Median Housing Price Model for D. M. Pan National Real Estate Company D. M. Pan National Real Estate Company are helping their realtors out by presenting them with a model to better determine the use of square footage as a benchmark for listing prices on homes. This model will help with predicting the median housing prices based square footage. Using linear regression is the best approach as it helps to discover the relationship between square footage and listing price and is used to predict out put that is continuous value. On the linear regression model, we will use the square footage of a home to help predict a listing price based on samples of previous listings. In order to have proper random samplings, information was taken from all states and randomly added to the scatterplot. Using the square footage of each sample as a predictor variable, we will attempt to predict response variable which would be the listing price. Above on the scatterplot chart provided you can see how the prices tend to move up with higher square footage. This information comes from 50 samples of the regional information provided. Even with different market areas you can still see the movement. Based on the information provided with the 50 random samples we can determine that with such a broad spectrum we still only have 1 outliner that places the listing price higher than average. The outliner just means that this particular sample is from a region with a higher price range, but majority of data is consistent. The data’s linear line is showing the positive of higher square foot equals higher price. While our sample show a higher statistic rating on prices then what the random sampling provides us, it still shows our main prediction of the higher the square foot the higher the listing price should be. A regression model can be created because a relationship has been found between our two variables, square footage and listing price of homes. The linear regression shows a positive association with the two variables increasing together. The calculated correlation coefficient would be .378. The linear relationship is considered weak when the value of r is greater than .3 but less than .5. The regression equation is y=152x+5 R-squared would be .7 giving the equation a moderate strength. The slope of the line is positive, and the intercept is 152. Using the equation to predict a listing price for a 2,800 sq ft. This would equal $427,005. In conclusion, with the information provided from a random sample of the regions and a statistic chart of national averages we can predict a fair listing price for clients. This gives real estate agents of D.M. Pan National Real Estate Company a model to help give clients a reasonable outlook at buying and selling properties in their area with sampling of data from the area in question.

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