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Housing Price Prediction Model for D.M. Pan National Real Estate Company
Juanita Onasanya
Department of Math, Southern New Hampshire University
MAT 240: Applied Statistics
Ole Forsberg
March 17, 2024
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Housing Price Prediction Model for D.M. Pan National Real Estate Company
Regression Equation
Y= 80.658x+91775
Determine
r
The interaction between the square footage and the asking price equals 0.817961337, or
0.818. With the present circumstances, R equals 0.818. The connection between square footage
and selling price becomes especially high when the outcome's value approaches 1.0. Since
square footage grows, so does the selling price; hence, the R coefficient is positive, indicating
that selling prices keep growing.
Examine the Slope and Intercepts
Both the slope and intercept in this particular instance were challenging variables to
specify. You'll find a few auctions of property lacking dwellings or square footage that wouldn't
be practicable throughout the United States of America. Whenever a residence is erected, its
slope and intercept structure expand in square footage, making it reasonable.
R-squared Coefficient
The ratio of R square is the R-correlation multiplied by itself, which is 0.669061 (which was
rounded to 0.67). Additionally, it indicates that approximately 67 percent of houses in this area
are priced based on square footage. The value of the coefficient of predictability denotes the
increases in Y that the X-variable confirms. The variance is obvious from the property's asking
prices, which rise as the square footage rises.
Conclusions
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There is a clear association between the national statistics supplied with the sampled data
from the East and North Central Regions. The national average is 2,111, while the sampled East
North Central Region is 1,697. The national average is greater compared to the sampled area.
The normative deviation indicates that the national average is 921, but the sampled data for the
East North Region is 561. The national average is greater than the sample values. The minimal
national average is 1,101, whereas the sampled data is 1,113. The selected data shows a higher
minimum square footage than the national average. The first-quarter statistics likewise show a
larger disparity between the national average (1,626) and the sampled data (1,330). The median
for the national average (1,881) differs from the sampled data (1,574). The disparities between
the national average (2,215) and the East North Central Region's sampled data (1,873) persist in
the third quarter statistics. The maximum is dramatically different, since the national average is
6,516, while the East North Central Region sampled data is 3,525.
The slope demonstrates that rising square footage mostly costs dwellings in the United
States. In the same way that the regression equation validates the data results, typical residences
range in size from 1,500 to, for certain cases, 3,000 square feet, with prices ranging from
$246,400 for 1,560 square feet to $323,300 for 3,408 square feet. The square footage graph
might be best utilized to forecast and exactly determine the average square foot house selling
price variation throughout a certain location.
