Multiple Regression Analysis
Case #28, Housing Prices II
Keller Graduate School of Management – GM533
Ryan D. Lee
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.
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
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
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