Tag Archives: economics

The Mule on Mortgages

Banks, Central Banks and Money

One misconception about the mechanics of money that I mentioned in my last post is the idea that banks can hoard their reserves at the central bank* rather than lending them out.

Here I will explain why this idea simply does not make sense, but no more casinos and gaming chips. No more senior croupiers and casino cashiers. I will dispense with the metaphor and instead stick to a more prosaic explanation, looking at interactions between banks and central banks.

All banks have their own accounts with the central bank. Often these are called “reserve accounts”, although in Australia they are called “exchange settlement accounts” (ESAs). As the Australian terminology suggests, the primary function of these accounts is to facilitate settlement of transactions that take place between banks. To keep it simple here, I will stick to the terminology of “reserve accounts”.

To see how this works, imagine I make a $100 purchase from a shop on my credit card. If the shop banks with the same bank as I do, all that happens is that our bank increases the balance of my credit card by $100 and also increases the balance in the shop’s bank account by $100. With a couple of simple accounting entries and no movement of any physical currency, the transaction is complete. In fact, as was discussed in the casino money post, this simultaneous $100 loan advance to me and $100 deposit raising for the shop has effectively “created” an additional $100 of money in the economy that was not there before.

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How Money Works

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Over the last couple of years as the global financial crisis unfolded, a subject I have spent a lot of time thinking about is the nature of money. I have been planning a blog post on the topic and the time has finally come.

The catalyst for finally writing this post was attending last week’s 16th national conference on unemployment at the University of Newcastle, hosted by the Centre of Full Employment and Equity (CofFEE). I found myself there because the centre’s director, Professor Bill Mitchell, is the author of billy blog, which I read regularly. Bill’s research and advocacy in the area of unemployment and underemployment is firmly rooted in a detailed understanding of how money works in a modern economy (hence the appeal for me) and the implications these mechanics have for government spending policy. This theme also underpinned many of the talks at the conference and the program included a panel discussion on the subject of “Modern Monetary Theory”. The panel comprised Bill Mitchell, Randy Wray and Warren Mosler, all strong advocates of what is sometimes referred to as “chartalism”. Along with another billy blog regular, Ramanan, I was invited to participate by providing a brief wrap-up at the end of the discussion.

But how hard can it really be to understand how money works? You earn it and you spend it or save it. Or, as the textbooks would have it, money serves as both a medium of exchange and a store of wealth. Is there anything more to say?

In fact there is. Most people and, indeed, many economists have not given very much thought to the mechanics of money and this leads to a number of misconceptions, all of which have made frequent appearances in the press and in political debate around the world over the course of the financial crisis. One example is the suggestion that the UK government could run out of money, an idea given further credence by the decision of rating agency Standard & Poor’s to put the UK’s rating on “negative outlook”. Even Barack Obama seems to be saying that the US is running out of money. The fact is, governments in many developed countries simply cannot run out of money. China could (but it is very unlikely) and so could member states of the European Monetary Union, but the US, UK, Japan and Australia could not. I will explain why here. In later posts I will continue the theme of the mechanics of money and will look at other misconceptions such as the idea that banks can “hoard” their reserves at central banks or that government deficits inexorably lead to high interest rates (the short answer to this one is: look at Japan).

In this post I will start with the basics of how money works and cover the following points:

how lending can “create” money
the limits to money creation
the difference between “fiat” money and money that is convertible on demand

A useful parallel to money in a real economy can be found in gaming chips in a casino. So, imagine a fairly standard sort of casino. You walk in, James Bond-style, hand over a thousand dollars to the cashier and get a pile of chips in return. The chips are marked with various denominations and total one thousand. This is an old-fashioned sort of casino: every game is played on a green felt table, there is not a poker machine in sight and, of course, you need your chips to play. To make your stay easy, you can also use your chips to buy drinks and snacks. When you have finished your evening’s play, you can redeem any chips you have not gambled away for cash.

There might be hundreds of thousands of dollars worth of chips circulating around the casino, but so far behind every chip is a corresponding amount of money sitting in the cashier’s safe. If we call this money the casino’s “reserves”, then the chip supply in circulation around the tables is equal to the casino’s dollar reserves. Of course, there might be a few cases of chips in the croupier’s office and even a chip-pressing machine in the basement, but these chips are not yet in circulation. They are just waiting to be handed over to the next patron who walks in the door with a full wallet. Under this regime, every gambler can be completely certain that they will be able to redeem their winnings at the end of the night.

While your thousand dollar stake might seem like a lot, there are a few high-rollers who frequent the place who like to play with much larger sums. Rather than producing chips with very high denominations, this casino has introduced convenient “smart chip cards”. High rollers can pay the cashier as much money as they like and the cashier will add it to the virtual chip balance on their smart cards. At every gaming table, the croupier has a card reader which can be used to debit the balance on the card in return for actual chips. This means that the total chip supply in circulation is the sum of actual chips and virtual chip balances on the smart cards. But still, this chip supply is matched by money in the cashier’s safe.

Now suppose you are a trusted regular at the casino and one night you turn up short of cash. No problem, the casino is happy to advance you your thousand dollars in return for a quickly scribbled IOU with your signature. Your credit is good. You take your $1,000-worth of chips and walk to the Blackjack table. But now something has changed. The total chip supply in the casino is $1,000 higher than the money in the cashier’s safe. In theory this could be a problem. You could immediately lose the $1,000 in chips and walk out. Then if everyone in the casino wanted to redeem their chips, there would not be enough money to go around. But, it isn’t likely to be a problem in practice. The casino operates 24 hours a day and so there are always far more than $1,000 in chips in circulation. On top of that, the house takes a decent cut on the tables, so it would not take very long for the casino to win back over $1,000-worth of chips and then $1,000 can be held back from the profits that the cashier regularly sends up to the manager’s office. In fact, the credit seems so safe, the casino decides to offer credit more widely. While they are at it, they introduce a few other innovations, like offering lucky door prizes in chips, which also adds to the supply of chips in circulation without a corresponding increase in money in the cashier’s safe.

These loans that the casino has introduced give it the ability to “create” an additional supply of chips. But not all lending creates new chips. If instead of borrowing from the house, you had offered your IOU to a high-rolling friend you would still get your $1,000 in chips for the evening, but you got them from your friend so the chip supply does not change.

The new lending arrangements are working well, but the system is limited by the fact that the cashier does not know all of the patrons very well, and is naturally being very cautious about who to lend chips to. To manage this bottleneck, the casino decides to allow senior croupiers to provide loans to gamblers they know well as long as they take responsibility for the credit-worthiness of the borrower. So now getting credit is simply a matter of providing an IOU to the senior croupier who knows you best and he or she will charge up your smart chip card. If you need actual chips, that is not a problem either as the senior croupier has a stash under the table borrowed from the cashier. Of course, the croupier is taking a bit of a risk providing you with this advance since the house expects him or her to make good any amounts you do not repay. So to make it worth their while, you give the croupier a few chips for their trouble each time you need an advance. This works so well that the cashier no longer offers loans directly to anyone other than the senior croupiers.

As successful as the new arrangements are, the casino does have to be very careful about putting strict limits on the number of chips that the senior croupiers can create through lending. Otherwise, the day may come when there are simply too many chips and not enough money in the safe and a successful gambler may walk up to the cashier to cash in their chips only to find that the cashier does not have enough money in the safe. Word will spread and everyone will want their money back, but the casino will be unable to oblige. It would be bankrupt. So while there may be no limit to the number of chips that the casino could physically manufacture (and of course it has complete control of smart chip card balances), there is a constraint on the number it can put into circulation. This constraint is a direct consequence of the fact that chips are redeemable for cash.

The analogy to the real economy should be clear here. The cashier operates like a central bank and government treasury combined. The senior croupiers are the banks. Chips are money and smart chip card balances correspond to bank account balances. In the same way that senior croupier lending effectively creates new chips, so bank lending adds to the money supply in an economy. But what is the analogy to the money in the cashier’s safe? While central banks around the world do maintain reserves of gold and foreign currencies (think of all the US dollars that the central bank of China has), for many countries the analogy breaks down in one important respect.

The casino made a commitment to redeem your chips for cash. Some central banks do make similar commitments. In the days of the gold standard, central banks in Australia, the US, the UK and elsewhere would exchange currency for gold. Of course there were times, as in war, when this convertibility was suspended, but in those days having something backing money was seen as just as important as having money backing chips in a casino. The gold standard system was abandoned after the second world war and instead, under the Bretton Woods system, domestic currencies could be exchanged at the central bank for a fixed number of US dollars. This system collapsed in turn in the 1970s. Today, some countries such as China do maintain currencies pegged to the US dollar (or some other currency) and so still make a commitment of convertibility. However, most countries have adopted so-called “fiat” money. The word fiat is Latin for “let it be” and fiat money does not derive its value from any form of backing. It is declared to be money, and so it is. Many people still assume that Australian dollars are in fact backed by something, but if you tried to take a $10 note to the Reserve Bank of Australia, you would be lucky to get two $5 notes in return. You could certainly not be assured of getting any particular amount of gold or US dollars.

Some people find the entire concept of fiat money deeply disturbing and pine for a return to the “real” money days of the gold standard. But fiat money is in fact an extremely powerful innovation. In the casino analogy, the cashier must always be careful about how many chips are put into circulation to avoid the crisis of being unable to convert chips back to cash. However, in a country with fiat money, the central bank makes no convertibility commitments, so this risk simply does not exist. It has monopoly power in the creation of currency. So, the government simply cannot run out of money. There may be very good reasons for a government to curb its spending. For example, it may not want to add too much to demand in the economy because it is concerned about inflation. But running out of money is not one of those reasons, whatever the president of the United States may think.

I will leave it there for now, as this post is long enough already. But, stay tuned for more on the macroeconomic implications of a modern fiat money system.

Petrol Price Update

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Another five months on since my last petrol price update and oil prices have continued to rise, but so has the value of the Australian dollar. So while crude oil prices in US dollars are up around 75% since their lows in February, they are only up 29% in Australian dollar terms.

West Texas Intermediate Oil Prices

The Australian dollar has been rising steadily for the last six months, pushed along by the Reserve Bank of Australia which has started raising their target cash rate. Higher interest rates in Australia make it more attractive for offshore investors to buy Australian securities and they have to buy Australian dollars to do so. Australian investors holding foreign assets may do the same.

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Fertility Declines Don’t Reverse with Development

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In this follow-up guest post on The Stubborn Mule, Mark Lauer takes a closer look at the relationship between national development and fertility rates.

STOP PRESS: Switzerland’s population would be decimated in just two generations if it weren’t for advances in their development.

At least, that’s what the modelling in a recent Nature paper projects. The paper, widely reported in The New York Times, The Washington Post and The Economist, amongst others, was the subject of my recent Stubborn Mule guest post. In that post, I shared an animated chart and some statistical arguments that cast doubt on the paper’s conclusion. In this post, I’ll take a firmer stance: the conclusion is plain wrong. But to understand why, we’ll have to delve a little deeper into their model. Still, I’ll try to keep things as non-technical as possible.

First, let’s recap the evidence presented in the paper. It comprised three parts: a snapshot chart (republished in most of the reportage), a trajectory chart, and the results of an econometric model. As argued in my earlier post, the snapshot is misleading for several reasons, not least the distorted scales. And the trajectory chart suffers from a serious statistical bias, also explained in my earlier post. I’ll reproduce here my chart showing the same information without the bias.

That leaves the econometric model. From reading the paper, where details of the model are sketchy, I had wrongly inferred that the model suffered the same statistical bias as the trajectory chart. I have since looked at the supplementary information for the paper, and at the SAS code used to run the model. From these, it is clear that a fixed HDI threshold of 0.86 is used to define when a country’s fertility should begin to increase. So there’s no statistical bias. However, I discovered far more serious problems.

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Is There a Baby Bounce?

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In this first ever guest post on The Stubborn Mule, Mark Lauer takes a careful look at the relationship between national development and fertility rates.

Recently The Economist and the Washington Post reported a research paper in Nature on the relationship between development and fertility across a large number of countries. The main conclusion of the paper is that, once countries get beyond a certain level of development, their fertility rates cease to fall and begin to rise again dramatically. In this post I’ll show an animated view of the data that casts serious doubt on this conclusion, and explain where I believe the researchers went wrong.

But first, let’s review the data. The World Bank publishes the World Development Indicators Online, which includes time series by country of the Total Fertility Rate (TFR). This statistic is an estimate of the number of children each woman would be expected to have if she bore them according to current national age-specific fertility rates throughout her lifetime. In 2005, Australia’s TFR was 1.77, while Niger’s was 7.67 and the Ukraine’s only 1.2.

The Human Development Index (HDI) is defined by the UN as a measure of development, and combines life expectancy, literacy, school enrolments and GDP. Using these statistics, again from the World Bank database, the paper’s authors construct annual time series of HDI by country from 1975 until 2005. For example, in 2005, Australia’s HDI was 0.966, the highest amongst all 143 countries in the data set. Ukraine’s HDI was 0.786, while poor old Niger’s was just 0.3.

A figure from the paper was reproduced by The Economist; it shows two snapshots of the relationship between HDI and TFR, one from 1975 and one from 2005. Both show the well-known fact that as development increases, fertility generally falls. However, the 2005 picture appears to show that countries with an HDI above a certain threshold become more fertile again as they develop further. A fitted curve on the chart suggests that TFR rises from 1.5 to 2.0 as HDI goes from 0.92 to 0.98.

Of course, this is only a snapshot. If there really is a consistent positive influence of advanced development on fertility, then we ought to see it in the trajectories through time for individual countries. So to explore this, I’ve used a Mathematica notebook to generate an animated bubble chart. The full source code is on GitHub, including a PDF version for anyone without Mathematica but still curious. After downloading the data directly from Nature’s website, the program plots one bubble per country, with area proportional to the country’s current population.

Unlike with the figure in The Economist, here it is difficult to see any turn upwards in fertility rates at high development levels. In fact, the entire shape of the figure looks different. This is because the figure in The Economist uses axes that over-emphasise changes in the lower right corner. It uses a logarithmic scale for TFR and a reflected logarithmic scale for HDI (actually the negative of the logarithm of 1.0 minus the HDI). These rather strange choices aren’t mentioned in the paper, so you’ll have to look closely at their tick labels to notice this.

To help focus on the critical region, I’ve also zoomed in on the bottom right hand corner in the following version of the bubble chart.

One interesting feature of these charts is that one large Asian country, namely Russia, and a collection of smaller European countries, dart leftwards during the period 1989 to 1997. The smaller countries are all eastern European ones, like Romania, Bulgaria and the Ukraine (within Mathematica you can hover over the bubbles to find this out, and even pause, forward or rewind the animation). In the former Soviet Union and its satellites, the transition from communism to capitalism brought a crushing combination of higher mortality and lower fertility. In Russia, this continues today. One side effect of this is to create a cluster of low fertility countries near the threshold HDI of 0.86 in the 2005 snapshot. This enhances the impression in the snapshot that fertility switches direction beyond this development level.

But the paper’s conclusion isn’t just based on these snapshots. The authors fit a sophisticated econometric model to the time series of all 37 countries that reached an HDI of 0.85, a model that is even supposed to account for time fixed-effects (changes in TFR due only to the passage of time). They find that the threshold at which fertility reverses is 0.86, and that beyond this

an HDI increase of 0.05 results in an increase of the TFR by 0.204.

This means that countries which develop from an HDI of 0.92 to 0.98 should see an increase in TFR of about 0.25. This is only about half as steep as the curve in their snapshot figure, but is still a significant rate of increase.

However, even this rate is rather surprising. Amongst all 37 countries, only two exhibit such a steep rise in fertility relative to development between the year they first reach an HDI of 0.86 and 2005, and one of these only barely. The latter country is the United States, which manages to raise TFR by 0.211 per 0.05 increase in HDI. The other is the Czech Republic, which only reaches an HDI of 0.86 in 2001, and so only covers four years. Here is a plot of the trajectories of all countries that reached an HDI of 0.86, beginning in the first year they did this. Most of them actually show decreases in TFR.

So how do the authors of the paper manage a statistically significant result (at the 0.1% level) that is so widely different from the data? The answer could well lie in their choice of the reference year, the year in which they consider each country to have passed the threshold. Rather than using a fixed threshold as I’ve done above, they express TFR

relative to the lowest TFR that was observed while a country’s HDI was within the window of 0.85–0.9. The reference year is the first year in which this lowest TFR is observed.

In other words, their definition of when a country reaches the threshold depends on its path of TFR values. In particular, they choose the year when TFR is at its lowest.

Does this choice statistically bias the subsequent trajectories of TFR upwards? I leave this question as a simple statistical exercise for the reader, but I will mention that the window of 0.85–0.9 is wider than it looks. Amongst countries that reached an HDI of 0.9, the average time taken to pass through that window is almost 15 years, while the entire data set only covers 30 years.

Finally I’d like to thank Sean for offering this space for my meandering thoughts. I hope you enjoy the charts. And remember, don’t believe everything you see in The Economist.

UPDATE:

To show that the statistical bias identified above is substantial, I’ve programmed a quick simulation to measure it. The simulation makes some assumptions about distributions, and estimates parameters from the original data. As such it gives only a rough indication of the size of the bias – there are many alternative possibilities, which would lead to larger or smaller biases, especially within a more complex econometric estimation.

In the simulation, each of the advanced countries begins exactly where it was in the year that it first reached an HDI of 0.85. Thereafter, a trajectory is randomly generated for each country, with zero mean for changes in fertility. That is, in the simulation, fertility does not increase on average at all¹. As in the paper, a threshold is found for each country based on the year with lowest TFR within the HDI window. All shifts in TFR thereafter are used to measure the impact of HDI on TFR (which is actually non-existent).

Here is a sample of the trajectories so generated, along with the fitted response from the paper.

The resulting simulations find, on average, that a 0.06 increase in HDI leads to an increase of about 0.075 in TFR, despite that fact that there is no connection whatsoever. The range of results is quite broad, with an increase of 0.12 in TFR also being a likely outcome. This is half of the value found in the paper; in other words, simulations of a simplified case where HDI does not influence TFR at all, can easily generate half of the paper’s result.

Of course, if the result is not due to statistical bias, then the authors can easily prove this. They need only rerun their analysis using a fixed HDI threshold, rather than one that depends on the path of TFR. Until they do, their conclusion will remain dubious.

¹ For the technically minded, the HDI follows a random walk with drift and volatility matching those of advanced countries, and the TFR follows an uncorrelated random walk with volatility matching the advanced countries, but with zero drift. The full source code and results have been uploaded to the Github repository.

FURTHER UPDATE:

More details can be found in the follow-up post to this one, Fertility Declines Don’t Reverse with Development.

Where Have the Fish Come From?

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After reading my posts on the international arms trade, a friend thought I might be interested in some data on the international trade in fish. While I know almost as little about fish as about arms, I always welcome good data. The data in question is published by the Food and Agriculture Organization (FAO) of the United Nations. The FAO also hosts FAOStat, which looks like an interesting data repository. If I can get myself a subscription to this service, it may provide the subject matter for future posts on the Mule.

But back to the fish. The first point my correspondent made was that many fish exporters are also importers. Among the top 50 importers of fish, all but 16 countries also appear in the list of the top 50 exporters. The chart below* gives an indication of the relative scale of fish imports and exports in 2006 of the top 10 importing countries. Of these big importers, only China and Denmark export even more fish than they import.

Fish Trade by the Top 10 Importers (2006)

But the real mystery my fishy correspondent alerted me to is the difference between total worldwide imports and exports of fish. According to the figures, total worldwide imports of fish amounted to US $89.6 billion while exports only amounted to US $85.9 billion. That would appear to mean that US $3.7 billion worth of fish was imported in 2006 from nowhere! While I am sure that statistics of this kind may not be too accurate, the report does report each country’s trade figures to the nearest US $1000, so it seems to be a big difference. I speculated that some countries were not admitting to exporting whale meat to Japan, but my correspondent pointed out that whales are not fish. While the US Supreme court has ruled that tomatoes are vegetables, I do not know their view on whales, and this is probably not the answer anyway. Any theories out there, readers?

At the suggestion of singingfish, I will be making available the code used to produce charts here on the Stubborn Mule. Most of the charts are produced using the R statistical package, which is free and open-source. R can be downloaded here. The data and code for the chart above is here. I will gradually add the code for charts from older posts as well.

UPDATE: I forgot to mention that my correspondent also suggested fish rain as an explanation. I, however, am not convinced. Regardless of the original source, I am sure most countries would treat fish rain as a natural bounty rather than an import.

* Tip for reading the chart: there is no label on the right hand side for the USA and no label on the left for Denmark, but following the lines should make it obvious where they would be if there was room.

The Big Arms Traders

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My last post looked at the international arms trade. Taking data from SIPRI, I produced maps showing arms exports for a number of countries, including Australia and the USA. While these maps gave an indication of the spread of arms trading, it did not show which are the biggest overall importers and exporters of arms.

To remedy this, I have created two “word clouds”. The first shows arms importers. The size of the text varies with the total value of arms imported over the period 1980 to 2008 (figures are adjusted for inflation and are expressed in 1990 US dollars). The three biggest arms importers over this period were India ($58 billion), Japan ($37 billion) and Saudi Arabia ($35 billion). Australia’s imports over this period totaled $15 billion.

Arms Importers (1980-2008)

The word cloud for exporters is far more concentrated. Between them the USA and Russia* accounted for almost 65% of total arms exports, with exports of $60 billion and $48 billion respectively. France then comes in at a distant third with exports totaling just under $12 billion.

Arms Exporters (1980-2008)

If you like the look of these word clouds, you can easily create your own. With Wordle you can create word clouds which are based on word frequency. This example is based on words used here on the Stubborn Mule (notice the prominent appearance of the word “debt”). For a bit more flexibility, IBM have a freely available Word-Cloud Generator, which can either work on word frequencies or take columns of words and numbers. It is written in java and is very easy to configure and run. I used it to produce the images in this post.

* As in the previous post, figures for the USSR and Russia have been aggregated.

The Arms Trade

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Yesterday iconoclastic commentator on technology, politics and culture, Stilgherrian, shared an interesting discovery on twitter. He had come across the website of the Stockholm International Peace Research Institute (SIPRI) and their Arms Transfer Database. SIPRI has been monitoring international arms trades since 1968 and in the process have assembled an extraordinary database with details of all international transfers of major conventional weapons since 1950. Since March 2007 this database has been available online.

The business of international arms trading is certainly not within my area of expertise, but a rich data-set like this presents a perfect opportunity for a type of data visualization that has not yet appear on the blog: maps. The SIPRI database provides “Trend Indicator Value (TIV)” tables which aggregate trade values between countries. Values are inflation-adjusted, expressed in 1990 US dollars.

Starting with Australia, the data shows that the total value of arms imported by Australia from 1980 onwards exceed exports by a factor of almost 30 times. Imports are largely sourced from North America and Europe, while exports are spread more broadly and include a range of Asian and Pacific countries. Click on the charts to see larger images.

Arms transfers from Australia (1980-2008)

Arms transfers to Australia (1980-2008)

Needless to say, the distribution of arms transfers in and out of the USA looks very different. Over the last 30 years, the USA has exported arms to well over 100 countries across every continent other than Antarctica.

Arms transfers from the USA (1980-2008)

Arms transfers to the USA (1980-2008)

Another big exporter of arms to a wide range of countries is the United Kingdom.

Arms transfers from the UK (1980-2008)

Arms transfers to the UK (1980-2008)

Russia offers a rather different distribution of arms transfers. Russia has exported arms to almost 100 counties, most notably China, but since 1980 has only imported from Germany, Poland and the Ukraine.

Arms transfers from Russia (1980-2008)

Arms transfers to Russia (1980-2008)

I will not offer any further comment on this data, but will leave the maps to speak for themselves. If you would like to see a map for any other countries, feel free to contact me on twitter, @seancarmody. I will add them to this flickr image set.

UPDATE: As Mark Lauer correctly pointed out, these maps were originally inaccurate when it came to countries which were formerly part of the Soviet Union. This has now been corrected in the maps above.

Deleveraging and Australian Property Prices

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car-small A few weeks ago, I had a preliminary look at Australian property prices. That post focused on rental yields and argued that the fact that property prices have consisently outpaced inflation over the last 10-15 years can be associated with a steady decline in rental yields which has been matched by a decline in real yields in other asset classes. What I did not address was the argument that debt deleveraging will lead to a collapse in property prices just as it has done in the US. That is the subject of today’s post.

The Bubble

The bubble argument is a compelling one. The chart below shows the growth in Sydney property prices over the last 24 years. Prices rose fairly consistently over this period at an annualised rate of almost 7%. Over this period, inflation averaged around 3% per annum, so property prices grew at a rate of approximately 4%. This means since 1985, the cost of a typical house has risen by a disconcerting 123% over and above inflation. Little wonder that many people see the property market as a bubble waiting to burst.

Sydney Property Prices (1985-2009)*

The fuel driving the property market has been the rapid growth in household debt, most of which has been in the form of mortgage debt. The next chart is taken from Park the Debt Truck!, a post which looks at trends in Government and household debt in Australia. The highlighted regions show the periods of Labor federal governments. Household debt began its upward trajectory during the Hawke and Keating years, but really gathered pace during the Howard years. With the help of continually extended first-time home-buyer grants, growth is yet to slow now that Rudd has come to power.

Govt and Household Debt

Government and Household Debt in Australia

This expansion of debt has been a key factor driving up property prices. Without the easy access to money, the pool of potential home-buyers would be far smaller and with less demand pressure, prices would not have risen so fast. A very similar pattern was evident in the US, but in late 2006 the process began to lose steam. Property prices faltered, debt became harder to obtain, borrowers began to default on their loans leading to foreclosure sales which put further downward pressure on prices. The bubble was bursting.

So far I am in agreement with the property bubble school of thought. Where I part ways is concluding that Australia will inevitably experience the same fate, resuting in a collapse in property prices, possibly in the range of 30 to 40%.

Deleveraging

Words can be powerful. Once you use the word “bubble” to describe price rises, it seems almost inevitable that the bubble must burst. Similarly, “reducing debt” sounds like a good thing, while “deleveraging” sounds like a far more ominous destructive process. But all deleveraging really means is debt reduction and it can happen in a number of ways:

borrowers use savings to gradually pay down debt
borrowers sell assets to pay down debt
borrowers default on their loan

When it comes to borrowers selling assets, in some cases this may be voluntary. But it may be that they are forced to sell. A good example is in the case of margin loans to purchase shares. If the share price falls, the lender will make “margin call”, requiring the borrower to repay some of the loan. Selling some or all of the shares may be the only way to raise the money required. When borrowers default on a secured loan (such as a mortgage), the lender will usually sell the asset securing the loan in an attempt to recover some of the money lent. In this situation the emphasis is usually on ensuring a speedy sale rather than maximising the sale price.

Forced sales are the ideal conditions for a price collapse, particularly if lenders have become reluctant to finance new borrowers. If debt reduction takes the form of gradual repayment, the pressure on prices is far less. There will certainly be less demand for assets than during a period of rapid debt increase, but this can simply result in neglible growth in asset prices for an extended period of time rather than a price collapse.

To understand what form debt reduction will take, it is not enough to consider the amount of debt. The form of the debt is very important. Some of the key characteristics that will influence the outcome include:

the term of the loan (the length of time before it must be repaid)
repayment triggers (such as margin calls)
interest rates

Short-term loans can be very dangerous. In 2007, the non-bank lender RAMS learned this the hard way. It had relied heavily on very short-term funding (known as “extendible asset-backed commercial”) and back when the global financial crisis was simply known as a liquidity crisis, RAMS found itself unable to refinance this debt. It’s business collapsed and it was purchased by Westpac for a fraction of the price at which the company had been listed only months before.

The most common type of loan with repayment trigger is a margin loan. There is no doubt that a significant factor in the dramatic falls in the Australian sharemarket over 2008 was forced selling by investors who had used margin loans to purchase their shares. There are also other sorts of loan features than can be problematic for borrowers. Another one of the corporate victims of the financial crisis was Allco Finance. It turned out that they had a “market capitalisation clause” attached to their bank debt. This was like a margin call on the value of their own company and was an important factor in the collapse of the company.

Even if borrowers have long-term loans and are not forced to repay early, if they are unable to meet interest payments, they will be in trouble. A common feature of the US “sub-prime” mortgages at the root of the financial crisis was that interest rates were initially low but then “stepped up” a couple of years after the mortgage was originated. While the market was strong, this was not a problem due to the popular practice of “flipping” the property: selling it for a higher price before the interest rate increased. Once prices began to fall, the step-ups became a problem and mortgage delinquencies (falling behind in payments) and defaults began to rise. In some states, the phenomenon was exacerbated by laws that allowed borrowers to simply walk away from their property, leaving it to the lender, who had no further recourse to pursue the borrower for losses. On top of all this, rapidly rising unemployment put further stress on borrowers’ ability to service their mortgages.

So, how do Australian mortgages look on these criteria? The standard Australian mortgage is a 25-30 year mortgage with no repayment triggers. Most mortgages are variable rate and, despite the banks not passing through all the central bank rate cuts, mortgage rates are at historically low levels. In part due to the regulatory framework of the Uniform Consumer Credit Code (UCCC), lending standards in Australia have been fairly conservative compared to the US and elsewhere. The Australian equivalent of the sub-prime mortgages, so-called “low doc” or “non-conforming” mortgages, represent a much smaller proportion of the market. Many lenders cap loan-to-value ratios (LVR) at 95% and require the borrower to pay mortgage insurance for LVRs over 80%, which encourages many borrowers to keep their loans below 80% of the value of the property. Interest step-ups are rare. Mortgages are all full recourse.

The result is that while US mortgage foreclosure and delinquency rates have accelerated rapidly, they have only drifted up slightly in Australia. It is not easy to obtain consistent, comparable statistics. For example, deliquency data may be reported in terms of payments that are 30 days or more past due, 60 days or more or 90 days or more. Of course, figures for 30 days or more will always be higher than 90 days or more. Nevertheless, the difference in trends is clear in the chart below which shows recent delinquency rates for a variety of Australian and US mortgages both prime and otherwise. The highest delinquency rates for Australia are for the CBA 30 days+ low doc mortgages. Even so, delinquencies are lower even than for US prime agency mortgages 60 days+ past due.

Delinquency Rates (III) Delinquency Rates in Australia and the US**

All of this means that the foreclosure rate remains far lower in Australia than in the US. Combined with the fact that mortgage finance is still increasing, due largely to the ongoing first-time home-buyers grant, there has still been little pressure on Australian property prices. In fact, reports from RP Data-Rismark suggest prices are on the rise once more (although I will give more credence to the data from the Australian Bureau of Statistics which is to be released in August).

Once the support of the first-time home-buyers grant is removed, I do expect the property market to weaken. Prices are even likely to fall once more with the resulting reduction in demand. However, without a sustained rise in mortgage default rates, I expect deleveraging to take the form of an extended lacklustre period for the property market. Turnover is likely to be low as home-owners are reluctant to crystallise losses, in many cases convincing themselves that their house is “really” worth more. Even investors may content themselves reducing the size of their debt, continuing to earn rent and claim tax deductions on their interest payments.

The biggest risk that I see to the Australian property market is a sharp increase in unemployment which could trigger an increase in mortgage defaults. To date, forecasters have continued to be confounded by the slow increases in unemployment and now the Reserve Bank is even showing signs of optimism for the Australian economy.

Australian property prices have certainly grown rapidly over recent years. Driven by rapid debt expansion, prices have probably risen too far too fast. But, calling it a bubble does not mean it will burst, nor does using the term “deleveraging” mean that prices will inevitably follow the same pattern as the US. In the early 1990s, Australia fell into recession and the commercial property market almost brought down one of our major banks. Meanwhile, house prices in the United Kingdom collapsed. Despite all of this, in Australia, residential prices simply slowed their growth for a number of years. I strongly suspect we will see the same thing happen over the next few years.

* Source: Stapledon

** Source: Westpac, CBA, Fannie Mae, Bloomberg.

By the way, notice anything unusual in the picture at the top?

UPDATE: Thanks to Damien and mobastik for drawing my attention to this paper by Glenn Stevens of the Reserve Bank of Australia. It includes a chart comparing delinquency data for the US, UK, Canada and Australia. The data is attributed to APRA, the Canadian Bankers’ Association, Council of Mortgage Lenders (UK) and the FDIC. Since these bodies do not appear to make the data readily available, I have pinched the data from the chart and uploaded it to Swivel. It paints a very similar picture to the chart above.

Delinquency: US, UK, Canada and Australia Mortgage Delinquency Rates