“I think it is a mistake to assume that a riskless, easy, guaranteed way to prosperity is to be leveraged into property. It isn’t going to be that easy.” (RBA Governor Glenn Stevens, Sunrise Program March 29 2010)I applaud Glenn Stevens for making the above statement on national television. It was both courageous, and a succinct and accurate statement of the delusion that has come to dominate economic thinking in Australia. He effectively acknowledged that Australia has succumbed to a Ponzi Scheme: the belief that the entire country can make a living from unearned income. This something that, until recently, most public and private commentators have been strenuously denying. The great pity is that this realisation was so long in coming, while the farce is that one wing of Australia’s government has now declared its intention to bring down a Ponzi Scheme that the other wing is trying to maintain.
The data that led Stevens to this realisation is pretty obvious: the most recent quarter saw the largest increase in house prices since the ABS began keeping records in 1986.
The role of the Federal Government in causing this bubble–and earlier ones–via the First Home Owners Grant is also obvious. While previous manipulations of the market by the Grant turned a tepid rate of increase into a bubble, this time the Grant turned the fastest rate of fall in house prices into its greatest rate of increase. The current volatility of house prices is telling: eight of the ten biggest movements–in both directions–have occurred in the last two years.
With the RBA likely to increase rates specifically to prick this bubble, the volatility will doubtless continue. But even without the RBA’s expected–and I have to say justified–anti-bubble interest rate intervention, the real estate market is, as Stevens argued, far from a stable route to riches.
House Prices are Not NormalOne of the great fallacies of conventional “neoclassical” economics that encouraged behaviour that caused the GFC was the proposition that asset prices are “Normal”–in the sense that their volatility fits the pattern described by the “Normal Distribution”.
The superficial beauty of the Normal Distribution is that the behaviour of a variable can be reduced to just two numbers–its mean and standard deviation. But its real, deep beauty is that if a variable follows a Normal Distribution, then extreme events are vanishingly rare. if a variable moves normally, then:
- Movements of 5 standard deviations or more above or below the mean are so rare as to be effectively non-existent; and
- Their rarity means that they play no significant role in shaping the system: its behaviour is completely described by the events that fall within the +/- 5 standard deviations range.
For example, the average daily movement on the Dow Jones since 1914 is 0.028%, while the standard deviation is 1.13%. If stock market price movements were “Normal”, there would have been just one daily decline of more than 4.5 percent since 1914. In fact, there were 100 such falls, out of a total of 24,593 daily movements in the Index–fully 100 times as many falls as a Normal distribution would predict.
Nor could those falls be ignored in the long run: they caused a collective 612.5 percent fall in the Index, when the sum of all the percentage movements since 1914 is 688 percent.
Anyone who relies upon the Normal Distribution when investing in the stock market is ultimately on a hiding to nothing to lose his shirt, because the Normal Distribution seriously underestimates the odds and the importance of extreme volatility in share prices. A far better guide to how share prices actually behave is the “Power Law”, as well as Didier Sornette’s research based on an analogy to earthquakes.
So how do house prices stack up? Though we have a far shorter time series for house prices than for shares, one thing is for certain: house prices are not Normal. The mean quarterly change in the ABS series for nominal house prices since 1986 is 1.24%, and the standard deviation is 1.786%. If house prices were Normal, the distribution of quarterly changes would look like the red line in the next chart; the actual pattern is shown by the blue bars.
The vast majority of quarterly movements are below the mean, with the largest number–27 out of the 93 quarters–registering just above zero change (an average of 0.267% increase for the quarter). The high overall average of 1.24% growth per quarter in nominal house prices is driven by the smaller number of quarters (26 out of the 93) with increases above the average.
The data is skewed in time as well as magnitude. A truly Normal distribution would have no time pattern to the data, with a large movement just as likely to be followed by a small one. The actual distribution has long periods of low increases with clusters of large changes–and these have increasingly involved large falls as time has gone on. The next chart compares the actual pattern of price movements (in red) to a simulated random pattern (the black crosses).
There are several movements–especially the -3.4% and +7% recorded since the GFC began–which are outside the standard range for a Normal Distribution. They are not so far outside that we can categorically say that a Power Law accurately describes house price movements, as we can with share prices. But the odds are that these two leveraged asset classes share the same fundamental dynamics.
The FHOG of Real EstateIt should also come as no surprise that the First Home Owners Grant scheme significantly distorts the housing market. From the statistics, there is no doubt that the true beneficiaries of the scheme are vendors, real estate agents, and lenders–not first home buyers.
There are several ways to slice and dice the data on this point: there are years when there was no Grant, and years when there was a grant in some form or another; periods prior to the introduction of a Grant, or a change in its magnitude, and periods after the change; and periods when the Grant was doubled. The following charts show these dissections.
Periods without a FHOG had significantly lower growth in house prices, and significantly lower volatility in prices. The average quarterly price change without a FHOG was a mere 0.44%–one third of the average for the entire series. The volatility was also substantially lower, with all movements being between -1 and +2.5%.
Periods with a FHOG had both substantially higher average price rises (2% p.a. vs 1.25% for the entire set) and substantially greater volatility (ranging from -3.4% to + 7%).
A closer look at the impact of the FHOG shows that its role is that of a storm trooper for the housing market. The next chart looks at the movements in house prices in the 2 years after an introduction or change to the Scheme, and in particular at what happens to prices in the 2 years after the payment was doubled (in 2001 under Howard and 2008 under Rudd). The “Pre-FHOG” is all other quarters apart from these 2 year segments.
All but one of the large increases in house prices (4% or more in a quarter) occurred after the FHOG was doubled, while the average quarterly change in prices was over 2.9 percent. If the FHOG is the real estate sector’s storm trooper, then doubling the FHOG is its Panzer division.
The next table summarises the statistical properties of house price changes, including “Kurtosis”–a measure of how peaked the distribution is compared to the Normal Distribution–and “Skew”–a measure of how biased the distribution is towards above or below mean movements. Periods without a FHOG have a peaked distribution (Kurtosis greater than zero) and few price changes below this peak with many above (Skew greater than zero); periods with a FHOG have a flattened distribution (Kurtosis below zero, meaning that price movements are more widely dispersed), and a negative skew (meaning that there are more price movements below the mean than above).
The role of the FHOG in causing house prices to rise faster than consumer prices is even more apparent if we consider the annual CPI-deflated series–but what is then also obvious is its decreasing effectiveness over time. When rolling annual price changes are considered–a more realistic time frame for changes in house prices, since this is a slow moving asset market–the biggest price inflation bang for the FHOG buck was back in 1988, when the rate of increase hit almost 30%. Howard’s doubling could only score a 16.5% maximum rate of growth of real house prices; Rudd’s has thus far peaked at 11.25%–though this omits the impact of the most recent 7% increase in nominal house prices (since CPI numbers are only available till December 2010).
The real house price data emphasises the message that the real beneficiaries of this government intervention are not first home buyers, but vendors, real estate agents, and banks–in increasing order of benefit.
The vendors benefit from a higher price; the agents benefit from higher turnover and fees; while the banks benefit from the increased mortgage debt that first home buyers–and then the vendors they sell to–take on in order to buy into a government-supported Ponzi Scheme. The banks and mortgage lenders in general have been the biggest beneficiaries as mortgage debt has risen from under 20% of GDP in 1990 to over 85% at the end of 2009.
The revival of this Ponzi Scheme played a key role in Australia’s sidestep of the GFC. As is obvious in the next chart, the mortgage debt to GDP ratio began to fall prior to the First Home Vendors Boost, but then accelerated once the Boost was available.
The Australian economy has thus returned to debt-driven growth, with the household sector carrying the full burden for the private sector. I remain sceptical this period of debt-driven growth will last as long as in previous bubbles when our private debt to GDP ratio was half what it is today.
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