How to Predict the Selling Price of a House
Executive Summary
An analysis was done to find an equation that predicts the selling price of a house. The data used in this research analysis to predict the selling price of a house is shown in the Bryant/Smith Case 28 (See Appendix 1).
The null hypothesis stated that there is not a relationship between the selling price of a house and its characteristics. The alternate hypothesis stated that there is a relationship between the selling price of a house and its characteristics. A 95% confidence level was chosen and a prediction interval which is a confidence interval estimate of a predicted value of the selling price used. The MegaStat output of a Regression Analysis of the Bryant/Smith Case 28 data was used as the basis to calculate the multiple regression equation as the prediction point. The point prediction of the selling price of a house corresponding to the combination 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) given on the MegaStat output. The MegaStat output tells us that the p-value associated with the variables (Square Foot, Garage, Basement and Age) are less than 0.01 level of confidence, therefore we have very strong evidence that these variables are significantly related to the selling price and thus, are very important in this model. Also, since the p-value associated with Bed and Heat was 0.0248 and 0.0199 respectively, we have a close to strong evidence that they are important. The results from the data calculation indicated that the null hypothesis should be rejected and the alternate hypothesis should be accepted. The purpose of this research is to find an equation that predicts the selling price of a house. Developments in housing prices are of great interest to householders, policy-makers and those involved in the housing industry. This has been the case in a number of countries where house price developments are having significant macroeconomic
An analysis was done to find an equation that predicts the selling price of a house. The data used in this research analysis to predict the selling price of a house is shown in the Bryant/Smith Case 28 (See Appendix 1).
The null hypothesis stated that there is not a relationship between the selling price of a house and its characteristics. The alternate hypothesis stated that there is a relationship between the selling price of a house and its characteristics. A 95% confidence level was chosen and a prediction interval which is a confidence interval estimate of a predicted value of the selling price used. The MegaStat output of a Regression Analysis of the Bryant/Smith Case 28 data was used as the basis to calculate the multiple regression equation as the prediction point. The point prediction of the selling price of a house corresponding to the combination 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) given on the MegaStat output. The MegaStat output tells us that the p-value associated with the variables (Square Foot, Garage, Basement and Age) are less than 0.01 level of confidence, therefore we have very strong evidence that these variables are significantly related to the selling price and thus, are very important in this model. Also, since the p-value associated with Bed and Heat was 0.0248 and 0.0199 respectively, we have a close to strong evidence that they are important. The results from the data calculation indicated that the null hypothesis should be rejected and the alternate hypothesis should be accepted. The purpose of this research is to find an equation that predicts the selling price of a house. Developments in housing prices are of great interest to householders, policy-makers and those involved in the housing industry. This has been the case in a number of countries where house price developments are having significant macroeconomic
References: Bailey, Martin J., Richard F. Muth and Hugh O. Nourse, “A Regression Method for Real Estate Price Index Construction”, Journal of the American Statistical Association 58 (1963): 933#150; 42. Case, Karl E., and Robert J