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.