A few weeks ago, I took a look at property taxation in the US, Canada, and New Zealand. I found that Auckland homes are taxed lightly by comparison – rates average 0.39% of house value. Property tax rates are twice as high in most of the other cities I looked at. In some cases – e.g. Houston, where property taxes average 2.31% of home value – they are much, much higher.
This should come as no surprise to anyone who’s read the literature. For example, Grimes and Coleman (2009) find that New Zealand under-taxes property:
McLeod et al (2001; p.26) showed that the proportion of taxation raised through property taxes was lower in New Zealand than in Australia or the United States. Taking into account all levels of government (federal, state and local), Grimes (2003) found New Zealand’s share of property taxes in government revenue was relatively low, at 5.7%, compared with a (20 country) OECD average of 8.3%. As a share of GDP, New Zealand’s property tax share was also relatively low at 1.8% compared with the average rate of 2.4%.
They go on to suggest that introducing even a relatively small land tax could result in fairly large changes to land prices. It’s intuitively sensible that higher property taxes would discourage people from bidding up prices – the more they pay for land, the more they pay in taxes!
Stu recently took a look at the same research, and some of the broader trends, and concluded that higher property taxes could take some heat off the housing market. But how much does property tax really matter for housing affordability?
To get a rough sense of the relationship, I’ve put together a chart showing the relationship between property tax rates (x axis) and Demographia’s “median multiple” measure of house prices relative to incomes (y axis). It includes data on all 59 US, Canadian, and New Zealand cities with a population over 1 million.
Notice the substantial negative correlation between property taxes and median multiples. There is a strong tendency for places with lower property taxes to have higher house prices, and vice versa. The least “affordable” places all have relatively low property taxes.
Overall, this chart suggests two things: First, New Zealand’s relatively low property taxes may contribute to our relatively high house prices. Notice how Auckland’s house prices seem to fit the overall trend in the data.
Second, as I’ve previouslyargued, Demographia’s analysis is largely meaningless as they have failed to account for the full range of explanatory variables, from interest rates to tax policies to economic fortunes.
Here’s another view on the same data. I’ve taken the natural logarithm of both variables to smooth out the relationship, and put a trend-line through the data points. This simple bit of analysis suggests that:
About 44% of the variations in median multiple can be “explained” by differences in property tax rates. For a bivariate regression, this is quite high.
The slope of the regression line suggests that, within this sample of cities, a 10% increase in the property tax rate is associated with a 4.6% reduction in the median multiple. Again, that’s a quite strong relationship.
I don’t think we can draw any firm policy conclusions from this data, but it certainly suggests that our low property taxes are worth investigating as a cause of our high house prices. In the words of xkcd, “Correlation doesn’t imply causation, but it does waggle its eyebrows suggestively and gesture furtively while mouthing ‘look over there’.”
Finally, it’s worth taking a closer look at the four US cities with the highest median multiples – the top data points on the left hand side of the first chart. They are all large cities in California – San Francisco, Los Angeles, San Jose (i.e. Silicon Valley), and Sacramento (the state capitol). They provide a great illustration of why failing to account for multiple, correlated explanatory variables can undermine an analysis of house prices.
There’s also a great irony underlying the use of LOS [traffic Level of Service] as part of CEQA’s environmental impact checklist. It seems self-evident that bike projects are favorable to the environment, but the use of LOS to evaluate them can sometimes imply quite the opposite. The person who filed the 2005 lawsuit against the San Francisco master bike plan, for instance, suggested that because bike lanes raise LOS they also raise congestion and car idling, and thereby cause pollution.
That’s not the only contradictory aspect of LOS. Case in point: a developer whose building fails an LOS threshold can mitigate the environmental impact by widening the street, which of course would attract more cars and pollution. So instead of encouraging dense development and lower vehicle mileage — the hallmarks of a transit-first city — San Francisco’s use of LOS as part of CEQA actually discourages livable design. In a three-part series on LOS at Streetsblog, one transportation consultant called LOS the “single greatest promoter of sprawl and the single greatest obstacle to transit oriented development” in California.
However, CEQA and its absurd requirements for traffic assessments (etc) aren’t the only thing going on in California. State-level laws have also constrained local governments’ ability to raise property taxes. Proposition 13, a citizen-initiated referendum passed in 1978, caps property tax rates at 1% and fixes them to the value of the house at the time it was purchased, plus a 2% annual increment for inflation.
This has had a number of perverse effects, including stripping away funding from California’s formerly excellent primary and secondary education and setting it on a path of decline. (Prop 13 is basically exhibit A in the case against binding referenda.) It has also distorted the housing market. Because home-owners know their property taxes won’t increase if the value of their house increases, they may be more willing to speculate on capital gains.
A 1982 paper by economist Kenneth Rosen offers empirical support for this hypothesis – he found that reductions in property tax rates were almost immediately followed by proportional increases in house prices.
Consequently, it would be foolish to analyse the Californian housing market without attempting to control for both taxation and planning policy. If you only looked at one policy, your conclusions would be biased by mis-attributing the effects of the other policy. (That’s precisely what Demographia seems to have done, by the way.)
What’s true for California is also true for New Zealand. I find it hard to take seriously the claims of people who attribute housing affordability solely to regulatory policy and fail to consider the potential impact of our low property taxes.
What do you make of the data on property taxes and house prices?
My lasttwo posts about Demographia’s analysis of house prices prompted quite a bit of discussion. I thought that it may be worth expanding on my points and clarifying why they mean that we should take Demographia’s conclusions with a large grain of salt.
Economists (and statisticians) have a term for what Demographia has done: “omitted variable bias”. This can occur when there are multiple variables that have a causal impact on an outcome. If we attempt to understand that outcome without considering all explanatory variables, we run the risk of biasing our conclusions.
Economists are trained to identify and address issues arising from omitted variable bias. Here, for example, is a comment on the topic from a widely used undergraduate econometrics textbook:
Now suppose that, rather than including an irrelevant variable, we omit a variable that actually belongs in the true (or population) model. This is often called the problem of excluding a relevant variable or underspecifying the model. We claimed in Chapter 2 and earlier in this chapter that this problem generally causes the OLS [ordinary least squares regression] estimators to be biased.
Now, I realise that’s a bit obscure, so let me be more specific. Here’s a list of (some) factors that can influence house prices, with a view on their expected impact:
Expectations for future population and economic growth
Future expectations tend to be capitalised into house prices – i.e. prices will tend to be higher in areas with better growth prospects
Consumer and natural amenities
People are willing to pay more to live in nicer places
Interest rates (and availability of finance)
Lower interest rates enable people to afford a larger mortgage on a given level of income
Construction sector productivity
Lower productivity will increase the cost of supplying new dwellings
Tax policies, including capital gains taxes and mortgage interest deductions
Taxation of capital gains will reduce willingness to invest in housing for capital gains
Tax subsidies for mortgage-holders will tend to push prices up
Other housing market policies, such as rent subsidies or public housing provision
Rent subsidies tend to be captured by landlords and thus will tend to push up prices
Ongoing construction of state housing will add supply to the low end of the market and thus constrain price increases
Urban planning policies
Policies that constrain the development of new housing in desirable areas, or make it more costly or uncertain to develop, will reduce supply and push up prices
Demographia only addresses one of those seven variables. Because they fail to account for the other six potentially explanatory variables, their estimates of the welfare impact of urban planning policies are not likely to be reliable. Without controlling for other potential explanations for high housing prices, it’s impossible to say whether their conclusions about any individual city are correct or not.
Consequently, my recommendation to people seeking to understand the impact of urban planning policies on housing costs is simple: Ignore Demographia and go read the relevant economics literature instead. For those who are interested in doing so, here are a few papers that I have learned a lot from. They apply a range of modelling approaches, but what they have in common is that they undertake a detailed analysis of rules, rather than making sweeping and unsupported generalisations:
Glaeser, Gyourko and Saks (2005) studied the impact of building height limits in Manhattan by looking at the gap between observed sale prices and the marginal cost to add another floor to high-rise buildings. They find that constraining the supply of high-rise apartments imposed a significant “regulatory tax” on residents.
Grimes and Liang (2007) took at look at land prices around Auckland’s Metropolitan Urban Limit, finding evidence of a “boundary discontinuity”. They interpreted this as evidence that the the MUL is overly restrictive.
Kulish et al (2011) developed a hypothetical model of the impact of several factors on housing and transport costs. They modelled density restrictions as well as increased transport costs and lower building productivity, finding that building height limits raise housing costs and increase sprawl. I have previously discussed their findings.
MRCagney (2013) examined the impact of minimum parking requirements in Auckland, finding that they impose a loss on developers and businesses by forcing them to over-supply parking. They also cause worse congestion, meaning that not even drivers benefit. Luke C briefly discussed this study in a post on the Unitary Plan parking rules.
My favourite economics paper on planning regulations is Cheshire and Sheppard’s 2002 study on the impact of greenbelt rules in Reading, UK. The authors observed that greenbelts have both positive and negative effects. On one hand, they limit the supply of land for new housing, which drives up costs. On the other hand, they give residents access to public open spaces, which people like. Rather than ignoring this trade-off, they used house price data to model it.
Overall, Cheshire and Sheppard did find that allowing development in Reading’s greenbelt would make people better off. However, they also found that a failure to consider the amenities produced by planning rules would have resulted in too high an estimate of the gains in wellbeing. In other words: right direction, wrong magnitude.
In light of the evidence, my view is that failing to account for urban amenities and other explanatory variables in an analysis of the impact of supply restrictions can result in two errors:
First, it can make us over-optimistic about the degree to which loosening rules will affect housing prices. That’s not to say that less restrictive planning regulations couldn’t make us better off – just that we should not expect house prices to fall by 60-75% as Demographia implies when it says that Auckland should have a median multiple of 3.
Second, it can lead to perverse outcomes, by encouraging us to eliminate rules that are serving a useful purpose. Often (although not always) planning rules are managing the external social or environmental costs associated with some developments. A proper cost-benefit analysis – which Demographia has not done – will consider both the pluses and minuses of rules.
In short, housing markets are complex, and any analysis needs to consider that fact. To reiterate my point from last week: Demographia takes an inappropriate, overly simplistic view of house prices. This may be good for grabbing headlines, but it’s not good economics.
To recap: urban economics suggests that differences in the level of the median house price to median household income ratio between cities can be interpreted as differences in livability. All else equal, people should be willing to pay more to live in cities that offer better quality of life.
But how should we interpret changes to the median multiple from year to year? If a city’s median multiple rises from 5 to 5.5, does that mean that the city suddenly got 10% more livable? Or did something else happen?
Demographia is very certain that higher median multiples are the product of one thing, and one thing only: limits on sprawl into greenfield areas. Here’s Don Brash, former Governor of the Reserve Bank of New Zealand and former head of several right-wing political parties, laying out that view in Demographia’s 2008 report:
Once again, the Demographia survey leads inevitably to one clear conclusion: 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.
With that in mind, Demographia’s data seems to indicate that housing in Los Angeles and Las Vegas (as well as many other US cities) suddenly became a lot more affordable in the late 2000s. It’s obvious that they must have removed their Metropolitan Urban Limits – how else to explain such a big drop? Oh, wait…
(It’s slightly disturbing that our Reserve Bank used to be run by a man who doesn’t believe that interest rates and credit conditions can affect house prices. But I digress.)
Here’s a graph of changes in Demographia’s median multiple estimate for Auckland since 2004. We haven’t seen the same drastic swings as in the US, where the housing bubble and bust was pronounced, but house prices have risen relative to incomes. (Although, as Stu found in his analysis of different measures of housing costs, this hasn’t flowed through to rents or mortgage payments, due in part to changes in interest rates.)
This change has been especially pronounced in the last few years. Since 2012, Auckland’s median multiple has risen roughly 22%. Does this mean that we’ve become 22% more “livable” during that time?
With all due respect to the good work done by Auckland Council and Auckland Transport since their inception, probably not. So we need to look for alternative explanations, of which there are several. I’ll focus on three in particular.
The first potential explanation is that there has been a market failure. Residential construction slumped massively during the Global Financial Crisis, with the most significant reductions occurring in the supply of apartments and attached dwellings. Here’s a graph that John Polkinghorne put together illustrating that trend:
Incomes and employment have mostly come back from the recession, but construction has been a bit slow to respond. I suspect this reflects technical constraints within the development sector, as it takes a while to organise finance, find sites, and hire the cranes, bulldozers, and blokes/blokesses in hardhats. Until they get into gear, there may be a bit of an undersupply – but one that will tend to sort itself out.
The second potential explanation is that the introduction of Auckland’s Unitary Plan has caused people to expect housing supply to be more constrained in the future. While the Unitary Plan envisages the gradual expansion of the city’s Metropolitan Urban Limit to meet new demand for greenfield suburbs, it maintains extensive controls on the supply of new dwellings in accessible, high amenity areas. Moreover, the plan actually got significantly more restrictive following the consultation process.
Transportblog has highlighted this issue a number of times before. To recap, here’s a map (from Koordinates) that shows where the Unitary Plan got more restrictive as a result of consultation. The areas in red have been down-zoned to restrict development, while areas in green have been up-zoned. The large orange areas show future greenfield land. As you can see, there are not a lot of opportunities to develop new dwellings in the isthmus and lower North Shore, while West Auckland has been happy to facilitate growth:
Timing is important here. Demographia’s figures suggest that there was a jump in house prices relative to incomes between the end of 2012 and the end of 2013. This coincides with the notification of the Unitary Plan in September 2013, which, as described above, will ease greenfield land supply while limiting development opportunities in the inner suburbs.
However, there is also a third potential explanation: that our rising house prices reflect increasing awareness of Auckland’s great quality of life. For most of the last decade, our city has been near the top in Mercer’s Quality of Living Survey. It’s been ranked third for five years running.
So maybe the story is that potential migrants and investors have observed that, by international standards, Auckland offers high quality of life at an affordable price. And they are in the process of arbitraging that away.
I’ve illustrated that process in the following graph, which shows the relationship between Demographia’s median multiple (X axis) and Mercer’s Quality of Living Survey (Y axis). The trend-line is estimated based on 2012 data. I’ve also plotted Auckland’s median multiple and Mercer index in 2015.
The red dot that represents Auckland is moving towards the trend-line, suggest that its prices are catching up with its livability.
Previous studieshave found that growth in New Zealand house prices is strongly correlated with net migration – i.e. migrants tend to bid up house prices. Net migration to New Zealand did, in fact, start picking up in 2013 – around the time that Demographia’s estimate of the price to income ratio began to rise. Perhaps this is evidence for the “amenity arbitrage” hypothesis?
So, which explanation is true? Honestly, it’s impossible to say without a lot more detailed analysis. My point in writing this is not to argue that there is a single explanation for changes in Auckland’s house prices, but to point out that there are many possible explanations. Housing markets are affected by a number of factors, and it’s inappropriate to focus on one without controlling for the rest.
This is, essentially, why Demographia’s analysis fails. Rather than articulating a model that encompasses all of the potential explanatory factors, they have settled on a single number and insisted that it must be interpreted in a single way. It’s hard to see the value in that approach. And it’s definitely not good economics.
Every year since 2005, pro-sprawl think-tank Demographia has published a new edition of its “International Housing Affordability Survey“. They report a “median multiple” measure of housing affordability that compares median house prices to median household incomes within a number of cities, mostly in the English-speaking world.
Demographia’s aim, in publishing this data, is to argue that “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.” By this, they mean building car-dependent suburbs on the urban fringe – and nothing else.
Another Demographia-approved urban paradise.
A number of people, including Todd Litman and Stu Donovan (on Transportblog), have taken aim at Demographia’s empirical analysis and choice of metrics. Unfortunately, Demographia is unwilling to open up its analysis and methodology for an independent peer reviewed, so it’s difficult to referee those claims.
Here, I want to take a look at the issue from a different perspective. Basically, the urban economics literature suggests that Demographia’s chosen measures do not mean what they think they mean. And they almost certainly do not prove the case they’re trying to make.Before I explain why, let’s start out with a quick look at the data. According to Demographia’s 2015 report:
The most “affordable” cities included the likes of Detroit (median multiple of 2.1), Cleveland (2.6), and Houston (3.5)
The “unaffordable” cities included most large Australian cities, including Sydney (9.8) and Melbourne (8.7), many “coastal” North American cities, such as Los Angeles (8.0), San Francisco (9.2), Vancouver (10.6), New York (6.1), and Boston (5.4)
All New Zealand cities were on the “unaffordable” end of the spectrum, ranging from Palmerston North (4.1) and Dunedin (4.6) to Christchurch (6.1), Tauranga (6.8) and Auckland (8.2).
In other words, there’s a quite large range of median multiples. This raises a quite obvious question: Why are people willing to pay so much more to live in some places? Why live in “unaffordable” San Francisco when “affordable” Houston is just down the road? Why live in Auckland when housing is relatively cheaper in Dunedin?
Why would anyone want to live in a large, multicultural city located between two beautiful harbours in a subtropical climate? Sheer madness.
Urban economists have studied this phenomenon in detail, and observed that there is an omitted variable in Demographia’s equation: the differing amenities offered by different cities. If a city offers good natural amenities or consumer amenities, people will be willing to pay more to live there. Conversely, if a place isn’t particularly nice, people won’t be willing to pay much for houses there. (Common sense, really.)
It is quite common in discussions of housing affordability to focus on the share of income being spent on housing, as if this is a natural measure of the degree to which housing affordability is a problem within an area. The spatial equilibrium assumption suggests that this measure is not particularly meaningful or helpful.
In short, urban economics suggests that we should interpret a high median multiple as an indication that a city offers great amenity for its residents, rather than an indication of bad policies. I tested this hypothesis by looking at the correlation between the (2012) Demographia median multiple figures and two international quality of living rankings. I found that there was a positive correlation between median multiples and livability.
Here’s the correlation between the median multiple (X axis) and Mercer’s 2012 Quality of Living Survey (Y axis; lower numbers indicate higher rankings). Once again, a positive correlation, with 31 data points:
In other words, high house prices relative to incomes are a good indicator that a city is a nice place to live. Rather than proving that Metropolitan Urban Limits inevitably push up house prices, Demographia’s median multiple seems to simply measure cities’ relative levels of amenity. When they argue that all cities should have a median multiple of under three, they are arguing for an absurdity: that all cities should offer the exact same level of amenity to their residents.
If we wanted to accomplish that, we’d have to destroy most of the things that make great cities great. This might make housing cheaper, but it wouldn’t make us any better off in a broader sense. That’s because it would require us to:
Bulldoze the Waitakere Ranges and use the spoil to fill in the Hauraki Gulf – to ensure that Auckland didn’t have any natural advantages over a flat, inland city like Hamilton
Dynamite the historic boulevards of Paris and replace them with American-style subdivisions and malls – to ensure that Paris didn’t offer anything that Houston doesn’t
Ban any venture capital or startup activity in San Francisco, to ensure that it doesn’t offer any agglomeration economies that don’t exist in Detroit
Build large screens over sunny cities like Tauranga and Brisbane – to ensure that they don’t have nicer weather than Moscow or Toronto.
But Demographia’s not aware of this. Their analysis is overly simplistic. The only thing it reveals is the authors’ grievous failure to understand the basics of urban economics. It’s no wonder that Demographia has never tried to have its studies peer reviewed or published in academic journals. Their claims aren’t supported by any valid conceptual model. But I guess that’s what happens when you get an urban planner and a former property developer to do an economist’s job…
Demographia is a pro-sprawl think tank in the USA that publishes density and house price data for cities across the world. They’re often seen using their statistics to argue that the only way to deliver affordable housing is with suburban fringe expansion into greenfields land.
Demographia’s approach to calculating density is simple but misleading. They have simply calculated the total number of people in each city and divided it by the total land area covered by that city – including unpopulated areas like parks and reserves. This measure of average density is actually quite irrelevant. For example, Demographia uses it to claim that Los Angeles is more dense than New York.
As Peter outlined in this recent post, what is much more relevant is the density of the neighbourhood that the average person lives in, rather than the density of the average acre of land in the city.
For argument’s sake, consider a village of one hundred acres with one hundred residents. By the measure of area weighted density the density is simple, one person per acre. Does this represent the reality of how people live? Well it could, if everyone lived alone in a separate house on an acre of land. But what if they didn’t? What if everyone lived in a single apartment block in the middle of town that sat on one acre of land? Well then the density the people actually live at would be 100 people per acre. That’s a hundred times more dense… but the same density by Demographia’s measure!
And what if it were something more complex? What if a quarter of the town lived in the one apartment building, half lived in eighth-acre sections around it, and the remaining quarter were spread out over the rest of the land? Doesn’t that sound a bit closer to the reality of most cities? One person per acre means nothing for this theoretical town, half the people live at eight times that density and a quarter at fifty times the density.
Just in case you’re still unsure, in the image below the dots represent dwellings and each box has the same number of dots in it. Overall they have the same average density however in reality they would feel like two very different places.
In short population-weighted density is a much better indicator of the density of the neighbourhoods people chose to live in, and a much better way to describe cities and housing.
With that in mind, it was a simple task to take Peter’s data and throw it on a chart. In simple terms these show how many people live in neighbourhoods (Census meshblocks to be precise) grouped by density in Auckland, Wellington and Christchurch. I’ve also picked out the density level that Demographia says each city is.
It’s easy to see a few things here. First of all we can see that neighbourhood density can vary quite a lot within cities. One number just can’t describe how people live. Second, we can see there is something of a bell curve. Most people live within the middle range of densities, those living very low or very high are small in number, but that middle is actually quite broad. Third, we can see how far off Demographia is. Their supposed summary statistic isn’t anywhere near the middle of the curve, it’s actually near the bottom in each case.
For example it seems the Demographia figure describes the density at which roughly five percent of Aucklanders live. Nine percent live at lower densities, and 86% live at higher densities. Many people live in neighbourhoods that are two or three times more dense than Demographia’s misleading average. In short, Demographia’s figures are irrelevant for the vast majority of Aucklanders (and Wellingtonians, and Christchurchers). They don’t reflect how the majority of people choose to live.
My first post suggested Demographia’s primary findings were not supported by independent evidence, such as alternative “rent-income” and “home affordability” indicators. My second post then outlined some issues with their “median-multiple” indicator (calculated as the median house price divided by the median household income).
This post will now refine some of these criticisms, before outlining some of my own ideas on the causes of housing affordability issues in New Zealand. First I wanted to tease out some of implicit assumptions that underpin Demographia’s “median-multiple” indicator, namely:
Median-matching: This issue was best articulated by James H: “the median-multiple indicator carries an assumption: that the income-earners at and around the median are the same group who demand the houses priced at and around the median, and that result can be extrapolated for other price-income pairings. As mentioned in the post, that excludes measurement for a large group of earners at many income levels who are in fact happy to rent for a range of reasons. Also I doubt whether median houses are often bought by median earners for a variety of reasons including life stages, geographical differences etc.”
Independent inputs: This issue relates to the fact that the two inputs into the median-multiple indicator (house prices are income) are actually not independent of each other. Consider a situation, for example, where most of the houses in New Zealand were being bought and sold by relatively wealthy households, and that these households subsequently experienced high income growth, while incomes for the general population remained broadly unchanged. In this situation the median house price (and median-multiple indicator) would rise simply because income growth was concentrated within the same people that were purchasing properties, rather than because housing was becoming less affordable.
The second issue is quite important, because it implies that changes in income may in fact impact on house prices. The figure below illustrates the input data used by Demographia for Australia. In this graph, we find a strong positive correlation between median household income (x-axis) and median house prices (y-axis).
This suggests that locations with high incomes have more expensive housing (surprise surprise!). More specifically, it suggests that for every $1 increase in median household income there is a corresponding $5.1 increase in median house price. While that sounds like a lot, note that income is measured p.a. whereas house prices are “total.”
My final comment is on the relevance of Demographia’s indicator. i.e. the “so what” question? It seems that the parts of New Zealand with the highest median-multiple ratios, such as Auckland, are actually attracting the fastest population growth, as illustrated below. Of course, this may reflect other factors that are at play, but it does suggest that our housing affordability (at least as measured by the median-multiple indicator) is not yet significant enough to drive people away.
Thus, population growth in these places seems to be going the other direction from what Demographia would expect – we are increasingly moving to areas that they consider to be unaffordable. Based on this evidence, I’d suggest that the median-multiple indicator used by Demographia is not a good measure of housing affordability. Instead, it seems to measure:
The degree to which income growth is invested in housing; and
Population growth (which will tend to push up property prices but suppress income growth).
It’s not clear to me that the median-multiple indicator measures housing affordability, and nor is it clear to me that the urban containment policies pursued by local governments are binding to the degree that they have major impacts on property prices. They may be – but Demographia’s indicator does not, and cannot, tell you that.
My personal view is that the primary impact of local government regulations is not through the constraints they place on land supply (i.e. urban containment), but actually through the barriers they create to the development of more compact and affordable housing. Here’s some examples of regulations pursued by local governments in New Zealand that seem likely to restrict the supply of affordable housing:
Minimum lot sizes – i.e. “all ye who have less money shall be forced to purchase land you don’t want.”
Minimum apartment sizes – i.e. “all ye who have less money shall be forced to purchase living space you don’t want.”
Minimum parking requirements – i.e. “all ye who have less money shall be forced to pay for vehicles you don’t own”.
Maximum height limits – i.e. “all ye who chose to live like rats are consigned to perish like rats – on the street.”
Heritage protections – i.e. “all ye who don’t have the money to renovate a villa shall live elsewhere.”
In my experience these policies are often more binding constraints than the availability of land. So my suggestion is that housing affordability has less to do with policies that favour urban containment (as Demographia and the National Party would have you believe) than they are to do with the plethora of policies that suppress more intensive and affordable housing. I’d go as far as to say that most of our policy settings have a systematic bias against the development of compact and affordable housing.
In this light, it seems that recent political announcements have missed the mark. National are deluding themselves into thinking that the release of land on the urban periphery will deliver meaningful and sustained reductions in the cost of land, and by extension housing. Labour and the Greens, meanwhile, seem intent on using government capital to build our way out of the problem – which is not only expensive but also runs the risk (at least on the surface) of building the wrong kinds of houses in the wrong places. None of these three parties seems to yet acknowledge that some of our issues with housing affordability may be the result of policies that prevent urban intensification.
So instead of writing the foreword to next year’s (deeply flawed) Demographia report, I’d suggest that Bill English – and other National cabinet Ministers – should be writing letters in support of proposals to develop apartments, town houses, and units in places like Milford and Orakei. And more importantly, they should be making submissions on aspects of the draft Unitary Plan that support and/or prevent more compact and affordable accommodation options. Onya Bill.
Yesterday’s post considered the recently released Demographia survey on housing affordability. Thanks to everyone who commented; the discussion was useful for honing my thoughts on follow-up posts. Such as this.
But first let’s re-cap: Demographia’s key findings were 1) New Zealand has increasingly unaffordable housing and 2) this is the direct result of urban containment policies.
The main issue I took with the Demographia report in yesterday’s post was 1) the lack of strong economic justification/references supporting their housing affordability indicator of choice (namely the median-multiple ratio) and 2) the lack of discussion/investigation of potential alternative indicators.
Indeed, my quick web search threw up at least two alternative indicators of housing affordability, namely the rent-multiple ratio and the home affordability index, neither of which appeared to lend much support to Demographia’s findings. Of course, this does not prove their conclusions are incorrect, but it does suggest they are premature.
In this post I wanted to look beneath the hood of Demographia’s housing affordability indicator a little more. The reason being that when you do you start to see what they are measuring and, perhaps more importantly, what they are not measuring. In Demographia’s case, they calculated their housing affordability indicator as follows:
Median-multiple = median house price / gross median household income per annum
This then measures, in a simple sense, the cost of the median home relative to the median household income. While that may sound reasonable enough on the surface, the devil is in the detail. Two of the more obvious issues with Demographia’s indicator that spring to my mind are discussed in the following paragraphs.
Demographia’s definition of “income” excludes taxes and transfers. This is pertinent for at least two reasons:
First, some taxes have direct impacts on property prices, e.g. local rates. These will simultaneously tend to affect property prices (higher rates = lower property prices) and post-tax income (lower), but not gross income. Somewhat perversely, this would mean that jurisdictions with higher property taxes would tend to exhibit more affordable housing, at least according to Demographia’s indicator.
Second, most taxes directly impact on a household’s disposable income and in turn affects their ability to afford housing. In New Zealand tax rates have changed considerably over time, especially for different segments of the population. Consider for example the impact of Working for Families on demand for certain types of housing.
Such issues mean that the median-multiple housing affordability indicator, as it appears that Demographia have applied it will not pick up on relevant differences in taxes and transfers, both spatially and temporally.
The spatial differences are likely to be fairly minimal within a country like NZ – where local taxes don’t vary that much from place to place – but this is certainly not the case when making international comparisons. Many countries have much higher rates of property taxes (and even local income taxes) that will tend to impact on house prices and thereby affect their housing affordability relative compared to New Zealand.
On the other hand, the temporal differences introduced by changes in domestic tax and transfer policies are likely to be fairly large, even within a country. The potential impacts on housing affordability of recent tax changes to the top personal tax rate, ability to claim capital depreciation on properties, and commercial tax rates are hard to predict in advance. Tax impacts may well spill over national boundaries as well; NZ’s lack of capital gains tax, for example, is frequently quoted by my Australian colleagues as a primary driver of their decision to invest in New Zealand’s property market.
These issues would make me extremely cautious about drawing broad, sweeping conclusions on trends on housing affordability both within and between countries simply based on the median-multiple indicator.
You don’t measure the affordability of cookies based on the cost of buying the cookie factory.
The point is that housing is a actually a type of good, or more specifically a service, which is “produced” by a house. You can gain access to housing without necessarily buying the factory that produces it, i.e. rent a house. Obviously, some people do this already and they’re called “renters.” Like me.
Even in New Zealand many people rent by choice. And in many countries in central and northern Europe renting is even more prevalent. But the key takeaway message is that the affordability of housing, which is what Demographia sets out to investigate, is probably better measured (from an economic perspective) using rents rather than house prices. This is especially true for low income households that are more likely to rent.
And that’s why I’d place more emphasis on the graph produced by the Productivity Commission, which calculated the ratio of rents to household disposable income over time than the median-multiple indicator presented by the Demographia study. This showed the rent to income ratio in New Zealand declining since the 1990s, contrary to Demographia’s findings and casting some not inconsiderable doubt on their conclusions.
My preference for using rents is also related to the first point on the impacts of taxes on house prices: Unlike houses, which are an asset, rents measure the cost of housing services. I suspect it’s far easier to “net out” the impact of services taxes in various jurisdictions, i.e. GST, on rents than it is to adjust for changes in the myriad of other income and asset taxes that might affect house pricing.
That’s all for tonight, but tomorrow’s another day and I’m already fomenting ideas on the next Demographia post; in the meantime I’d welcome your comments/suggestions/criticisms.
The annual Demographia Housing Affordability report is out – this time with its forward written by Bill English – and just like every other Demographia study it suggests that more land needs to be opened up for urban sprawl in order to bring down housing prices. There are a number of different flaws in Demographia’s analysis (for example it’s based on pre-tax income, it ignores the infrastructure costs of servicing sprawl and it ignores the additional transport costs of living on the urban edge) but I’ll ignore those for now, instead focusing on a pretty simple question – does Auckland really have a land supply shortage?
I think it’s fairly widely agreed that an important factor in Auckland’s rising house prices is a lack of housing supply: simply not enough dwellings are being built. The Auckland Plan talks about the need to build around 13,000 houses a year, every year, over the next 30 years and the fact that we’ve only been building around 3,000 dwellings a year in recent times:What you can also see in the graph above is that Auckland was able to build its required amount of housing during the middle of last decade (the very years when housing prices increased the fastest from memory) and that the number of detached dwellings as well as the number of apartments built per year has fallen dramatically since about 2004. As large greenfield areas such as Silverdale North, Flat Bush, Hingaia, Hobsonville and parts of Takanini have become available for development over the past six or seven years, it’s interesting that we have actually seen a decline in detached structures built rather than a further increase.
Furthermore, Auckland has a lot of areas for future greenfield development working their way through the planning process at the moment or already operative. This is shown in the Auckland Plan’s development strategy map – with yellow indicating “pipeline” (which I assume means that it’s in the process of becoming operative) and “operative” (ready to go I assume) greenfield land. Operative is shown in red and pipeline land in yellow: In fact, the Auckland Plan states that the biggest chunk of growth in the first 10 years of the Plan will take place in these areas:
Personally I think it’s likely that Auckland won’t see anywhere near that amount of greenfield development over the next 10 years – not because there won’t be enough land available (as I said the process for opening up that land is underway already) but rather because there’s unlikely to be the market demand for houses in these peripheral locations.
But in any case, I don’t think that supply new houses in these areas is likely to do much about housing affordability because there’s actually not a housing affordability problem in peripheral parts of Auckland. For example searching Papakura area properties under $400,000 returns not far off half the houses for sale (224 out of 569) in that area:
Auckland’s average house prices are dragged up by the extraordinary prices paid for places in the inner suburbs because that’s seemingly where people really want to live. If there’s heaps of available greenfield land on the urban edge, a significant number of relatively affordable houses already available on the urban edge and the planning in place for a huge amount more greenfield land on the urban edge, I just can’t buy into the hypothesis of Auckland having a land supply shortage.
What we have is a housing supply shortage, particularly in the inner suburbs where people want to live. And the way to fix that is by making intensification easier through getting rid of minimum parking requirements and getting rid of density controls. It’ll be interesting to see whether the Unitary Plan tackles this real issue rather than the non-existent land supply shortage spun by property developers who want to make a pile of money by bringing their land inside the urban limits.
*** Spoiler alert: The title of this post is somewhat hyperbolic ***
Demographia’s “9th Annual International Housing Affordability Survey” has just been released and is receiving a lot of attention in various media outlets, such as the NZHerald. Indeed, NZ ‘s connection to the report is relatively strong – it was co-authored by a kiwi and the foreword is written by our very own Minister of Finance.
For those not in the know, the primary objective of the Demographia report is to evaluate housing affordability across a selection of “anglo” countries, namely Australia, New Zealand, Canada, the U.S., the U.K. and Ireland. This is a very admirable objective; after all if policy makers can better understand the complex range of factors affecting housing affordability, then this can in turn support more informed debate and policy settings.
Demographia measure housing affordability using the so-called “median-multiple” indicator, which they define as follows:
Housing affordability = Median house price / Median household income.
This is a pretty simple indicator: Take the median house price and divide by the median household income and, voila, you have a multiple describing the price of housing relative to incomes. Demographia then collect a swathe of data on house prices and incomes for all cities with populations of 1.0 million or more in Australia, New Zealand, Ireland, U.K., Canada, and the United States. Their results over time are shown below.
Since 2004 the trend in the median-multiple measure has diverged between countries; it increased in Australia, New Zealand, and Canada; stayed broadly constant in the U.S.; but declined in the U.K. and Ireland. While these are fairly innocuous results, the Demographia report then concludes (p. 3):
Overwhelming economic evidence indicates that urban containment policies, especially urban growth boundaries raise the price of housing relative to income. This inevitably leads to a reduced standard of living and increases poverty rates, because the unnecessarily higher costs of housing leave households with less discretionary income to spend on other goods and services. The higher costs ripple into rental markets, tightening the budgets of lower income households, who already suffer from lower discretionary incomes. The principal problem is the failure to maintain a “competitive land supply.” Brookings Institution economist Anthony Downs describes the process, noting that more urban growth boundaries can convey monopolistic pricing power on sellers of land if sufficient supply is not available, which, all things being equal, is likely to raise the price of land and housing that is built on it.
I read that and thought “hmm, that’s fairly strong stuff.” So at this point I thought it was worth stepping back a little.
First let’s examine the two key arguments the Demographia report advances in support of the median-multiple measure of housing affordability (p. 6):
It is simpler than other measures, which are “often not well-understood outside of the financial sector.”
The second reason given is rather vacuous and, frankly, a little condescending to anyone who does not work in the financial sector. And believe me, many economists do not work in the financial sector; at least not anymore.
On the other hand the first reason offered as justification for using the indicator is more understandable: If the median-multiple indicator is used by a range of reputable international organisations then it likely has more merit as measure of global differences in housing affordability.
At that point I tried to follow the sources provided in the Demographia report. Doing raised some fairly important issues: The World Bank link, for example, takes you to a relatively obscure web-page that appears to date from 1992, while the Harvard link appears to be an on-line catalogue of the indicators used in the U.N. report, rather than an independent publication attesting to the merits of the median-multiple indicator.
That leaves us with one “independent” reference, namely the U.N., lending credibility to the use of the median-multiple indicator of housing affordability. Following that link, however, reveals that the median-multiple indicator is but one of a myriad of indicators and checklists identified by the U.N. And perhaps more importantly, the U.N. do not present the median-multiple indicator in isolation, but instead consider it as one of two possible “housing affordability” ratios, as illustrated below.
Righto, so the median-multiple measure can be calculated using either median house prices or house rents. At this stage I was perplexed: Why does the Demographia study not (from what I can tell) mention the ratio of house rents to income as as a possible alternative indicator?
So I did some more digging, and found this graph in the Productivity Commission’s final report on housing affordability in NZ, which considered median rent to household (disposable) income, as illustrated below. In many ways this indicator is more comprehensive than that originally identified in the U.N. report, because it considers after-tax income.
Based on this graph the Productivity Commission concludes (p. 4):
During the house price boom, rents increased at around the same rate as generalised inflation. Across territorial authorities, rents grew in a relatively tight range of 2.3% per year (in Dunedin City) to 8.2% per year (in Buller District). In all cases, rent increases were significantly less than real house price inflation and the ratio of house prices to rents increased markedly, a departure from the long-term broadly stable relationship.
This apparently benign aggregate situation disguises a more difficult position for renters on lower incomes. In particular, people in the lowest two income quintiles spend a much higher proportion of their income on rent than people on higher incomes (Figure 0.5). Even though the situation appears to have improved since the late 1990s, those in the two lower income quintiles still spend, on average, more than 30% of their disposable income on rent, after allowing for government assistance.
Oh dear Daisy: It seems that the Productivity Commission has – using the other housing affordability indicator recommended by the U.N. study referenced by the Demographia report – come to a different conclusion: That housing affordability in NZ has been improving since the late 1990s.
Now at this point I want to caution that these other indicators do not prove that Demographia is necessarily wrong, only that their use of indicators may be too limited. Usefully, the Productivity Commission includes another indicator of housing affordability, namely the “home affordability index” (compiled by Massey University). This index considers the relationship between the costs of servicing a mortgage on the median house and median household income:
This indicator suggests that home affordability has been declining for about the last 4 years. Perhaps more importantly, the 2008 peak in the home affordability index (indicating relatively unaffordable homes) does not seem to be significantly higher than earlier peaks in, for example, 1989 and 1996.
So where does this leave the Demographia report? Well on I’m afraid to say that a first investigation throws up very little corroborating evidence to support their key conclusions, namely that the current price of housing in New Zealand is “unaffordable” relative to historical norms. That’s not to say that they’re wrong, only that their conclusions are not fully supported by the available evidence.
Nor is this to suggest that more affordable housing is not a valid objective: I certainly think it is. And just because the current price of housing is comparable to historical trends, we should still be interested in making housing more affordable, because as Deomgraphia note it is perhaps the most basic of human needs. So while I question Demographia’s analysis and conclusions, the subject is nonetheless very important and worth considering in more detail.
I only wish that 1) they had the time/energy to analyse these issues in more detail within their existing research and 2) news organisations did a little more research before reporting the results of studies like this. While I have more to say on this issue (and hope to do so in future posts), it’s now time for me to up stumps and head for tea (i.e. bed). Until next time …
One of the common excuses for why public transport supposedly “won’t work in Auckland” and why we need to continue to plow money into motorways, is that Auckland is supposedly “too low density” for public transport. In fact, aspiring Auckland Super City Mayor John Banks went so far as to say that Auckland was the “second most spread out city in the world” (after Los Angeles) in a Guest Post on Aucklandtrains. He used this “fact” to justify why Auckland needs to “compete its motorway network” as quickly as possible.
But is this true? How does Auckland’s population density compare with other cities around the world? How does its land area compare with cities in Australia and the USA – for example? Is Auckland anywhere near the second most spread out city in the world? What about Los Angeles?
Fortunately, Demographia (who I am often quite sceptical about when it comes to planning matters, but who seem to have a reasonably good grasp of this issue) have undertaken an enormously in depth study into city sizes, city population and population densities. Perhaps what is most interesting about their work is how they calculate where each urban area begins and ends – which actually fits together quite nicely with how I tend to think of the issue: I like the idea that we’re not measuring “bits of cities”, such as simply the inner part of the New York urban area – which of course has very high densities but isn’t just what New York is made up of.
For a start, I suppose that it makes sense to look at what the biggest cities in the world are by population – here are the top 20: Auckland doesn’t even make it into the top 200, which roughly corresponds with the number of cities in the world with more than 2 million people.
The next thing that’s very interesting to look at is the physical size of cities in the world – which of these urban areas covers the most space. I remember as a kid hearing that Auckland was physically the same size as London, but is that true?
Well according to the table above, the Auckland urban area was 531 square kilometres in size, making it about a third the size of London. Also interesting to note that Auckland’s about half the size of Perth, and less than a third of the size of the Brisbane metropolitan area. At the top of the list, we see that the New York metropolitan area is the biggest built-up urban area in the world, by size, followed by Tokyo. The New York metropolitan area is about 22 times the physical size of Auckland. By size, Auckland is actually the 181st largest city in the world.
Turning to population density, it is really interesting to see how Auckland compares with other cities in Australia and the USA. Most of the really high density cities in the world are in developing nations, which shows why Auckland and many other large and well known cities end up ranked so low. Yet we still see that Auckland’s population density is a bit higher than Sydney’s and significantly higher than all the other major cities in Australia. Interestingly enough though, one city that has a higher population density than Auckland is Los Angeles. In fact, Los Angeles has the highest density of any city in the USA – not because it has a really dense core like New York does, but because throughout Los Angeles the lot sizes are generally pretty small, a similar situation to what we have in Auckland.
So what does all of this mean? Well for one it shows that any time someone says “Auckland’s population density is too low for public transport to work” you can absolutely say that they’re talking rubbish. It also probably means that simple population density isn’t necessarily the ultimate defining issue about whether a city’s urban form is suitable for public transport or not. The way in which that population density is structured (small lots evenly spread throughout the city or higher and lower density nodes) might matter more, the concentration of jobs in certain areas might also make more of a difference (although remember that Vancouver has a lower percentage of jobs in its CBD than Auckland).
Ultimately, what this all probably means is that the popularity of public transport is likely to be based more on the quality of the system than it is on the urban form of the city. Sure, there are many things we can and must do to structure our city more efficiently and sustainably, but let’s stop making the excuse that Auckland is too spread out for public transport to work. Because, as the above tables show, that’s complete rubbish.