Over the holidays, I read William Fischel’s new (2015) book on urban planning, Zoning Rules! The Economics of Land Use Regulation. It’s an important, interesting, and – fortunately for me – readable book on the topic. Fischel draws upon three or four decades of research on the topic, as well as his experience as a member of his local zoning board in New Hampshire.
Zoning, or urban planning more generally, exerts a strong influence on the shape of cities. It influences where people live and work, as well as the housing choices and prices that they face. It is a dull-sounding topic with important ramifications – very similar to transport policy in that regard. And, like transport, it arouses a surprising level of passion.
In the book, Fischel addresses two main topics:
- First, how did zoning / urban planning arise and proliferate? A century ago, cities did not have comprehensive zoning codes that defined how intensely people can develop land and what types of activities can happen in different places. Now, virtually every city has zoning / planning regulations. What changed?
- Second, under what conditions is zoning economically efficient? Many commentators and researchers have critiqued the cost of planning rules, but there are also benefits – and complex interactions with other policies such as local government property taxation. What ties all this together?
In the first part of this review, I’ll discuss Fischel’s (convincing and well researched) answer to the first question.
Let’s start with a common story about why bad planning regulations exist. Here’s Finance Minister Bill English fingering some suspects in his recent speech on the topic:
Your prospects of being able to buy a house are directly related to the decisions made by planning officials about the availability of land, the environmental standards they apply to building, and the way infrastructure is allocated.
It’s very difficult to understand how planners do that, even though the consequences for the community and the economy are significant.
Central government has had the opportunity to sit alongside councils to understand how they make their decisions.
Some of those decisions appear quite arbitrary.
They can be driven by the tastes of individuals who have the power to make decisions.
English argues that planning rules are imposed in top-down fashion by council planning staff. In this narrative, planning rules exist because local governments have chosen to supply them to us in preference to other models.
Fischel argues that this story is backwards: urban planning rules have generally not come about due to top-down bureaucratic decision-making, but as a result of bottom-up democratic pressure. Politically active home-owners, or “homevoters”, advocate for tighter planning restrictions. Because the majority of the average homeowner’s wealth is tied up in their home, they have a strong incentive to vote to prevent developments that might put the value of their home at risk. In planning, ideas are secondary to self-interest:
Public officials respond to the interests of their constituents, and public ideologies such as city beautification, hearth-and-home, and environmentalism come to the fore when they serve the interests of property owners. [Zoning Rules!, p 215]
There is a significant amount of empirical support for the “homevoter hypothesis”. Saiz (2010) found that US cities with more severe geographic constraints also have more restrictive planning rules – suggesting that people who own expensive property are more likely to vote to limit development. (I recently reviewed Saiz’s paper.) At an international level, Germany and Switzerland have the lowest home ownership rates in the OECD and also some of the most affordable housing. At a hyper-local level, case studies of the great down-zoning of Los Angeles reveal the key role played by a relatively small number of vocal homeowner activists.
But timing also matters, as zoning is a relatively new phenomenon. Fischel identifies two critical periods in the development of urban planning. First, zoning was invented and subsequently spread quickly through America in the 1920s. Second, in the 1970s, zoning was tightened significantly, with increasing restrictions on both density and suburban expansion. Here was the result in Los Angeles:
Synchronised changes of this nature require synchronised causes. Fischel argues that zoning first arose as a result of a transport revolution in the 1910s and 20s:
As trucks, buses, and cars replaced rail-bound modes of transportation, suburban residential districts could no longer rely on nuisance law, informal pressures, control of rail routes, and piecemeal covenants to protect their residential investments from incompatible use. Zoning was a response to potential insults to their homes from near-nuisances transported to their neighborhoods by footloose trucks and buses. [Zoning Rules!, p 216]
The urban planning clampdown of the 1970s occurred as a result of a more complex mix of factors. The backdrop to these changes was the subsidised expansion of home ownership after World War II: governments handed out subsidised mortgages like candy, thus expanding the number of homevoters. Fischel identifies six main factors that increased homeowners’ demand for tighter zoning controls, and made it easier for them to get what they wanted:
…the three demand factors that led to the 1970s growth control movement were (a) the growing suburbanization of employment (as opposed to just residences) resulting from the construction of the interstate highway system and the spread of containerized shipping; (b) the expansion of equalitarian legal principles that derived from the civil rights movement of the 1960s; and (c) the sudden growth of housing values in the portfolio of homeowners [resulting from high 1970s inflation]. The three elements that facilitated the supply of exclusion were (a) the expansion of legal standing to opponents of development; (b) the federalization of the environmental movement that dawned on the national scene in 1970; and (c) state legislation that established multilayered review of many projects that were formerly regarded as entirely local. [Zoning Rules!, p. 217]
The details of the story are different in New Zealand than in the US. Our history with zoning as a means of racial exclusion is nowhere near as shameful as America’s. And planning legislation has followed a different (and, I hope, more efficient) course than in the US. But many of the key elements are likely to be similar. New Zealand cities have experienced the same revolutions in urban transport and suburbanised population and employment to a similar extent. And, of course, we also have a class of stroppy “homevoters” who will advocate for tighter planning regulations to maintain or increase their property values.
If you think that urban planning rules should be changed, it’s essential to understand the bottom-up drivers of those rules. Many critics of zoning are oblivious to the popularity of zoning among a vocal segment of home-owners. As City Observatory’s Daniel Hertz recently wrote:
anyone who thinks there is a “consensus” about the damage caused by too-strict zoning ought to attend the next community development meeting in their neighborhood.
Fischel’s excellent history of zoning is a useful reminder that urban planning policies generally arise as a result of pressure from homeowners, not as a result of a conspiracy of planners. Consequently, the path to reform or liberalisation of planning rules is a difficult one for local government politicians to walk. If they vote for significant loosening of planning rules, they increase their risk of losing the next election. And successful challengers may simply turn around and tighten the rules back up again.
Fischel’s awareness of that dynamic flavours his policy recommendations. Zoning Rules! closes with no proposals for sweeping change. Instead, it proposes various ideas for how a challenging “bottom-up” dynamic could be incrementally improved. At the top of the list is an important long-term play: reduce the demand for strict planning rules by cutting back tax subsidies for home ownership, like New Zealand’s lack of a comprehensive capital gains tax.
Next week: Fischel’s analysis of the economic efficiency of zoning.
This is the second and final post discussing some broad ideas for building a better city. The first post discussed the dynamic nature of cities and argued that a focus on appropriate pricing and incentive mechanisms was important to managing urban ills without stifling beneficial change. This part discusses how we might identify policy areas in need of improvement, and why we should care about efficient policy.
2. Cost benefit analysis is important for identifying opportunities to improve policies
Over the last 170 years, New Zealand cities have been shaped by a wide range of policies, ranging from planning regulations to public investments to government land-holdings to tax and subsidy policies. Many of those policies serve (or served) a useful purpose, and most are good intentioned. But it’s almost inconceivable that all of them are efficient. Cities are dynamic places, and policies put in place in past decades can easily become outmoded and begin to distort prices and limit people’s choices.
Fortunately, as I have tried to suggest in the previous post, we’ve got more policy alternatives than we think. We don’t have to use yesteryear’s solutions to solve next year’s problems. But how do we know what we might have to change?
In my view, cost benefit analysis, or CBA, has an important role to play in identifying which policies are good and which need to change. If you want to learn more about CBA, you can delve into the technical guidelines published by organisations like the Treasury and the NZ Transport Agency. But CBA is not really as complicated as the tedious official guidance makes it sounds. It boils down to a rather simple question:
“This policy has some benefits and some costs. Do we expect the benefits to exceed the costs?”
If the answer is no, perhaps we should do something different instead.
Cost benefit analysis can be applied to a wide range of policy questions. It’s commonly applied to evaluate public investment options – that’s what NZTA uses it for – and less widely used elsewhere. But the principles can readily be extended to a wide range of urban policies. In a paper I wrote for this year’s NZAE conference, I looked at a couple of approaches to evaluating the costs and benefits of planning regulations. I found that analysis of property sales could be used to identify cases where planning rules distorted prices and prevented people from making useful investments – as well as cases where planning rules could provide benefits by managing localised externalities.
Here are a few good examples of how CBA can help in making better decisions.
Back in 2009, the new National-led Government worked with the Green Party to design and implement an insulation and clean heating subsidy programme. In the first four years of the programme, roughly 180,000 homes were insulated and heat pumps were installed in around 60,000 homes. A cost benefit analysis undertaken in 2011 (Grimes et al, 2011) found that the project’s benefits exceeded the costs by a factor of five:
The overall results suggest that the programme as a whole has had sizeable net benefits, with our central estimate of programme benefits being almost five times resource costs attributable to the programme. The central estimate of gross benefits for the programme is $1.28 billion compared with resource costs of $0.33 billion, a net benefit of $0.95 billion.
This finding provided a strong rationale to extend the programme to 2016 and trial a rental warrant of fitness programme to improve home weathertightness and safety performance in selected NZ cities.
A second good example is the Ministry of Transport’s recent analysis of the economic performance of state highway investment. I reviewed their analysis in a series of posts last year (parts 1, 2, 3, 4). Among other things, they found that the economic efficiency of road spending had fallen since 2008, with projects with low benefit-cost ratios being selected over projects with higher BCRs.
This is a valuable finding that should be taken seriously by policymakers and the public. It suggests that there may be opportunities to significantly improve the value that we are getting out of transport investment. That being said, it also suggests that policymakers have chosen to take a more optimistic view about project benefits than indicated by conventional CBA procedures. Sometimes this is warranted – it’s difficult to accurately account for some benefits – and sometimes it’s not.
This reminds me of a third example. I was struck by recent comments by Wellington’s Deputy Mayor about the city’s new publicly funded convention centre and film museum:
Deputy Mayor Justin Lester said the museum would become New Zealand’s most significant man-made attraction and an international draw card.
“It’s a little bit when Disneyland first opened in California, but in a Wellington context… In the 150th year since Wellington became New Zealand’s capital, there are only a handful of moments that rival the significance of this announcement.”
I haven’t read the business case, so I don’t know what assumptions were made to sell the project. But if the financial and economic forecasts require Wellington to become the “Disneyland of the south”, I would be very nervous.
This leads on to a very important consideration when using CBA results: To get the real story, it’s important to dive into the data and calculations that sit behind the headline figures. In some cases, people make claims about projects that are not backed up by their analysis. For example, they may require unlikely things to happen in order for the hypothesised benefits to materialise. In these cases, a properly-done CBA should also provide you with a means of understanding the risks inherent with the policy.
3. In an efficient city, there is time and space left over to lead a good life
But why is efficiency important? At the start of this post, I argued that good urban policies facilitate agglomeration economies in both production and consumption. This, in turn, enables cities to succeed in attracting new businesses and new residents. (The alternative of urban stagnation or economic decline is not really very appealing.)
Or, as Stu argued in a post last year, efficient urban policies provide us with an abundance of good things. Efficient urban planning policies allow us to have abundant, affordably priced housing, without sacrificing public goods. Efficient transport policies enable us to have abundant access. Good management of public assets allows us to combine a productive economy with a good supply of public goods.
Furthermore, generally prioritising efficiency in our urban policies means that we will have more resources left over for all the non-economic things that make life beautiful and enjoyable. For example, allowing people to use land efficiently by building more housing in areas that are accessible to jobs and amenities will also allow them to avoid commuting long distances to sprawlsville. This in turn frees up time to spend with the ultimate in non-economic investments – children and families.
Last December, I saw how things could be a little different. It was the first day that LightPath / Te Ara I Whiti cycleway was open. (Incidentally, I think it should be called PinkPath.) I skipped the mass ride organized by Bike Auckland in favour of a drink elsewhere, but checked out the new cycleway on the bike ride home.
Now, I wasn’t extraordinarily enthusiastic about the project. It seemed to be too far over to the west edge of town and so I wasn’t sure how many people would want to use it. But it has surprised me. I’ve been up and down it eight or ten times since then, and there are always people out on it, even at 10pm. They are having fun cycling – a relatively new concept for Auckland.
PinkPath was designed to be fun to cycle on and fun to see from a distance. It sends a message: “Auckland will give you new choices about how to travel. Rock up on your bike.” It didn’t cost much – less than 1% of Auckland’s annual transport budget and a disused motorway off-ramp. But that money and space can easily be consumed by inefficiency elsewhere in the system.
Which leads me to my conclusion: the good things in life are not necessarily expensive, but they can easily be crowded out by bad urban policies. So get the prices right, do some CBA, and live a better life in a better city.
This is the first half of a two-part series of posts. It summarises a few ideas that have been banging around the back of my head for a while – basically, an attempt to answer the question: “What can economics do for cities?” In this part, I discuss a couple of important concepts: agglomeration economies, which underpin cities’ existence and ongoing success, and the potential role of pricing mechanisms for managing urban ills.
What do cities do?
Cities mean different things to different people. They are places to work, places to play, places to invest, places to consume, places to conduct politics, places to realise one’s individuality, places to blend into the crowd. (And many, many more things beside.)
In fact, one of the features of a successful city is that it can mean different things to different people, and attract and retain them for different reasons. Cities exist because they are efficient and diverse.
Economists use the term agglomeration economies to describe the advantages of urban scale and density. If you operate a business, locating in a city will allow you to access more workers, more customers, and more new ideas. But even if not, an urban location still offers advantages – more restaurants and retailers, a larger dating pool, better access to education and healthcare, and more choices about how to work, live, and get around.
New research from the Netherlands finds that agglomeration economies in both production and consumption are important, albeit to a different extent in different cities. Furthermore, ignoring agglomeration economies is a risky proposition for cities:
As history has shown (see, for example, what happened to Detroit or the decline in the population of Amsterdam and Rotterdam referred to above), current successes provide no guarantees for the future. This is what Gibrat’s law tells us, growth is independent of current size. Future growth is therefore largely independent of past success. The chances for policymakers that try to row against the tide are small. A successful policy requires to ‘go with the flow’. Large investments in infrastructure in a declining city do not satisfy any real demand but lead to large financial burdens for the local population, making these cities even less attractive. However, policy can make a difference in growing cities. In order to remain on the short list of hot spots, policymakers in these cities have two margins to work on.
- First, the city has to be attractive for innovative entrepreneurs and enterprises to locate their business.
- Second, the city has to be an attractive choice for high-educated top talent as a place to live in.
In other words, urban success is a dynamic process. Cities can’t stand still – they must be capable of attracting new people and generating new ideas and opportunities. Simply identifying some things that people like about a city and then freezing them in amber is a recipe for long-term urban failure.
1. Incentives and prices matter, so it’s important to get them right
We need change, but we don’t necessarily need change at all cost. Most development is good, but some has deleterious side-effects. A new factory may contaminate local air and water quality. A coal-fired power plant will damage our climate. A new subdivision may pump traffic onto congested roads. A new retailer may attract more people to park on already-crowded streets.
Policy responses to these challenges can heavy-handed and inefficient. While negative (and positive!) spillovers are abundant in cities, some cures may be worse than the disease. A good example is minimum parking requirements, or MPRs, which require new developments to provide a defined minimum amount of parking. The aim of this policy is to prevent parking from spilling over onto neighbouring streets and properties.
Unfortunately, MPRs tend to be both inefficient and ineffective. They are inefficient because (a) there is usually poor evidence for choosing minimum ratios, meaning that many businesses and households are compelled to purchase more parking than they need and (b) they tend to be more costly than alternative approaches to parking management. Furthermore, they are often ineffective, as people continue to complain about a lack of parking even in places where MPRs have led to a major oversupply.
Better pricing is often a better alternative to blunt policy instruments. As any economist will tell you, if you want less of something, put up the price! This approach is applicable to a wide range of policy areas, especially in cities. For example:
There are several important advantages to using prices, rather than regulations or construction, to discourage negative spillovers. First, pricing respects people’s ability to make good choices. If we had a carbon tax, it wouldn’t prevent someone from burning petrol or farming cows. But it would make them pay the full social cost of those choices.
Second, prices can change in response to new information. AT’s new parking policy is a good example of this – they will monitor demand for on-street parking and tweak the prices up if occupancy is too high. This reduces the risk of screwing things up due to forecasting errors.
Third, and most importantly, prices provide governments, businesses, and households better information, which can enable them to make better decisions. Over time, this will result in significant dynamic efficiencies. For example, congestion pricing will help transport agencies plan infrastructure upgrades. Rather than having to guess whether people will value expanded roads – which frequently leads to errors – they will be able to measure the actual value that people place on travel.
Tomorrow: Part 2.
How transport projects are evaluated has always been of interest to me. I believe that although the standard cost benefit analysis approach that lies behind the NZTA economic evaluation manual has its flaws, the resulting BCR is still an important factor in determining whether a project, or a particular project option, should proceed. I don’t really buy the argument that a project with an unfavorable BCR should be trumped by “strategic” reasons to enable it to proceed. If the strategic reason is any good it will probably be reflected in the BCR, particularly if wider economic benefits (WEBs) are taken into account. I put the Puhoi to Warkworth business case in this category (BCR 0.92) , along with the eye-wateringly expensive Additional Waitemata Harbour Crossing for cars and trucks (BCR 0.4).
Recently I took a look at a number of documents on the East-West Connections project – formerly called the East-West Link, released by the NZTA . At this stage they’ve completed an “Indicative Business Case” (IBC) – essentially, an initial investigation of the options for improving connectivity in the area. They’ve published the IBC alongside a number of technical appendices.
This is a welcome step as this is the first time that the wider public is getting a decent look at the project, including all of the options on the table. NZTA’s decision to release the full documentation, without redacting large sections of the analysis, is really good for transparency.
So let’s take a high-level look at some of the specific trade-offs between costs and benefits of the different options. To jump right to it, NZTA’s conclusion is that Option F should be progressed to a Detailed Business Case. Here’s a picture of Option F, which involves a new highway along the Onehunga foreshore:
For context, Options C, D and E also involved building new roads (part of the way) along the foreshore, while Options A and B entailed upgrades to existing roads, including freight lanes. A summary assessment of this “short list” is available in Appendix O of the business case.
NZTA conducted a cost-benefit analysis (CBA) of these six options. CBA for transport projects typically compares:
- The financial costs to build and operate the project, and
- The monetised economic benefits of the project, including user benefits such as travel time savings, vehicle operating cost savings, and reliability improvements and other benefits such as vehicle emissions reductions (or increases) and effects on economic productivity (“agglomeration”).
While CBA does have some weaknesses, largely due to shortcomings in the modelling tools available to us, it’s a conceptually robust way to assess project options. Especially in the case of road projects, NZTA’s approach should capture the majority of the economic benefits arising from projects, including productivity improvements for freight users.
This is the summary table:
You can see the net present value of the total benefits exceed the total costs for each option – i.e. the total benefit-cost ratio (BCR) for each option is above 1.
However, there are two problems.
The first is that the NZTA has made somewhat arbitrary assumptions about agglomeration benefits, which in theory reflect the productivity gains arising from improving connectivity between businesses. Rather than formally modelling it using the procedure specified in Appendix A10 of NZTA’s Economic Evaluation Manual, they’ve simply assumed that agglomeration impacts will add 25% on top of transport user benefits for each option.
As Stu previously highlighted in the case of the Mill Road highway, which included a similar “fudge factor” for agglomeration benefits, there is no real reason to do this (other than the rather circular argument that the same thing is being done for other projects).
In the case of East-West Connections, there is a stronger argument to be made for agglomeration benefits, as this project will serve a busy commercial/industrial area. However, it’s still necessary to do the analysis to establish their existence and magnitude! I (perhaps cynically) suspect that the 25% figure has simply been used to make the BCRs all appear higher than they otherwise would be. For public relations purposes it is preferable to have a higher BCR than a low one, even though the purpose of the exercise is an option evaluation rather than an assessment of the absolute economic worth of the project.
The second problem with this table is that it is not consistent with NZTA’s own requirements. Section 2.8 of the EEM sets out requirements for calculating and reporting BCRs. That section requires an incremental analysis of costs and benefits:
In other words, if you are choosing between two options, one of which is considerably more expensive than the other, it’s not enough to say that the more costly option has a BCR above 1. It’s actually necessary to show that the added (incremental) benefits of the costly option exceed the added (incremental) costs.
This is an important step in cost-benefit analysis as it shows you whether spending that extra bit of money for a more expensive solution is justified. Failing to do an incremental CBA is basically an invitation for gold-plating and overspending – i.e. find a worthwhile project, and then jack up the costs as high as possible.
So let’s take a look at an incremental BCR analysis of the East-West options. For simplicity I’ve focused only on Options A, B, and F – the two cheapest options, and NZTA’s preferred option. (Options C, D, and E are fairly similar to F in terms of total costs and total benefits – including them wouldn’t get a different result.)
Here’s a picture of Option A, which is an upgrade of SH20 and the existing Nielson St route to SH1:
And here’s Option B, which is pretty similar but also adds a south-facing ramp to SH1:
I’ve ranked the options from least to most expensive:
- Option A has total costs of $200 million and total benefits of $850 million. Consequently, it has an incremental BCR (relative to spending nothing) of 4.3. In other words, Option A seems like a good project.
- Option B has total costs of $500m and total benefits of $1650m. This means that it has incremental costs of $300m (i.e. $500m-$200m) and incremental benefits of $800m (i.e. $1650m-$850m). Its incremental BCR, compared to Option A, is therefore 2.7. This suggests that it’s well worth spending the extra money for Option B.
- Option F has total costs of $800m and total benefits of $1550m. Relative to Option B, its incremental costs are $300m and its incremental benefits are -$100m. Its incremental BCR is therefore -0.3.
In other words, if the NZTA were to follow their own economic evaluation manual it shows that Option F is not great value for money. It costs a lot more while actually delivering fewer economic benefits than Option B. Negative BCRs are generally not a positive sign that a project is a good idea.
This isn’t to say that we shouldn’t build Option F, or that we should build Option B. There may be some significant positive or negative effects that aren’t captured in this analysis and that may tip things in a different direction. For example, existing traffic modelling tools may not capture travel time reliability benefits very well. Similarly, we haven’t taken a look at environmental costs – on the one hand, Option F paves over the remainder of the Onehunga foreshore, which is negative; on the other, it potentially moves trucks away from the town centre and residential areas, which might be a good thing.
Admittedly I’m not a professional economist, but to me the incremental BCR analysis does highlight several questions that need to be answered:
- Given the fact that Option F costs more than Option B while delivering fewer quantified economic benefits, is there evidence that other unquantified benefits, such as travel time reliability, are sufficiently large to justify the added costs?
- Given that the project is primarily intended to improve convenience for freight users, has the government asked freight companies and shippers in the area if they would be willing to invest their own money to pay for Option F?
- Given the results of the Basin Reserve Flyover hearings, in which a Board of Inquiry found that the incremental economic benefits of the Flyover weren’t sufficient to outweigh the added environmental/amenity costs, is there a risk that approval for Option F won’t be forthcoming?
And finally, given the results of an incremental BCR analysis, isn’t there a case to also progress Option B for a more detailed assessment in the next stage of the work, given that it maximises economic benefits at a lower cost?
I have been pondering a comment in William Fischel’s generally excellent new book on zoning to the effect that:
…suburbanization and reduced urban density are worldwide phenomena. All but 16 of the 120 urban areas on every continent grew outward and reduced their overall population densities in the last decade of the previous millennium, even as almost all of them grew in total population.
This is an interesting claim, but one that I find very difficult to reconcile with the evidence on other “big picture” changes observed in cities over the last three decades.
In recent decades, agglomeration economies have gotten stronger and the structure of advanced urban economies has changed. This has in turn increased the premium that people place on proximity and centrality – a phenomenon well illustrated by Grimes and Liang (2007), who show how close proximity to the city centre shifted from being a “disamenity” to an “amenity” during the 1990s:
Relatedly, regulations limiting density are increasingly binding (and, in places like Auckland, San Francisco and New York, more costly than regulations limiting sprawl). This was nicely illustrated by three recent pieces of research that showed that (a) Auckland’s legacy planning regulations imposed twice as many costs on apartments as standalone houses, and that (b) the cost of legacy councils’ building height limits were higher than the cost of the city’s former urban growth boundary:
So colour me perplexed. On the one hand, I have a very respected and knowledgeable economist telling me that cities are getting less dense. On the other hand, I have a mass of evidence, often compiled by other respected and knowledgeable economists, that suggests that densities should be increasing, not decreasing.
As it turns out, Fischel’s claim is what my mathematician friends describe as “true but trivial”. It’s not inaccurate, but it doesn’t tell you anything interesting about the world either. The idea that urban densities are getting lower is in fact a statistical artefact – i.e. it’s a “fact” that arises from the way that Fischel has calculated population densities.
Calculating population densities is a slightly arcane subject. Last year, I wrote a short paper that explored alternative measures and explained why we should prefer a population-weighted density measure to a simple average density:
The most common approach to measuring population density is simply to divide the total population of a city by the total land area of the city. As shown above, this approach will tend to underestimate the density of cities with large expanses of lightly populated exurban land. However, this approach is commonly used for international comparisons due to the fact that it relatively straightforward to calculate…
The population-weighted density measure was introduced by Barnes (2001) to correct for the weaknesses of the simple average density measure. This measure was recently used by the US Census Bureau to produce consistent and meaningful data on American cities (Wilson et al, 2012). As the example above suggests, it more accurately reflects the density at which the average city resident is living (Eidlin, 2010).
Population-weighted density is estimated by calculating the density of all individual neighbourhoods within a city, assigning each neighbourhood a weight equal to its share of the city’s total population, and summing up the weighted density of all neighbourhoods. In other words, if a dense inner-city neighbourhood has ten times as many people as an outlying suburban neighbourhood, the inner-city area would be weighted ten times as heavily as the suburban area.
In other words, average density measures the density of the average hectare of land in the city, even if hardly anyone lives there, while population-weighted density measures the density of the neighbourhood that the average citizen lives in. Average density is not, therefore, a very meaningful number if you want insight about how people are living in a city.
Moreover, it turns out that a city’s average density can be declining even though every part of the city is getting more dense! This sounds counterintuitive, so I’ve put together a simple model showing how it can happen.
You can download the model here in case you’re interested in playing with it. For simplicity, I’ve assumed a linear city that extends in one direction from a centre. (This readily generalises to a city that extends in multiple directions.) Population densities are highest near the centre and decline exponentially with distance. (The distance decay parameter I’ve used loosely approximates observed outcomes in big NZ/Aus cities.)
I’ve tested the impact of a 20% increase in the urban population. In the model, intensification accounts for around 60% of growth, meaning that all existing developed areas get ~12% denser. The remaining 40% of growth occurs in low-density greenfield areas, which expand the city’s footprint by 33%.
Mathematically-minded readers will see where this is going. But for the visual learners out there, here’s what it looks like on a graph:
Because the urban footprint has expanded by 33% while the population has only grown by 20%, the city’s average density has dropped by 10%. But if you look at the graph, you can see that it would be ridiculous to say that the city is spreading out and de-densifying. In fact, every part of the city is getting more dense, and most growth is occurring within built up areas.
In other words, Fischel is wrong to say that cities are getting less dense. Horizontal growth is certainly happening – but so is vertical growth.
I would argue that the latter trend – towards proximity, density, and efficient use of land – is the more significant of the two. We are currently seeing big changes in the function, structure, and use of cities. Recognising and responding to those changes is important, and policies that unwittingly stifle them will have large economic and social costs.
That’s why it’s important to measure density (and other urban phenomena) accurately – bad measures often contribute to bad policy decisions.
How do you think cities are growing?
Back in July, I went down to Wellington for this year’s New Zealand Association of Economists conference. I really enjoy NZAE – people attend because they’re genuinely excited about sharing their ideas and learning from other people. (Stu Donovan and John Polkinghorne were also there.)
I was presenting a paper on using hedonic analysis of property sales to assess and compare the costs and benefits of planning regulations. The empirical side of the paper was an analysis of the impact of dwelling size, lot size, location, and amenities such as the presence of old buildings on property sale prices.
I used these results to consider the rationale for heritage preservation policies. In doing so, I asked three key questions:
- Is there evidence of positive spillovers (“externalities”, in economese) associated with old buildings?
- How large are those spillovers relative to other things that people value, such as living close to the city centre or having more living space?
- Is a blanket heritage control that limits the demolition of building likely to be optimal? In other words, are the positive spillovers from old buildings large enough to justify making it more difficult to develop in some areas?
The first question is very important. As I discussed the other week, people argue that old buildings should be preserved because they are valuable to their inhabitants. To my mind, that is not a good case for government to get involved. If heritage buildings are mainly valuable to their inhabitants, then those people can probably sort things out without the need for any rules.
But if there are positive spillovers from heritage, there may be a case to regulate. That’s because decisions made by a property owner about whether to demolish a heritage property may not take into account the impacts that their decisions may have on other people.
Many – although certainly not all! – old buildings have aesthetically pleasing exteriors. Simply put, they’re nice to look at. (This may simply reflect a selection process – i.e. people built ugly buildings 100 years ago, but they’ve been demolished.) The presence of these buildings can make an area more attractive for passers-by and other residents.
Central Post Office – now known as Britomart (Source)
There are a number of ways that we can measure the public value of aesthetically pleasing old buildings. For example, people may visit areas with more old buildings more often and spend more time walking the streets. (Although I caution that there’s a risk of omitted variable bias here, as areas with older buildings also tend to have older, more walkable street networks.) They may spend more money in shops in these area. Or, importantly, they may be willing to pay higher prices to live around old buildings and enjoy their aesthetic characteristics more frequently.
In my paper, I used residential property sale data to identify the existence of positive spillovers from old buildings. I’ll spare you the details of the number-crunching, but basically, I used four years of recent property sales data to determine whether people are willing to pay higher prices to live near old (pre-1940) buildings.
The results suggest that there are modest positive spillovers from old buildings. On average, every additional pre-1940 building in a neighbourhood was associated with a 0.3% increase in the price paid for neighbouring dwellings. Some individual buildings are likely to have stronger spillovers, of course – not all old buildings are created equal! And there are likely to be some spillovers that aren’t captured in residential property prices.
But as heritage policy is often a very local event – people tend to advocate for the preservation of buildings in their suburb or neighbourhood – it’s likely that this measure captures many of the spillovers that matter. Which leads us on to the third question: When is a blanket heritage control likely to be optimal?
The downside of a blanket control is that it will make it more difficult (or even impossible) for people to redevelop sites or make additions to existing homes. My analysis of recent property sales showed that the quantity of floorspace has a strong effect on property values. I estimated that a 10% increase in the size of a dwelling was associated with a 4.8% increase in its sale price, holding all other factors constant.
Based on this result, I asked: How much additional floorspace would be required to fully offset the loss of aesthetic spillovers from neighbouring pre-1940 buildings? In other words, what’s the point at which people might be indifferent between preserving heritage and getting opportunities to intensify their properties?
The results are mapped below. Darker greens and blues indicate areas with larger positive spillovers from old buildings. Yellow colours indicate areas where there are few if any spillovers. Of course, there are likely to be a number of subtleties that I wasn’t able to pick up in the data, such as the quality of heritage properties in different areas.
Change in floorspace required to offset loss of heritage spillovers (Source: Nunns, 2015)
One interesting thing about this map is that it suggests that the value of heritage preservation may be relatively low compared to the value of opportunities for intensification almost everywhere in the city. Even in the most heritage-y parts of Devonport and Ponsonby, it would only take a 30-40% increase in floorspace to fully compensate for the loss of localised spillovers from all the pre-1940 buildings in the neighbourhood. That isn’t an unreasonable possibility given that these areas have standalone houses sitting on crazily expensive land. (And the fact that many of these buildings would be preserved by their owners anyway.)
So what should we make of this?
First, an important caveat: these results are not definitive. They’re based on a piece of quantitative analysis that captures overall trends but omits qualitative aspects of the aesthetics of old buildings. In some areas, it may under-estimate the contribution of individual buildings that are especially attractive. In others, it will over-estimate the magnitude of spillovers, because the old buildings in the area are simply not that flash.
But even taking that caveat into mind, there may be room to optimise heritage preservation by focusing blanket heritage controls in areas where evidence of positive spillovers is strongest. So it’s encouraging to see that Auckland Council is refining its position on heritage controls in the Unitary Plan. (And dispiriting to see the NZ Herald’s alarmist one-sided take on the issue. Pro tip to the editors: articles like this are why I do not buy your newspaper. I spend money on other print media, so you’re missing out.)
It’s also worth remembering that blanket controls aren’t the only way to preserve heritage. Heritage schedules can be used to target protections to individual buildings with notable aesthetic or historic value. And councils can directly fund the preservation of notable buildings by buying up and renovating them. In some cases, these may be a more efficient way of ensuring that we maintain the good bits of the city at a reasonable cost.
What do you think an optimal heritage preservation policy would look like?
Last week, I introduced the concept of elasticity of supply with respect to price as a useful measure of housing market dynamics. Supply elasticities measure how responsive builders are to an increase in demand. In other words, when people turn up wanting dwellings, how quickly do the tradies start building more?
Supply elasticity can in turn have a big, long-run effect on prices. If the building sector is consistently slow to respond, it creates the condition for an ongoing shortfall in supply, which means that people will bid up prices more.
My post last week took a look at some of the (limited) international comparisons of planning regulations, which seem to indicate that New Zealand is not an especially poor performer. For example, consent processing time is relatively fast and efficient compared with other OECD countries.
However, regulations are only part of the picture. For example, Patrick wrote a good post a while back looking at Auckland’s geographic constraints:
Intuitively, we’d expect Auckland’s limited supply of developable land to have an effect on housing supply dynamics. But how much of an effect should we expect?
The empirical literature provides us with a reasonable estimate. A 2010 paper by MIT economist Albert Saiz (Massachusetts, not Manukau) measures constraints on land availability in large US cities and uses them to estimate the effect on housing supply.
Saiz finds that there are large differences in land availability between different cities. For example, “flatland” cities like Atlanta or Houston have very little area constrained by lakes, rivers, oceans, or steep slopes. Over 90% of the area around these cities is available for development. Coastal cities like San Francisco, San Diego, or Miami, on the other hand, might be able to develop less than 1/3 of the surrounding area.
Saiz concludes that:
Quantitatively, a movement across the interquartile range in geographic land availability in an average-regulated metropolitan area of 1 million is associated with shifting from a housing supply elasticity of approximately 2.45 to one of 1.25. Moving to the ninetieth percentile of land constraints (as in San Diego, where 60% of the area within its 50-km radius is not developable) pushes average housing supply elasticities down further to 0.91.
Translated from economese, this means that cities with less developable land have housing markets that respond more slowly to increased demand. (Or, as non-economists might say, duh.) For context, an elasticity of 0.91 indicates that a 10% increase in house prices is met by a 9.1% increase in housing supply. Even if regulations are held constant, a “flatland” city is expected to have a more responsive housing market than a coastal city with lots of hills.
In other words, when people compare Houston’s house prices with San Francisco’s or New York’s, they’re not comparing like with like. Geography matters quite a lot!
So what does Auckland’s geography look like? A 2014 NZIER paper modelled the effect of geographic and regulatory barriers on the city’s house prices. The authors conclude that: “relative to even Australian cities Auckland’s twin harbours severely restrict the availability of well-located land close to the city centre.” Overall, they estimate that less than one-third of the area around Auckland is available for development – most of the rest is water:
In other words, Auckland has very severe geographic constraints. In terms of the availability of developable land, it’s similar to hilly coastal cities like San Diego. Saiz estimated that a city of around Auckland’s size with an average level of planning regulations would have a supply elasticity of 0.91. So: does Auckland perform better or worse than this in practice?
A 2010 study by Arthur Grimes and Andrew Aitken provides some relevant data. Using data at a district council level, they looked at how quickly new dwellings were built in response to “shocks” in demand such as increases in net migration. Their key conclusion was that housing supply in New Zealand’s urban areas tends to be a little bit more responsive than supply in rural areas:
If we divide regions into urban and rural, we find faster adjustment in urban areas (average γ1i = 0.0093) than in rural areas (average γ1i = 0.0064). This result is consistent with an active development industry, based principally in cities, facilitating new construction.
In other words, the authors estimate a supply elasticity of around 0.93 for NZ’s urban areas (principally Auckland). This is almost exactly what we would predict based on Auckland’s geography. The implication of this is that Auckland’s housing market functions more or less as expected given its geography – we don’t have to assume unusually restrictive planning regulations to explain the observed outcomes.
There are a couple lessons we can draw from this.
First, Auckland’s geography is a primary driver of the city’s housing supply dynamics. If we have higher house prices than we’d like, it’s partly because we have less land for housing. As I’ve written before, some analyses of Auckland’s high house prices fall prey to omitted variable bias – i.e. ignoring important causal variables and thus over-estimating the impact of specific policies. This can result in flawed policy recommendations.
Second, we shouldn’t compound constrained geography with bad policy. Because Auckland doesn’t have much developable land, there is an even stronger incentive to use land efficiently. (A fact with implications for transport policy, planning policy, tax policy, and publicly-owned land.) Land-hungry policies might not be too bad in a land-abundant place like Houston, but requiring Auckland to follow a similar pattern is economically calamitous.
As most New Zealand cities are also heavily constrained by geography, this challenge isn’t unique to Auckland. But it’s also not all bad: the interplay of mountains, volcanoes, harbours, and oceans is what makes New Zealand such a beautiful place to live. Let’s build cities that enable us to get the best out of it.
How should we think through the dynamics of housing markets?
Conceptually, there’s a very simple answer and a very complex one. The simple version is that housing is just another market, shaped by the interaction of demand – i.e. people turning up with money to buy dwellings – and supply – people building new dwellings to meet demand. Policies can affect the supply side (e.g. by making it more costly or difficult to build new dwellings), the demand side (e.g. by subsidising home ownership), or both (e.g. by imposing supply restrictions to produce local amenities like parks).
And then there’s the complex story, in which we have to think about things like:
- Interactions between owner-occupation, renting, and property investment
- The impact of mortgage lending practices and asset values on housing
- The durable nature of housing, which means that prices can overshoot in a declining market
- The geography of jobs, amenities, and housing supply – not all locations are equally desirable, which means that houses in the wrong place don’t do much good
- Government provision of housing services (e.g. state homes) and subsidies for property ownership or renting
- Industrial organisation in the building sector, including firm size and structure and supply of skilled labour
- A wide range of local and central government regulations covering building materials, performance standards for dwellings, and the bulk, form, and location of dwellings
- Etc, etc, etc.
So it’s not usually possible to fully explain housing market dynamics with a simple supply and demand story. However, it’s often useful to start with a clear understanding of that story.
So with that in mind, here’s a key concept for analysing housing market dynamics: elasticity of housing supply. In an earlier post on public transport fares, I introduced the idea of elasticity of demand, which measures how responsive people’s demand for a good or service is to higher (or lower) prices. Supply elasticities are much same idea, but on the supply side of the equation.
Elasticity of housing supply is an important concept because it provides an indication of how many new dwellings will be constructed in response to an increase in prices (or demand). For example, an elasticity of less than 1 would indicate that developers are relatively unresponsive to increased demand – i.e. if prices rise by 10%, it will cause new housing construction to increase by less than 10%.
It’s easy to see why this is an important metric. In the aggregate, a relatively “inelastic” supply will mean that the housing stock will struggle to meet demand in a growing city. But aggregate lasticities aren’t everything – if new dwellings can’t be built in areas that are proximate to jobs and amenities, bad things will still happen
Supply elasticities can be measured empirically by looking at how markets have evolved in the past. In fact, a number of people have done just that.
In their 2012 housing affordability inquiry, the Productivity Commission surveyed some of this literature (see pages 33-34 of their final report). They published this chart comparing long-run elasticity of housing supply in 21 OECD countries, including New Zealand. Remember, higher numbers indicate more responsive housing supply:
New Zealand’s elasticity was around 0.7 – on the inelastic side, but still within the top 1/3 of the countries in the study. In other words, neither terrible nor fantastic. We have historically had a more elastic supply of housing than the UK or Australia, but we’ve lagged behind several Scandinavian countries as well as Japan, Canada, and the US.
Now, elasticity of supply is influenced by a number of factors. Building industry capability and productivity plays an important role. So do geographic constraints – a topic I’ll come back to in a future post. State house construction can also play a role, by ensuring that building activity doesn’t bottom out when prices dip. And, of course, planning regulations and consenting processes play a role. But how much of a role?
Unfortunately, we don’t have any good international comparisons of planning policies. However, the World Bank’s annual Ease of Doing Business report publishes some data on the ease of obtaining building consents, which provides a rough indication of the stringency of countries’ planning processes.
Here’s the upper echelons of their 2015 rankings. As you can see, New Zealand is ranked as the second easiest place to do business. When it comes with dealing with construction permits, we’re ranked 13th – ahead of countries like the United States (46) and United Kingdom (45) but behind Hong Kong (1), Singapore (2), and, oddly, Iraq (9).
Here’s a bit more detail on how Auckland’s consenting processes stack up. We have fewer procedures, a shorter consenting timeframe, and a lower consenting cost than the average OECD country:
So what does all this data mean? I think there are a few lessons we can – and can’t – learn from it.
The first is that perhaps we don’t have as many problems as we think we do. I have to admit that I was surprised by these figures. I was expecting our elasticity of supply to be lower and our consenting processes to be ranked lower. But perhaps – as with Auckland’s congestion – our problems aren’t that bad when put in international perspective. Kiwis do tend to prefer doing things efficiently, and NZ’s not large enough to require overly cumbersome bureaucratic machinery.
The second thing is that there is room to improve. There is almost always room to improve. New Zealand’s housing supply is still inelastic, which suggests that we may have trouble accommodating growth. Although the World Bank’s data on the ease of obtaining building permits seems to suggest that regulatory processes are less onerous here than many other places, who really knows? There are likely to be gremlins in any bureaucratic process.
The third lesson is that there are multiple paths to a well-functioning housing market. The countries with the highest elasticities of housing supply don’t have a lot in common with each other when it comes to policy frameworks. The US has a different set of policies than Japan or the Scandinavian countries. And it’s also the case that some countries have affordable and livable housing even though their elasticity of supply is low – Germany or the Netherlands, for example.
This is, in a way, really good news. We don’t have to go searching for a single “silver bullet” policy framework. There are different paths we could go down to improve the functioning of our housing market.
What do you make of these comparisons?
Last month, I took a look at the costs and benefits of publicly owned golf courses (Part 1, Part 2, Part 3). A few key findings from that analysis:
- Golf courses are different from public parks, as they can only be used by a small number of paying customers
- The benefit of redeveloping golf courses to offer a mix of new neighbourhoods and public parks could be as much as nine times higher than the benefit of the status quo to golfers
- Publicly owned golf courses don’t pay their fair share of rates, meaning that the rest of us have to pay higher taxes.
A key concept running through this analysis is the idea of an “opportunity cost“. We often face mutually exclusive choices – i.e. if we choose one thing, we can’t have the other. In those situations, the “cost” of getting one thing is giving up the opportunity to have the other.
Calvin and Hobbes illustrate the concept of mutually exclusive choices quite nicely:
In the case of publicly owned golf courses, our choices are fairly simple: If we keep them open for golf, we give up the opportunity to have public parks, other sports fields, or housing on them. And if we convert them to other uses, we give up the opportunity to golf now, and the option to choose a different set of uses at some future date.
However, there are other ways to think about the opportunity cost of publicly owned golf courses. For example, what happens when a local government wants to sell down assets, e.g. to free up capital for new investments? If they refuse to consider selling the golf course, what else do they sell instead?
(This isn’t to say that asset sales are necessarily a good idea – that’s really an issue that must be assessed on a case-by-case basis. When politicians propose to sell assets, I think that it’s essential that they are specific about (a) exactly how a sale would lead to better outcomes in the affected market, (b) exactly why they need the money – no vague promises of wish-fulfilment slush funds please! – and (c) how they will avoid losing money on the sale through poor timing.)
In 2002 the former Auckland City Council decided to sell down some of its publicly-owned assets, including some of its shares in the Auckland Airport and its entire public housing stock. The proposed sale of the public housing, most of which housed elderly Aucklanders on low incomes, stirred up opposition. As a result, central government got involved, and purchased the properties through Housing New Zealand:
The Government has offered to buy Auckland City Council’s pensioner and residential property portfolio.
On Monday, 30 September 2002 Cabinet approved an agreement negotiated between Housing New Zealand Corporation and the council.
Housing New Zealand will pay a total of $83 million for the Council’s two portfolios:
1542 pensioner rental units, on 50 sites, with a book value of $101 million. 129 residential units, with a book value of $31 million.
This reflects the full market value for residential housing and a discount for pensioner housing – which takes into account the fact that these sites will always be retained for social housing and that Housing New Zealand Corporation is committed to a fast tracked redevelopment programme.
In short, Auckland City Council earned $83 million for the sale of 1671 public housing units. The deal didn’t increase the total amount of housing in the city, as it didn’t release any land for new development or more intensive redevelopment. Furthermore, although Housing New Zealand was able to keep the units available as social housing, it probably had a bit less money to build new social housing in Auckland that year.
However, as I found when I looked at the benefits of alternative options for Chamberlain Park, the Council could retain a third of the golf course as a new public park and still earn more from selling the land for housing development. Even accounting for the fact that house prices have approximately doubled since 2002, it’s not even close – the golf course is worth about 50% more than the council housing ($240m vs ~$160m).
In 2002, when Council decided to sell some assets, the “opportunity cost” of not considering selling a golf course was having to sell the council housing instead. But the same choice also applies in reverse in the present day. If Auckland Council wanted to get back into the social housing game, to alleviate the impact of the city’s current housing affordability challenges, perhaps it could fund it with the proceeds from golf course sales?
1600 council flats or a single golf course: which do you think has a greater social value?
Last week, I took a quick look at the relationship between gentrification and the preservation of historic buildings. People often argue that preserving old buildings as they are is a good way of preserving the culture and community of an area.
This does not seem to be true in practice. For example, regulating to preserve villas in Ponsonby didn’t prevent the suburb from gentrifying. It may have actually contributed to gentrification by making it more difficult to build new housing to serve increased demand for living there. In this context, every lawyer moving into the area inevitably displaced an artist, musician, or working-class family.
So there seems to be some confusion about what exactly is accomplished by historic preservation. And unfortunately, this confusion seems to get worse, not better, when people start using the language of economics to talk about the issue.
To give an example, here’s an article on the topic written by Donovan Rypkema, an internationally recognised consultant on the economics of heritage. (He gave an Auckland Conversations talk here in March 2015.) Rypkema identifies five “economic values” generated by heritage preservation.
I want to focus on the last two “values” that he identifies. On the one hand, he says, we know that old buildings are valuable because they sell for higher prices than similar, newer buildings:
The United States is a country obsessed with property rights. As a result, the area that has been studied most frequently is the effect of historic districts on property values. The most common result? Properties within historic districts appreciate at greater rates than the local market overall, and they appreciate faster than similar non-designated neighborhoods. The worst case is that historic district houses appreciate at rates equivalent to the overall local market.
In England, they’ve found that a pre-1919 house is worth on average 20% more than an equivalent house from a more recent era, and the premium becomes even greater for an earlier historic home. On the commercial side, the Royal Institute of Chartered Surveyors has tracked the rates of return for heritage office buildings for the past 21 years and found listed buildings have consistently outperformed the comparable unlisted buildings. Similar analyses in Canada demonstrated that 1) heritage buildings had performed much better than average in the market place over the last 30 years, 2) there is no evidence that designation reduces property values, and 3) the price of heritage houses was not affected by cyclical downturns in property values.
And on the other hand, he argues that old buildings are valuable because they provide cheap space for start-up businesses:
Small Business Incubation
An underappreciated contribution of historic buildings is their role as natural incubators of small businesses. In America, 85% of all net new jobs are created by firms employing less than 20 people. That ratio is similar in Europe and even greater in the developing world. One of the few costs firms of that size can control is rent. A major contribution to the local economy is the relative affordability of older buildings. It is no accident that the creative, imaginative, start up firm is not located in the office park or the shopping center–they cannot afford the rents there. Historic buildings become natural incubators, usually with no subsidy of any kind.
Pioneer Square in Seattle is one of the great historic commercial neighborhoods in America. The business association asked firms why they chose that neighborhood. The most common answer: it was an historic district. The second most common answer: the lower cost of occupancy.
Now, I hope you see the problem with Rypkema’s argument. Old buildings cannot simultaneously be both cheap and expensive, like some kind of Schrödinger’s historic preservation district. Either the buildings are commanding high prices – and thus attracting well-heeled tenants – or they aren’t.
Due to its internal inconsistency, Rypkema’s argument would seem to imply that there is always a case to preserve any old building. (Or any new building, for that matter.) If the building is expensive: Preserve it, it’s valuable to its owners! If the building is cheap: Preserve it, it’s valuable to its occupants!
But I don’t think that makes sense. As I wrote last week, we should be more interested in the social and economic processes going on within buildings, rather than the buildings themselves. There is an link between buildings and social processes, but it’s not very direct. That’s because an individual building can serve multiple functions over its life-span. An office building that starts life as an A-grade location for a commercial law firm may turn into a B- or C-grade tenancy for small professional service firms or tech start-ups. This can also happen in reverse: a run-down old building can be refurbished to attract A-grade tenants again.
Does this imply that there is no rationale for preserving historic buildings? No – there often is a good argument for historic preservation. Many (but not all) old buildings are attractive and aesthetically pleasing. Their presence in a neighbourhood or a downtown area can have positive “spillovers” to neighbours and passers-by.
If those spillovers are large, which may be true in the case of especially notable buildings or even specific areas with a number of attractive buildings all from a single era, it can be worth regulating to preserve the buildings. But that is a case for heritage preservation that can and should be expressed directly – it’s about the aesthetics of the building and the characteristics of the place! – rather than indirectly, as Rypkema does when talking about job creation and property values and yadda yadda.
What do you think about the economics of heritage preservation?