How to reduce Auckland house prices

Arthur Grimes, the former chair of the Reserve Bank board, caused a stir with his proposal to crash Auckland house prices by 40% by building lots more housing:

My call for policies to drive a house price collapse is driven by my personal value judgement that it’s great for young families and families on lower incomes, to be able to afford to buy a house if they wish to do so. My concern is not for older, richer families, couples or individuals who already own their own (highly appreciated) house…

Research at Motu (accessible from www.motu.org.nz, and published in international scholarly journals) shows that (given current interest rates and incomes) a 1% increase in the number of dwellings relative to the population leads to a reduction in house prices of around 2.2%. Thus a 40% fall in house prices means that the number of dwellings in Auckland would have to expand by around 18% relative to the current dwelling stock. On top of that, the stock has to increase to reflect population growth. So with, 2% population growth per annum, the stock of dwellings in Auckland would have to increase by roughly 30% if prices were to fall by 40% over the next 6 years.

There are currently around 500,000 dwellings in Auckland. A 30% increase in dwelling numbers over 6 years translates into an extra 150,000 houses over that time – i.e. 25,000 extra houses per year for each of the next 6 years. This estimate contrasts with much smaller (and nonsensical) estimates of housing shortages that are often quoted. The reason is that those smaller estimates (e.g. 10,000 extra houses) would just leave prices where they are!

Now, I’m not quite as confident that the spillovers from a large drop in housing prices could be contained without significant effects on financial stability or the real economy. And there are definitely many challenges to technical feasibility, including zoning, infrastructure capacity, and availability of builders. But I fully agree with Arthur’s framework for thinking through supply and price dynamics.

It’s a welcome break from much of the usual commentary about the supply and demand balance in Auckland’s housing market. For example, it’s common to hear statements like, “Auckland’s population grew 22% between the 2001 and 2013 Censuses, while the number of dwellings in the city increased by 19%. Therefore we’re only 3% short.”

I’d describe this as the “quantity surveying” approach to market analysis – i.e. count up the number of people, count up the number of homes, and compare the difference. It’s not good economics, as it completely fails to consider:

  1. Desired outcomes from the housing market: As Arthur observes, an appropriate goal is for young people and low-income families to be able to access the housing market. We’re trying to achieve lower prices, not a certain ratio of dwellings to people.
  2. Underlying relationships between supply, demand, and prices: As Arthur points out, in order for prices to fall, the housing stock must be rising faster than population. Exactly how much faster depends upon the degree to which there is pent-up demand for housing that hasn’t been met in the past.

With that in mind, let’s take a closer look at Arthur’s figures. He argues that:

the stock of dwellings in Auckland would have to increase by roughly 30% if prices were to fall by 40% over the next 6 years… [which] translates into an extra 150,000 houses over that time – i.e. 25,000 extra houses per year for each of the next 6 years.

Why is an additional 30% increase in dwellings needed when Auckland’s population has only grown by around 30% over the last 15 years? Doesn’t that seem a bit too large?

From a quantity surveying perspective it does, but from an economic perspective, it’s totally sensible. To see why, we need to think about what rising housing prices mean. If the supply of a good – housing in this case – is constrained, prices must rise until some potential buyers give up and go elsewhere. In the Auckland housing market, that could mean:

  • Moving elsewhere in New Zealand – surely a factor behind recent price rises in Hamilton, Tauranga, and beyond
  • Moving overseas – a total loss of that person’s capabilities to the NZ economy
  • Staying in Auckland and living in overcrowded or unsafe housing – which disadvantages them and costs the public health system and social agencies
  • Living in a car or on the streets – it’s frankly appalling that I even have to mention this.

The fact that Auckland’s prices have been rising more rapidly than prices in the rest of the country (which are affected by the same bank lending conditions and macroeconomic trends as Auckland) is an indication that price-driven rationing is probably occurring. There is likely to be a significant amount of pent-up demand for Auckland housing – and if we figured out how to meet it, the city would get bigger. (Leading to, among other things, increased agglomeration economies.)

Finally, it’s worth discussing Arthur’s thoughts about where new housing should be constructed. He takes the SFBARF view: build absolutely everything everywhere:

So how can we get these extra houses and where can they go? Some people favour intensification and some favour expansion of the city’s footprint. The size of the task means that both are required.

Auckland is lucky that it has plenty of farmland around it and – contrary to popular myth – farmland is almost worthless in farming uses compared with residential use. Expansion is therefore required, but with a proviso. A change in the zoning of rural land to residential gives the existing landowner a massive uplift in value – i.e. a multi-million dollar gift from the community. To my mind, this value should accrue to the community that grants the zoning change. The Public Works Act could conceivably be used (or changed) to enable Council to buy rural land at a premium (say 50%) above the rural land value and then all extra value uplift would accrue to the Council to be used for infrastructure and services for the enlarged community.

Auckland also has plenty of opportunities for intensification in areas where developers would wish to intensify and where people wish to live. For instance, Tamaki Drive is ready made for high-rise apartments where tens of thousands of people would no doubt wish to purchase apartments. Of course climate change may make development on Tamaki Drive a risk, but a few blocks back from the sea – on the ridges overlooking the harbour – would work just as well. Lift the restrictions on the heights of new developments, and I expect that we would see an utter transformation in the intensity of housing from Orakei through to Glendowie.

Why can’t we do it with sprawl alone? There are two answers. The technical answer, which we’ve extensively covered on the blog in the past, is that new infrastructure to greenfield areas is expensive and time-consuming to develop. The time lag to intensify sites that are already served with infrastructure can be smaller, provided that consenting is straightforward.

However, the economic answer is, to my mind, even more important. Research into the determinants of property prices in Auckland consistently finds that proximity to the coast and proximity to the centre are two of the most important attributes for buyers. People value the amenities that come with coastal living – that’s a significant part of the attraction of being in Auckland – and they value the consumer amenities and employment accessibility that are concentrated in the centre.

A sprawl-only plan may work from a quantity surveying perspective – it would raise the ratio of dwellings to people – but it would mean growing away from the coast and away from the centre. The next swathes of farmland to be developed – east of Flat Bush, west of Orewa, and north of Kumeu – are all a long way inland.

This will work for some people, but for many it will miss the point of living in Auckland. If we are to meet growing demand, we will also have to think hard about how new residents will access to the city’s man-made and natural amenities.

Remember the last time house prices crashed 40%?

More commentary on this later on, but for now I’m just going to drop in some data.

Former Reserve Bank chair Arthur Grimes commented last week in The Spinoff:

In March 2016, the REINZ Auckland median house price reached $820,000. Four years previously, it was $495,000 – that’s a 66% increase in 4 years. What’s more alarming is that in 2012, many people considered that house prices were already getting out of reach for most people. That was particularly the case for young people and low income earners.

That extraordinary increase – coupled with the already high level in 2012 – was behind my call to a recent Auckland Conversations event that policy-makers should strive to cause a 40% collapse in house prices to bring the median back to around $500,000.

This sounds like a bit of a crazy idea. But the even crazier thing is that it’s happened before.

In the early 1970s, New Zealand experienced a rapid increase in house prices caused by, among other things, a swift run-up in immigration and a shortage of builders and building materials. Between 1971 and 1974 real house prices increased by 60%. This caused alarm, and the government responded by loosening planning controls to allow more flats to be built in cities. Then the 1973 oil shock hit, net migration turned negative, and the economy entered into a prolonged slide. (Thanks Muldoon!)

From 1974 to 1980, house prices fell by around 40% in real terms. By the end of the decade houses were no more valuable than they had been at the start. That’s shown in the following graph, which I’ve compiled from Reserve Bank data on long-run house prices and consumer price inflation.

Real house prices 1962-2015 chart

In principle, the same thing could happen today, given the right confluence of supply and demand shocks. But there’s an important difference between the 1970s and the 2010s: consumer price inflation.

Back then, overall price levels were inflating at double-digit rates. As a result, all that it took to get house prices back in line with wages (and prices for everything else) was for them to stand still for a few years. In dollar terms, house prices actually held constant from 1974 to 1980, while prices for everything else increased around them.

Today, consumer price inflation has dropped to almost zero. This means that getting real house prices back in line with incomes, at least in the short term, will require prices to fall in dollar terms. That is, understandably, a scary prospect for politicians, bankers, and homeowners. But it could happen.

Geography and housing supply dynamics

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:

AKL from space

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:

NZIER Auckland's narrow geography

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.

Double EMU Orakei Basin

Briefing papers 2: “Understanding housing affordability”

AUT’s Briefing Papers initiative has kindly allowed us to syndicate their recent series on housing. The second briefing paper is written by Motu Institute economist Arthur Grimes:

Housing affordability is a multi-faceted, complex issue. Concentration on just one aspect of the issue – be it housing supply, land supply, interest rates, construction costs or migration – will miss important aspects of why house prices vary in different locations at different times. In this briefing paper, we illustrate how differing facets of the housing market combine to produce diverse housing outcomes.

Common sense and observation of recent trends indicates that house prices reflect variables such as: population and migration, land availability (which is affected by both geographical and planning constraints), construction costs and financial factors (credit availability and interest rates). Government-funded rent and mortgage subsidies will also affect how much people can afford for housing and so will affect prices. These factors are included here in a simple framework, providing a systematic approach to understand house price outcomes. The approach is based on, and extends, published papers by the author[1] as well as other influential papers on housing markets.

Four important relationships

The framework draws on four simple relationships that are at the core of determining housing market outcomes. These four key relationships combine to give outcomes (at the city level) for: house prices, population, land prices, and the housing stock (i.e. the number of dwellings in the city). These outcomes are driven by developments in other factors such as finance costs, construction costs and natural and civic amenities.

The first key relationship is for house prices. Theoretical and empirical work shows that house prices are determined primarily by the ratio of population to the housing stock and by finance costs. As the population rises relative to the available housing stock, house prices increase since people have to bid more to purchase (or rent) a dwelling. As interest rates (and other costs of servicing a mortgage) decline, people can afford to increase their expenditure on housing, so house prices rise. Another (often overlooked) feature of financing costs may also be very relevant for house prices. The greater are government-funded subsidies such as accommodation supplement, the greater is the amount that most renters and lower-income house buyers can afford to spend on housing, so rents and house prices will increase in price.

The second key relationship is for regional population. People are attracted to regions that have high wages, attractive natural amenities and attractive civic amenities. Their choice of location is also affected by the cost of housing (both rental and owner-occupied). If population is very responsive to housing costs then extra supply of dwellings may have little effect in dampening house prices since demand (through internal and external migration) will increase to fill the extra dwellings at near existing prices.

The third key relationship to consider is the responsiveness of new housing supply to changes in prices and costs. Over time, the supply of houses increases (or stagnates) until such time as the market price of houses equates to the sum of all costs of producing a new house. These costs include the price of land associated with the dwelling (i.e. the “lot price”), construction and other costs (including regulatory costs),[2] plus a normal rate of return on capital. The sum of these costs ultimately equals the house price, with changes in the lot price being the most important factor in equating house prices to costs.

This brings us to the fourth key relationship, which is for lot prices. For a given city, the average lot price rises as the local population rises. As the population increases, the price of existing land close to the city centre (or to town centres) becomes more sought after so increases in price. While the lot price on the urban fringe may stay low – though this will be determined crucially by the strength of planning and geographic constraints – the increased price of land in existing parts of the city will increase the average lot price of the city.

The four relationships described above all relate to long term outcomes. While short term outcomes may diverge temporarily from the long term relationships (e.g. due to “fads” or to short term migration swings), an understanding of these four relationships enables us to trace through how economic shocks (e.g. a change in global interest rates) or policy shifts (e.g. a change in costs levied on developers) eventually affect house prices, land prices, population and the housing stock.

Examples

We demonstrate the inter-relationships within the housing market by adopting a systems approach that incorporates all four long term relationships just described. We can use this system to evaluate the effects of various developments on housing outcomes. We base our model on published estimates of the effects of the various factors on New Zealand housing market outcomes.[3] Informed by recent Auckland experience, we allow the lot price to form half of the total house price (e.g. a $700,000 house comprises land worth $350,000 and a building worth $350,000). We do not have strong evidence on the effects of population increase on land prices, but assume initially that a 1% rise in population results in a 1% rise in average land values; we also assume that a 1% rise in house prices results in a 0.2% decline in population as people migrate out of the city.

These parameters give us a baseline estimate of responsiveness of the housing market to specific developments. The development that we will concentrate on here is an increase in the number of people wishing to move to Auckland. The assumed increase in population is equal to 10% of Auckland’s existing population holding all other factors constant (we term this a “population shock”). In fact, all other factors will not be constant and the adjustments to these other factors (such as house prices) will affect the final number of people who end up moving to the city.

The first group of bars in the accompanying figure show the baseline estimate of the impact of a 10% flow of people to Auckland on house prices, land prices, actual population and the number of dwellings. Land prices rise (by an estimated 9.1%) in response to the influx of people and this land price rise pushes up house prices by 4.4%. The rise in house prices has two effects. It leads to greater house-building, though this is constrained somewhat by the higher land prices, so the number of dwellings increases by only 6.9%. Higher house prices also deter some people from moving to Auckland, so the population rises by around 9% rather than the full 10% of people initially wanting to move to Auckland. There are now more people per house than previously since the population rises faster than does the housing stock (i.e. crowding increases).

The second group of bars simulates the same population shock but now we assume that land prices are very responsive to the extra population[4] (e.g. because planning laws are very restrictive in allowing extra land for housing). Now we find that land prices skyrocket by almost 25% in response to the same population pressures. This leads to an 11.6% rise in house prices. These house price rises curtail the population inflow, so that population increases by only 7.6% (rather than the 9.1% in the baseline simulation). The skyrocketing land price restricts new housing development so that the stock of dwellings now increases by only 2.4% and household crowding becomes much more severe than before.

Thirdly, we assume the same responsiveness of land prices to population as in the baseline simulation, but now change the responsiveness of population to house prices. We now assume that every 1% rise in house prices reduces population by 1% (rather than by just 0.2% in the previous two simulations). This yields the third set of bars in the figure, again in response to the initial 10% population shock. The rise in land and house prices following the initial population pressures now deters people from settling in Auckland so that the population increase is just 6.6%. The reduced population inflow means that there is less pressure on house prices which now increase by just over 3%. Crowding pressures are no longer as severe as in the other cases as the housing stock increases by 5%, just short of the population increase.

What do we learn from these examples?

These simulations tell us that each aspect of the housing market interacts with each other aspect as a system. One cannot look just at housing supply, or housing demand or immigration to judge its impact on housing outcomes. All these aspects must be considered together. The same shock (e.g. a sudden rise in the number of people wishing to settle in Auckland) can have very different housing outcomes depending on the responsiveness of some factors to others.

Two facets of responsiveness that we have concentrated on here are: (i) the response of population to house prices, and (ii) the response of land prices to population. If population is very responsive to house prices, then house and land price movements will be more muted than when the responsiveness is low. This factor essentially reflects one aspect of the demand for housing.

The second factor relates to the supply of housing. If land prices are very responsive to population size, then house and land prices will react strongly to an influx of population. That the same shock can lead to either a 6% rise or a 25% rise in land prices depending on just these two factors shows the importance of considering all responses in determining what happens to housing outcomes following economic shocks.

We may have little power to affect how people’s location choice responds to house prices, but we do have some ability to affect how responsive land prices are to population pressures. Planning laws that constrain the amount of greenfields land available for new housing or that limit the ability to intensify in existing areas (i.e. the ability to use less land per dwelling) increase the responsiveness of land prices to population inflows. The increased rise in land prices then induces a larger rise in house prices for the same population shock.

Thus while many demand and supply factors interact with each other to affect house prices, we are able to moderate their final impacts through policy choices. It then becomes a political economy question as to whether or not we wish to implement policies to counteract a situation whereby house prices have become so high as to price many ordinary New Zealanders (and almost all poorer New Zealanders) out of buying a house in Auckland. Our work shows that we do not need to accept this situation.

One avenue to help address it is to increase the availability of land (both through enabling intensification and opening up greenfields land) so as to bring land prices and house prices back down to more affordable levels. Even then, however, we have to consider how all the factors appearing in our four relationships interact with each other to understand the overall effects of such a policy change on the outcomes of policy interest. If population flows are rather unresponsive to house prices, freeing up the land supply should bring down house prices and have little effect on population levels. By contrast, if population flows are very responsive to house prices, then freeing up land supply will be reflected principally in an influx of population with only a secondary effect on land and house prices. The responsiveness of population is an empirical issue, but one that may be affected by some policy settings such as migration policy and housing assistance policies. For instance, if housing assistance does not rise with an increase in housing costs, then a house price or rent rise may deter significant numbers of people from living in Auckland. Again, it is a political economy issue as to how the populace and policy-makers wish to treat these various outcomes.

graph-9

 


[1] Arthur Grimes & Andrew Aitken (2010) “Housing Supply, Land Costs and Price Adjustment”, Real Estate Economics, 38(2), 325-353; and Arthur Grimes & Sean Hyland (2015) “Housing Markets and the Global Financial Crisis: The Complex Dynamics of a Credit Shock”, Contemporary Economic Policy, 33(2), 315-333.[2] An analysis of regulatory costs in relation to Auckland housing is reported in: Arthur Grimes & Ian Mitchell (2015) “Impacts of Planning Rules, Regulations, Uncertainty and Delay on Residential Property Development,” Motu Working Paper 15-02, www.motu.org.nz.

[3] For instance, based on Grimes and Hyland, op. cit., we assume that house prices rise by 2.2% if the population rises by 1% relative to the housing stock in a city.

[4] Specifically we now assume that a 1% rise in population induces a 3% rise in land prices.

The urban planning conundrum

Since I moved to Auckland, I’ve been trying to make sense of local trends in house prices. Why have they risen over the last decade? Will they keep going up, or crash back down to earth? What’s driving all this?

Over the last few years, a lot of the focus has been put on the role of planning regulations in pushing up prices. I’m sympathetic to this focus, as it looks like there may be a few barriers to building the kind of city I’d like to live in. But I also suspect that the causes are a bit more complex than planning alone. After all, housing markets are big, confusing (and confused) things.

Others are more confident that planning was what done it. For example, the annual Demographia Housing Affordability Survey states, quite confidently, that planning regulations – an “institutional failure at the local level” – are the main cause of high house prices:

The purpose of the Demographia Surveys is to alert the public and policy-makers if housing exceeds 3.0 times annual household incomes, that there is institutional failure at the local level. The political and regulatory impediments with respect to land supply and infrastructure provision must be dealt with.

Former Reserve Bank governor (and former leader of both National and ACT) Don Brash made matters even clearer in his foreword to the 2008 edition:

The affordability of housing is overwhelmingly a function of just one thing, the extent to which governments place artificial restrictions on the supply of residential land.

In short, planning rules – especially metropolitan urban limits – are bad. Very bad. They are the main reason that houses in some places are unaffordable, which Demographia defines, somewhat arbitrarily, as median house prices that are more than three times the median household income:

Demographia affordability categories

Now, according to Demographia, Auckland currently has a “median multiple” of 8.2: “severely unaffordable”. Based on their figures, the median house price in the city at the end of 2014 was $613,000. (This actually seems low – perhaps they’ve converted it to USD?)

In order for Auckland to meet Demographia’s definition of an “affordable” market, house prices would have to fall by $390,000, or 63%. Given that they think that urban planning/metropolitan urban limit policy is the main cause of high house prices, we can take this as their estimate of the cost of those policies. $390,000, per house.

This is obviously a very large number. If house prices fell back to the “affordable” level, it would have ruinous effects on NZ’s financial system and household wealth. (Frankly, this does not seem like a very good outcome.)

So that’s Demographia’s estimate. Recently, several New Zealand economists have taken a more detailed look at the costs of planning regulations in Auckland. For the most part, they analyse the impact of planning rules that were put in place by previous councils:

  • Motu’s paper on the costs of planning regulations concludes that they add between $32,500-$60,000 to the cost of a new standalone house and $65,000-$110,000 to the cost of a new apartment. These figures were sourced from a survey of developers and accounted for the impact of a number of individual rules ranging from balcony rules to section size controls.
  • NZIER’s paper models the impact of urban limits and height/density limits. (This paper uses a similar approach to the one I discussed here.) It concludes that these two rules cost households the equivalent of $1800 per annum. In present value terms, this is somewhere on the order of $30-40,000 per household (depending upon what discount rate you use).

[Disclaimer: I know the authors of both studies and have a great deal of respect for their work. In a professional capacity, I provided comments on earlier drafts of both papers. This is somewhat unavoidable given the size of New Zealand…]

Here’s a chart comparing Motu and NZIER’s estimates of the cost of planning regulations with Demographia’s estimate:

costs of planning regulation chart

In other words, these estimates suggest that Auckland’s planning regulations explain only 10-30% of the difference between Auckland house prices and a median multiple of 3. Even if we add together the estimates from the two papers, we don’t get anywhere near explaining the gap.

So: whose estimates of the cost of planning should we believe?

Personally, I trust Motu and NZIER’s analysis, as it’s backed up by empirical research and/or economic modelling, whereas Demographia’s is mainly justified by rather repetitive and self-referential haranguing. Consequently, I don’t think we can conclude that planning regulations are a sufficient explanation for Auckland’s relatively high house prices.

That doesn’t necessarily mean that we should be sanguine about planning regulations. While things will change under the Unitary Plan, the costs associated with existing rules are reasonably large. And, in contradiction to Demographia’s claims that constraints on greenfield land supply are the biggest problem, both papers find that existing regulations place higher costs on higher-density developments.

The Motu paper finds that existing planning rules add twice as much cost to apartment developments as to standalone houses. Similarly, the NZIER paper finds that height limits (and other controls on density) are slightly more costly than urban limits. (This is partly because Auckland is mostly surrounded by water, meaning that preventing land from being used efficiently is much more deleterious than it would be in a city with more land.)

If these issues aren’t addressed, it may be more difficult to get the Auckland that we want: a city that gives people better housing choices, that lets them live in places that are accessible to jobs and amenities, and which efficiently accommodates the next Aucklander.

What do you think about the cost of planning?

Parking is massively oversupplied in US cities

When people discuss the costs of car-centric transport systems, they tend to tend to talk about congestion, fuel costs, crashes, or, if they’re environmentally-minded, carbon emissions.

However, one of the largest costs of auto-dependency is hidden in plain sight: the cost of providing parking spaces. The financial cost of providing parking spaces can be staggering. According to Todd Litman, “most communities have three to six parking spaces per vehicle (one a home, one at the worksite, plus spaces at various destinations such as stores, schools and parks)”. As car parks occupy around 30 m2 apiece, this means 90-180 m2 per car.

In Auckland, where suburban land prices range from around $250/m2 (west and south Auckland) to over $1000/m2 (inner isthmus, lower North Shore), surface parking would cost $22-90,000 per car. That’s more expensive than the cars that occupy those spaces!

mcc-coloured

Buildings are in red. Parks are in green. Everything else is roads and carparking.

Moreover,  land that is devoted solely to cars cannot be put to higher and better uses, such as dwellings, businesses, or public spaces. In a successful city, we would expect the value of those other uses to continue rising, meaning that the opportunity cost of car parking will also rise. Space is expensive in cities, and parking is an inherently inefficient use of land.

This spatial inefficiency is exacerbated by the fact that many cities have ended up with more car parking than is necessary. Eric Jaffe in Citylab reports on some important new research on parking oversupply in US cities:

Some new research reminds us just how oversupplied parking really tends to be in American metro areas: in a word, enormously. Rachel Weinberger and Joshua Karlin-Resnick of Nelson\Nygaard Consulting Associates analyzed parking studies of 27 mixed-use districts across the United States and found “parking was universally oversupplied, in many cases quite significantly.” On average across the cases, parking supply exceeded demand by 65 percent.

[…]

The researchers focused on districts with both residential and retail developments in a variety of settings—17 suburbs, 6 cities, and 4 towns—mostly in New England or California. (Interestingly, a third of the areas were documented as having the impression that local parking was scarce.) By looking at previous parking studies in these areas, as well as satellite imagery via Google Earth, they identified existing parking supplies and peak weekday and weekend demands.

Critically, the researchers also took into account the accepted practice of supplying 15 percent more spaces than necessary—a sort of buffer zone that reduces the congestion caused by drivers circling for spaces.

In all 27 districts, spanning places with 420 spaces to those with 6,600 spaces, Weinberger and Karlin-Resnick found an oversupply of parking over and above the buffer zone. The oversupply ranged from 6 percent up to 253 percent across the study areas (below, the highest over-suppliers). And in the nine areas that had believed parking to be scarce, the oversupply ranged from 6 percent to 82 percent.

us parking oversupply

These are pretty extraordinary findings. An average oversupply of 65% means that two out of every five parking spaces are, essentially, useless. We would never tolerate such waste in any other part of our economy – if, for example, two out of every five meatworks were sitting idle, we would start shutting down the unprofitable ones.

I highly recommend reading the rest of the article, as there are a number of other interesting findings in the research. One in particular stood out:

Interestingly, a third of the areas were documented as having the impression that local parking was scarce.

The researchers found that this was not correct – parking was in fact oversupplied in each one of these areas. Policymakers and businesses in these areas significantly overestimated the amount of parking that was truly required. It’s common to hear retailers complaining about the loss of on-street parking for cycle lanes and bus lanes, but the evidence suggests that we should treat their claims with caution.

The same thought occurred to me when reading the recent Motu paper on the cost of planning regulations. Based on a survey of 16 Auckland-based developers, the authors concluded that:

There were diverse views of the impact of car parking requirements on developments, reflecting differing development types. CBD apartment developers, particularly those developing at the affordable end of the market, prefer to include fewer car parks. They saw car parks as a cost to the development as the market value of a park was less than the cost of including them on the development. In contrast to CBD apartment developers’ views, suburban apartment developers tended to favour offering more car parks.

However, some of the comments from developers made me wonder whether they had also fallen into the trap of overestimating parking requirements:

“The optimal number of car parks in a suburban apartment development targeting the mid to upper end of the market is 2 to 3 per unit with additional common parking for guests”

Now, I haven’t been keeping a close eye on suburban apartment developments, but I’d be extremely surprised if developers were actually building three car parks per unit. If anything, the trend seems to be for fewer car parks. For example, the Merchant Quarter apartments in New Lynn have unbundled parking, while the apartments planned for Alexandra Park will have only one car park apiece.

Do you think Auckland has a parking oversupply? If so, what should we do about it?

What will happen as we build our “missing modes”?

Last year I started to take a look at demand for new transport investments. I found that demand for toll roads has massively underperformed, showing that people are unwilling to pay for new roads. On the other hand, demand for new public transport facilities has taken off more rapidly than projected. All alternative modes are growing rapidly in Auckland, while driving has stayed flat for the past decade.

ResizedImage600304-FutureDemand-Diagram1

Why won’t it grow? We thought it would grow!

The conundrum is, basically: Why is this happening? I argued that declining willingness to pay for new roads is consistent with a saturated market – i.e. all the people who value driving are already on the road. But that doesn’t explain why demand for public transport, walking, and cycling has been so robust over the past decade.

Here, I want to investigate a potential reason for the boom in demand for Auckland’s “missing modes”: the “complete network” effect. I discussed this briefly in a post on the benefits of cycle investments:

Importantly, the researchers found that a larger, more ambitious programme of cycle upgrades will deliver a higher benefit-cost ratio than a smaller programme. This is what economists sometimes call the “complete network” effect – in effect, the more places you can get to easily and safely on a bicycle, the more likely you will be to cycle. (This is also why Facebook has so many users: You have to have an account because everybody else also has an account!)

Here, I want to take a deeper look at demand for relatively new, expanding networks. A 2008 working paper by Arthur Grimes (“The role of infrastructure in developing New Zealand’s economy”, pdf“) provided some historical data on how demand evolved for two important 19th-century infrastructure networks: telegraphs and railways. Grimes suggests that growth in demand on these networks followed an “S-shaped pattern” of rapid initial growth, a period of modest growth, and then a second period of rapid growth after the network reached a certain size:

A forecaster in 1866 would have had little ability to judge the extent of use of the new infrastructure over subsequent years given the lack of precedent for it. A forecaster in 1896, having seen 15 years of constant messages per person may confidently have forecast a stable outlook for that variable over the coming decade. He would have been mistaken almost by a factor of two within ten years.

Grimes’ data is summarised in the following graphs, with telegraphs on the left and railways on the right. The bottom two graphs show the “S-curve” in per-capita demand clearly:

Grimes (2008) infrastructure uptake curve

Source: Grimes (2008)

This nonlinear pattern in demand is likely to reflect two factors. First, growth in demand is fast at first because infrastructure builders start by constructing the best projects – i.e. the ones that will attract the most customers quickest. Once these projects are built, the next ones attract demand more slowly – roughly at the rate of population growth.

Second, the later upturn in the curve occurs after the network reaches a sufficient “critical mass” to become increasingly useful for more purposes. This is the complete network effect in action: filling in the missing links in a network can enable it to serve many more trips (or messages).

I would argue that demand for Auckland’s “missing modes” is following a similar trend. So: Where are we on the “S-curve”?

First, we cannot expect an uptick in demand after the construction of Waterview finishes off Auckland’s motorway network. While Waterview is a sensible stopping place for expansions of Auckland’s motorway network, it is at best a marginal improvement in the city’s road networks. There are already a number of roads that connect the north and northwest to the south.

Second, in public transport, I would argue that we are probably on the tipping point to sustained rapid growth:

  • We’ve got an existing bus network which supports steady if not spectacular growth in demand. Auckland Transport is currently in the process of reorganising it into a New Network that provides more frequent all-day services that serve many more destinations than before. This could easily lead to a boom in bus trips.
  • We have an existing rail network that has experienced a revival in demand since the development of Britomart in 2003. The City Rail Link will transform the usefulness of the rail network by breaking out the bottleneck in the city centre and enabling a doubling in train frequencies.
  • New rapid transit infrastructure can capture significant new demand when it’s made available – as the Northern Busway has done.
perth-patronage

Improving rail networks can experience big jumps in demand.

Third, the cycling network is probably a few steps behind in the process. There’s likely to be a period of steady if not spectacular growth in demand as new projects come online, but under NZTA and AT’s current investment plans there will be gaps in the network for a number of years. At a certain point, though, the gaps between safe cycle infrastructure will be filled in, enabling rapid growth in demand as cycling becomes safe and useful for many more trips.

When cycling seems safe and easy, lots of people cycle.

When cycling seems safe and easy, lots of people cycle (Source)

In short, the “S-shaped pattern” of uptake for new transport networks will shape demand within New Zealand’s cities following new investments in public transport, walking and cycling, just as it has done on previous infrastructure networks. The only question is: Are we willing to invest in our “missing modes” to make them increasingly useful for more and more trips?