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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Author: Brandon Donnelly

  • 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

  • Climate gentrification is reshaping coastal cities

    Last year, Jesse Keenan, Thomas Hill, and Anurag Gumber of Harvard University, published a research paper called, Climate gentrification: from theory to empiricism in Miami-Dade County, Florida.

    What they were trying to uncover was a possible relationship between climate change and single-family home pricing in places, like Miami, that are vulnerable to sea level rise and flooding. This phenomenon is colloquially referred to as “climate gentrification.”

    One of the things that they uncovered through their work was, in fact, a positive correlation between the rate of price appreciation of single-family homes in Miami-Dade County and incremental measures of higher elevation. In other words: there’s value in higher ground.

    Recent reports (like this one from the WSJ) that Little Haiti in Miami is experiencing a surge in investment, seem to, at least partially, support this finding. Little Haiti sits about twice as high as Miami Beach, which is only about 4 feet above sea level.

    Here is a diagram from the WSJ showing the change in home prices since 2018:

    I’m not sure that this diagram necessarily reinforces the above finding. Mid-Beach in Miami Beach is shown as having an 8% gain, and yet it sits, like pretty much the rest of the Beach, within a 100-year floodplain. But already Miami is looking to manage the impacts of, “gentrification that is accelerated by climate change.”

  • 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

  • Architecture of density

    Photographer Michael Wolf died at his home in Hong Kong this week. He was 64. Even if you don’t recognize the name, I am sure that many of you have seen his work. Perhaps his most famous project was “Architecture of Density”, which had him documenting Hong Kong’s extreme urban density from 2003 to 2014. Here is a photo (taken from his website):

    Michael was born in Germany, but grew up in Canada (he went to school in midtown Toronto) and in the United States. He eventually settled in Hong Kong and Paris — both of which became muses for his work. Michael made a career out of documenting cities and the life that happens within them. If you aren’t familiar with his work, I would encourage you to check it out here.

    Photo: Michael Wolf

  • Atlas of Urban Expansion

    Since 2012, a team at New York University has been working on something called the Atlas of Urban Expansion. What they are doing is collecting and analyzing data related to the quantity and quality of urban growth around the world. Everything from population densities to how well the streets were laid out during each geographic expansion.

    The Atlas defines a city as having at least 100,000 people, which is a commonly used benchmark. According to this definition, there were 4,245 cities on the planet as of 2010. Included in their study is a representative sample of 200 of them, all of which can be found here.

    They are also, rightly, looking at each city in terms of its extrema tectorum — the limits of its built-up area. This is as opposed to using administrative boundaries, which wouldn’t be as relevant in a study like this.

    I really like the animations that they created depicting urban growth from 1800 to 2014, because they show: (1) where each city started (the dark nucleus); (2) how different urban shapes emerge as a result of geography, transport, and other factors; and (3) how land consumptive many of our cities have become in recent years.

    Image: Atlas of Urban Expansion

  • Thoughts on Autonomy Day

    This past Monday, Tesla held an event for its investors called “Autonomy Day.” It was livestreamed, but if you missed it, here’s the video. It’s almost 4 hours long, though the first hour is just footage of Tesla vehicles driving around. I’m assuming it was background content.

    I’ll be honest in that I haven’t watched it all. But there’s a lot here if you want to get into the inner workings of how their self-driving cars work. Musk also promises, at the event, that Tesla will have level 5 autonomy ready by the middle of next year (2020). At that level, you will no longer need to pay attention to the road as a driver.

    Along with this autonomy, the company plans to start rolling out “robotaxis” and a ride-hailing app that will allow owners to rent out their cars. Musk is predicting that this could generate upwards of $30,000 in profit per year for owners. Of course, at this point, nobody really believes any of these promises. Musk is notorious for overselling.

    But let’s imagine that robotaxis are the future. Maybe it won’t happen by the middle of 2020. But it will happen at some point.

    If taxis are automated machines that drive people around all day and then go and park somewhere during off-peak times, where do they want to go and park? Does autonomy all of a sudden disconnect the locations of owners and parking, because your car will simply come to you when you need it?

    And what do these feature mean for parking supply? Presumably (and we have talked about this before on this blog), you need less parking and it wants to be in locations where the real estate values are less. But because of this, I bet that we’re going to need to start — and get really good at — pricing road usage.

    What are your thoughts?

  • 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

  • From urban to suburban

    The US Census Bureau just released its population estimates for 2018. As has been the case in previous years, the counties that added the most people (largest numeric growth) are all located in the south and west. Texas holds 4 out of the top 10 spots.

    Here is a Tweetstorm by Jed Kolko, the chief economist of Indeed, with a couple of graphs summarizing the findings (click through to see the full thread):

    Despite the narrative that people are returning to cities and urban centers, the data is pretty clear: the flow of domestic migration within the US is largely from dense urban counties to more suburban — and affordable — ones. Big cities are expensive.

  • The capital of the future: Shanghai

    Joe Berridge’s recent opinion piece in the Globe and Mail makes the case for why Shanghai is destined to become “the capital of future.” Brash city building, massive scale, and entrepreneurial hustle are among some of the reasons why he believes the city is on a path to global supremacy. And similar to other great capitals, it has benefited from a strategic geographic position on an important waterway — in this case the Yangtze River.

    By way of comparison, Toronto is said to be the fastest growing urban region in both North America and Europe right now. We add somewhere around 125,000 people each year. Shanghai, on the other hand, is adding between 700,000 and 800,000 people each year — much of it from internal migration. The city currently has a population of around 24 million people and it is expected to grow to somewhere between 35 and 45 million people by 2050. (Figures from the Globe.)

    Notwithstanding all of our successes as a global city region, as I was reading Berridge’s piece I couldn’t help but come back to this comparison. Shanghai opened its first subway line in 1993. Today it has one of the most extensive networks in the world; whereas, it would probably take Toronto this long to figure out if that first line should be light rail or a below-grade subway. And we haven’t even gotten to the number of stops yet.

    But that’s one of the differences between top-down and bottom-up city building: speed.

    Photo by Denys Nevozhai on Unsplash

  • Young people are driving a lot less

    As a kid growing up in the suburbs, I got my driver’s license the day I turned 16. Being able to drive was a big deal. But we know that this desire to drive has been changing in profound ways. Here’s some recent stats on the percentage of licensed drivers in the US by age (taken from the WSJ):

    In 1983, about 46% of 16-year-olds had a driver’s license. By 2014, this number had dropped to 24.5%, which is the lowest it has been in recent years, and was probably impacted by the broader economy. As of 2017, this number was up to about 26%.

    If you’re a car company, I would imagine that these are pretty important numbers. They represent the top of the sales funnel. Most people probably like to have a driver’s license in hand before they go out and buy a car.

    Supposedly, some people in Detroit are betting that young people will still eventually buy a car. And when they do, it’ll be a nice big one like an SUV or a truck. But, the data suggests that it is not just young people who are eschewing driving.

    Here’s some data from the University of Michigan Transportation Research Institute (via NPR), looking at the proportion of licensed drivers in the US by all age categories:

    While the biggest drop has certainly happened among younger generations, licensing is still down for older cohorts. Based on these numbers, we don’t hit parity until somewhere around 50 to 54 years old.

    And the only cohorts where licensing has increased significantly are when people reach over 55. Over 70 is up by a huge margin — more than the drop among 16 year olds — which is probably a symptom of people living longer.

    Some of this decrease among young people can probably be attributed to delayed family formation and people living in denser urban environments, where it is more convenient to get around without a car. But I don’t think that’s all of it.

    Which suggests to me that the race to autonomy is a pretty important one to win.