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But they lost money last quarter while the market was still rising. Seems there was some problem with their prediction process.
by dsizzle 5y ago
But they lost money last quarter while the market was still rising. Seems there was some problem with their prediction process.
- sudosysgen 5y agoTheir plan wasn't to flip homes in a month or two as far as I'm aware so that's expected if they get out.
- rossdavidh 5y agoRumor is that they had to put their thumb on the scales (i.e. tweak the model) to get enough sellers to sell to them. In other words, if paying what their model actually thought was the right price, not many people sold to them. Instead of saying "our division's whole business model won't work, you should fire us", they tried to cut the margin too close, resulting in losses which got the CEO's attention to the problem. This kind of thing is difficult to confirm from the outside, of course. But that they adjusted the model to pay more towards the end is pretty widely known.
- dsizzle 5y agoDo you have a reference for this? Is part of the rumor that not everyone in the organization knew this was happening?
- rossdavidh 5y agoNo, the rumor was just that they put their thumb on the scale. https://ryxcommar.com/2021/11/06/zillow-prophet-time-series-and-prices/ https://ryxcommar.com/2021/11/06/zillow-prophet-time-series-... "Speaking of middle managers, word on the street is that Zillow Offers put their thumb on the scale of the algorithm to make it engage in more aggressive trades. Manually adjusting an algorithm isn’t necessarily a bad thing, but you need to do it for the right reasons. And clearly that didn’t end up working out..." That not everyone in the organization knew that, is just me speculating.
- dsizzle 5y agoAh, I was trying to find that article earlier today. (I searched "Sean Spicer data science Twitter" to no avail, ha.) Actually that piece also hints at an uneven understanding of these issues across the organization: > In one tweet, I semi-joked that what happened was lower and mid level employees convinced upper management that the algorithm was 99% accurate by hiding the caveats of what “99% accuracy” means.