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The Impact Of Income On Home Prices

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Review of Subject In this report, the question “How much of the changes in the median selling price of homes in a city can be explained by the changes in median income of that city?” is answered. Home ownership is an important aspect of one’s life stages, and home prices are determined by demand and supply. The demand curve is affected by the one’s income, such that as one’s income increases, one is more willing to pay a higher price for the same quantity of goods (Baye & Prince, 2014). However, there are many other factors that might affect the demand curve, e.g. no. of children, in the household, the perceived quality of education in the school district, or the number of job positions (filled or open) around the city. According to Burda …show more content…

In real world situations, often one needs to analyze the relationship between one dependent variable and a set of more than one independent variables. In this case, the bivariate model can be extended to include additional independent variables. The general multiple regression equation is y = a + b1x1 + b2x2 + b3x3 + … + bkxk, where a is the intercept and bk is the coefficient of the independent variable xk. For example, in finding out whether home price of a city is related to factors such as household income, number of households, unemployment rate, and quality of education in that city, one could develop a linear model using the multiple regression analysis. First, collect the home price data as the dependent variable, and income data, unemployment data etc. as independent variables. Second, apply the regression in Minitab software. Third, analyze the output of the regression model for the R-square, the p-values of the coefficient of the variables, and the VIF values. The concept of the coefficient of determination, R-square, is the same in both the simple regression model and multiple regression model. It is the percentage of variations of the dependent variable explained by the changes in the set of independent variables (Lind, Marchal, & Wathen, 2015). The R-square can also be obtained which tells how good the overall fit of

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