the demand for new houses robert t. gordon mba 570
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The Demand for New Houses
Robert T. Gordon
MBA 570
ABSTRACT
The demand theory was used to determine if the demand for new
homes is explained by overall market conditions.
INTRODUCTION
Real Estate boom High levels of unemployment Low interest rates Metropolitan areas Mid-West
Historical Studies
“Housing and Economics” by William F. Solomon (http://www.personal.psu.edu/users/w/f/wfs120/ist110_wfs/Portfolio.html )
Attributes increased demand for housing to the increasing “civilian non-institutional population” of the U.S.
Historical Studies cont.
Federal Reserve bank of San Francisco indicates that the demand for new houses is a factor of job growth,
http://www.frbsf.org/econrsrch/wklyltr/wklyltr99/el99-24.html
Excluded variables
Unemployment Rate Population FFR Total Production Index Consumer Price Index DSPI (disposable personal income) Average Price
The Model
Qnh = f(M1, FXYEN, HS, DJI)
Where Qnh= Quantity of new homes demanded in the U.S. M1 = M1 (Money Stock). FX = Foreign Exchange rate. Yen = Japanese Yen HS = New housing starts. DJI = Dow Jones Industrial Average
Null Hypothesis
H0: The demand for new homes is explained by market conditions
Parameters
Variable Type Expected Sign
Actual Sign
M1 Exogenous + +
FX Exogenous + +
HS Exogenous + -
DJI Exogenous + +
Variable Description
M1 (Money Stock) is a measure of total money supply. The M1 money supply includes only checkable demand deposits.
Variables cont..
FX & Yen represents the dollar to yen foreign exchange rate.
Variables cont..
HS represents new housing starts
DJI represents the Dow Jones Industrial Average.
Source Data
Data was gathered from the following web sites. Economagic.com: Economic Time Series Page U.S. Department of Commerce: Bureau
of Economic Analysis (http://www.bea.doc.gov/bea/an/nipaguid.pdf)
ANOVA
Sources SSQ MSQ Df F-Value
Model 39896 9973.994 4 128.849Error 12772.4 77.408 165 P-ValueC.Total 52668.4 169 0.00001
ANOVA TableDep: US sold (000's)
P-Value
The P-Value noted in the ANOVA Table (P=0.00001) indicates a confidence level greater than 99.99%. The F-Value is statistically significant. A statistically significant proportion of the total variation in the dependent variable is explained.
ANOVA
Association Test
MLE Stats
Root MSE 8.798 Lambda ====> n/cSSQ(Res) 12772.4 LogLiklihood ====> n/cDep.Mean 68.788Coef of Var (CV)
12.79
R-Squared 75.75%Adj R-Squared 75.16%
ANOVA TableDep: US sold (000's)
R2
The R-squared noted (75.75%) indicates that 75.75% of the variation in the dependent variable is explained by the variation in the independent variables.
Adjusted R2
The adjusted R-squared noted (75.16%) has properly adjusted for the number of independent variables.
Change is immaterial in this case, it is important to have an accurate portrayal of the information.
Adjusted R-squared indicates that 75.16% of the variation in the dependent variable is explained by variation in the independent variables.
ANOVA cont..
Auto Correlation
Diagnostic Tests
Rho 0.632 White's Test for Homoscedasticity
====> 23.667
Durbin 0.721 P-Value for White's ====> 0.05024Durbin H n/cD Low Limit 1.679 Average VIF ====> 1.762
D Upper Limit 1.788
Ho: Rho = 0 Suggested Transformation
Rho: Pos & Neg
Reject First Differences
Rho: Positive Do Not Reject
Correlation for Normality
====> 0.9962
Rho: Negative Reject Approx. Critical Value
====> 0.999
ANOVA TableDep: US sold (000's)
Durbin-Watson Statistic
The information indicates that there is auto-correlation. The Durbin statistic (.721) is unsatisfactory.
The null hypothesis (Ho: Rho = 0) “Reject” indication in ORS.
This was resolved using First Differencing.
First Difference
Auto Correlation
Diagnostic Tests
Rho 0.043 White's Test for Homoscedasticity
====> n/c
Durbin 1.861 P-Value for White's ====> n/cDurbin H n/cD Low Limit 1.679 Average VIF ====> 1.028D Upper Limit 1.788Ho: Rho = 0 Suggested
TransformationRho: Pos & Neg
Do Not Reject
Rho: Positive Do Not Reject
Correlation for Normality
====> 0.9931
Rho: Negative DoNot Reject
Approx. Critical Value
====> 0.999
ANOVA TableDep: US sold (000's)
Multicollinearity
As noted in the ANOVA tab, the average VIF comes out to 1.762.
This is far below the acceptable limit of “10”. NOT deemed problematic. Multicollinearity is not a problem.
White’s Test
P-Value for the White’s test is .05024 indicating a confidence level less than 95%. This means that the residual error terms are homoskedastic. This is a satisfactory outcome and we accept the null hypothesis .
Constant Variance
Regression Constant Variance TestDependent Variable: US sold (000's)
Predicted95908580757065605550454035
Resid
ual
30
25
20
15
10
5
0
-5
-10
-15
-20
-25
Normal Probability
Normal Probability PlotDependent Variable: US sold (000's)
Expected Residual20151050-5-10-15-20
Sort
ed R
esid
ual
30
25
20
15
10
5
0
-5
-10
-15
-20
-25
Parameters
Parameter Standard t For Ho: P-Value PartialVariable Estimate Error Est = 0 (95%=0.
05)Corr VIF
Intercept -100.232 15.068 -6.652 0.00001 -0.211 n/aM1 MONEY STOCK
0.099 0.009 10.965 0.00001 0.422 2.396
YEN -DOLLAR FX
0.392 0.071 5.552 0.00001 0.157 1.588
Housing Starts
-0.008 0.003 -2.248 0.02432 -0.03 1.118
Dow 0.002 0 6.496 0.00001 0.204 1.946Dependent: US sold
(000's)
Regression Parameters
Elasticities
M1 MONEY STOCK
YEN -DOLLA
R FX
Housing Starts
Dow
Average==> 1.6822 0.7077 -0.02716 0.21157
Elasticity
Conclusion
As money supply increases (M1) the demand for new homes will increase.
As the dollar grows stronger against the Yen, the demand for new homes will increase.
As housing starts increase, demand for new homes will slightly decrease.
As the DJI average increases, the demand for new homes will increase.
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