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Why if it is flat (flat=1), the increase in price is not   and is %

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The real estate agency supports the view that price is size-inelastic. In order to assess this hypothesis a second model is estimated where the variables PRICE, SQRFT and LBDRMS are replaced by their logarithms (variables LPRICE, LSQRFT and LBDRMS, respectively). The results are reported in Table 4.21 Table 4.2 coefficient -0.677 std. error t-ratio p-value 0.3350 0.698 -0.970 const LSQRFT IS LE ELAT 0.815 0.099 8.254 0.0000 0.029 0 0661 0.054 1.862 Adjusted R-squared P-value(F) 0.567 0.552 R-squared F(3, 84) 0.000 36.72 4.3. State the estimat 4.4. Which fat deqa elated with PRICE at a -10 % ? Give an interpretation of the results of Table 4.2 concerning the significant variable(s). 4.5. Is the assumption that price is size-inelastic corect at a 10 %? State your comments analytically. (variable FLAT takes the value 1 if it is a flat, and 0 otherwise). 4.3. State the estimated equation of Table 4.2 LPRICE = -0.677 0.815 LSQRFT +0.028 BDRMS 0.054 FLAT 4.4. Which factors are significantly related with PRICE at a =10%? Give an interpretation of the results concerning the significant variable(s) The significant factors at a-10% are the ones with corresponding p-values smaller than 0.1. Therefore, the significant factors are LSQRFT (p-value 0.00) and FLAT (p-value = 0.0661). According to the results in Table 4.2, an increase of SQRFT by 1% is expected to correspond to an increase in PRICE by 0.815% ceteris paribus. Because FLAT s a dummy variable, the results in table 4.2 imply that a flat has on average 100(e0.054 1)% = 5.55% igher price than a house of the same size and same number of bedrooms, which is not a flat. exp 0.054 1) * 100 erp(0.054

Answer 1

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