BADM 5140PS 6 - The Best Model for Predicting Home Prices...

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The Best Model for Predicting Home Prices - A Multi-regression Analysis of homes at Hemlock Farms Problem Set #6 BADM 5140 Dr. D Brandon Kear Harold Riddle Hui Li Nicolas Bilweis Jed Dahl
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I. Executive Summary We were recently hired as a market analysis group by a private investor to conduct research on what causes house prices to increase or decrease in the hemlock farms area. We have been provided by Hemlock Farms with the following variables: list price, hot tub, lake view, bathrooms, bedrooms, loft/den, finished basement, and acres. We examine the data using several multiple regression methods (VIF, Best subset and stepwise methods). The best model wil l keep the best independent variables predicting asking price for houses and dismiss the others. After we evaluated all the possible combinations of independent variables, we found the most appropriate multiple regression model to predict the asking price of house. The best model consists of the following independent variables: number of rooms, whether or not the house has a hot tub or not, and whether or not the house has a Lakeview or not. Therefore, using the best model, a house with 6 rooms, a hot tub, and a lake view, will have a predictive value of $574,514.30. We recommend the investor to refrain from extrapolation. For future research we also recommend including other variables in the regression analysis, including personal disposable income, unemployment rate, consumer sentiment, and the change in CPI, etc. II. Statement of the problem A private investor, whom requests that his /her identity remains anonymous, hired us as a market analysis group that specializes in predicting housing prices. After providing us with data on 61 homes our investor wants us to be able to predict which variables change house prices in the Hemlock Farms area and to what extent each variable influences the market price. We will need to test which independent variables are significant in predicting the dependent variable using VIF, Best subset and stepwise methods. Our goal: "to find statistically significant evidence clarifying which variables, if any, are directly impacting market price." Once we have completed our statistical research, we built the most appropriate multiple regression models to predict the asking price of house. It will be given to our investor to evaluate and use as he/she deems fit. III. Statement of research objective The Hemlock farms have provided us information on 61 homes that were on sale for July, 2010. The single dependent variable is “Home Sales Price”; and the eight independent variables include: List Price, Hot Tub, Lake View, Bathrooms, Bedrooms, Loft/Den, Finished Basement, and Acres. Our job is to create a regression model to predict the price of home sales in Hemlock Farms. IV. Assumptions
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This note was uploaded on 07/21/2011 for the course BADM 5140 taught by Professor Rag during the Spring '11 term at East Tennessee State University.

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BADM 5140PS 6 - The Best Model for Predicting Home Prices...

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