Finding bias in an example

In the following example, there is a significant business impact to finding bias in data.

The housing data company Zillow recently backed out of the iBuying business. Zillow is a US-based company that lists housing information for average consumers to look at. iBuying is the term used for instant buying and involves Zillow buying properties directly and then selling them for a profit (in theory). Zillow found that their estimations (or zestimates) were off by a large factor, which led to the company pulling out of that area. Maybe we can find out why.

In this scenario, we will try to find where bias could have entered the system in something such as a zestimate. To give you a framework to work through, we'll walk through steps and each type of bias discussed earlier to think through it. This is important, as you might not instantly jump to a certain type of bias unless you see it. This is an issue, as everyone has a bias toward looking for something...