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 ( 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,  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 VariableTypeExpected Sign Actual Sign M1Exogenous++ FXExogenous++ HSExogenous+- DJIExogenous++

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 (

ANOVA

P-Value  The P-Value noted in the ANOVA Table (P= ) 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

R2R2  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 R 2  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..

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

Multicollinearity  As noted in the ANOVA tab, the average VIF comes out to  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 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

Normal Probability

Parameters

Elasticities

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.