Building the Skyline

The Birth & Growth of Manhattan's Skyscrapers

by Jason Barr 4 Comments

Escape from New York?: Density and the Coronavirus Trajectory

Jason M. Barr and Troy Tassier        written on April 3, 2020, posted on April 20.

Note: A shorter version of this post was written for Scientific American, which was posted on April 17th.

The Density Paradox

Anyone who follows the journalist Matt Yglesias on Twitter knows his style. He’s sardonic, facetious, and relies on barbs to make his point. His recent tweet on March 23, 2020, at 8:10 pm EST is a perfect example:

The moral of corona virus is that we should adopt the kind of low-density living patterns associated with Asian countries like South Korea, Japan, Taiwan, and Singapore that have successfully controlled its spread.

Though the tweet oozes sarcasm, it highlights how southeast Asian cities—known for their hyper-density—not only were invaded by the coronavirus pandemic but also figured out ways to slow its spread without destroying the very essence of what makes cities so successful. But his tweet contrasts sharply with the perception that New York City—the epicenter of the epidemic in the United States—is somehow to blame for causing so much damage. For example, the New York Times on March 23rd ran an article entitled, “Density Is New York City’s Big ‘Enemy’ in the coronavirus Fight.” Journalist, Brian Rosenthal, writes:

New York has tried to slow the spread of the coronavirus by closing its schools, shutting down its nonessential businesses and urging its residents to stay home almost around the clock. But it faces a distinct obstacle in trying to stem new cases: its cheek-by-jowl density.

This kind of sentiment begs the question: what is the role of population density in spreading the virus? Are big cities worse than smaller ones? Does New York deserve such harsh criticism?

Scenes from Coronavirus, NYC. Sources: Top left, top right, bottom left, bottom right.

How Contagions Conjugate

Before we answer these questions, let’s take a step back and focus on the process of infectious diseases in general. For this, we need to assume that no preventive measures are taken, and everyone goes about business as usual, without social distancing or self-isolation. To understand how widely and quickly an infectious disease will spread, epidemiologists refer to something called the reproduction number or simply, R. It measures the average number of people that an infected individual will infect over the course of their infection.

Among extant diseases, measles has the highest reproduction number, at about 15. That is to say, assuming that the measles vaccine didn’t exist, one person with measles, if left untreated or unquarantined, can expect to pass it along to 15 others (hence the 2019 crises). For influenza, that number is only about 1.5. The current evidence puts the coronavirus at around 2.65—not as bad as the measles, but worse than the typical flu strain.

The Reproduction Number

The number of new cases on any given day is directly tied to the reproduction number. Say, for example, that R is two. This means that the average infected person passes it on to two people, who then collectively pass it on to four people, who then collectively pass it on to eight people and so on. Then, assuming the virus began with a single person, the number of new cases on a given day is directly tied to the reproduction number and the number of days since “patient zero” was infected.

But to know why and how fast the disease spreads, we need to know more about the reproduction number and what determines if it is large or small. It turns out that the reproduction number is determined by four elements. Thus, to see the role that cities play in spreading the virus, we need to look more deeply at those components.

Contact Rate

First is the contact rate. It is a measure of the average number of contacts people have with other people on a given day. Extroverts will have more contacts than their introverted counterparts. People who take public transportation and interact with many co-workers or customers each day will have more contacts than those that commute by car and sit at a solitary desk. Children in a crowded school typically have more contacts than their grandparents. Those sitting at a computer screen in Area 51 monitoring for extraterrestrial life will have fewer contacts than D.J.s. (but maybe more with Martians or Klingons).

If we take all the people in a society and calculate how frequently they mix on average, then we get the contact rate.  One thing to consider, however, is that contacts are not the same for all infectious diseases. Influenza and HIV spread through very different mechanisms, and thus the count of contacts differs greatly as well. (More generally, the structure of social contacts, who interacts with whom and who they interact with as well, can also play a role, but we ignore that for the time being.)

Transmission Rate

Second is the transmission rate, which is the likelihood that any one individual will pass the disease onto someone else. Not all diseases are equally transmissible. Some infectious diseases, such as HIV, are relatively difficult to transmit. The CDC estimates that only 63 out of 10,000 exposures to shared needles will result in the transmission of HIV. Estimates for the common cold are much higher, 2%-40% depending on age. Coronavirus appears to be in the lower end of this range, perhaps around 1%-5%, although the data is far from complete. One of the key problems with the coronavirus is that even if an infected person chats with only a few other people on a given day, there is a significant chance that she passes on the infection.

Duration

Third is the time it takes for the disease to work its way through the human body from infection to illness to recovery. In the case of the Corona Virus, the average time is about a month—two weeks between infection and first symptom and then about two weeks until recovery (or death).

Susceptibility

Last is the fraction of people at a given time who are susceptible to the disease. In the case of a novel virus that is newly introduced into a population, such as Corona Virus, susceptibility is typically near 100%. One may recall the stories of Hernan Cortes carrying smallpox to the Aztecs, who were decimated from its spread. As the disease spreads and people recover, that fraction of susceptible people in a population will fall because of developed immunity.

The Fire

In summary, the reproduction number—the average number of infections a person will bestow to their fellow humans —is determined by the product of the contact rate, the transmission rate, duration of infection, and fraction of susceptible people in the population.

To offer an analogy, we can think of an infectious disease as operating like a fire. A spark ignites the wood, which burns brightly and hot, until finally, all the fuel is spent, and the fire goes out. The reproduction number determines how hot and bright the fire burns. If R is bigger than one, more and more people are infected, and the fire grows larger. But, as the epidemic grows, more people are infected and then recover to become immune; this means that the fraction of susceptible people is smaller.

In turn, as this fraction gets smaller, the reproduction number gets smaller and smaller, eventually dropping below one. It is at this point that the fire has reached the peak of its heat and begins to die out and eventually disappears because there are no longer enough susceptible people to keep the fire going. It may be that people have recovered to become immune or that people simply die. This is the story of the Black Death, which killed somewhere between 75 and 200 million people in the mid-14th century. Once started, it burned until the hosts were buried in the ground.

The Role of Density

But what role does density play in these fires? The intuition that population density increases the propensity of an epidemic to spread in cities is correct in the sense that increased density likely leads to an increase in the contact rate of individuals, which makes the reproduction number larger, leading to more infections in dense areas. If you live in an apartment building, commute in a subway, work in a skyscraper and take your lunches in crowded diners, you are more likely to interact with someone who passes the infection along to you.

And, you’re likely to spread the infection more widely than others living in less dense areas. Thus, the reproduction number should be bigger in large dense cities and lead to bigger fires. But, how much bigger and how much of an effect will it have?  We aim to answer this question below for the current stage of the epidemic in the US.

The Missing Role of Time

If we think about an epidemic spreading across a spectrum of cities, the reproduction number misses a portion of the story. An infectious disease doesn’t enter all cities at the same time. In the U.S., we heard notable stories of outbreaks on the west and east costs early in the epidemic. Only more recently are we are hearing about New Orleans, Detroit, and other urban areas in North America. The reproduction number tells us little about how the epidemic first appears and spreads through these cities and across the globe.

To think through this spread, imagine each city or town as a small forest of trees, once a spark begins a fire anywhere in the city, the rest of the forest will burn until the fuel is spent. Because dense cities have higher contact rates, they will burn a little faster and a little longer, but what is currently happening in New York City and New Orleans will be replicated in cities across the country as soon as a spark arrives. As the epidemic fire burns, it will spread to nearby areas: New Rochelle, NY lead to New York City and nearby places in New Jersey and Connecticut.  But, as people travel between more distant areas of the country, this creates new sparks in new locations, and new fires start.

Thus, a traveler from elsewhere arrives in New Orleans for Mardi Gras or an otherwise anonymous spring-breaker arrives in Miami, and a new fire begins. Because we are early in an epidemic, we haven’t seen how the sparks distribute across the landscape. But, the growing fires will be bigger in areas where the fire started earlier.

Contact Rate versus Sparks: Time is of the Essence

As discussed above, the reproduction rate is given by four variables: the contact rate, the transmission rate, the disease duration, and the fraction of the population that remains susceptible. But for analyzing the coronavirus epidemic in the U.S. during the last month, the transmission rate and duration rate are likely constant across the U.S. As for susceptibility, since we are analyzing things relatively early in the epidemic (at least for the U.S.), it’s safe to assume for now that the susceptibility rate across the U.S. is also constant and likely close to 100%. Even in a city with a large outbreak like New York, with more than 30,000 cases, it is still far below 1% of the population.

But the contact rate across the country is likely affected by factors, such as population density or economic density (like regional gross domestic product). At the same time, we can measure the number of sparks which could start a fire by, for example, by the presence of a major international airport. Finally, we can investigate timing by looking at when each fire started, measured by the number of cases in each county in early March, a little over a month after the first confirmed case in the U.S.

Left: Number of COVID-19 Cases on March 27, 2020 in U.S. Counties that Had at Least Once Case vs. County Population Density (both axes in logs). Right: Number of COVID-19 Cases on March 27, 2020 in U.S. Counties that Had at Least Once Case vs. Number of Says since First Case in County. Sources:

The Density Effect

To this end, we have performed a statistical (regression) analysis of daily county-level COVID-19 cases as of March 27, 2020 (day 66 of the epidemic in the U.S.), along with several potential explanatory variables, in an effort to see how density, airports, and timing help to determine the current spread of cases across the United States. (Technical details are here.) The results show that population density does matter but is not as large as the popular media would have you believe. In fact, on average, an increase in county-level population density by 20% increases the number of cases by about 11-12%, on average (conditional on having at least one case of the virus). In other words, more populous counties are likely to have fewer cases on a per capita basis than their sparser counterparts.

We find, however, that what matters more than population density is the presence of a very large airport. A county with an airport that has at least one million passengers is likely to have nearly double the cases as compared to counties with no or smaller airports.

Further, counties that had early cases of COVID-19 have much larger case counts today. Our findings suggest that if a county had a least one case on March 1 (day 40), then by March 27, they are likely to have nearly double the cases of counties without an early case. The timing of early case arrivals is much more important than that of population density.  This suggests that, at this early stage of the epidemic, the current distribution of cases in the U.S. is determined more by the arrival of early cases into a county and less by the density of the county itself. There are no low-density, low-risk counties, just counties that haven’t been found by the pandemic yet.

Around the World

It’s also worth stressing that when we look at the epicenters around the world, we see that they frequently are not located in the largest cities in each of the respective countries. In Italy, the epicenter was in northern Italy around Milan, not in Rome, which has twice as many people. In China, the epicenter was in Wuhan, China’s 10th largest city. If population density were really the reason for the epidemic, the COVID-19 epicenters would have been in Rome and Shanghai, China’s largest city. Timing and bad luck is simply a more important driver.

The Future Course of the Virus

The analysis of density discussed here is based on the premise that the virus is free to run its course without any interventions. This is a reasonable assumption in the early stages in the U.S. before governments were able to mobilize resources and enact isolation measures. Now that states around the country have begun to fight back, there is evidence these measures are slowing its spread. This suggests the question of which policies are best to lower the reproduction rate? Do self-isolation, social distancing, or testing provide better means for fighting back? Are cities better places to be if you get infected? These are questions we’ll return to in the future when the virus has finally been vanquished.

by Jason Barr 11 Comments

In a City FAR, FAR Away: Building Height Regulations and Their Impacts

Jason M. Barr      March 17, 2020

Your New Job

Imagine the governor of your state announces that she is going to create a brand-new city out of whole cloth. The state has purchased 200 square miles (519 km2) of farmland (around the size of Chicago) and will begin auctioning off lots for development. The governor is working with officials to build the transportation and infrastructure network. But she has turned to you with a critical mission: to formulate the regulations about building heights. Your job, as Chief Building Heights Planner, is to develop a series of plans about how dense or tall the structures can be throughout the city.[1] Where should you begin?

Options

You can think of the possibilities as existing along a continuum. On one side is to write no rules and allow buildings to be as tall or as dense as developers want. If someone builds a 100-story skyscraper in the middle of a leafy residential neighborhood, so be it. If builders cover 100% of the lot and put their neighbors in darkness, that’s okay too.

The other extreme is to order the city entirely. Say you build a 3D model in your office and arrange the exact scale, density, and height for every structure. There can be no deviation. Building #1 must go here and must be so many stories. Building #2 must go there and have exactly so many floors, and so on.

Option three, of course, is somewhere in between, where you designate some rules on height and density, and let the real estate community figure out the rest. But even within this option, there’s a wide spectrum. Do you set limits for everyone? Do you restrict hieght in some places but not others?

What Do these Four Buildings Have in Common? All of these Brooklyn structures have a floor area ratio of 3. Clockwise from top left: 181 Clermont Ave (2007, 6 floors), 761 Atlantic Avenue (1974, 31 floors), 109 3rd Avenue (1900, 4 floors),  1630 41 Street (1926, 4 floors). Source: OASIS NYC Map.

How FAR Can You Go?

After doing a little research, you find one popular regulatory tool is to limit the so-called floor area ratio (FAR).[2] The FAR dictates how much building space can be constructed based on the lot size. For example, in an older (pre-car) dense apartment-based neighborhood, the rules might specify a maximum FAR of 3. This means that on a 2,000 square foot (186 m2) lot, the most a new building can have is 6,000 square feet (557 m2) of living space.

If we assume, for example, that each household in the building rents 600 square feet (58 m2), then that building will have ten units. A FAR increase to 4, means the structure can be 8,000 square feet (743 m2), with 13 units. If each apartment, on average, holds two people, the FAR-3 building has 20 people, and the FAR-4 building has 26 people.

Building Heights

FAR limits say little about building heights directly. In a FAR-regulated city, the height is chosen by the developer, based on what works best for her. In the FAR-3 example, she can build a three-story building that takes up the entire lot, or she can erect a six-story building that covers only half the lot. Yet both will hold the same number of people.

Thus FAR limits mean that building bulk and height are substitutes—the taller the building goes, the narrower it must be, with more open space surrounding. This strategy presumably mitigates the negative impacts of tall buildings, such as shadows on neighboring properties. For these reasons, FAR regulations are popular among planners. They are a simple way to reduce the density of both people and buildings.

Wheel of FARtune?

By how do planners choose the “correct” FAR values across the city? The answer is: they just pick numbers that seem right—usually some higher numbers in the central core (say 8 to 15), medium numbers in the dense residential rings (say 2 to 4), and much smaller figures further out (typically one or less). These values are often based on what “feels” appropriate, or are politically salable, and are not tied to any economic research that attempts to weigh the costs of restrictions versus their benefits. A survey of maximum FARs in central cities around the world shows that planners have chosen a wide range of numbers. Their arbitrariness is also supported by the research discussed below.

Average Maximum Allowable Floor Area Ratios Versus Distance from City Center for New York, Chicago, and Bengaluru. For each city, I took the average of the maximum allowable FAR for buildings in 1 kilometer rings from the center city. In New York, the average allowable FAR within 1 KM of the center is about 9. For Chicago, it is about 12, and in Bengaluru it’s about 1. Sources: New York: NYC PLUTO, Chicago: Building footprint shapefile, Bengaluru: Breuckner and Bertaud (2005). Note: Average FARs were not weighted by lot size.

FARnomics: The Economics of FAR Regulations

Recently, economists have begun to question the wisdom of choosing urban FARs based on planners’ notions of what constitutes the “good” city. Economic analysis about FAR regulations starts with a series of questions: What would density look like without any FAR limits? Under what conditions would restrictions help improve city well-being? When would FAR restrictions make things worse?

The Order without the Design

As Alain Bertaud discusses in his 2018 book, Order without Design: How Markets Shape Cities, a bustling metropolis without any FAR restrictions will generate a natural order. In the central city, where land values are high, developers will be incentivized to provide taller builders on relatively small lots. As land values decline moving away from the center, buildings will get shorter, on average.

A well-functioning land market will also create a predictable pattern for population density. Near the center, where land is expensive, households will live in apartment buildings. Further away, where lots are cheaper, people will occupy larger houses. Thus, density will be high near the center and fall from there. This outcome is based on the choices of households and represents what they prefer—better accessibility to the center versus more housing and open space further away. The price of land is the nexus between what households want and what kinds of real estate will be constructed. Overly burdensome regulations will distort land prices and prevent them from efficiently allocating housing demand with housing supply.

Key Assumptions

The optimality of this natural order is based on a few assumptions. First is that there are no negative spillovers or externalities from this arrangement. That is, the tall buildings in the city don’t create excessive shadows, promote hyper-congestion, or generate undue health or social problems. Additionally, economists assume that residents or visitors are indifferent to the building designs and shapes that would spring up when the real estate market is relatively open. Tall or dense buildings, it is assumed, don’t offend people’s aesthetic sensibilities.[3]

The presence of these possible harmful elements has been the driving force and justification for imposing FAR restrictions. City officials argue they are necessary to relieve congestion, provide open space, improve urban health, and make the city “beautiful.” But the economist would respond: if this is so, what are the “right” set of restrictions (assuming that restrictions are the best way to solve these problems) that best balances the trade-offs. The truth is, as of this writing, no one knows. The proper calculations have yet to be done.

The Case of New York

Take New York City as an example. The city’s FAR-based regulations were enacted in 1961. They were designed to reduce density as compared to the 1916 zoning codes, which were seen as not being restrictive enough. While small FAR changes (rezonings) in different neighborhoods occur from time to time, there has been no systematic change in the regulations in over half a century. In other words, the same FAR codes, more or less, are in effect today as when John F. Kennedy was President and when The Andy Griffith Show was one of the most popular TV shows in the nation. (If you’re young enough not to know what I’m talking about then I think I’ve made my point.) Once FAR regulations are imposed, they are nearly impossible to dislodge, despite mounting evidence that they are harming the city.

Furthermore, in 1961, when these regulations were set, planners with who felt that promoting the tenants of modernism was surely the path to urban utopia. Clearly, by 1975, when New York City was ready to declare bankruptcy (see “Ford to City: Drop Dead”), utopia was on the minds of few. Sticking with these outdated zoning rules is like trying to use a Commodore 64 computer to process “big data” data.

Estimating the Impacts of FAR Regulations

Recently, however, the urban economist Jan Brueckner, of the University of California-Irvine, and co-authors have studied the impacts of FAR restrictions around the world. His research asks the question: What are the costs of excessive FAR restrictions, imposed in the name of improving the common good?

Bengaluru (Bangalore)

Indian cities have some of the lowest central-city FAR values in the world. Planners have established them, evidently, as a backdoor way to impose their notion of a better city, and because officials couldn’t provide the city services needed to support a dense urban core. As Brueckner and Bertaud (2005) write, “It is believed that ‘excessive’ density results in a loss of environmental quality and increased traffic congestion. In addition, higher densities would place greater demands on urban infrastructure, which Indian cities, plagued by weak technical capacities and inadequate tax revenue, feel ill-equipped to provide at appropriate levels.”

Unlike New York (with a maximum central-city FAR of 15), and Shanghai (with a maximum central-city FAR of 8), Bengaluru has a central-city FAR capped at less than 1.5. This number is considered reasonable for a suburban residential neighborhood of garden apartments, but absurdly low for a central business district in a metropolis with 8.4 million people.

Maximum Allowable Floor Area Ratios in Central Areas for Selected Cities. Source: Brueckner and Sridhar (2012).

In their paper, Brueckner and Bertaud explore the economics of these excessive restrictions. They demonstrate that these FAR limits promote denser development in the suburbs than would otherwise be the case, and create larger, more sprawling cities. The attempt to limit building heights in the center to reduce congestion inadvertently creates more congestion by promoting more driving. If we think about the (lightly-regulated) city as a block of clay. The draconian FAR restrictions are tantamount to smashing the clay. The result is a flatter, wider entity.

They perform some calculations for Bengaluru, and find that the excessive sprawl reduces average household income from extra commuting by 2%. It’s also worth noting that, in recent years, Bengaluru has achieved an international reputation as a tech hub. Limiting densities may have other consequences, such as reducing the opportunities of firms and workers to interact and learn from each other. These restrictions are likely reducing citywide productivity than otherwise.

101 Indian Cities

In a 2012 paper, Brueckner and his collaborator, Kala Seetharam Sridhar, investigated the impact of FAR restrictions on urban sprawl for 101 municipalities throughout India. First, they find a wide distribution of maximum central-city FARs. The lowest city has 1, and the highest has 4.125. The average city in the sample has a central-city FAR of 2.87. Again, compared to other cities around the world, these figures are low.

They next study how these FAR restrictions impact the total land area of municipalities, which is a measure of urban sprawl. Based on their analysis, they conclude that if the average city in India raised its central-city FAR by one unit (e.g., go from 2 to 3), the, it would need 20% less land area to hold the same amount of people. Or, on the flip side, on average, a city that decides to lower its central-city FAR by one is generating an increase in its land area by 20%.

Land Values in China

A well-functioning land market is the main means by which the city orders itself. If land values rise at a specific place, it signals that the residents want taller buildings. If FAR regulations are overly tight, they can lower the value of land, which will then generate buildings that are economically “too short,” denying residents the opportunity to live and work in the location of their choice.

Brueckner, and his co-authors, Shihe Fu, Yizhen Gu, and Junfu Zhang, study the land market in China. Unlike the U.S., there is no private property. Instead, all the urban land is “owned” by the respective municipal governments, who sell long-term leases to developers (70 years for residential and 50 years for commercial). The revenue from these land-leases is used to fund infrastructure and other government projects.

Because municipal officials control this land, they can impose FAR regulations on the developers when they win the right to build. We tend to think of modern Chinese housing as mostly dense, high-rise apartments. This is true, but municipal officials frequently require developers to adhere to relatively low FARs of between 2 and 3. The effect is to create a Corbusian city of tower-in-the-park complexes.

Findings

To study the question of how these FAR restrictions impact land (lease) values, they constructed a very large data set with over 50,000 transactions across more than 200 cities from 2002-2011. Each land-lease transaction also includes the maximum FAR requirement. Across cities, they find that the average residential FAR is only about 2.4.

The authors also find that the higher the FAR restrictions, the greater it reduces land values. Loosening these restrictions would allow land prices to more accurately reflect the benefit of various locations throughout the city. The results suggest that the current FAR values are actually not allowing central locations to be as dense as residents would want.

The Trade-offs

Since Chinese officials lease the land, one would think they have the incentives to provide even looser FARs to increase their revenue streams. However, local leaders must balance the revenues from these land leases with the costs of building infrastructure. So the FAR restrictions may not only be related to notions of sound design principles but also limiting urban density growth to something that will not overly burden the current level of infrastructure. Or, as Brueckner et al. hypothesize, “In this sense, local officials behave as net revenue maximizers, taking into account the public costs associated with land development into account.”

However, if city size or budgetary issues were important than the data would show a relationship with FAR restrictions. But, based on their analysis, they do not find evidence that this is so. Instead,  they find little explanation for FAR stringencies across China, aside from the fact that places with ancient historical sites are stricter. Their results seem to indicate that FAR limits are based on the preferences of city officials rather than on underlying economic principles.

High-rise living in four Chinese cities. Clockwise from top left: Shenzhen, Changchun, Shanghai and Hong Kong. Photos by Jason M. Barr

Land Values in the United States

Brueckner and his co-author, Ruchi Singh (2020), study land values and FAR stringency across U.S. cities. The lower the average allowable FARs, the lower the land values are likely to be, and hence the greater is the “gap” between what residents want and what developers can supply. In particular, they investigate how FAR regulations impact land prices in New York, Chicago, Washington DC, Boston, and San Francisco from 2000 to 2018.

Their analysis allows them to estimate the gaps. They find that New York City is supplying only about 70-77% of the demand. For Washington DC, this number is between 60 to 72%, which makes sense given that DC also has height caps. Boston is supplying around 78 to 83%. And Chicago is at 92 to 94%.

Interestingly, they find that San Francisco’s gap is only between 90 to 93% of what the market wants. Their findings are likely being driven by the relative stringency in each city’s central business district. The demand to live and work in Manhattan is quite strong.[4] Unlike San Francisco’s business district, which “competes” with Silicon Valley, Manhattan has few direct local competitors for its financial and tech services. Similarly for Chicago and Boston.

What Would you Do?

I asked Professor Brueckner to summarize the findings from his body of work. He indicates that, in short, city planners should be careful for what they wish for. He says,

FAR limits reduce housing supply, raise prices and lead to longer commutes. If there are no good reasons for the restrictions, such as better neighborhood ambience, the outcome is a loss for city residents.

As Brueckner’s research shows, the path to getting things right begins with measuring the costs of excessive restrictions. Not going too far with FAR stringency can help a city reduce the negative impacts of density, without eliminating or reducing its benefits. And now, with this in mind, what have you decided to do in your role as Chief Building Heights Planner? If you want to take a look at my planning ideas, you’ll have to stay tuned for a future post on the subject.

 

—

[1] You will be part of a team that is developing other rules, including building codes, requirements for health and safety, as well as regulations about land uses. Your decisions will be made in consultation with them.

[2] “Plot ratio,” “floor space ratio,” “floor space index,” “gross plot ratio,” and “site ratio” are other terms also used to designate the FAR.

[3] To the best of my knowledge, there is little research on how residents feel about different kinds of architecture, building shapes and arrangements, and their willingness to pay to be in these neighborhoods. Architects and planners, who have strong feelings about what is right and good, are usually the ones, along with politicians, writing the rules about building heights and densities. There is research to support the notion that walkability and mixed uses are good for residents’ well-being. These findings suggest that there is too much automobile usage and too many zoning restrictions. Good policies can reverse be undone by them as well.

[4] A similar 2019 study for New York City was performed by Brueckner’s student, Byunggeor Moon. Here Moon did the analysis with properties bought to be torn down, rather than vacant lots as in Brueckner and Ruchi (2020). Moon’s findings compare similarly to those of Brueckner and Ruchi.

 

 

by Jason Barr 6 Comments

Is Gentrification Good or Bad?

Jason M. Barr        November 18, 2019

Across U.S. cities, perhaps nothing is more controversial than the issue of gentrification—the process of wealthier residents moving into lower-income, central city communities. Those who are against it claim it destroys cities and harms residents, while proponents say it helps neighborhoods grow and become livelier. So, which is it? Is gentrification good or bad? To answer this, we need to move beyond feelings and rhetoric and try to get a sense of what’s really going on. One of the best ways to do this is to employ rigorous social science methods with large data sets of individuals. This post highlights recent studies that that investigate what the data suggest about the consequences of gentrification.

The Key Findings

While we will review the findings in more detail below, if there’s one theme that runs throughout them, it’s that the terrible, sky-is-falling rhetoric on gentrification is not born out by the data—at least for the issues discussed here. Displacement—if it exists at all—is relatively slight. Luxury condos do not appear to promote rising rents for lower-income residents. On the other hand, gentrification can promote more diversity and integration and has little impact on children’s health. That’s not to say that gentrification is entirely benign or beneficial. The data do point to some negative consequences, such as a reduction in local employment and slightly higher anxiety levels among children. But taken together, the research suggests that gentrification in and of itself is not as harmful as its detractors claim.

Fears about Gentrification Regularly Made Good Headline Copy.

The Bad

Those opposed to gentrification claim it promotes a series of negative impacts for residents. They say that it pushes up housing prices for the poor. As well, it irrevocably alters the vibe of neighborhoods—erasing its gritty, authentic soul with cold glass towers for the ultra-rich. Just as importantly, it drives the displacement of low-income families from their communities—forcing them to seek out cheaper shelter further away. Gentrification attracts expensive chain stores which don’t hire local workers, and who sell products that the low-income residents either don’t want or can’t afford. In short, opponents say gentrification is bad because it pits the lower-income incumbents against upper-income entrants, who always seem to win.

The Good

Proponents of gentrification might disagree on the degree to which it drives displacement and argue that an inflow of money is a good thing. With money comes resources and amenities. Older houses are renovated; stores that formerly avoided the area now bring in new products and services, as well as new employment opportunities. Money flowing into the neighborhood can only help—improving schools, reducing crime, prompting infrastructure upgrades, and so on. In short, gentrification is a tide that lifts all boats.

The Research

In the last few years, economists have begun to tackle the issue of gentrification using modern data-oriented research methods to tease out its causes and consequences. Here we are going to look at several possible impacts: displacement, neighborhood demographics and integration, children’s health, local employment, and high-end residential construction and housing costs. Specifically, I’m going to review six studies, four of which were written or published in 2019, one in 2017, and one in 2016. They represent the state-of-the-art work.

Experimental Design

With gentrification, residents feel like it’s bad. They see changes in their neighborhoods and rising prices, and they conclude that the influx of higher-income people must be the cause. But the problem with going with one’s gut is that it can lead to a confusion of correlation with causation. The key to seeing if there is a true gentrification effect is to establish a kind of quasi-scientific experimental design. The studies discussed below do this by going back to some starting period—say the year 2000—to first identify neighborhoods that are “gentrifiable” in that they are low-income, central-city neighborhoods.

From this pool, the researchers then place them into one of two “bins”—those that gentrified—i.e., saw big jumps in educated upper-income residents—and those that did not—i.e., remained relatively low income. In this way, the authors create a “treatment group”—those neighborhoods that gentrified—and a “control group”—those that did not. Then a possible impact of gentrification, such as employment opportunities or children’s health, can be investigated. The point is that we can’t make claims about gentrification without identifying a benchmark or reference group that has a similar conditions, except for gentrification.

All Else Equal

Another important aspect of this work is the use of regression analysis which allows researchers to identify a gentrification effect or not, controlling for or holding constant, a host of other factors that might determine some outcome. Cities are dynamic places. It’s hard to know how much of new employment opportunities or mental health issues are due specifically to gentrification, rather than the many other factors that are going on in people’s lives and their neighborhoods. The “controlling for” aspect that comes from the statistical methods allows for a gentrification effect to be better isolated among the “fog” of all the changes happenings on a day-to-day basis.

Gentrification and Displacement

Arguably, the most controversial issue about gentrification is that of displacement. When upper-income people move into a neighborhood, landlords—presumably—raise rents beyond what the low-income households can pay. They are thus forced to seek affordable housing elsewhere, either further away from the center or outside the city altogether. One of the main problems in trying to measure displacement accurately is that low-income households move quite frequently, independent of anything that might be related to gentrification. So, the objective is to measure how much of their movement is due directly to gentrification rather than from any number of other reasons that might have nothing to do with gentrification, such as the desire to be closer to family or changes in life circumstances.

Furthermore, if prices across the entire city are rising, people may leave their neighborhoods because of the general, citywide inflation in housing costs, which render their current homes unaffordable, and not for any specific localized reasons due to the wealthier entrants. By using regression analysis, researchers can decipher whether mobility is determined by gentrification per se or by rising prices more broadly.

Additionally, it’s theoretically possible that each time a low-income household leaves the neighborhood for reasons independent of gentrification, a high-income family moves in. In this case, no one is displaced. If this was the mechanism by which change occurred, long-time residents who see wealthy people moving in might draw the false conclusion that those who left were displaced, especially when the racial or ethnic composition begins to become noticeably different.

Gentrifying Neighborhoods in Philadelphia (in red). Source: Ding, et. al. (2016).

Philadelphia

Research by Lei Ding, Jackelyn Hwang, and Eileen Divringi, in their 2016 paper, “Gentrification and Residential Mobility in Philadelphia,” focus on the specific question of displacement. The authors obtained very fine-grained data for more than 50,000 adults living in Philadelphia between 2002 and 2014. The data set contains the census tract (usually a handful of blocks; and which comprises a small neighborhood) where residents were living and where they moved to (if they moved), and this can be exploited to determine how much of their mobility is being driven by gentrification.

The authors first define neighborhoods that are “eligible to gentrify,” or “gentrifiable,” if they had median household incomes below the citywide value at the initial time of analysis, in the year 2000. Then from this group, they define those neighborhoods as “gentrifying” if they experienced rapid increases in real estate values and in college-educated residents.

They investigate the likelihood of a resident moving from one census tract to another by comparing movement rates for those in non-gentrifying neighborhoods to those in gentrifying neighborhoods, controlling for a host of other factors, separate from gentrification, that might impact whether a person moves or not. Given their data set, they focus on residents without mortgages and who have low credit scores—which is a proxy for low-income, rent-paying residents. Based on their analysis, they conclude that

the results suggest that the low-score residents and low-score residents without mortgages in gentrifying neighborhoods are generally no more likely to move than similar residents in nongentrifying neighborhoods.

In short, they do not find strong evidence of displacement.

New York

A paper with a similar approach, but for New York City, is “Does Gentrification Displace Poor Children and their Families? New Evidence from Medicaid data in New York City,” by Kacie Dragan, Ingrid Gould Ellen, and Sherry Glied. In this case, the authors use fine-grained data from a New York State Medicaid database. Here the focus is on the possible displacement of children in low-income households.

Specifically, they use the database to track where 56,992 New York City children were living and whether they moved or not from 2009 to 2015. They were also able to determine whether they lived in public housing, subsidized housing (such as rent-stabilized), or free-market housing. In a similar spirit as the study above, they define neighborhoods (census tracts) as “gentrifiable” as ones that have average incomes in the bottom 40th percentile of all census tracts in New York City. Then from this group, they define those that gentrified as experiencing the most rapid growth of college-educated in-movers from 2009 onward.

The Findings

Comparing children in those census tracts that gentrified versus those that did not, they find that low-income children who were living in gentrifying neighborhoods were no more likely to move than children who lived in non-gentrifying census tracts. This finding holds even for low-income children who lived in market-rate rental housing.  They also write,

Contrary to fears voiced by many, we find that the average low-income child who starts out in areas that later gentrify experiences a reduction in neighborhood poverty, mainly because the majority do not move as neighborhood income rises around them.

In short, the authors again do not find evidence of displacement, and they find evidence of improvements in the quality of children’s neighborhoods.

Gentrification and Integration

Most cities in the U.S. that appear diverse on paper are segregated in practice. At the very local level, people frequently live in neighborhoods characterized by high levels of racial or ethnic homogeneity. But gentrification has the potential to reverse this trend in the sense that it is normally associated with an influx of college educated white households into minority areas. The problem is that these in-movers are often seen as interlopers or invaders. Or as an Atlantic Monthly article put it, “The way a lot of African American and Latino people experience gentrification is a form of colonization. The gentrifiers are not wanting to share—they’re wanting to take over.”[1]

But, if, as most people believe, integrated communities are beneficial to residents and society, the question remains about how gentrification affects the composition of neighborhoods.  The  paper “Gentrification and Fair Housing: Does Gentrification Further Integration?”, by Ingrid Gould Ellen and Gerard Torrats-Espinosa, studies how gentrification impacts the racial and ethnic composition of inner-city communities. Here they investigate 412 cities throughout the United States. Similar to above, they define the set of low-income gentrifiable neighborhoods (census tracts) and then split them into those that gentrified versus those that did not, based on the growth in their relative incomes. They find that

the neighborhoods that have integrated through gentrification have remained racially integrated for longer periods of time than the conventional wisdom suggests. Many are seeing little change in their white population share in the decades following gentrification. Indeed, neighborhoods that became integrated through gentrification appeared to be more racially stable than those that integrated through households of color moving into predominantly white neighborhoods.

Gentrification and Local Employment

Another assumption about gentrification is that the wealthy interlopers bring in their expensive business, such as Whole Foods and Starbucks, and hire people from far away. But the question remains, how does the entry of new establishments in gentrifying communities affect employment opportunities for low-income locals? This question is investigated in “Does Gentrification Increase employment opportunities in low-income neighborhoods?“, by Rachel Meltzer and Pooya Ghorbani.

On the one hand, new businesses can generate opportunities for locals since they can see the “Help Wanted” signs as they walk past the stores, or they may be more likely to hear about new jobs from friends. On the other hand, if the new businesses require higher skills or less labor in general, or use in-house hiring mechanisms, as might be the case for large chains, then opportunities for locals may be diminished.

Here, the authors investigate the case of job growth in the New York City metropolitan region from 2002 to 2011.   Similar to above, they first identify the pool of gentrifiable neighborhoods (census tracts) as those that are in the bottom 20% in the ranking of median household income in 2000. They then separate them into two groups—those that gentrified and those that did not based on income growth. Their focus is on studying what they call live-work zones—rings around each census tract that represent areas where one is within walking distance or an easy commute to work (they study three zone sizes, 1/3-mile, 1-mile, and 2-mile rings).

The Findings

Here the results are somewhat mixed. The authors find that jobs going to residents who live very close by decline (that is, jobs the within the same census tract or 1/3-mile zone). But, as the definition of live-work zone expands (to say within the one-mile area), gentrification is associated with higher employment going to local workers. They write,

We find that employment effects from gentrification are quite localized. Incumbent residents experience meaningful job losses within their home census tract, even while jobs overall increase. These job losses are concentrated in service and goods-producing sectors and low- and moderate-wage positions; but local residents do see gains in higher-wage jobs in very proximate live-work zones and lower-wage jobs slightly farther away.

In other words, employment effects are quite mixed. Closer to home, jobs seem to become less available to residents, but employment opportunities still exist within surrounding neighborhoods.

Rapidly Gentrifying (light blue) and Moderately Gentrifying (dark blue) Neighborhoods in New York City. Source: Dragan et al. (2019).

Gentrification and Children’s Health

One impact of gentrification that has not received much attention relates to children’s health. This issue is investigated in the paper, “Gentrification and The Health Of Low-Income Children In New York City,” by Kacie Dragan, Ingrid Gould Ellen, and Sherry Glied, who also authored the paper on New York City discussed above.  For poorer families remaining in gentrifying neighborhoods, how do the young fare as wealthier people move in?  Gentrification can potentially improve health because of increased safety, access to nutritious foods, better parks, and perhaps more healthcare itself, if new clinics or medical facilities move in. On the other hand, rapid changes might lead to more uncertainty and loss of friends, and children may see their parents suffer from financial stress and anxiety if prices rise rapidly.

Using the Medicaid data discussed above, the authors looked at four measures of children’s well-being: obesity, asthma, attention deficit hyperactivity disorder (ADHD), and anxiety or depression. Again, similar to the other studies, they compare gentrifying neighborhoods to those that did not.

They find that

children who started out in areas that rapidly gentrified in the period 2009–15 did not differ meaningfully or significantly from children in similar areas that remained low SES [socioeconomic status] in terms of their 2015–17 rates of hospitalizations … or in terms of diagnoses for overweight or obesity, attention deficit hyperactivity disorder or conduct disorder, or asthma at the end of the period (by ages 9–11) However, the difference between the two groups of children with respect to anxiety or depression diagnoses was significant: We saw a 1.56-percentage-point increase (8.69 percent versus 7.13 percent; p < 0:01) in the prevalence of anxiety or depression among those who started out in areas that rapidly gentrified relative to those in areas that remained low SES.

In short, they do find a bump in anxiety rates, but otherwise, no other differences. The drivers of this increase is not explored, but it is an important question for future work.

Gentrification and Housing Prices

Lastly, gentrification is a hot-button issue because the assumption is that when high-end construction appears, it means rising prices for everyone. A recently-completed study by Xiaodi Li, “Do New Housing Units in Your Backyard Raise Your Rents?“, explores how the construction of high-end residential buildings impacts rents in nearby buildings across New York City from 2002 to 2013. The Law of Supply says that as more units are built, it will increase the total quantity of housing and thus lower prices, all else equal. The demand side relates to the spillovers associated with new construction—if the high-end units also draw in more amenities or help reduce crime, for example, then people would be willing to pay more for housing in these places, and prices will rise. Li’s goal is to separate these two elements and see which is more important.

The Chicken or the Egg?

The measurement problem here is to disentangle a kind of chicken-and-egg conundrum: do rising rents drive new construction, or does new construction drive rising rents? To investigate the latter, Li controls for the timing of new construction by looking at both when new a building permit is issued and when it opens up for occupancy. The idea is to look at how rents are impacted by building openings, controlling for the permitting dates. Openings represent the supply effect, where the permitting times can control for the draw of the neighborhood if it is gentrifying. Another measurement strategy she uses is to focus on how rents are impacted only within 500 feet of a new building. By looking at very localized relationships, she can more accurately calculate price changes due to new completions, rather than other things going on in the neighborhood or city.

The Findings

Based on her analysis, she concludes that

for every 10% increase in the housing stock, rents decrease 1% … within 500 feet. In addition, I show that new high-rises attract new restaurants, which is consistent with the hypothesis about amenity effects. However, I find that the supply effect is larger, causing net reductions in the rents and sales prices of nearby residential properties.

In other words, her results validate the Law of Supply, which says that adding more housing can help reduce prices. It is important to remember, however, that the statistical method finds a reduction in rents from new buildings, holding everything else constant. But, in reality, everything else is not fixed. In particular, prices across the city are, in general, rising. So based on casual observation, it’s virtually impossible to mentally calculate how much prices would have grown otherwise, if there were no new additions.

Housing Price Indexes for New York City, 2003-2019Q2 (2003=100). The left graph is an index of residential building prices in NYC by borough–not adjusted for inflation. The right graph is the same set of indexes, but adjusted for inflation. Both the nominal and real prices of housing have been rising over the last 15+ years. Most people probably use nominal prices when thinking about housing costs, which gives the impression that they are rising faster than is the case (i.e., as compared to the inflation-adjusted prices). Source: Indexes created by Jason Barr from data provided by New York City.

Concluding Remarks

Though taken together, these studies find minimal impacts from gentrification, I have, due to space constraints, necessarily given short shrift to other elements of these scholars’ research. I do not want to give a false impression that each study only finds the one thing that I reported. Instead, each uses their data sets to study details and questions beyond what I have discussed. Their work teases out more nuanced impacts. I hope to return to these issues in a future post.

The Mechanisms?

These studies, however, generally do not identify the underlying processes or mechanisms that are in play. It calls to mind the need for additional studies. We can imagine any number of reasons why displacement effects are not being found. For example, cities with rent control or public housing might protect low-income residents and so what is being measured are the protections (though Dragan et al. find no higher rates of displacement in market-rate homes). Furthermore, gentrification is often associated with high-end construction. So, the lack of an impact may be from entering residents living in new units or moving into housing that becomes vacant through normal turnover.

But we could also envision that a displacement effect is being muted by an equally opposing “stay-at-any-cost” effect. For example, we can imagine two sets of low-income households in a gentrifying community. First are those who refuse to move, despite rising costs, because they prefer the new neighborhood or believe it’s better for their children. They thus absorb the expenses and pay them through either working more hours, taking in roommates or lodgers, or reducing expenditures on other things. And then we can imagine another set that is priced out or no longer likes the neighborhood and leaves. So here we might not see any net effect because the must-stayers equally balance those displaced.

More to Come

The findings reviewed here are but a few that look at the impacts of gentrification. In future posts, I will discuss more of them, as well as studies that look at the drivers of gentrification. As a whole, this body of work can also aid city officials in designing better policies that help those harmed without generating unintended consequences. There is still much to discuss. Stay tuned.

—

[1] This quote is reproduced in Ellen and Torrats-Espinosa’s article discussed in the section, but the original 2017 article in the Atlantic Monthly is here.

 

by Jason Barr 1 Comment

Skyscrapers, Housing, and Cities: A Q&A Interview with Ingrid Gould Ellen (Part II)

Jason M. Barr    July 1, 2019

Ingrid Gould Ellen is the Paulette Goddard Professor of Urban Policy and Planning at New York University, and a Faculty Director at the NYU Furman Center for Real Estate and Urban Policy. She is the author of Sharing America’s Neighborhoods: The Prospects for Stable Racial Integration and editor of The Dream Revisited: Contemporary Debates About Housing, Segregation and Opportunity.

On Housing Supply

JB: In your recent article, “Supply Skepticism: Housing Supply and Affordability,” in Housing Policy Debate, you discuss some of the reasons given by the opponents of new construction in large, older cities. Can you elaborate on some of them and the rebuttals you provide in the article? What does the evidence say about the relationship between housing supply and affordability?

IGE: While new developments have long faced opposition from NIMBY homeowners, who want to maintain their home values, the article highlights the opposition that now comes from renters and affordable housing advocates, who worry that new development will actually raise rather than lower rents and prices. This dynamic was on full display in the recent California debate on SB50, where some tenant advocates continued to oppose upzoning in transit-rich areas due to the perceived risk of displacement. Our article analyzes four commonly voiced arguments voiced by this new group of supply skeptics.

The first argument is that land in many high-cost cities is so constrained that any market rate housing will come at the direct expense of affordable homes.  The second challenges the concept of filtering (the idea that new market rate housing causes other housing to filter to lower income households), maintaining that adding supply at the top end will do little or nothing to alleviate affordability challenges in lower-priced segments of the market.  The third argument is that new housing will simply induce more demand and that any decreases in price resulting from additional supply will be fully offset.  Finally, the fourth argument focuses on localized spillover effects and asserts new housing will increase rents and trigger displacement in the immediately surrounding neighborhood, even if it relieves price pressure across the metro-area.

Many of these arguments are plausible, but our review of the literature concludes that on balance, research suggests easing barriers to new construction make housing more affordable.  That said, there are a number of research gaps.  Most notably, we need more research examining how new market-rate developments affect prices and rents in the immediately surrounding neighborhood.  There is some emerging research currently underway that should help to shed light on this question.

Finally, and importantly, we argue that allowing more new housing will not fully address affordability challenges; subsidies or other incentives are also needed to meet the housing needs of the lowest income households.

On Housing Supply and Governing

JB: Related to this, today, the realities of governing a city means that, frequently, good economics makes bad politics, and good politics is bad economics. For example, if a mayor proposes a large upzoning of a particular area to increase density and housing affordability, it’s likely to be shot down. It seems to me that if researchers want to have more say in the local, state, and regional policy process, they need to offer policy solutions that consider the challenging political realities that motivate mayors and local officials. A lot of social science and economic research has shown that restrictive zoning raises housing prices; but the policy implication that city governments should just go ahead and loosen zoning restrictions is not helpful for a mayor worrying about re-election. What are your thoughts on this?

I think it’s helpful to emphasize the need for balance in local housing policy.  Reducing barriers to new supply is critical, but such efforts should be paired with efforts to create and preserve subsidized housing, to help households access and afford private-market homes and to protect against displacement.  Such balanced approaches are not only likely to be more effective in addressing affordability challenges, but are also more likely to garner political support from the wide coalition of interests needed to advance desired policy changes.

New York’s Coop City. This housing complex in the northern Bronx, completed in 1973, was arguably the last attempt by the city to build large-scale, non-public housing for lower- and middle-income residents. Source: Wikipedia.

On Cities and Climate Change

JB: You’ve done research on the effects of Hurricane Sandy on New York’s economy. Can you describe some of this research and your findings?

IGE: I’ve been collaborating with Rachel Meltzer and Xiaodi Li on a set of papers about the impacts of Hurricane Sandy on local businesses and real estate markets. Our paper on local businesses finds that Sandy had a significant and persistent effect on neighborhood establishments, particularly small retailers with a more local customer base. After Sandy, retail establishments exposed to higher storm surge flooding experienced larger losses in jobs and sales revenues compared to retail establishments with no or little exposure to inundation.  We also see evidence that retailers exposed to high levels of flooding were more likely to close.

In our second paper, we examine effects on local real estate markets.  Our preliminary results indicate that property prices fell after the storm, but price declines were concentrated among properties directly physically damaged and their immediate neighbors.  Properties in the flood zone that saw little damage from the storm saw little change in price, suggesting that homebuyers did not change their global assessment of risk for flood prone areas.  Further, properties in higher income neighborhoods experienced smaller price discounts and recovered more quickly compared to those in lower income areas.

On Climate Change and Urban Resilience

JB: Going forward, what do you think cities can do to be more resilient to better withstand sea-level rise and storm surges? And how can these policies be enacted without placing an undue burden on residents, who already have high housing and living expenses?  What are your thoughts on Mayor De Blasio’s plan (adapted from former Mayor Bloomberg’s plan) to build a “Seaport City,” expanding Manhattan through infill into the East River?

IGE: Climate change is an enormous challenge for coastal cities, and there is no silver bullet, especially in dense urban areas – we can’t just put skyscrapers on stilts.  Certainly we shouldn’t ask taxpayers to shoulder the risk of building of homes in flood zones, as is our current model under the national flood insurance program. And we should probably subsidize efforts to make existing buildings throughout the city more resilient.  It’s quite likely that more sweeping efforts will also be needed to protect cities from flooding, such as building seawalls or dikes that can protect neighborhoods from surging waters.  Some creative designers have come up ideas for building landscaped hills that not only shield cities from floods during storm but also serve as recreational spaces.

Flooded homes in Tuckerton, N.J., on Oct. 30, 2012, after Hurricane Sandy made landfall on the southern New Jersey coastline on Oct. 29. Source: US Coast Guard via AFP/Getty Images.

On the Future of Large Cities

JB: In a recent op-ed on Bloomberg.com, the economist Tyler Cowen offers a rather bleak prospect for the large cities like New York, and San Francisco. His basic argument is that in a city with two groups–renters and owners–the owners push restrictions on new construction to increase their property values, while the renters are reluctant to push for urban improvements in infrastructure and services because they would just wind up increasing their rents (and benefiting property owners). His conclusion:

In a NIMBY-controlled world, many of our most glorious cities will slowly but surely fall apart. Voters just won’t care enough about the quality of public services, and furthermore many voters might end up using their votes to express their ideology (even more so than usual) rather than to elect leaders to solve problems….. If America’s cities continue to deteriorate in so many areas, the social costs of building restrictions and high rents will spread — even to the most development-friendly parts of America and the world.

Would you like to offer any thoughts or a rebuttal on this? Is the future of New York a return to the 1970s?

IGE: I’m less pessimistic.  Over the past decade or two, we’ve seen a growing share of college-educated adults choose to live in downtown areas.  There’s some debate about the underlying causes.  In work with Keren Horn and Davin Reed, we argued that part of the story is declining crime.  Others have pointed to long work hours leading to a desire for shorter commutes, while others have highlighted a growing taste for urban amenities.  But whatever the cause, I don’t expect that we will see the kind of suburban flight that characterized U.S. cities during the 1970s.  And the quality of life in U.S. cities today is dramatically improved from what it was in that decade.  Streets are safer, parks are nicer, and fiscal outlook is far better.

I worry more about the consequences of rising rents, which threaten to price out all but high-income households in many of our cities and undermine the very diversity that makes cities so vital.  So, we certainly need to continue to push for an ample supply of housing, serving a range of incomes.

Continue reading Part I of this Q&A Interview with Ingrid Gould Ellen here.

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