> 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 datasets.
However I think where the hubris lay is in how they thought they could leapfrog all the way to an automated solution before building a competency as a house-flipping company.
From what I understand, where they failed was partly in building a rich enough model to properly account for the less easily quantifiable elements which ultimately account for a property's value. I.e. the price per square foot might make a property look like a steal, while something like a sewer main nearby, or problematic neighbor could radically change the value proposition to anyone standing at the site. That's a non-trivial problem to solve for even the best ML and it's not clear how you would automate this.
If you ask me, instead of focusing on building an automated price discovery system, they should have started by trying to build a quality home-flipping organization, and figuring out how to super-charge manual work using their datasets. Over time you might find ways to optimize the process and increase the level of automation to scale output relative to head-count.
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).
According to Matt Levine's recent column, while you might think that, it wasn't what sunk them in practice. Bidding low in fact worked; it just was inherently limited in scale, which is why they switched to bidding higher. Unfortunately, being wrong in the other direction is very bad.
"I know, I know, the traders are saying: “No, this is stupid, your algorithms will not be 100% precise, some of your ‘lowball’ bids will in fact be too high, and those will be the ones that sellers accept. You’ll get adverse selection and end up losing money.” But that was not Zillow’s actual experience in the first quarter! The actual experience is presumably that some people accidentally got too-high bids, realized they were good and accepted them, but mostly Zillow sent too-low bids to everyone, and some people, for whatever irrational reason — market ignorance or financial necessity or laziness or whatever — accepted the too-low bids. The general point is that there is no reason at all to think that the people on the other side of these trades from Zillow are generally better informed than Zillow is. Sure they know more about their houses than Zillow does, but Zillow knows more about the market, and has more money"
"If you systematically bid too low, you will not do many trades, but you will make a lot of money on each trade. If you systematically bid too high, you will lose money on each trade, and also you will do a whole ton of trades. This is much worse!"
I think a useful analogy here is trade-ins. Everybody knows that a private sale gets you a better price for a car than selling to a dealer. But a lot of people don't want the headaches that come with one. Why wouldn't it be the same for housing, especially when people may have life circumstances that mean they need to sell ASAP?
Because of shear dollar amount. Let's say on trade, you can sell your car to a dealership for $8000. Privately, maybe you can get $10000 or 10,500. Now, of course, $2,500 is not nothing but for some people, that is a trade they're willing to make to simply get the car off of their hands rather than go thru with a full-scale private sale process.
On the other hand, for a house that you could sell "instantly" for, say, $450,000, but that you could potentially get by selling privately for $480,000 or $500,000, that is now leaving 10 times more dollars on the table. Proportionally, the difference between the car and the house might be similar but in absolute dollars, it's a huge difference.
1. Your numbers are somewhat arbitrarily chosen... a car could easily be worth $40k and in some markets a home could be worth $200k.
2. Precisely because the house is worth so much, most people cannot afford to pay two mortgages, or their mortgage and rent on a similar home, at the same time, so if they must move by a certain date, they're under pressure to sell.
3. If you decide to sell to Opendoor you can pretty sure that the sale is going to go through; no worries about financing, for instance.
4. It's true that the numbers are larger in absolute terms, but people are often not rational in this way. Plenty of sellers leave tens of thousands on the table for somewhat frivolous reasons.
5. There is a lot of fat as far as people skimming off the top of transactions -- brokers, title companies, etc. IBuyers can get much better rates on these services by being bulk buyers, meaning they don't actually have to come that far off of the next-best offer to make a profit.
I think the question of fungibility comes into play here, too. If I’m a HFT and I accidentally post a too-high bid for Anacott Steel then there are well-capitalized players in a position to sell me a whole lot of Anacott until I lower the bid. (They may even be other HFTs who can naked short it to me.) But if I’m an iBuyer and post a too-high bid for 742 Evergreen Terrace, only the Simpson family can hit that bid, and only the one time. If I’m systematically overbidding, then that’s bad, but not every counterparty is informed enough to take advantage (or willing to stomach the considerable transaction costs), and there’s not a well-capitalized player to step in and arbitrage away the difference.
I don't think Levine successfully disputes the importance of adverse selection here. Sure, if you make a million low-ball offers, a handful will be accepted for God knows what reason. That doesn't mean that adverse selection isn't a serious danger when you attempt to scale up, as Zillow did.
I mean, why did Zillow go straight from making profitable purchases of few houses to losing money on tons? If there's some sweet spot in the middle that would have allowed them to scale up profitably, would it really be that hard to land on that spot?
I think it's obvious that homeowners know much more about the houses and neighborhoods they live in than Zillow, and this creates a big risk for Zillow.
I think he was saying that while no doubt it occurs on a case by case basis, it appears to be relatively insignificant either way.
When they were paying less than market price, they still made a lot per deal, and when they were paying more than market price, adverse selection hardly pushed them over the edge.
If you make "a million high ball offers" then the fact that some of them are particularly bad deals isn't the core problem.
I'm not 100% confident of this but it seems like the picture that is being painted.
And also, that it is particularly hard to hit the spot in between - I don't know if he's correct about that.
I won't say he's definitely wrong but it seems odd to argue that Zillow has so much market knowledge and money as to render adverse selection irrelevant, and yet they just couldn't resist making tons of unprofitable offers out of sheer impatience to scale up. What happened to all that market knowledge? There's got to be more to it than this.
This is where we get into internal incentives and theory of a firm.
My first project at a FANG made something like $42M for the company its first year, about $100M in total. There were 3 engineers working on it, and it cost the company maybe $500K in total. Great return on investment, right?
Except my Director wasn't in charge of making money, his job was to increase user happiness, typically measured as the proportion of interactions that were "successful". And this project didn't do this - since it encouraged browsing behavior (poking around without a goal), it actually decreased "successful" interactions. So the project was canceled and threatened with unlaunching about 18 months in.
I was like "Well can I buy it off you? $42M might be rounding error for you, but I'd love to have a business with a $42M ARR." But ultimately this was a no-go as well, because it used company infrastructure, user data, sale relationships, etc and negotiating that contract (along with all the legal and reputational risks to the parent company) would've cost a bunch more than $42M. Ultimately he was like "Well, if it's making that much money, maybe it should be transferred over to department that's actually in charge of making money", and that's where it landed for the next few years, until $100M became rounding error in the company's annual revenue and it wasn't worth that executive's time to sponsor it.
Projects have to move the needle for the executive that sponsors them, otherwise it's not worth their attention. Zillow made about $2.7B in 2019, with 47% gross margins. If Zillow Offers was profitable but could only flip say 1000 houses/year at a profit of $50K/house, that's only $50M, basically 2% of their existing revenue. It just doesn't move the needle for the shareholders, which means it won't move the needle for the CEO, which means it's not worth his attention. They needed it to be a substantial fraction of sales in the U.S. - if it had made a profit of $100K on $3M homes/year, that's a $3B business, more than double Zillow's existing revenue, at potentially higher margins.
This doesn't sound right. 2% of the firm's revenue in its first year, combined with a plausible growth trajectory to 5x or 50x that, is great!
I don't doubt that your €42m project got shut down, and that your employer might well have been right to do it. But this particular Zillow project wasn't shut down because it didn't move the needle - if anything, it was shut down because it did.
It sounds like the problem with Zillow Offers is that it didn't have the plausible growth trajectory to 5x or 50x that. It could be a profitable $50M business or an unprofitable $1B business, but not a profitable $1B business. The earnings call shutting it down mentioned that the company wasn't getting the economies of scale that they thought they would be getting.
> I think it's obvious that homeowners know much more about the houses and neighborhoods they live in than Zillow, and this creates a big risk for Zillow.
I really question the idea that most people have any idea what their home is worth without relying on... Services like Zillow. Especially if they've lived there a while.
Most people don't know what their home is worth, and if you get two appraisers or realtors to give you an estimate they'll likely be quite different.
Similarly, if you think a house is worth $500k but you list it for $600k, you don't know if someone will decide they love that house and they're willing to pay that amount or not.
That's one reason Opendoor tends to list houses at a fair premium to what they paid, as sometimes someone decides it is worth it and they make 20%+ on homes like that.
Yeah, and by the same token, you don't know if the buyer offering the top amount is really going to pull through, giving an offer from OpenDoor a certain appeal.
Where is that done? I've bought in two states and sold in one and typically the way it's done is you have a contingency in the home sale that lets you walk if you don't like what your inspector finds. A seller could theoretically do their own but there's little upside, since this just creates a record of any issue that turns up that you now can't claim ignorance of.
In Oakland at the moment the sellers typically get the inspection done, provide the report in the disclosure packet, and then expect an offer with inspection contingency waived.
On the downside, there are the obvious risks in the structure of the incentives - in principle these may be sufficiently mitigated by the need to maintain a good reputation among buyers' agents; in practice they may not be.
On the upside, having had the opportunity (and motivation) to read many tens of such reports meant that I was (I believe) much better at reading them - and had a sense of what was typical amongst the homes I was looking at - by the time I read through the report for the house I wound up buying, and there haven't been any surprises so far...
I wonder -- does it really matter if the previous homeowner is more informed than Zillow? For things like "annoying neighbor" or other hard to quantify/quickly detect annoyances, the buyer doesn't know about those things either, so I guess the information asymmetry is almost 100% in Zillow's favor, right?
The buyer might not realise about the annoying neighbour (unless the most annoying thing about the neighbour is the mess they leave everywhere) but will definitely pick up on things that Zillow's algorithm doesn't.
no - because machine data of the deal is not complete, therefore cannot be represented in the models. As any computer-vision researcher knows, the code sometimes does not see what is "obvious" to almost any person.
This is one of the things that's still useful about agents. They want to keep doing deals with each other, so the selling agent is motivated to pass on information like this.
Maybe that's been your experience. Mine has been that they're eager to cover up issues and explain away those they can't. I mean no hate, that's their job.
It has been my experience. My agent took a particular likening to me for whatever reason, probably because I'm a very low drama client, and took me around on the open houses that are mostly intended for agents to connect. In my town these happen on Wednesday morning. Selling agent usually has some basic amenities out like a coffee station, pastries, occasionally pizza. A large number of agents just cruise through continuously. It's something of a festive atmosphere. I was usually the only client vs agent in these situations, and while all agents have a degree of salesmanship to what they do, I saw first hand very frank discussions of potential problems that would cause a deal to feel sour after it was done.
As an agent you make your money on the overall volume, not squeezing the last bit out of any particular deal. Near as I can tell getting a reputation for deception is nearly career suicide unless you're at the very low end of the market. I think you're making a fundamental mistake in thinking about incentives in the context of a single sale vs having a career in a business that is very strongly driven by personal networking. This is a common fallacy in microeconomic thinking.
My observation where I live is that there are somewhat exclusive networks and not everybody is in the same one -- they might be clustered around catering to a clientele speaking a particular foreign language, for instance, and be somewhat apart from more "mainstream" agents. Anyway, I personally watched the selling agent try and convince me and my agent that the water damage in a property I was looking at was hardly damage at all and anyway, it doesn't rain in California, so don't worry about it. Sure, that's just an anecdote. So is your story, though.
I don't doubt that two agents who had frequently worked with each other amicably might be more frank, but I do doubt that agents all, or even mostly, presumptively do this with every counterparty they meet.
If you bid low, almost no one will take the opposite side of the deal, so your overall deal flow is low and total profit is low (even if margins are high).
If you ‘open up’ the flood gates on the other end, then yes you’ll do a lot of deal flow - Buyers sense a sucker - and open up a lot of opportunities for matches. It just so happens you’re also losing your shirt.
It’s easy to ‘make money’ (close deals) by giving money to people, and losing money in the actual business.
Rabois talks a lot of noise about how well they're doing, but Opendoor is a miniscule fraction of the size of Zillow, and it's hard to know exactly how good Opendoor is doing anyway. We'll see as the years roll on with a lot of economic uncertainty coming up.
Isn't is also true that the original pricing algorithm was built for a very different purpose? It was useful for getting a ball park estimate of value, but it was hardly accurate in the underwriting sense (for the reasons you point out). The hubris of assuming that those prices were so accurate that Zillow was willing to buy at them sight unseen is mind blowing, particularly when one takes into account the adverse selection (if Zillow's estimate is above what I can get from in-person bidders, I am more likely to take it than if the error is in the other direction).
Yes, you might discover that your average price is accurate, which is just fine for a reporting site. But beneath that there could be some structure, for instance there might be blobs of houses that your model makes too cheap vs reality, and blobs that are too expensive. If those are identifiable, eg via some sort of local knowledge, you might find that people will sell you houses that you've marked too high, but you can't buy the ones that you've marked too low.
This “annoying neighbor” problem keeps getting cited as an example of something that affects housing prices that the automatic algorithms don’t know about, but does it really exist? What is the mechanism by which the potential buyers find out about the annoying neighbor? I’m highly skeptical. I’ve had annoying neighbors before and there is zero chance that any potential buyers knew about them when I sold.
Are people interviewing all the neighbors before making a house purchase? Are those neighbors not incentivized to withhold any negative information about the neighborhood because it affects their own property values?
Two covered cars in the driveway, bushes growing out halfway into the sidewalk, house in disrepair, taller than allowed fencing with Beware of Dog signs, it’s sometimes not hard to spot someone you don’t want to live next to.
Huh. Some variation of this house exists on any given block in my city except maybe the neighborhoods where the prices are well over $2 million. No HOAs. I guess my threshold for “annoying neighbor” is a bit different from yours.
Sure, these houses exist everywhere, but most people avoid buying houses next to them. If Zillow bought such a house they’d take a loss to unload it unless they baked it into their model.
I think I’m just used to a housing market (seattle) where that is such a tiny blip it couldn’t possibly affect the price. Maybe untrue elsewhere.
Edit: it also seems like a small thing to worry about at a point in time since neighbors change. Annoying neighbors move away, or a new annoying neighbor can buy the house next door at any time. I’m not paying extra for something that is completely out of my control and could change tomorrow. The potential for annoying neighbors is not a feature of a given house so much as a feature of all houses, in my mind. But this attitude is probably more prevalent in people who don’t live in neighborhoods governed by HOAs where certain “annoying” behaviors are regulated away.
I can't speak for everyone. But I definitely think that a lot of people are put off by buying a house next to a Beware of Dog guy even though they know that one could move in tomorrow. Also, in California, a lot of people are in neighborhoods who could never afford to move in but own the home and don't have to worry about increasing taxes.
Where I am all the houses sell for above asking, often before an open house. It seems hard to imagine a buyer would even have a chance to get scared off by the neighbors.
Even if their ML model provided excellent predictions, another potential problem they may not have accounted for is adverse selection: the only takers may have been on houses whose bids were too high.
It seems very difficult for me to imagine that they didn't anticipate adverse selection and try to account for it. I mean, clearly something went wrong somewhere, but I feel like people are acting like they had never heard of adverse selection until now.
Makes me wonder if Zillow wasn't planning to "tune" the models, surely they wouldn't want to publicly publish what they think things are worth and then offer 5% less. I wonder whether accurate estimates or making money from flipping would have dictated their decision-making...
[W]hy 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.
Indeed, I believe this is what OpenDoor does. From The Economist article [1],
"They [OpenDoor] charge a fee for the services they provide: buying and selling homes immediately, with zero fuss. The quick in-and-out makes them more like marketmakers than property investors, who buy to hold.
...
"A former Zillow employee told Business Insider that management had been hellbent on catching up with Opendoor, the front-runner. In order to compete, the employee alleged, the company pushed to offer generous deals to potential clients. It called this “Project Ketchup”. Now it has its own fake blood on its hands."
> a market-maker for homes at a large scale…a house-flipping company
These are different things.
Archetypal market making involves simultaneously buying and selling an asset. Flipping involves buying, improving and later selling. One might be able to deal with the heterogeneity of houses by operating at scale. (Zillow attempted this.) One might also deal with the delay between buying and selling by hedging. (Zillow never seems to have thought about this.) But the improvement function makes what Zillow attempted fundamentally separate from market making.
They weren’t paid to provide liquidity. If anything, they paid a premium for scale and immediacy. They were a real estate operation masquerading as a tech outfit. WeWork in different stripes.
Theres a good video here from a british hedge fund manager on why zillows real estate market making doesn't make sense: https://youtu.be/eDc4saE5m9k
The main insights are that market makers hold assets for a short period of time making money on the spread between buyers and sellers offers. Zillow had to hold on to houses for a long time and was speculating that the houses would be worth more in the future which is not market making.
> Archetypal market making involves simultaneously buying and selling an asset
Does it? I worked for a few years for a market maker, and that's not what we did. Simultaneous buying and selling is what the arb guys did. We'd buy and sell with generally short hold times. Which makes sense to me given that the exchange has market makers to provide liquidity. If something can be simultaneously bought and sold, then the market-maker is unnecessary.
> Simultaneous buying and selling is what the arb guys did. We'd buy and sell with generally short hold times
The ideal market maker is arbitraging (and eliminating the arbitrage-able inefficiency). That’s why humans were replaced by faster-trading machines everywhere they could be. In most cases, the arbitrage is synthetic or approximate, e.g. hedging an options or swaps book. But a fundamental separation between speculating and marketing making is the latter does not take a view on the assets per se, and should not be betting on their future price movement.
No market maker always achieves the ideal. But they tend towards it. Zillow didn’t have that tendency. In fact, they erected fundamental obstacles between themselves and that ideal.
Sorry, what's your source for this archetype? I thought maybe the place I worked for was just weird, but I've just looked at a half-dozen sources and as far as I can tell, we were pretty typical.
I’d have to dig up the textbook sources, but the key bit is in the definition: market makers quote a two-sided market and make money from the spread [1], i.e. buying at the bid and selling at the offer. If it happens simultaneously, that’s ideal. Every second one is long or short, risk and cost are incurred. Market makers seek to minimise and manage these.
In practice, arbitrage is tough. So most market makers simulate simultaneity by hedging. For example, if longs are accumulating (e.g. due to specialist obligations) one might open shorts or buy positional puts or wing it by shorting SPYs.
> Saying they simultaneously buy/sell is wrong/confusing
That wasn’t claimed. What was said is the archetype is simultaneity. That is 100% accurate for how the term “market maker” has been used, globally, since at least 1999. (Pre-GLB/LTCM and post-ECN, the term was used more broadly.)
Drift from simultaneity incurs cost and risk. Those costs and risks must be managed. If you aren’t thinking in those terms, you aren’t market making.
Zillow’s downfall mirrors that of the money-centre banks in securities dealing post-GLB leading up to the crisis. What does and does not constitute market making, which is risky but less so than leveraged day trading, was a huge area of policy concern. When non MMs think of themselves as market makers, there is a predictable set of risks they get downed by. Zillow, like so many others, fell prey to that misconception. (There is loose analogy in the ABS markets, where banks holding inventory of esoteric products, either badly hedged or hedged with a busted counterparty, got hosed.)
You can't garauntee your (bid/ask) resting orders are executed against in the same epsilonic time window, nor would you want to. No market making practioners would think in these terms.
I think you maybe have some misunderstandings around the practicalities limit orders and market microstructure (not withstand some theoretical model of risk free market-making, which has broadly been superseded, if you care about the theory at all).
> 1) a statement, pattern of behavior, prototype, "first" form, or a main model that other statements, patterns of behavior, and objects copy, emulate, or "merge" into. Informal synonyms frequently used for this definition include "standard example," "basic example," and the longer-form "archetypal example;" mathematical archetypes often appear as "canonical examples."
> 2) the Platonic concept of pure form, believed to embody the fundamental characteristics of a thing.
The confusion between you two seems (to me at least) to fit almost entirely within the difference between those two definition. If you are describing the ideal market maker as essentially performing arbitrage, that seems to fit the second definition pretty well, right?
Meanwhile if wpietri says that most of the work at his believed-to-be-typical example of a market maker was doing non-arbitrage stuff, that'd make sense, right? I guess in most places the main work would be managing the divergence from idealness.
That could be it. Except that if market-makers were ideal in that sense, they wouldn't need to exist. If a buyer and a seller simultaneously exist at a given price, they can just trade with one another. Market-makers are valuable to markets only when they provide liquidity through non-simultaneous buy/sell pairs.
I think it also leaves out that not every market maker wants to be flat instantly. The one I worked for, and at least some of our peers were sometimes happy to hold inventory for a bit when they thought the market would even out.
> one I worked for, and at least some of our peers were sometimes happy to hold inventory for a bit
May I ask if this was before or after GLB (1999, for the kids following at home)? (My experience as a derivative market maker was post GLB and post crisis.)
Managing a flat book is less exciting than placing directional bets. It’s also less profitable, at least when the bets go well. Add to that the complexity of market making, particularly when derivatives enter the equation, and you get the two decades–following the advent of ECNs, turbocharged by GLB and ending in the financial crisis—in which market making desks were proprietary traders first and liquidity providers second.
Also, if you buy a swap and I simultaneously sell stock, there is both a simultaneous trade and liquidity being provisioned in a value-adding way.
By marking it up and/or appreciation. They buy it for $500/sqft and then rent it for $100/sqft. In that hypothetical the breakeven is 5 years, plus overhead.
If the occupancy doesn’t work out in their favor, they may still make it up in appreciation.
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.
This is a critical concept that appears to be poorly understood in at least the web development circles I run in.
It reminds me of one of my favorite Bill Gates quotes:
"The first rule of any technology used in a business is that automation applied to an efficient operation will magnify the efficiency. The second is that automation applied to an inefficient operation will magnify the inefficiency."
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.
Personally, I would treat the GP's mindset of "inventing workflows" differently than your mindset of redesigning at "poorly designed processes".
Yes, a poorly designed process sucks but it works at some level. That means the rough flow of it is figured out. Yes, there are exceptions and complications and all kinds of odd things but it's fundamentally different. It's not "from scratch" as you're taking an existing working-but-broken process where you know the input, know the output, and rethinking everything in between.
In an "inventing" scenario, you have what you think should be the input, a notion of what the output should be, and you're trying to build towards that notion.. without the validation that you're thinking of it correctly.
The first is a harder social problem (aka getting people to change) while the second is a harder technical problem.
Ultimately you have to build within your sphere of competence. If you have a well-established but inefficient manual process, it may sometimes be the case that burning it down and replacing it with a tech-driven approach might be the best way forward.
But if you are trying to solve a novel problem, and the proposed solution involves "ML will magically predict the future", you'd better have a very good idea of exactly how the problems will be solved, or else you're probably better off starting with good old-fashioned human intelligence.
Ideally, you want to understand the whole process from beginning to end, including all the complex edge cases, before trying to automate it, then automate the whole shebang in one giant undertaking. You need a tremendous amount of high-level buy-in to pull this off, as people will have to wait and suffer with the old process until you're completely done building the new one.
What often ends up happening is a large manual processes is automated bit by bit, and you end up with the situation you describe: a poorly designed manual process painstakingly replicated in code. Full automation is often never actually achieved here.
The absolute worst thing to do, though, is to begin automating the thing without fully understanding it. It's putting rocket boosters on your self-driving car without first understanding the rules of the road.
It’s more complex than this in practice because in a large organization you have a significant change management component to every process change, whereas automation of an existing process immediately frees up bandwidth even if the process isn’t great. I interview global executives for a living; I hear this every day and I fully believe it.
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.
Taking some hopelessly byzantine, spreadsheet-driven process and “automating” it by building a Rube Goldberg scripting framework around it is the kind of totally stupid automation that doesn’t work.
Actually getting down to surface level and understanding fundamentally what each of those humans is accomplishing via those spreadsheets, extracting that all the way back out to a domain model and process flow diagram, and then selectively replacing process steps, whole cloth, with tech designed to be an actual subservice with SLA targets, is the right way to do it.
Throwing the spreadsheets and/or humans out altogether and starting “from scratch” is so exceedingly and needlessly risky from an information loss and hubris point that, well, good luck, but you’re nearly certain to fail.
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 startup that was attempting to model credit risk by building a binary classier for defaulting, and trying to figure out a way to use this to score people for credit (obviously they chose to ignore the fact that there is a huge industry with decades of experience in assessing consumer credit risk).
They focused exclusively on finding more advanced models to get better AUC without even realizing that that's not important. I mentioned that the most simplistic credit score model should at least model P(default|info) and then set the interest rate to - P(default|X)/(P(default|X)-1) to break even and they couldn't comprehend this basic reasoning. It was doubly hilarious since their population's base default rate was such that the solution to this equation was higher than the legal limit they could charge for interest.
In the early part of the current startup/tech boom there was a focus on "disruption", the idea that new ideas could easily dominate old ways of doing things. But for many industries, such as credit/lending and real estate, you should at least understand the basic principles of how these "old ways" work before trying to disrupt them.
> 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.
That distribution is still a point estimate for a multinomial, not truly the distribution of your certainty in that estimate itself. This is essentially a generalization of logistic regression, which will of course give the probability of a binary outcome, but in order to understand the variance of your prediction itself you need to take into account the uncertainty around your parameters themselves.
This can be done for neural networks, through either bootsrap resampling of the training data or more formal bayesian neural networks, both of these are fairly computationally intensive and not typically done in practice.
This is the key insight. Systems like Zillow's model a dozen or so big factors that are easy to collect (square footage, exact location, nearby comps, sales history, etc.) -- and then treat the rest as minor random noise.
Minor? Usually.
Random? Not at all. A minor annoyance like a cracked driveway ($1,500 to fix) is also likely to be associated with older kitchen appliances, faulty water pressure, deteriorating deck; poorly seated windows, etc. And then, buying that house for what the algo tells you -- or even algo minus 3% -- isn't likely to be a happy choice. Its fair market price may be algo minus 10% or worse.
Also worth bearing in mind, the Realtor community is not going to make life easy for Zillow. Once it's known that Zillow is loading up on clunkers, buyers' agents are likely to tell their customers: There's a Zillow house on the market, too. It's probably got problems. I'd demand a full inspection and some indemnities if I were you.
Common flaw of market disruptors. They assume that the existing players will remain neutral and indifferent to their arrival. The real world tends to be much tougher.
anectodal evidence, but yes, an apartment I lived in was 30k cheaper than everything in the house, and the estimate. So I was really happy to have caught a bargain.
Then after moving in it turned out the neighbors were unbearable. The father had developed a DIY habit during covid and he would regularly put together furniture at 11PM. Then they had two monsters for kids. Would literally jump around uncontrollably for hours, and for some reason, through some defect or lack in the sound proofing, the sounds were even amplified. Every time they went down to the playground the kids were literally screaming at the top of their lungs the whole way down. I nearly lost my sanity and fortunately could sell it at at 2k loss in a year.
Apart from that of course you can sue anybody for anything (with more or less success); why would that be the case here? I mean isnt if neighbours are annoying a subjective thing? Do you know of any court ruling which implies one has to disclose the state of the neighbours?
There is a famous (among first year law students) case[1] that seems relevant given the nature of the issue is one a buyer would not reasonably be able to ascertain on their own. One possible point of differentiation: ghosts are a permanent defect on the value of the property while loud children living next door would probably only torment the homeowner for a decade or so at most.
The opinion is famous not just for its unusual fact pattern but also because the Judge clearly had quite a lot of fun working in other-worldly puns and references while writing it.
Despite quoting the ruling at length multiple times in the article, there's not a single link to the actual ruling on that page, so far as I can find. Never change, Wikipedia.
Yes, and even if something is super annoying, it might be legal. For example they listened to music until 2-3 in the morning a lot of cases. Thumping reggaeton. And while it for sure wasn't over legal limit by decibel, the bass made my bed shake.
You indeed can if there are nuisance neighbors and I believe that is considered a material fact in most states. However, most documents, I believe contain a Real Estate Transfer Disclosure Statement which would have had a line indicating a "yes" if there were nuisance neighbors and it would have been up to you to ask for more details.
Yes, you'd have to be prepare to counter that with e.g. testimony from neighbors, or friends who heard the previous owners complain. It's not a slam-dunk, but it is an actionable tort.
Also good luck getting positive ROI on that multi-year lawsuit over things that buyer can reasonably say ‘never bothered me really, I don’t know what they’re talking about - sounds like they’re just irritated at life’. Especially when you factor in all the legal fees and several years of hassle going through the courts.
None of these things are necessarily unusual in most neighborhoods. All at once is irritating to most people - but hard to objectively prove are a true nuisance in a legal sense.
Or a house full of cats. I had a 'cat lady' friend who struggled to sell her home because she had 13 cats. 13 'indoor' cats. Even at a great price the house would not sell. Enter the wonderful folks at Zillow that bought her house based purely on the numbers. Last I heard they still hadn't been able to move that house at any price.
Okay even accepting that Zillow made big unforced errors, that doesn't sound believable. Like, they don't make the offer conditional on someone looking at it in person for red flags?
In my area, OpenDoor was buying sight unseen. All they required was a 10 minute zoom call where you walk them through the house. Zillow actually sent out someone to do a home inspection (although a bit more cursory than what a typical buyer would pay for). This may have varied by market though.
Walking through a house with a high quality N95 mask in a hurry you might not notice the cat pee smell - or chalk it up to there being 13 cats and once they're gone the smell will go away.
Indoor cats don't magically get diseases. Just like an indoor pet bat isn't going to magically contract rabies. They might if you are letting them out to go roam the terrain. I'm really tired of these silly perpetuated mythologies.
All it takes is one exposure, and your "indoor" cat can infect your remaining pets (and you).
This entire topic is is marred with silly nonsense like "your only risk is mishandling cat feces", which totally ignores things like:
1) The cat litter box is covered in microscopic parasite-containing cat feces
2) The cat tracks feces-covered litter out of the box into the living space, which further spreads the parasite. If you walk barefoot through your house, you will step on cat liter pieces eventually which are again covered in microscopic feces.
3) The cat steps in litter covered in cat feces from other cats, gets it on its paws, which it then licks, starting the infection cycle over again. Multiple cats can easily keep a perpetual infection cycle going.
4) Cats, even well trained ones, routinely climb on top of furniture/eating/cooking surface and coat them in their feces. My parents were shocked after installing an interior camera when they saw their "well-behaved cats that never do that" walk all over their dinner table, kitchen counters, open and go inside cabinets etc etc. They are smart enough to know when you are around or not, and to not act like this when you can see them.
5) Cats have sharp claws, and even well behaved ones can accidentally scratch you or scratch a toddler that gets too close etc. This will then directly introduce cat feces to your blood stream.
Once exposed, the initial symptoms are mild flu-like symptoms and not a huge deal (unless you are pregnant, in which you will likely experience a miscarriage. Pregnant women should not be exposed to cats, or be in a household that has indoor cats). The real issue is the long term cysts they leave behind in your brain, causing a latent inflammation and immune response that seemingly interacts with your dopamine system in poorly understood ways. For example, latent cyst-stage toxoplasmosis significantly increases your risk of developing schizophrenia.
Bathing cats can give them infections if the water gets in their ears. Generally, they clean themselves in a freshly cleaned litter box, what's called a "sand bath" by rolling around. They also clean themselves by licking themselves. They do a very good job, I've been around cats my whole life and none of them smell except perchance the fat ones that cannot lick their butts and sometimes have a bit of a dirty butt.
I'm not sure if you asked the question in regard to disease -- but even indoor cat poop will not carry diseases. Perhaps E. coli or other bacteria already in the cat's gut (and our guts too), but toxoplasmosis is not going to pop up unless it's in the cat food (uncooked pork, etc.) or the cat already has it. It is another common mythology that somehow poop has "disease" in it. It has no more disease than we already possess...
I'm sorry, but is this a joke and I'm just missing it? I find it hard to believe that you are seriously suggesting a cat rolling around in a feces-covered litter box is "cleaning itself".
>freshly cleaned litter box
There is essentially no such thing as this. You would need to completely remove the litter and sanitize the box with bleach _each time your cat poops_. No one does this, they scoop out poop and put the contaminated scoop in a bucket or something. The next time the cat enters the "clean" box it is covered in it's own feces, and your scoop acts as a cross-media reinfection device. Litter boxes are, in practice, perpetually dirty things.
Cats are not "clean" after they lick themselves, if anything this behavior is primarily responsible for the cultivation of a parasite lifecycle that has co-evolved to the cat's gut.
Cats, especially ones who share litter boxes, routinely get covered in potentially parasite-bearing feces via litter boxes and ingest it via grooming. This allows for easy additional spread/cross infection.
"Cats lick themselves, therefore they are clean" is a long-discredited but persistent myth.
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.
Yes, even after extensive renovations cat pee smell can persist and some people are bothered by that smell. Im one of those people but I do like cats and wouldn’t mind having cats if they wondered around the neighborhood rather than be inside only.
There are billions of old and ill small animals and birds each year who would normally be taken care by various predators. Around humans pretty much only cats can do that important and necessary job. The popular Smithsonian study that people like to cite which states billions of killed by cats somehow fails to mention what share of killed are ill/old vs healthy. There is no surprise though in such a glaring deficiency of that study once one learns that that Smithsonian team has a researcher convicted for animal, specifically toward cats, torture.
Another aspect - rats, a human civilization companion, raid nests for eggs thus decimating birds population around humans. By controlling rats cats help to maintain birds population.
This is not true, see my reply to your sibling comment for a more detailed answer. Toxoplasmosis is highly correlated with severe mental disorders like schizophrenia.
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.
>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
The fact that a house is for sale at a given price, but has not sold after some time, is a strong signal that it's overpriced. The longer it's been sitting, the stronger that signal is. They'd be crazy not to include that data in the Zestimate.
Now, if it's extremely fast, eg they adjust the price down within a day or so, then it seems a little ridiculous. OTOH the Zestimate has always been a rough indicator at best.
When you reduce price on a house listed on Zillow and Redfin right now, it bumps it to the top of taxonomy-based cues on those sites because of the information update, which increases overall recommendations/listing promotion to potential buyers. It's a new step in getting a property sold introduced by technology dynamics. Price reductions in traditional real estate listings worked differently (You were not refreshed on MRIS). with everyone performing price reductions though, that can have mis-leading effect on economic indicators, and it can also create harmful price reduction "panic" in certain markets though, so this is going to be a burgeoning issue moving forward.
I am luckily both a web developer and knowledgeable about real estate, most people don't properly understand the dynamics that are impacted/introduced into the market by technology and algorithms... People assume the traditional real estate market rules are still in play primarily still, but technology has complicated everything... That's also why Zillow overbought homes, because people making critical decisions too often put "traditional pre-tech" real estate market concerns over considering modern impacts of IT to their decisions.
My house sold within 2.5 months overall, it was not on the market for a long time.
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.
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.
I think the opposite: there is not much valuable data, it is just noise.
It is very difficult to go from what users browse to what they actually buy. People very often say one thing, then do something completely different.
And sometimes they browse stuff just to make sure that their current decision is correct, so they will look at a lot of items they're not going to buy.
(oh, and everybody and their mother knows photos are important. No need for ML to find that out)
Can you not imagine how useful it is to know user data e.g. what neighborhoods receive the most clicks, what type of homes generate the most favorites, how long people view one listing vs. another, … that is unrelated to public MLS data?
> Can you not imagine how useful it is to know user data e.g. what neighborhoods receive the most clicks, what type of homes generate the most favorites, how long people view one listing vs. another, … that is unrelated to public MLS data?
Click data is much less valuable that the recent sale price data available in MLS. Using 90s style dwell time and click counts would likely yeild a lot of very noisy data. False positives from people's browser reopening with 15 tabs looking at different houses. False positives from social and paid advertising boosting a particular home or neighborhood's numbers. False positives from enterprising real estate entrepreneurs doing everything they can to get the clicks up in areas they own property to drive up prices. Meanwhile, the recent sale prices tell you much more, with certainty and are very expensive to manipulate.
Maybe, but I was under the impression that Redfin/Trulia/Realtor.com would have the same information.
Also, unless Zillow started imposing confidentiality agreements on their bids, then competing buyers would just have to bid $1 more without their dataset, right?
Zillow is the largest aggregator. They own Trulia. I can squint and see the thought process here by Zillow, though execution, as evident, did not go as planned.
My understanding is that they believed they had an advantage in terms of buyer intent information. Everyone can see who buys what, but Zillow has access to more information about how people shop for homes, and the events leading up to the actual sale.
> However I think where the hubris lay is in how they thought they could leapfrog all the way to an automated solution before building a competency as a house-flipping company.
In my mind this is the problem with consultants who try to automate processes. It’s really difficult (maybe even impossible?) to successfully write a program to make a computer do $thing if you don’t understand the intricacies of how to do $thing manually.
Would investors pour their money if they were more conservative and said something along the lines of "look, this is a very complex subject driven by and for humans, we should hire a bunch of non-technical people with relevant industry experience and try to make some bucks of profit before going full scale engineering and AI"?
Theoretically investors should reward a realistic and well-reasoned business plan, and punish hand-wavy science fiction. The fact that this is largely not the case (cough metaverse cough) is probably an indicator about how frothy the market currently is.
Very much this! I can count on one hand the number of successful businesses I've seen that use machine learning to do some magic inferences humans could never do. I've seen hundreds where humans are pretty good at doing their job, but machines can do it faster, more consistently and cheaper.
One huge difficult-to-quantify risk is the public opinion risk of being a very high profile company that flips houses. If it had succeeded to actually push up housing prices considerably, the whole company could be destroyed in the court of public opinion and therefore probably would be destroyed legally, regulatorally, and legislatively as well.
Even if Zillow had an algorithm that was 100% accurate at predicting current house prices, the housing market is just incompatible with market making. A market maker isn’t exposed to changes in the price, they clip the ticket on providing liquidity regardless of price direction. Zillow may have been able to successfully speculate on house prices with an accurate model, but they would not be a market maker.
Houses trade slowly, so would sit on Zillows books for a long time (days/months). Market makers on the stock market can have assets sit on the books for under a second. Houses are not fungible, which extenuates the slow trade problem.
When we were looking for a house we rejected many because of the “wrong sort” of neighbor, ascertained entirely (and possibly erroneously) without meeting them. I doubt Zillow can model that with public data.
Orrr maybe someone in the org could've practiced some basic morality and compassion and refrained from further contributing to the housing shortage. Just because you can make money being a sociopath doesn't mean you should...
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...
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.
> 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 practical sense as you seem to be indicating. For example, in San Saba, Texas, 20-30% of the households are under the federal poverty threshold. The median household income in the town of San Saba is about $32K/yr. People just don't have any excess cash so the maintenance on dwellings is neglected. That means folks become extremely thrifty and resourceful patching what needs to be patched, very cheaply, if not for free. Some dwellings simply aren't maintained and one day won't be there anymore.
Families live on small budgets, don't require much and generally just "get by". The municipal and county governments have very small budgets but extremely resourceful staff who accomplish a lot with very little. Everyone comes together as a community when needed (see: February 2021 freeze event) and it all works very efficiently, actually.
To someone who is not from here and who doesn't understand that dynamic, they might see those properties as you described and believe a tragedy was unfolding. But that doesn't reflect reality on the ground vis-a-vis my neighbors.
I'm in rural TN; not that different a place at all. I'm not speaking of family owned homes tho. I'm talking about the Abandoned, uninhabited homes that are now owned by some out of state thing per county records... which is a lot of them. LLC's and INCs whom I believe have the properties valued highly on some book somewhere and haven't done anything to maintain them.
Our local Craigslists always have "Property inspector" jobs listed. You go take some cell phone shots of buildings to prove they exist. The people I have spoken to who have done those say they didn't bother going to the places as often as not and took pics of some neighbors house. Even when people actually do that job and document the true state of these properties I can't help but suspect the information is buried or lost because thats not the narrative management would want.
The actual family owned housing stock got better the last two years, our population doubled for the last 3/4ths of 2020, and all those relatives did a lot of renovation and rebuilding.
I actually used to work with people from East Tennessee for the past 2.5 years. They described how the Knoxville area was growing like crazy with folks from the coastal states moving there.
I understand what you're saying. The ripple effect created by that dynamic would unjustifiably inflate local property values, reducing affordability for locals, creating synthetic demand by reducing supply as the land could otherwise be auctioned.
West TN; we've got that happening too. The neighbor's $750k McMansion has ludicrous "market value" implications for the hunting camp trailers beside it and the doublewide up the road.
and (ahem) East TN is more "western Arlington VA" IMO. I said rural. I'd have to walk a half mile to get a decent rifle shot at a neighbor. It's getting too crowded here.
You just described my road here in Henderson County,TN. We bought 100 acres and built a dream home. In the last years (ish) 4 new single wides went in and a couple of new smaller homes. We were told during the entire build that we will never get out of it what we are putting into it and we don't care. We aren't building for resale, selling it is our kid's problem.
But there are so many abandoned places out here. People have just walked away and never looked back. We had one across the road that over the last 10 years the woods has reclaimed and unless you knew that it was there, you would drive right past it.
My observations passing through rural Texas matches this. You frequently see houses that probably only served 1 maybe 2 generations and then they are in a poor condition uninhabitable by even those folks used to roughing it. Housing stock in rural areas just doesn’t last long.
FWIW I lived in San Diego and saw this too. Houses bought by parents for $50k in 1973 ended up being unmaintainable for some of the families even with Prop 13 keeping their taxes low. Then you'd have houses passes to kids, sometimes with drug problems, but in any case, no resources or knowledge sufficient to maintain a house.
> In your opinion, what do you think the most effective way to help these families out might be?
This is a question that I'm well-positioned to answer. I moved to this rural area in 2018 after living in Austin for 24 years. I immediately looked for ways to volunteer and help.
I developed relationships with elected and community leaders, started my own "technology incubator" to teach technology skills and classes. I explored establishing a regional technology council with my county judge and Texas state leadership. The community liked that I was volunteering but the actual uptake, expending effort to learn and implement what I was teaching, wasn't there. They didn't know what to do with it. The gap between their world and the world we know at HN was too wide to be bridged effectively.
My experience is applicable to every problem here where someone thinks they may be able to help in some way. Whether it's teaching job skills, helping those who are addicted to meth or whatever, I believe people can't be helped if they don't want to expend the effort to get from A to B themselves.
There are many reasons for this, why offering to help in an economically-depressed or disadvantaged community doesn't yield results. Locals are apathetic, comfortable living in the middle of nowhere with very low expectations, or else they have poor self-esteem and don't believe they can do better.
I don't "push" anymore. I just try to be empathetic and understand their situations. This past Thanksgiving I asked the community to tell me if anyone was unable to get a turkey for Thanksgiving and would like one. Two families responded; I was glad to help. It's little things like that which I can do to help their situation which I feel is the best approach now.
Edited to add: There is an organization here called "Mission San Saba" where a group of ~30 volunteers will pick one house per year to renovate, typically for an older or economically-disadvantaged family. That has been very successful here.
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.
So Texas funds education at the state level via property taxes? That sounds surprisingly progressive of them.
Washington state does something similar, though it’s more of a subsidy. Education is still mainly funded locally, but the state kicks in with its own funding for poorer districts, so Seattle property taxes subsidize schools across the state in Spokane.
You'd be surprised at how progressive southern states are. The Texas state motto is literally "Friendship." Now, I don't know much about Texas but I did live in Arizona for a few years. It surprised me more than a bit, as someone who grew up in New York.
Arizona legalized medical marijuana quite early, followed by recreational marijuana. Their medicaid program AHCCCS [1] is extremely comprehensive and even pays for Uber/Lyft to the doctor's office and back. Patients are able to see a great selection of GPs and specialists, and the copay is always $0. The accompanying drug plan is comprehensive, also with a copay of $0. AHCCCS will approve expensive modern drugs like Rozeram (supercharged melatonin analog for sleep) if the sufficient documentation of reasonable need is provided.
Cactuses are protected from destruction by law, and must be transplanted when doing clearing for construction. You may find the idea of being able to own a firearm without a license to be unpalatable but the state largely remains very safe crime-wise (perhaps due to that?)
I miss living in Arizona. It's a beautiful state with very caring folk. I saw almost no homeless folks in Phoenix. Folks there seem to really care about their fellow citizens. Southern hospitality is for sure a thing, take it from a daft boy from Brooklyn!
> Cactuses are protected from destruction by law, and must be transplanted when doing clearing for construction. You may find the idea of being able to own a firearm without a license to be unpalatable but the state largely remains very safe crime-wise (perhaps due to that?)
My mom lived in Tucson and decided on a visit that I might want to go shooting with her and her boyfriend at the time. Suffice it to say, it didn't go well. BTW, Arizona does very poorly in crime rate (10th highest for violent crime, 3rd highest for property crime), especially Phoenix and Tucson (but is very urban, so there is that also). I'm not sure why you consider it safe crime wise when the numbers say otherwise. They also do very poorly in education (rank 48th). I was really surprised they could beat New Mexico and Louisiana (https://www.wmicentral.com/news/latest_news/arizona-ranks-48...).
It is beautiful. I would love to live in Tucson someday, but with the bad schools, it would have to be after my kid was done with school and I retired.
> I miss living in Arizona. It's a beautiful state with very caring folk. I saw almost no homeless folks in Phoenix. Folks there seem to really care about their fellow citizens. Southern hospitality is for sure a thing, take it from a daft boy from Brooklyn!
When I was a kid, I took a greyhound bus from Vicksburg MS to Seattle WA via the southwest approach (I later did the northwest route, which wasn't as interesting). People would get on the bus from various prisons in Texas (the bus stopped a lot at prisons), New Mexico and Arizona...and were all going to LA. Why bother being homeless in Phoenix (when summers can kill) if LA isn't that far away? Heck, that applies to Texas as well, not just Arizona.
>When I was a kid, I took a greyhound bus from Vicksburg MS to Seattle WA via the southwest approach (I later did the northwest route, which wasn't as interesting). People would get on the bus from various prisons in Texas (the bus stopped a lot at prisons), New Mexico and Arizona...and were all going to LA. Why bother being homeless in Phoenix (when summers can kill) if LA isn't that far away? Heck, that applies to Texas as well, not just Arizona.
You know I wonder if anyone has done a study on how much of the homeless in LA are from out of state. Is it even possible? There is like 65k+ homeless just in LA country. I don't know how they are going to fix this.
There's a point-in-time count of homeless that happens annually nationwide. I believe they capture this information (well, it's more along the lines of how long did they live in the area before becoming homeless).
How is the diversity? Is there integration or do different ethnic groups just keep to themselves. Is there upward mobility for some groups but not others?
What about issues like Joe Arpaio and his policies affecting the community. I can't speak for your experience but I wonder are you really seeing the whole picture?
How do you feel Arizona will cope with the coming climate change? At the rate its going Arizona may become unsafe to live in in 10-15 years.
I remember hearing that 15 years ago. I remember hearing about how we are due for an earthquake that will break California into two. That was in the 80/90s. We had great fears of a new ice age in 60/70s.
I don't know what the future holds but thinking Arizona will become unsafe in 10-15 due to climate change is a little foolish.
There were never great fears of a new ice age in the 60s/70s.
> This distinction can be made because a serious review of the literature shows that there was no such “scientific verity”. There are anecdotes that can be plucked from the record, primarily from the popular media. But a rigorous review by Tom Peterson and William Connolley (with some minor help from myself) shows that, even as the planet was in a short term cooling trend in the 1970s, concerns about greenhouse warming dominated the scientific literature. The paper documenting our results has been accepted for publication in the Bulletin of the American Meteorological Society, and Doyle Rice did a nice job summarizing the paper last week in USA Today.2
Sounds like you have "heard" a lot of different things over the years.
Have you look at any actual data? Have you seen the trends in the data that have been going in the wrong direction? At the end of the day, unless the direction changes, it does not matter if it happens in 10 or 15 years or may take longer. It will happen.
I have hard data that a quake so big never happened.
I have a book from the 70s that talks about the up coming ice age with data to back up his claim.
Have I seen trends going in the wrong direction? It depends on what trends and what start point you pick. Currently things are trending lower than a few years ago as carbon output is being reduced in many countries. I remember when the target was keeping it under 3% degrees than lower than 2% now it sits at 1 1/2%. Soon it will be 1% global temp increase. So things do seem to be trending in the right direction.
Last time I looked at a map it was next to Mexico which is as far south as one can travel before leaving the United States. Where do you consider it to be?
> It sounds like that funding decision predates the current crop of state leadership
If anything, today it is the folks in Austin (who are predominantly politically liberal) who decry their property taxes being used to fund rural school districts.
People who are actually from Texas know that we help each other out. That's how we roll.
That was when "Southern Conservative Democrat" was still a thing. Republicans were reviled in the South because they were literally the party of Lincoln, the most unpopular politician among southern whites for a long time (suffice it to say, black southerners had no problem voting for Republicans when they were allowed to vote at all). The turning point didn't really start until Nixon's southern strategy, and took a three decades to finish.
I'm still surprised that Texas would distribute property taxes equally like that for education. Even if they were run by Democrats, they were never run by the liberal kind.
in our county, 20%+ of the housing is idle, owned by out-of-state companies, some of whom pay property taxes and some dont.
I've seen this personally, too. A house I rented until a couple of years ago was owned by a Chinese company, which also owned half of the other houses on the block. We all paid rent to the same LLC that forwarded the cash overseas, and did almost zero maintenance.
I think the housing market is so fucked no one really grasps the scale of the problem.
One thing I don't see discusses very often is the affect that large "master-planned communities" have on a city's housing prices. I've seen at least three cities where mega developers like Howard Hughes Corp own massive tracts of land, but instead of building houses, sit on that land waiting for the price of housing to go up. Sometimes the developers are very open about it. Sometimes not. But instead of allowing a free market to develop 5,000 new homes, they develop one lot here and one lot there.
Or worse — I've seen them build hundreds of homes and then sit on them, empty and vacant, waiting for prices to climb high enough to put the houses on the market. Again, a drip at a time, to keep the housing supply artificially small so they can boost their profits. Meanwhile, people have nowhere to live.
I don't understand why the US does not push back on Chinese ownership of property. From what I understand, there are so many restrictions of owning land in China as a foreigner:
1. The property must be residential
2. If the property is commercial, a foreigner must incorporate in China
3. You may own only one property
4. You must possess a long-term visa
5. You cannot be a landlord, as a foreigner
6. You must pay a 1% deposit and an initial 30% of the purchase price to the seller in RMB, if you are obtaining a mortgage
I think there needs to be a campaign to educate the US population on how lopsided the system is. Only then will politicians start to get massive heat to enact at least equivalent restrictions. Right now its all the rich and foreigners picking away at the carcass that is America.
> The county isn't auctioning off because of tax default anymore, no one was buying these places at $100.
What's the issue with out of state companies owning rural properties nobody wants? If the market is heating up, maybe it's time to run tax auctions again.
In WA state, if there's no bidders, the county retains the land and will auction it again when someone expresses interest (or it some cases, can sell it to a neighboring land holder without auction, like for the 1930s era tax foreclosure I bought last year)
They'll eventually be reclaimed and re-titled one way or another I'm sure. I'm concerned with the larger implications, if my supposition is correct that they are being accounted more valuable than they actually are. These are the leftovers of Countrywide mortgage bonds and such I think.
That's been true for rural areas since the green revolution in the 1950s changed agriculture and manufacturing went overseas. Even if there is still a mine in the hills outside of town, there are fewer jobs at that mine than there were when the town was built out 100 years ago.
Jobs generate demand for homes. Homes cost a lot in areas where there's been more jobs added than homes. In the last decade, the bay area has added seven jobs per every unit of housing constructed.
Vacant housing is only a problem in constrained areas. The vast majority of the U.S. is not constrained. Your summer cabin in Montana isn't depriving anyone of a home, because it isn't driving up prices. Your unoccupied condo in Manhattan is depriving someone of a home, but I suspect that there are not so many of these.
Yeah, when most people talk about vacant homes, they don't understand that almost all of those homes are just between tenants. They are not actually being left vacant.
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.
The problem with those scenarios is that for every one that made a killing in real estate there’s plenty that barely broke even. The winners think they have some special sauce … maybe rhey did, maybe theg didn’t.
The problem is that their blogging about it attracts the people that want to get rich quick and they are the ones likely to lose their shirts.
It's just a bubble: owners want the value to increase, county or city wants the value to increase (to get more tax money), everyone wants the supply to be very limited to increase the price and the value, it's a Munchausen pulling himself by the hair from the swamp. In this case 1 million is not what it used to be.
Honestly, this is the most important point. Rampant speculation, amount other things, is already destroying housing affordability ( Hi from Melbourne Australia, where I'll probably need $150k for a deposit on nothing special at all, in an inconvenient location ). It's massively increasing wealth inequality, at a rate that seems to be increasing.
If this kind of algorithmic speculation took off, not only are we likely to see the algorithms themselves form feedback loops to push prices up far higher than otherwise, even if they don't sell directly to each other, I believe it will drastically increase the size and rate of the boom bust cycle.. and we're doing that to _peoples homes_.
The scope of human suffering possible here is huge, the societal damage massive.
"data is fungible?" i think the author does not understand what fungibility means. if something is fungible, that means that any unit of it is exactly the same as any other unit of it. fungible data would be random data, which wouldn't help you predict or model anything.
i think zillow also did not understand what fungibility means. real estate is not fungible. in fact, it's anti-fungible- that's why there are huge diligence processes that exist around most real estate transactions. maybe floors in an office building may be fungible, but residences are definitely not- with all their quirks, customizations and problems.
this whole argument that they failed because the ceo of zillow didn't have big balls is pretty putrid. pair this with the word salad of misused words and twisting of quotes and i'd say this is probably one of the worst pieces i've ever seen posted here. i feel worse for having given it any time at all.
the simple fact is that the ceo of zillow didn't know what they were doing, had a team that (supposedly) applied facebook's infrastructure scaling prediction library to house prices and then attempted to apply a market making mindset to the real estate market at scale. not only is this probably something nobody should try to do, considering we contribute so much in tax dollars to first time homebuying incentives as it's recognized that the housing market is where j q public can start to build wealth, it's also probably something that nobody could do (well, at scale, with machines) given that housing is not fungible.
sure, financial instruments are fungible, maybe even late model cars, but definitely not houses. doesn't take a scientist to spot that.
While no doubt Zillow made many of these mistakes, I think the reality is more sobering that the author of the article realizes. The more grim possibility, is that Zillow got out of the house buying business, not because they weren't good enough at it, but because they _were_ good enough at it to realize that it was at the top.
If buyers want more now for their house, than it can be sold for in a few months time (which is necessary for renovations and other prep for sale), then there is no ML (and no non-ML) method to make money. Either you overpay and lose money, or you don't overpay and you don't buy any houses.
In that situation, the only smart play, is to get out of the market. Zillow is, no doubt, not perfect. But they have a lot of knowledge of the housing market, and they thought it was time to get out entirely. I think the author of the article either isn't able, or doesn't want, to consider that Zillow might have been exactly correct in doing so.
While this sounds plausible, I think there are a couple of factors that work against this theory:
1) Why layoff your data science division if they are predicting with accuracy?
2a) If you have enough conviction to call the top of the market, why sell off so much housing at a huge loss? Zillow are the only participant in the residential real estate market losing money right now.
2b) If you see signals of a forthcoming housing crash, why not short the housing market?
The simplest explanation is that Zillow was poorly run.
It may have been poorly run, and nonetheless correct that:
- they could not buy houses without overpaying (relative to what they could sell them for a few months down the line)
- the housing market would not recover for several years (so no need to keep that extra 25% of your labor force, especially if you anticipate a decline in revenue from real estate agents coming soon)
I believe there are real estate ETFs that can easily be shorted. Many of the more complex contracts and instruments still exist, there are just more limitations on how banks can invest in them.
Zillow wasn’t aiming to build a business making bets on the housing market. They wanted to become a market maker for housing, profiting off the spread and not caring about the underlying price movements. Being a (good) market maker is still profitable in a falling market.
The issue is that the housing market is just unsuitable for this strategy. Houses aren’t fungible, and they are very slow to trade. So Zillow ended up in a position where rather than clipping the ticket on spread, they were actually quite exposed to house price movements.
You are absolutely correct. But, in a different situation where sellers were willing to sell at a price that was likely to still look like a good idea in a few months, they would not have realized that this wasn't a good idea (yet). It was, I think, inevitable that they would get out of this, because (as you point out) the housing market is not suitable for a market-maker business. But I don't think it was inevitable that they got out at the top; they could have held on and given in to the inevitable six months or a year after the slide had begun. I think it's to the CEO's credit that they got out sooner than that.
Rumor 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.
"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.
Ah, 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.
It's land, there is no top. It's a finite resource. Buy any property in the U.S. and hold for 15 years and I would be shocked if you didn't make out even if you had 2008 in between.
> Buy any property in the U.S. and hold for 15 years and I would be shocked if you didn't make out even if you had 2008 in between.
Vast swaths of rural Midwest and northeast with little industry and declining population definitely did not make out, especially factoring in property taxes and the opportunity cost of not investing in VOO as a near risk free alternative.
Perhaps, although I suppose there were Japanese property buyers who thought so also. But a CEO can't run at a loss for 15, 10, or probably even 5 years before getting booted, either by the board or a hostile takeover.
I 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 understand. Facebook/Google's data is basically worthless to anybody but Facebook/Google. The data has value because it is derived from their own processes, which in this case are the requests and context of each product surface.
Yeah, I'm gonna say that romanticizing mass surveillance is a bit much. Cambridge Analytica, the five eyes countries, Clearview - all these are using Facebook and Google's data to great effect.
Facebook and Google's data are not their own. That data is comprised of private lives, stripped bare pixel by pixel, bit by bit, and it's offensive to frame it as if they're doing something alchemical and special with it. Google's search dominance came from something special, creating the right algorithm and seizing the first mover advantage, but the relentless and ruthless invasion of privacy is a rent seeking race to the bottom.
All of the ills of the internet and political turmoil in the west from algorithmic amplification are the brainchilren of Facebook and Google. It turns out that "tailoring search results" and "targeted advertisement" are excuses for something that can cost far more than a society might want to pay.
I'm not going to engage in a flame war over this, but suffice it to say that this is pretty much exactly the misunderstanding I was referring to with that quote.
Most data Facebook collects is of the form (user saw this post, user clicked/did not click this post). That data's value is tightly coupled to the process Facebook used to decide whether or not to cause the user to see that post. The data only has value in the context of iterating on that process.
You’re also absolutely right that the social media content: the photos, the sentiments, the likes, the connections, should not in any way “belong” to FB/G.
The data that does belong to them, and that is useless to anyone else, are the outputs from their sentiment analyzer service,
the weights and trigger conditions for their content ranking algorithms, the intermediate outputs of their ML evaluations, etc.
GP, and the article, are saying: look there first. Try to start by truly understanding “what you already know, but aren’t paying enough attention to,” and don’t just treat the problem as “needs more data.”
But seriously, a lot of your claims are flimsy or misinformed, which goes to credibility. Cambridge Analytica was a huge nothingburger that had no actual effect on US elections or Brexit. Google did not have first mover advantage, they were so late to the search engine game that it caused them trouble in their early financing. Show us a real, known harm from Clearview. Google and Facebook are not breaking any laws, so how can they be "invading privacy"? The bottom line is, people love FAANG tech, and are happy to trade their data to use it. And one of the reasons is because they are not experiencing real harm, in spite of what HN's white knights would have us believe.
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).
While the point the article makes is true — it costs money to acquire the real world data, the comparison to credit underwriting is misguided. Underwriting credit is fundamentally different than predicting house prices.
In particular when you’re auto-underwriting credit it’s not typically an origination-for-sale model. So the value of the loan is the present value of the future payments, less the future value of defaults, less the cost of acquiring the customer.
Historically those things can be modeled pretty accurately and the aspects that can’t be modeled accurately can often be hedged or eliminated by the law of large numbers. The innovation of the new ML underwriting with respect to accuracy is at the margins. The real disruption is the speed and cost. (Disclosure: I worked at a SMB fin tech and we reran multiple credit models for a million customers and past customers every night.)
If Zillow were getting into the rental business, in some ways it might have been easier for them. But they needed to model where they could sell an illiquid asset which is a much harder and much less well understood problem. And yes with enough capital to plow through and the appropriate risk attitude they could likely have gotten the handle on what their pipeline was really going to look like. But it’s hardly the same problem as credit underwriting.
I think the title is highly misleading. The main point here is that Zillow simply had no idea what it takes to be a market maker and their pool was picked off by savvy traders.
Good tweetstorms with technical explanations on how that happened:
I'll second that this article is just wrong. Zillow burned plenty of money in their Offers business. The problem is that all that spending revealed that they performed poorly in a questionable market segment.
Ultimately they were really bad as flippers. More often than not paying more than market price for the homes they bought.
I think the root problem is that this was a panic move. They saw Open Door's success and thought they had no choice but to try and replicate it. But its a questionable business move for Zillow and ultimately they couldn't make it work
Zillow offered to buy my home at 30% more than everyone else in the market for cash, without an inspection, and I wasn't even looking to sell it at the time.
The article offers no new or inside information, just more armchair quarterbacking. I'm surprised that it is getting traction on HN. I think it says more about the zeitgeist than it does about the (lack of) insightful-ness of the content.
Yeah, it just repeats the Narrative in a giant post hoc. "Zillow uses ML models in some way; Zillow failed; QED, ML models are dangerous." Except the reporting by Bloomberg and insiders is that Zillow failed because they overrode the models predicting lower prices and bought like drunken sailors, and it's just a story of yet another marketmaker being run over by the market. So sad, too bad, largely irrelevant to the tech world, yet, it looks like it's entering the mythology of ML up there with Cambridge Analytica or the tank story - unkillable by mere facts or tardy reporting.
As someone who upvoted but didn't care much for the content, I think it's worth mentioning that sometimes/often I upvote for the currently occurring conversation to get more eyes, not for the link. I haven't seen too many specifics on the Zillow collapse and I've learned a nice deal through many of the comments here, most not having much at all to do with the article.
I think the article makes an interesting point about this being the first of many, but I disagree with the initial tone of the article. It seemed to paint Zillow as being afraid of loss. On the contrary, I viewed Zillow as demonstrating good common sense and an ability to make hard decisions. To me it shows that they aren't committing the sunken cost fallacy, and are willing to cut an entire 25% of the company and take massive losses so they can redirect themselves towards better objectives.
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.
>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.
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.
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.
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
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.
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.
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.
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 operational failure.
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.
I think the tone is appropriate, because the issue is a bit more subtle than that. Zillow was afraid to plan for the large losses necessary to gather the only data that counts, i.e. the data that is the outcome of their own processes.
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.
Zillow is passing on an infinite number of potentially profitable enterprises. The reason they attempted this one is because they thought they already had good enough models to avoid taking large losses. If you read their statements, it is clear the reason Zillow is abandoning the this effort is because of inaccuracies in their models not just because they were spooked by losing money. They were also spooked last quarter by making too much money!
> [T]hey thought they already had good enough models to avoid taking large losses.
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.
Their model was something like buy houses for 'market_price(house) * 95%' and then sell them for 'market_price(house)'. The article argues that they should have devised a core complex model for asking prices, but an equally viable strategy would be to make sure their market price estimations were sufficiently accurate. That doesn't take any company specific information so it is entirely plausible (although false) that their Zestimate values would work well enough.
Wait. There is a lot of messaging telling entrepreneurs to try to de-risk their new ventures. The common pattern I observe is having a new ideas and de-risking it into a successful business.
> The common pattern I observe is having a new ideas and de-risking it into a successful business.
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.
You could use that argument to justify spending more money on any unprofitable venture. If you discover that some market segment is higher risk or lower profit than you expected, that is a good reason to consider course correcting.
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.
I hear the division was toxic which makes more sense than all of this.
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.
I think a key point that is missed is the feedback cycle time. Real time bidding advertising has I believe a number of the listed concerns however the feedback time is maybe hours at most and might be milliseconds. So the risk is in general a lot smaller and worst case you just lose some of the money you spent that day/week. With long term assets you could lose months worth of investments before your feedback loop fully kicks in.
In the original Foundation books by Asimov, the conceit of "Psychohistory" was similar to the concept of machine learning for pricing: The future can be predicted _if people aren't aware of the prediction to change their behavior in relation to it_
This is similar to 'adverse selection' in real life & in Zillow's model. The article makes a nod to this, but seems to imply that if you train your model on that adverse selection, you can come out ahead after paying to learn about it.
To me that kind of misses the point. Adverse Selection isn't a static feature of the landscape you can identify and avoid, it is people understanding what you understand, adapting, and responding. Train your model with adversaries trying to beat it, then you'll maybe counter the specific first round strategies they use, and they'll learn new ones and beat your new model with their 2nd round strategies. It's a continuous game. Your requirement to gather a corpus of training data will keep you in the 2nd turn of a game where the wins are biased to whoever has the 1st move.
Basically the COMEX changed the rules explicitly to disadvantage the Hunt Brothers. The changes made to margin requirements is what made the difference here. I don't think anyone could claim that the silver market is an entirely free market, I remember last year a press release where the COMEX said they weren't sure how much they actually had in their vaults in eligible and registered, with a plus/minus 50% figure being given on their estimates. I can't think of any other major market where someone would come out and say they didn't know how much inventory they had and that their best estimate could be 50% off. And the participants in the silver market are still rather ridiculous to this day: https://www.reuters.com/business/finance/jpmorgan-pay-60-mln...
Would some cryptocurrency stuff count? We have no idea how much a handful of whales control Bitcoin or Eth. The tether thing seems really shaky too with how much they actually have in reserves. Same with a number of exchanges or major market players.
Cryptocurrency is also a bit wonky because of always including forever lost access to a solid percentage of the currency. Bitcoin is the most notable.
Tether is an enormous fraud and the financial reporting of the reserves has just never been up to generally accepted standards.
The thing with crypto is that much like some of these other commodity markets there's less real trading volume than many people think (there's been a lot of wash trading going on: https://cryptobriefing.com/binance-wash-trading-icebergs-tip...). Where crypto is very different from the futures markets is that you can just buy the stuff directly because the costs of holding it are much lower. Say I want to invest in oil, it's a massive pain in the ass to build warehousing to start taking delivery, whereas something like crypto is much easier for a company or individual to hold. From this point of view there's very real non-regulatory reasons why trading futures for oil makes sense whereas this is not so for cryptocurrencies.
Bitcoin is best example. Somehow currency we aren't sure how much is reachable anymore should come some sort of gold standard... Like at any moment significant fraction of it could be dumped on market. Probably won't, but it is not entirely certain...
I don’t get it. Wiki only says they failed because of the other institution changing the rules because of them. What’s the analogy to housing or Zillow?
Sure they failed. But the only data we have is that they failed because of something very specific which doesn’t relate to much else.
This is what most profit seeking strategies can miss. Their designers (consciously or not) can't help but to stop thinking through their plan at the profit step and just assume "rinse and repeat" forever after.
Real estate is one of the few markets where non-experts can make money, where it’s not a hyper-liquid winner-take-all game. Coupled with this is the fact that housing is a necessity and owning a home leads people to invest in their communities more than if they were renting, I think it’s a good thing if Zillow (and OpenDoor, etc.) fail at pushing everyday people out of the business of real estate investing. Here’s hoping we see some regulation—the illiquidity of the home buying market is not a problem that needs to be solved.
Opendoor doesn't compete with real estate investors, they compete with realtors and mortgage brokers.
Opendoor's primary benefit is to enable people to move when they otherwise could not easily do so, creating more liquidity and matching supply and demand (often number of bedrooms in house to number of bedrooms now needed).
The challenge with moving is that most people need to sell their current house before they can afford (or even know what they can afford) to buy their next home. Opendoor lets a family buy that next home with its cash, then list their current home on the market or sell it to the company so they avoid the double mortgage or double move (home->rental->home)
Does Opendoor avoid some of the standard x% realtor fees on either or both of the transactions? Reduced fees could easily make a huge difference to expected profitability.
In contrast, “Zillow Seeks to Sell 7,000 Homes for $2.8 Billion” so Zillow lost more than a few percentage points.
Framing it as machine learning undersells the problem.
It's a hybrid model trading in an adversarial, real-dollar environment. The leverage comes from having a small human team trade big volume, much more than they could possibly trade directly, by augmenting their human abilities with automation and a model. Or seen from the other side, it's a model with human oversight.
Any system like that is high risk, high reward. All the successful ones started out by losing a lot of money. Paypal lost an incredible amount to fraud before they started breaking even. OpenDoor lost an incredible amount to mispricing, and took on a ton of balance sheet risk, before their business really started working.
I think Opendoor still does poorly in new markets, but then it improves as they work out the quirks of the local market and start asking the right questions and collecting the right information.
A big part of Opendoor is creating the right apps and processes to collect this information to feed their models. The machine learning part is important, but can give the false impression it's just about data scientists crunching numbers at head office, when in reality there's a huge real-world operational machine that's driving it.
Let's not forget there was also a huge public outcry on HN and in other places when it came out that they were buying and selling real estate. So if it was socially detrimental to the company's image, and they didn't have the corporate will to collect enough data for it to become profitable, it's a no-brainer that they would get rid of it.
I see this as a victory for us calling out companies for immoral behavior.
Yeah, reading this article I couldn't help but notice that there would be a massive conflict of interest in Zillow entering the real estate market, and would probably create externalities. Seems like a win and strange that the author is so derisive of the company for doing what is probably in all of our best interests.
It's telling that the article opens with a clip of Alec Baldwin talking about needing "brass balls" from Glengarry Glen Ross, seemingly oblivious to the fact that it's a dark comedy mocking cutthroat sales culture.
Zillow lost money, because they were hit really hard during pandemic.
This article does not mention that. Instead, the rest of the article deals with Linkedin-wisdom and hard platitudes, such that it is not possible to build a good model on someone else's data (as if Zillow even was).
Data scientists remarking on the Zillow fold, are like psychiatrists or engineers remarking on non-clients and bridges build by others. They know nothing about the business, about the constraints, about how the estimates are consumed. They end up silly, but without good information coming from Zillow, we assign value to their analysis, purely on Twitter-soundbite-ability and internet-authority.
How exactly were they hit hard during pandemic? Their main income stream is the funnel of services they provide (or get a cut of) through lead generation (agents, title, insurance, etc...).
During the time of the pandemic, the house prices rose and so did the volume. If anything, they made out like a bandit.
“You cannot bootstrap off an existing dataset. Full stop. These datasets can contain implicit assumptions or associations that you are not aware of. This is the original sin of many a algorithmic risk underwriting startup”
False. You can definitely bootstrap and adjust the model as you either gather more data yourself or get more outside data. You can also build confidence intervals around the model predictions and decide how you want to proceed based on that. There is lots you can do with that initial model.
The essence of the article is that they underestimated how flawed their algorithms are and how hard it is to build a good lasting algorithm in a dynamic world.
Many seasoned wall street algorithms have suffered many times over 5 decades, and when they fail we call them black swan events.
And wall street algorithms should be easier because securities are fungible. One share of AAPL is the same as another. Houses are not like that. Real estate is local, local, local. Every house has a hundred unique attributes that each potential buyer will value differently.
That’s not what I read in that article at all. What I read was that their data and methodology was flawed, and they weren’t willing to pay the price to fix it.
Zillow thought they already had enough data and accurate enough models to buy and sell houses profitably. The last two quarters proved they didn’t. In the first quarter they were puzzled by making too much money and in the second they lost a whole bunch
The author is arguing that they should have pivoted from “we already have models” to “we’re intentionally gambling hundreds of millions of dollars so we can build good models over the next few years”. That might be a good strategy for a startup with loads of VC money and no other products, but it makes less sense for a more established company to risk going under on that bet
Their methodology might have been flawed. The author is speculating.
He uses Zillow to explain how datasets – especially the ones with money tied-in – can’t be trusted blindly. Building a high-quality dataset is an expensive endeavour.
I wonder why so few here question the basic assumption of injecting from above, machine-learning models to extract profit, into a vital part of the reproductive cycle of human families.
> At a high level, the story of Zillow Offers is a story of our industry at its best.
Not in my book. All I see is the price of real estate being driven up by corporate greed and the individual home-buyer being shut out of the market.
Is it wrong of me to hate "flippers" (be they corporate or private)? Pure capitalists will tell me that every property sold went to the highest bidder — in the case of a flipper winning they were willing (able) to risk the capital to hopefully turn a profit on the flip.
I suspect if you dig deeper you might find sales going to flippers because they had 100% cash offers, because they are better at "the game". I see no reason to punish prospective first-time home owners in this sort of market.
The answer is to build houses and print money to buy them with.
If houses were a (much) smaller bet for the buyer, there would be more flexibility to build houses where demand exists and a faster, lower-drama exit for people who don't like the changing nature of their in-demand neighborhood.
The inertia created when people have their life savings tied up in their house perpetuates the problem of affordability, by making the areas that have the most mismatched supply vs demand the least likely to deal with the problem.
People would probably suggest regulation or taxes to “fix” the problem but I think the root of the issue is artificially low interest rates, freewheeling lenders (again), and the fact that there are few other places to deploy your money and get some yield. There is also the tax benefits of owning income properties which should probably be looked at.
The process of building new structures is filled with so much regulatory friction that it is impossible for the average person to even consider building their own home.
Which regulations would you relax? Surely there is some unnecessary red tape, but it's not as if building regulations have been developed for fun, it's largely in response to safety issues and so on.
Cosmetic stuff, square footage requirements, height requirements, parking requirements. Basic structural engineering, fire safety, etc. requirements of course would stay but if local code is more stringent than national you might take a look at it (e.g. things like a local code requiring copper pipes when PVC is acceptable and much cheaper).
Unless there's a need to build to a higher standard with longer maintenance periods, so that housing stock can have a longer life. Houses exist for decades, better to build for that without needing maintenance but perhaps costing more initially.
Developers will always attempt to skimp on quality to save/make more money. Even people building their own home will sometimes try to avoid compliance. That's why the regulations are there.
GP obviously meant PEX, which is another plastic. (For people not familiar with modern plumbing: PEX is used for supply; PVC is used for drainage to the sewer.)
Maybe that’s what they meant, but PVC is also used for water distribution, cold water supply, and sprinkler piping (obviously all cold water but still pressurized supply applications), so it’s not at all clear that when they said PVC they meant PEX. CPVC is rated for domestic hot water supply, so they could have meant that as well. I’d say PEX was an underdog rather than the obvious alternative meaning.
Quite a lot of regulations have nothing to do with safety. Minimum set-backs, minimum parking requirements, maximum building heights, etc. All of these add cost and reduce density.
Single-family zoning is another local government policy that is absolutely intended to constrain development, not improve safety.
Well as someone who lives in a fairly regulated housing market (Berlin) I'm happy about all the regulations you've mentioned as they prevent negative externalities which would benefit real-estate developers at the cost of everyone else. Imo targeting a specific population density is within the mandate of local government, as too-high density causes all sorts of issues from traffic to health and everything in between. If you want to unchain developers on density, I invite you to take a 10km drive in Delhi or Bangkok and tell me if the cost generated on a daily basis in terms of time and stress is worth it.
I am in favor of finding ways to encourage more housing, but what you're calling for is essentially to invite favela housing in the developed world.
Seriously? Berlin's tower blocks are the favelas of the developed world.
Also, the reason why Berlin hasn't had the same pressure is because it is one of the few cities in the developed world that has actually shrunk over a multi-decade period. It is very easy to limit population density when there is no pressure on housing. And, ofc, the historical division of the city meant that it had to develop more than one centre. These factors aside, afaik, the development of Berlin hasn't been exceptional...they built suburbs when there was pressure on housing in the early 20th century, built public transport, those suburbs eventually integrated into the city...very few cities have grown through greater intensity in the centre because cost is prohibitive, regardless of regulations.
Nothing to do with regulations, everything to do with historical circumstance (also, the guy you replying to is quite correct...if you actually look at housing regulations in the US, they have been a tool for racial/economic segregation...being real, that is why the limit on multi-family housing exists, the US has very low population density, saying they will become Delhi if they reduce regulations is hysterical).
You've proven my point exactly. Many of the the Plattenbauten (GDR-era block housing) could not be built in Berlin today because of current regulations. Berlin is targeting a moderate population density with mixed-use neighbourhood, which is the recommendation of subject-matter experts and makes it in general a very nice place to live. There's a huge desire by developers to increase density within Berlin and I have no doubt it would increase massively if there was no check against this.
Nobody is arguing that the entire US would become like Delhi, but can you seriously hold the opinion that housing deregulation would not result in mass production of low-quality housing near major population centers?
There are problems with extremes on both ends. For instance the NIMBY-driven housing policy in SF is not what is needed to create sustainable housing. But is a somewhat unique case, and it doesn't mean an extreme swing in the other direction towards deregulation would lead to a good outcome. Sensible housing regulation is undoubtedly a requirement for sustainable urbanization.
Surely you can grasp that "and so on" implies other reasons than the one explicitly stated. Negative externalities are another example of a valid target of regulation.
It's not about safety, it's also about amenity and suitability and sustainability. In some areas, density is important given the population, in others its not.
Parking requirements are about local traffic management as well. Set backs are about ensuring natural light. Some local regulation is about NIMBYism or HOAism, that sort of thing is where reform might be better addressed.
Lack of set-back rules do not prevent building houses with set-back. Lack of parking law does not prevent building parking. Developers will not build density if it isn't a profitable use of the land -- i.e., important given the population. Rezoning to permit density does not immediately replace all existing structures.
Mandating these things is some of that "local regulation tied into NIMBYism" you mention.
Profitability isn't the only important metric here. It might be profitable for developers to increase density well beyond the point where it causes measurable negative externalities towards everyone occupying an over-crowded place.
Because there's a societal need to ensure that the housing stock is safe and effective. We invest (or should) a lot of our taxes into local amenities to ensure that housing is provided the best environment. Transport, schooling, roads, etc.
That housing should also be up to a similar standard in terms of its externalities like pollution and energy efficiency etc.
We have regulations for air travel, for car emissions and efficiency, why should housing be any different?
We have regulations for air travel, and that raises the price for air travel, which means some people can't afford air travel.
We have regulations for car emissions, and that raises the price for cars, which means some people can't afford cars.
We have regulations for housing, and that raises the price for housing, which means some people can't afford housing.
How many people should not be able to afford housing? Is the number of people who currently can't afford housing too low, or too high? Should we increase regulations for housing, or decrease them? Are we making the right trade-offs?
> Because there's a societal need to ensure that the housing stock is safe and effective.
And this is accomplished via building codes, which are rigorous and applied almost uniformly in the U.S.
> We invest (or should) a lot of our taxes into local amenities to ensure that housing is provided the best environment. Transport, schooling, roads, etc.
And this is the model that has made blue cities unaffordable for the poor. They're not environmentally friendly either, their schools are awful, amenities poor, and transportation lacking. It would be hard to find one single issue where there is even parity of centrally planned quality-of-life concerns in blue cities vs red cities.
The question is not "regulations" persay. There is no magical regulation slider bar that can be adjusted to optimal result. It's what those regulations seek to accomplish. In many U.S. urban metros, those regulations are targeted to what city policy thinks the owners should do with their property, and not what they want to do with it. It's not clear those regulations have had their intended effect.
Yes, you are wrong to hate flippers. You are wrong to hate anyone who is working hard to make an honest living. Yes, flipping is hard work.
All successful work probably displaces someone else in some way. If you're good at your job, you're "denying" that job to someone less skilled. If you work in software, you're automating things that would require more labor if done manually. Fortunately, humans can pivot.
Either hate everyone, or hate no-one. You can't just hate flippers.
I think there are ways to make money that are socially negative value -- e.g., theft is a pretty obvious one, or bitcoin mining.
House flipping isn't a social negative. They're doing a productive activity and producing value. They aren't long-term speculators removing housing stock from the market. It's essentially home renovation, done by a 3rd party owner.
Bitcoin people believe it is a moral good (myself included), and have extremely strong arguments in favor of that view, which are rooted in morality and economics. Thus, when you make a snide anti-bitcoin remark without actual content, you come across as trying to invalidate bitcoin through mere peer pressure, which we all know is juvenile.
It's like Trump voters who criticize "libtards." We all know it's not a valid way to discuss something.
In fact, the expansion of the fiat money supply enriches the wealthy through the Cantillon effect. Then, because the value of money is going down, they pile into assets like housing. For instance, the US is becoming a nation of renters due to this effect. The stock market is similarly distorted. We need bitcoin because we need an objective form of money. That would allow stocks and houses to stop being stores of wealth and reflect their true economic value, which would be a huge boon to everybody.
I'm guessing the energy thing is what you think your anti-bitcoin argument would be. Bitcoin mining is also such an efficient market that in the long run, only the most efficient forms of energy--such as nuclear and geothermal--will be viable for it. Bitcoin is already helping to advance "green" energy. This is abundantly clear to people involved in the mining industry.
Ethics is what we decide it is. How about not condemning practically everybody as some kind of sinner, the way you are? That's a counterproductive view that reeks of Christianity.
It doesn’t even matter if it’s hard work. Flippers take advantage of a market inefficiency, and just like everyone who does that, they make the market less inefficient. That’s a good thing even when it’s easy.
When Conglomocorp sells one of their houses, they just get the cash and can realize profits.
When average homeowner Joe sells their house they still have to live somewhere. They must immediately use that money for another house, which is also inflated. The higher sale price doesn’t matter.
Average non-homeowner Joe trying to buy a first house is SOL.
Flippers also reduce the supply of housing units for the time they are unoccupied, which could be months or years, especially when sold to other flippers. More liquidity is not necessarily a good thing for the housing market, as it tends to increase the number of speculators on the market, contributing to a vicious cycle where more homes are being flipped between speculators than actually occupied by people who need them.
It pains me to watch people apply simplistic theoretical laws of supply and demand to something as complicated as housing. The map is not the territory. There are massive costs to increasing supply, as well as psychological/community costs to moving homes, which are not cleanly captured in any Economics 101 textbook.
Here’s a thought experiment: if I told you the market was going to be less liquid and you may not be able to easily sell the house you’re about to buy, wouldn’t that change your behavior?
I think you’ve bought into the tik tok narrative that somehow it’s zillows fault that houses are expensive.
I'm buying housing for myself. It would be a potential relevant thing to take into account between choices, but I still need a house.
I'm buying a long term asset, so the liquidity of the housing market is not relevant to me, unless I'm actually buying for a specific short term, like a planned work period.
Liquidity of the housing market is only important to the agents and the loan originators because they make money on the flow.
> if I told you the market was going to be less liquid and you may not be able to easily sell the house you’re about to buy, wouldn’t that change your behavior?
No. Like a normal person, I bought my house to live in and to improve and to stay in for a long period of time. It isn't a speculative investment vehicle.
It seems like you’re presenting a straw man. Being able to sell your house and not be tied to one home for life is a reasonable desire that has nothing to do with speculative investment.
No, it's that if I need to sell it I've structured my finances and my life to be able to take time to do it--because I've intentionally made decisions with the remodeling in my home to be suboptimal for selling anyway. I'd have to put up a wall and reroute a bunch of plumbing for my laundry room off my master bedroom so I could turn it back into a bedroom because that's what the dollar-signs-for-eyes crew values, so why would I be worried about selling it in 48 hours?
This is an industry I actually know a teensy bit about. Normal people don't need to sell a house in two weeks. That is an abnormal condition brought on by stupid, disinterested money flooding the market and the idea that everyone must be hyper-mobile all the time is one brought on by the more deranged, "humans exclusively acting as work producing automatons" part of a market economy. Solidity and permanence are valuable. I'd go so far as to say that when you take into account the benefits of long-term residence and ownership--and they are benefits you will not see in your ML model, such as a cohesive neighborhood where you actually know and maybe even interact with the people who live around you--that it might even be a positive to discourage market thrash. You know. For humans, and not investors.
> structured my finances and my life to be able to take time to do it
so you do agree that it takes time to buy and sell real estate. The reason liquidity is better for market efficiency is that liquidity allows the price of the asset to move towards the "true" price, where either party of the transaction doesn't feel they've been cheated.
If you claim that house flippers are "cheating" the long term buyers, then you must also agree that the market is currently inefficient, and that the long term buyers is paying above the "true" price. Liquidity would actually alleviate that problem!
if you don't agree that flippers are cheating their price higher, then you must also agree that the long term buyers are getting a better deal.
So either way, liquidity makes the market more efficient, and results in the "true" price of the asset to be revealed sooner and easier.
It’s clear your approach to housing is consistent with your life choices. I think maybe you are just thinking your approach is “normal” and the right way to do things.
A lot of people buy starter homes, or homes in areas they do not plan to stay 10+ years, or homes they outgrow. That’s all normal too.
> if I told you the market was going to be less liquid and you may not be able to easily sell the house you’re about to buy, wouldn’t that change your behavior?
It would not change my physiological need for shelter, no
“Oh I might not be able to sell this for a profit in two years, guess I’ll die in the street”
Arguably, it's good for both. Buyers have more quality inventory to choose from and can purchase a home with lower risk of getting trapped in it permanently. Sellers get faster sales with a higher floor on prices.
Liquidity is NOT good in a dire-necessity supply-constrained market like housing, because it invites capital which could've been spent elsewhere to lock up unnecessary housing units (houses are empty while being flipped), further constraining supply of a critical resource.
Imagine if drinking water was treated as a speculative asset, with large percentages of a countries water supply being stored in tanks and sold back-and-forth on paper between capital-rich investors instead of actually being pumped to where it was needed through pipes.
> Imagine if drinking water was treated as a speculative asset, with large percentages of a countries water supply being stored in tanks
then you'd see people not waste any water at all, and fix any pipe leakage, and conserve water, and use water efficient agriculture methods etc.
The price of a commodity determines how much and how easily it is available. The fact that water is so liquid (both in terms of the price, as well as being an actual liquid) is because of the high amount of investments made into obtaining it over the past centuries. Liquidity of any asset (or commodity) is a good feature to have imho.
Both. In an illiquid market, it takes a long time to find buyers or sellers. You are incentivized to overprice (if selling) or underprice (if buying) and wait a long time to see if someone will match you. A liquid housing market means people can buy or sell the house at the "right" price without waiting many months or years.
As a seller, would you rather wait a year to make a bit more money? That wouldn't be good. That would be crappy.
Sure, but the more important point is that the market represents housing. Having housing empty is pointless, so market speculation is that is not about the rent from the asset but only its capital growth leads to that exact outcome, empty housing has lower expenses and depreciation in reality, which helps maintain the asset's value better than if someone was living in it.
I haven't heard of housing sitting empty. Even if there is a flipper in the chain of possession, houses end up getting acquired by long-term investors who rent them out or people who actually live in them. As far as I'm aware.
> A machine learning organization thinks of risk entirely differently than an automated risk underwriting organization.
It's possible and maybe even advisable to use machine learning in the automated risk underwriting business, but it is a different setup / set of objectives.
As the author notes, IMO the adversarial and antifraud aspect of risk underwriting turns it less into a straight-up estimation problem and much more into a game theory type of problem. ML models can assist in evaluating risk, but you do indeed have to be preocuppied by your risk as a party to the transaction in the first place, and not just trying to predict prices as a third party observer (which by itself is pretty riskless).
> Because when you have a hammer, everything tends to look like a nail and when you have TensorFlow, everything tends to look like an ML problem.
And if you have billions of dollars in cheap capital, everything looks like an investment problem.
Which is ultimately the suggestion of this article: "Why aren't you more like Wall Street?"
The implications are exactly the opposite of Zillow being an innovative company. If they require billions of dollars in deep pockets (nbd) and a restructuring of their org to be more like old-school operators, all signs point to existing players as more fundamentally correct about the strategy required to succeed in the space.
If the assumption is that you're going to lose half your money up front, then my plan would be to make sure "my money" is as little as possible: learn based on smaller bets. It sounds like Zillow built the Sea Dragon first, when they should have started with the Redstone and moved toward the Saturn V.
If Zillow thought they had all the data they needed, there would have been little harm starting with $100 million in properties -- if the loss there ended up being $5 million, they would have known immediately something was up and that they had work to do.
All of this reads like a Dickensian nightmare, where corporations have bought up all the water and air.
This is ridiculous, we need much better regulation on this stuff.
I wonder if higher property taxes would help a bit? If you own a 'home' then you're going to be paying for the water, school, electricity infrastructure whether you use electricity, water, or not.
Of course, that would be gamed hard and would have to be strongly regulated as well.
But that, and vacant property taxes, limits on some other things, and some other adjustments might help.
Feels analogous to the history of the collateralized debt obligation debacle where the models used to value CDOs were trained on data that no longer resembled reality. At least Zillow can live to fight another day, where as Stan O'Neal put all of Merrill Lynch's chips in with one of the biggest make-or-break gambles in the history of finance and the market turned against it, rendering Merrill to a fatally wounded company bailed out by Bank of America.
I think some reasons Zillow lost were that their pricing and risk processes were terribly underdeveloped in order to scale fast, their models were obviously inaccurate, and they didn't understand the difference between an acquisition cohort and resale cohort, and specifically how much the tail sales of an acquisition cohort determines profitability.
This is the same folly as Long Term Capital Management.
You're not going to be able to reliably model asset prices at the resolution and accuracy needed to front run the market for a long period of time. This case was worse because the "Zestimate" directly created a feedback loop that moved the underlying asset prices higher.
Zillow's mistake is that they thought their AI could replace human buyers instead of augment them.
Most AIs today are for augmentation, not replacement. Vehicle autopilots are a perfect example. The ones that are commercially available aren't capable of replacing the human, they just augment the human's abilities.
Realtor and Mortgage Lending industry is very slow in tech adoption and adaptation of digital strategy. Unless there is a lift across the industry on the buy-sell-marketplace together, such mishaps will occur. This industry will fail if injected with viral nature of social media algorithms.
If Zillow had "figured it all out" on solving the magic pricing problem, they could have put the entire appraisal industry out of business. Well, guess what, they didn't and they didn't even come close.
No "machine learning" model can successfully peer into the inside of the home, and in the walls, or in the plumbing, to get an accurate sense of the worth of the home because no machine learning model would have that information. It isn't available until you actually go and look, with real human eyes, into the house.
Always has been that way. Always will be that way. AI is great for when you need to tame a firehose and make millisecond decisions. But there's a 90 year old in Omaha who is better than the best AI.
Is it fair to call this the result of “AI thinking?” Meaning that urge to automate away human involvement, because —after all—-if people are involved in analyzing data and decision making, then clearly the AI isn’t finished let.
Getting people to initially sign up through bonuses causes a lot of money to be shed, and are thus not profitable until people renew (without the bonus) the second year. I remember seeing the CEO of Chase saying he was excited that they lost billions in the new sapphire card because it meant they had so many members
There's a lot of information that is only available to MLS members. Zillow used to not have access to this information, but they slowly brokered deals with MLSes around the country to get it.
Yeah, each MLS org has its own data set which is a giant pain in the ass for the newspapers who are publishing listings for the MLSes in their areas. I don't know if they ever standardized it, but I know that one of the first tasks I had as a new dev for a newspaper back in the early 00s was to build a tool to take the data and normalize it into a single CSV.
In order to actually understand true risk (to create a profitable model), you’ll actually have to experiment and lose money in order to bootstrap your own ML model. Taking data acquired elsewhere and hoping it can make your own model instantly profitable isn’t possible.
There's something really funny about white collar office worker businessmen talking about how it takes balls of steel to do what they do. Ok bro, sure. Trackballs of steel maybe
which assumes risk is (i) normally distributed and (ii) a source of reward. For most people, however, risk looks like Theranos or the Fukushima accident or the Challenger distaster.
It's unbelievable that a machine learning model trained to predict house prices based on experience would be accurate in the face of events like the COVID-19 pandemic or what will happen when the Fed raises interest rates. You can model risks like that, but to the extent that you're working from experience you are working from a database from the 1929 Crash, South Sea Bubble, etc.
(B) Mark Levine wrote a good article about how you'd exploit such a predictive model. If you consistently gave people low offers, a few people would accept them. You would get a high rate of return but could invest little capital.
To invest more capital you have to make more offers that get accepted, that is, give better prices. Your rate of return goes down and if there is shrinkage from errors, accidents, etc. you could get a negative return.
It's that "tendency towards a declining rate of profit" that Marx warned about.
(C) The analogy with stock market market makers doesn't sound good when you consider the differing timescales.
Market makers are isolated from some risk because of the length of their holdings. Yet, they make profits by exploiting the stochastics of a stationary market (e.g. if you don't like the price at time t1, you will usually get a better price at t2) but they lose money when markets move definitively in one direction or another.
That kind of trader heads for the bathroom when things go South and in the interest of being orderly markets impose sanctions on market makers who do the natural thing and press the "STOP & UNWIND ALL POSITIONS" button when it gets tough.
In the case of Zillow I see holding times that go on for weeks or months and all kinds of real world risk like planning to do certain renovations but having to delay the work because out of 20 things you need from Home Depot they only have 16 of them.
Buy low, sell high. You need “data science” to do this? $VNQ is up 33% since 2016. Do you realize how dumb and bad you have to be to lose money on real estate in this time? I imagine randomly picking homes off the MLS would have yielded better returns in the last five years than whatever Tableau-powered nonsense the biz ops analysts at Zillow used. The entire iBuying concept is a farce, completely divorced from basic fundamental analysis.
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 datasets.
However I think where the hubris lay is in how they thought they could leapfrog all the way to an automated solution before building a competency as a house-flipping company.
From what I understand, where they failed was partly in building a rich enough model to properly account for the less easily quantifiable elements which ultimately account for a property's value. I.e. the price per square foot might make a property look like a steal, while something like a sewer main nearby, or problematic neighbor could radically change the value proposition to anyone standing at the site. That's a non-trivial problem to solve for even the best ML and it's not clear how you would automate this.
If you ask me, instead of focusing on building an automated price discovery system, they should have started by trying to build a quality home-flipping organization, and figuring out how to super-charge manual work using their datasets. Over time you might find ways to optimize the process and increase the level of automation to scale output relative to head-count.