# Suburban Transit-Oriented Development

Here’s a Google Maps image of Southport, a section of Fairfield, Connecticut with its own Metro-North commuter rail station:

Here’s an image at the same scale of Bourg-la-Reine, an inner suburb of Paris on the RER B, at the junction between the line’s two southern branches:

At Bourg-la-Reine, the buildings just east of the station are high-rise. There are local community amenities, including walkable schools, supermarkets, and pharmacies, and people can comfortably live in this suburb without a car. This generates significant RER traffic at all hours of day: outbound trains are often standing-room only until they reach this station even in midday, outside rush hour.

At Southport, there are a few townhouses near the station. But the roads are wide and hostile to pedestrians, and the nearest supermarket closes at 6 pm, too late for commuters returning from the city. Car ownership approaches 100%, and nobody rides the trains except to get to office jobs at the traditional peak hour in Manhattan (or perhaps Stamford).

The difference between the two places is so stark that they can barely be compared. Southport has 317 inbound boardings per weekday. Of those, 263, or 83%, are in the morning rush hour; the Metro-North-wide average is 63%, and the average on the SNCF-operated parts of the RER and Transilien is about 46%. Bourg-la-Reine has 4.5 million annual riders, about 16,000 on an ordinary working day.

A huge part of the difference is about service provision – Bourg-la-Reine has a train every five minutes midday, Southport a train every hour. But it’s not just about service. The RER has stations farther out, with somewhat less intense service, such as a train every 15 minutes, with comparable ridership. And the LIRR and Metro-North have little off-peak ridership even at stations with more frequent service, such as Mineola and Hicksville. Transit-oriented development (TOD) is as important as good service in such cases.

I bring up Southport because the RPA just dropped a study about suburban TOD that grades every New York commuter rail station between 0 and 3, and gives Southport the highest mark, 3. The RPA study looks at zoning within 800 meters of each station and considers whether there’s a parcel of land that permits multifamily housing with a floor are ratio higher than 1.25. Southport has such lots, supporting some townhouses, so according to the RPA it gets full marks, even though, by RER standards, it is like every other American car-oriented suburb.

Based on this methodology, the RPA identifies a number of good suburbs, and even comes to policy conclusions. It proposes more TOD in the mold of existing exurban New York examples, such as Patchogue. The model for the program is the real reason the RPA study is so weak: rather than calling into attention the big differences between land use at suburban stations in New York versus in Paris (or any number of big European cities with suburban rapid transit), it overfocuses on small differences within auto-oriented suburbia.

Some of the ultimate conclusions are not terrible. For example, the RPA is proposing linking federal infrastructure development to permitting more multifamily housing. This would improve things. However, the problem with this is twofold. First, it is unrealistic – the federal government gave up decades ago on enforcing fair housing laws, and has no interest in attempting to make exclusionary suburbs behave. Were I to propose this, hordes of American commenters would yell at me for not understanding American politics. And second, it misunderstands the nature of the problem, and ends up proposing something that, while unrealistic, is still low-impact.

The best way to understand the problem with the study is what author Moses Gates told me on Twitter when I started attacking it. He said that the RPA was looking at zoning rather than actual development. Since there is zoning permitting multifamily development within the prescribed radius at Southport, it gets full marks. With my understanding of what good TOD looks like, I would be able to say that this is clearly so bad the methodology must be changed; on Twitter I suggested looking at zoning within 300 meters of the station rather than 800, since the highest-intensity development should be right next to the station. I also suggested looking at supportive nonresidential uses, especially supermarkets. A development that isn’t walkable to retail at reasonable hours is not TOD.

The RPA does not think in this language. It thinks in terms of internal differences within the US. Occasionally it deigns to learn from London, but London’s suburban development is auto-oriented by European standards (transit mode share in the London commuter belt is at best in the teens, often in the single digits). Learning from anywhere else in the world, especially places that don’t speak English, is too difficult. This means that the RPA could not reach the correct conclusion, namely, that there is no such thing as an American suburb with TOD. The only exception I can come up with in the United States involves Arlington, on the Washington Metro, and Arlington is no longer considered a suburb, but really a full-fledged city in a different state, like Jersey City.

The other thing the RPA missed is that it drew too large a radius. TOD at a train station should include townhouses 800 meters out – but it’s more important to include high-rise residential construction next to the train station and mid-rise apartment buildings 500 meters out. Giving American suburbs latitude to place TOD so far from the station means they will act like Southport and allow small amounts of multifamily housing out of the way, while surrounding the station itself with parking, a tennis court, and large single-family houses with private swimming pools. This is not hypothetical: suburbs in New Jersey have reacted to court rulings mandating affordable housing by permitting apartments at the edge of town, far from supporting retail and jobs, and keeping the town core single-family.

Because the RPA missed the vast differences in outcomes between the US and France, it missed some useful lessons:

• States should centralize land use decisionmaking rather than give every small suburb full autonomy.
• TOD doesn’t need to be fully mixed-use, but there should be some local retail right next to housing.
• Housing should be high-density right next to the station. A floor area ratio of 1.25 is not enough.
• Publicly-funded social housing should be next to train stations, in the city as well as in the suburbs, and this is especially important in expensive suburbs, which aren’t building enough affordable housing.

Without suburban TOD, any regional rail system is incomplete. I wish I could have covered it at my talk, but I didn’t have time. Good service needs to run to dense suburbs, or at least suburbs with dense development within walking distance of the station. It needs to extend the transit city deep into suburbia, rather than using peak-only commuter rail to extend the auto-oriented suburbs into the city.

# Neighborhoods With Excess Capacity

In New York, the tech industry has clustered in the Meatpacking District, around 14th Street and 8th Avenue. Google’s building (the company’s largest office outside the Googleplex) is there, Samsung’s New York offices are there, startup incubators are there with co-working spaces. Stephen Smith has called for commercial upzoning there (on YIMBY three years ago, and on Twitter just now), despite NIMBY objections. He argues not only that there is pent-up demand for office space, but also that there is excess subway capacity there: “the L train’s capacity west of Union Square is essentially unlimited, after the hordes from Brooklyn headed to destinations east of Broadway change for the 4/5/6 and N/Q/R.” While his other arguments for upzoning are solid, this one is incorrect, and I’d like to explain which areas have excess capacity and which don’t.

Two years ago, I wrote this post about modeling transit crowding. The model is primitive – it assumes a one-dimensional city, 100% mode share, and independent job and residence distributions. For the purposes of this post, cities A, B, and C from the model are not relevant (they have perfect mixture of jobs and residences); cities D, E, and F, with separation of residences and jobs, are more relevant, with city F, with partial mixture, the most useful.

The results of the model are fairly predictable. In the morning peak, transit vehicles (or roads!) fill up toward the center as they pass through residential areas, and then empty in the commercial core. This means that more residences outward of the point of greatest congestion, and more jobs inward of it, add more crowding; more jobs outward of the point, and more residences inward of it, do not. More jobs on the other side of city center add to crowding, because people still ride through the point of greatest crowding.

On the L, the point of greatest crowding is between Bedford Avenue (the last stop in Brooklyn) and First Avenue (the first in Manhattan). This means that more residential development on the L in Brooklyn and more commercial development in Manhattan would add crowding – even commercial development on the West Side would attract riders living in Brooklyn, who would ride through the overcrowded segment under the East River. The other subway lines serving the Meatpacking District suffer from the same problem: those are the 2 and 3 at 7th Avenue and 14th Street, and the A, C, and E at 8th Avenue. With Second Avenue Subway having taken some crowds off the 4 and 5 on the East Side, it’s likely the 2, 3, and E are the most crowded subway lines in New York today (the A has more room). Yes, most riders on those lines get off in Midtown, but it doesn’t matter, because riders from the Upper West Side and Queens, attracted to new jobs in the Meatpacking District, would still ride through the most crowded point, at the entry to Midtown.

So if not the Meatpacking District, where is it better to add jobs, purely from the perspective of subway crowding? Superficially, the answer is to mix them across the residential parts of the city. But here, my model runs into problems with mode share. The model says that adding jobs in (say) Downtown Brooklyn increases subway crowding, because of riders from Uptown Manhattan riding to the south. Per the model, it’s best to add jobs on the side with more crowding, which is the north and Queens sectors, not the Brooklyn sector, where only the L is very crowded. This means, more jobs on the Upper East and West Sides, and maybe also in Long Island City, near Queensboro Plaza.

But in reality, there is some travel segmentation in New York. People who work on the Upper East and West Sides probably live in those neighborhoods or in Harlem and the Bronx, and people who work in Downtown Brooklyn probably live elsewhere in Brooklyn. Yes, it’s possible to commute between the Upper East Side and Downtown Brooklyn, but people would not ordinarily choose to do so – the commute is long and crowded (because of all the Midtown-bound workers), and there isn’t much saving on rent. People might still do it for various reasons, like a two-body problem or moving frequently between jobs – this is why through-running is important – but it’s much less common than living and working on the same side of city center.

So most likely, office development in Downtown Brooklyn would mainly attract ridership from within Brooklyn. Extra ridership from Uptown Manhattan and the Bronx is likely to be small. The upshot is that locations outside the most crowded point on each inbound subway line are likely to lead to large gains in subway ridership without much additional crowding.

I bring up Downtown Brooklyn and not just the Upper West and East Sides because it is better-connected to more bedroom communities by subway. These include the Lower East Side and Chinatown, Long Island City, and nearly all of Brooklyn. Long Island City is also highly accessible, from much of Queens and the parts of Brooklyn on the G train. But the Upper West and East Sides aren’t so accessible because of the lack of good east-west subway options.

Of course, the situation on the ground is different. New York is desperate to add tech jobs in Downtown Brooklyn, but the tech industry insists on clustering in the Meatpacking District. There’s only so much a city can force developers to site themselves in the areas most convenient for infrastructure. But from a long-term capacity standpoint, it’s in New York’s interest to encourage commercial development outside the Manhattan core, especially in areas that get decent subway service from multiple directions, like Long Island City, Downtown Brooklyn, and maybe Jamaica.

It would be easier if there were more service targeted at off-core destinations. This is part of why I harp on regional rail all the time – the LIRR would be able to serve Downtown Brooklyn and Jamaica better if it didn’t exist just for the benefit of suburban salarymen working in Midtown. But this also includes Triboro, which would give multidirectional service to nodes including Jackson Heights, the Bronx Hub, and Brooklyn College. This would encourage developers to build commercial at these nodes, which suffer from poor access to workers today.

Note that opening circumferential transit, in this model, has the opposite of the expected effect on radial lines. Normally, a new transit line reduces demand on parallel lines and increases demand on intersecting lines, which runs the risk of overloading them. But if a circumferential line encourages office development at intersection points with radials, it will still encourage more ridership on the radials, but this ridership will completely miss the congested inner portions of the radials.

# Meme Weeding: Land Value Capture

Last month’s Patreon poll was about meme weeding – that is, which popular meme in public transit I should take apart. The options were fare caps on the model of London, popular among some US reformers; wait assessment, a schedule adherence metric for trains I briefly complained about on Vox as used in New York; and land value capture/tax increment financing/the Hong Kong model. The last option won.

Good public transit creates substantial value to its users, who get better commutes. It’s an amenity, much like good schools, access to good health care, and clean air. As such, it creates value in the surrounding community, even for non-users: store owners who get better sales when there’s better transportation access to their business, workers who can take local jobs created by commuters to city center, and landowners who can sell real estate at a higher price. All of these positive externalities give reason to subsidize public transit. But in the last case, the positive impact on property values, it’s tempting to directly use the higher land values to fund transit operations; in some cases, this is bundled into a deal creating transit-oriented development to boost ridership. In either case, this is a bad way of funding transit, offering easy opportunities for corruption.

Value capture comes in several flavors:

• In Japan, most urban private railroads develop the areas they serve, with department stores at the city end and housing at the suburban end.
• In Hong Kong, the government sells undeveloped land to the now-privatized subway operator, the MTR, for high-density redevelopment.
• In the US and increasingly Canada, local governments use tax increment funding (TIF), in which they build value-enhancing public infrastructure either by levying impact fees on development that benefits from it or by programming bonds against expected growth in property taxes.

In both Hong Kong and the major cities of Japan, urban rail operations are profitable. It is not the case that value capture subsidizes otherwise-money losing transit in either country, nor anywhere I know of; this did not prevent Jay Walder, then the head of New York’s MTA, from plugging the MTR model as a way of funding transit in New York. What’s true is that the real estate schemes have higher margins than rail operations, which is why JR East, the most urban of the remnants of Japan National Railways, aims to get into the game as well and develop shopping centers near its main stations. However, rail operations alone in these countries are profitable, due to a combination of high crowding levels and low operating costs.

The Japanese use case is entirely private, and does not to my knowledge involve corruption. But the Hong Kong use case is public, and does. For all the crowing about it in Anglo-American media (the Atlantic called it a “unique genius” and the Guardian said it supported subsidy-free operations), it’s a hidden subsidy. The state sells the land to the MTR, and the MTR alone, at the rate of undeveloped outlying land. Then the MTR develops it, raising its value. Other developers would be willing to pay much better, since they can expect to build high-density housing and have the MTR connect it to Central. This way, the government would pocket the profits coming from higher value on its land. Instead, it surreptitiously hands over these profits to the MTR.

While Western media crows about Hong Kong as an example of success, local media excoriates the corruption involves. Here’s the South China Morning Post on the MTR model:

The rail and property model was never anything but a delusion to which only Hong Kong bureaucrats could be subject. It traded on the odd notion that you cannot assign a value to property until you actually dispose of it.

Thus if you give the MTR the land above its stations, these sites suddenly and magically acquire value and the proceeds cover the cost of building the railway lines. Ain’t magic wonderful? We got the MTR for free.

Stephen Smith dealt with this issue in 2013, when he was still writing for NextCity. He explained the local corruption angle, the fact that MTR rail operations are profitable on their own, and the lack of undeveloped land for the state to sell in most first-world cities. (Conversely, one of his arguments, about construction costs, doesn’t seem too relevant: Hong Kong’s construction costs are probably similar to London’s and certainly higher than Paris’s, and doing value capture in Paris would be an urban renewal disaster.)

Stephen also tackles American examples of value capture. With no state-owned land to sell to the public transit agency at below-market prices, American cities instead rely on expected property taxes, or sometimes levy special fees on developers for letting them build TOD. Stephen talks about scale issues with the TIF-funded 7 extension in New York, but there are multiple other problems. For one, the 7 extension’s Hudson Yards terminus turned out to be less desirable than initially thought, requiring the city to give tax breaks. See for examples stories here, here, and here.

But there are more fundamental problems with the approach. The biggest one is the quality of governance. TIF is an attractive-looking option in American jurisdictions that recoil at raising direct taxes to pay for service. This means that as happened in New York, it is tempting for cities to promise property tax windfall, issue bonds, and then let successor governments raise taxes or cut services to pay interest. This opaqueness makes it easier to build bad projects. When the government promises especially high benefit-cost ratios, it can also keep issuing new bonds if there are budget overruns, which means there is no incentive for cost control.

TIF also requires the city to use zoning to create a shortage of land in order to entice developers to pay extra to build where it wants them to. Stephen complains that New York reamed problems on upzoning in Midtown East, one of the few locations in Manhattan where developers are willing to build supertall office towers without any tax breaks; the new zoning plan, in the works since he was writing for NextCity in 2013, only just passed. Another such location is probably the Meatpacking District, near the Google building at 14th and 8th, now the city’s tech hub – there is no tall office construction there due to the power of high-income residential NIMBYs. Were the city to loosen zoning in these areas and permit companies that need a prime location to set up offices in these areas, it would find it even harder to entice developers to build in a lower-demand area like Hudson Yards. Midtown East and the Meatpacking District are replete with subway lines, but there are no new plans for construction there, so the city wouldn’t do a TIF there.

The same problem, of TOD-reliant funding requiring the city to restrict development away from targeted investment areas, also works in reverse: it encourages development-oriented transit. In 2007, Dan Doctoroff, then a deputy mayor and now head of Google’s Sidewalk Labs, opposed Second Avenue Subway, on the grounds that the area is already developed. Second Avenue Subway was eventually built, but the 7 extension omitted a stop in an already-developed area amidst cost overruns, as Bloomberg prioritized Hudson Yards. This is not restricted to New York: San Francisco is more interested in a subway to Parkmerced than in a subway under Geary, the busiest bus route, busier than the subway-surface light rail branch serving Parkmerced today. Smaller American cities propose core connectors, aiming promoting redevelopment in and around city center. This in turn means ignoring low-income neighborhoods, where there is no developer interest in new buildings except as part of a gentrification process.

These problems are for targeted investments. But when there is more widespread TOD, TIF ends up being a tax on transit users. Cities build roads without levying special taxes on sprawling development, whether it sprawls by virtue of being near the highway or by virtue of being far from public transit. When they build transit, they sometimes tax TOD, which means they are giving developers and residents tax incentives to locate away from public transit.

Hong Kong is not the right model for any TOD scheme; its corruption problems are immense. It’s a shiny object for Americans (and other Anglophone Westerners), who are attracted to the allure of the exotic foreigner, like a premodern illiterate attributing magic to the written word. Instead of replicating its most questionable aspect, it’s better to look at models that are attractive even to local corruption watchdogs.

This means funding public transit and other services out of transparent, broad-based taxes. Paris uses a payroll tax, varying the rate so as to be higher in the city (2.95%) than in the outer suburbs (1.6%). Everyone will hate them, especially people who don’t use transit and don’t view it as directly necessary for their lives. This is why they work. They compel the transit agency to run efficient service, to stave off opposition from aggrieved center-right middle-class voters, and to run it well, to stave off opposition from populists (“why am I being taxed for trains that break down?”). They leave no room for waste, for cronyism, or for slush funds for favored causes, precisely because they’re hard to pass.

It’s easy to see why politicians avoid such funding sources. The democratic deficit of local governance in the US is immense, and that of Canada is only somewhat better. Nobody wants to lose an election over raising taxes, even in cities where the political spectrum runs from the center leftward. Value capture sounds like a good, innovative idea to fund government without hated taxation, and its abuses are hidden from sight. Even as it forces city residents to endure opaque fees (never call them taxes!), it wins accolades to politicians who propose it. No wonder it continues despite its failures.

# Climate Apartheid

There are floods in Houston, thanks to Hurricane Harvey‘s making landfall close to the city, dumping about a meter of rain within just a few days. So far, the best explanation I’ve seen about the city’s drainage system is Matt Corbett’s tweetstorm, about how the city keeps building flood control systems but due to population growth they are perpetually five years behind current development. The confirmed death toll so far is 30. There is a connection to climate change: warmer ocean temperatures make tropical storms more likely, and also make it likelier that they will move slowly and dump more rain onto one area; as a result, Harvey is the Houston region’s third 500-year flood in three years, while one neighborhood was hit by three such floods in a decade.

There are floods all over South Asia, thanks to unusually strong summer monsoon rains. Mumbai got about 200 mm of rain in 12 hours yesterday. The worst impact is in more rural areas and smaller cities in the north, including Bangladesh, West Bengal, Nepal, Bihar, and Uttar Pradesh. The confirmed death toll so far is 1,200, of whom only 6 are in Mumbai – Bihar and Nepal seem like the worst-hit areas. There is once again a connection to climate change: the seasonal monsoon rainfall in South Asia is fairly stable, but extreme events dumping more than 100 or 150 mm of rain on one day are happening at increasing frequency, and climate models predict an increase in extreme rainfall events.

It is not my intention to attack American media for undercovering India. Rather, it is my intention to attack American public intellectuals and wonks for proposing adaptation to climate change. This means building flood walls to protect low-lying cities at risk of storm surges like New York, using zoning and public investments to steer development toward higher ground, and building infrastructure to deal with higher future flood risk. This contrasts with reducing the extent of climate change in the first place, called mitigation in environmentalist parlance, by reducing greenhouse gas emissions.

Examples of calls for adaptation, in lieu of or in addition to mitigation, abound:

• The Obama administration passed a directive requiring flood control standards for federally-funded projects in areas vulnerable to climate change. The Trump administration rescinded this directive, to widespread criticism from liberals in the media, for example in the Guardian.
• ThinkProgress just published an article calling for both adaptation and mitigation.
• ProPublica’s investigative reporting about Houston mentions that Fort Lauderdale and Boulder are both addressing adaptation in their long-term city plans, and compares Houston negatively with them.
• CNN negatively compared US flood control efforts with the Dutch Delta Works. The article does mention American climate change denial, but talks about the Netherlands’ flood control and not about its pro-bike transportation policy, doing its part to mitigate catastrophic climate change.
• In a personal conversation with ReThinkNYC‘s Jim Venturi about transit-oriented development near Secaucus Station, he said that the area is vulnerable to climate change, at only 2 meters above sea level. I don’t want to blame him, because he might have been channeling the Regional Plan Association or statewide plans, but one of those three (ReThink, RPA, the state) is giving up the United States’ best TOD spot on climate adaptation grounds.

All of these adaptation plans should be prohibited on grounds of climate apartheid. The term climate apartheid is not my own: it comes from Desmond Tutu, who says,

[Link, PDF-p. 181] For most people in rich countries adaptation has so far been a relatively painfree process. Cushioned by heating and cooling systems, they can adapt to extreme weather with the flick of a thermostat. Confronted with the threat of floods, governments can protect the residents of London, Los Angeles and Tokyo with elaborate climate defence systems. In some countries, climate change has even brought benign effects, such as longer growing seasons for farmers.

Now consider what adaptation means for the world’s poorest and most vulnerable people—the 2.6 billion living on less than US\$2 a day. How does an impoverished woman farmer in Malawi adapt when more frequent droughts and less rainfall cut production? Perhaps by cutting already inadequate household nutrition, or by taking her children out of school. How does a slum dweller living beneath plastic sheets and corrugated tin in a slum in Manila or Port-au-Prince adapt to the threat posed by more intense cyclones? And how are people living in the great deltas of the Ganges and the Mekong supposed to adapt to the inundation of their homes and lands?

There’s a lot of nuance to add on top of Tutu’s admonition. The most important is that Mumbai is not Port-au-Prince or Malawi; it’s not even Bihar. But it’s not New York or the Netherlands either. Catastrophic flooding is still a serious risk, and its urban policy gives the poor a choice between substandard slum housing in flood-prone areas and housing projects in suburbs far from any jobs. And this is the richest city in India, while India is richer than practically every African state between South Africa and the Sahara. The poorest countries in the world, in turn, have very low emissions, even relative to their low GDPs – Bangladesh emits the equivalent of 1 metric ton of CO2 in greenhouse gases per capita annually.

Moreover, we can already know what climate adaptation will really mean. A 1-meter rise in sea level is projected to flood 17.5% of Bangladesh, corresponding at today’s population level to about 25 million people. Now, add the effects of crop failures: David Roberts quotes a paywalled Nature Climate Change article saying that rice yields go down by 10% per degree of nighttime temperature above 26. The minimal scenario would dwarf the Syrian refugee crisis, affecting 5 million people, by an order of magnitude. A more catastrophic scenario, involving flooding in Nigeria and increased droughts in the interior of Africa, could dwarf the Syrian crisis by two orders of magnitude.

The reaction to migration crises has been to militarize the border, to push the refugees away before they can get close and attract local sympathy. The US built a wall along much of the border with Mexico, long before Trump; Europe is stepping up patrols in the Mediterranean, and as we speak France is trying to open detention camps in Libya. This is not just a first-world reaction: India is fencing its border with Bangladesh. Climate adaptation means a little bit of money for flood control schemes, and a lot of money for pushing away refugees on threat of gunning them down, and building an entire apparatus of intermediate detention camps to be able to pretend that it’s not the fault of the US or Europe or Japan that the refugees are dying.

The implication is that, in parallel with the Anti-Ballistic Missile (ABM) Treaty in the Cold War, adaptation should be banned throughout the developed world. A country that spends money on trying to avoid the consequences of climate change is unlikely to be interested in avoiding it in the first place – just as a country with ABM protections is unlikely to be interested in avoiding nuclear war. Tackling a global problem like climate change requires ensuring that no single high-emissions country or economic bloc can insulate itself from its consequences.

Far from discouraging development in floodplains, first-world governments should prohibit any consideration of post-2000 temperature rise. If Secaucus is terra firma today, New Jersey should be compelled to treat it as terra firma forever and develop it as the TOD site that it is. If sea levels rise, none of the residents will be as negatively affected as the median Bangladeshi, but the residents might still agitate for future mitigation.

# Infrastructure for Mature Cities

A post by Aaron Renn just made me remember something I said in the Straphangers Campaign forum ten years ago. I complained that New York was building too little subway infrastructure – where were Second Avenue Subway, Utica, Nostrand, various outer extensions in Queens and the Bronx that we crayonistas liked? Shanghai, I told people in the forum, was building a lot of subway lines at once, so why couldn’t New York? The answer is not about construction costs. Ten years ago, China’s construction costs relative to local incomes were about the same as those of New York; even today, the difference is small. Rather, it is that China is a fast-growing economy that’s spending a lot of its resources on managing this growth, whereas the US is a mature economy without infrastructure problems as urgent as those of developing countries.

Aaron posits that American cities are too conservative, in the sense of being timid rather than in the sense of being on the political right. He gives examples of forward-looking infrastructure projects that New York engaged in from the early 19th century to the middle of the 20th century: the Manhattan grid, the Erie Canal, the Croton Aqueduct, the subway, the Robert Moses-era highways and parks. Today, nothing of the sort happens. Aaron of course recognizes that “New, rapidly growing cities need lots of new infrastructure and plans. Mature cities need less new infrastructure.” The difference is that for me, this is where this line of questioning ends. New York is a mature city, and doesn’t need grand plans; it needs to invest in infrastructure based on the assumption that it will never again grow quickly.

If not grand plans like building the Manhattan grid far beyond the city’s then-built up area, then what should a mature city do? Aaron talks about dreaming big, and there is something to that, but it would take a profoundly different approach from what New York did when its population grew by 50% every decade. I stress that, as with my last post critiquing another blog post, I agree with a substantial part of what Aaron says and imagine that Aaron will treat many of the solutions I posit here as positive examples of thinking big.

Rationalization of Government

Mature societies have accumulated a great deal of kludge at all levels, coming from social structures and government programs that served the needs of previous generations, often with political compromises that are hard to understand today. Welfare programs are usually a kludge of different social security programs (for the disabled, for retirees, for various classes of unemployed people, sometimes even for students), housing benefits, reduced tax rates for staple goods like food, child credit, and in the US food stamps. A good deal of the impetus for basic income is specifically about consolidating the kludge into a single cash benefit with a consistent effective marginal tax rate.

In transportation, bus networks have often evolved incrementally, with each change making sense in local context. When a new housing development opened, the nearest bus would be extended to serve it. In Israel, which grew late enough to grow around buses and not rail, this was also true of dedicated industrial zones. In cities that used to have streetcar networks, some buses just follow the old streetcar routes; the Washington bus system even today distinguishes between former streetcars (which have numbers) and routes that were never streetcars (which use letters). Jarrett Walker‘s bus network redesigns are partly about reorganizing such systems around modern needs, based on modern understanding of the principles behind transit ridership.

Governance often needs to be rationalized as well. In the early 20th century, it was important to connect outlying neighborhoods to city center, and connections between lines were less important. This led to excessively radial surface transit (rapid transit is always radial), but also to rail lines that don’t always connect to one another well. Sometimes due to historical contingency the lines are run by separate agencies and have uncoordinated schedules and different fare systems charging extra for transfers. Occasionally even the same agency charges for bus-rail transfers, often because of a history of separate private operators before the public takeover. In the US and Canada, the special status of commuter rail, with different unions, fares, schedules, and management is of particular concern, because several cities could use commuter rail to supplement the rest of the transit network.

In New York, this points toward the following agenda:

• Modernization of commuter rail, with full fare integration with the subway and buses, proof-of-payment fare collection to reduce operating costs, high off-peak frequency on the local lines, and through-running where there is infrastructure for it (i.e. Penn Station).
• Some bus service reorganization. New York already has extensive frequent buses, but some of its network is still questionable, for example some branches of the Third/Lexington and Madison/Fifth one-way pairs in Harlem.
• Subway reorganization. The subway branches too much, and at several places it could have higher capacity if it reduced the extent of reverse-branching; see discussion here and in comments here. Some elevated lines could also see their stops change to support better transfers, including the J/M/Z at Broadway and Manhattan to transfer to the G, and maybe even the 7 at 108th Street to enable a transfer to a straightened Q23 bus.
• Fare integration with PATH, and demolition of the false walls between the PATH and the F/M trains on Sixth Avenue, to enable cross-platform transfers.

Serve, Don’t Shape

There are two models for building new infrastructure: serve, and shape. Serve means focusing on present-day economic and demographic patterns. Shape means expecting the project to change these patterns, the “build it and they will come” approach. When New York built the 7 train to Flushing, Flushing already existed as a town center but much of the area between Long Island City and Flushing was open farmland. I’ve argued before that third-world cities should use the shape model. In contrast, mature cities, including the entire developed world except a few American Sunbelt cities and analogs in Canada and Australia, should use the serve model.

The serve model flies in the face of the belief that public transit can induce profound changes in urban layout. In reality, some local transit-oriented development is possible, but the main center of New York will remain Midtown; so far Hudson Yards seems like a flop. In the suburbs, more extensive redevelopment is possible, with apartment buildings and mixed uses near train stations. But these suburbs, built after WW2, are less mature than the city proper. In fast-growing cities in North America outside the traditional manufacturing belt the shape model still has validity – Vancouver, still a relatively new city region in the 1980s, got to shape itself using SkyTrain. But in New York, there is no chance.

This also has some ethnic implications. Jarrett likes to plan routes without much regard for social circumstances, except perhaps to give more bus service to a lower-income area with lower car ownership. But in reality, it is possible to see ethnic ties in origin-and-destination transit trips. This is why there are internal Chinatown buses connecting Chinatown, Flushing, and Sunset Park, and a bus connecting two different ultra-Orthodox neighborhoods in Brooklyn. In Washington, there is origin and destination data, and there are noticeable ties between black neighborhoods, such as Anacostia and Columbia Heights.

In a mature city with stable ethnic boundaries (Harlem has been black for ninety years), it is possible to plan infrastructure around ethnic travel patterns. This means that as New York disentangles subway lines to reduce branching, it should try choosing one-seat rides that facilitate known social ties, such as between Harlem and Bedford-Stuyvesant. While New York’s ethnic groups are generally integrated, this has special significance in areas with a mixture of linguistic or religious groups with very little intermarriage, such as Israel, which has two large unassimilated minorities (Arabs, and ultra-Orthodox Jews); Israeli transportation planning should whenever possible take into account special ultra-Orthodox travel needs (e.g. large families) and intra-ethnic connections such as between Bnei Brak and Jerusalem or between Jaffa and Nazareth.

Integrated Planning

A few years ago, I wrote a post I can no longer find talking about building the minimum rail infrastructure required for a given service plan. In comments, Keep Houston Houston replied that no, this makes it really difficult to add future capacity if demand grows. For example, a single-track line with meets optimized for half-hourly service requires total redesign if demand grows to justify 20-minute frequency. In a growing city, this means infrastructure should be planned for future-proofing, with double track everywhere, no reliance on timed overtakes, and so on. In a mature city, this isn’t a problem – growth is usually predictable.

It is relatively easy to integrate infrastructure planning and scheduling based on today’s travel patterns, and impossible to integrate them based on the future travel patterns of a fast-growing city such as Lagos or Nairobi. But in a slow-growing city like New York, future integration isn’t much harder than present-day integration. Alone among North American cities, New York has high transit mode share, making such integration even easier – transit usage could double with Herculean effort, but there is no chance that a real transit revival would quadruple it or more, unlike in cities that are relatively clean slates like Los Angeles.

Since the mature city does not need too much new infrastructure, it is useful to build infrastructure to primarily use existing infrastructure more efficiently. One example of this is S-Bahn tunnels connecting two stub-end lines; these are also useful in growing cities (Berlin built the Stadtbahn in the 1880s), but in mature cities their relative usefulness is higher, because they use preexisting infrastructure. This is not restricted to commuter rail: there is a perennial plan in New York to build a short tunnel between PATH at World Trade Center and the 6 train at City Hall and run through-service, using the fact that PATH’s loading gauge is similar to that of the numbered subway lines.

In New York, this suggests the following transit priorities:

• Open commuter rail lines and stations based on the quality of transfers to the subway and the key bus routes. For example, Penn Station Access for Metro-North should include a stop at Pelham Parkway for easy transfer to the Bx12 bus, and a stop at Astoria for easy transfer to the subway.
• Investigate whether a PATH-6 connection is feasible; it would require no new stations, but there would be construction difficulties since the existing World Trade Center PATH station platforms are in a loop.
• Change subway construction priorities to emphasize lines that reduce rather than add branching. In particular, Nostrand may be a higher priority than Utica, and both may be higher priorities than phases 3 and 4 of Second Avenue Subway. A subway line under Northern Boulevard in Queens may not be feasible without an entirely new Manhattan trunk line.
• Build commuter rail tunnels for through-running. The Gateway project should include a connection to Grand Central rather than Penn Station South, and should already bake in a choice of which commuter lines on each side match to which commuter lines on the other side. Plan for commuter rail lines through Lower Manhattan, connecting the LIRR in Brooklyn with New Jersey Transit’s Erie Lines, and, accordingly, do not connect any of the lines planned for this system to Penn Station (such as with the circuitous Secaucus Loop in the Gateway project).

Conclusion

New York still needs infrastructure investment, like every other city. Such investment requires thinking outside the box, and may look radical if it forces different agencies to cooperate or even amalgamate. But in reality the amount of construction required is not extensive. More deeply, New York will not look radically different in the future from how it looks today. Technological fantasies of driverless flying cars aside, New York’s future growth is necessarily slow and predictable, and cities in that situation need to invest in infrastructure accordingly.

In my post about third-world transit, I posited an epistemological principle that if the presence of a certain trait makes a certain solution more useful, then the absence of the trait should make the solution less useful. The shape vs. serve argument comes from this principle. The same is true of the emphasis on consolidating the kludge into a coherent whole and then building strategically to support this consolidation. A fast-growing city has no time to consolidate, and who’s to say that today’s consolidation won’t be a kludge in thirty years? A mature city has time, and has little to worry about rapid change obsoleting present-day methods.

But at the same time, the same epistemology means that these changes are less critical in a mature city. In the third world, everything is terrible; in the first world, most things are fine. New York’s transportation problems are painful for commuters, but ultimately, they will not paralyze the city. It will do well even if it doesn’t build a single kilometer of subway in the future. Nothing is indispensable; this means that, in the face of high costs, often the correct alternative may be No Build. This illustrates the importance of improving cost-effectiveness (equally important in the third world, but there the problem is the opposite – too many things are indispensable and there isn’t enough money for all of them).

I emphasize that this does not mean transportation is unimportant. That New York will not be destroyed if it stops building new infrastructure does not mean that new infrastructure is of no use for the city. The city needs to be able to facilitate future economic and demographic growth and solve lingering social problems, and better infrastructure, done right, can play a role in that. New York will most likely look similar in 2067 to how it looks in 2017, but it can still use better infrastructure to be a better and more developed city by then.

# Cities Should not Encourage Home Ownership

There’s a discussion on Twitter about home ownership. In the US, there are periodic calls to abolish the mortgage interest deduction on various grounds: it discriminates against low-income renters, it benefits people in higher tax brackets (i.e. the rich), it is a subsidy to the suburbs. Matt Bruenig, one of the strongest voices on the American socialist left writing about policy, makes an anti-racist argument: per a 2015 report from Demos (p. 12), home ownership contributes to a racial wealth gap, since whites enjoy higher returns than blacks and Hispanics.

In this post, I’m going to make a more general point: home ownership is a questionable individual choice, and a bad regional choice. Governments, from the urban to the national level, should not encourage it in any way.

Home ownership as wealth

Real estate, like any other asset, is a source of wealth. People buy it as an investment, which they can borrow against (“second mortgage”), bequeath to their children, or sell in retirement. In this, it’s no different from any other asset. The more down-to-earth use is as a source of savings: retirees who own a house or apartment debt-free, having finished paying off their mortgage, are not at risk of eviction if their pensions are limited. Near-retirees who lose their jobs are in a similar situation – they have lower immediate expenses than if they rented.

The problem is that this form of saving works in reverse for everyone who is of working age. A 40-year-old who loses their job might want to live off of savings while looking for a job of equivalent skill level and pay. If their savings are largely in their house, this is difficult, for two reasons:

1. The house is less liquid than stocks – it’s hard to sell a quarter of it.
2. The house is likelier to lose value when the owner needs it the most.

The second point is true to some extent of all pro-cyclical assets (e.g. stocks), but especially of real estate. Workers are more likely to be laid off in recessions, when pro-cyclical assets lose value. Counter-cyclical ones, like sovereign bonds, rise in value, but have lower returns in the long run, creating the familiar risk/returns tradeoff. Housing in that sense is no different from stocks.

However, in one sense, housing is different: it is especially sensitive to the state of the local economy. The American economy today is stronger than it was thirty years ago, but the Detroit economy is not, and people who bought houses in Detroit have had their home values wiped. In this way, home ownership makes people less capable of moving to places with better jobs.

Home ownership and NIMBYism

One of the points made by William Fischel in his writings about zoning and NIMBYism is that the impetus for this behavior comes from homeowners trying to safeguard the value of their investment. Per Fischel, since most homeowners’ entire savings are locked up in one risky asset, they are risk-averse when it comes to any neighborhood change, leading to NIMBYism. Renters are more flexible. So are the richest people, who have a broad array of investments (and often multiple apartments and houses): upper-crust NIMBYism is often the domain of the upper middle class rather than of the top 1%.

It is the general interest of society to have less NIMBYism and looser zoning. This is true even at the level of the individual city. It’s in the interest of San Francisco to be able to build more housing and more office space, even to replace single-family houses in outer areas near Muni Metro with mid-rise apartment buildings. And the higher the level of government, the more upzoning makes sense.

Condos and governance

As Ed Glaeser mentions in a 2011 paper, more than 85% of single-family houses in the US are owned, and more than 85% of apartments in buildings with 3 or more units are rented. Glaeser explains how this turns home ownership incentives into incentives for single-family housing. But it also affects how new multifamily housing looks.

Traditional mid-rise buildings are owned by a single landlord, who rents them to individual tenants. Newer buildings are either rentals or condos. Condos have more complex governing boards, and in extreme cases end up having the same social dynamics of suburbs: people who enjoy telling others what to do make rules about behavior.

Policy solutions

The American practice of making mortgage interest tax-deductible is not common across the developed world. But there are more widespread policies that still treat homeowner wealth preferentially to other kinds of investments. There are no capital gains taxes on real estate appreciation, subject to constraints to make sure individual homeowners are not taxed but large-scale commercial developers are. Most countries also fail to tax imputed rents. Switzerland does tax imputed rents, but is unusual in doing so: Swiss homeowners owe taxes on the rents they’d be getting if they rented out their properties at fair market value.

There doesn’t need to be double taxation. In other words, housing should be taxed as personal consumption (so mortgage interest is not deductible, but there are no imputed rents) or as business expenses (so interest is deductible, but there are imputed rents). But it should be single-taxed, because it is not a state interest to depopulate the cities to create a class of suburban NIMBYs, who affect petty aristocratic manners when times are good but turn into a precariat when times are bad.

# Safer Streets: Design is Better Than Enforcement

As some American cities are attempting to reduce the number of car accident fatalities, under the umbrella of Vision Zero, the growing topic is one of traffic enforcement. Streetsblog has long documented many instances in which the police treats any case in which a car runs over a pedestrian as a no-fault accident, even when the driver was committing such traffic violations as driving on the sidewalk. In addition to enforcement, there’s emphasis on reducing the speed limit in urban areas, from 30 to 20 miles per hour, based on past campaigns in Europe, where speeds were reduced from 50 km/h to 30. Unfortunately, street design for lower speeds and greater traffic safety has taken a back seat. This is not the best way to improve street safety, and is not the standard practice in the countries that have reduced car accident rates the most successfully, namely the UK and the Scandinavian countries.

On high-speed roads, one of the most important causes of fatal accidents is the combination of driver fatigue and sleepiness. For some studies on this problem, see here, here, and here. The second link in particular brings up the problem of monotony: if a road presents fewer stimuli to the driver, the driver is more likely to become less vigilant, increasing the probability of an accident. One study goes on and shows that higher speed actually increases monotony, since drivers have less time to register such stimuli as other cars on the road, but this was obtained in controlled conditions, and its literature review says that most studies find no effect of speed. I emphasize that this does not mean that lower speed limits are ineffective: there’s evidence that reducing highway speed limit does reduce accident rates, with multiple studies collected in a Guardian article, and lower accident rates in France since the state installed an extensive system of speed cameras.

But while speed limit reductions offer useful safety benefits, it is important to design the roads to be slower, and not just tell drivers to go slower. Road monotony is especially common in the United States; per the second study again,

While comparing self-reported driving fatigue in the US and Norway, Sagberg (1999) suggests that the higher prevalence of self reported drowsy driving found in the US may be due to differences in road geometry, design and environment, as well as exposure. He argues that the risk of falling asleep is higher on straight, monotonous roads in situations of low traffic, where boredom is likely to occur. This type of roads is more common in the US than in Norway.

The studies I have consulted look primarily at highways and rural roads; I have not found comparable literature on urban roads, except one study that, in a controlled simulation, shows that drivers are better at gauging their own alertness levels on urban arterials than on rural roads. That said, urban arterials share many design traits that lead to monotony, especially in the United States and Canada:

• They are usually straight, forming a grid rather than taking haphazard routes originating from premodern or early-industrial roads.
• They are wide: 4-6 lanes at a minimum, often with a median. Lanes are likely to be wide, closer to 3.7 meters than the more typical urban 3 meters.
• Development on them usually does not form a strong enclosure, but instead commercial developments are only 1-2 stories, with setbacks and front and side parking lots.

Such roads are called stroads in the language of Charles Marohn, who focuses on issues of their auto-centric, pedestrian-hostile nature. Based on the studies about monotony, I would add that even ignoring pedestrians entirely, they are less safe than slower roads, which prime drivers to be more alert and to speed less. It is better to design roads to have more frequent stimuli: trees, sidewalks with pedestrians, commercial development, residential development to the extent people are willing to live on top of a busy road.

Regarding lane width, one study finds that roads are the safest when lanes are 3-3.2 meters wide, because of the effects of wider lanes on driver speeds. A CityLab article on the same subject from two years ago includes references to several studies that argue that wide lanes offer no safety benefit for drivers, but are hostile to pedestrians and cyclists.

This approach, of reducing speed via road design rather than enforcement, is common in Scandinavia. Stockholm has a few urban freeways, but few arterials in the center, and many of those arterials have seen changes giving away space from cars to public transit and pedestrians. Thus, Götgatan is partly pedestrianized, and Odengatan has center bus lanes and only one moving car lane in each direction; the most important of Stockholm’s streets, Sveavägen, has several moving car lanes in each direction, but is flanked on both sides by medium-rise buildings without setbacks, and speeds are rarely high.

When enforcement happens, the great successes, for example in France under the Sarkozy administration, involve automation. Red light cameras have a long history and are controversial, and in France, Sarkozy lowered the speed limits on many roads and stepped up speed camera enforcement. The UK has extensive camera enforcement as well. Human enforcement exists, but is less common than speed cameras. Thus, the two main policy planks Vision Zero should fight for in the US are,

1. Road redesign: narrower lanes, wider sidewalks, trees, and dedicated bus and bike lanes in order to reduce the number of car lanes as well as provide more room for alternatives. Zoning laws that mandate front setbacks should be repealed, and ideally so should commercial height limits on arterials. In central cities, some road segments should be closed off to cars, if the intensity of urban activities can fill the space with pedestrians.
2. Lower speed limits in the cities, enforced by cameras; fines should be high enough to have some deterrent effect, but not so high that they will drive low-income drivers bankrupt.

It is especially important to come up with solutions that do not rely on extensive human enforcement in the US, because of its longstanding problem with police brutality and racism. The expression “driving while black” is common in the US, due to bias the police in the US (and Canada) exhibits against black people. In Europe, even when bias against certain minorities is as bad as in the US, overall police brutality levels are lower in the US by factors ranging from 20 to 100 (see for example data here). In my Twitter feed, black American urbanists express reluctance to so much as call the police on nonviolent crime, fearing that cops would treat them as suspects even if they are the victims. When it comes to urban traffic safety – and so far, Vision Zero in the US is an urban movement – this is compounded by the fact that blacks and other minorities are overrepresented in the cities.

This means that, in the special conditions of US policing, it’s crucial to prevent Vision Zero from becoming yet another pretext for Driving While Black arrests. As it happens, it does not require large changes from best practices in Europe, because those best practices do not involve extensive contact between traffic police and drivers.

Recall last year’s post by Adonia Lugo, accusing Vision Zero of copying policy from Northern Europe and not from low-income American minority communities. As I said a year ago, Adonia is wrong – first in her belief that foreign knowledge is less important than local US knowledge, and second in her accusation that US Vision Zero advocates copy European solutions too much. To the contrary, what I see is that the tone among US street safety advocates overfocuses on punitive enforcement of drivers who violate the speed limit or break other law. Adapting a problem that in Europe is solved predominantly with street design and technology (speed cameras don’t notice the driver’s skin color), they instead call for more policing, perhaps because mainstream (i.e. white) American culture is used to accepting excessive police presence.

# Greenbelts Help Cars

A number of major cities, most notably London, have designated areas around their built-up areas as green belts, in which development is restricted, in an attempt to curb urban sprawl. The towns within the green belt are not permitted to grow as much as they would in an unrestricted setting, where the built-up areas would merge into a large contiguous urban area. Seeking access to jobs in the urban core, many commuters instead live beyond the greenbelt and commute over long distances. There has been some this policy’s effect on housing prices, for example in Ottawa and in London by YIMBY. In the US, this policy is less common than in Britain and Canada, but exists in Oregon in the form of the urban growth boundaries (UGBs), especially around Portland. The effect has been the same, replacing a continuous sprawling of the urban area with discontinuous suburbanization into many towns; the discontinuous form is also common in Israel and the Netherlands. In this post, I would like to explain how, independently of issues regarding sprawl, such policies are friendlier to drivers than to rail users.

Let us start by considering what affects the average speed of cars and what affects that of public transit. On a well-maintained freeway without traffic, a car can easily maintain 130 km/h, and good cars can do 160 or more on some stretches. In urban areas, these speeds are rarely achievable during the day; even moderate traffic makes it hard to go much beyond 110 or 120. Peak-direction commutes are invariably slower. Moreover, when the car gets off the freeway and onto at-grade arterial roads, the speed drops further, to perhaps 50 or less, depending on density and congestion.

Trains are less affected by congestion. On a well-maintained, straight line, a regional train can go at 160 km/h, or even 200 km/h for some rolling stock, even if headways are short. The busiest lines are typically much slower, but for different reasons: high regional and local traffic usually comes from high population density, which encourages short stop spacing, such that there may not be much opportunity for the train to go quickly. If the route is curvy, then high density also makes it more difficult to straighten the line by acquiring land on the inside of the curves. But by and large, slowdowns on trains come from the need to make station stops, rather than from additional traffic.

Let us now look at greenbelts of two kinds. In the first kind, there is legacy development within the greenbelt, as is common around London. See this example:

The greenbelt is naturally in green, the cities are the light blue circles with the large central one representing the big city, and the major transportation arteries (rail + freeway) are in black. The towns within the greenbelt are all small, because they formed along rail stops before mass motorization; the freeways were built along the preexisting transportation corridors. With mass motorization and suburbanization, more development formed right outside the greenbelt, this time consisting of towns of a variety of sizes, typically clustering near the freeways and railways for best access to the center.

The freeways in this example metro area are unlikely to be very congested. Their congestion comes from commuters into the city, and those are clustered outside the greenbelt, where development is less restricted. Freeways are widened based on the need to maintain a certain level of congestion, and in this case, this means relatively unimpeded traffic from the outside of the green belt right up until the road enters the big city. Under free development, there would be more suburbs closer to the city, and the freeway would be more congested there; travel times from outside the greenbelt would be longer, but more people would live closer to the center, so it would be a wash.

In contrast, the trains are still going to be slowed down by the intermediate stops. The small grandfathered suburbs have no chance of generating the rail traffic of larger suburbs or of in-city stops, but they still typically generate enough that shutting them down to speed traffic is unjustified, to say nothing of politically impossible. (House prices in the greenbelt are likely to be very high because of the tight restrictions, so the commuters there are rich people with clout.) What’s more, frequency is unlikely to be high, since demand from within the greenbelt is so weak. Under free development, there might still be more stops, but not very many – the additional traffic generated by more development in those suburbs would just lead to more ridership per stop, supporting higher frequency and thus making the service better rather than worse.

Let us now look at another greenbelt, without grandfathered suburbs, which is more common in Canada. This is the same map as before, with the in-greenbelt suburbs removed:

In theory, this suburban paradigm lets both trains and cars cruise through the unbuilt area. Overall commutes are longer because of the considerable extra distance traveled, but this distance is traversed at high speed by any mode; 120 km/h is eminently achievable.

In practice, why would there be a modern commuter line on any of these arteries? Commuter rail modernization is historically a piecemeal program, proceeding line by line, prioritizing the highest-trafficked corridors. In Paris, the first commuter line to be turned over to the Metro for operation compatible with city transit, the Ligne de Sceaux, has continuous urban development for nearly its entire length; a lightly-trafficked outer edge was abandoned shortly after the rest of the line was electrified in 1938. If the greenbelt was set up before there was significant suburbanization in the restricted area, it is unlikely that there would have been any reason to invest in a regional rail line; at most there may be a strong intercity line, but then retrofitting it to include slower regional traffic is expensive. Nor is there any case for extending a high-performing urban transit line to or beyond a greenbelt. Parts of Grand Paris Express, namely Lines 14 and 11, are extended from city center outward. In contrast, in London, where the greenbelt reduces density in the suburbs, high investment into regional rail focuses on constructing city-center tunnels in Crossrail and Crossrail 2 and connecting legacy lines to them. In cities that do not even have the amount of suburban development of the counties surrounding London, there is even less justification for constructing new transit.

The overall picture in which transit has an advantage over cars at high levels of density is why high levels of low-density sprawl are correlated with low transit usage. But I stress that even independently of sprawl, greenbelts are good for cars and bad for transit. A greenbelt with legacy railway suburbs is going to feature trains going at the normal speed of a major metro area, and cars going at the speed of a more spread out and less populated region. Even a greenbelt without development is good urban geography for cars and bad one for transit.

As a single exception, consider what happens when a greenbelt is reserved between two major nodes. In that specific case, an intercity line can more easily be repurposed for commuting purposes. The Providence Line is a good example: while there’s no formal greenbelt, tight zoning restrictions in New England even in the suburbs lead to very low density between Boston and Providence, which is nonetheless served by good infrastructure thanks to the strength of intercity rail travel. The MBTA does not make good use of this infrastructure, but that’s beside the point: there’s already a high-speed electrified commuter line between the two cities, with widely spaced intermediate stops allowing for high average speeds even on stopping trains and overtakes that are not too onerous; see posts of mine here and here. What’s more, intercity trains can be and are used for commutes from Providence to Boston. For an analogous example with a true greenbelt, Milton Keynes plays a role similar to Providence to London’s Boston.

However, this exception is uncommon. There aren’t enough Milton Keyneses on the main intercity lines to London, or Providences on the MBTA, to make it possible for enough transit users to suburbanize. In cities with contiguous urban development, such as Paris, it’s easier. The result of a greenbelt is that people who do not live in the constrained urban core are compelled to drive and have poor public transportation options. Once they drive, they have an incentive to use the car for more trips, creating more sprawl. This way, the greenbelt, a policy that is intended to curb sprawl and protect the environment, produces the exact opposite results: more driving, more long-distance commuting, a larger urban footprint far from the core.

# A Theory of Zoning and Local Decisionmaking

This weekend there’s a conference in the US, YIMBY 2016, by a national network of activists calling for more housing. I am not there, but I see various points raised there via social media. One is a presentation slide that says “NIMBYism is a collective action problem: no single neighborhood can lower prices by upzoning; might still be in everyone’s interest to upzone at city/state level.” I think this analysis is incorrect, and in explaining why, I’d like to talk about a theory of how homeowners use zoning to create a housing shortage to boost their own property values, and more generally how long-time residents of a city use zoning to keep out people who are not like them. In this view,zoning is the combination of a housing cartel, and a barrier to internal migration.

For years, I’ve had trouble with the housing cartel theory, because of a pair of observations. The first is that, contra the presentation at YIMBY, zoning is driven by homeowners rather than by renters; for an overview, see the work of William Fischel. The second is that restrictive zoning typically correlates with local decisionmaking, such as in a neighborhood or small city, while lax zoning typically correlates with higher-level decisionmaking, such as in a city with expansive municipal boundaries or in an entire province or country; see below for more on this correlation. These two observations together clash with the housing cartel theory, for the inverse of the reason in the above quote from the YIMBY presentation: it’s more effective to create a housing shortage in a large area than in a small one.

To a good approximation, land value equals (housing price – housing construction cost)*allowed density. If a small municipality upzones, then as in the quote, housing price doesn’t change much, but allowed density grows, raising the price a homeowner can get by selling their house to developers who’d build an apartment building. In contrast, if a large municipality upzones then housing prices will fall quite a bit as supply grows, and depending on the price elasticity, land value might well go down. If x = housing price/housing construction cost and e = price elasticity for housing, i.e. price is proportional to density^(-1/e), then maximum land value occurs when x = e/(e-1), provided e > 1; if e < 1 then maximum value occurs when x is arbitrarily large. Price elasticity is much higher in a small municipality, since even a large increase in local housing supply has a small effect on regional supply, limiting its ability to reduce prices. This implies that, to maximize homeowner value, small municipalities have an incentive to set density limits at a higher level than large municipalities, which will be seen in faster housing growth relative to population growth.

What we see is the exact opposite. Consider the following cases, none a perfect natural experiment, but all suggestive:

1. In the Bay Area, we can contrast San Francisco (a medium-size urban municipality), San Jose and generally Santa Clara County (San Jose is medium-size for a central city and very large for a suburb), and San Mateo County (comprising small and medium-size suburbs). San Mateo County is by far the stingiest of the three about permitting housing: over the last three years it’s averaged 1,000 new housing units per year (see here); in 2013, the corresponding figures elsewhere in the Bay Area were 2,277 new housing units in San Francisco and 5,245 in Santa Clara County. Per thousand people (not per housing unit), this is 2.63 in San Francisco, 2.73 in Santa Clara, and 1.31 in San Mateo. In Alameda County, comprising medium-size cities and suburbs, with a less hot housing market because of the distance from Silicon Valley jobs, growth was 2,474 units, 1.51 per 1,000 people. In small rich Silicon Valley municipalities like Palo Alto and Menlo Park, NIMBYs have effectively blocked apartment construction; in much larger and still rich San Jose, the city has a more pro-growth outlook.

2. Among the most important global cities – New York, Paris, London, and Tokyo – Tokyo has by far the fastest housing stock growth, nearly 2% a year; see article by Stephen Smith. In Japan, key land use decisions are made by the national government, whereas in Paris, London, and New York, decision is at a lower level. London builds more than New York and Paris; its municipal limit is much looser than Paris’s, with 8.5 million people to Paris’s 2.2 million even though their metro areas have similar populations. New York has a fairly loose limit as well, but the development process empowers lower-level community boards, even though the city has final authority.

3. Canada has a relatively permissive upzoning process, and in Ontario, the planning decisions are made at the provincial level, resulting in about 1.3% annual housing growth in Toronto in the previous decade; in the same period, San Jose’s annual housing growth was about 1% and San Francisco’s was 0.9%.

4. France has recently made a national-level effort to produce more housing in the Paris region, especially social housing, due to very high housing prices there. Last decade, housing production in Ile-de-France was down to about 30,000-35,000 per year, averaging to 2.6 per 1,000 people, similar to San Francisco; see PDF-pp. 4-5 here and the discussion here. With the new national and regional effort at producing more social housing, plans appear to be on track to produce 30,000 annual units of social housing alone in the next few years; see PDF-p. 6 here. With 7,000 annual units within city limits, Paris expects to build somewhat more per capita than the rest of the region.

In France, the combination of a national focus on reducing housing burden and the observation that higher-level decisionmaking produces more housing makes sense. But elsewhere, we need to ask how come homeowners aren’t able to more effectively block construction.

My theory is that the answer involves internal migration. Consider the situation of Palo Alto: with Stanford and many tech jobs, it is prime location, and many people want to move there. The homeowners are choosing the zoning rule that maximizes their ability to extract rents from those people, in both the conventional sense of the word rent and the economic sense. Now consider decisionmaking at the level of the entire state of California. California can raise housing prices even more effectively than Palo Alto can by restricting development, but unlike Palo Alto, California consists not just of residents of rich cities, but also of residents of other cities, who would like to move to Palo Alto. In the poorer parts of the state, there’s not much point in restrictive zoning, because there isn’t that much demand for new housing, except perhaps from people who cannot afford San Francisco or Los Angeles and are willing to endure long commutes. On the contrary, thanks to the strength of internal migration, a large fraction of prospective residents of Palo Alto live elsewhere in California. Nor do people in poor areas, where houses aren’t worth much as investments, gain much from raising house prices for themselves; the ability to move to where the good jobs are is worth more than raising housing prices by a few tens of thousands of dollars. This means that the general interest in California is to make Palo Alto cheaper rather than more expensive. The same is true of Japan and Tokyo, or France and Paris, or Ontario and Toronto.

While superficially similar to the point made in the presentation quoted at the beginning of this post, my theory asserts the opposite. The issue is not that individual municipalities see no benefit in upzoning since it wouldn’t reduce rents by much. It’s that they see net harm from upzoning precisely because it would reduce rents. It is not a collective action problem: it is a problem of disenfranchisement, in which the people who benefit from more development do not live in the neighborhoods where the development would be taking place. High-level decisionmaking means that people who would like to move to a rich area get as much of a vote in its development policy as people who already live there and have access to its amenities, chief of which is access to work. It disempowers the people who already have the privilege of living in these areas, and empowers the people who don’t but would like to.

Individual rich people can be virtuous. Rich communities never are. They are greedy, and write rules that keep others out and ruthlessly eliminate any local effort to give up their political power. They will erect borders and fences, exclude outsiders, and demagogue against revenue sharing, school integration, and upzoning. They will engage in limited charity – propping up their local poor (as San Francisco protects low-income lifelong San Franciscans via rent control), and engaging in symbolic, high-prestige giving, but avoid any challenge to their political power. Upzoning is not a collective action problem; it is a struggle for equal rights and equal access to jobs regardless of which neighborhood, city, or region one grew up in.

# Modeling Anchoring

Jarrett Walker has repeatedly called transit agencies and city zoning commissions to engage in anchoring: this means designing the city so that transit routes connect two dense centers, with less intense activity between them. For example, he gives Vancouver’s core east-west buses, which connect UBC with dense transit-oriented development on the Expo Line, with some extra activity at the Canada Line and less intense development in between; Vancouver has adopted his ideas, as seen on PDF-page 15 of a network design primer by Translink. In 2013, I criticized this in two posts, making an empirical argument comparing Vancouver’s east-west buses with its north-south buses, which are not so anchored. Jarrett considers the idea that anchoring is more efficient to be a geometric fact, and compared my empirical argument to trying to empirically compute the decimal expansion pi to be something other than 3.1415629… I promised that I would explain my criticism in more formal mathematical terms. Somewhat belatedly, I would like to explain.

First, as a general note, mathematics proves theorems about mathematics, and not about the world. My papers, and those of the other people in the field, have proven results about mathematical structures. For example, we can prove that an equation has solutions, or does not have any solutions. As soon as we try to talk about the real world, we stop doing pure math, and begin doing modeling. In some cases, the models use advanced math, and not just experiments: for example, superstring theory involves research-level math, with theorems of similar complexity to those of pure math. In other cases, the models use simpler math, and the chief difficulty is in empirical calibration: for example, transit ridership models involve relatively simple formulas (for example, the transfer penalty is a pair of numbers, as I explain here), but figuring out the numbers takes a lot of work.

With that in mind, let us model anchoring. Let us also be completely explicit about all the assumptions in our model. The city we will build will be much simpler than a real city, but it will still contain residences, jobs, and commuters. We will not deal with transfers; neither does the mental model Jarrett and TransLink use in arguing for anchoring (see PDF-p. 15 in the primer above again to see the thinking). For us, the city consists of a single line, going from west to east. The west is labeled 0, the east is labeled 1, and everything in between is labeled by numbers between 0 and 1. The city’s total population density is 1: this means that when we graph population density on the y-axis in terms of location on the x-axis, the total area under the curve is 1. Don’t worry too much about scaling – the units are all relative anyway.

Let us now graph three possible distributions of population density: uniform (A), center-dominant (B), and anchored (C).

Let us make one further assumption, for now: the distributions of residences and jobs are the same, and independent. In city (A), this means that jobs are uniformly distributed from 0 to 1, like residences, and a person who lives at any point x is equally likely to work at any point from 0 to 1, and is no more likely to work near x than anyone else. In city (B), this means that people are most likely to work at point 0.5, both if they live there and if they live near 0 or 1; in city (C), this means that people are most likely to work at 0 or 1, and that people who live at 0 are equally likely to work near 0 and near 1.

Finally, let us assume that there is no modal splitting and no induced demand: every employed person in the city rides the bus, exactly once a day in each direction, once going to work and once going back home, regardless of where they live and work. Nor do people shift their choice of when to work based on the network: everyone goes to work in the morning peak and comes back in the afternoon peak.

With these assumptions in mind, let us compute how crowded the buses will be. Because all three cities are symmetric, I am only going to show morning peak buses, and only in the eastbound direction. I will derive an exact formula in city (A), and simply state what the formulas are in the other two cities.

In city (A), at point x, the number of people who ride the eastbound morning buses equals the number of people who live to the west of x and work to the right of x. Because the population and job distributions are uniform, the proportion of people who live west of x is x, and the proportion of people who work east of x is 1-x. The population and job distributions are assumed independent, so the total crowding is x(1-x). Don’t worry too much about scaling again – it’s in relative units, where 1 means every single person in the city is riding the bus in that direction at that time. The formula y = x(1-x) has a peak when x = 0.5, and then y = 0.25. In cities (B) and (C), the formulas are:

(B): $y = \begin{cases}2x^2(1 - 2x^2) & \mbox{ if } x \leq 1/2\\ 2(1-x)^2(1 - 2(1-x)^2) & \mbox{ if } x > 1/2\end{cases}$

(C): $y = \begin{cases}(2x-2x^2)(1 - 2x + 2x^2) & \mbox{ if } x \leq 1/2\\ (2(1-x)-2(1-x)^2)(1 - 2(1-x) + 2(1-x)^2) & \mbox{ if } x > 1/2\end{cases}$

Here are their graphs:

Now, city B’s buses are almost completely empty when x < 0.25 or x > 0.75, and city C’s buses fill up faster than city A’s, so in that sense, the anchored city has more uniform bus crowding. But the point is that at equal total population and equal total transit usage, all three cities produce the exact same peak crowding: at the midpoint of the population distribution, which in our three cases is always x = 0.5, exactly a quarter of the employed population lives to the west and works to the east, and will pass through this point on public transit. Anchoring just makes the peak last longer, since people work farther from where they live and travel longer to get there. In a limiting case, in which the population density at 0 and 1 is infinite, with half the population living at 0 and half at 1, we will still get the exact same peak crowding, but it will last the entire way from 0 to 1, rather than just in the middle.

Note that there is no way to play with the population distribution to produce any different peak. As soon as we assume that jobs and residences are distributed identically, and the mode share is 100%, we will get a quarter of the population taking transit through the midpoint of the distribution.

If anything, the most efficient of the three distributions is B. This is because there’s so little ridership at the ends that it’s possible to run transit at lower frequency at the ends, overlaying a route that runs the entire way from 0 to 1 to a short-turn route from 0.25 to 0.75. Of course, cutting frequency makes service worse, but at the peak, the base frequency is sufficient. Imagine a 10-minute bus going all the way, with short-turning overlays beefing frequency to 5 minutes in the middle half. Since the same resources can more easily be distributed to providing more service in the center, city B can provide more service through the peak crowding point at the same cost, so it will actually be less crowded. This is the exact opposite of what TransLink claims, which is that city B would be overcrowded in the middle whereas city C would have full but not overcrowded buses the entire way (again, PDF-p. 15 of the primer).

In my empirical critique of anchoring, I noted that the unanchored routes actually perform better than the anchored ones in Vancouver, in the sense that they cost less per rider but also are less crowded at the peak, thanks to higher turnover. This is not an observation of the model. I will note that the differences in cost per rider are not large. The concept of turnover is not really within the model’s scope – the empirical claim is that the land use on the unanchored routes lends itself to short trips throughout the day, whereas on the anchored ones it lends itself to peak-only work trips, which produce more crowding for the same total number of riders. In my model, I’m explicitly ignoring the effect of land use on trips: there are no induced trips, just work trips at set times, with 100% mode share.

Let us now drop the assumption that jobs and residences are identically distributed. Realistically, cities have residential and commercial areas, and the model should be able to account for this. As one might expect, separation of residential and commercial uses makes the system more crowded, because travel is no longer symmetric. In fact, whereas under the assumption the peak crowding is always exactly a quarter of the population, if we drop the assumption the peak crowding is at a minimum a quarter, but can grow up to the entire population.

Consider the following cities, (D), (E), and (F). I am going to choose units so that the total residential density is 1/2 and so is the total job density, so combined they equal 1. City (D) has a CBD on one side and residences on the other, city (E) has a CBD in the center and residences on both sides, and city (F) is partially mixed-use, with a CBD in the center and residences both in the center and outside of it. Residences are in white, jobs are in dark gray, and the overlap between residences and jobs in city (F) is in light gray.

We again measure crowding on eastbound morning transit. We need to do some rescaling here, again letting 1 represent all workers in the city passing through the same point in the same direction. Without computing, we can tell that in city (D), at the point where the residential area meets the commercial area, which in this case is x = 0.75, the crowding level is 1: everyone lives to the west of this point and works to its east and must commute past it. Westbound morning traffic, in contrast, is zero. City (E) is symmetric, with peak crowding at 0.5, at the entry to the CBD from the west, in this case x = 0.375. City (F) has crowding linearly growing to 0.375 at the entry to the CBD, and then decreasing as passengers start to get off. The formula for eastbound crowding is,

(F): $y = \begin{cases}x & \mbox{ if } x < 3/8\\ x(5/2 - 4x) & \mbox{ if } 3/8 \leq x \leq 5/8\\ 0 & \mbox{ if } x > 5/8\end{cases}$

In city (F), the quarter of the population that lives in the CBD simply does not count for transit crowding. The reason is that, with the CBD occupying the central quarter of the city, at any point from x = 0.375 east, there are more people who live to the west of the CBD getting off than people living within the CBD getting on. This observation remains true down to when (for a symmetric city) a third of the population lives inside the CBD.

In city (B), it’s possible to use the fact that transit runs empty near the edges to run less service near the edges than in the center. Unfortunately, it is not possible to use the same trick in cities (E) and (F), not with conventional urban transit. The eastbound morning service is empty east of the CBD, but the westbound morning service fills up; east of the CBD, the westbound service is empty and the eastbound service fills up. If service has to be symmetric, for example if buses and trains run back and forth and make many trips during a single peak period, then it is not possible to short-turn eastbound service at the eastern edge of the CBD. In contrast, if it is possible to park service in the center, then it is possible to short-turn service and economize: examples include highway capacity for cars, since bridges can have peak-direction lanes, but also some peaky commuter buses and trains, which make a single trip into the CBD per vehicle in the morning, park there, and then make a single trip back in the afternoon. Transit cities relies on services that go back and forth rather than parking in the CBD, so such economies do not work well for them.

A corollary of the last observation is that mixed uses are better for transit than for cars. Cars can park in the CBD, so for them, it’s fine if the travel demand graph looks like that of city (E). Roads and bridges are designed to be narrower in the outskirts of the region and wider near the CBD, and peak-direction lanes can ensure efficient utilization of capacity. In contrast, buses and rapid transit trains have to circulate; to achieve comparable peak crowding, city (E) requires twice as much service as perfect mixed-use cities.

The upshot of this model is that the land use that best supports efficient use of public transit is mixed use. Since all rich cities have CBDs, they should work on encouraging more residential land uses in the center and more commercial uses outside the center, and not worry about the underlying distribution of combined residential and job density. Since CBDs are usually almost exclusively commercial, any additional people living in the center will not add to transit crowding, even as they ride transit to work and pay fares. In contrast, anchoring does not have any effect on peak crowding, and on the margins makes it worse in the sense that the maximum crowding level lasts longer. This implies that the current planning strategy in Vancouver should be changed from encouraging anchoring to fill trains and buses for longer to encouraging more residential growth Downtown and in other commercial centers and more commercial growth at suitable nodes outside the center.