What are some examples of questions that can be addressed using regression (hedonic) models in the context of housing prices?

Regression models, particularly hedonic models, are utilized to quantify the relationship between various features of a product or service and its price. In the context of housing prices, these models can help identify and measure the impact of various attributes on the overall value of homes.

The correct choice focuses on how the size of a lot influences housing prices. This is a quintessential application of regression analysis, where one can estimate the change in housing prices based on an increase or decrease in lot size. By including lot size as a variable in a regression model, analysts can determine its specific contribution to the price determination process, helping buyers, sellers, and developers understand market dynamics better.

In contrast, the other options do not directly relate to the quantification of price. While understanding common materials or the typical number of bedrooms in a house provides contextual information about housing, these insights do not lend themselves to the modeling necessary for regression analysis. Similarly, while the proximity of houses to high-voltage power lines may be a factor in determining desirability or value perception, it requires a more nuanced approach than what is captured through straightforward regression models focused on quantifiable impacts on price.