Question

Y = listing price
X1 = interior floor space (square footage of the house)
X2 = land size (square footage). This is usually represented by length of the front of the lot (in ft) X the depth of the lot (in ft). sizes range but a common lot size in Vancouver is 33 X 120. You will need to convert this to feet squared (area) before entering it into your spreadsheet (i.e., 3960)
X3 = number of bedrooms
X4 = number of bathrooms
X5 = age of the building (often listed as “Built in” year date. Thus, you will need to calculate it.)
X6 = walkability score
Key all data into an excel spreadsheet.
Note: some listings will not have all the data. If you are collecting data in a geographical area that does not provide the information above, please move onto another area. You will find that suburban areas around the Lower Mainland provide the above information quite readily.
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Also Note: you can use your mls search and reuse it later by emailing yourself the browser URL. Simply copy and paste the URL from the mls webpage into an email and mail it to yourself so you can continue your data collection at another time.
Use EXCEL to complete the following
1. Please create 6 scatter plots: Y versus each Xi for I = 1, 2, …6 and provide a comment with respect to the presence of an “approximate” linear relationship or not.
2. Determine the regression model that best predicts selling price.
3. Is the overall model significant? Carry out an overall F-test to determine this. Be certain to state the hypothesis, your decision rule and provide a concluding statement in the context of the problem.
4. Clearly state the meaning of the regression coefficients in the context of this problem.
5. Provide a proper Hypothesis Test at the 5% significance level on your estimated regression model to demonstrate if there is significant linear relationship between each independent and dependent variable.
6. How good is the fit of your model? Quote a measure from the regression output. Provide a clear statement of its meaning.
7. Check that the assumptions that are required by linear regression model are valid.
a. Generate a residual plot on the estimated model (residuals versus fitted values) and provide a commentary of what it means.
b. Generate a normal probability plot on the estimated model and provide a commentary of what it means.
c. Also, check for multicollinearity by creating a correlation matrix. Correlation stronger than + or – 0.6 between any two x variables indicates that they are somewhat redundant and may cause problems in your analysis. Do you have any such potential problems?
8. Does this model seem to be a good predictor of real estate market value? Draw conclusion with your team member. State why or why not you believe the model is a good/poor predictor. Also, suggest ways that the model could be improved.