1.
Data was collected from a random sample of 220 home sales from a community in 2003. Let P
denoted the selling price (in thousands of dollars), Bdr denote the number of bedrooms, Hsize
denote the size of the house (in square feet), Age denote the age of the house (in years), Poor
denote a dummy variable that is equal to 1 if the condition of the house is reported at poor anti is
zero otherwise, and View denote a dummy variable that is 1 if the house has a view of a nearby
mountain range and is zero otherwise. An estimated regression yields the following fitted
regression line: (a) Suppose that a homeowner of a 2500 square foot house removes a row of tall
trees that is blocking the view of the mountains from the house. What is the regression's
prediction for the increase in the value of the house? (b) Consider a house that has a view of the
mountains and is in poor condition. Suppose the homeowner adds 100 square feet to the house.
What is the regression?s prediction for the increase in the value of the house?
Solution
a) Hsize = 2500 sq feet View = 1 (post removing the trees, the house will have a view of the
nearby mountain) Therefore, keeping all other things constant, the house size and the view will
increase the price of the house as follows: Increase in P = 0.156*2500 + +25.5*1 +
0.005*2500*1 = 428 b) Increase in Hsize = 2500 sq feet View = 1 (View of mountains) Poor = 1
(Condition is poor) Therefore, keeping all other things constant, the house size, condition of the
house and the view will increase the price of the house as follows: Increase in P = 0.156*100 +
25.5*1 - 48.8*1 + 0.005*100*1 - 0.005*100*1 = -7.7 This means there is a decrease in the price
of the house by 7.7