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Zillow lost money because they weren't willing to lose money

stevenbuccini.com

151–160 of 386 posts

Re: Zillow lost money because they weren't willing to lose money

#151

Earlier quoted context omitted.

> I think the housing market is so fucked no one really grasps the scale of the problem. I don't think I agree with this assessment. I live in a very rural area two hours northwest of Austin, literally in the middle of nowhere. I've studied the local economy and understand how things work here. I think the characteristics you've identified in the rural housing supply are not unusual and also not as serious in a pract…

How are the schools funded?

Both local property taxes and property taxes from urban areas that are redistributed to rural communities by the state of Texas.

San Saba ISD is probably the best funded entity in the whole county. Every student has a laptop and home internet. The graduation rate is 100%. It's a small school; the senior class is only 50 students.

They built the new school in the middle of town, thus highlighting its position of import within the community.

Re: Zillow lost money because they weren't willing to lose money

#152

Earlier quoted context omitted.

Look up their “project ketchup”. Their managers overrode the models and cut both fees and reno cost to win more deals. The WSJ and Business Insider wrote about this. I was at Zillow for many years and the insiders I know tell me the articles are correct but just lacking some nuance. Many people leap to their own reasons why Zillow offers failed but the most proximate cause really does seem to be management and operat…

Saying that management bought at prices higher than model is not the same thing as saying they bought houses expecting to lose money. All that was said was that management increased the prices they would pay and changed the model so they could pay more. Nothing validates the model (again, this is a common-sense conclusion given the informational disparity that Zillow was at).

Right, they didn’t expect to lose money. They saw they were only closing 10% of deals and wanted to take a higher share from opendoor. They probably thought the market was going up fast and their models were too slow.

Re: Zillow lost money because they weren't willing to lose money

#153
post #28

> They thought they needed to build a machine learning model when they really needed to build an entirely new organization, one that possessed the technical and cultural mindset necessary to succeed in this space. I totally agree. It's not impossible to imagine their model working: why couldn't you serve as a market-maker for homes at a large scale, especially with the unique insights Zillow could have based on their…

Yes!! You nailed it! I spent a long career in software and now work in real estate, and you are spot on that they are not a company who understands real estate well enough to be buying and selling it. There are plenty of bad real estate agents in the world, but the amount of know-how and connections that good ones have is exactly the encapsulated in the examples you gave - local, specific knowledge that a national/international player isn’t going to have and isn’t going to be able to scale without a whole lot of human investment… gee wiz, kinda like real estate firms.

I wish I had the data that I assume they have internally, because watching their actions I’m not convinced they understand what questions would actually be interesting to explore with ml.

Re: Zillow lost money because they weren't willing to lose money

#154

Earlier quoted context omitted.

Not exactly. From what I read it’s almost completely benign in most humans.

Certainly it isn't a issue after they cats no longer live there and it's been cleaned to a reasonable standard. If it knocks 20 grand off the price of the house it's worth spending 2k to have everything deep cleaned.

Cat pee permanently stains flooring and is also extremely hard to get the smell out. 2k will not be nearly enough if there’s extensive cat damage. Wood floors turn black with it and must be replaced.

Re: Zillow lost money because they weren't willing to lose money

#155
post #54

Good riddance. If large-scale house flipping took off, we might actually end up in a scenario where housing was treated as a speculative asset, with empty houses getting flipped between investors looking to make a quick buck, further lowering the supply of actual places to live (because housing units remain empty while being flipped), driving up the cost for families who just want a place to live. Oh wait...

Half way through I was already clicking "Reply" thinking "...is this guy for real?!", only to see the "Oh wait..." The amount of social media content revolving around "how I became a milionaire/how I reached my first million" and the common factor is "I bought a house in 201*", then I'd say something is a bit off... Either there's massive speculation, or 1 million isn't what it used to be, or worst: both.

Or $1M has different purchasing power in different places.

Re: Zillow lost money because they weren't willing to lose money

#156
post #28

> They thought they needed to build a machine learning model when they really needed to build an entirely new organization, one that possessed the technical and cultural mindset necessary to succeed in this space. I totally agree. It's not impossible to imagine their model working: why couldn't you serve as a market-maker for homes at a large scale, especially with the unique insights Zillow could have based on their…

It's a pretty interesting discussion as I sold my house just this year, and was frequently watching Zillow trends and information.

Originally the estimate on Zillow said my house was 20% over the value I actually sold my house for just last month. I listed with a traditional realtor for a 5% commission, because when I looked up the service and other fees for Zillow sales, I found they included around 20% of cost for buying homes and closing within generally 10 days.

As I listed my house, and as I reduced price on it for it to gain attention, I noticed the zillow estimate also went down to always stay below my listed price. I believe the estimate that both Zillow and Redfin display prominently were purely based on what my list price was changed to last, not on any meaningful algorithm, which can be very harmful to sellers and buyers, because it makes the process a bit deceptive by nature. Luckily Zillow also displays the price history on homes, which apparently cannot be "gamed" as much as the "zestimate" can be. Another thing I noticed was that the view stats on my listing that zillow regularly provided changed, even after days passed, that was very concerning because stats of that kind aren't supposed to change... They indicate real interest in a property, that guide decisions for sellers to reduce price, and they also indicate what is truly a "hot home".

No matter what, there is always the "human factor" that can corrupt or even destroy any company, where realtors can game the process to maximize their own sales profit or positions, or where appraisers can inflate an estimate as a favor for a personal friend, even despite laws against doing so. In a bad economy, the lengths people will go to to suit their advantage are wild. This type of issue can never be properly addressed by any algorithm, and that's why trusting technology too much can so easily lead to failure in any setting.

Ultimately I am glad I did not sell to Zillow, because of all of the potential for hidden costs and because they manipulate the process even when you don't use their service, but I am not feeling sorry for them as a company... I felt the impact of their presence in the market whether I involved them or not, and that's a big problem when it comes to preserving the value of traditional investment and stable investment in a house that should be properly addressed by regulation.

Re: Zillow lost money because they weren't willing to lose money

#158
post #28

> They thought they needed to build a machine learning model when they really needed to build an entirely new organization, one that possessed the technical and cultural mindset necessary to succeed in this space. I totally agree. It's not impossible to imagine their model working: why couldn't you serve as a market-maker for homes at a large scale, especially with the unique insights Zillow could have based on their…

I don’t think it’s just that they had a poor model, but the combination of that and adverse selection.

If you pledge to purchase at the Zestimate then people who reasonably think they can get more than the Zestimate on the open market don’t have an incentive to sell their house to Zillow (besides convenience). But people who think the Zestimate is an over estimate will of course sell to Zillow. So instead of a normal distribution of actual value:estimated value you end up with a skew towards the end where the estimate is over the actual value.

Trading housing is very different from normal market making because houses are not fungible commodities like most securities are. For most entities trading securities at low frequency it does not really matter whether a market maker skims off a few pennies on their trade; it’s worth it for the liquidity. Houses are less liquid (because they are non fungible) so the liquidity is more valuable, but the price improvement routing around a MM can also be many percentage points of a trade because there are not only so many factors affecting their valuation, but also just chance and random noise (bidding war, a particular buyer falling in love with the property, not-price-conscious buyers).

Re: Zillow lost money because they weren't willing to lose money

#159
post #28

> They thought they needed to build a machine learning model when they really needed to build an entirely new organization, one that possessed the technical and cultural mindset necessary to succeed in this space. I totally agree. It's not impossible to imagine their model working: why couldn't you serve as a market-maker for homes at a large scale, especially with the unique insights Zillow could have based on their…

> a machine learning model Not to mention that generally ML models are not useful for assessing risk . ML nearly always focuses almost exclusively on some point estimate rather than a distribution of what you believe about a value. The former case is all about expectation and the latter about variance . Correctly modeling variance is far more essential to risk modeling than expectation alone. I recall talking to a st…

> Not to mention that generally ML models are not useful for assessing risk. ML nearly always focuses almost exclusively on some point estimate rather than a distribution of what you believe about a value.

It is actually quite a common practice to design neural networks that output probability distributions.

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