A realtor wishes to investigate what type of homes would attract residents in a neighborhood. The realtor collected sample data that contains the following information about some single-family houses recently sold in the neighborhood.

y = price in $1,000s (PRICE)
x₁ = age of the house in years (AGE)
x₂ = size of the house in square feet (SIZE)
x₃ = number of bedrooms (BEDROOMS)
x₄ = number of rooms (ROOMS)

The realtor conducted a regression analysis using the following model:

y = β₀ + β₁ x₁ + β₂ x₂ + β₃ x₃ + β₄ x₄ + ε

A portion of the regression results from Excel is shown below.

Regression Statistics
Multiple R	0.90
R Square	0.81
Adjusted R Square	0.79
Standard Error	13.10
Observations	45

ANOVA
	df	SS	MS	F	Significance F
Regression	4	28340.23	7085.06	41.31	1.07E-13
Residual	40	6860.06	171.50
Total	44	35200.29

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	21.56	9.17	2.35	0.02	3.03	40.09
AGE	-0.27	0.09	-2.89	0.01	-0.46	-0.08
SIZE	0.05	0.01	7.41	5.00E-09	0.04	0.06
BEDROOMS	-10.69	4.64	-2.30	0.03	-20.07	-1.30
ROOMS	3.75	2.19	1.71	0.09	-0.68	8.18

please answer both parts

The descriptive statistics of the data is given below:

	PRICE	AGE	SIZE	BEDROOMS	ROOMS
Mean	75.72	31.91	1,498.60	2.93	6.22
Median	71.00	26	1,486	3	6
Minimum	34.00	9	416	1	3
Maximum	152.50	102	3,600	5	10

Consider a house in the neighborhood that is 10 years old and has 2,400 square feet, 4 bedrooms, and 8 rooms. Based on the regression output, the predicted selling price of this house is __________.

For houses with a given square footage and a number of bedrooms, if the number of rooms stays the same, then for every one-year older in age of the house, the average price is expected to drop by $ __________.