But a recent paper by some engineering scholars suggests that this argument is oversimplified. They suggest that emissions from “operational impacts” (for example, day-to-day electricity and natural gas use) are more significant than construction impacts. As a result, the article concludes that in Toronto, annual greenhouse gas emissions for new high-rise multifamily buildings are on average 24 percent less than low-rise multifamily buildings.

I note, however, that the study is inconclusive in some ways- most notably because it includes a fairly small number of Toronto buildings, and (perhaps more importantly) because its high-rise category includes all buildings in the sample that are over five stories. Thus, it does not address whether there is a difference between 5-10 story midrises and much larger buildings.*

*Readers might be wondering why the authors did not find enough buildings to make such distinctions. First, the authors chose to focus on post-2009 buildings, thus limiting sample sizes. Second, the authors’ data was based on an earlier study with a limited building sample.

I discovered pretty quickly that the four most unequal large metros (measured by GIni coefficient) are Miami, New York, Los Angeles, and San Francisco: all high-cost metros. So at first glance, it thus appears that there is a strong correlation between inequality and housing prices.

But then I looked at the fifth through tenth most unequal metros, and a different picture emerged. These were, in order of Gini coefficient: Memphis, Houston, Boston, Tampa, Cleveland, and Chicago. All of these except Boston are low and medium cost metros; for example, the median home price in Memphis is under $300,000. And the differences between these metros and the high-cost metros are not enormous: San Francisco’s coefficient is .489 and Memphis’s is .486.

The least unequal metros with over a million people were mostly low and medium-cost metros,* but with a few exceptions: in Washington, Riverside and Denver the median home price was just over $600,000.

So what distinguishes the high-inequality/high-cost metros from the other high-inequality metros? As one might guess from conventional economic theory, income. The highest-cost high-inequality metros (Miami, New York, Los Angeles and San Francisco) have median household incomes over $90,000. So do Washington and Denver (although Riverside barely misses the cut at $87,000). By contrast, Memphis’s median income is just under $65,000, Cleveland’s is just under $68,000 and Houston’s is just under $80,000. In other words, high inequality plus high income = high prices. High inequality + low income = low prices.

*The bottom 10 included: Salt Lake City, Washington, Virginia Beach, Riverside, Grand Rapids, Kansas City, Minneapolis, Indianapolis, Denver, and Raleigh.

But state law is so stacked against housing that even when a locality is eager to allow new homes, it cannot necessarily do so. Residents of Sag Harbor whose anti-growth sentiments had failed to win in the normal political process instead sued over technicalities in the state environmental review, a review process written so broadly that basically any proposal can be subject to litigation if someone wants it badly enough.

Using Sag Harbor as my point of departure, I discussed new research on how New York can re-balance the checks and balances in its permitting process in a New York Daily News op-ed yesterday. Read the whole thing.

• Modern Housing for America, Gail Radford, 1996, (Housing Act of 1934 and Housing Act of 1937)
• The Wagner Housing Act: A Case Study of the Legislative Process, Timothy McDonnell, 1957 (Housing Act of 1937)\
• Housing Reform During the Truman Administration Richard Davies, 1966 (Housing Act of 1949)
• Government and Slum Housing: A Century of Frustration, 1969, Lawrence Friedman (Housing Act of 1954)
• Robert Clifton Weaver and the American City: The Life and Times of An Urban Reformer, Wendell Pritchett, 2008, (Fair Housing Act and HUD Act)
• Building Foundations: Housing and Federal Policy, edited by Denise DiPasquale & Langley C. Keyes, 1990 (Cranston-Gonzalez)
• The Housing Policy Revolution: Networks and Neighborhoods, David Erickson, 2009 (Cranston-Gonzalez)
• Act of Congress: How America’s Essential Institution Works, and How It Doesn’t, Robert Kaiser, 2013 (Dodd-Frank)
• Federal Government & Urban Housing (3rd ed.), R. Allen Hays, 2012 (all)

What’s right with the DSB argument? It does make sense to me that if rents go down far enough, new construction might not be profitable. For example, if construction costs are $200,000 per house and a new house will sell for $150,000, there will not be much new construction. (Even then, there will be some construction because developers might think prices will eventually go back up).

What’s wrong with the DSB argument? First of all, it assumes that rents will go down to some extent due to new construction- which, if true, would be a huge win compared to the status quo of ever-escalating rents. Let us suppose that in neighborhood X, rents have escalated to $2000 per month, but that developers will stop building if rents go below $1500 per month. First, this means that rents will go down 25 percent until developers stop building- which would be a very good thing.

Second, if developers will stop building when rents go below $1500, this means that they will start building when rents go above $1500. So this means that, in the absence of other factors limiting construction or raising construction costs,* rents will never go back up again. As soon as rents go up, developers will start building again to curb the rent increase.

By contrast, under the “business as usual” scenario (in which government limits construction to cater to the desires of local homeowners) rents keep going up and up forever. So even if the “developers will stop building” argument is correct, zoning deregulation will cause a decline in rents that will be far better than business as usual policies. The only unknown fact is how significant the rent decrease will be: because zoning exists everywhere (at least in the United States) we do not have any real-world examples.

To draw an analogy to climate policy: let us suppose that some policy would magically freeze carbon emissions at slightly below current levels. Would this be ideal? No. Would this be better than business as usual? Of course!

*Obviously I am oversimplifying here because often such factors exist: for example, Kevin Erdmann has written extensively about the impact of tight lending policies on housing markets.

I would have guessed that the most mixed-use American metros were old ones, with many neighborhoods built before zoning and widespread car ownership: New York, Philadelphia, Boston, Chicago.

But no: Austin is the US champ, followed by San Jose and Miami. At the opposite end, Chicagoland is off the charts in its degree of use segregation. There doesn’t seem to be a rhyme or reason: the four Texas cities are scattered across the chart. The two halves of the Bay Area (SF and San Jose) are on opposite ends, and not in the way I’d have expected. The Midwest is represented all over the chart.

Of course, having commercial and residential space in the same block group doesn’t mean that the two are integrated in a traditional neighborhood form.

Do Ho et al find this result because of patterns in how block groups are constructed? Or because some cities have much bigger downtown peaks? The latter appears to be part of the explanation for Chicago and Denver. Looking under the hook, the coefficient of variation of commercial floorspace per block group is 16 in Chicago, about 5x more than the median city, despite those two cities having the highest commercial space share. In the index, that makes a block group with 1000 residences and 10 shops look much more segregated if it’s in Chicago than if it’s in, say, Milwaukee.

The results don’t appear to come from some cities having bigger block groups than others, or larger commercial shares overall (except Chicago and Denver).

I would welcome complimentary approaches to understanding this question. Are my intuitive guesses ultimately more reliable? Or is the data-speaks approach telling us something important about mixed uses in the US?

(As for the rest of the paper, it’s very creative in squeezing a lot of results from limited data. But I’m skeptical of the core findings, which are based on the assumption that the crowding of workers per unit of commercial floorspace and residents per unit of residential floorspace are accurate proxies for prices.)

He reached this conclusion by asking a sample of 200 homeless people: “Where are you from?”  53 percent were from outside Los Angeles County, and “[n]early 40 percent” were from outside California.   Evidently, Rufo believes if you were not born in Los Angeles, you came there to gorge yourself on fentanyl or indulge yourself with social services.  The problem with this claim is that most NON-homeless people in Los Angeles County are also not born locally. According to table B05002 in the U.S. Census Bureau’s American Community Survey, only 5.05 million of the county’s 9.75 million residents were born anywhere in California, or 52 percent.   Because Rufo admits that 60 percent of Los Angeles homeless are from California, this means that homeless people are actually more likely to be from California than the citizenry as a whole.   (On the other hand, I am assuming that “from California” and “born in California” are the same thing, which may not be true.)

Moreover, as a logical matter, not being “from California” is not the same as “moving to California to use drugs without being arrested.”  If you moved to California after high school, was a stable, sober worker at first, became an alcoholic, lost your job, and then became homeless, obviously you a) are not from California yet b) did not move to California to use drugs with impunity.

Rufo is on firmer ground when he cites a Rand Corporation study showing that 36% of homeless people were not residents of Los Angeles County when they became homeless (p. 24).  Anyone who became homeless and then moved to Los Angeles for social services or warm weather would be in this category.  However, a large minority of these 36% were from other parts of California (presumably including nearby counties): only 22% of Los Angeles homeless became homeless out of state.

I have no idea how many of these 22% moved to California for the reasons suggested by Rufo.  But even if all of them did, Los Angeles still has an astoundingly high level of native homelessness.  According to a Brookings Institution estimate, Los Angeles has the fifth highest rate of homelessness among American cities (697 per 100,000 residents).  If we subtract the 22 percent, Los Angeles’s homeless rate is 544 per 100,000 residents- about 80 percent higher than the 49-city average, still the eleventh highest among 49 cities surveyed, and higher than all but five non-California cities.   Los Angeles has the third highest level of unsheltered homelessness (472 per 100,000 residents).  If you shave off 22 percent of that number, Los Angeles would have 369 per 100,000 residents, three times the 49-city average, the eighth highest rate among 49 cities, and higher than every non-California city but one (Portland).  

In other words: even if a significant percentage of Los Angeles homeless were the sort of social service tourists described by Rufo, Los Angeles would still have far more locally-grown homeless residents than less expensive cities- which supports the idea that yes, homelessness is a housing problem after all.

Instead, Hughes hypothesizes that underlying NIMBYism is in fact stronger in Europe – strong enough that it is rarely challenged and aroused. There are a lot of empirical claims here, and the story probably differs substantially country to country as it does region to region in the U.S.

It’s long past time to retire the idea that high housing costs are an Anglosphere problem. More research, commentary, and advocacy, especially from Europeans, would be a welcome addition to the global YIMBY discourse.

He acknowledges that orthodox economists believe that the law of supply and demand affects housing, and that thus more housing should equal lower prices.  But in Broken City, he argues that new housing (or maybe just new infill housing) means higher land prices and thus to higher housing costs.  His focuses heavily on Vancouver, British Columbia: he writes that “Vancouver, despite tripling the number of housing units within city limits since 1960, has been rewarded for its effort with the third highest housing prices in the world.” (p. 65).  

But Vancouver is just one example: Condon doesn’t compare it with cheaper cities.  Canada has other larg-ish cities, including Edmonton, Calgary, Montreal and Toronto.  Did those cities add more or less housing than Vancouver?  Did housing prices rise more rapidly or more slowly than in Vancouver?  Condon doesn’t address these issues.  He does mention in passing that housing prices have risen in Houston (which was allowed more housing than most North American cities),** but I am not sure he answers the question that motivates most YIMBYs: why is Houston so much cheaper than New York or Vancouver?***

Moreover, Condon’s description of Vancouver overlooks the fact that housing markets are regional and not bound by municipal lines.  What was going on in Vancouver’s suburbs? Were they building at a Vancouver-like pace, or did growth control policies limit suburban pricing and thus create a regionwide housing shortage?  Condon doesn’t address this issue.

His theoretical logic is that “When the city authorizes a doubling of market density.. the residual land price goes up in response… [thus there is] no substantial decrease in the cost per square foot of new housing.” (p. 196).  But these imaginary numbers seem arbitrary.  If density increases by 100 percent, why would land price increase by 100 percent instead of 20 percent or 50 percent?  I am not sure I understand his argument here.

And if land prices were a function of more housing construction, we would find that land prices exploded in places with lots of housing construction, but were stable or declining in places where “Not In My Back Yard” sentiment impeded housing construction.  But even a casual look at the American Enterprise Institute land cost database suggests otherwise: over the past decade or two, land costs has risen in most metro areas: not just in places with lots of new housing such as Austin, but in less permissive areas (such as San Francisco and its suburbs).

Unlike less sophisticated opponents of YIMBYism, he doesn’t argue that new housing is all bad, and he is all for housing owned by government and nonprofits.  But he also argues in favor of programs that make housing less profitable, such as impact fees, rent controls, and inclusionary zoning.  Some (if not most) economists believe that these policies discourage housing construction- but Condon argues that they make housing cheaper by reducing land costs, suggesting that if an extra tax makes a project unworkable, “the price for the land will be renegotiated down” (p. 163).  He points out that California cities “have been less timid than most states” in imposing impact fees (p. 162).  But since land and housing costs in California have exploded in recent years, this fact doesn’t really support his argument.

 He also praises inclusionary zoning because it encourages the creation of nonmarket housing and “lower[s] the prices of developable parcels” (p. 165)- in other words, reduces the demand for land.   In particular, he praised Portland, Oregon’s expansion of inclusionary zoning.  But after Portland passed its inclusionary zoning ordinance in 2016, land costs in Multnomah County (which includes Portland) increased from $1.06 million per acre in 2016 to $1.56 million in 2022.     Meanwhile, building activity crawled to a halt.  In 2017, almost 6000 permits were issued for Portland buildings with five or more units.  In 2024, fewer than 1000 such permits were issued.

He also praises inclusionary zoning in Cambridge, Mass.- where inclusionary zoning has apparently been far less harmful. But Cambridge’s program is far less restrictive, because inclusionary zoning is part of an overlay district- which means that landlords have the option of proceeding either under inclusionary zoning (which allows more units in exchange for more “affordable” units) or proceeding under the prior zoning code.

Similarly, he argues for rent control on the ground that it “quell[s] the appetite of international [investors] for a city’s land” (p. 142)- in other words, makes people less interested in being landlords, which supposedly will reduce land prices.  In New York, where I live, the state government implemented vacancy control, where rents don’t go up even when a tenant leaves.  As a result, landlords have warehoused thousands of rent-stabilized apartments, apparently because they would have to spend money on renovations to comply with housing costs, and are not allowed to raise rents enough to pay for the renovations.  Rather than declining, the median asking rent in each of New York’s five boroughs has increased by 50 percent or more between 2019 and 2026.

And as a broader theoretical matter, his relationship with the law of supply and demand seems inconsistent: on the one hand, he argues that housing supply doesn’t make housing cheaper, but on the other hand he argues that extra taxes reduce the cost of land by reducing demand for land.   

*For those of you who have not read this blog, YIMBY means “Yes In My Back Yard”, a slogan adopted by those of us who support expanded housing supply.   

**One might ask: if YIMBYism works, why have housing prices risen in Houston? But this question is easily answered: Houston has gained population far more rapidly than high-cost cities, leading to a demand explosion.     But even with this demand explosion, rents have risen more slowly there than in cities that have allowed less housing.  Nominal rents in Houston are about 3.29 times their 1980s level,  while rents in metro New York are five times their 1980s level.  

***Condon hints at an answer in his historical discussion: he points out in Chapter 4 that as American densities nosedived in the mid-20^th century, land costs went down.  So perhaps he believes that sprawl equals low land costs and density equals high land costs.  But Condon admits that densities in the English-speaking world are still very low compared to middle-income countries (p. 93)- so why hasn’t low density continued to bring land costs down?

Person A- infill is cheaper than sprawl because no one has to build new sewer lines!

Person B- Sprawl is cheaper because its more expensive to build in the city!

Person B’s post made me wonder: is suburbia always cheaper?

I decided to do a rough comparison: the cheapest new city houses vs. the cheapest new suburban houses of the same size. I went on Zillow and searched for houses that were a) built after 2020 and b) 2000-2500 square feet.

In some metros (even fairly inexpensive ones), suburbia clearly did seem to be cheaper. For example, the cheapest new house in Chicago started at around $600k, while similar options in Lake and McHenry Counties started at around $360-370k. In St. Louis, the cheapest new home cost around $450k, about a third more than the cheapest new houses in St. Louis and St. Charles Counties.

On the other hand, I found counterexamples. The cheapest new house in Atlanta cost $290k, about 15 percent below its counterparts in Cobb and Gwinnett Counties. The cheapest new house in Houston cost $225k, about the same as its counterpart in suburban Fort Bend County. The cheapest in Cleveland cost $288k, about the same as its counterpart in the suburban part of Cuyahoga County, slightly less than its counterpart in outer-suburban Lake County, and about 25 percent less than its counterpart in outer-suburban Geauga County. In Philadelphia, the cheapest new house is about $350k, about 10 percent lower than its counterpart in outer-suburban Chester County.

All of this is subject to two qualifications. First, this is not an academic “everything being equal” comparison: all I am trying to show is that it seems possible to build urban and suburban houses for similar prices. Second, it may be that at the true outer edges of suburbia, houses are cheaper than in the suburbs I have mentioned.