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 Size In(Size) (1) (2) (3) (4) 0.00048 (0.000041) 0.72 0.73 0.57 0.749 (0.057) (0.092) (2.05) (0.055) In(Size)2 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 Pool x 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), consider the expected increase in price from building a 465-square-foot addition to a house, holding everything else in the model constant. The upper bound for the 95% confidence interval of the expected increase from this addition is % Hint: Round your answer to three decimal places