The multiple regression analysis completed with the analysis toolpak for Excel shows a 95% R squared that has a low confidence level of significance as shown by the F statistic.
The set up is correct to predict house prices based on the independent variables. The null hypothesis requires a confidence level to be set (usually 95% at 2 standard deviations) to test whether or not (null hypothesis) that house price can be explained by the external factors at that level of confidence.
The overall predictive ability is the R-squared. The coefficients of each independent variable (+ or -) represent the relationships e.g. as Age increases, price decreases by a factor of 333.
While the analysis toolpak performs the raw regression analysis, it does not provide very useful descriptive information in order to interpret the results and validity of them. I suggest that you download and use our template from here which can be used fully for 30 days to conduct your analysis and provide advanced descriptive information as well as the ability to forecast for economic or business analysis.
