# Statistics - Keller Graduate School of Management...

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Keller Graduate School of Management Managerial Statistics (GM533) Course Project Housing Sales Price Predictor Model

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Housing Sales Price Predictor Model Page 2 TO: FROM: Eastville, Oregon’s Board of Realtors Realtor October 20, 2010 Housing Sales Price Predictor Model DATE: SUBJECT: Introduction Detrimental to any homeowner seeking to sell their home and to the professional agent representing them in the market is incorrect home pricing. Homeowners can lose out on achieving the full value of their property, brokers can miss out on profitable commissions and this can also have a negative effect on comparable area sales, thus affecting the local market as a whole. Often, the process for collecting, collating and interpreting data from a market analysis to arrive at a fair market price for a home can be cumbersome and more often than not inaccurate. What I have done here, using recent sample data of home sales in Eastville, Oregon is: 1. using statistical analysis, determine the more significant determinants of sales prices in Eastville and 2. develop an equation to more accurately predict the fair market value of a home The key basis of this analysis was the identification of the true statistically relevant determinants of sales prices; what features are most important in determining a homes’ value. The resulting equation should serve to streamline the market analysis process as well as provide a more accurate predictor of the value of area homes. I. Data and Statistical Methodology Using sample data from recent sales of 108 area homes (see Appendix 1); I have compiled a dataset containing information on important variables such as the following:
Housing Sales Price Predictor Model Page 3 PRICE SQ_FT BEDS BATHS HEAT STYLE selling price in thousands total sq ft in the house number of bedrooms number of bathrooms 0=gas forced air htng; 1=electric heat the architectural style of the home: 0=trilevel, 1=two-story, 2=ranch-styled GARAGE AGE FIRE number of cars that can fit in garage age of the home in years 0=no fireplace; 1=at least 1 fireplace BASEMENT 0=no basement; 1=basement SCHOOL 0=Eastville school district; 1=Apple Valley school district Important to note from the data:    the average sales price of Eastville home was approximately \$97,992 average garage size can hold two vehicles the average age of an Eastville home is 11 years In order to develop the model for the equation I used a statistical measure called ‘multiple regression analysis’ to determine the relationship or significance of important home features such as square footage, number of bedrooms/baths, heating, housing style, garage size, basement, age of the home, fireplace, and school district on home prices. II. Results Summary 

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## This note was uploaded on 12/18/2011 for the course HR HR1515 taught by Professor C.f.bernasconi during the Spring '11 term at Keller Graduate School of Management.

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Statistics - Keller Graduate School of Management...

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