Location, Location, Location An elegant solution to real estate pricing
Logically, square footage of a home and a home's price should be linearly correlated.
Also, logically, certain features should increase a home's price.
For example, we expect waterfront property to be more expensive per square foot than
an identical inland home.
Also, logically, we can expect certain features to have less of an impact on price at a certain
point.
For example, the incremental value of an additional bathroom is relative to other factors, such
as the size of a home.
Or state concretely, a 3rd bathroom plays a larger role in home value than a 6th bathroom.
The traditional approach to assessing a home's price is linear regression.
The issue is linear regression does not adapt well to the waterfront and bathroom scenario.
Alternatively, a tree-based machine learning method is well suited to handle the waterfront
property and bathroom scenario, but does not handle the linear correlation between price
and house size.
In this module we introduce a novel method that gets the best of both worlds.