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I agree, I think they realized it wouldn’t work and made a hard decision to save the company. Zillow realized the only time their ask was hit is when it was at

I agree, I think they realized it wouldn’t work and made a hard decision to save the company. 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.

marcinzm 5y ago
>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 $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.
- rpvnwnkl 5y ago
  They are out 200k. They bought for 100 too much and will have to sell for a 100 less than planned.
  - sokoloff 5y ago
    No, they bought for $1000K and sold for $900K. You can’t count the spread twice.
    - cwilkes 5y ago
      The spread kind of can be counted twice: if you tell management “we’re going to make $100k (10% return) in profit this year” and you end up paying $1000k and selling for $900k instead of $1100k like you planned … management is going to be less than pleased. They fronted you $1M with the expectation they would make $100k. Now they are losing $100k. So their own projections are screwed by $200k.
  - marcinzm 5y ago
    Not sure I follow. They buy for 1m so they're out 1m. Market value is irrelevant when bought. They sell for 900k, optimally, so they then get back 900k. In total they're out 100k (900k minus 1m). Not counting fees, market movement and assuming they sell optimally.
    - rpvnwnkl 5y ago
      lol yes I’m not sure what I was thinking. Thank you.
encoderer 5y ago
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 selectivity is what led to the bad basket of houses they now own.
- phire 5y ago
  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 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.
  - encoderer 5y ago
    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.
    - hogFeast 5y ago
      They didn't eliminate their fees (fee is the wrong term to use). Their model was built, maybe this changed, on being within 200bps of breakeven. Obviously, they only bought when the model would say: this will make money. Or are you saying they looked at the model, the model says you will lose money, and they decided to do it...that makes no sense, even for SV. 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.
Blackstone4 5y ago
It is called the winner's curse...at an auction, the highest bidder wins the asset but to do so they pay the highest price so better hope you are right when you win
- opinion-is-bad 5y ago
  The winners curse also creates the curious corollary that one should probably bid less, the more people are in the auction.
kwertyoowiyop 5y ago
Trying to beat an auction with a single offer (and still make a profit) sounds like a very difficult task, whether it’s done by a human or AI.

marcinzm 5y ago

>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 $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.

rpvnwnkl 5y ago
They are out 200k. They bought for 100 too much and will have to sell for a 100 less than planned.
- sokoloff 5y ago
  No, they bought for $1000K and sold for $900K. You can’t count the spread twice.
  - cwilkes 5y ago
    The spread kind of can be counted twice: if you tell management “we’re going to make $100k (10% return) in profit this year” and you end up paying $1000k and selling for $900k instead of $1100k like you planned … management is going to be less than pleased. They fronted you $1M with the expectation they would make $100k. Now they are losing $100k. So their own projections are screwed by $200k.
- marcinzm 5y ago
  Not sure I follow. They buy for 1m so they're out 1m. Market value is irrelevant when bought. They sell for 900k, optimally, so they then get back 900k. In total they're out 100k (900k minus 1m). Not counting fees, market movement and assuming they sell optimally.
  - rpvnwnkl 5y ago
    lol yes I’m not sure what I was thinking. Thank you.

encoderer 5y ago

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 selectivity is what led to the bad basket of houses they now own.

phire 5y ago
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 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.
- encoderer 5y ago
  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.
  - hogFeast 5y ago
    They didn't eliminate their fees (fee is the wrong term to use). Their model was built, maybe this changed, on being within 200bps of breakeven. Obviously, they only bought when the model would say: this will make money. Or are you saying they looked at the model, the model says you will lose money, and they decided to do it...that makes no sense, even for SV. 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.

Blackstone4 5y ago

It is called the winner's curse...at an auction, the highest bidder wins the asset but to do so they pay the highest price so better hope you are right when you win

opinion-is-bad 5y ago
The winners curse also creates the curious corollary that one should probably bid less, the more people are in the auction.

kwertyoowiyop 5y ago

Trying to beat an auction with a single offer (and still make a profit) sounds like a very difficult task, whether it’s done by a human or AI.