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Multiple Regression Analysis statics

# Multiple Regression Analysis statics - Multiple Regression...

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Multiple Regression Analysis Case #28, Housing Prices II Keller Graduate School of Management – GM533 Ryan D. Lee Executive Summary: In this report I will use a multiple regression analysis approach to predict the appropriate selling price of my home in Eastville, Oregon. This approach is a statistical analysis that will explain the correlation between several selling features (independent variables) with the selling price of a home (dependent variable). The value in this approach is that it provides a systematic approach that can be duplicated and used to help potential For Sale By Owner homeowners who are unsure how to price their home. Introduction: I am a homeowner in Eastville, Oregon. Like many homeowners these days I am looking for any way to not only save, but find additional income. I have been thinking about selling my home but do not want to pay the Realtor commissions, as my home has already lost some value with the declining economy. As a result I have decided to conduct a systematic approach to determine the value of my home using commonly sought after features in homes. This approach, has helped me determine the appropriate selling price for my home and can be duplicated and used by anyone else selling a home. As an entrepreneur I have decided to market my approach to other potential For Sale By Owner (FSBO) homeowners. The average commission paid to Realtors is between 5-6% of the selling price of your home, [ (Commissions, 2011) ]. While Realtors offer important services to those who are trying to complete a FSBO transaction it can be quite daunting without the right information. As I will explain in this report I have already done all of the ground work and thoroughly explain the process of a multiple regression analysis. The comparables that I used in my analysis are easily transferred to any market in the country. It is a turnkey approach to determine the appropriate value for your home allowing you to be a successful and more profitable home seller. The data used in this analysis came from a sample size (n) of 108 homes with varying features, all located in Eastville, Oregon. The features, or independent variables (X), that were compared in this analysis are listed below: * Square Feet – SQ FT (X1), total square feet * Bedrooms – BEDS (X2), number of bedrooms * Bathrooms – BATHS (X3), number of bathrooms * Heating – HEAT (X4), gas or electric, gas = 0, electric = 1 * Architectural Style – STYLE (X5), tri-level = 0, two story = 1, ranch styled = 2 * Garage – GARAGE (X6), number of cars that can fit in the garage * Age – AGE (X7), age of the home in years

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* Fire – FIRE (X8), no fireplace present = 0, at least one fireplace present = 1 * Basement – BASEMENT (X9), no basement = 0, basement present =1 * School – SCHOOL (X10), Eastville school district = 0, Apple Valley school district = 1 As previously mentioned to interpret the data and determine the relationship between the dependent variable (Y – Price) and the independent variables (X – selling features) I used a multiple regression analysis. Multiple Regression models “employ more than one independent
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Multiple Regression Analysis statics - Multiple Regression...

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