A linear regression model was built to predict the housing price (dependent variable) using the following independent variables (predictors):

renovated_no: =1 if the house was not renovated, =0 otherwise;

zipcode_98003: =1 house is located in zipcode 98003, =0 otherwise;

zipcode_98007: =1 house is located in zipcode 98007, =0 otherwise;

bedrooms: Number of bedrooms;

bathrooms: Number of bathrooms;

lot: Land space, which is converted using z-transformation;

sqft_above: The square footage of the interior housing space above ground level;

sqft_basement: The square footage of the interior housing space below ground level;

The model specification for the house price (dependent variable) is: 806967.904 238559.094 renovated_no 82677.643 zipcode_98003 + 101088.776 zipcode_98007 46967.721 bedrooms + 6016.794 bathrooms + 7980 lot + 298.285 sqft_above + 188.592 sqft_basement

Assume all predictors are significant. Based on the information above, answer the following questions. Show the steps how you obtain the result for all questions below for full credits.

For two houses, one that is located in zip code 98003 and was renovated and the other that is located in zip code 98007 but was not renovated, assuming the other conditions of the two houses are the same, which one has the higher expected price and what is the difference? Show the steps how you obtain the result.