Interpret the Slope and the Y-Intercept of the Least-Squares Regression $x$

Interpret the Slope and the y-intercept of the Least-Squares Regression Line
-A real estate magazine reported the results of a regression analysis designed to predict the price (y) , measured in dollars, of residential properties recently sold in a northern Virginia subdivision. One independent variable used to predict sale price is GLA, gross living area ( $x$ ) , measured in square feet. Data for 157 properties were used to fit the model, $\mathrm { y } = \beta _ { 0 } + \beta _ { 1 } \mathrm { x }$ . The results of the simple linear regression are provided below.
$$\hat { \mathrm { y } } = 96,600 + 22.5 \mathrm { x } \quad \mathrm { s } = 6500 \quad \mathrm { r } ^ { 2 } = - 0.77 \quad \mathrm { t } = 6.1 \text { (for testing } \beta _ { 1 } \text { ) }$$
Interpret the estimate of $\beta 0$ , the $y$ -intercept of the line.

A) There is no practical interpretation, since a gross living area of 0 is a nonsensical value.
B) All residential properties in Virginia will sell for at least $\$ 96,600$ .
C) About $95 \%$ of the observed sale prices fall within $\$ 96,600$ of the least squares line.
D) For every 1-sq ft. increase in GLA, we expect a property's sale price to increase $\$ 96,600$ .