To predict property value, real estate firms analyze historic data. Prices vary depending upon square footage as well other factors. D.M. Pan National Real Estate Company data will be used for this purpose. Data includes square footage as well as the associated values. In order to predict 2019 property values, a predictive model will also be built. This model uses square footage to project listing prices.
Real estate agents will be able, using the built model to determine if square footage is the best metric for setting the property listing price. This report will use regression lineare as the most appropriate statistical method. Linear is the expected result of the scatterplot. The response variable in this study will be the variable that we attempt to predict, such as listing prices. Contrary to the response variable, the predictor variables are the variables that influence or affect the outcome variable’s change, positively or negatively. The predictor variable in this instance is the square footage.
This investigation used a sample of 50 people. Excel used the RAND function to perform the sampling. () Function, which in essence randomizes data. In this case, the outcome variable of the analysis will be the listing prices in U.S. Dollars, while the predictor variable, the square footage, will be used.
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