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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 pvalue
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 pvalue 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, policymakers 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.
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 Spring '09
 JimLot

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