As a data scientist, you collect 60,000 data points for an online real estate listing company on 5,000 newly listed single-family homes (SFH) within the Central Valley. You develop the following inverse demand models to help find undervalued homes:

[tex]\[
\begin{array}{l}
\text{Model 1:} \\
y = 300 - 0.1x_1 + 200x_2 + 100x_3 + 0.05x_4 \\
\\
\text{Model 2:} \\
y = 300 - 0.1x_1 + 200x_2 + 100x_3 + 0.05x_4 + 0.04x_5 + 75x_6 - 0.1x_7 + 150x_8 - 20x_9 - 30x_{10} - 50x_{11}
\end{array}
\][/tex]

Data Table: Inverse Demand [tex]\((P^D)\)[/tex] Variables for Single-Family Homes

[tex]\[
\begin{array}{c|l|c|c|l|l}
\text{Data} & \text{Model Variables} & \text{Coefficient} & \uparrow \text{ or } \downarrow & \text{Endo- or Exogenous} & \text{Shift or Movement} \\
\hline
y & \text{Monthly Mortgage Price} & - & - & \text{Endogenous} & \text{Movement} \\
x_1 & \text{Qty. of SFH in the Market} & 0.1 & & & \\
x_2 & \text{No. of Bedrooms} & & & & \\
x_3 & \text{No. of Bathrooms} & & & & \\
x_4 & \text{Lot Size in Ft}^2 & & & & \\
x_5 & \text{Home Size in Ft}^2 & & & & \\
x_6 & \text{No. of Parking Spots} & & & & \text{Shift} \\
x_7 & \text{Miles Away From Freeway} & 0.1 & \downarrow & \text{Exogenous} & \\
x_8 & \text{Home Has a Pool} & & & & \\
x_9 & \text{Years Since Home Built} & & & & \\
x_{10} & \text{Mortgage Interest Rates} & & & & \\
x_{11} & \text{Neighborhood Crime Rate} & & & & \\
\end{array}
\][/tex]