Different AVMs are designed to deliver different types of valuations. And therein lies confusion.
Consumers don’t realize that there’s an AVM for nearly any purpose, which explains why different algorithms serve up different results, said Ann Regan, an executive product manager with real estate analytic firm CoreLogic. “The scores presented to consumers are not the same version that is being used by lenders to make decisions,” she said. “The consumer-facing AVMs are designed for consumer marketing purposes.”
For instance, more accurate models used by lenders do not include outliers — properties that sold for extremely high or low prices and that consequently would skew the averages and the comparable sales for a particular house, like yours. But models used by consumer websites, such as brokers’ sites and national listing sites, scoop in as much “sold” data as possible when concocting a valuation, because then they can claim to include all available data. That’s true, said Regan, but it’s more accurate to weed out misleading data.
AVMs used by lenders send along “confidence scores” that indicate how firm the estimate is. That is a factor typically not included alongside consumer AVMs, she added.
This is an interesting trade-off. The assumption is the consumer wants to see that all the data is accounted for, which makes it seem that the estimate is more worthwhile. More data = more accuracy. On the other hand, those that work with data know that measures of central tendency and variability can be thrown off by unusual cases, often known as outliers. If the value of a home is too high or too low, and there are many reasons why this could be the case, the rest of the data can be thrown off. If there are significant outliers, more data does not equal more accuracy.
Since this knowledge is out there (at least printed in a major newspaper), does this mean consumers will be informed of these algorithm features when they look at websites like Zillow? I imagine it could be tricky to easily explain how removing some of the housing comparison data is actually a good thing but if the long-term goal is better numeracy for the public, this could be a good addition to such websites.