Your first problem is trying to read Glaeser literally.
Take him seriously, not literally.
A literal reading will drive you mad because his housing analyis is not designed for coherence. It is designed to hide the economic role of land as distinct from capital (i.e. as a factor of production fixed in supply and earning economic rents), to misidentify the effects of land speculation as consequences of poor regulation, and to pursue deregulation, all in order to provide financial benefit to land owners.
As per my NZAE talk, I believe that neither Glaeser nor anyone else who claims that regulation makes housing expensive has any coherent theory of housing supply.
None of them can answer the simple question "why are houses built?" with anything approaching a coherent explanation.
Ironically, they have many answers (rather, the same answer, given many times) for why houses are NOT built. Which is a really neat trick, because it turns their scientific inadequacies into a strength. If they can't explain why prices aren't lower and quantities aren't higher, because they don't have a theory that works when it comes to explaining the facts, it means they can blame their chosen villain, which is regulation. In this way, incompetence is a great hole to fill with ideology.
For the benefit of your readers, here are several related ways to describe the problems with almost all US literature on housing supply, including Ed Glaeser's work. They're a slightly different take on your column:
1. This entire body of work implicitly assumes that houses are built if and when (and because) there exist arbitrage opportunities in present-day prices. This is the totality of their theory of supply. Their theory is that if the price of houses exceeds the price of construction, construction will go ahead. By implication, they think developers and landowners are myopic (i.e. not forward looking). The process of housing development in their theory of housing supply is one of 'myopic arbitrage', where no developer looks beyond present-day prices when deciding whether to build housing. In other words, if construction is profitable today, then unless encumbered by regulation, construction will go ahead, because developers and landowners in this theory are believed to be not smart enough to realise that it might be even more profitable to delay construction until tomorrow. There is no forward-looking behaviour in this theory. By assumption, therefore, there is never any vacant land already feasible to develop (since all land is developed as soon as first feasible), and there is certainly never any speculation or land banking (choose your term: either way it means not developing housing despite the change of use already being profitable), and therefore there also is no reason for these economists to go looking for, let alone try to understand, these empirical phenomena. According to the old management cliche, "you don't value what you don't measure", but for US housing economists, a better version is "you don't need to measure what you don't acknowledge".
2. They do not appreciate that "construction cost" is not the same thing as "supply cost". In the real world, construction is only one of the opportunity costs of developing a new home. To supply a new home in the real world means giving up the option to develop that land differently in future. Sacrificing that option is a true opportunity cost of supply. The true cost of supply therefore equals the cost of construction AND the value of land options foregone. What is that value? It is identical to the price of land, as set by developers themselves, competing to buy that land in the knowledge that they will be able to sell houses for so much more than construction cost because land is physically scarce and land rents can't be competed away ("they ain't making it any more"). Glaeser wonders why house prices don't match his measure of production cost (MPPC). One reason is that he doesn't include the actual price of land as a cost of production. As you point out, Glaeser's MPPC just includes an arbitrary scaling factor, in lieu of actual land prices. And as you point out, this is only suitable for a world where all locations are identical and location services just happen to be worth 20% of the value of structure services.
3. They use metrics that are blind to history, specifically, the existence of buildings already on the site that are not yet worth tearing down. One of the most bizarre things about comparing house prices to MPPC as a measure of a "regulatory wedge" that drives up house prices is that, even setting aside my previous point, the true opportunity cost of production on any infill site also includes the loss of capital extant on the site. When you must tear down valuable capital in order to supply new housing, that entails an opportunity cost, namely, the present value of the services the existing building could provide with no further investment. In the real world, this value acts as a hurdle to supplying new housing. But it doesn't exist in the MPPC concept, nor in the "marginal cost of land" concept used in the Glaeser + Gyourko "average cost vs marginal cost" method used to "identify" the price effect of planning restrictions. That makes the MPPC and the AC vs MC concepts only useful for vacant land, which is something people replicating those methods in the Australian and NZ contexts are clearly blind to. One of my favourite quotes from Kendall and Tulip (2018) is this: "some barrier has to stop people subdividing properties at low cost and then selling them at high market prices. We discuss several possibilities... and conclude that the most plausible explanation is that subdivision is illegal in large parts of our cities". Could that barrier possibly, just possibly, be the fact there are already buildings on almost all of the land in the cities Kendall and Tulip measure in their data?!?
"The true cost of supply therefore equals the cost of construction AND the value of land options foregone"
Thanks for framing this as an opportunity cost equation and looking at futures.
Based on my 30 years of experience in the heart of Silicon Valley, I have seen over and over how many times land that could be used for housing is built instead for office, because, here, office is extremely productive and generates rents far higher than residential rents. There's no competition really. Housing can expand in down-office markets but usually only on parcels already zoned residential.
My model of thumb has been for decades, "office crowds out housing. I believe its a huge factor that has been largely ignored. Even now, in down-office markets, developers are gaining approvals for millions of square feet of new office parks that they then land-bank by negotiating long-term developer's agreements to secure their future entitlements until markets return. Thus creating a housing demand futures market, and of course residential developers take note of it.
It's seems obvious here that housing supply is not only moderated by those parcels that become available when owners choose to make them so, but also because housing must compete with office when owners choose to redevelop.
Ugh, Glaeser. It was hilarious for me, as someone who spent a lot of his career trading options and modeling options to read the 2018 paper. My first take was, hey, building on some land is exercising an option to develop. I'm an options trader. I know that it is not optimal exercise behavior to exercise any option the moment it goes one penny in-the-money. But that's what his analytical framework is based on! That's actually how I discovered Cameron Murray's work -- ran across his paper on the importance of considering land banking... Don't sleep on Gyourko either. The Wharton Residential Land Use Restrictiveness Index, which now pops up everywhere in the literature, is a hot mess. In my particular branch of finance, we do a ton of Principal Components Analysis, and the use of it to create the WRLURI is more like abuse. Taking the first principal component and making that your metric? OK, let's replicate it with their data. Hey, wait a second, some of the variables have loadings with signs the opposite of what theory (and common sense -- after all, we are talking about land use rules, not particle physics) would suggest. Oh, and he mixes dichotomous and continous variables in the PCA. I mean, just a friggin' mess. And now the literature is riddled with regressions where WRLURI is in the set of independent variables. Their work is like the PFAS of housing economics. Dangerous forever chemicals that get everywhere...
I'd be interested to read more about the issues with the WRLUR index. I remember digging into it about 6-8 years ago and came to the view that it wasn't useful. But if there have been any recent and thorough critiques I'd be keen to read them.
Last time I delved into it deeply was 2021, so I need to go back to my notes. But I do remember that quite apart from methodological issues relating to PCA, there was a real problem with relying on surveys. I looked at the variables for the 3 jurisdictions where I knew the land use rules very well, and each had several "wrong" answers from the survey respondent. Apparently, I am not the first to notice the problem with surveys (local planning officials don't answer correctly quite often: https://www.tandfonline.com/doi/abs/10.1080/01944363.2019.1643253). Matt Mleczko did a paper more recently that tried to rebuild WRLURI using AI model to "read" actual code. That paper was very clear about methods so it could be reproduced, and in that one you had bizarre PCA results. Higher by-right heights led to a *more restrictive* final score, for instance.
One more observation on Glaeser. It's a long time ago, but I did undergrad at same institution where he was. He was considered a very good guy, personally, in a department that had more than it's share of disagreeable geniuses. He may get a pass partly because of that.
There is so much of interest in this post and following comments! Thanks.
Part of what makes these issues so important is that the more technical and professional work of economists, especially Glaeser, underwrites a far too simple common sense everyday economics about how Nimby regulatory barriers have disrupted what was, and would be, a virtuous supply and demand driven spatial equilibrium. In the beginning, property and land markets allocated locations properly, so it is said, such that each location was a perfect tradeoff of gains and losses at the margin. It is only when exogenous changes occur that the equilibrium is disrupted and that gains from arbitrage are created. Capitalizing on those, as Tim Helm notes, is what brings the market back into equilibrium. A dangerously seductive knowledge is created that is playing out in policy experiments that I worry are likely to cause harm or create unearned gains in ways that will shock many well-meaning Yimbys, who, after all, rightly want to undo the ways that mostly white middle-class suburbanites segregated spaces to their advantage and now protect valuable property.
So, it is particularly worrisome to see many researchers and policy advocates relying on what they take to be ‘proven’ truths about the costs of regulation, the ‘regulatory tax’ that is supposed to be at the core of the housing affordability crisis.
By way of a contribution to this knowledge critique, it is instructive to see how Glaeser (and various co-authors) pivoted from an analysis that once argued that high property values were justified by the amenity value of locations in productive and warmer places and hipper places. In some ways, the analysis was similar to the supply-side analysis of the proper price of a location based on competitive construction costs. But it was a demand side ‘consumer city’ theory that argued that rising prices in California, e.g., were due to demand-side clustering on the part of those willing to pay more for locations that allowed them to be around others who shared their cultural tastes and educational backgrounds.
In both the supply-led and demand-led cases, Glaeser asserts that there is a kind of natural spatial equilibrium as an analytical starting point based only on competitive costs and subjective valuations. But the demand-side theory was a first take on explaining rising housing prices. In the beginning... there was a spatial equilibrium that left each person indifferent to where they lived. Each benefit they would receive by moving somewhere was offset by an equal value cost. This builds on the well-known monocentric city model that shows falling density and falling property and land value per square foot as you move farther from the most desirable locations with good jobs. You pay with higher commute times within a city. Then Glaeser takes this model and really works over the inter-city tradeoffs. This is where the trouble mushrooms. In his Consumer City papers, Glaeser argues that people are moving to places were wages are higher but where costs of locations have grown even faster. That suggests that people are putting up with a decline in net benefits, a loss of welfare. But if they are doing it, there must be a hidden benefit, because people are not fools. This is what I call a residual form of thinking: an inequality arises and the magnitude is a measure of what is presumed to behind it. There is no detailed theory, but there is a lot of hand waving at various trends that align with Glaeser’s interpretation. For example, here are Glaeser, Kolko, and Saiz from their 2000 “Consumer City” essay:
“In cities with more educated populations, rents have gone up more quickly than wages since 1970—the natural interpretation of this fact is that while productivity has risen in places with more educated workers, quality of life has risen faster.” p3. Then econometric work is produced to show that people value what they describe ‘critical urban amenities’ that include “a rich variety of service and consumer goods”, good weather; architectural and other forms of beauty; good transport; good public services. In short, a new equilibrium is established that warrants higher prices as a reflection of higher willingness to pay among skilled people who value urban locations. The inequality was not a barrier, not a problem, not a misallocation.
It is interesting that Murray finds that in the later work, focused on regulation and barriers to elastic housing supply, the demand-side features of urban locations that people are willing to pay more for have vanished from the analysis. Now, we switch to the supply-side to find that rising prices are inefficient barriers and must be opposed.
Despite this difference, I think there is a similar method at work, one that defines a quantitative deviation from a baseline equilibrium and then accounts for it less than rigorous ways. Murray and others have done a real service by drilling into the assumptions, the micro conundrums, and lack of detailed understanding of land development decisions on the supply side. It could be that this earlier demand side work was more on the mark as it was delving into hard to measure subjective valuations of locations. I have a working hypothesis that there has been an epochal change in the way people value locations. It has become a more important part of our consumption over time, more closely tied to personal development and understanding. And then institutional path dependent practices and expectations enforce this higher prices.
Be that as it may, there is so much more to understand about how and why prices of locations have changed, so much more detailed research that needs to occur, before we ground sweeping reforms in overheated quantitative measures of the costs of regulation. To put this another way, I hope that we can make some progress in disaggregating the many aspects of land and location value and how and why it changes. I don’t doubt that some forms of regulation have added scarcity value to locations that are protected from incremental density development. But how much, in what cases are there offsetting benefits we haven’t counted? In what ways have developers gamed the system to find some forms of exclusion profitable? Part of what’s appealing about the current centripetal pull toward a few key barriers, is that there is some truth to the costs of Nimby protectionism. But it validates a false and dangerous reliance on a simplistic Econ 101 model of housing as a competitive good that is deeply misleading. This grounding can only have bad effects on the kinds of action and vision it authorizes and those options that it excludes.
I found lots of interest in your comment. I think you're basically arguing that, in various ways, the regulatory tax literature neglects the demand-side drivers of price. I totally agree.
To take Cameron's example of this neglect, in the real world, people pay more for better located land (higher land prices per dwelling), and are therefore prepared to pay more to construct housing space on well-located land (higher construction costs per dwelling), making a fixed construction cost assumption wildly wrong. As Cameron points out, this assumption effectively predicts that all locations are identical and no higher-density dwellings would ever exist (which, to put it mildly, is unrealistic).
Another example of the neglect of the demand side is the way regulatory tax ideas ignore spatial equilibrium. If the real world, if upzoning reduced some "zoning wedge" that in some certain place was pushing up house prices, then housing costs would fall in that place, and people would move there. When would that process come to an end? When a new equilibrium is established with identical quality of life to locational alternatives. If amenity was no lower, how could prices be lower? It wouldn't be a quality of life equilibrium.
One bizarre example of ignoring spatial equilibrium I saw in NZ was an Infrastructure Commission paper that argued that, had certain policy settings been different from the 1980s onwards, rents in NZ's largest city (Auckland) would have been 50% lower today. Yet this paper also assumed that people would not have migrated in response (that is, the numbers were based on a closed-city AMM model). This kind of stuff is ludicrous. All modelling is wrong - i.e. no model is reality - but some modelling is so wrong as to be worse than useless.
I see many illogical consequences of the dominant line of thinking that zoning makes housing more expensive by reducing supply. Here are a few.
First, this idea cannot be reconciled with the idea that migration equalises quality of life and thereby re-establishes spatial equilibrium. If regulatory change makes supply lower or higher than in some counterfactual, then population (demand) will be lower or higher too, because anything else implies non-equilibrium. That means any zoning effect on price, up or down, can only be a short run one. That is fine. In the long run we're all dead. Short run gains are what we live for. But it's useless to estimate a short-run gain based on an erroneous long run model in which people do not migrate.
Two, the supposed zoning effect on house prices works via restrictive zoning making land prices higher and upzoning therefore making land prices lower. But in what world does giving landowners MORE rights in their bundle of property rights make that bundle worth LESS money? It's illogical. In the real world, it goes the other way. Yes, of course, higher density housing allows a higher land price to be spread over more dwellings, which means the land value per dwelling and price per dwelling can be made lower after upzoning, which is success of sorts, but is not "cheaper housing" due to increased competition and higher supply, but is just reflective of compositional change, with the comparison of old and new dwelling prices (and land value per dwellings) not being a like-for-like comparison. Comparing, on the one hand, a detached house on $1m of land with, on the other, each of 10 apartments on that site having $0.2m of land value after upzoning, means comparing apples with oranges. We can only meaningfully say that zoning makes housing more or less expensive if we're comparing like with like.
Ignoring the demand side also leads to troubling interpretations of empirics. For example, dwelling rents seem to have fallen in Auckland relative to elsewhere in NZ, even on a like-for-like basis controlling for compositional changes. This is universally interpreted as a good thing. But why? Do the lower rents result from more supply having made rents fall, while leaving willingness-to-pay for housing unchanged (and causing quality of life to rise)? Or is it because willingness-to-pay for housing fell, due to declining urban amenity (with quality of life maybe remaining unchanged)? Unless we acknowledge that house prices reflect locational attributes, then we might be mistakenly attributing price changes to supply-side factors.
I'm only on read #1. It takes me a while to digest. And ..... thanks for starting the take-down on Glaeser. Tread carefully here. Glaeser has a lot of legacy mind-share, deserved or not. If you vet him respectfully and authoritatively it will further a much-needed academic pushback on the absurd academic housing dialogue here in the USA.
As an aside, on your point about land prices and locations. Lest there be any doubt about the true variability of land costs go here: https://www.fhfa.gov/research/papers/wp1901
"Working Paper 19-01: The Price of Residential Land for Counties, ZIP codes, and Census Tracts in the United States"
They provide an updated (2024) data base for all census, zip, etc locations in the US. Apparently land-prices are not a fixed multiple of construction costs and do vary quite widely both within cities and across cities. Who knew?
For a thorough takedown of The WRLURI, see the discussion in Murtaza Baxamusa's 2020 book A New Model for Housing Finance, pp. 104-110.
The disease is catching: the Terner Center for Housing Innovation at UC Berkeley, the go-to consultancy for the California Legislature, has devised its own such survey. Critiquing the Terner survey, I cited Baxamusa's work:
"In his book A New Model for Housing Finance, published in June by Routledge, San Diego-based planner and USC instructor Murtaza Baxamusa wrote:
'Studies on the impacts of land use regulations on housing are mostly biased. They evaluate the cost of the regulation to the developer but ignore the benefits of regulation to the public.'
Baxamusa’s objections to 'one of the most commonly used indicators,' the Wharton Land-Use Regulation Index, also apply to the Terner study:
'It is a static model based on a point in time survey, whereas the variables that are being tracked, such as permits and prices, are dynamic. Even the political climate changes over time. The sample selection bias raises questions on who filled out the survey and when?'
Baxamusa called out the Wharton researchers’ question about the importance of 'community pressure.' That question, he wrote, 'is clearly leading, especially since there was not a balanced "developer pressure" in the choices.' The Terner survey has the same bias."
MATCHR is Mercatus Center's extension of the Terner index. Another attempt at dimensional reduction -- uses Terner's survey methodology and then uses factor analysis to reduce it all to a single index.
I have a pet theory that the reason econ is more hierarchical then other fields is that the average economist defaults to believing the feilds status ladder is a meritocracy that produces a positive outcome of efficiently identifying the most competent people. They dont seem to consider that less talented academics might somehow find correct ideas or that the status ladder could not be meritocratic.
Regardless of the cause I think the observation about the status gradient is a true one. Econ seems to elavate academics from a small group of universitities way about everybody else. Same thing with academic journals were the big five completely dominate.
Overall I think economics is too insular and part of the solution to the problems you describe is to have economics ideas discussed more widely by outsiders. Economics theories should be bullshit tested by outsiders for credibility.
Just my ideas could be wrong.
Had another thought the other day that maybe if you went to work for a hedge fund/ fin institution and helped them to make a lot of money your ideas might gain more credibility! Could be a crazy idea though.
Of course all academic fields are have the problem I described in the first para but naive/idealistic belief in meritocracy might be a particularly neoclasical economist thing?
Your first problem is trying to read Glaeser literally.
Take him seriously, not literally.
A literal reading will drive you mad because his housing analyis is not designed for coherence. It is designed to hide the economic role of land as distinct from capital (i.e. as a factor of production fixed in supply and earning economic rents), to misidentify the effects of land speculation as consequences of poor regulation, and to pursue deregulation, all in order to provide financial benefit to land owners.
As per my NZAE talk, I believe that neither Glaeser nor anyone else who claims that regulation makes housing expensive has any coherent theory of housing supply.
None of them can answer the simple question "why are houses built?" with anything approaching a coherent explanation.
Ironically, they have many answers (rather, the same answer, given many times) for why houses are NOT built. Which is a really neat trick, because it turns their scientific inadequacies into a strength. If they can't explain why prices aren't lower and quantities aren't higher, because they don't have a theory that works when it comes to explaining the facts, it means they can blame their chosen villain, which is regulation. In this way, incompetence is a great hole to fill with ideology.
For the benefit of your readers, here are several related ways to describe the problems with almost all US literature on housing supply, including Ed Glaeser's work. They're a slightly different take on your column:
1. This entire body of work implicitly assumes that houses are built if and when (and because) there exist arbitrage opportunities in present-day prices. This is the totality of their theory of supply. Their theory is that if the price of houses exceeds the price of construction, construction will go ahead. By implication, they think developers and landowners are myopic (i.e. not forward looking). The process of housing development in their theory of housing supply is one of 'myopic arbitrage', where no developer looks beyond present-day prices when deciding whether to build housing. In other words, if construction is profitable today, then unless encumbered by regulation, construction will go ahead, because developers and landowners in this theory are believed to be not smart enough to realise that it might be even more profitable to delay construction until tomorrow. There is no forward-looking behaviour in this theory. By assumption, therefore, there is never any vacant land already feasible to develop (since all land is developed as soon as first feasible), and there is certainly never any speculation or land banking (choose your term: either way it means not developing housing despite the change of use already being profitable), and therefore there also is no reason for these economists to go looking for, let alone try to understand, these empirical phenomena. According to the old management cliche, "you don't value what you don't measure", but for US housing economists, a better version is "you don't need to measure what you don't acknowledge".
2. They do not appreciate that "construction cost" is not the same thing as "supply cost". In the real world, construction is only one of the opportunity costs of developing a new home. To supply a new home in the real world means giving up the option to develop that land differently in future. Sacrificing that option is a true opportunity cost of supply. The true cost of supply therefore equals the cost of construction AND the value of land options foregone. What is that value? It is identical to the price of land, as set by developers themselves, competing to buy that land in the knowledge that they will be able to sell houses for so much more than construction cost because land is physically scarce and land rents can't be competed away ("they ain't making it any more"). Glaeser wonders why house prices don't match his measure of production cost (MPPC). One reason is that he doesn't include the actual price of land as a cost of production. As you point out, Glaeser's MPPC just includes an arbitrary scaling factor, in lieu of actual land prices. And as you point out, this is only suitable for a world where all locations are identical and location services just happen to be worth 20% of the value of structure services.
3. They use metrics that are blind to history, specifically, the existence of buildings already on the site that are not yet worth tearing down. One of the most bizarre things about comparing house prices to MPPC as a measure of a "regulatory wedge" that drives up house prices is that, even setting aside my previous point, the true opportunity cost of production on any infill site also includes the loss of capital extant on the site. When you must tear down valuable capital in order to supply new housing, that entails an opportunity cost, namely, the present value of the services the existing building could provide with no further investment. In the real world, this value acts as a hurdle to supplying new housing. But it doesn't exist in the MPPC concept, nor in the "marginal cost of land" concept used in the Glaeser + Gyourko "average cost vs marginal cost" method used to "identify" the price effect of planning restrictions. That makes the MPPC and the AC vs MC concepts only useful for vacant land, which is something people replicating those methods in the Australian and NZ contexts are clearly blind to. One of my favourite quotes from Kendall and Tulip (2018) is this: "some barrier has to stop people subdividing properties at low cost and then selling them at high market prices. We discuss several possibilities... and conclude that the most plausible explanation is that subdivision is illegal in large parts of our cities". Could that barrier possibly, just possibly, be the fact there are already buildings on almost all of the land in the cities Kendall and Tulip measure in their data?!?
"The true cost of supply therefore equals the cost of construction AND the value of land options foregone"
Thanks for framing this as an opportunity cost equation and looking at futures.
Based on my 30 years of experience in the heart of Silicon Valley, I have seen over and over how many times land that could be used for housing is built instead for office, because, here, office is extremely productive and generates rents far higher than residential rents. There's no competition really. Housing can expand in down-office markets but usually only on parcels already zoned residential.
My model of thumb has been for decades, "office crowds out housing. I believe its a huge factor that has been largely ignored. Even now, in down-office markets, developers are gaining approvals for millions of square feet of new office parks that they then land-bank by negotiating long-term developer's agreements to secure their future entitlements until markets return. Thus creating a housing demand futures market, and of course residential developers take note of it.
It's seems obvious here that housing supply is not only moderated by those parcels that become available when owners choose to make them so, but also because housing must compete with office when owners choose to redevelop.
Ugh, Glaeser. It was hilarious for me, as someone who spent a lot of his career trading options and modeling options to read the 2018 paper. My first take was, hey, building on some land is exercising an option to develop. I'm an options trader. I know that it is not optimal exercise behavior to exercise any option the moment it goes one penny in-the-money. But that's what his analytical framework is based on! That's actually how I discovered Cameron Murray's work -- ran across his paper on the importance of considering land banking... Don't sleep on Gyourko either. The Wharton Residential Land Use Restrictiveness Index, which now pops up everywhere in the literature, is a hot mess. In my particular branch of finance, we do a ton of Principal Components Analysis, and the use of it to create the WRLURI is more like abuse. Taking the first principal component and making that your metric? OK, let's replicate it with their data. Hey, wait a second, some of the variables have loadings with signs the opposite of what theory (and common sense -- after all, we are talking about land use rules, not particle physics) would suggest. Oh, and he mixes dichotomous and continous variables in the PCA. I mean, just a friggin' mess. And now the literature is riddled with regressions where WRLURI is in the set of independent variables. Their work is like the PFAS of housing economics. Dangerous forever chemicals that get everywhere...
Great comment. Thanks.
I'd be interested to read more about the issues with the WRLUR index. I remember digging into it about 6-8 years ago and came to the view that it wasn't useful. But if there have been any recent and thorough critiques I'd be keen to read them.
Last time I delved into it deeply was 2021, so I need to go back to my notes. But I do remember that quite apart from methodological issues relating to PCA, there was a real problem with relying on surveys. I looked at the variables for the 3 jurisdictions where I knew the land use rules very well, and each had several "wrong" answers from the survey respondent. Apparently, I am not the first to notice the problem with surveys (local planning officials don't answer correctly quite often: https://www.tandfonline.com/doi/abs/10.1080/01944363.2019.1643253). Matt Mleczko did a paper more recently that tried to rebuild WRLURI using AI model to "read" actual code. That paper was very clear about methods so it could be reproduced, and in that one you had bizarre PCA results. Higher by-right heights led to a *more restrictive* final score, for instance.
One more observation on Glaeser. It's a long time ago, but I did undergrad at same institution where he was. He was considered a very good guy, personally, in a department that had more than it's share of disagreeable geniuses. He may get a pass partly because of that.
There is so much of interest in this post and following comments! Thanks.
Part of what makes these issues so important is that the more technical and professional work of economists, especially Glaeser, underwrites a far too simple common sense everyday economics about how Nimby regulatory barriers have disrupted what was, and would be, a virtuous supply and demand driven spatial equilibrium. In the beginning, property and land markets allocated locations properly, so it is said, such that each location was a perfect tradeoff of gains and losses at the margin. It is only when exogenous changes occur that the equilibrium is disrupted and that gains from arbitrage are created. Capitalizing on those, as Tim Helm notes, is what brings the market back into equilibrium. A dangerously seductive knowledge is created that is playing out in policy experiments that I worry are likely to cause harm or create unearned gains in ways that will shock many well-meaning Yimbys, who, after all, rightly want to undo the ways that mostly white middle-class suburbanites segregated spaces to their advantage and now protect valuable property.
So, it is particularly worrisome to see many researchers and policy advocates relying on what they take to be ‘proven’ truths about the costs of regulation, the ‘regulatory tax’ that is supposed to be at the core of the housing affordability crisis.
By way of a contribution to this knowledge critique, it is instructive to see how Glaeser (and various co-authors) pivoted from an analysis that once argued that high property values were justified by the amenity value of locations in productive and warmer places and hipper places. In some ways, the analysis was similar to the supply-side analysis of the proper price of a location based on competitive construction costs. But it was a demand side ‘consumer city’ theory that argued that rising prices in California, e.g., were due to demand-side clustering on the part of those willing to pay more for locations that allowed them to be around others who shared their cultural tastes and educational backgrounds.
In both the supply-led and demand-led cases, Glaeser asserts that there is a kind of natural spatial equilibrium as an analytical starting point based only on competitive costs and subjective valuations. But the demand-side theory was a first take on explaining rising housing prices. In the beginning... there was a spatial equilibrium that left each person indifferent to where they lived. Each benefit they would receive by moving somewhere was offset by an equal value cost. This builds on the well-known monocentric city model that shows falling density and falling property and land value per square foot as you move farther from the most desirable locations with good jobs. You pay with higher commute times within a city. Then Glaeser takes this model and really works over the inter-city tradeoffs. This is where the trouble mushrooms. In his Consumer City papers, Glaeser argues that people are moving to places were wages are higher but where costs of locations have grown even faster. That suggests that people are putting up with a decline in net benefits, a loss of welfare. But if they are doing it, there must be a hidden benefit, because people are not fools. This is what I call a residual form of thinking: an inequality arises and the magnitude is a measure of what is presumed to behind it. There is no detailed theory, but there is a lot of hand waving at various trends that align with Glaeser’s interpretation. For example, here are Glaeser, Kolko, and Saiz from their 2000 “Consumer City” essay:
“In cities with more educated populations, rents have gone up more quickly than wages since 1970—the natural interpretation of this fact is that while productivity has risen in places with more educated workers, quality of life has risen faster.” p3. Then econometric work is produced to show that people value what they describe ‘critical urban amenities’ that include “a rich variety of service and consumer goods”, good weather; architectural and other forms of beauty; good transport; good public services. In short, a new equilibrium is established that warrants higher prices as a reflection of higher willingness to pay among skilled people who value urban locations. The inequality was not a barrier, not a problem, not a misallocation.
It is interesting that Murray finds that in the later work, focused on regulation and barriers to elastic housing supply, the demand-side features of urban locations that people are willing to pay more for have vanished from the analysis. Now, we switch to the supply-side to find that rising prices are inefficient barriers and must be opposed.
Despite this difference, I think there is a similar method at work, one that defines a quantitative deviation from a baseline equilibrium and then accounts for it less than rigorous ways. Murray and others have done a real service by drilling into the assumptions, the micro conundrums, and lack of detailed understanding of land development decisions on the supply side. It could be that this earlier demand side work was more on the mark as it was delving into hard to measure subjective valuations of locations. I have a working hypothesis that there has been an epochal change in the way people value locations. It has become a more important part of our consumption over time, more closely tied to personal development and understanding. And then institutional path dependent practices and expectations enforce this higher prices.
Be that as it may, there is so much more to understand about how and why prices of locations have changed, so much more detailed research that needs to occur, before we ground sweeping reforms in overheated quantitative measures of the costs of regulation. To put this another way, I hope that we can make some progress in disaggregating the many aspects of land and location value and how and why it changes. I don’t doubt that some forms of regulation have added scarcity value to locations that are protected from incremental density development. But how much, in what cases are there offsetting benefits we haven’t counted? In what ways have developers gamed the system to find some forms of exclusion profitable? Part of what’s appealing about the current centripetal pull toward a few key barriers, is that there is some truth to the costs of Nimby protectionism. But it validates a false and dangerous reliance on a simplistic Econ 101 model of housing as a competitive good that is deeply misleading. This grounding can only have bad effects on the kinds of action and vision it authorizes and those options that it excludes.
Hi Jonathan,
I found lots of interest in your comment. I think you're basically arguing that, in various ways, the regulatory tax literature neglects the demand-side drivers of price. I totally agree.
To take Cameron's example of this neglect, in the real world, people pay more for better located land (higher land prices per dwelling), and are therefore prepared to pay more to construct housing space on well-located land (higher construction costs per dwelling), making a fixed construction cost assumption wildly wrong. As Cameron points out, this assumption effectively predicts that all locations are identical and no higher-density dwellings would ever exist (which, to put it mildly, is unrealistic).
Another example of the neglect of the demand side is the way regulatory tax ideas ignore spatial equilibrium. If the real world, if upzoning reduced some "zoning wedge" that in some certain place was pushing up house prices, then housing costs would fall in that place, and people would move there. When would that process come to an end? When a new equilibrium is established with identical quality of life to locational alternatives. If amenity was no lower, how could prices be lower? It wouldn't be a quality of life equilibrium.
One bizarre example of ignoring spatial equilibrium I saw in NZ was an Infrastructure Commission paper that argued that, had certain policy settings been different from the 1980s onwards, rents in NZ's largest city (Auckland) would have been 50% lower today. Yet this paper also assumed that people would not have migrated in response (that is, the numbers were based on a closed-city AMM model). This kind of stuff is ludicrous. All modelling is wrong - i.e. no model is reality - but some modelling is so wrong as to be worse than useless.
I see many illogical consequences of the dominant line of thinking that zoning makes housing more expensive by reducing supply. Here are a few.
First, this idea cannot be reconciled with the idea that migration equalises quality of life and thereby re-establishes spatial equilibrium. If regulatory change makes supply lower or higher than in some counterfactual, then population (demand) will be lower or higher too, because anything else implies non-equilibrium. That means any zoning effect on price, up or down, can only be a short run one. That is fine. In the long run we're all dead. Short run gains are what we live for. But it's useless to estimate a short-run gain based on an erroneous long run model in which people do not migrate.
Two, the supposed zoning effect on house prices works via restrictive zoning making land prices higher and upzoning therefore making land prices lower. But in what world does giving landowners MORE rights in their bundle of property rights make that bundle worth LESS money? It's illogical. In the real world, it goes the other way. Yes, of course, higher density housing allows a higher land price to be spread over more dwellings, which means the land value per dwelling and price per dwelling can be made lower after upzoning, which is success of sorts, but is not "cheaper housing" due to increased competition and higher supply, but is just reflective of compositional change, with the comparison of old and new dwelling prices (and land value per dwellings) not being a like-for-like comparison. Comparing, on the one hand, a detached house on $1m of land with, on the other, each of 10 apartments on that site having $0.2m of land value after upzoning, means comparing apples with oranges. We can only meaningfully say that zoning makes housing more or less expensive if we're comparing like with like.
Ignoring the demand side also leads to troubling interpretations of empirics. For example, dwelling rents seem to have fallen in Auckland relative to elsewhere in NZ, even on a like-for-like basis controlling for compositional changes. This is universally interpreted as a good thing. But why? Do the lower rents result from more supply having made rents fall, while leaving willingness-to-pay for housing unchanged (and causing quality of life to rise)? Or is it because willingness-to-pay for housing fell, due to declining urban amenity (with quality of life maybe remaining unchanged)? Unless we acknowledge that house prices reflect locational attributes, then we might be mistakenly attributing price changes to supply-side factors.
Thanks again for your comment.
I'm only on read #1. It takes me a while to digest. And ..... thanks for starting the take-down on Glaeser. Tread carefully here. Glaeser has a lot of legacy mind-share, deserved or not. If you vet him respectfully and authoritatively it will further a much-needed academic pushback on the absurd academic housing dialogue here in the USA.
As an aside, on your point about land prices and locations. Lest there be any doubt about the true variability of land costs go here: https://www.fhfa.gov/research/papers/wp1901
"Working Paper 19-01: The Price of Residential Land for Counties, ZIP codes, and Census Tracts in the United States"
They provide an updated (2024) data base for all census, zip, etc locations in the US. Apparently land-prices are not a fixed multiple of construction costs and do vary quite widely both within cities and across cities. Who knew?
For a thorough takedown of The WRLURI, see the discussion in Murtaza Baxamusa's 2020 book A New Model for Housing Finance, pp. 104-110.
The disease is catching: the Terner Center for Housing Innovation at UC Berkeley, the go-to consultancy for the California Legislature, has devised its own such survey. Critiquing the Terner survey, I cited Baxamusa's work:
"In his book A New Model for Housing Finance, published in June by Routledge, San Diego-based planner and USC instructor Murtaza Baxamusa wrote:
'Studies on the impacts of land use regulations on housing are mostly biased. They evaluate the cost of the regulation to the developer but ignore the benefits of regulation to the public.'
Baxamusa’s objections to 'one of the most commonly used indicators,' the Wharton Land-Use Regulation Index, also apply to the Terner study:
'It is a static model based on a point in time survey, whereas the variables that are being tracked, such as permits and prices, are dynamic. Even the political climate changes over time. The sample selection bias raises questions on who filled out the survey and when?'
Baxamusa called out the Wharton researchers’ question about the importance of 'community pressure.' That question, he wrote, 'is clearly leading, especially since there was not a balanced "developer pressure" in the choices.' The Terner survey has the same bias."
Is the Termer measure the MATCHR index? Yup, another biased mess.
Don't know. Here's a link to the Terner index: https://ternercenter.berkeley.edu/blog/new-resources-land-use-california/
MATCHR is Mercatus Center's extension of the Terner index. Another attempt at dimensional reduction -- uses Terner's survey methodology and then uses factor analysis to reduce it all to a single index.
Thanks. That's ominous.
I have a pet theory that the reason econ is more hierarchical then other fields is that the average economist defaults to believing the feilds status ladder is a meritocracy that produces a positive outcome of efficiently identifying the most competent people. They dont seem to consider that less talented academics might somehow find correct ideas or that the status ladder could not be meritocratic.
Regardless of the cause I think the observation about the status gradient is a true one. Econ seems to elavate academics from a small group of universitities way about everybody else. Same thing with academic journals were the big five completely dominate.
Overall I think economics is too insular and part of the solution to the problems you describe is to have economics ideas discussed more widely by outsiders. Economics theories should be bullshit tested by outsiders for credibility.
Just my ideas could be wrong.
Had another thought the other day that maybe if you went to work for a hedge fund/ fin institution and helped them to make a lot of money your ideas might gain more credibility! Could be a crazy idea though.
Of course all academic fields are have the problem I described in the first para but naive/idealistic belief in meritocracy might be a particularly neoclasical economist thing?
p.s. Tim, I would love to read or listen to your. NZAE talk that you refer to. Is there a link you can send? Thanks, Jonathan