Tag Archives: charts

Is Australia taking its fair share of asylum-seekers?

In Crikey this week, Bernard Keane made the point that Australia accepts a disproportionately small number of asylum-seekers given our population size. So, where exactly do we rank in the world in terms of generosity towards displaced persons? The United Nations Refugee Agency provides a wide range of statistics about refugees and asylum-seekers. The latest monthly data gives the number of asylum-seeker applications by country for 2009 up to and including August. The chart below shows a ranking of the 44 countries who reported accepting asylum-seekers over this period. Australia finds itself well down the list in 20th place. Mind you, the United States ranks a few spots behind us and, despite having a better reputation when it comes to taking refugees, New Zealand is even further behind. Malta is by far the most welcoming country for refugees.

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Does Switzerland have the world’s best universities?

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Today @jgzebra drew my attention to the Times Higher Education league table of the top 200 univerities in the world. A quick glance at the list shows two US universities in the top three and six in the top 10. And indeed the United States dominates the results, claiming 54 spots out of the 200. The United Kingdom comes in next, taking 29 spots.

Country Count in Top 200 Universities List

Of course, this tally does not take into account the differing sizes of each country: with a population of over 300 million people, you would expect a good showing from the United States. So the obvious question is, what would the national ranking look like if population were taken into account? Rather than doing this based on the number of appearances each country makes in the list, I aggregated the overall “score” awarded to each univerity (which combines scores based on surveys of peers, employers, staff and students, citations and international staff and students) and then ranked each country by aggregate score per million population*.

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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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Crime Around The Corner

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Observant visitors to this blog may have noticed the recent appearance of a “wiki” button at the top of the page. This links to the recently established Stubborn Mule wiki, which I plan to use as a repository of information relevant in some way to the blog. Since so many of the posts here focus on data analysis, I have started with a collection of links to useful sources of data online, particularly economics and finance data.

The latest link I have added is to the New South Wales Bureau of Crime Statistics & Research (while I did not include it in the economics and finance section, maybe it does belong there). This site includes a research data set which provides monthly crime data going back to 1995 broken down by local government (council) area and offence type. Needless to say, the first thing I was interested to learn was the level of criminality in my own local area, particularly as I moved here only very recently.

The chart below shows the total number of crimes in the various offence categories for 2008 in my local government area of Marrickville. While I was not surprised to see theft coming in at the top of the list, there were a few oddities further down. I was initially surprised to see driving offences at the bottom of the list. My driving is, of course, impeccable but I do not know if the same is true of all of my neighbours, not to mention visitors to the area. Digging further, I discovered that from 2003 onwards*, the figures for driving offences have been zero for all areas and transport regulatory offences have leapt up. So, presumably there has been a classification change. One mystery solved.

Marrickville Crime Count (2008)

More intriguing is blackmail and extortion. Until 2008, the highest rate this crime had reached in Marrickville was four cases per year and in three years, the figure was zero. Yet, in 2008, there were nine cases of blackmail and extortion. What lies behind this wave of blackmail around the corner? Mystery not solved.

This led me to examine other trends through time. Starting with theft, I was gratified to learn that 2008 was the lowest year for theft since these records began. I am hoping 2009 will be lower still.

Occurrences of Theft in Marrickille

A look at prostitution also suggests the area has become more law-abiding after a significant spike in offences in 2001.

Occurrences of Prostitution Offences in Marrickville

As for serious crime, Marrickville experienced three homicides in 2008. The total number of homicides in the area since 1995 is 66, putting Marrickville in a somewhat disturbing 14th place out of 155 local government areas, although these two have been reducing over recent years. For those interested in the most murderous areas in New South Wales, here is a list of the top five areas in terms of total homicides since 1995. Any country readers will note that all of these local government areas are in Sydney (the area in the table labelled “Sydney” encompasses only the central business district and some inner-city suburbs).

Area	Homicides
Fairfield	242
Sydney	327
Blacktown	136
Liverpool	102
Parramatta	82

* The historical data for Marrickville is in the “Files” section of the blog.

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 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.

Love is Old-Fashioned, Sex Less So

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Following on from my post on Visualizing the Hottest 100, I noticed that the UK’s Guardian newspaper has published a list of 1000 songs to hear before you die*. The list was assembled from nominations posted by readers. Even before looking at the list, I suspected that the demographic profile of the Guardian’s readers may be a little different to that of Triple J’s listeners. A look at the distribution of year of release in the two lists bears that out.

	Hottest 100	Guardian 1000
Minimum	1965	1916
1st quartile	1984	1968
Median	1994	1977
3rd quartile	1997	1988
Maximum	2008	2008

Year of Release “Five Number” Statistics

In fact, fully 14% of the tracks in the Guardian’s list were released before the earliest track in the Hottest 100. Interestingly, that track was Bob Dylan’s “Like A Rolling Stone”, which also features in the Guardian’s list.

While the 1000 songs are not presented in any particular rank order, they are grouped by “theme”. The themes are heartbreak, life and death, love, party sonds, people and places, politics and protest and, of course, sex. This allows us to investigate the evolution over time of these different themes.

The chart below is a “box and whisker plot”, also known more prosaically as a “box plot”. It provides a graphical representation of the distribution over songs in each theme by year of release. The box shows the “interquartile range”, from the 1st quartile to the 3rd quartile. This means that half the songs fall inside the box, while a quarter were released in earlier years and a quarter in later years. The solid band shows the median year, which is the year right in the middle of the distribution. The light grey line shows the average year of release. Since most of the distributions are skewed to the left (early years) right (later years) in the interquartile range [see UPDATE below], the mean is a bit higher than the median. The “whiskers” on the plot extend no more than 1.5 times the width of the box. Any outliers beyond the whiskers are shown as points.

Box Plot (II)

Distribution of Year of Release

So what can be made of these distributions? It looks as though love songs are not as popular as they once were and people and places have fared worse still. But while love may be old-fashioned, sex and party songs have become more prevalent and there is still plenty of heartbreak.

And what of the most popular artists? The three most successful artists in Triple J’s Hottest 100 were Nirvana, Jeff Buckley and Radiohead. Nirvana and Radiohead managed one song each in the Guardian’s list: “Lithium” and “Paranoid Android” respectively (both in the life and death theme). Jeff did not make the list, although his father Tim did, with the song “On Top”. The artist with the most entries in the Guardian’s list was Bob Dylan, and the top 12 features a few who did not make it into the Hottest 100 at all, including Randy Newman, Frank Sinatra and The Kinks.

Bob Dylan	24
The Beatles	19
David Bowie	9
Randy Newman	8
The Rolling Stones	8
Elvis Presley	6
Frank Sinatra	6
Madonna	6
Marvin Gaye	6
Prince	6
The Beach Boys	6
The Kinks	6

It’s hard to read much more than that into these numbers, but importantly it gave me the opportunity to use a box and whisker plot which this blog has been sorely lacking.

UPDATE: As Mark has commented, this is a bit of a dodgy explanation. There is only so much that can be deduced about a distribution from a box and whisker plot (appealing though they may be). This histogram shows the distribution of the year of release for life and death songs.

Histogram: Life and Death Year of Release

Life and Death Theme Histogram

Mark also pointed out that the box and whisker plot does not really show the relative popularity of the different themes over time. I haven’t used pie charts yet, but I am not a fan, so I have come up with a mosaic plot instead.

Mosaic (II)

This confirms the decline in popularity of the love theme, but suggests that, while sex boomed in the 1990s, it has lost ground again in the 21st century. Heartbreak and party songs are the most popular themes of the current decade. The chart also shows that there are more songs in the list from the 60s and 70s than from the 90s, again a departure from the Hottest 100.

I have added this chart to the Guardian Datastore photo pool on flickr.

* To be precise, there are only 988 different songs in the list (and six are duplicated, each appearing in two different categories).

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

Visualizing the Hottest 100

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Today radio station Triple J finished broadcasting their Hottest 100 tracks of all time, the first all-time vote since 1998. For those outside Australia and not familiar with the tradition of the Hottest 100, it began back in 1989 and results are determined by listener votes. After two more years the format changed and votes were restricted to tracks released over the previous year, presumably because the top 10 became a list of the usual suspects. Since then 1998 and this year have been the only all-time hottest votes. A traditional favourite, Love Will Tear Us Apart by Joy Division, which was #1 in two of the first three all-time charts only made it to #4 this year, but Nirvana’s Smells Like Teen Spirit was #1 in the third and again in 1989 1998 and this year it made it to #1 for a third time.

Thanks to the wonderful collaborative spirit of Web 2.0, this year’s full list is already up on Wikipedia, complete with the year of release of each track. This allows me to indulge in my data mining hobby, which is why I am posting here rather than over on the The Music Blogs. So, inspired by a suggestion from Mark Lauer, a regular Mule reader (and careful sub-editor), here is a look at the distribution of the hottest 100 tracks by year of release.

chart

Hottest 100 Track Ranking by Year of Release

While the density certainly increases after about 1995, reflecting a lot of new entrants since the early charts, there is no clear trend along the 45 degree line (and, for the technically-minded, the R² is about 0.1%). So, while there are not as many oldies in the chart, those oldies that do make it in are just as likely to rank well as the newer entrants. To make the most of the R code I wrote to produce this chart, here is the same thing showing artist name rather than track name.

artists

Hottest 100 Artist Ranking by Year of Release

To get a better sense of the distribution of rank and year, here is a chart that just shows the location of the tracks by year and rank.

points

Hottest 100 Rank versus Year of Release

Seeing the data just as points like this shows a concentration of tracks released around the mid-90s. A histogram of the year of release confirms this.

hist

Of course, I’m sure this says more about the demographics of voters than the preponderance of true classics in the 90s.

UPDATE: In this tweet, @nicwalmsley suggested an artist scoring system: 100 points for ranking 1st, 1 point for ranking 100th. As he notes, this system puts Radiohead, Jeff Buckley and Nirvana in 1st, 2nd and 3rd place respectively. Here are the top 10 artists by this measure.

Radiohead	343
Jeff Buckley	269
Nirvana	188
Powderfinger	154
Metallica	152
The Beatles	149
The Smashing Pumpkins	139
Pearl Jam	138
Michael Jackson	135
Pink Floyd	13

FURTHER UPDATE: @Warlach has laboured hard to assemble the full Hottest 100 as a blip.fm playlist.

YET ANOTHER UPDATE: In case you are wondering about the geographic mix, as expected the list is dominated by the US and the UK.

USA	45
UK	37
Australia	15
France	2
Jamaica	1

The pedants should note that I’ve counted System of a Down in the USA (rather than USA/Armenia) and Crowded House as Australia (rather than Australia/New Zealand). I hope that doesn’t offend our Kiwi cousins!