3-3 Assignment Real Estate Analysis Part II
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Housing Price Prediction Model for D.M. Pan Real Estate Company DiAngeles Lino
MAT 240: Applied Statics Matthew Elwer
January 27,2024
3-3 Assignment: Real Estate Analysis Part II
Regression Equation Based on the graph, the regression equation is: Y=101.9x + 39576 Determine r
Finding the absolute value of r = 0.965 requires me to first take the square footage (x variable) and the listing price (y variable) to calculate r. The two variables have a strong positive linear relationship, as evidenced by the value's proximity to unity. As one might anticipate, the listing price rises in tandem with the home's square footage.
Examine the Slope and Intercepts According to the sample, the slope is $101.90, meaning that a home's listing price will typically rise by $101.90 for every square foot that is added. In the sample, $39,576 is the intercept. The sample's x value is expressed in square feet, and the lowest value that can be obtained is 1607, which is far from zero. This means that the intercept cannot be meaningfully interpreted until the
x values are close to zero. As a result, since the data is based on house prices rather than just bare
land transactions, there is no way to meaningfully interpret the intercept and calculate the land value.
R-squared Coefficient R square in the above scatterplot is equal to 0.9306, and its decimal representation is
93.06. R-squared indicates the proportion of the listing price prediction error that is removed
when the square footage is subjected to least-squares regression. A coefficient of determination
is expressed as R-squared. This indicates that 93.6 percent of the variance in the median listing
price can be accounted for by changes in the median square feet.
Conclusion
To summarize, the mean, median, and standard deviation of listing prices in the East North Central Region sample are all less than those found in the National Summary of Statistics. In contrast to the national figures, the square footage's mean, median, and standard deviation are larger. In comparison to most of the United States, this would suggest that you could get larger homes in the South East Central Region for a lower listing price. Price variations per square foot can be found with the aid of the slope value. The price in the sample region will go up by $10,190 for every 100 square feet. Predictions outside of the range of the available data should not be made using the above graph. The range of square footage between 1607 and 6498 is where the graph would be most useful.
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