# Finding the price of a home using regression

Essay by JHolliday7University, Master'sA, January 2009

James Holliday

GM533

12/13/2008

Executive Summary

The analysis in this paper was utilized to find an equation that predicts the selling price of a house. The null hypothesis states that there is no clear and definitive relationship between the selling price of a house and the characteristics of the house. The alternate hypothesis states that there is definitely a relationship between the selling price of a house and the characteristics. A 95% confidence level and a confidence interval estimate of a predicted value of the selling price were used. The MegaStat output of a Regression Analysis of the data was used as the foundation to calculate the multiple regression equation. The point prediction of the selling price of a house corresponding to the variations of values of the independent variables is: Y = -12.5988 + 0.0383(X1) + 4.3573(X2) -14.5371(X3) + 16.0610(X4) + 11.3576(X5) - 1.2168(X6) which is given on the MegaStat output later in this paper.

The MegaStat output shows that there is very strong evidence that these variables are definitely related to the selling price and indeed very important in this model. The results from the data show that the alternate hypothesis should be accepted.

Introduction and Purpose

The purpose of this analysis is to find the equation that predicts the selling price of a house. While the focus of this paper is predicting the selling price of a house in Eastville, Oregon, the method discussed in this paper would also be easily utilized for data from other areas or countries. One major problem in measuring housing price growth results from the inconsistencies of transactions. To be meaningful, price data should be based on transaction prices rather than valuations.

One of the most important things you need to know when selling a house is the maximum you should pay...