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.
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?
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?
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.
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.
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.
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.
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.
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.
This recent post by Sam Altman (of Y Combinator) on how to achieve outlier success was just passed around our office. And it’s so fucking good that I decided to regurgitate it here on the blog by listing all 13 of his thoughts along with some of his most salient points. All of the words below are his (not mine), but most of his words are missing. I wanted to make a more condensed version so that you could easily print out this post and affix it to your desk. I am about to do that. Thanks for a great post, Sam.
1. Compound yourself
I think the biggest competitive advantage in business—either for a company or for an individual’s career—is long-term thinking with a broad view of how different systems in the world are going to come together. One of the notable aspects of compound growth is that the furthest out years are the most important. In a world where almost no one takes a truly long-term view, the market richly rewards those who do.
2. Have almost too much self-belief
Self-belief is immensely powerful. The most successful people I know believe in themselves almost to the point of delusion. Cultivate this early. As you get more data points that your judgment is good and you can consistently deliver results, trust yourself more. If you don’t believe in yourself, it’s hard to let yourself have contrarian ideas about the future. But this is where most value gets created.
3. Learn to think independently
Entrepreneurship is very difficult to teach because original thinking is very difficult to teach. School is not set up to teach this—in fact, it generally rewards the opposite. So you have to cultivate it on your own.
4. Get good at “sales”
All great careers, to some degree, become sales jobs. You have to evangelize your plans to customers, prospective employees, the press, investors, etc. This requires an inspiring vision, strong communication skills, some degree of charisma, and evidence of execution ability.
Getting good at communication—particularly written communication—is an investment worth making. My best advice for communicating clearly is to first make sure your thinking is clear and then use plain, concise language.
5. Make it easy to take risks
It’s often easier to take risks early in your career; you don’t have much to lose, and you potentially have a lot to gain. Once you’ve gotten yourself to a point where you have your basic obligations covered you should try to make it easy to take risks. Look for small bets you can make where you lose 1x if you’re wrong but make 100x if it works. Then make a bigger bet in that direction.
6. Focus
Once you have figured out what to do, be unstoppable about getting your small handful of priorities accomplished quickly. I have yet to meet a slow-moving person who is very successful.
7. Work hard
I think people who pretend you can be super successful professionally without working most of the time (for some period of your life) are doing a disservice. In fact, work stamina seems to be one of the biggest predictors of long-term success.
8. Be bold
If you are making progress on an important problem, you will have a constant tailwind of people wanting to help you. Let yourself grow more ambitious, and don’t be afraid to work on what you really want to work on.
9. Be willful
People have an enormous capacity to make things happen. A combination of self-doubt, giving up too early, and not pushing hard enough prevents most people from ever reaching anywhere near their potential.
10. Be hard to compete with
Most people do whatever most people they hang out with do. This mimetic behavior is usually a mistake—if you’re doing the same thing everyone else is doing, you will not be hard to compete with.
11. Build a network
Great work requires teams. Developing a network of talented people to work with—sometimes closely, sometimes loosely—is an essential part of a great career. The size of the network of really talented people you know often becomes the limiter for what you can accomplish.
12. You get rich by owning things
The biggest economic misunderstanding of my childhood was that people got rich from high salaries. Though there are some exceptions—entertainers for example —almost no one in the history of the Forbes list has gotten there with a salary.
You get truly rich by owning things that increase rapidly in value.
13. Be internally driven
The most successful people I know are primarily internally driven; they do what they do to impress themselves and because they feel compelled to make something happen in the world. After you’ve made enough money to buy whatever you want and gotten enough social status that it stops being fun to get more, this is the only force I know of that will continue to drive you to higher levels of performance.
Earlier this month it was announced that the on-demand electric scooter and bike startup, Lime, had closed a $310 million series D round. This values the 18-month old company at around $2.4 billion and brings its total raise to $867.1 million. For comparison, Bird — its main competitor — has raised around $400 million.
These numbers should tell you about the kind of growth that the “micromobility” startup is seeing. They are now in 15 countries and its riders have taken over 34 million trips. In the last 7 months alone, the company reports that it has seen a 5.5x increase in ridership. They are seen as an affordable last-mile solution. Supposedly 1/3 of its users report an income of less than $50,000 per year.
Lime entered the Canadian market last fall via Waterloo. They have yet to expand anywhere else, though I suspect we’ll see them in Toronto this spring/summer. One of the barriers is that their scooters (with airless tires) aren’t equipped to deal with snow, so they currently pack them up during the winter months.
This is in addition to the regulatory challenges they are facing in cities all around the world. But like Uber, I am sure there is a compromise to be had.
