Question

This is most likely the result of a multicollinearity problem with the model. This is likely because bedrooms, bathrooms, and living area are all roughly measuring the same thing: The size of the property.

The coefficient for BEDROOMS is negative, which seems counterintuitive. Choose the BEST explanation for this result.

Answer 1

One possible explanation for the negative coefficient for BEDROOMS could be that in the dataset, properties with fewer bedrooms tend to have larger living areas or more bathrooms, which are both positively associated with higher prices. Therefore, the negative coefficient for BEDROOMS may be capturing the indirect effect of these other predictors on the target variable, rather than a direct effect of the number of bedrooms itself. Additionally, there may be some omitted variables that are correlated with BEDROOMS, such as location or age of the property, that are driving this result. It would be worth exploring these possibilities further to get a better understanding of the relationship between the predictor variables and the target variable in the model.