measure from the Commerce Department, suggesting that

respondents have reasonably accurate and unbiased estimates of the market values of their homes. The

labor income risk is evaluated as the residuals from the random effect estimation of household total labor

income, based on their human capital. The annual stock market return is measured as the S&P 500 index

return with dividends reinvested.

3. Model

I use a simple measure of the covariance between labor income risk and self-reported house values:

∑

1993

1980

)(

ititii

HeHWCov

Where H

is the self-reported house value for household i in year t.

is residual term from the following

model for labor income by Carroll and Samwick (1997)

itititit

ititit

pgp

++=

−1

In each period

, a household i with a set of characteristic variables receives labor income

. In the

decomposition of the logarithm of wage income,

is the permanent component, which is defined as the

amount of log labor income the household receives in the absence of any transitory income shocks. In each

period, the permanent component grows by a factor g.

is a shock to permanent income and

is a

transitory shock to the logarithm of labor income. It is assumed that both permanent and transitory shocks

are normally distributed, i.e.

),0(

iid

σε

),0(

iid

ση

. I further assume that

can be

predicted linearly with a vector X

it-1

of household demographics at time

−

. So we can have

From this equation, I calculate the conditional mean and variance of log labor income:

This model was also employed in Viceira(01) and Vissing-J∅rgensen(00).

See also Vissing-J∅rgensen(00) and Angerer (03)

)1(lnln

11 −−

−

++=

itititititit

gww

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