Tag: zillow

• And that’s a wrap

  

  My time in the mountains has come to an end. I’m on a flight back to Toronto and about to start watching old Bond movies (as one should). If you don’t ski or snowboard or do any other winter sports, it’s maybe hard to relate to this, but the mountains are a truly special place. I’m always sad to leave them. In my opinion, there’s no better place to disconnect and recalibrate.

  And even though I’ve been mostly disconnected, it has been hard not to miss the hype around the new Apple Vision Pro, which was released into the wild this week. I haven’t tried one yet, but every review that I have read or watched seems to come to the same general conclusion: “Wow, this thing is incredible. It feels like a glimpse into the future of computing.”

  If you’re looking for a comprehensive technical overview of the device, you should check out Marques Brownlee’s video, here. But if you’re just looking to get a sense of what it might be like to, you know, wander around New York City wearing one, you should definitely check out Casey Neistat’s video, here.

  Now I think there’s no question that there’s a dystopian element to all of this. When Casey is standing around and watching a butterfly eat his donut, he looks pretty strange from the outside. Only he is seeing the butterfly. But then again, we all look pretty weird standing around staring down at our phones all the time. Maybe this will help us become less disconnected. I don’t know.

  Either way, it’s hard not to imagine this changing — a lot. I mean, here’s just one small example. Already Zillow has an app for Vision Pro that allows you to tour homes for sale. Assuming it’s as good as everyone says it is, I can’t imagine anyone going to physically tour a home ever again, unless they’re really serious and/or ready to put in an offer.

  Of course, there’s also no shortage of people saying that this device is simply too expensive. But I think that misses the point. This is version one. At this point, Apple just needs to be directionally right about what they are calling “spatial computing.” (They don’t want you to call it VR.) Because if they are right, the price will come down and then we’ll all be watching old Bond movies on these devices.

  2024.02.03

• Every home is for sale; it’s just a question of price

  Over the last few weeks, a number of people have told me that, when it comes to their current home, they have a number in mind. They more or less said, “I’ve already spoken with my husband/wife about it and, if someone were to offer us $X, we would sell and move immediately.”

  What’s fascinating about this is that it’s a form of housing supply that generally doesn’t exist anywhere right now. Sure, the people I was speaking with would sell and move for a price, but how does something like this actually happen? How do buyers find them?

  I suppose it could happen through word of mouth. I now know their prices and so if someone I know were interested in such homes, I could tell them. It is a low probability, but it’s still a possibility. Alternatively, someone (an agent or otherwise) might just show up on their doorstep and make them an offer. My dad actually sold his last home this way.

  But again, how likely is this to happen? It doesn’t seem scalable. And this is why Zillow used to have something called a “Make Me Move” listing. Rather than a traditional listing, it was a listing for, “I don’t necessarily need to sell, but if you offered me $X, I would move.” For whatever reason, though, Zillow no longer offers this service. Presumably, it’s because it wasn’t working. Hmm.

  Here’s how I’m thinking about it.

  Today, most housing markets are binary. A home is either for sale or it’s not. Sometimes enterprising people manage to secure an “off-market home”, but generally speaking the market is binary. If a home isn’t for sale, most people don’t usually bother with it. Mostly because they can’t easily find it.

  But market conventions aside, the conversations I’ve been having suggest that it’s actually more of a gradient. On the one side are people who really don’t want to sell. Maybe they’re never sellers. Let’s pretend that the home has been in their family for generations and so to convince them to sell you’d probably have to offer them an absurdly high price and that might not even do it.

  On the other end of this gradient are people who are ready to sell today. In an extreme example, they might even need to sell by a certain date, or else. In this case, a below-market price could get them to sell. They are highly motivated and one sure-fire way to increase speed is to lower price.

  But for everyone else in between, it is a big unknown gray area where price and desire to sell are, I would think, inversely correlated. As desire to sell increases, expectations around price probably need to come down until they reach a point where the market can bear it and a transaction will occur. This is my hypothesis at least.

  But if it’s true, and there’s a big untapped gray area, then the housing market is a lot bigger than we think it is.

  2023.07.07

• Opendoor wants to be a transaction layer for homes

  We have spoken a lot over the years about Opendoor. And for a period of time, iBuying seemed like a very good idea. Zillow go into it. Redfin got into it. Everybody was iBuying. But then this year everybody started losing money, mostly due to algorithms that could not contend with falling prices.

  It turns out that being a market maker for homes can be a tough business because there is a lag between when you buy the home and when you hope to sell it. And so right now, few people want to be an iBuyer. Zillow no longer does it. Redfin no longer does it. And Opendoor’s stock is, at the time of writing this post, down 87.19% YTD.

  It is pretty easy to be pessimistic on this space, and that pessimism may be warranted. Though it may not be. My thinking has always been as follows. The process of buying and selling a home will eventually move online. The industry is ripe for change and there is no debating that. The real question is: how the hell do you do it? Everybody, including me in my late 20s, has tried.

  Two-sided marketplaces are tricky, because you always run into a chicken-and-egg problem. If you don’t have buyers, no seller is going to bother with your real estate marketplace. And if you don’t have sellers (i.e. homes), no buyer is going to bother with your real estate marketplace. So generally speaking, the way to build a marketplace is to start with one side, somehow get them on and using the platform, and then open it up to the other side.

  And this is exactly what iBuying hopes to do. Today it is largely a tool for sellers. It is a tool that says, “I will give you instant liquidity for your home so you don’t have to worry or care about who might actually buy it.” This is, of course, convenient for sellers, which is why people have been using it; but it is capital intensive and, as we have seen this year, it transfers some risk to the iBuyer.

  In the world of Opendoor, they call this a first-party (1P) transaction. It is them buying directly from sellers. But the larger vision is for Opendoor to become more of a transaction layer and instead just facilitate third-party (3P) transactions. This is currently being done through Opendoor Exclusives and the objective here is to match buyers and sellers directly, so that Opendoor can avoid taking on the risk of actually owning homes for a period of time.

  Will this work? I don’t really know. But I do think it is exciting and I do think it is the way to think about what Opendoor is ultimately trying to do with their business.

  Reminder: I am long $OPEN

  2022.11.23

• Listed.fun (and something else)

  This is a fun little passion project by Airbnb-engineer Andrew Pariser and someone known as Potch. The way it works is that it shows you a picture of a recently sold property, and you have to guess what it sold for. You get a bunch of guesses, and after each one, you are given more information about the property and some feedback on how close you are. To win, you need to get within 1%.

  When I tried it out, my initial guess was way off (too high). Toronto has trained me well. I also wasn’t sure where Evansville, Indiana was, so that bit of information didn’t really help me. But the arrows telling me I was way too high, certainly did. The reality is that it’s pretty hard to guess the value of a home if you don’t know where it is, you can’t see interior photos, and you generally don’t have enough information.

  But what if you were from Evansville, Indiana and what if you did have enough information? I bet that the guestimates would actually be pretty accurate. This idea of crowd-sourcing market information and pulling wisdom from crowds has long interested me, because price discovery is a major pain point for real estate. Sure you can look at comparable sales and current listings, but that is not an exact science. Neither are algorithms.

  But what if there was a way to test the market and get pricing feedback before you actually list? Would you trust it more than Zillow’s algorithm? This is something that I’m working on testing right now through a passion project called Unlyst. Myself and a few others are working on a very simple product that will be released this fall. If you’d like to follow along, sign up here.

  2022.09.09

• Opendoor is creating too many rentals

  Steven Levy over at Wired recently wrote a short piece comparing Opendoor’s iBuying approach to what Zillow was doing when it was in the space. (Thank you Robert Wright for forwarding me the article.)

  As we have talked about before, the fundamental problem with Zillow’s model is that it couldn’t accurately predict where home prices were going. It was losing too much money and so they shut down that side of their business.

  The article talks about Opendoor’s approach and how they’ve spent the last 8 years refining a valuation model/approach that is now apparently pretty accurate. That’s positive. But here’s another excerpt that I found particularly interesting:

  There’s one controversial aspect of the business model that Wong didn’t bring up. It appears that when companies like Zillow and Opendoor can’t easily sell a home, the fallback is what’s called an “institutional sale.” All iBuyers sell a small but not insignificant percentage to institutional investors with aspirations of being “mega-landlords.” While the marketing materials of the iBuyers emphasize clean sunny rooms and frictionless transactions, that segment of the market involves hedge funds like KKR and Blackstone snapping up properties for rental, limiting the inventory available for families seeking homes. Even the Biden administration has weighed in on the evils of this trend: “Large investor purchases of single-family homes and conversion into rental properties speeds the transition of neighborhoods from homeownership to rental and drives up home prices for lower cost homes, making it harder for aspiring first-time and first-generation home buyers, among others, to buy a home,” said a recent White House dispatch.

  It’s interesting for two reasons.

  First, these highly tuned valuation models are now being used to scale the acquisition of single family homes. No specific figures are given, but Levy speculates that some iBuyers could be feeding up to 20% of their homes to institutional buyers. Economies of scale are a challenge with this asset class. Here technology is helping.

  Second, I don’t like the tone toward renters in the above White House dispatch: “[It] speeds the transition of neighborhoods from homeownership to rental.” This line in particular implies that renting is perceived as being suboptimal to homeownership and that “speeding”’ towards the former is something that should be avoided for reasons of social good.

  Even the words that are used here suggest biases. A single-family home is called, well, a home. But a rented one is a rental property. I reckon that a home is a home regardless of whether it’s low-rise, high-rise, rented, or owned.

  2022.02.18

• The Zillow postmortem

  The postmortems surrounding Zillow’s exit from the algorithmic home-flipping business are starting to surface. Here’s an article from the WSJ and here’s Matt Levine’s take on it. The latter piece is very Levine-like and is called, “Zillow tried to make less money.”

  The obvious story is that Zillow’s algorithms were not valuing homes correctly. But the story is more nuanced than this. In Q1 of this year, Zillow’s home flipping business was actually more profitable than it had initially expected. And that’s because its algorithms were consistently undervaluing homes. So when it did transact, it was doing so at favorable / low cost bases.

  The problem was that the company was not transacting enough and there was a fear of losing ground to competitors like Opendoor. Apparently only about 10% of people who requested an offer from Zillow actually ended up accepting it. Margins were good, but volumes were too low.

  

  So what Zillow did was tweak its algorithm to be more aggressive (see above chart from the WSJ). But this created the opposite problem: low/negative margins, higher volumes.

  Once again, it shows you some of the challenges with bringing real estate online. The supply of homes is largely heterogenous and there are a lot of qualitative factors that play into what someone is willing to pay.

  2021.11.18

• Market making vs. home trading

  Matt Levine’s latest column is a good follow-up to yesterday’s post about Zillow exiting the algorithmic home-buying business. In it, he talks about the differences between being a market maker and being a trader of homes. Part of his argument is that if you’re a pure market maker then, in theory, you don’t really care about where home values are going. Because either way, you’re just earning a spread.

  Here’s an excerpt:

  A market maker is someone who buys and sells an asset in order to profit from the spread, not someone who accurately forecasts the price of an asset six months from now. End users want to buy or sell stocks or bonds or houses, they want to do it quickly at a predictable price, so they go to a market maker who will provide that service. The market maker buys from sellers and sells from buyers and does its best to match them up; ideally it buys an asset from a seller and resells it to a buyer within a fairly short time. It collects a “spread” from the buyer and seller: It buys from the buyer at a bit less than the fair market price, and sells to the seller at a bit more than the fair market price, because it is providing them a valuable service, the service of “immediacy” or “liquidity,” the service of always being available to buy or sell. 

  The problem with real estate is that you’re not able to buy and sell with the same kind of rapidity:

  But in the house business you can’t generally buy a house in the morning and sell it in the afternoon. You sign a contract to buy a house in the morning, then you do an inspection and title search and stuff, then a few weeks later you close on the house and deliver the money, then you spruce up the house a bit, then you wait for a buyer to come in — which takes, not seconds as it does in the stock market, but days or weeks or months — then you show the house to the buyer, then you sign a contract to sell it, then they do an inspection and title search and stuff, then you wait around for them to get a mortgage, then a few months later you close on the sale.

  This is an important distinction. And so he argues that what we’re actually talking about is the business of trading homes, which means that you have to have a view (and hopefully some conviction) on where home prices are going to go in the future. Sometimes you will be wrong. But that’s okay, as long as you’re right more often than you’re wrong.

  2021.11.03

• Zillow exits algorithmic home-flipping business

  Things are happening in the algorithmic home-flipping business right now.

  A few weeks ago I wrote about Zillow pausing this part of its business. It was then later revealed that the company was set to take a loss on many/most of the homes that it had purchased through this “iBuying” division. In October, it listed some 250 homes in Phoenix and on average they were priced about 6.2% below what they had bought them for.

  So it is perhaps no surprise that today the company announced that it will be the exiting the business of buying high and selling low. Turns out this isn’t good for business.

  But does this mean that the model doesn’t work or that Zillow simply didn’t have its algorithms tuned correctly? Following the news, competitor Opendoor took to Twitter to reassure everyone that the digitization of real estate is still well underway:

  We are in the midst of a generational shift towards a fully digital experience, with a large, unmet need for a seamless experience in real estate.

  — Opendoor (@Opendoor) November 2, 2021

  Opendoor also announced today that it will be expanding technical hiring into Canada — starting first with Toronto. The plan is to hire upwards of 100 people over the next several years. Presumably this is about access to talent, but presumably it also means that Opendoor is looking toward one day expanding into Canada.

  Stay tuned.

  Disclosure: I continue to be long $OPEN.

  2021.11.02

• Zillow pauses algorithmic homebuying business

  Zillow just announced that it has paused its (algorithmic) US homebuying business for the remainder of this year. The company acquired some 3,800 homes in Q2 of this year and, apparently, it now has a backlog of repairs and sales to work through. As a reminder, this business model, which is sometimes referred to as iBuying, is based on using algorithms to quickly value and buy homes (mostly online). The homes are then renovated and flipped for a profit. The problem, as most of you know, is that this pandemic has, among other things, disrupted construction supply chains and made it difficult to hire people. That has hurt the renovation component of this model.

  Today’s news was bad for Zillow’s stock, but good for Opendoor’s stock, which is their main competitor. Opendoor subsequently came out and announced that they remain open for business. (Disclosure: I am long $OPEN). But this announcement is perhaps a good reminder that buying and selling real estate remains a different animal than, say, buying and selling stocks. And so there are some perfectly understandable reasons for why real estate hasn’t been disrupted by the internet in the same way that other industries have. Matt Levine does a great job explaining this in his recent column, “Sorry, Zillow’s Computer Can’t Buy Your House Right Now.”

  Here’s an excerpt:

  “I’ll pay you $350,000 for your house as long as a human can go out there, look around, and make sure that price isn’t wildly off” is an interesting model but it’s not quite the same as “push this button to sell your house for $350,000.” And “I’ll pay $350,000 for a house and then send out a crew to replace the carpets” is not quite the same as “I’ll pay $350,000 for a house and flip it 20 minutes later for $355,000, collecting a small spread for providing liquidity.” Computerization has come into the housing market, but it hasn’t taken it over yet.

  One of the challenges is that the supply of homes is heterogeneous, even in a suburban community or in a multi-family building where you might have the same set of floor plans that repeat. Because maybe the home has been renovated and fit out entirely in gold. Or maybe it’s the opposite and it has been poorly maintained. There are variables to contend with that have historically necessitated more rather than less human involvement. Homes are also something that don’t trade all that frequently, which is less than optimal when it comes to online marketplaces.

  But what if buying and selling a home was dramatically cheaper and easier to do? How often would people actually do it? Presumably more often. I agree with Matt that “computerization” hasn’t taken over the real estate industry just yet. But algorithmic homebuying still appears to be one of the more promising approaches.

  2021.10.18

• An international travel boom is coming

  

  

  I was reading up on vaccine passports this morning. What is clear is that countries are scrambling to figure this out right now, though I understand Israel is already up and running, as is South Korea, which has a system built on top of the blockchain. (This feels like a great use case for the technology.)

  What is also clear (see above charts) is that many countries are highly motivated to figure this out sooner rather than later. The geographies that are weighted toward tourism dollars don’t want to miss out on yet another summer travel season. And given how dominant Europe is in terms of international arrivals, I suspect that they might end up leading the way in terms of rolling out some form of internationally accepted passport system. I would imagine that true universality is going to be a challenge though.

  Domestic travel in the US has already bounced back in a significant way. Looking at TSA screenings for the first half of this month (May 2021), travel right now is at about 70% of 2019 volumes. This is in comparison to just under 10% last year (May 2020). Once international travel gets streamlined in the second half of this year, I’m sure the same thing will happen on that front.

  One of my predictions at the beginning of this year was that we would see an explosion in global travel, probably in the second half of the year. I stand by that view. Many/most of us have spent the last year in various forms of lockdown and many/most of us have spent the last year with almost no work-life balance (a symptom of WFH).

  According to some recent data from home website Zillow, the company saw traffic skyrocket in 2020 from 1.5 billion visits to 9.6 billion visits (compared to the year prior). This is people looking at homes, and, in many cases, looking at homes that are more expensive than what they currently own. Real estate websites, you could argue, became a form of escapism last year, which is something that travel is normally pretty good at.

  People are restless and ready to unplug. I reckon that’s going to happen in a meaningful way later this year.

  Charts: Financial Times

  2021.05.23