Zillow realized the only time their ask was hit is when it was at a premium to the actual market price. If they used competitive offers, they’d never have the winning bid. In a hot market where you’re offering a premium, you’re going to have owners of lower quality properties accepting your offer, while owners of higher quality properties have more offers to select from.
Zillow got left holding a bag of lemons and decided to get out before buying the whole lemon grove.
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 $900k while the other could sell for $1.1. Zillow made the right average offer of $1million to both but the buyers/sellers actually had more information. So the 1.1m seller didn't take Zillow's offer while the 900k seller did. Now Zillow was out $100k essentially not counting fees.
They fronted you $1M with the expectation they would make $100k. Now they are losing $100k. So their own projections are screwed by $200k.
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 selectivity is what led to the bad basket of houses they now own.
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 too little and the seller will go elsewhere.
Even if they get their estimates right 99% of the time, the 1% of cases where they get it wrong will slowly drain money out of the scheme.
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.
Flip this around, are you saying that if the model was correct they wouldn't have made money? The problem was the model saying something was a good buy when it wasn't. The model was bad. Sellers do have good information, at least better than Zillow.
Generally, this is a misconception about how things like quant investing actually work (this was an attempt to apply quant investing to housing). Some people, usually people without actual market knowledge, view quant systems as providing greater information. In reality, most quant systems are just responding to changes in liquidity. The amount of actual fundamental information these systems provide is very minimal, and will always be beaten by a knowledgeable human. The reason why is simple: there is a huge amount of private, non-quantifiable information with these domains (and this is true in investing and property, doing this in resi housing is nonsensical).
I have seen fundamental quant investing work but only when you combine quantitative work with a knowledgeable human. I have seen the same thing in sports betting syndicates too (it does vary though, in some games quantitative data does capture more of the relevant information and machines can beat humans in those instances...but if there is substantial private, non-quantifiable information then it stops working).
This is hard for people to accept because lots of people spend lots of time and effort at university being taught that ML is effective. But ML is only as good as the information you put in. The demise of value factor investing is a perfect example: collect a ton of PHd quants and finance professors, they start doing fundamental investing but without doing any research themselves, and it has done nothing but haemorrhage cash. It takes an extraordinary amount of education to supress common sense here.
You have to understand the domain. You have to understand the information you are putting in. Zillow did neither, they thought ML would save them.
Many people leap to their own reasons why Zillow offers failed but the most proximate cause really does seem to be management and operational failure.
Planning to lose money takes nerve. Zillow tried to avoid avoid the pain, and ended up abandoning what might be a profitable enterprise (for someone else) in the future.
Risk aversion and launching a new business strategy do not work well together.
That is a common pattern, but when you see a company launch a new venture and the primary goal is to not lose money, often, the desire not to lose money leads to decisions that prevent actually making money.
Around 2008, some investment banks famously had a single division manage to lose significantly more money than the entire rest of the company made over the same time period. Zillow not wanting to replicate their mistake isn't necessarily a bad decision.
CEO said cut! Way to go!
This loss was not immaterial but it also wasnt too material as they werent even leveraged on the homes. They had orders of magnitude more capital to risk if they really chose to dive into this or take it at least to real estate 2008 levels. Far from it.
That's a fair point; the essay doesn't do much to distinguish whether they didn't know they needed to take losses, or couldn't take the pain of the losses.
Nevertheless, it's a pretty good analysis of what a company needs to do, in order to build a model relevant to their own actual business. They need to both know about the pain involved, and be prepared to take it. (And even then it might not work!) Third-party data (and suffering) might not be a good substitute.