She wants to get as many offers as possible, so she does not want to set the asking price of the house too high since that would result in fewer people considering the property. But I told her that we do not want to set the asking price too low, which would lead us to sell the property at a low price leaving us without cash to buy another one.

Of course, she wants me to do all the work. She asked me

§ to place an asking price which is neither too low nor too high

§ to appraise the selling price of our house

§ and to forecast the possible discount we will have to give to the buyer

I went to my realtor friend John, in order to have him give me an idea about where to price my house. Since he went out of business ten years ago, he could not help me very much. The only thing I got from him was a ten-year old folder with old houses’ data.

After having looked at this data, I remembered that I was taking your Statistics class at the University of Texas at Dallas. I remembered dimly that we did multiple regressions and could set up predictive models with that. Maybe this was the perfect way to determine an asking price, forecast th

The third regression omitted the “Time on Market” variable. This regression continued to show variables with a P-value greater than 0.05. This variable with the highest P-value of 0.605384724 was “Square Footage” and was eliminated from future consideration. R2 and Standard Error values were nearly constant.

The “Difference AP-SP” column of data was used to represent the “y” independent value in the forecasting model. We used the remaining columns of data to represent the “x” dependent values in the forecasting model (excluding the “Selling Price,” the “Asking Price,” and the random number columns data). Once again, I had to plot the different values of “xi” against the “y” to establish if a relationship existed between each dependant variable and the independent variable, and to verify that the relationship is not curvilinear. Thus, we needed to decide which given data would be relevant to achieve our goal of predicting the discount price.

Some topics in this essay:
Standard Error, Selling Price, Square R2, Texas Dallas, Predicted Price, Relevant Data, Significance Significance, Average Size, Washington Chicago, , standard error, selling price, price house, forecasting model, independent variables, evaluation difference ap-sp, price evaluation, independent variable, evaluation difference, dependent variable, difference ap-sp, evaluation selling price, developing forecasting model, price selling price, model difference price,