MAT_240_Module_Three_Real_Estate_Analysis_Part_II_.doc MAT_240 Median Housing Price Pred

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MAT_240_Module_Three_Real_Estate_Analysis_Part_II_.doc MAT_240 Median Housing Price Prediction Model for D.M. Pan Real Estate Company Southern New Hampshire University Module Two Notes In the sca... tter plot about, the independent variable (x) is the median square feet, while the dependent variable (y) is the median listing price ($). The independent variable would be useful for making predictions as the square footage does not change for an individual house, only the value would change based on any renovations made to home or property value. Regression Equation The regression equation for the scatter plot from project 1 is Y=195.5x-50564. Determine r R is the correlation between the predicted values and observed values of ˜y™. In my analysis R=0.4464. This tells me there is a strong positive linear relationship between the x and y variable. Examine the Slope and Intercepts Slope= 195.95 Intercept = -$50,564 Y=195.95(0)-50564 Y= -$50,564- The intercept remains the same R-squared Coefficient The R2 measures how the square footage of a house determines the price the house should be listed. Based on my scatter plot, the R2 is .1993, which means that 19% of the price is explained by the square footage of the house. Conclusions ï‚·The square footage for homes based on my selected region is aligned with the median range for the square footage of homes nationally. ï‚·For every additional 100 square feet the price goes up $19,595 (y=slope*100) ï‚·Y=195.95(1200)-50563.75/ $184,576 would be the listing price. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . .. .. . . . . . . . . . . . . . . . . . .. . . . . [Show More]

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