Regression estimated with OLS from 866 transactions of oceanfront houses

  1. Here is a fitted simple regression estimated with OLS from 866 transactions of oceanfront houses along the Oregon coast: β„Žπ‘œπ‘’π‘ π‘’π‘π‘Ÿπ‘–π‘π‘’ Μ‚ = 632577 βˆ’ 130555π‘“π‘™π‘œπ‘œπ‘‘π‘Ÿπ‘–π‘ π‘˜ Where houseprice is the sales price ($) of the house, and floodrisk is a binary variable equal to one if the home has been classified as at risk of flooding, and equal to zero otherwise.

a. Interpret the estimated coefficient on floodrisk.

b. The R2 measure for this model is 0.0094. Interpret. An alternative specification of the same model uses the logged price of property as the dependent variable: log (β„Žπ‘œπ‘šπ‘’π‘π‘Ÿπ‘–π‘π‘’ Μ‚ ) = 13.14 βˆ’ 0.273π‘“π‘™π‘œπ‘œπ‘‘π‘Ÿπ‘–π‘ π‘˜

c. Interpret the estimated coefficient on floodrisk using this model with the logged dependent variable.

d. Explain why the log transformation is used in econometric models.

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