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.
Images: Bank of England
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.
Images: Bank of England
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.
Photo by Jordi Moncasi on Unsplash
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.
Image: Econsult
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.
Photo by Jordi Moncasi on Unsplash
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.
Image: Econsult
Share Dialog
Share Dialog
Share Dialog
Share Dialog
Share Dialog
Share Dialog
