This is really well written. Thanks for sharing.
Zillow lost money because they weren't willing to lose money
61–70 of 386 posts
Re: Zillow lost money because they weren't willing to lose money
#62Earlier quoted context omitted.
People forget that tech is able to automate workflows. You don’t often yield success when you attempt to automate and invent the workflows in parallel.
I would say the opposite is true. Dying companies are stuck in their own routines because they're trying to automate their poorly designed processes that require humans at multiple steps. Smart companies are designing newer, better processes that are enabled by tech. Starting from scratch can be a huge advantage.
This is a statement I would have agreed with wholeheartedly 20 years ago, and that I disagree with wholeheartedly now.
Re: Zillow lost money because they weren't willing to lose money
#63> 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…
> 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 datasets. Why would Zillow have unique insights? With the exception of Texas, I thought real estate sales information is public information in the US.
I have given Zillow a lot of non-public information over years of searching.
How many people search on bedrooms but not bathrooms? When people search on both, what’s the pattern they use? If we highlight prices and BRs on the map does that give more clicks than just prices? How important are photos (times 50 different questions there)? How strong a signal is repeat views spaced over time? Saving a house to favorites? Sending a link to a friend? Clicking on comps in the neighborhood? Which comps do people zero in on (as evidenced by spending more time on the page)? How strong a signal is sending a message to the real estate agent on the listing? What areas of the country are seeing an uptick in search traffic? How long between claiming a house as an owner on the site, updating the information, and listing it for sale?
They are sitting on a (well-earned) treasure trove of data and it’s not unreasonable to think they could use that to be better informed than another buyer without that information.
Where they seem to have failed is in not augmenting that advantageous data with regular old boots-on-the-ground observations.
Re: Zillow lost money because they weren't willing to lose money
#64I really liked this quote, which is also true of machine learning organizations at large tech companies: The most valuable data is not social data, ... but your own data because every dataset that you’re looking at internally describes your own process, including your bugs, ... building models from your own data is the only way to build a really successful system. This is one thing that a lot of outsiders do not unde…
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Re: Zillow lost money because they weren't willing to lose money
#65> 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…
> 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 datasets. Why would Zillow have unique insights? With the exception of Texas, I thought real estate sales information is public information in the US.
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Re: Zillow lost money because they weren't willing to lose money
#66Earlier quoted context omitted.
I would say the opposite is true. Dying companies are stuck in their own routines because they're trying to automate their poorly designed processes that require humans at multiple steps. Smart companies are designing newer, better processes that are enabled by tech. Starting from scratch can be a huge advantage.
This is a statement I would have agreed with wholeheartedly 20 years ago, and that I disagree with wholeheartedly now.
I'd be curious to learn why. I've seen the pain of companies tricked into thinking robotic process automation to do their horrendous excel workflows is a good idea. I've seen the benefit of a decent python data engineer with a small AWS budget.
The techier folks definitely have a different set of problems but the speed at which hings get done is night and day. Companies with old school work patterns (which, in my personal experience, means dusty old banks) are terminally entrenched in their ways.
Re: Zillow lost money because they weren't willing to lose money
#67Earlier quoted context omitted.
>If they used competitive offers, they’d never have the winning bid. Why do you assume that, seems like a cash buyout would be a great deal for many sellers if it was at the appropriate price. Issue is I think that Zillow's information was less granular than what the buyers/sellers had. Let's say Zillow priced two houses near each other at 1million each. However one was close to a busy road so would only sell for $90…
They are out 200k. They bought for 100 too much and will have to sell for a 100 less than planned.
No, they bought for $1000K and sold for $900K. You can’t count the spread twice.
Re: Zillow lost money because they weren't willing to lose money
#68Good 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...
My wife did some work for the Census last year. Our extremely rural neighborhood has lots of unused housing, some for a decade+. That work got her out to see some of the places not visible from the roads, and increased our awareness of the scale of the problem.
At a guess, in our county, 20%+ of the housing is idle, owned by out-of-state companies, some of whom pay property taxes and some dont. The county isn't auctioning off because of tax default anymore, no one was buying these places at $100. Many of these places are complete teardowns now; some actually no longer exist, having burned or apparently been scrapped. The tax assessments on those have not been adjusted, for the few i checked.
I think the housing market is so fucked no one really grasps the scale of the problem.
Re: Zillow lost money because they weren't willing to lose money
#69I really liked this quote, which is also true of machine learning organizations at large tech companies: The most valuable data is not social data, ... but your own data because every dataset that you’re looking at internally describes your own process, including your bugs, ... building models from your own data is the only way to build a really successful system. This is one thing that a lot of outsiders do not unde…
It makes sense when you ask the question another way: "What is the likelihood that a preexisting assemblage of data contains all the nuances for my specific process?"
Some domains are intricately mapped in available data (e.g. equity pricing), but most, and especially most physical, are not (e.g. freight transportation).
Re: Zillow lost money because they weren't willing to lose money
#70Earlier quoted context omitted.
I’m not saying you’re wrong, but this is an over simplification. Sellers are not guaranteed a “market price” so there is room to trade a small margin for guarantees and hassle free home selling. The problem seems more that they were not getting “enough” houses doing it this way, especially competing against Opendoor, and so they had to bid higher and on more properties in order to hit “scale”. And that lack of select…
No, that's not the issue. The issue is that their machine learning model can't possibly be 100% accurate, there will be some amount of error that is shaped in a normal curve. If their model overestimates the market value, they end up massively overshooting their goal price of "slightly less than market value", the seller accepts and they lose money. If their model underestimates the market value, they will offer way…
Sellers don’t have perfect information about the value of their home. They get a market value estimate from a realtor but that is just an estimate.
Of course iBuyers can’t perfectly forecast the market but that is why they add 3-7% fees, a very large buffer on a house purchase.
Again, this is where Zillow ran into problems: they reduced or eliminated that fee to win more deals versus opendoor.