Case 28: Housing Prices

In: Business and Management

Submitted By mcuaresma924
Words 2098
Pages 9
Case 28: Housing Prices
GM533 Managerial Statistics
April 11, 2012

I’m conducting an analysis between the price of a home in Eastville, Oregon and the factors which develop a home’s price. The data is analyzed using ANOVA (Analysis of Variance) and multiple regression hypothesis testing procedures. The regression analysis will help create a multiple regression fit which will incorporate the ten predictor variables of a home’s price. After the regression analysis is complete, global and local ANOVA tests will help eliminate the insignificant predictor variables and create the net significant regression equation. Even though the sample size is only representative of the houses in Oregon, the general trends that affect house prices are the same. Therefore, this multiple regression model will be useful in its predictions but limited in it’s use on more complex homes. In addition, there are more factors than the ten discussed that affect the price of a home (i.e. the economy, inflation, etc.); therefore the tool has limited power.
As a homeowner, one should know his or her home’s accurate price in the market. This is an extremely important fact to know, so a person can decide when it’s appropriate to sell their home. There are several tools out there already such as Zillow.com but, these websites are giving prices that are estimated from the original price of a home. Using a regression equation will give the ability to have a second opinion on his or her home’s price. Even though the price of a home is affected by many factors outside of the criteria used, the simple multiple regression equation can give a rough estimate of the price range one should place on a home.
The multiple regression equation must be simple and effective. Incorporating variables that have little or no influence on purchasing price, such as house color, will further confuse the user and…...

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