Brandon Donnelly

Daily insights for city builders, delivered every morning at 6 AM ET. I’m Brandon Donnelly — a Toronto-based real estate developer and founder of Globizen. I’ve been writing here since 2013.

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Tag: tech

  • Landed is helping teachers buy homes

    The average salary of a teacher in the United States was approximately $61,730 last year. This can make homeownership in high cost areas a challenge.

    Here is a chart from Curbed:

    Landed is trying to solve this problem by offering downpayment assistance to “essential professionals” — starting first with teachers — so that they can buy homes in and near the communities that they serve.

    The way it works is pretty simple.

    They’ll contribute up to half of a traditional 20% downpayment — so 10% of the value of the home — in exchange for a 25% share in any future gains, or losses.

    Put differently, for every 1% that Landed contributes, it takes 2.5% of any future appreciation (or depreciation). However, on an equity basis, they are actually putting up 50% of the required cash (in the maximum scenario) in order to get 25% of any future gains.

    There’s no monthly payment associated with Landed’s money, but it does need to be repaid at the end of 30 years or when the homeowner exits the agreement, whichever comes first. Homeowners are free to repay Landed at any time should they decide to sell the property or they just want to pay them out.

    Landed pitches the service as another version of “the bank of mom and dad.” And for many prospective homeowners, I am sure that it makes all the difference in the world.

    At first glance, it would seem that each homeowner also benefits from a kind of positive leverage. They only put up 50% of the required equity, but they get to enjoy 75% of the potential gains. However, each homeowner is also responsible for 100% of the carrying costs.

    I ran a couple of quick return scenarios, assuming a $500,000 purchase price and a 10 year hold, in order to test whether Landed or the homeowner would receive a higher IRR once the property gets sold.

    I didn’t carry any transaction costs, but I did factor in principal recapture, as well as utilities, insurance, and maintenance.

    My rough numbers suggest that it depends on the annual rate of appreciation. If appreciation stays close to the rate of inflation, it could tip in favor of Landed because they don’t put out any money after t = 0.

    But at higher rates of appreciation, the homeowner starts to benefit from the favorable 75/25 split at the end of the hold period.

    Either way, Landed is providing a service to people who may not otherwise be able to afford to buy a home. That has value. Here’s some more information on how it works, in case you’re interested.

  • Uber’s seed investors made this much money

    $UBER went public on Friday. Notwithstanding the initial stumble, Uber will go down in history as one of the most lucrative venture capital investments of all time.

    The stock is down from its IPO price of $45 per share, but at that price, the initial seed investment of $510,000 that First Round Capital made back in 2010 was worth about $2.5 billion on Friday.

    Here is a list of some of the other notable investors from Uber’s seed round and what their initial investments grew to over the course of 9 years (chart from the WSJ):

    Of course, for every Uber, there are many more failed companies. And for every investor who turns $5,000 into nearly $25 million, there are many more who decided to pass on the opportunity.

    In the case of Uber, many early investors couldn’t see how the product could go mainstream. It initially started upmarket with limousines, which was actually a clever way to hack the chicken-and-egg problem that plagues marketplaces.

    Many also wondered how many metro areas outside of San Francisco had the kind of urban density and supply and demand drivers to support this kind of a service.

    Today, some nine years later and many billionaires later, lots of people — including myself — are still wondering: Will Uber turn out to be a great (i.e. profitable) business? Hindsight is always 20/20.

  • Redfin is rolling out an online purchase option for homes

    There’s a lot of money at work right now trying to reinvent the way that homes are bought and sold. Perhaps the most popular trend is “instant buying” or algorithmic home buying. I have been writing about this for years, mostly because of Opendoor. But now there are lots of companies competing in this space. With this model, home sellers get the benefit of an almost immediate sale, though usually it’s at a slightly lower price.

    Redfin, on the other hand, is returning to something that it first tried out back in 2006: a buy now button on its online listings. It failed back then. But maybe it was simply too early. The feature allows unrepresented buyers — that is, buyers without an agent — to make online offers. Naturally, it’s far from a single click process. But when accepted, the seller ends up paying about half the amount of commission.

    According to the New York Times, the company started testing the feature in late March in the Boston area. Of the 120 homes listed on Redfin with a “start an offer” button, 5 ended up being purchased via an online bid. That’s more than I would have expected. But Redfin positions these offers as being the stronger option because they save sellers money. There’s also an option to tour the home on your own.

    Given this initial response, the company is now working to roll out this feature nationally, market by market. Is this the future of home buying?

  • One great big exit (not the Brexit kind)

    Wired’s oral history of how the London startup scene came to be is a good reminder that, typically, a city needs some great big exits (acquisition or IPO) to really kickstart an ecosystem. In the case of Silicon Valley, you could perhaps trace things back to Fairchild Semiconductor (1950s). But a more recent example of this phenomenon would be the PayPal Mafia, whose members have gone on to found Tesla, LinkedIn, YouTube, and other companies that you may have heard of.

    Put simply: success begets success. When a startup does really well and the founders and employees of that company get rich, it is likely that many will go on to found/fund other successful companies in that same city. In the case of London, that catalytic startup was arguably Skype (at least according to Wired). Microsoft acquired the company in 2011 for $8.5 billion, giving birth to the Skype Mafia. Of course, that wasn’t the only ingredient, but it sure helped (excerpt from Wired):

    Since 2008, according to data compiled by Dealroom.co, the UK has created 60 unicorns (tech companies valued at $1bn or more) – 35 per cent of the 169 created across Europe and Israel. In the past three years, the UK has created more unicorns (25) than France, Germany, the Netherlands and Sweden combined (19). And London has produced 23 unicorns with a combined value of $132bn, compared with Berlin’s eight, worth $32bn.

    The world has changed since Skype was founded. It’s now cool to be doing a startup. But given that every city seems to be trying to establish a thriving startup scene, I think it’s valuable to point out just how important a single big exit can be, not just for the people within the company, but for the broader city. Easier said than done, right?

    Photo by Benjamin Davies on Unsplash

  • The WeWork of vacation rentals

    The word on the street is that Sonder — the marketplace for vacation rentals and competitor to Airbnb — is close to finalizing a $200 million investment round that would value the company at $1 billion.

    I first wrote about Sonder back in 2016 after I met someone from their business development team here in Toronto. I have yet to stay in a Sonder, but I’ve looked at their rentals a few times.

    One of the main differences between Sonder and Airbnb is that the former head leases their rental supply. And they do this by trying to go higher up on the food chain and partner with developers and real estate operators.

    In this regard, they are similar to WeWork. And it allows them to sit somewhere in between Airbnb and a conventional hotel. The supply is distributed, but the service offering is more consistent.

    Of course, this arguably makes their business model slower (they have to negotiate leases) and more costly (they’re committing to fixed costs). So it becomes a question of: How valuable is that consistent service offering?

    Lately when I travel, I’ve been trending more toward hotels, as opposed to Airbnb-like rentals. I like the experiences that many hotels are now focused on creating and I like knowing that if my flight arrives late (in a place like Brazil), I’ll be able to get into my room.

    I guess consistency does matter.

    Photo by Spencer Watson on Unsplash

  • The artificial intelligence bias

    Machine learning is one of the most important trends in tech right now. But like anything new, it naturally raises a number of important questions and concerns. Benedict Evan’s most recent blog post provides a good explanation of what he refers to as the artificial intelligence bias. Here are a couple of excerpts that I found interesting.

    What machine learning does:

    With machine learning, we don’t use hand-written rules to recognise X or Y. Instead, we take a thousand examples of X and a thousand examples of Y, and we get the computer to build a model based on statistical analysis of those examples. Then we can give that model a new data point and it says, with a given degree of accuracy, whether it fits example set X or example set Y. Machine learning uses data to generate a model, rather than a human being writing the model. This produces startlingly good results, particularly for recognition or pattern-finding problems, and this is the reason why the whole tech industry is being remade around machine learning.

    The rub:

    However, there’s a catch. In the real world, your thousand (or hundred thousand, or million) examples of X and Y also contain A, B, J, L, O, R, and P. Those may not be evenly distributed, and they may be prominent enough that the system pays more attention to L and R than it does to X.

    What AI isn’t:

    I often think that the term ‘artificial intelligence’ is deeply unhelpful in conversations like this. It creates the largely false impression that we have actually created, well, intelligence – that we are somehow on a path to HAL 9000 or Skynet – towards something that actually understands. We aren’t.

    The conclusion:

    Hence, it is completely false to say that ‘AI is maths, so it cannot be biased’. But it is equally false to say that ML is ‘inherently biased’. ML finds patterns in data – what patterns depends on the data, and the data is up to us, and what we do with it is up to us. Machine learning is much better at doing certain things than people, just as a dog is much better at finding drugs than people, but you wouldn’t convict someone on a dog’s evidence. And dogs are much more intelligent than any machine learning.

    Photo by Ales Nesetril on Unsplash

  • Liquidity network effect

    Uber filed its S-1 last week in anticipation of going public in May. The WSJ reported on it, here. These are always interesting documents because you get access to previously private information. Here we can see that Uber’s ride-hailing market share in the US is down to 67% (as of February 2019) from 78% two years earlier. Revenue from this business line — which is the company’s biggest — also seems to have levelled off (chart from the WSJ):

    The ride-hailing business today has become a commodity. A lot of people, myself included, simply check to see which service is the cheapest (usually it’s Uber vs. Lyft). So this space feels to me like a giant race to build the biggest network and get to something new, whether that be autonomous vehicles or delivery drones. Uber calls this creating a “liquidity network effect.” Here’s an excerpt from the S-1:

    We have a massive, efficient, and intelligent network consisting of tens of millions of Drivers, consumers, restaurants, shippers, carriers, and dockless e-bikes and e-scooters, as well as underlying data, technology, and shared infrastructure. Our network becomes smarter with every trip. In over 700 cities around the world, our network powers movement at the touch of a button for millions, and we hope eventually billions, of people. We have massive network scale and liquidity, with 1.5 billion Trips and an average wait time of five minutes for a rider to be picked up by a Driver in the quarter ended December 31, 2018. Every node we add to our network increases liquidity, and we intend to continue to add more Drivers, consumers, restaurants, shippers, carriers, and dockless e-bikes and e-scooters. We also hope to add autonomous vehicles, delivery drones, and vertical takeoff and landing vehicles to our network, along with other future innovations. Our strategy is to create the largest network in each market so that we can have the greatest liquidity network effect, which we believe leads to a margin advantage.

    If you’d like to download a full copy of their filing, click here.

  • Experimenting at the right scale

    Jeff Bezos published his annual letter to shareowners this week. You can find it here. And as is his usual practice, he has attached his 1997 letter to shareholders at the bottom of it. This is his “Day 1” and he clearly likes the reminder.

    I was somewhat surprised to learn that 58% of physical gross merchandise sales on Amazon are now by independent third-party sellers. This number has been steadily increasing almost every year since 1999.

    And this is despite the fact that first party sales — products sold by Amazon — have grown at a compound annual growth rate (CAGR) of 25% during this same time period. Amazon excels at the fulfillment component and you can have them do that for you as a third-party seller.

    There are a number of other interesting facts sprinkled throughout the letter, but I particularly liked the bits on “intuition, curiosity, and the power of wandering.” Here is an excerpt on how Amazon is working to scale the size of its failures:

    As a company grows, everything needs to scale, including the size of your failed experiments. If the size of your failures isn’t growing, you’re not going to be inventing at a size that can actually move the needle. Amazon will be experimenting at the right scale for a company of our size if we occasionally have multibillion-dollar failures. Of course, we won’t undertake such experiments cavalierly. We will work hard to make them good bets, but not all good bets will ultimately pay out. This kind of large-scale risk taking is part of the service we as a large company can provide to our customers and to society. The good news for shareowners is that a single big winning bet can more than cover the cost of many losers.

    A lot has already been said and written about accepting failure in life and business. Nobody wants to fail, but it can happen when you’re trying to “imagine the impossible.”

    The two nuances here are that failures should scale along with the company. And that “large-scale risk taking” can actually be construed as a service. It might mean that the impossible becomes possible.

  • IPOs and home prices

    Fred Wilson made an interesting remark in his recent post about the current “IPO bonanza” that is taking place in the tech space. He is, of course, talking about the recent IPO of Lyft, the recent S-1 filings from Pinterest and others, and the expected filings from Uber, Airbnb, and so on.

    After listing the benefits of going public, he went on to say that this bonanza will surely also mean that it is going to become even more unaffordable in the Bay Area. Part of this is perhaps self-serving, since he operates a VC firm out of NYC. (Take your money and move to NYC.)

    But the data suggests that there is truth to this.

    When Twitter when public in 2013, it was estimated that it created some 1,600 millionaires. This is great for the local startup ecosystem as many of these beneficiaries could go on to found their own companies and create a whole new batch of jobs. The money gets recycled.

    But what does it do to the local housing market — especially a supply-constrained one like that of the Bay Area where it is difficult to build?

    In 2018, Barney Hartman-Glaser, Mark Thibodeau, and Jiro Yoshida penned a paper called, Cash to Spend: IPO Wealth and House Prices. In it, they looked at the impact of IPOs on local home prices in California from 1993 through to 2017.

    What they found, among other things, was a “positive and significant association between local house price changes and firms going public.” The price increases were also found to be the greatest the closer you get to the headquarters of the firm that just went public.

    If you’d like to download a copy of the paper, you can do that here.

  • Forum on Future Cities: Urban Intelligence

    MIT Senseable City Lab and the World Economic Forum’s Global Future Council on Cities and Urbanization are hosting a conference next month on the impact that artificial intelligence is having on our cities. Here is a summary of the event:

    As AI (Artificial Intelligence) becomes ubiquitous, it transforms many aspects of the environment we live in. In cities, AI is opening up a new era of an endlessly reconfigurable environment. Empowered by robust computers and elegant algorithms that can handle massive data sets, cities can make more informed decisions and create feedback loops between humans and the urban environment. It is what we call the raise of UI (urban intelligence).

    The 2019 Forum on Future Cities, organized by MIT Senseable City Lab and the World Economic Forum’s Global Future Council on Cities and Urbanization, will focus on four aspects of the UI transformation: autonomous vehicles, ubiquitous data collection, advanced data analytics, and governing innovation. Panelists include mayors, academics, senior industry leaders and members of civil society to explore such topics from different points of view, highlighting the scientific and technological challenges, the critical collective decisions we as a society will have to make, and the exciting possibilities ahead.

    The forum takes place on April 12th in Cambridge, Massachusetts. And since it looks to deal with many of the topics that we talk about on this blog, I figured that some of you might be interested in attending. If so, you can register here.