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.
We have spoken a lot about pedestrian fatalities over the years (here, here, and here are a few posts), and, if there is a general rule of thumb, it is that pedestrians are safer in dense urban environments where there are a lot of other people walking around.
But another important factor might be average vehicle size. Here is a recent study by Justin Tyndall that combined US pedestrian crash data with car sizes to come up with the effect of front-end vehicle height on pedestrian death probability. This is an important metric because larger/higher front-ends are more likely to fatally hit someone in their chest and/or head.
What was ultimately found was that a 10 cm increase in front-end height — which is really not a lot — causes a 22% increase in pedestrian fatality risk! Meaning that something as simple as reducing front-end heights could reduce pedestrian fatalities. By his estimation, a 1.25 m height cap would reduce US pedestrian deaths by about 509 people each year.
This is pretty interesting, especially considering that average car sizes seem to keep going up.
Here’s an interesting paper from WFH Research that looks at, “the evolution of working from home.” Not surprisingly, remote work tends to vary by industry, with tech being the most likely to work from home and with hospitality & food services the least likely.
By extension, WFH prevalence also appears to correlate with population density. This largely has to do with the kinds of jobs that center themselves in big and dense cities. This is interesting because one conventional way to think about cities is that they are places where businesses and people cluster to accumulate wealth. That clustering is still happening, but work is evolving.
And that is always the case.
Overall, the authors conclude that about 40% of US employees are now working at least one day a week at home, and that just over 11% are fully remote. They also argue that fully remote work lowers average productivity by about 10-20%, but that hybrid work is closer to flat. Interestingly enough, opinions on productivity differ whether you ask employees or managers.
North American cities have long had a problem with apartment buildings.
One the one hand, they were viewed as an important requirement for world-class status. Regardless of whether there was an economic imperative to build in this way, you needed grand buildings to communicate that you were an important and sophisticated city.
But on the other hand, apartments were viewed as clearly inferior to low-rise houses. Apartments were too dense; they were thought to morally corrupt people (infidelity meant just walking down the hall); and by definition — until the rise of condominiums — they were filled with renters.
One of the first apartment houses to be completed in the city was the Alexandra Palace Apartments (pictured above) on University Avenue near Elm Street:
The next building to be completed, the Alexandra, on University Avenue, was on an even grander scale. It was promoted by the Union Trust Company, but subsequently owned by the specially constituted Alexandra Palace Co. Ltd., and opened in 1904. The building, of stone, brick and steel construction, comprised 72 suites on seven floors; it also included dining rooms. In 1905 more than a quarter of its suites were vacant, mainly on the upper floors (although the very top floor was fully occupied); its tenants included a leading judge, two barristers, a professor, a doctor and a prominent real estate agent, but otherwise its social standing did not quite match that of St George Mansions. In 1915 occupants included Professor James Mavor. There were more tenants aged in their thirties than in St George Mansions, but overall the average age of 42 and household size of 2.6 was not dissimilar.
But perhaps the most interesting part of the paper is Toronto’s reaction to this apartment boom. We moved to stop it:
Nonetheless, it is clear that the attempted invasion of high-status single- family areas in Parkdale and, more especially, Rosedale and Avenue-St Clair, provided the catalyst to action. For all the moral outrage and sanitary evidence, there was little concern as long as apartments stayed downtown or in lower-status neighbourhoods. This becomes even more apparent when we examine what happened in the months following the passage of the by-laws.
Toronto’s housing stock has changed dramatically over the last 100 years or so, and we are now nearly 50% apartments/condominiums over 5 storeys. But at the same time, some things seem to never change.
People have noted that walking seems to have a special relation to creativity. The philosopher Friedrich Nietzsche (1889) wrote, “All truly great thoughts are conceived by walking” (Aphorism 34). The current research puts such observations on solid footing. Four studies demonstrate that walking increases creative ideation. The effect is not simply due to the increased perceptual stimulation of moving through an environment, but rather it is due to walking. Whether one is outdoors or on a treadmill, walking improves the generation of novel yet appropriate ideas, and the effect even extends to when people sit down to do their creative work shortly after.
The results were a bit inconclusive as to whether outdoor walking is better than other forms of walking, so for now we will just say that walking — in general — is good for creative thinking. But where my mind immediately goes is: Does this finding scale up?
In other words, if you were to take two different cities — City A where everybody, for the most part drives, and City B where everybody, for the most part, walks — could you find any evidence that City B was on average more creative than City A?
I guess one way you could measure this is through patents. And if you were to look at patents per capita in the US, you’d likely find cities like Princeton (NJ), Redmond (WA), and cities in Silicon Valley near the top of the list. I’m not sure there’s an obvious correlation here.
But it is kind of interesting to think about a possible relationship between urban form and creativity.
In 2020, Berlin implemented a rent cap that applied city-wide to both new and existing rental housing contracts. The policy was later found to be unconstitutional and so as of April 2021 this is no longer in place. But for a brief period of time, and for better or for worse, Berlin had a blanket rent control policy. Berlin is, of course, not alone when it comes to rent caps. They are seen by some as a solution to rising home prices, gentrification, and displacement. But do they actually work?
This recent working paper argues that the answer is no. And that there are other better tools available. Yes, overall rents do tend to decline. But when you have a city-wide policy, it means that rents also decline for high-income households. And in this paper, the economists argue that this tends to benefit the rich more than the poor. Rent caps also tend to decrease overall housing supply, which, as we all know, is counterproductive when you’re trying to make something more affordable/accessible.
But perhaps the key argument is this one here: Rent controls create a misallocation of housing that can actually decrease overall welfare for lower-income households. The reason behind this is that homes stop getting allocated to those who value it and need it the most. Instead, you get people who may be overhoused or underhoused, but who remain firmly put because of what are below-market rents.
There are a number of ways in which this distortion might play out. But it could involve someone with a very large older apartment who now no longer needs a large apartment, but is staying put because of their favorable and irreplaceable rent structure. This in turn precludes someone who desperately needs a large apartment from finding a suitable place. And since overall supply has also decreased because of the controls, the problem is exacerbated.
It can all get a bit complicated, but if you’re interested in this topic, here is another technical research paper from Edward Glaeser and Erzo Luttmer that deals specifically with the misallocation of housing under rent controls.
Earlier this year, Enrico Moretti, who is a professor at UC Berkeley, published this research paper looking at the effect of high-tech clusters on productivity and innovation. (I am unclear if there is any relationship to the Italian brewing company Birra Moretti.)
One of the things he looks at in the paper is the decline of Kodak. Headquartered in Rochester, New York, Kodak famously missed the transition to digital photography. And so by the late 1990s, they were forced to start letting people go. The result was an almost 50% decline in the size of the entire “high-tech cluster” in Rochester.
But what Moretti goes on to test in his paper is the impact that this employment decline had on productivity and innovation outside of Kodak and outside of the photography sector (but within Rochester). And what he found was that between 1996 and 2007, the productivity of non-Kodak inventors dropped by about 20%!
This, of course, is one of the great features of cities. Even if you’re not working at some big company with lots of smart people, just being in the same city, on the same block, or within the same office building, can make you more productive. It turns out that business ecosystems are pretty interconnected. Spillovers are important.
For more on this topic, check out this recent Wired article by Viviane Callier. In it she makes the case that remote work is going to negatively impact productivity and innovation over the long run.
We know that innovation and economic growth tends to be unevenly distributed. This is the bull case for living in cities and, more particularly, for living in certain cities. But of course, the big question these days is whether or not our little work from home experiment has proven that, for the first time ever, work can now decentralize.
Well here is a unique study that looked at 29 disruptive technologies over the last two decades in the United States. Using three main sources — patents, job postings, and hundreds of thousands of earnings calls — the team traced where new innovations/technologies have tended to emerge and then how they spread (or didn’t spread) across the rest of the US.
Their initial findings won’t surprise regular readers of this blog. There are indeed a certain number of pioneering superstar cities. Within their list of new disruptive innovations, the team found that about 40.2% of them came from California. The next “super-cluster” was along the Boston-Washington corridor in the northeast with ~21.2%. By narrowing down their list to “disruptive patents”, as opposed to all patents, innovation looks even spikier.
Next the team looked at how these disruptive technologies tend to diffuse across the country. This is where job postings and earnings calls come into play. New technology gets created in California garage. Cool. But at what point do CEOs across the country start talking about it and hiring people who are capable of doing things with it? This next figure shows that diffusion at various time intervals.
Now here are the important takeaways. New disruptive technologies clearly take time to spread. However, high-skilled hiring tends to spread much more slowly than low-skilled hiring. This kind of makes sense as you’ve got a built up and entrenched knowledge base in these pioneering locations.
But what this also means is that pioneering locations tend to maintain their hegemony for quite some time — decades. The high-paying jobs stick closer to home for much longer, presumably because geography makes it harder to transfer knowledge. This is, of course, based on historical data. But I remain highly suspect that Zoom calls can really disrupt the importance of our superstar cities.
Levered assets, such as real estate, tend to have prices that are correlated with interest rates. Lower rates usually translate into higher asset prices. We are living through this kind of environment right now. And so it is generally valuable to have a view on where rates might go next.
To do that, it can be helpful to look back at history. And a lot of the time, that look goes as far back as the second half of the 20th century. I wasn’t buying real estate in the 1970s and 1980s, but I am often reminded — by people older than me — that this was a period of high inflation and high interest rates.
But what about an even longer period of time?
Paul Schmelzing (visiting researcher at the Bank of England) has a pioneering working paper that was published last year which looks at global interest rates over a 707 year time horizon. His research spans the period of 1311 to 2018 and uses archives and many other sources to try and reconstruct annual rates across the world’s advanced economies.
Below are two charts from the paper that I found interesting. The first represents the data that was used to weight long-term debt yields across the various advanced economies. My how things change when you take a long enough view. It also shows the share of advanced economy real GDP that is captured by the study (it’s about ~80% — the red line below).
The second chart shows the headline global real rate from 1317 to 2018. And what Schmelzing discovers is that even when you look across many different monetary and fiscal regimes, real interest rates have never really ever been stable. In fact, when you look as far back as the 14th century, real interest rates have on average declined about 0.6 to 1.6 basis points per year.
So part of his argument is that what we are seeing today maybe isn’t all that strange; it’s actually expected. For a copy of the full working paper, click here.
There is data to suggest that on-demand (OD) mobility services — such as Uber — are increasing vehicle kilometers traveled (i.e. causing greater traffic congestion) by inducing people away from public transit and other forms of urban mobility. This is potentially even more of an issue right now with most urban transit agencies looking at massive budget shortfalls.
But there’s potentially another way to look at this problem. A recent study led by Dániel Kondor of the MIT Senseable City Lab has looked at not only vehicle kilometers traveled but also something that the team calls the “minimum parking problem.” What is the minimum amount of parking that you need assuming a world with more on-demand mobility, and eventually autonomous vehicles?
To try and answer this problem the researchers looked at the small city-state of Singapore. With a population of about 5.6 million people and somewhere around 1 million vehicles, Singapore actually has one of the lowest number of private vehicles per capita in the developed world. Even still, it has some 1.37 million parking spaces taking up valuable room.
What the team found was that on-demand mobility could reduce parking infrastructure needs in Singapore by as much as 86%. This is the absolute minimum number, which would take the current estimate of 1.37 million spots down to about 189,000 — a significant reduction.
However, the tradeoff is that it could increase vehicle kilometers traveled by about 24%. Without ample parking, their model assumes that these on-demand vehicles would need to “deadhead” between trips. That is, drive around aimlessly while they wait for their next passenger. Demand isn’t usually neat and tidy.
However, it’s worth noting that the above percentage increase assumes that if people were instead driving themselves around that they always found a parking spot as soon as they arrived at their destination. This, as we all know, is not often the case, and so this increase is probably a worst case scenario.
Nevertheless, the team did also find that a 57% reduction in parking could be achieved with only a modest 1.3% increase in vehicle kilometers traveled. This, to me, is meaningful because it says that you could, in theory, cut parking supply in at least half and not much would happen in the way of traffic congestion.
It would, however, free up a bunch of space for things like bicycle lanes, green space, and other valuable urban amenities. Now, if on-demand vehicles are pulling people away from transit, then maybe we’re no better off. But if the alternative is people driving and parking everywhere they go, then it would seem that there are much better uses for that space.
Richard Voith and Jing Liu of Philadelphia-based Econsult, along with a bunch of other smart coauthors, have just published a working paper looking at the effects of the Low-Income Housing Tax Credit (LIHTC) on home prices. More specifically, they looked at the impact that LIHTC-financed properties have had in Los Angeles — both in low-income and high-income neighborhoods, as well as when it’s the first LIHTC development in the area or a subsequent one. Some of you might be assuming that low-income housing is likely to create downward pressure on home prices. But the authors found the opposite to be true. Below is the paper’s abstract. If you’d like to download a copy of the full working paper, you can do that over here.
Abstract: While there is widespread agreement about the importance of the Low-Income Housing Tax Credit (LIHTC) in addressing the country’s affordable housing needs, there is less certainty about the effects of LIHTC-financed properties on their surrounding neighborhoods. A growing body of research has largely refuted the argument that affordable housing properties in and of themselves have negative effects on local property values and increase crime rates. Several key questions remain essentially unanswered, however. First, for how long do the observed spillover benefits of LIHTC construction last? Second, does the development of multiple LIHTC properties in a neighborhood have an additive, supplemental effect on surrounding conditions, or is there a threshold at which the concentration of such properties – and the predominantly low-income individuals they house – negatively affects the neighborhood?
In this paper, we focus on Los Angeles County, a large, diverse urban area with significant affordability challenges. Drawing upon both public and proprietary property sales data, we conduct interrupted time series analyses to ascertain whether property value trends differed prior and subsequent to the introduction of a LIHTC-financed property in the community. We find that LIHTC properties positively impact surrounding housing values across the spectrum of Los Angeles’ neighborhoods. Further the concentration of multiple LIHTC properties in a neighborhood additively increases housing prices up to ½ mile away. Finally, these effects though of greater magnitude in lower-income neighborhoods, are fully present in high-income neighborhoods.
