One of the common criticisms of new housing is that it's designed for rich people and that it does nothing to help the housing situation of average citizens. The YIMBY response to this is, "Well, yes, it does actually, because supply eases overall housing pressures and because of the filtering effect." This is the process whereby housing becomes gradually more affordable and available to people as new housing is built and vacancies are created. But most people don't like this explanation. It feels slow and indirect.
Here's something that might help.
In this recent study, researchers looked at the downstream effects of a new condominium tower in Honolulu called The Central Ala Moana. Completed in 2021, the building contains 512 units, of which 60% are income-restricted (310 units) and 40% are market-rate (202 units). It was developed under a state affordable-housing program that gave the developer height and density bonuses, plus fee waivers totalling about $13 million in exchange for delivering income-restricted units. (In my opinion, this is directionally preferable to unfunded inclusionary zoning mandates.)
Using address-history microdata, the researchers tracked who moved into the new condominium tower, and constructed detailed vacancy chains across multiple rounds of moves. Here's what they discovered:
Among documented vacancies, the 202 market-rate units produced 87 downstream vacancies (0.43 vacancies per initial unit), while the 310 income-restricted units produced 90 (0.29 vacancies per unit). Thus, market-rate units are more likely to generate a downstream vacancy. The main mechanism is new household formation: movers into income-restricted units are more likely to be a newly formed household, leaving family or roommates at the prior address and thus preventing a vacancy from being created.
In absolute numbers, they found that the completion of the building induced more than 500 local vacancies in the three years after construction, by setting off a chain of moves. Importantly, the researchers also found that the homes being vacated were, on average, about 40% less expensive than those in The Central. So even though a new building may be more expensive than the existing housing stock (which is generally the case or else the development wouldn't happen), it does generate benefits.
It eases overall housing supply constraints and expands affordability in the local housing market.
Cover photo by Michael Olsen on Unsplash
Towards the end of last year, Meta released SAM 3, which stands for the third version of its Segment Anything Model. The way it generally works is that it allows you to detect, edit, and experiment with things in images and videos. For example, if you were looking at a video of a street, you could ask it to find all the scooters (which I did below), count the number of pedestrians wearing black pants, blur all the license plates on the cars, and so on.
Engaging in physical activity is unequivocally associated with improved health outcomes. But are certain physical activities better than others? And what might the implications be for how we design our cities?
Here is a brand new study that examined the relationship between specific types of physical activity and the risk of death, using two large cohort studies with more than 30 years of self-reported data.
The study included information on walking, jogging, running, cycling (including stationary machines), lap swimming, tennis, climbing flights of stairs, rowing, and weight training.
It's important to note that this is an observational study using self-reported data. There are limitations to this. One question mark is around intensity. When someone reports swimming for an hour, it could be vigorous or casual. And the researchers note that long, low-intensity physical activities could bias the observed associations toward the null.
With this caveat out of the way, here's what they found:
One of the common criticisms of new housing is that it's designed for rich people and that it does nothing to help the housing situation of average citizens. The YIMBY response to this is, "Well, yes, it does actually, because supply eases overall housing pressures and because of the filtering effect." This is the process whereby housing becomes gradually more affordable and available to people as new housing is built and vacancies are created. But most people don't like this explanation. It feels slow and indirect.
Here's something that might help.
In this recent study, researchers looked at the downstream effects of a new condominium tower in Honolulu called The Central Ala Moana. Completed in 2021, the building contains 512 units, of which 60% are income-restricted (310 units) and 40% are market-rate (202 units). It was developed under a state affordable-housing program that gave the developer height and density bonuses, plus fee waivers totalling about $13 million in exchange for delivering income-restricted units. (In my opinion, this is directionally preferable to unfunded inclusionary zoning mandates.)
Using address-history microdata, the researchers tracked who moved into the new condominium tower, and constructed detailed vacancy chains across multiple rounds of moves. Here's what they discovered:
Among documented vacancies, the 202 market-rate units produced 87 downstream vacancies (0.43 vacancies per initial unit), while the 310 income-restricted units produced 90 (0.29 vacancies per unit). Thus, market-rate units are more likely to generate a downstream vacancy. The main mechanism is new household formation: movers into income-restricted units are more likely to be a newly formed household, leaving family or roommates at the prior address and thus preventing a vacancy from being created.
In absolute numbers, they found that the completion of the building induced more than 500 local vacancies in the three years after construction, by setting off a chain of moves. Importantly, the researchers also found that the homes being vacated were, on average, about 40% less expensive than those in The Central. So even though a new building may be more expensive than the existing housing stock (which is generally the case or else the development wouldn't happen), it does generate benefits.
It eases overall housing supply constraints and expands affordability in the local housing market.
Cover photo by Michael Olsen on Unsplash
Towards the end of last year, Meta released SAM 3, which stands for the third version of its Segment Anything Model. The way it generally works is that it allows you to detect, edit, and experiment with things in images and videos. For example, if you were looking at a video of a street, you could ask it to find all the scooters (which I did below), count the number of pedestrians wearing black pants, blur all the license plates on the cars, and so on.
Engaging in physical activity is unequivocally associated with improved health outcomes. But are certain physical activities better than others? And what might the implications be for how we design our cities?
Here is a brand new study that examined the relationship between specific types of physical activity and the risk of death, using two large cohort studies with more than 30 years of self-reported data.
The study included information on walking, jogging, running, cycling (including stationary machines), lap swimming, tennis, climbing flights of stairs, rowing, and weight training.
It's important to note that this is an observational study using self-reported data. There are limitations to this. One question mark is around intensity. When someone reports swimming for an hour, it could be vigorous or casual. And the researchers note that long, low-intensity physical activities could bias the observed associations toward the null.
With this caveat out of the way, here's what they found:
This is immediately useful for a company like Meta because it allows for object-level modifications across its content creation platforms. So if you took a video of someone dancing and you desperately wanted to give them a bobblehead, SAM 3, I'm told, would allow you to quickly do that. Other AI models, such as Gemini, can also segment, but supposedly the SAM models are better and more precise at this specific task.
Beyond bobblehead videos, the potential of this model seems enormous for real estate, cities, and, of course, many other things. Using the above image as an example, you can quickly imagine SAM 3 being used to count and track modal splits across a city, and then make planning decisions based on real-time data.
People are also using it for real estate purposes. Pair the model with satellite images, and you can ask it to tell you how many houses have a pool, which houses recently had their roof replaced (and have solar panels), how many cars are parked on a street, how many cars are parked at Canadian Tire, and the average building lot coverage in an area.
You could also use it to swap out finishes in a real estate listing (including in videos), and get material/area takeoffs ahead of a construction project. I don't know for sure, but I would also imagine that this model would make a great building condition inspector. Come to think of it, I'd love a SAM 3 that could walk our construction sites and document every little detail!
Of course, a lot of these use cases are already being tackled. But the models are getting that much better. And that will lead to even more innovation.
Cover photo by Above Horizon on Unsplash
Their two key findings were that (1) most physical activities lower mortality rates in a non-linear way when you do more of them, and (2) mixing different physical activities is associated with lower mortality, independent of total activity levels. Variety is good.
Interestingly enough, the most effective activity at lowering overall mortality is the simplest one: walking. It was found to reduce all-cause mortality by about 17%. This is the difference, or maximum observed benefit, between the highest walking group and a sedentary baseline.
Once again, the data clearly shows that walkable cities can help produce meaningfully better health outcomes. So, if, like me, you subscribe to the philosophy that there's no greater luxury in life than our health, well, then there's perhaps no greater luxury than living in a walkable city.
Cover photo by Alain ROUILLER on Unsplash
This is immediately useful for a company like Meta because it allows for object-level modifications across its content creation platforms. So if you took a video of someone dancing and you desperately wanted to give them a bobblehead, SAM 3, I'm told, would allow you to quickly do that. Other AI models, such as Gemini, can also segment, but supposedly the SAM models are better and more precise at this specific task.
Beyond bobblehead videos, the potential of this model seems enormous for real estate, cities, and, of course, many other things. Using the above image as an example, you can quickly imagine SAM 3 being used to count and track modal splits across a city, and then make planning decisions based on real-time data.
People are also using it for real estate purposes. Pair the model with satellite images, and you can ask it to tell you how many houses have a pool, which houses recently had their roof replaced (and have solar panels), how many cars are parked on a street, how many cars are parked at Canadian Tire, and the average building lot coverage in an area.
You could also use it to swap out finishes in a real estate listing (including in videos), and get material/area takeoffs ahead of a construction project. I don't know for sure, but I would also imagine that this model would make a great building condition inspector. Come to think of it, I'd love a SAM 3 that could walk our construction sites and document every little detail!
Of course, a lot of these use cases are already being tackled. But the models are getting that much better. And that will lead to even more innovation.
Cover photo by Above Horizon on Unsplash
Their two key findings were that (1) most physical activities lower mortality rates in a non-linear way when you do more of them, and (2) mixing different physical activities is associated with lower mortality, independent of total activity levels. Variety is good.
Interestingly enough, the most effective activity at lowering overall mortality is the simplest one: walking. It was found to reduce all-cause mortality by about 17%. This is the difference, or maximum observed benefit, between the highest walking group and a sedentary baseline.
Once again, the data clearly shows that walkable cities can help produce meaningfully better health outcomes. So, if, like me, you subscribe to the philosophy that there's no greater luxury in life than our health, well, then there's perhaps no greater luxury than living in a walkable city.
Cover photo by Alain ROUILLER on Unsplash
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