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Cute fact about the Zillow thing is that even if Zillow's model was more accurate than local agent valuations, local agents/property owners can still win on average because they get to choose which houses to buy/sell.
Easy to see this with a simple, dumb model. Generate a data set of 100k houses with "true" values uniformly at random between $250k and $2.5m, and generate Zillow valuations (true value + 5% random noise) and agent valuations (true value + 10% random noise)pic.twitter.com/slVks94pWN
Say the agent sells to Zillow if the price offered (Z's valuation) is more than 20% above what the agent thinks the house is worth, i.e. the deals that happen are the ones in yellow below.pic.twitter.com/uV5IB0egZM
Let's be optimistic and assume that Zillow manages to sell the houses for their true value (haha). They buy 6,000 houses at an average loss of $50k/house before fees and a total loss of > $300m, all due to adverse selection. This is what their distribution of P&L looks like.pic.twitter.com/5LFvYNNuxW
So they basically need to completely vertically integrate by buildings their houses or sharing data with developers somehow
Very nice explanation, thanks! The curse of any market maker is adverse selection, even if you have a better model! Same result would happen to Citadel if they always offered to trade at mid.
The lesson from this explanation would be that there’s a lot of work in between getting a signal and building a trading strategy. They are not one of the same. Separate steps. Both very important.
Machine learning goes to an illiquid market. You love to see it.pic.twitter.com/CH4v3leEck
Will Zillow start doing PFOF to only see houses from the retail owners that haven't listed or heavily reached yet.
One big adverse selection experiment
Just like any trade if you’re getting insta filled it’s most likely not good. In Zillow’s case they kept putting it on over and over.
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