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 impacts. However, the construction of aggregate measures of housing prices is not a straightforward exercise, and involves addressing a number of conceptual and practical issues. This paper aims to provide a computationally simple method of addressing some of these issues. While the focus of this paper is on predicting the selling price of a house in Eastville, Oregon, the method outlined in this paper would also be feasible and readily adaptable for data from other areas or countries. One major problem in measuring housing price growth results from the infrequency of transactions and the heterogeneous nature of the housing stock. To be meaningful, price data should be based on transactions prices rather than valuations. One of most important things you need to know when selling a house is the maximum you should pay for a property so that you can make your desired profit. The key to determining your maximum cash offer is knowing how to predict the value without relying on Realtors. There are many different house price indexes that can be obtained to get the latest information on property prices and the patterns and trends of growth. In essence, there are so many different guides with so much differing information that it becomes almost an impossible task to know which one you can trust to be accurate. A perfect house price would only report on the actual purchase price of every completed property. To further enhance this information the type of property and any seasonal adjustments should be included. At this time this information is not obtainable and such an index does not exist and coupled with the effects of short term house price inflation or property price volatility, the house price index becomes a very complex equation. There are many problems with predicting house prices due to the nature of the market where no sale is the same and a house that is identical to another can sell for a different price for any number of reasons. This could be due to the...

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