Median Housing Price Prediction Model for D. M. Pan National Real Estate Company 1
Report: Housing Price Prediction Model for D. M. Pan National Real Estate Company
TESTBANKSNERD
Southern New Hampshire University
, Median Housing Price Model for D. M. Pan National Real Estate Company 2
Introduction
This report aims to construct a prediction model for listing home prices by square footage
through linear regression analysis. Therefore, the research question for this report is;
Research question: Is square footage a good predictor of listing prices for homes sold in 2019?
Consequently, answering this question will help the D. M. Pan National Real Estate Company
set better listing prices and help the real estate agents in charge of the listings develop their
pricing strategies. Linear regression will be used because of its suitability to this context as it
enables the analysis and measurement of the strength of the association between two numerical
variables, that is square footage and housing prices. Additionally, it will assist us in determining
the extent to which the price of housing can be forecasted based on the size of the house. Thus,
when performing linear regression, we anticipate seeing a strong positive correlation between
square footage and housing prices as we observe the scatterplot. The dots should ideally be
arranged in such a manner that it appears to be a linear relation in a positive slope. This is
important because, on a scatterplot, the strength and direction of the relationship are easily seen
at first glance.
Furthermore, in a simple linear regression model, there are dependent (y) and
independent or predictor (x) variables. The dependent variable is the variable being predicted,
while the predictor variable, or the independent variable, is used to make the prediction. In this
case, the square footage of the house is the predictor variable because it is an observable
characteristic of houses that can affect their cost. On the other hand, the response variable or the
dependent variable is the housing price. Therefore, this approach will help determine how
Report: Housing Price Prediction Model for D. M. Pan National Real Estate Company
TESTBANKSNERD
Southern New Hampshire University
, Median Housing Price Model for D. M. Pan National Real Estate Company 2
Introduction
This report aims to construct a prediction model for listing home prices by square footage
through linear regression analysis. Therefore, the research question for this report is;
Research question: Is square footage a good predictor of listing prices for homes sold in 2019?
Consequently, answering this question will help the D. M. Pan National Real Estate Company
set better listing prices and help the real estate agents in charge of the listings develop their
pricing strategies. Linear regression will be used because of its suitability to this context as it
enables the analysis and measurement of the strength of the association between two numerical
variables, that is square footage and housing prices. Additionally, it will assist us in determining
the extent to which the price of housing can be forecasted based on the size of the house. Thus,
when performing linear regression, we anticipate seeing a strong positive correlation between
square footage and housing prices as we observe the scatterplot. The dots should ideally be
arranged in such a manner that it appears to be a linear relation in a positive slope. This is
important because, on a scatterplot, the strength and direction of the relationship are easily seen
at first glance.
Furthermore, in a simple linear regression model, there are dependent (y) and
independent or predictor (x) variables. The dependent variable is the variable being predicted,
while the predictor variable, or the independent variable, is used to make the prediction. In this
case, the square footage of the house is the predictor variable because it is an observable
characteristic of houses that can affect their cost. On the other hand, the response variable or the
dependent variable is the housing price. Therefore, this approach will help determine how
