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.
Below is a list of the 44 cities found in the 2018 Global Power City Index by the Mori Memorial Foundation’s Institute for Urban Strategies.
The index ranks the major cities of the world according to their “magnetism”, which they generally define as a city’s ability to attract people, capital, and businesses from around the world.
As with all rankings, the output depends entirely on the methodology that you use. The GPCI seems to have the right executive committee in place. It includes global city authorities like Saskia Sassen. But that’s not really the point of today’s post.
Beside each city, I have added the average highs and lows (in celsius) for both the coldest and hottest months of the year. For cities in the northern hemisphere, these are typically January and July/August, respectively.
I have also added the spread between the hottest and coldest months to get a sense of variability. I always find it interesting to see how cities like Singapore, Kuala Lumpur, Jakarta, and Mumbai basically stay the same temperature all year round.
When you look at this list, remember that you can ski in Dubai.
All weather data taken from the NOAA (National Oceanic and Atmospheric Administration).
People move to cities for a whole host of reasons, whether it be for more money, more affordable housing, and/or better weather. The fastest growing cities in the US, for example, tend to be in the south where it’s warmer and where housing supply is more elastic. However, we also know that “consumer leisure amenities” increasingly factor into this decision.
A new research paper by Gerald A. Carlino (Federal Reserve Bank of Philadelphia) and Albert Saiz (MIT) has tried to quantify this relationship by looking at the perceived beauty of a place. To do this, they analyzed the number of tourist visits and the number of “crowdsourced picturesque locations” in a metro area. Read: Instagrammable moments.
What they found was that beauty, not surprisingly, matters (much like it does in other facets of life). Between 1990-2010, metro areas that were perceived as being “twice as picturesque” experienced greater population growth — about 10 percentage points higher. These metro areas also attracted a higher percentage of educated individuals and experienced greater housing appreciation.
If you’d like to download a copy of Beautiful city: Leisure amenities and urban growth, click here.
On February 1, 2017, an inclusionary zoning ordinance came into effect in Portland, mandating that all new residential projects with 20 or more units dedicate a portion of the building to affordable housing.
For the first year, the requirement was 8% of all units for households earning 60% of the Area Median Income or 16% of all units for households earning 80% of the AMI. I’m not sure if it was or is possible to do a blend of the two income levels.
After the first year, the requirement was supposed to step up to 10% and 20% of all units, respectively. But that step up was never enacted, which had many industry analysts arguing that it was a clear signal the ordinance was not performing as intended.
According to Joe Cortright of City Observatory (which is based in Portland), the new ordinance largely resulted in 3 things happening:
(1) Developers rushed to get new applications in during the transition period so that they would not be subjected to the new IZ rules; (2) applications increased for projects with less than 20 units (avoid the rules by building smaller); and (3), following the initial transition surge, building permit applications, as a whole, dropped off.
This last point is what usually comes up in debates around inclusionary zoning. Does the requirement to build affordable housing actually reduce overall housing supply?
I’ve written about this before, but the math is pretty simple. Inclusionary zoning policies are a drag on revenue and a direct cost to the project. What that means is that something else will need to give in order for the numbers to balance.
That could come in the form of lower costs (such as an impact fee abatement) or in higher rents on the balance of the units. But this latter approach is easier said than done. Sometimes you need to wait for the market to “catch up”, which could be what some developers in Portland are doing.
They’re waiting for housing to get more expensive — overall — so they can then offset the pro forma drag from the affordable units.
When many (or perhaps most) people think of Miami/Miami Beach, they think of its beaches and resorts. And that is certainly a mainstay of the region’s offering. But over the past few decades, Miami has also emerged as an important global city (albeit at a more regional scale) and as a center for art and culture. Miami has the second largest concentration of international banks in the United States after New York, which begins to speak to the region’s importance for Latin America.
New York City is what it is today because it was the port of entry for new immigrants coming to the United States. This same phenomenon is what reshaped the Miami economy, starting first with Cuban exiles. Today, the city remains a refuge for Latin Americans searching for greater political and economic stability. As my friend from Miami likes to tell me, “the best thing about Miami is that it’s so close to the United States.”
I’m enjoying this book and I bet some of you will as well.
The European Union uses something called the Nomenclature of Territorial Units for Statistics (or NUTS) in order to geographically subdivide its member states and collect statistical data. There are three scales: NUTS 1, 2, and 3. And generally speaking, they follow existing administrative boundaries. Or at least that’s the goal.
In addition to being used for collecting statistical data, they also form the basis for how the EU implements its “cohesion policies“, which are designed to direct funds toward less developed regions within the Union based on GDP per capita (PPP).
Because these NUTS impact funding allocations and because they can be redrawn if certain criteria are met, there’s not surprisingly an incentive to gerrymander. The Pudding has a great visual essay that explains why this happens and how Hungary split its central region into two new ones, isolating its capital, Budapest.
Big cities tend to be, of course, more developed than their surrounding areas. But it is interesting to see by how much. Though an extreme outlier, the Inner London – West region has a GDP per capita that is 625% the EU average (2017 numbers).
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?
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.”
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.
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):
TL:DR from today's 2018 Census local population estimates:
Suburbanization marches on.
Suburbs and mid-size metros grew fastest in 2018. Small rural uptick. Biggest metros shrank. Let's dig deeper. (thread) pic.twitter.com/d4GLPAoVfC
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.
Jens von Bergmann (data analyst and mathematician); Nathanael Lauster (sociologist); and Douglas Harris (law professor) have been working since 2018 on a study of how condominiums are used and occupied across Canada. The goal is to use the results to better inform public and academic debate.
They recently presented some of their early findings at the National Housing Conference in Ottawa and have since made that information public. It is still a work in progress, but already there are some interesting takeaways. To start, here is a chart showing occupied housing units in Canada and in select CMAs:
Not surprisingly, Canada is broadly speaking a nation of single-detached houses. But in our three largest cities — Toronto, Montreal, and Vancouver — apartments/condominiums are doing a lot of the heavy lifting.
Vancouver has the highest proportion of condominiums. It is a geographically constrained metro area and it is one of the first cities in the country to adopt condominiums as a housing tenure. And in Montreal, there are more apartments under 5 storeys than there are single-detached houses. Not surprising. There’s no “missing middle” in this city.
But the really interesting question is, how are these condominiums being used and occupied? It’s a challenging question to answer, which is why it’s so often debated, but here’s what the researchers have found so far:
The owner and renter categories are self-explanatory. Temporary, which is the least common type of tenure, is where the owner has declared their principal residence as being somewhere else. In other words, the condominium is a second home.
The vacant category is effectively that city’s condominium rental vacancy rate. These are condominium units which are empty, but that are at the same time listed for rent. There are relatively few of these. In Toronto and Vancouver they’re virtually non-existent in this dataset (2016).
Finally, we get to unoccupied units. This one is tricky and the researchers aren’t exactly clear on what is driving this number. They chalk it up, at least partially, to the flexible nature of condominiums. For example, it could be empty because the unit is switching from owner-occupied to rental, or vice versa.
That said, it is very interesting to note that Toronto and Vancouver actually have the lowest percentage of unoccupied condominium units. This may be surprising to some of you given the public discourse around investor units in these two cities.
Generally, they found that in Canada’s three largest metro areas, the following rule of thumb seems to apply: For every 10 condominium units built, 6 will become owner-occupied, 3 will enter the rental stock, and 1 will go unoccupied. Does that seem right to you?
If you’d like to dig into the methodology that the researchers used, you can do that over here at Mountain Doodles. All of the charts and data used in this post were taken from there.
