Interpreting housing market indicators

I was motivated by this article in The Economist about housing costs in London to really dig down to how most economists think about housing supply and land markets. Today I want to share a toy version of the model I use to understand land and housing markets, and potential supply issues within them.

The model 
At time=0 a city has 20 people in 10 homes owned by out-of-town investors. 1 person from each home earns $52,000 per year, the other is a dependent. Rent for each home is $250 per week and each home is 100sqm in size. In sum, per capita income is $26,000, rent to income ratio is 25%, and floor space is 50sqm per person.

Say supply is perfectly restricted. No new homes can be built at all but population increases 10%. Now there are 11 income earners supporting 11 dependents. Average occupancy must rise from 2 to 2.2 people per home. 

Household income rises from $1,000 to $1,100 per week. Rents will stay around 25% of incomes so now rent is $275 per week. Floor space is down from 50sqm to 45.5sqm per person. Rent paid per capita is the same. 

In this scenario what measures can signal a supply-side squeeze? Clearly we need to be looking at an increase in the rent to individual income and a decline in the sqm per person as evidence of a supply side squeeze. 

But once you introduce two more critical features to the model - geography and income distribution - things get very interesting.

Geography
We can add geography to our toy model very easily by having the 10 identical homes on a single road heading away from the city centre. They are equally spaced, and the marginal travel cost is $20 per week for each home as distance increases from the city. 

In the base case we have 2 people per house willing to spend 34% of their income on a home with zero travel cost (the 1st home on the road). 

The first house would rent for $340pw, the next $320, then the third at $300 etc up until the 10th house is rented for $170pw. Remember, the housing cost (including commuting time) is $340per week for each household. The average and median rent is still $250pw or 25% of household income. 

The left graph below summarises this simple linear geographic set up, where a single home is at each point heading away from the city centre, and price a household is willing to pay for each location is falling with distance.

In the situation where all supply is constrained and the population grows by 10%, as in the first example we see that that prices simply increase in accordance with the increased income of the average household. 

But what if supply is only constrained over the first half of the road? No new homes can be built in the area occupied by the current 5 closest homes (as stylistically shown in the middle graph above). 

The outcome is that the density of the second half of the road increases by 20%. 

Prices at each location are identical, but there are now 1.2 homes on average at every position on the road from house 5 onwards. The mean rent has fallen from $250 to $245 per home because the new homes are only in the inferior locations (the same rent-to-income ratios and same floorspace per capita would exist). 

Had there been a ‘greenbelt’ style constraint, in that no new homes could be built in the furthest half of the road (where homes 6 to 10 are located), the number of dwellings in the inner half would have increased by 20% to accommodate the new people. In this case the new average rent would be $255 per dwelling in average. This is because all the new homes are in superior positions. Every single household is still spending 34% of their income, or $340 per week on housing and commuting, and each resident has the same size home. 

In either of these cases supply could not be said to be constrained in the city as a whole, despite half of the city being unable to expand dwelling numbers. Yet we see by virtue of location of growth that mean rents will change substantially. To summarise the impacts of geographically constrained supply we can observe the following metrics. 

The critical difference between local constraints and absolute city wide constraints is that the sqm of housing per person does not fall. However, this doesn’t mean that some households might be willing to trade household size for location, in which case the housing space per person may shrink in areas close to the city despite the city itself not being supply constrained and the fringes being able to be easily developed.

Income distribution 
We can add another dimension to this toy model in the form of a stylised income distribution. Income differences allow wealthier individuals to use their income to outbid for superior locations. 

Say the average individual still earns $52,000 per year ($1000pw), but that the wealthiest individual earns $125,000 and the poorest $13,400. Each household in between earns 80% of the previous household, starting at the wealthiest. 

We also convert the $20/week incremental commute cost to a commute-time cost based on the wage rate of each household. Thus the commute cost to the first home costs $20 for the average income earner, but $48 of time for the high income earner, and just $5.17 for the lowest income earner. 

What happens in this situation is that households sort themselves into inferior/superior locations based on incomes. But it also means that the rental gradient is much steeper than the income gradient. 

Let’s see how this works. 

Imagine that only the worst located home is vacant, and only the poorest household is in need of a home. How does the bargain over rent occur? Clearly the poor household has no alternative option but to rent an existing home - they can’t buy a piece of land and built their own. Another land owner could build a home for them and rent it, but they would have legitimate worries about being underbid by the current owner of the vacant home. 

This home owner also has the outside option to attract higher income earners away from other homes by lowering the price a little. Essentially, the owner of this last home has all the negotiating power to extract the full willingness to pay from the renter. In this case willingness to pay from the poorest household is 35% of their income minus commuting costs, or $112 per week. 

The same logic applies to any of the homes that are vacant at any point in time. Thus the home owners are able to extract rents because of their monopoly bargaining position. We end up with the following situation. 

The critical indicators from our three types of supply constraint in this model are summarised below. Note that while the mean of the income distribution is still $52,000 or $1,000 per week, the presence of very high incomes skews the rent distribution by a large degree. The income distribution of the new population (after growth) is assumed to be identical to the existing residents.

We learn that increasing income inequality actually increases the mean and median measures of rents, despite affordability being identical (34% of income on housing and commuting). The most important measure is the sqm per person if one wants to discuss housing supply constraints. Any other measure needs to control for income and geographical distributions. 

Concluding remarks 

What we realise from this simple toy model is that rents are a function of incomes, willingness to pay for superior locations, commute time and city structure, and ultimately the institutional bargaining power of land owners. 

Think about it. If all tenants could collectively bargain by agreeing to only pay half the rent they currently pay, and no one would outbid that new halved price in order to move to a better location, then rents fall by half. Real wages would rise dramatically, and real land prices would plummet. 

We also learn that most aggregate measures of housing affordability suffer from biases due to geographical and income distributions. Thus to make any solid claims about the affordability of the market household level data is required. If we measure housing within a city boundary, yet the urban area extends beyond that boundary, we will always find worrying measures because we haven't monitored the price of homes at the actual urban fringe (Yes it is still possible to buy a 3 bedroom home on the urban fringe and within commuting distance to Brisbane for less than $300 per week).

If we are seriously about housing affordability, we need to shift the bargaining power towards tenants. A transition period to higher land taxes would provide incentives to construct housing rather than hold out for increasing future values of development sites. Better standard contract conditions on regulated residential rental contracts (for example the inclusion of limits to rent increases) would also tilt the bargaining power in favour of tenants. 

Please share this article. Tips, suggestions, comments and requests to rumplestatskin@gmail.com + follow me on Twitter @rumplestatskin

Ford closes... along with common sense

Now that Ford has finally closed its assembly plant we have a reason for the economics crowd to reveal the biases in their ‘model thinking’.

Take Possum Comitatus, aka Scott Steel, who seems to think that if Australia had a few more people we would make more stuff (is that more per capita Poss?).

The lesson that shan't speak it's name - if we'd like to keep making stuff in Australia, we need more people in Australia
— Possum Comitatus (@Pollytics) May 23, 2013

Which is a bit of nonsense really.

A Twitter argument broke out between Possum, Nicholas Gruen, and our own The Prince. 

Nick - Nonsense, we’ll always make things in Australia, just less than some other countries.

Prince - Sorry, that’s BS. Switzerland, Norway, Finland. All manufacturing giants but minnows in population. It’s how you structure econ.

Possum - That utilise the EU population as a local population with common consumption demands. Ahem.

Possum - The Australian market isn’t large enough to sustain many goods specifically customised for Australia. Cars are the perfect example

Possum - Volume matters for stuff like this

Nick - So we’d export - like Volvo. Alas we woke up to that issue too late and too lethargically

Possum - We’d export to where? What other country in the world, using cars as an example - has a similar spectrum of conditions?

Prince - Population is not a cure for structural imbalances - makes house prices higher.. is that the real goal here?

Possum - Go and stick Finland, Switzerland and Norway in the middle of the Indian ocean. Reckon they’d be a manufacturing base?

Possum - Population is scale. Want cheaper house prices, build more houses.

Sorry Possum. Population is population. Scale is scale. The conflation of domestic population and industry scale, and therefore competitiveness, is one that regularly occurs. It is number two on my population myths list.

That large scale manufacturing is shifting to low cost countries is a product of globalisation - the multi-decade process of reducing barriers to international trade. It is a sign of our wealth relative wealth, but also a product of the management of our foreign account balances.

Switzerland protected its local trade-exposed industries by protecting its currency. German manufacturers are benefiting from the weak Euro, while Japan’s attempts at stimulating economy activity revolve around containing the value of the Yen.

Richard Tsukamasa Green actually has a very nice reply, outlining how in a situation of protected local markets being opened up to international trade.  He deserves quoting at length

In short, companies that produce for a market in their own country have an advantage when exporting. If we have increasing returns to scale, that is it keeps getting cheaper to produce more once you’re already producing, then the efficient, cheap producers are those who are already producing.
...
If so, then when it comes to trade, the countries who were producing widgets for their own market are those that provide it cheapest to everyone else. The home market has become part of the country’s comparative advantage.
...
Most importantly there aren’t too many cases I can think of where this advantage has been maintained without government props. Other elements of comparative advantage, like wages levels or training, seem to outweigh lingering home market effects – the massive amounts of computer hardware out of South East Asia isn’t due to their love of PCs, nor do Chinese consumers exhibit a love for…everything manufactured. The most valuable thing seems to be know how, and that is the most mobile of production factors.
The other is hat it makes the most sense when countries have been operating as autarchies and then BAM, international trade. That possibly made sense in the world of 1985 following five odd decades of global protectionism, but not now. Any developing industry will start with many countries as potential locations, regardless of where the consumer lies. The home market effect would only hold if transport costs are high so manufacturing close to customers is cheaper, but then that the lowering of costs once things get going are so great they more than offset the cost of transport.

I fully concur that this myth persists because in the period of reducing trade barriers larger domestic markets did provide a 'home market' advantage.  But this is not the world of 2013.

Let me respond to Possum point by point.

[Finland, Norway, Switzerland] “utilise the EU population as a local population with common consumption demands.”

I’m not sure what to make of this. Is he saying that foreign populations that demand the same goods generate a larger potential market? If so, that is my argument.

The Australian market isn’t large enough to sustain many goods specifically customised for Australia. Cars are the perfect example

Which is an astounding fact considering the number of different cars available from Japanese, European and US manufacturers. There must be at least 20 major brands in the Australian market, and over a hundred different models of car available. Whatever customisation they require seems a simple enough task.

After all, if Japan, Germany and Sweden can export a completed car suitably customised to Australian conditions to us, why can’t we do the same in reverse?

We’d export to where? What other country in the world, using cars as an example - has a similar spectrum of conditions?

I have no idea what ‘spectrum of conditions’ means, but the answer to the first part is easy - anywhere. If we are going to be an exporter we manufacture to the conditions of the destination countries regardless of whether they are identical to our own.

I find it funny because in Melbourne we have Boeing’s largest manufacturing base outside of North America. We assemble Volvo trucks. In fact over at Manufacturer’s Monthly there is a whole list of the companies ‘making stuff’ in Australia. It seems we can make stuff after all.

The problem of course is that the relative size of our natural resource production. Coal, iron ore and gold make up 40% of our exports. We then have education, tourism, and a whole bunch of other primary resources (gas, wheat, alumina, copper ore, beef). 

The big long term economic questions that the Ford decision reminds us about are

1. Do we value diversity of economic production?
2. How do we want to manage our external position given the volatility of the resources cycle?

Government debt hysteria

It’s budget time.  That means it’s time to switch off from the mainstream business news for a couple of weeks.  To help get you through I have a couple of notes about the ridiculous government debt hysteria that has broken out in this country in the past decade, and in many troubled nations (save Japan) since the financial crisis.

1. Nonsensical comparisons 
First cab off the rank, debt to GDP. GDP is a measure of the volume of all transactions in the economy.  How is it related to the debt held by an institution that forms a minor fraction of the economy.  Why not compare BHP debt to Australia GDP?  The very fact that this nonsense ratio is considered important by macroeconomists as a determinant of anything is quite bizarre.

In any case, Australia is a world leader in low government debt.

Source: Wikipedia

Moreover, shouldn’t we consider the assets of an entity when considering its debts? Mmm.  What would the assets of the the Australian government be worth? This is a trick question.   There is basically no way of estimating the value of the nation’s shared public assets despite some valiant attempts.  These attempts put Australia’s wealth at over $6 trillion in 2008.  That’s around $300,000 per person.

But isn’t a better measure of the ‘sustainability of debt’ the interest cost to government revenue ratio? For example, here is a nice comparison of interest expense as a percentage of central government revenue.  Australia barely makes a showing.

Should we not also consider that the same entity, in effect, sets the interest rate on those debts. As Dean Baker points out, this power really negates the ‘future burden’ of debt that the ‘household view’ of government budgets suggest.

And with all this focus on the crazy debt to GDP ratio, somehow the debts of the private sector are ignored.

2. Money is a human creation 
Humans made every rule there is, and every rule can be changed. Keep this in mind.

But we also need to think about what interest payments actually are.  They are transfers from the debtor to creditors.  So when government pays interest on its debt, it is simply creating a transfer payment from taxpayers to holders of government bonds.  In many cases, these will be the same people or entities. Sometimes the owners of bonds will be foreign, but they only get paid interest in Australia dollars, which need to be spent within Australia, circulating through the economy.

Furthermore, the government can allow inflation to reduce the real size of the transfer involved with servicing its debt, and it can set the interest rate it pays.

We also need to fully comprehend Paul Krugman’s analysis of the Capitol HIll Babysitting Co-op Crisis. Put simple, the baby-sitting co-op created their own monetary system which fell into recession for lack of money.  Those who were running low on ‘baby sitting money’ because they had redeemed their vouchers over short period were worried about their shortage of remaining vouchers and stopped going out.  Those who had accumulated vouchers were worried about being unable to earn back their wealth should they start spending.

Notice that the money issued actually facilitated wealth inequality in the co-op.  Some families had very few vouchers left while the others were accumulating voucher wealth. We shouldn’t be surprised about this relationship between widening wealth gaps and business cycles.  And we shouldn’t be surprised that ancient remedies involved resetting debt periodically though jubilees - which would have worked perfectly well in the babysitting coop.

Ultimately we make the rules and can deal with the social and economics outcomes that arise from our economic system however we choose.  Fundamentally these choices are moral ones.

3. Equilibrium analysis does not apply
Most detractors of government debt seem to have a model of the economy in mind where the economy is in its magical equilibrium.  Then the government spends money, and since the model is always in equilibrium, the extra spending crowds out other spending exactly.  You might think I’m simplifying the argument.  I’m not.  There is not even money in the model at all.

The scope for adjustment to monetary policy, tax policy, inflation, all of which can reduce the debt interest size over time, are never discussed.

Remember debt and money are human inventions. Look at Japan to see what is possible and why we need considered analysis based on a proper understanding of money.

I hope, given the horrid state of quantitative macro that the RBA’s conference at the end of this year attracts some more robust analysis.

Why Mathematica for economics?

Readers would have noticed that some of my previous posts containing interactive graphs that require Mathematica CDF Player to view. Which might leave you wondering why I choose to use Mathematica as my computational tool for ‘doing economics’.

So I thought I might outline here why I do. 

Image source

And the answer is, mostly, because it was easy to learn. 

1. The documentation in Mathematica is very comprehensive and delivered in a notebook format (Mathematica’s ‘front end’ interface), so you can modify the examples and test what your modification does to the output. 

Many useful functions are built in, and even better, functions are typically named by their real mathematical name. 

For me, I need a tool that can handle analysis of graphs and networks, can do symbolic manipulation, produce charts, handle diverse file types, analyse textual data (for matching names and other strings in large data sets). I have scraped data from websites using Mathematica, analysed social networks, and more.

Now, Mathematica is surely not the only tool for these jobs. Many like the open source R, others like Matlab, and for basic regressions Stata is a common tool. I don’t want to start a debate about which tool is better for which job - ultimately it is a user choice about investing their time to learn one or the other, and about the ongoing usability. 

2. I like the integration of different functionality. There are real and electronic books written completely in Mathematica. It has built-in slide show options to have live and interactive calculations and charts in presentations. Easy interactive tools and web deployment (but viewers do need the free CDF player). 

3. One thing that Mathematica users seem to emphasise is functional programming. Rather than writing loops to build up calculations, you simply write the function and it can automatically map across lists and various other data structures. Once you get used to this, you won’t want to write out loops again. This also means it is easy to get quite complex calculations coded up quickly. 

4. A very helpful user group, especially at StackExchange. 

Lastly the RBA produces their charts and does a lot of analysis with Mathematica (although using custom packages). Some useful Mathematica tips from the RBA’s Luci Ellis are here, and here is a useful blog on how the dynamic graphing capabilities make good teaching tools. 

Life of an economics student?

Having studied the fundamental post-graduate economic courses last year I feel I can comment on this quite scary YouTube clip. Basically, the public perception of what economists do and the tools they use to understand the economic is completely out of whack with what most really do.

Yes, there are research frontiers in many areas that factor in the political and social realities we read about, but should it not be the case that all economists are equipped to understand these realities?

Teaching of economics really needs to get out of the 1980s and embrace all the knowledge the field has attained in the past couple of decades into their core curriculum.

Enjoy.

More housing market signals

My recent post about timing the Australian property cycle concluded that, all things considered, the period over the next 2-3 years will probably the best time to buy since the late 1990s.

My message, if it wasn’t clear, is that if you have been holding off purchasing a home because of the risk of capital losses, then these risks are probably lower now than at any time in the past decade.  Maybe prices will be a couple of percent lower at the end of next year, but I have a hard time wrapping my mind around downward price movement more severe than a couple more years of the slow melt, or around 3% in nominal terms.  The chances of price gains is also now much higher.

I described in the past how each Australian city has its own cycle, and that aggregate data is may need to be assessed against local indicators. Sydney will probably be the first to start the next price cycle.

Now don't take this as a thumbs up from me for housing price growth.  High asset prices are not a particularly desirable feature of an economy.  However my strongly held view is that asset prices should not form part of the debate over housing affordability.  It is like having a debate over the affordability if steel by looking at the price of BHP shares.  No, the asset price will be subject to the whims of financial markets, and the affordability of steel can only be observed by looking at the price of steel.  In housing markets, land prices are asset price, and rental prices are the actual market price for housing.

Whether we also desire for social reasons broad access to the housing asset market, then we may consider severe changes to policy in this arena.

Further, I am merely observing the features of previous cycles.  This is a slightly better approach than just extrapolating recent trends, but there is no particularly strong theoretical reason why the next cycle should be identical to the last.  Though I do expect common features.

So what sort of indicators are crucial in observing the bottom of the cycle?

Dwelling turnover
What we are looking for in this indicator is a slight uptick. We have bobbed along the bottom for three years now.  Can this go on, or are we due for a correction?  Will those reluctant landlords cash in once prices have stabilised?  Once turnover starts to noticeably increase, perhaps break through 5% toward 6%, I will have more confidence that it is a relatively advantageous time to buy.

Turning point of mortgage payments to income ratio 

We should see bottoming out of mortgage payments to household incomes at the bottom of the cycle.  With further interest rate cuts expected this year, this indicator should fall quickly below 8% in the next two years. 

Turning point in housing credit

Housing credit growth has been on the decline since the end of the national boom in 2003. However the short periods of increasing rates of growth also produced price gains.  It’s now been ten years since the peak, and a modest turn around looks imminent, especially considering the pattern of the second derivative of housing credit which is surging towards positive territory.

Falling rents

It may seem like an odd indicator, but falling rents (in terms of rent to income ratios) is a signature of increasing prices.  The last 5 years have seen rents tighten in relation to income.  This might be over for now and if we see this indicator start to fall I will have even more confidence about where we are in the cycle.

Of course, I noted earlier that the next housing price cycle will be far less severe than the last.  This is not a prediction of a huge nominal gains, but of relative returns from entering housing in 2014-2015 compared to the last 6 years.  For those interested in getting into the market it is time to start paying close attention to the market in your area.

Watts' model of cascading network failure

I have written in the past about how social and economic networks are a necessary ingredient for a proper understanding of economic patterns. The rise of social network platforms like Facebook and Twitter has allowed a thorough analysis of empirical regularities seen in networks in the social domain. Stephen Wolfram has a great blog about the regularities observed in Facebook data scraped using WolframAlpha.

One of the more interesting networks models is Duncan Watts' model of cascading network failure. Simply, each node in the network has a specific tolerance to failure, and it the share of adjoining nodes that have failed exceeds this tolerance, that node will also fail. For example, a node could have a tolerance of 0.5, so if more than half of its neighbours have failed, it will also fail, leading to a cascade of failures of its other neighbours.

This simple model is quite versatile. It suggests underlying mechanisms behind how fashion fads arise, or why investors tend to go with the herd, or why some industries produce superstars even though no one can objectively tell the difference in the quality of their skills. 

The methodological individualism so fondly embraced by the economics crowd has at its core the concept of utility, but stops short of answering the far more important question – where does our utility function come from if not our environment and our interactions with others? A model of networks can help explain the source of utility and begin to give a picture of how unique cultures and customs arise. 

In any case, I have generated an animated version of the model that simulates over a random network, with 5 random nodes ‘shocked’ to initiate the model. The histogram shows how many of the 20 different shocks have led to cascades of failure of a particular number of nodes. 

Enjoy. Follow me on Twitter. And please share.

Timing the residential property cycle

One interpretation of recent data is that investors seem happy to jump back into Australian residential property markets. Perhaps due to a search for yield. Perhaps from foreign cash seeking a safe harbour. Or perhaps it’s simply time for the Aussie love affair to be rekindled. Holes are over. It’s houses turn.

With these winds of change in the air maybe it is time to take a step back and look at the long term property cycle itself.

Property industry types talk about the cycle like a mythical being - unless you have witnessed it yourself you won’t know how aggressive the beast can be to your leveraged finances.

Long term regularity of asset price cycles is an intriguing proposition. Is the 18 year cycle really a good rule of thumb? If so, why don’t investors expect the cycle, and remove it through their anticipatory actions?

A simple answer might be that investors would anticipate the cycle if credit markets would allow it. But the banks supplying credit are themselves constrained by previous movements of the market. Thus the interaction of prices and the willingness to supply credit seems to be pretty decent explanation of the peculiar regularity of long term cycles. Thanks Minksy.

One way to think about the nature of the cycle is in terms of returns from yield compared to capital growth. At the bottom of the cycle equities, including property, are seen as risky places to preserve capital. During the boom expectations of capital growth return, and equities become the assets to hold.

If this truly reflects some fundamental emergent dynamic in the economy, a simple rule of thumb is to buy the high yields at the bottom of the cycle, and sell capital gains at the top.

But how do we know when yields are high? We need a relative measure rather than an absolute measure.

In the past I have used the mortgage rate divided by gross yield as a measure of the relative value of residential property. The theoretical picture is the the mortgage rate is a good proxy for the yields, net of capital growth, available in the economic generally. Gains above this rate typically arise from capital gains.

When the gross yield is close to the mortgage rate, theory says that the price is reflecting an expectation of low capital gains. But that would be wrong, given that it is the same theory predicts and equilibrium asset price.

In reality this would be a good time to buy.

The theoretical explanation is that these low growth expectations arise from recent experience of low growth - the same feedback that feeds the cycle upswing when high prices feed into expectation. If markets are myopic, you can forget about finding anything useful about expectations in the prices themselves.

So where’s my evidence for this? The graph below is an update from a previous post. With recent rental growth, price falls, and falling interest rates, this simple measure is showing that now is a good time to buy.

I have also created a second measure - the mortgage payment per dollar of a 30 year loan divided by the yield. The second measure adjusts for the fact that the cost of buying asset, in addition to the cost of interest, is a higher portion of the total cost at lower interest rates.

What is more surprising is the regularity of a head and shoulders-type pattern - similar tops and bottoms, and a similar period to the cycle, in this case 15 years. Not too far from the 18 year rule of thumb. And not too far off the stylised asset price cycle seen so regularly when discussing the latest housing boom

Given this regularity, and the strong buy signal, my internal model of the market suggests two possible future paths.

1. Renewed cycle

A great time to buy in most capital cities was around 1998. This year preceded a boom in Sydney, that cascaded across the country for the next 8 years. My chart shows the cycle at around 15 years, meaning this year is a good time to buy. The 18 year rule of thumb is then 2016 - just three years time.

Given the expected resources shock in the second half of this year and early next, I would not be in a rush. Also it may be wise to get better signals about the direction of international markets, particularly the US before leveraging into Australian housing.

I expect to be on the lookout for well located land in about two years time unless I get strong signals that the second path is playing out.

2. Stagnation

Given the weight of private debt, the already low interest rates across most of the developed economies, and a general reluctance for increased public spending to maintain employment and stimulate private investment, could we be heading to a long credit-constrained stagnation that requires major price adjustments in wages, rents, and currencies.

I have no good reason to believe one way or another. Political outcomes in Europe, China and Japan will be critical, as will our domestic adjustment following the mining investment peak.

My gut says that the fundamentals of continued current account deficits, which reflect inflow of foreign asset demand, and scope for much lower mortgage rates will probably allow for another cycle to ramp up by 2017.

I don’t expect it to be as severe as the last cycle for a few reasons.

• Inflation will be low unless the AUD falls significantly. Thus real gains could be high without such dramatic nominal gains.
• Mortgage rates still have scope to fall to around 4% in the next two years.
• But I expect the memory of the financial crisis and a stricter regulatory environment will mean tighter bank lending
• The demographic shift of baby boomers seeking to get out of negatively geared residential property will dampen capital gains

If most investors are myopic, those who consider the long term will have an advantage in any market. And what we have seen here is that we seem to be in a relatively attractive period for buying residential property assets. Just remember to consider all the other macro and political factors in your own assessment of the market.

Australian age-dependency ratios

Everywhere you turn it seems that higher rates of population growth are seen as a 'solution' to an ageing population.  Here's one recent example.  My general views on this matter are found here.

At the very least there should be a publicly accessible model of population growth to verify the claims being made in this debate.  The productivity commission has modelled population growth for this purpose, although the intricacies of the model are not at all clear or public.

My first step towards this is to actually look at the historical demographic record in Australia.  As you can see from the interactive chart below, the country's age dependency ratio has been steadily increasing since at least the 1970s.

Offsetting this age dependency has been a quite dramatic decline in youth dependency as fertility rates fall. The total dependency, or number of children and retirement age people per working age person is at record lows.

The next step is the add some features to this model to allow a choice of assumptions about immigration rates (and ages) as well as birth and death rates, to see exactly what how immigration policy is affecting demographics and whether there are some circumstances in which the 'immigration solves ageing' slogan may hold.

Enjoy.

Are there supply curves in a theory of return-seeking firms

In the theory of return-seeking firms there is no supply curve as such.  There are simply reactions by firms given their expectations about 1) the persistence of a demand shock, 2) their competitiveness.

Under normal conditions where demand increases in line with expectations, mark-up pricing that is set at a level to discourage competitor entry, can continue to be used.  However, there are many pricing options available to a firm to win market share (a discussion for a later post). 

The below model shows the case of three firms in a market.  The rate of return earned at the starting position is proportional to the market power/competitiveness of the industry.  The theory has nothing to say about whether three firms will result in reduced competition.  Competition, or lack thereof, is an artifact of local monopolies, regulatory frameworks, capital barriers and so forth. In a market with free entry and local competition, three firms can easily be very competitive. 

A shift in the demand curve in this model need not have any special impacts on prices under any period of analysis.  There are no assumptions about the slope of a supply curve.  What exists is an ability to interpret price changes as evidence of market/monopoly power.  For example, if demand for oil tankers increased over a short period, ship builders would have years to increase their mark-ups and returns before a competitor could become established.  However, they may choose not to take all the possible increase in returns to decrease the attractiveness for a new competitor, or to win market share from an existing competitor - no use making high return now, but being forced to accept very low returns in the future when new firms enter the market.  

The price setting during a short term demand shock is not at all the result of costs faced by firms, but of market power. 

To recap, an unexpected sudden shift in demand can provide temporary monopoly power for firms currently in the market (since the shift is beyond the planned capital investments in the market). In markets where new capital takes many years of investment, or there are regulatory barriers to investment, higher prices would be expected.  However in markets where production is highly competitive between established firms vying for market share,  sudden shifts in market demand may lead to falling prices. 

The below interactive graph the demand shock slider shifts the demand curve.  The market power slider sets the starting market power and shows that higher mark-ups / returns will be acheived with greater market power. The checkbox allows market power to be related to demand shocks to demonstrate the case that even in apparently competitive markets unexpected demand shocks might themselves create temporary market power.