I have a dataset of home listings, in R. Here is a sample:
City Price Term Size Bedrooms Bathrooms Description Appleton 3400 12 1150 1 2 Good location Appleton 3780 12 1600 7 2 Nice area; Barstow 1400 6 900 3 2 Shady st Raleigh 3700 12 1400 1 1 Quiet st, *veranda* Ames 2200 12 1300 3 2 Good location Ames 3400 12 1150 1 2 Good location, *veranda*
What I would like to do is ascertain which cities have the highest premiums and discounts for verandas. Certainly one way to do this would be to build two datasets: get cities WITHOUT verandas (i.e regular listings), and get cities WITH verandas. I could then get the mean rent per city using aggregate, and calculate the difference in mean between the two datasets.
But, I think there is a better way. The above approach does not take into consideration confounding factors like Size, Bedrooms, Bathrooms, etc. How might I take a multiple regression approach which would look at the impact of "veranda-ness" while controlling for other factors? I still want to answer the question of which cities have highest premiums/discounts, only using a more quantitative, robust approach.
I'm familiar with R's lm function and how to run a regression, but not sure how to extrapolate that to answer the question.
Thanks in advance!