Question

Data were collected from a random sample of 220 home sales from a community. Let Price denote the selling price (in $1000), BDR denote the number of bedrooms, Bath denote the number of bathrooms, Hsize denote the size of the house (in square feet), Lsize denote the lot size (in square feet), Age denote the age of the house (in years), and Poor denote a binary variable that is equal to 1 if the condition of the house is reported as “poor.” An estimated regression yields:

Price=

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Answer #1

(a)

The coefficient of Bath is 23.4

So, addition of Bathroom increases the price by 23.4 * $1000 = $23400

The coefficient of Hsize is 0.156

Addition of 100 sq ft of size increases the price by 0.156 * 100 $1000 = $15600

Expected increase in the value of the house = $23400 + $15600 = $39000

(b)

The coefficient of Poor is -48.8

So, loss of  in value for poor condition = 48.8 * 1000 = $48800

(c)

= adjusted R squared = 0.72

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Answer 1

(a)

The coefficient of Bath is 23.4

So, addition of Bathroom increases the price by 23.4 * $1000 = $23400

The coefficient of Hsize is 0.156

Addition of 100 sq ft of size increases the price by 0.156 * 100 $1000 = $15600

Expected increase in the value of the house = $23400 + $15600 = $39000

(b)

The coefficient of Poor is -48.8

So, loss of  in value for poor condition = 48.8 * 1000 = $48800

(c)

= adjusted R squared = 0.72