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.
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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
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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
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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
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Great, simple example. Kudos
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Still not getting it - is there a simpler way to think about this? E.g. both the market and a trader could get a stock valuation wrong (trader more wrong), but the trader can choose when to participate. If the trader sticks to overcorrections -> makes money?
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If Zillow had a better valuation method than the agent, the agent would only sell to Zillow when Zillow’s valuation exceeds their own estimate by a significant amount since they have to discount their own valuation by the higher variance + they have the option to wait
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Pretty sure this is a parallel to carvana and Carmax models, except they can seek out and acquire in demand inventory at auctions, in addition to what owners bring them.
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This is the crucial point - just what is the reason why wouldn't *any price* above the agent's valuation seal the deal?
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I think because the agent has the option to wait, and in an uncertain market they expect someone to make an offer above their valuation. Cf. winner’s curse in auction theory.
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