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Adam Pilz
Econometrics
Empirical Project
I. Introduction
Over the last 15 years, investors seeking better than average returns have brought
their money to the US marketplace. During the 1990’s, stock markets roared. People
were lured by new technology companies that seemed to have endless growth
possibilities. These possibilities, coupled with seemingly endless profits for those who
jumped in the market, gave way to what Alan Greenspan referred to as “irrational
exuberance” and the worst stock market crash since the Great depression. For example,
the Nasdaq composite alone lost 76.8% of it’s value from March 2000 through September
2002.1
After the tech bubble busted and the attacks of September 11, the Federal Reserve
continued to lower interest rates attempting to save a struggling US economy. The
exceptionally low rates spurred a housing boom that lasted until mid-2006. During this
time, investors again rushed to the US marketplace to chase large profits being made in
real estate that was appreciating at unsustainable rates. As owners began to default on
highly leveraged loans, the housing market quickly soured and another crash in
valuations followed. Phoenix and San Diego have seen declines of 20.6% and 20.7%
respectively, from June 2006 through January 2008.2
From 1990 through 2005 financial and real estate wealth have increased and
decreased dramatically. More than 60% of the wealth creation in the 1990’s was due to
1
http://finance.yahoo.com/echarts?s=%5EIXIC#chart1:symbol=^ixic;range=5y;indicator=dividend;charttyp
e=line;crosshair=on;logscale=on;source=undefined
2
http://www2.standardandpoors.com/spf/pdf/index/CS_HomePrice_History_032544.xls
the rising value of stock holdings (Poterba, 2000). Between the end of 1989 and the end
of 1999, the real net worth of U.S. households increased by $15 trillion, or by more than
50% (Poterba, 2000). These gains were not to be long lived. When combining losses of
all major U.S. stock markets, U.S. stocks lost 50.2% of their value, or 7.4 trillion,
between March 2000 and October 2002 (Zweig, 2003). I could imagine no better time to
conduct a study examining wealth effects on consumption. Looking back, I see a
spectacular decade of lessons waiting from which to be learned. We have witnessed a
stock market crash and a housing market crash within one decade. The wealth that has
been destroyed in real estate may affect the economy in the next couple of years. With a
possible recession in our future, it would be interesting to know if consumer spending
will continue or if it will decrease because of the change in real estate value. I will test to
see if rising home values positively effects consumption. If real estate wealth has no
effect on consumption, we would expect any recession to be short lived.
Section II below provides a brief review of the literature on the different wealth
effects and why it is important to draw a distinction between the two. Section III
describes the formulation of the model to be used. Section IV provides sources of the
data and their description. Section V provides the estimation of the model. Section VI
reviews the hypothesis testing. Section VII analyzes the results. Section VIII reveals the
conclusions and limitations of this study.
II. Literature Review
There have been many works published on stock market wealth effects. Mankiw
and Zeldes (1991) and Attanasio, Banks, and Tanner (1998), found that the spending of
stockholders is more highly correlated with their gains from the stock market than that of
nonstockholders, which supports a direct effect. However, Poterba and Samwick (1995)
use other tests and conclude that the evidence points to a small role for direct effects.
However, both conclude that the effect is positive. To my knowledge, no study has been
conducted on indirect effects, which would be the stock market gains of person A
affecting the spending of person B.
It is not a wise assumption that the wealth effect for the stock market is the same
for real estate. There are many reasons why consumption may be differently affected by
the form of wealth that is held. Case, Quigley, and Shiller(2005, pg.5) presented five
possible reasons:
“First, increases in measured wealth of different kinds may be viewed by
households as temporary or uncertain. Second, households may have a
bequest motive which is strengthened by tax laws that favor holding
appreciated assets until death. Third, households may view the
accumulation of some kinds of wealth as an end in itself. Fourth,
households may not find it easy to measure their wealth, and may not even
know what it is from time to time. The unrealized capital gains held by
households in asset markets may be transitory, but asset prices can be
measured with far more precision in thick markets with many active
traders. Fifth, people may segregate different kinds of wealth into separate
“mental accounts,” which are then framed quite differently.”
Shefrin and Thaler(1988) suggest that when people frame different assets, they feel that
some are more appropriate to use for current spending while others are set aside for longterm savings.
The effects of real estate wealth have been explored mostly in the recent past.
Earlier papers asserted conclusions that housing wealth had little effect on consumption,
Elliott (1980). However, the results of this paper were heavily criticized by Peek (1983)
and Bhatia (1987) who questioned Elliot’s methods of estimating real nonfinancial
wealth. Campbell and Cocco (2005) found a large, positive wealth effect for old
homeowners that exceeds that of young homeowners. Li and Yao (2006) also
incorporate borrowing constraints to predict that consumption of young and old
homeowners is more sensitive to home price changes than that of middle-aged
homeowners. Juster, Lupton, Smith, and Stafford (2006) estimate zero effect of housing
capital gains on consumption for a sample of Panel Study of Income Dynamics (PSID)
households over the period 1984-1994.
A recent study, and the one this paper is modeled closely to, included both wealth
effects in a comparative framework. Case, Quigley, and Shiller (2005) found at best
weak evidence of a stock market wealth effect. However, they did find strong evidence
that real estate wealth has an important effect on consumption. They estimate a home
price consumption elasticity between 5% and 8% in U.S. state-level data. They remind
us that their conclusions may not predict consumption with accuracy, but they maintain
the conclusion that changes in real estate wealth should be considered to have a larger
impact than changes in financial wealth in influencing household consumption.
Besides Case, Quigley, and Shiller, (2005) little research has been done recently
to estimate the differing wealth effects in the same model. Also, the Case, Quigley, and
Shiller (2005) model used data ending in 2000, which does not include the housing
bubble that has occurred in the last 5 years. An analysis of these effects including more
recent data is needed.
III. Formulation of the Model
John Maynard Keynes developed a mathematical function to express consumer
spending as one term called the "consumption function". The model I will use will
mimic this mathematical function but have some differences. The consumption function
calculates the amount of total consumption in an economy. It is made up of “autonomous
consumption” that is not influenced by current income and “induced consumption” that is
influenced by the economy's income level. This is the formulation of a basic
consumption function.
C = 0 + 1 Yd
where C = total consumption, 0 = autonomous consumption, 1 = the marginal
propensity to consume, and Yd = disposable income. The second term (1 Yd )is induced
consumption. Autonomous consumption is the level of consumption when income equals
zero. The marginal propensity to consume measures the changes in consumption
induced by changes in income.
My model will include disposable personal income, financial wealth, and a
measure nationwide housing values all adjusted to per capita, chained 2000 dollars.
Since this model requires the use of time series data, I was required to manipulate the
data to compensate for the influence of the previous quarter, also known as “lag.” The
model is as follows:
(PCTCHNGCONSUMP) = 0 + 1 (PCTCHNGINCOME)1 + 2
(PCTCHNGFINANCIAL)2+ 3 (PCTCHNGHOUSING)1 + u
Where 0=autonomous consumption, PCTCHNGINCOME= is the percent
change of the difference in disposable personal income from the previous quarter. It is
computed by dividing the difference of the series by the lagged value of the series, then
multiplying by 100{(DIF/LAG)*100}, 1= the marginal propensity to consume from
changes in PCTCHNGINCOME , PCTCHNGFINANCIAL= is the percent change
of the difference in financial wealth from the previous quarter computed in the same
fashion, 2.= the marginal propensity to consume from changes in
PCTCHNGFINANCIAL, PCTCHNGHOUSING= is the percent change of the
difference in the value of owner occupied housing from the previous quarter also
computed in the same fashion, 3= the marginal propensity to consume from changes in
PCTCHNGHOUSING.
PCTCHNGINCOME is included to account for changes in disposable personal
income that occurred from quarter to quarter and is estimated to have a positive effect on
consumption. PCTCHNGINCOME, as well as the next two variables are assumed to
be positive based on the absolute income hypothesis of John Maynard Keynes. Keynes
hypothesized that the changes in income and consumption would not be equal and that
the marginal propensity to consume would be less than one. The financial
(PCTCHNGFINANCIAL) and real estate (PCTCHNGHOUSING) wealth effects
are included to account for the changes in consumption that can be attributed to the
respective effect. The inclusion of both of these variables is what relates my model to
that of Case, Quigley, and Shiller (2005). An error variable is included to account for
any change in consumption that is not accrued by a variable represented in the model. I
have not included a variable for potential future income which is suggested by Milton
Friedman’s permanent income hypothesis because I felt it was out of the scope of this
study. PCTCHNGCONSUMP is the percent change of the difference in personal
consumption expenditures from the previous quarter and is estimated in the same fashion
as the other variables. PCTCHNGCONSUMP is estimated to be positively influenced
by the variables represented in the model.
IV. Data Sources and Description
The data was received from various primary sources. The time frame considered
by this study ranges from the first quarter of 1985 through fourth quarter 2007. Personal
consumption expenditures was obtained from the Bureau of Economic Analysis website3.
It includes the price of expenditures made by households, including those made on behalf
of households. Disposable personal income is also received from the Bureau of
Economic Analysis website4. The estimation of disposable personal income is personal
income less personal tax and nontax payments. Both sets of data are recorded by quarter
and can be obtained “per capita” in chained 2000 dollars from the BEA.
3
4
http://www.bea.gov/national/nipaweb/IndexP.htm#P
http://www.bea.gov/national/nipaweb/TableView.asp?SelectedTable=58&FirstYear=2002&LastYear=200
4&Freq=Qtr
Estimates of financial wealth were derived from the Federal Reserve website5
under the Flow of Funds accounts. From the FOF accounts, I computed the sum of
corporate equities held by the household sector, pension fund reserves, and mutual funds
to comprise the aggregate level by quarter. This level was then divided by the same
population estimates from the BEA used for PCE and DPI, and chained in 2000 dollars.
The households and nonprofit organizations sector consists of individual households
(including farm households) and nonprofit organizations such as charitable organizations,
private foundations, schools, churches, labor unions, and hospitals. Nonprofits account
for about 6 percent of the sector’s total financial assets, but they own a larger share of
some of the individual financial instruments held by the sector. (The sector is often
referred to as the ‘‘household’’ sector, but nonprofit organizations are included because
data for them are not available separately except for the years 1987 through 1996.) For
most categories of financial assets and liabilities, the values for the household sector are
calculated as residuals. That is, amounts held or owed by the other sectors are subtracted
from known totals, and the remainders are assumed to be the amounts held or owed by
the household sector.
The calculation used to derive housing wealth:
Vt=Rt Nt It Vo
Where, Vt = aggregate value of owner occupied housing in quarter t, Rt = homeownership
rate in quarter t, Nt = number of households in quarter t, It = S&P/Case-Shiller U.S.
5
http://www.federalreserve.gov/RELEASES/z1/Current/annuals/a1985-1994.pdf
http://www.federalreserve.gov/RELEASES/z1/Current/annuals/a1995-2004.pdf
http://www.federalreserve.gov/RELEASES/z1/Current/annuals/a2005-2007.pdf
National Home Price Index in quarter t (Ii1 = 1, for 2000:I), and Vo = mean home price in
the base year, 2000.
I acquired homeownership rates and number of households from the Bureau of the
Census website6. They were multiplied to obtain the number of owner occupied
households in the US. Then I multiplied the number of owner occupied households by
the median home price in 2000 (from the Bureau of the Census7) and normalized this by
the S&P/Case-Shiller U.S. National Home Price Index which uses 2000 as the base year.
This was the procedure used by Case, Quigley, and Shiller (2005) to get an average value
for the same real estate throughout the test period adjusted by a real estate value index.
When those steps where completed, I had an aggregate value of owner occupied housing
for the entire US each year throughout the test period. This value was then divided by the
same population estimates from the BEA used for PCE and DPI, and chained in 2000
dollars.
The S&P/Case-Shiller U.S. National Home Price Index tracks the value of singlefamily housing within the United States and will be extracted from the Standard and
Poor’s website8. This index is provided free of charge by S&P. The index is a composite
of single-family home price indices for the nine U.S. Census divisions. Calculating
historical estimates of the U.S. national index requires choosing base periods and
estimating of the aggregate value of single-family housing for those periods. The
decennial U.S. Census provides estimates of the aggregate value of single-family housing
6
http://www.census.gov/hhes/www/housing/hvs/historic/histt14.html
http://www.census.gov/hhes/www/housing/hvs/historic/histt13.html
7
8
http://www.census.gov/prod/cen2000/phc-2-1-pt1.pdf
http://www2.standardandpoors.com/spf/pdf/index/cs_national_values_022603.xls
units for the Census divisions. The last two decennial Census years, 1990 and 2000, were
chosen as the base periods. The aggregate value estimates for the 1990 base period were
used to calculate composite index data for the period from the first quarter of 1987 to the
fourth quarter of 1999, while the 2000 base period estimates were used to calculate data
from the first quarter of 2000 until the present. The divisor for both of these periods is set
so that the composite index equals 100 in the first quarter of 2000.
V. Model Estimation
The model estimated by OLS is:
(PCTCHNGCONSUMP) = .47077 + .13321 (PCTCHNGINCOME) +
.00864 (PCTCHNGFINANCIAL)+ .03844 (PCTCHNGHOUSING)
VI. Hypothesis Testing
The OLS results of my model are reported in Table 5. The T-value for the
income variable is 2.32 and this appears to be the only variable that is statistically
significant at the 95% level(except for the intercept). The T-value for the housing
variable is 1.50 which makes it significant only at the 85% level. The T-value for the
financial variable is .96 and given its p-value, there is a 34.06% chance that the true value
of this parameter estimate is zero.
I used Pearson Correlation Coefficients to test for multicollinearity. The results
are shown in table 2. Multicollinearity appears to be nonexistent between the
independent variables in the model as the largest correlation is between financial wealth
and housing wealth at -.15394. A possible problem may be the size of the standard error
between the income variable and the financial and housing variables. To resolve any
further questions of multicollinearity, separate regressions models for each of the wealth
variables with the income variable were estimated. The results are Table 3 and Table 4.
The T-values remained almost the same when compared the original model. The T-value
for the financial variable in the separate model was (.73) compared to (.96) in the
combined model. The T-value for the housing variable was (1.37) compared with the
combined model (1.50).
VII. Results
Table 5 reveals an Adjusted R^2 of (.0582) which tells us that the model of
income, financial wealth, and housing wealth only explains 5.82% of the variation that
we see in consumption. The RMSE reveals to us the standard error in the regression
which is (.43486). When we compare that to the dependent mean of the regression
(.53053) we see the reliability of the model is questionable.
From the model we see that a 1% change in the difference of income leads to a
13.321% change in the difference of consumption. The parameter estimate for the real
estate variable tells us a 1% change in the difference of real estate wealth leads to a
3.844% change in the difference of consumption. The last parameter estimate is for
financial wealth. This variable tells us a 1% change in the difference of financial wealth
leads to a .864% change in the difference of consumption. Case, Quigley, and Shiller
(2005) estimated that the effect of housing market wealth on consumption was larger than
that of financial wealth which is partly represented in this model. The parameter estimate
is much larger for the housing variable than for financial variable, and is closer to some
statistical significance.
VIII. Conclusions and Limitations
In order to observe the impact of the recent housing bubble I have formulated an
OLS regression model to test the effects on consumption. The last ten years has also
included a stock market bubble for which I have included a variable to test for effects. It
appears that only income can be said to have an effect on consumption. This conclusion
has merit. Just as suggested by Shefrin and Thaler,(1988) when citizens allocate money
into different assets, they may frame them for different uses. This would suggest that the
profits from the housing and stock markets would not be spent in later quarters. Other
possible solutions as to why the financial and real estate effects may be small,
nonexistent, or impossible to detect:

It is possible that the individuals who are willing to invest don’t frame
the assets, but that they have a low marginal propensity to consume.

For the Financial effect, it is possible that most of the profits were
made by financial institutions.

For the Housing effect, it is possible that the consumers who
purchased homes using the “exotic” adjustable rate mortgages had the
interest rates reset high enough that they did not have enough money
to spend even with the wealth created by the rapid appreciation.

Also for the Housing and Financial effect, it is possible that foreign
investors made profits in the US and spent those profits at home
instead of here, resulting in no change in domestic consumption.
There are certain limitations to this model. This model may not include all of the
adjustments needed to properly estimate time series data. I was able to use lag functions
for this model but the data used was quarterly. It is likely that all four of the variables in
this model have lag that extends longer than one quarter for which I adjusted.
Other limitations are lack of appropriate data. I did not have data on which
individuals made money in each of the bubbles and lost money in each of the downturns.
I also do not have data on which individuals spent funds and how much they spent. This
required me to adjust all the data in per capita terms diluting effects. It is likely that the
gains and losses are concentrated in certain sectors of individuals where the effects may
have been very large. If this data is ever available, further study should be conducted to
ascertain the true effects of changes in real estate and financial wealth.
References
Attanasio, Orazio, James Banks, and Sarah Tanner. “Asset Holdings and
Consumption Volatility.” NBER working paper no. 6567, May 1998.
Bhatia, Kul. “Real Estate Assets and Consumer Spending,” Quarterly Journal of
Economics, 1987, Vol. 102, 437-443.
Campbell, John and Joao Cocco. “How do house prices affect consumption?
Evidence from micro data", NBER Working Paper no. 11534, 2005.
Case, Karl, John Quigley, and Robert Shiller. “Comparing Wealth Effects:
The Stock Market versus the Housing Market," Advances in
Macroeconomics, 2005, Vol. 5 Issue 1, 5.
Elliott, J. Walter. “Wealth and Wealth Proxies in a Permanent Income Model.”
Quarterly Journal of Economics, 1980, Vol. 95, 509-535.
Juster, F. Thomas, Joseph P. Lupton, James P. Smith, and Frank Stafford.
“The Decline in Household Saving and the Wealth Effect." The Review of
Economics and Statistics, February 2006, Vol. 88, 56-78.
Li, Wenli and Rui Yao. “The Life Cycle Effects of House Price Changes." Journal of
Money, Credit and Banking, May 2006, Vol. 13, 103-113.
Peek, Joe, “Capital Gains and Personal Saving Behavior,” Journal of Money,
Credit, and Banking, June 1983, Vol. 15, 1-23.
Poterba, James M. “Stock Market Wealth and Consumption.” Journal of
Economic Perspectives, May 2000, Vol.14, 99-118.
Poterba, James and Andrew Samwick. “Stock Ownership Patterns, Stock Market
Fluctuations, and Consumption.” Brookings Papers on Economic Activity.
no. 2. 1995.
Shefrin, Hersh and Richard Thaler. “The Behavioral Life-Cycle Hypothesis.”
Economic Inquiry, September 1988, Vol. 26, 609-643.
Zweig, Jason. The Intelligent Investor. New York, NewYork: HarperCollins
Publishers, 2003.
http://www.bea.gov/national/nipaweb/IndexP.htm#P
http://www.federalreserve.gov/RELEASES/z1/Current/annuals/a1985-1994.pdf
http://www.federalreserve.gov/RELEASES/z1/Current/annuals/a1995-2004.pdf
http://www.federalreserve.gov/RELEASES/z1/Current/annuals/a2005-2007.pdf
http://www2.standardandpoors.com/spf/pdf/index/cs_national_values_022603.xls
http://www.census.gov/hhes/www/housing/hvs/historic/histt14.html
http://www.census.gov/hhes/www/housing/hvs/historic/histt13.html
http://www.census.gov/prod/2007pubs/08statab/pop.pdf
http://www.census.gov/prod/cen2000/phc-2-1-pt1.pdf
http://www.bea.gov/national/nipaweb/TableView.asp?SelectedTable=58&FirstYear=200
2 &LastYear=2004&Freq=Qtr
Table 1.
Description of Variables
Variable
Variable Descrption
real per capita percent
change of disposable
personal income from the
PCTCHNGINCOME
previous quarter*
real per capita percent
change in financial wealth
from the previous
PCTCHNGFINANCIAL quarter*
real per capita percent
change in the value of
owner occupied housing
from the previous
PCTCHNGHOUSING
quarter*
real per capita percent
change in personal
consumption expenditures
from the previous
PCTCHNGCONSUMP quarter*
*number is calculated: (DIF/LAG)x100
N
Descrptive
Statistics:
Mean
(St. Dev.)
MIN
MAX
-2.197
2.5429
83
0.43497
-0.8384
83
0.44004
-5.3942
83
-0.05172
1.89234
-6.3466
3.62089
83
0.53053
0.44808
-1.0667
1.46223
.15.59094 13.5789
Table 2.
Pearson
Correlation
Coefficients
PCTCHNG
CONSUMP
PCTCHNG
CONSUMP
1
PCTCHNG
HOUSING
0.14751
PCTCHNG
HOUSING
0.14751
0.07007
0.24628
(0.1833)
(0.529)
(0.0248)
1
-0.15394
0.00483
(0.1647)
(0.9654)
1
-0.03612
(0.1833)
PCTCHNGF
INANCIAL
PCTCHNGI
NCOME
PCTCHNGF PCTCHNGI
INANCIAL
NCOME
0.07007
-0.15394
(0.529)
(0.1647)
0.24628
0.00483
-0.03612
(0.0248)
(0.9654)
(0.7458)
(0.7458)
1
Table
3.
Regression analysis:
dependent variable
pctchngconsumption
Predicted
Independent Variable sign
intercept
pctchngfinancial
(+)
pctchngincome
(+)
Summary Statisics
N
Adj. R^2
RMSE
F-Statistic
Parameter Estimate
0.46972
0.00657
0.13315
TValue
8.62*
0.73
2.31**
83
0.0436
0.43821
2.87
Note: The individual statistic is significant to the *99% level, the **95%
level
Table
4.
Regression analysis:
dependent variable
pctchngconsumption
Independent Variable
intercept
pctchnghousing
pctchngincome
Summary Statisics
N
Adj. R^2
RMSE
F-Statistic
Predicted sign
(+)
(+)
Parameter Estimate
0.47523
0.03465
0.13125
83
0.0591
0.43464
3.58
Note: The individual statistic is significant to the *99% level, **95% level
TValue
8.83*
1.37
2.29**
Table
5.
Regression analysis:
dependent variable
pctchngconsumption
Independent Variable
intercept
pctchngfinancial
pctchnghousing
pctchngincome
Predicted sign
(+)
(+)
(+)
Parameter Estimate
0.47077
0.00864
0.03844
0.13321
Summary Statisics
N
83
Adj. R^2
0.0582
RMSE
0.43486
F-Statistic
2.69
Dependent Mean
.53053
Note: The individual statistic is significant to the *99% level, the **95%
level
TValue
8.71*
0.96
1.5
2.32**