Suppose that a researcher collects data on houses that have sold in a particular neighborhood over the past year and obtains the regression results in the table shown below. Dependent variable: In(Price) Regressor (1) (2) (3) (4) (5) Size 0.00048 (0.000041) 0.72 0.73 0.57 0.749 In(Size) (0.057) (0.092) (2.05) (0.055) In(Size) 0.0087 (0.16) 0.0043 Bedrooms (0.038) 0.084 0.076 0.083 0.088 0.085 Pool (0.036) (0.037) (0.039) (0.036) (0.034) 0.039 0.027 0.027 0.027 0.027 View (0.032) (0.031) (0.028) (0.032) (0.033) 0.0024 Poolx View (0.12) 0.21 0.16 0.18 0.15 0.19 Condition (0.046) (0.038) (0.036) (0.039) (0.035) 11.49 6.69 6.71 7.07 6.63 Intercept (0.073) (0.41) (0.54) (7.51) (0.44) Summary Statistics SER 0.103 0.101 0.108 0.106 0.104 R 0.72 0.75 0.76 0.74 0.73 Variable definitions: Price sale price ($); Size house size (in square feet); Bedrooms number of bedrooms; Pool = binary variable (1 if house has a swimming pool, 0 otherwise); View = binary variable (1 if house has a nice view, 0 otherwise); Condition binary variable (1 if real estate agent reports house is in excellent condition, 0 otherwise). Using the results in column (1), what is the expected increase in price of building a 506-square-foot addition to a house, holding everything else in the model constant, is