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The telco confusopoly

The one frustration that started me blogging more than three years ago was the confusing pricing practices of phone and internet service providers. It was quite obvious to me that their 'plans' where meant to be confusing to ensure the consumer could not easily identify the cheapest provider. Today, the Australian Communications and Media Authority (ACMA) has released a report that recommends improving price information for telecommunications contracts to avoid a 'confusopoly' (here).  Amongst other things -

The authority also wants to prohibit what it says are misleading advertising practices, such as the use of the term "cap" on mobile and broadband plans.

"It's not a cap, it's not a maximum, it's a minimum," Mr Chapman said

"We want to prohibit that unless its a genuine hard cap, so that if you exceed your limit the service ends or you get the opportunity to upgrade." (here)

Most recently I have been comparing mobile phone plans. Some of the cheap plans don't allow you to call 13, 1300 and 1800 numbers under the cap, and they all have different call rates, flag fall and penalties for exceeding cap limits. To actually compare providers you need to know your calling needs in advance and have the mathematical skills to run this call profile, and other scenarios, through a model of each available phone plan. Insanity.

As I previously wrote -

By consciously manipulating these two criteria of a free market [low barriers to entry and perfect, or at least good, information], all firms in the market are able to avoid a state of true competition that would produce the most efficient allocation of services, and are able to artificially inflate the value of the commodity, hence producing more profit for each firm in the market.

This is not meant to sound like a conspiracy, because indeed each firm does not need to meet in back rooms with the other firms in the market and all agree to limit customer information and the comparability of their products. They each simply need to aspire to the great marketing ideal of product differentiation, a concept that is fundamentally designed to artificially eliminate direct competition by removing direct comparability.
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The power of product differentiation, through its ability to remove comparability and create an information gap to distort what could be a perfectly competitive market, can be demonstrated by the case of the term life insurance market in the US in the late 1990s. There was a mysterious and dramatic drop in prices across all firms that did not correlate to price drops in other forms of insurance, which themselves where steadily rising.

According to economist Steven D. Levitt, this can be attributed to the realisation of a perfect market through the power of the internet. Although term life insurance policies had been quite homogeneous before this period of time, the process of shopping around for the cheapest price had been convoluted and time consuming, whereas websites such as Quotesmith.com suddenly made the process almost instantaneous.

In just a few years, the value of the term life insurance market in the US had dropped by USD$1bilion because of the new found ease of comparability. What insurance firm would want this to happen? Even if you were a small player in the market, say a 1% market share, your turnover had just dropped by $10million. It is perhaps one of the great recent examples of the power of perfect competition in allocating resources efficiently, yet possibly one of the greatest blunders by the insurance industry.

I believe that the power of private enterprise is its innovative response to the financial risks it incurs, but with very simple regulation the innovative confusopoly, which comes at a cost to cosumers, can easily be avoided.  Indeed, most of the pushback against the telco confusopoly is from webpages which keep up-to-date tabs on plans from each service provider and enable you to take some rough guesses about future use and compare the cost effectiveness of each offering (eg here)

GDP down 1.2% for the March qtr

In the next 24 hours there will be a frenzy of economic commentary about the national accounts data and the importance of the last quarter's figure for the RBA board meeting next Tuesday.  My money is on no move by the RBA and more poor economic data this year.

For interest, below is the GDP per capita and Real net national disposable income per capita over the past deacde.  Notice the last few years (since end 2007) have been very flat for GDP per capita, and volatile but not really moving for net national disposable income per capita.

Brisbane and Perth housing slide continues

RP Data-Rismark released their dwelling price data for April today (here).  Brisbane and Perth are leading the price slide with Sydney and Canberra showing small gains.  This follows a stream of poor economic data recently.

I March 2010 I suggested that the next interest rate move by the RBA would be down.  I was wrong.  They increased another 75 basis points in total in their April, May and November decisions. 

My reason for suggesting they must move down is that the economy was much weaker than they anticipated, and the outlook far less bouyant.  Given this recent data one must think that their optimism is slowly fading.

2011 will be a very interesting year indeed. 

Learning to judge risk

No, this is not a post on financial risk. It is about child development and learning to judge risks yourself (a hot topic in my household).

As an economist parent this article, and the comments that follow, is very interesting. It begins...

Play equipment designed by  "safety nazis " doesn't allow children to learn from risk-taking, an expert has warned.

More kids aged two to seven were getting injured in playgrounds because they didn't know how to take calculated risks.

While it may seem obvious, learning to take risks involves... taking risks! There is an old saying that epitomises this attitude – if you want to learn to swim, jump in the water.

But it seems that Councils are not going to replace their plastic low velocity slippery slides and bouncy foam ground covers with splintered old wooden climbing frames in hurry. The experts still haven’t grasped the implications of their research. They conclude with the following advice.

To improve playgrounds, Ms Walsh suggested longer and bigger slides built into embankments to eliminate falls.

Also, smooth boulders for balancing, shallow ponds for exploring and plenty of vegetation to provide nooks and crannies for children to crawl around.

But if children learn from risk taking, shouldn’t they build high fast slides, with no ground protection and sharp jagged boulders for balancing and deep ponds for exploring?

In any case, the finger pointing at engineers and playground designers was thoroughly dismissed in the comments, with molly-coddling litigious parents copping a bit of heat.

Getting my head examined - a Chris Joye rebuttal

Don't get me wrong. I agree with Chris Joye on some things - lowering the inflation target (perhaps not right at the moment), pushing for a more streamlined NBN, and supporting Malcolm Turnbull's political ambitions.

But when it comes to the housing market the guy with all the numbers is happy to overlook the strikingly obvious and adores a verbal stouch with his foes - the group he calls 'housing nutters'. In fact he just recently recommended the following 

At the same time, anyone who claims that a 1% year-on-year retracement in dwelling values is a major asset-class event (cf. the share market frequently falling more than 5% on a given day) needs their head examined, with the greatest of respect. And I sincerely meant that latter caveat: you genuinely should seek medical advice if you are convinced that house prices are plummeting. 

Let's take his advice and examine what is in my head (noting that I don't believe a 1% year-on-year fall in national home prices in isolation is a concern). 

Joye often likes to draw attention to the low volatility of the housing market compared to the share market (eg here and here). But he neglects a few important differences. 

1. The housing market has at best monthly data only. Moreover each month's price data point is essentially an average. The share market would be far less volatile if you measured it that way and averaged away each month's price extremes. Not that volatility represent risk in any case. 

2. The share market is an equity market. If you want to compare like with like you need to compare the change in home equity to the change in share prices. If there is $1.7trillion in housing debt outstanding against $3.5trillion worth of housing, you can double any housing price change to calculate the change in equity of homeowners on average. Of course prices are set at the margins so perhaps for the price setting buyers and sellers the leverage, and importance of small price movements, is even greater. 

3. The negatively geared investor sets the market price (apart from the recent burst of FHBs). This means they are losing money every year. Any small decline in value decreases their equity substantially in addition to losses already incurred. 

4. The marginal homebuyer is heavily leveraged - 80% plus. This is not the case in the share market. Remember, leverage works to improve both gains and losses. 

5. The wealth effect is much stronger in the housing market than other markets, particularly due to leveraging and the sheer size of the asset compared to household incomes. 

6. Cost of home ownership is much greater than simply the interest cost. For most homes around 25% of the gross rent is spent on annual costs (refer to 3.) 

7. Housing market crashes, while they feel almost spontaneous, actually take some years to eventuate.

Irish housing - 5 years to fall 28%, or 0.41%/month

US housing - 6 years to fall 31%, or 0.38%/month

UK - 2 years to fall 21%, or 0.8%/month (and still 18% down from their peak 4 years later) 

8. Lastly, I feel sorry for anyone who shared Joye's property optimism and bought into the Brisbane or Perth property markets in the past two years.  While the share market hasn't been crash hot, 6% returns on cash has been pretty flash.

Anyway, if these notes are a sign of a mad man, well so be it ;-)

Wealth effect driven by the housing market

Leith van Onselen over at Macrobusiness has written a couple of very important and timely articles on the wealth effect. Put simply, the wealth effect is an increase in spending that accompanies and increase in perceived wealth.

In relation to housing, this paper suggests the wealth effect increases our propensity to consume by 9c per dollar of increased housing value (which is further supported here). So if the housing stock of Australia is valued at $3trillion (some say between 3.5 and 4 trillion), and market values increase 10% in a year, then we will spend on average 9% of the $300billion of new 'wealth', or $27 billion - with $6 billion of spending occurring prior to the end of the next quarter.

Importantly, this money is spent before it is earned by selling the asset. The easy access to home equity lending has been a contributing factor to the size of this effect, enabling households to spend their capital gains before they have been realised which increases their financial risk.

There are few readily available studies about the size of this effect in reverse, but if the same values hold in both directions we can look at some interesting scenarios.

If prices fall 2.5% nationally over a quarter then we lose $75billion of perceived wealth, with an immediate reduction in spending in the following quarter/half year of about $1.5billion and ongoing reductions in spending totalling $7billion

With about $1.7trillion of bank loans outstanding, that is about the same effect on spending as an increase in interest rates of 0.25% and keeping them there for two years (which will mean $4billion extra is spent on interest repayments per year). This of course assumes that house prices are not dramatically affected.  Indeed, if we consider that interest rate moves are likely to also bring down home prices, we can expect a much greater effect from the monetary lever.

That’s why house price falls of just a few percent can cascade into a crash so easily.

I would suggest the reason the wealth effect in relation to housing is much higher than found elsewhere is that many people who benefit/lose from house price changes are highly geared, which increases/decreases their equity more quickly for a given price change.

On this note I would add that you can’t directly compare share market volatility to house price volatility, since the share market is an equity market. To make a direct comparison you need to compare the volatility of the equity component of the housing market with share market, or the volatility of the share market value plus the value of debts held by those listed businesses to the housing market.

Peter Schiff predictions



Peter Schiff was ridiculed for years when he predicted that the US housing bubble and credit binge would result in a massive asset price bust and recession.  You could describe his economic philosophy as Austrian, and as the recent Keynes and Hayek rap video explains, the bust is a direct consequence of the boom, not some seperate economic event that can be avoided through government intervention.

In this early 2009 video Schiff predicts the collapse of the US dollar and makes some very astute observations that may resonate with Australians.  Enjoy.

1980s Texas Housing Bubble Myth - A Reply

Recently the debate on the price impacts of planning regulations has been a hot topic here and elsewhere. Leith van Onselen at Macrobusiness is one of the more sophisticated proponents of supply side impacts on home prices and recently responded to a comment of mine about Houston Texas. My comment was that if Houston is an example of how responsive supply can help cities avoid house price volatility, why did Houston experience a house price bubble in the 1980s?

Leith argued that Houston's apparent price bubble was a mere mild blip, especially on the grounds of price to income multiples. In his typically evenhanded fashion Leith does note many of the demand side factors at play during that time - the oil boom, liberalisation of loan standards and population growth. He brings together these points with the following conclusion.

What makes Texas’ home price performance in the early 1980s particularly impressive is that prices managed to remain relatively stable in the face of significant demand-side influences that should have caused home prices to rise significantly and then crash.

An additional point is made that Houston has managed to avoid the 2000s property bubble infecting most of the rest of the US, and much of the world.

My reply.

Houston prices declined around 40% in real terms following the 1982 market peak - that is indeed volatile - and it took 15 years for prices to recover in nominal terms.  The Case-Shiller 10 city index has dropped by a similar amount since the US peak in 2006 (30.5% nominally) - so much for the volatility aspect.

But why do prices in Houston still appear so dramatically affordable?

One major reason is the relatively high property tax rate.

Property tax rates in Houston more that doubled from 1984 to 2007 becoming one of the highest rates in the US. Depending on your area you can pay between 2-3% of your properties improved market value in annual State taxes, while the US National average is 1.04%.

One would expect areas with higher property taxes to have structurally lower prices, reduced price volatility, and much lower price to income ratios.

An illustrative example is shown below. The three comparisons are intended to roughly represent the early 1980s, the early 2000s and today. The Houston property tax rates increase from two to three percent, while the comparison taxes increase from half to one percent. Interest rates also represent mortgage rates at the time.

From these examples we can see that from just this single factor, the property tax differential, we should expect prices in Houston to currently be structurally around 30% lower than national averages (more on the impact of the property tax differential here).

An important factor at play in this example is that at lower interest rates a fixed percentage property tax leads to greater price differences. Therefore, over time, we would expect Houston to home prices to be a smaller fraction of comparable homes elsewhere as the property tax differential has a greater price impact at lower interest rates. Remember in the table above, rents and returns are the same for each comparison - only the tax rate is different.

Of course, this does not mean that housing is lower cost. It just means that cost of housing is borne by annual tax obligations rather than capitalised in the price. A far better comparison of whether housing is structurally cheaper in Houston would be to compare quality-adjusted rents to incomes over time and across cities.

Perhaps once the property tax differential and other demand side factors are properly considered we will see Houston's supply-side impact on housing prices diminish to zero.

Lastly I would add that the memory of such a deep and prolonged property price slump would be motivation enough to dampen speculative housing demand in Houston. Who in their right mind would bid up prices in Houston knowing that increased tax liability and the history of dramatic losses on the property market?

Evidence of supply-side effects on home prices remains elusive.  

A sign of desperate times?

Saw this advertisement today in the Financial Review.  I haven't seen anything like it before but it reeks of desperation.  Is it some kind of joke?

I like the first part of the fine print "Real Estate agents tell me I can get $2.1million for my luxury home but..."

Rent seeking behaviour - scare campaigns



From the Hungry Beast

Housing stimulus idea

From Crikey:

Local bike paths mean higher house prices

That is one housing stimulus package I would be happy to see implemented.  From the comments section:

More accurate take on alternate headline –
Bikeways found to be desirable trait amongst urban home buyers.
Temporary shortage of bikeways elevates prices in areas with bike paths.
Once all areas have bike paths, price distortion will be nil.

Captain Planet

Demand shocks – the details

Many pundits claim that increasing population increases demand. In technical economic jargon it shifts the curve to the right. But what we rarely see is an exploration of the two types of demand shock and the different potential price impacts.

The first type, as mentioned above, shifts the downward sloping demand curve to the right. This means there are more people with the same willingness to pay.

The second type is a shift of the demand curve up upwards. This represents a willingness to pay more for the same goods from the same number of people.

It’s pretty clear from this distinction which of these factors is in play in Australian housing.

Let’s examine the first case. Consider an auction with 10 cars for sale and 50 bidders willing to pay $1000 for a car. You add another 50 people to the mix each also willing to pay $1000. If I’m not being clear enough, this is analogous to increasing population in the housing context.

When the auction is run with the original 50 bidders each car sells for $1,000. When the auction is run with 100 bidders, the 10 cars still sell for $1,000 each.

This is an extreme scenario but does demonstrate a very real point. Unless the new potential buyers are willing to pay more for the same items as the existing buyers, the price won’t rise. At most, a second price auction (the result of an English open auction with heterogeneous preferences) becomes closer to a first price sealed bid auction by the addition of another bidder willing to bid at a price between the second and first price (read up on some auction theory here).

As I said before, new buyers, even a small cohort of the total market, can influence the price level if they are willing to pay more than the existing buyers, since prices are set at the margins. Imagine our car auction once again, and we give 10 of our original 50 bidders and extra $500 to spend on the car. What is the new result? All cars sell for $1500 each to these 10 bidders. Even though they were just 20% of the original market of buyers, they dragged the price up 50%. In fact even if there were thousands of buyers willing and able to pay $1000 for those 10 cars, the ten people willing to pay $1500 would always win.

Prices are set at the margin by those with the highest willingness to pay.

This example, where the demand curve is shifted vertically to reflect an increased willingness to pay by the same number of buyers, is shown graphically in the right hand side graph above.

But, you say, the key problem here is that supply is being overlooked - in the simplified examples demand curves are horizontal and there is no supply response. But look at the graphs again. Not only is the demand curve a more acceptable shape, but supply does respond in both circumstances. In the first, where the demand profile of buyers shifts horizontally supply does respond enabling prices to remain at their equilibrium level.

However, when the demand curve shifts vertically, it doesn’t matter how much supply responds to price increases, it cannot be a mechanism to bring prices back down (except under extreme assumptions about the shape of demand and supply and the limits of peoples willingness to pay at the top and bottom of the market).

Of course, in the end this analysis is probably unecessary. The value of housing arises from the rents - its ability to generate revenue or provide a service. Since we haven’t seen rents outpace inflation significantly for any extended period in the past two decades, one must be quite certain that the cause of prices being much higher than a rational present value of future cash flows is pure demand side speculation.

Economics, Real Estate and the Supply of Land

As a general rule, economists relying on supply and demand curves without properly discussing the assumptions that sit underneath their graphs can be ignored.

Alan Evans' book Economics, Real Estate and the Supply of Land is an effort to refute Ricardian notions of land supply and rent, and offer an alternative neoclassical theory of land supply. The arguments in this book are taken by many who believe that reducing government involvement in town planning will decrease the price of housing. Evans’ reasoning is questionable to say the least, and supported by elaborate graphs with often biased assumptions and interpretations.

One of Evans’ aims is to refute the Ricardian proposition ‘that the price of land is high because the price of corn [read: houses] is high, and not vice versa’.

To do this he constructs a model economy with a fixed land supply where two agricultural uses compete for land – potatoes and corn. In the figure below we see his construction of this economy on the left, with demand for corn inverted so that the intersection of corn and potato demand determines the equilibrium share of land devoted to each crop, and the equilibrium rent of land at point A.

He then proceeds to add a demand shock to potatoes ‘for some reason’. The new blue line represents the new increased demand for potatoes which enables potato growers to bid up prices for land previously grown for corn and reduce the amount of land used to grow corn. He concludes with the following -

Now it is quite clear that the increase in the rent of land is not caused by the increase in the price of corn. Exactly the reverse is true. The price of corn has risen because the price of land has risen.
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The rent for land is not solely determined by the demand for the product.


His conclusions are wrong.

First, it is still quite clear that at the new equilibrium the price of land for corn is still determined by the new higher price for corn. You could just as easily argue that every time a potato grower buys land from a corn grower he decreases the output of corn and the price of corn rises, thereby leading to an increase in the rents of land available for growing corn.

Second, he fails to notice that all he has done with the model is to demonstrate the inflation mechanism following an increase in money supply for one purpose. He increases total demand (potatoes plus corn) but shifts preferences towards potatoes so that corn demand is constant. The end result of his demand increase is to increase all prices in the model economy – potatoes, corn and rent.

Followers of Say would jump straight to this conclusion. You can’t simply increase total demand in the economy – demand is comprised of supply.

An actual demand shock, which models a change in preferences from corn to potatoes, is shown in the right hand side figure. You will notice that total demand remains constant and therefore the rents for this fixed quantity of land also remain constant.

So no, land rents do not determine prices. Prices determine rents.

Another example of poor reasoning is when Evans argues against a 100% land value tax. He argues that a tax of that nature would ‘freeze’ land development because there would be no incentive for a owner of agricultural land to sell his land to a developer for housing development, since he would not capture any of the value uplift. The rent achieved by the owner of the land will remain the same as when it is rented to the farmer – zero.

Yet in chapter 8 he argues that the value of land grows in anticipation of future higher value uses. In these cases, when the site is genuinely worth more as housing, the tax would be at a rate that reflects that higher value, and not the agricultural value. Therefore, the owner of the land will be facing a tax on the land value for housing while only receiving rents at agricultural values. As the city expands and the value of his land for housing surpasses the value for agriculture, he has a great incentive to sell or develop immediately to avoid losses.

Although I don’t support a 100% land value tax, I do support shifting the tax burden towards land and fixed rights to natural resources.

What we do learn from this book is that even the experts are prone to bias that affects their ability to apply objective logic and reason.