Real Estate Data Set - 1099 Words

This task involves trying to give an educated guess on a linear regression model for pricing real estate using a real facts date set using numbers and facts. Normally two approaches are used for valuing a real estate property: income and sales comparison. The sales assessment approach values a real estate property based on sale prices of similar properties. In this case the properties with familiar individuality are basically on the same price level, it would be typical to use a linear regression model to complete this demonstration. Our team will examine the significance of numerous independent variables to a real estate value. Two important strategies to regression testing is to run all test, and run a subset of the test based on a†¦show more content†¦The correlation is 41% between Bdrm and Size. We have concluded that there is no problem of multicollinearity in the data. We also found that Price and Size have relatively large scales compared with other variables. So it is natural to consider the logarithmic form of these two variables. We will run a new regression in which the regressor is log (price) and the regressand Size is replaced by its logarithm log (size). The R-squared of this new model is around 60%, even a little lower than the original model. The statistical significance of other independent variables keeps almost unaffected. The analysis of coefficients in this model with logarithms is not intuitive. So we still stick to the reduced linear model estimated in (a). We analyzed the OLS residuals to check for possible heteroskedasticity for a cross-sectional data set. We computed the fitted values of Price and OLS residuals in our model. Then residuals are sorted by fitted values of Price, and a scatter diagram of residuals is plotted against the fitted values in Figure 2. It is observed that all points are fairly scattered and there is no obvious pattern for the dependence of the residuals on the fitted values. We conclude that a linear model with homoskedastic errors is satisfactory for this data set. We evaluated the validity of this linear regression model by checking the accuracy of its prediction. The linear model is estimated by using 100 out of 105Show MoreRelatedThe First Data Set Is From Real Estate Business Intelligence2397 Words   |  10 PagesThe first data set was from Real Estate Business Intelligence (RBI), formerly the custom solutions branch of the Mid-Atlantic Multiple Listing Service (MLS)/Metropolitan Regional Information Systems (MRIS). RBI provided a custom dataset for all 366,542 recorded homes sales in the state of Maryland from January 1, 2010 to March 1, 2016 and their corresponding home characteristics. 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