Am trying to create a similar chart to Prof. Keen’s above for house prices to disposable income. I’ve got data covering the period 1959 – 2006:

* Total Disposable Income from the RBA’s G12 product (“GDP Income Components”)

* Dr. Stapledon’s median house price (extracted from his PhD thesis, Table 2.5, 2nd column)

* Population from the ABS’s 3105.0.65.001 product (“Australian Historical Population”)

In order to produce the chart I’m converting the RBA Total Disposable Income series to a Disposable Income per Capita series by dividing by the ABS total population series. Next I divide the Mendian House Price series by the Disposable Income per Capita to arrive at a “Years of disposable income to purchase median house” series.

However there is clearly something wrong as I am getting nonsene numbers. To explain [using ballpark figures]:

In 1959, Total Disposable Income was $3 trillion, which was spread across a population of 10 million, meaning Australians only had $300 per capita of disposable income. Dr. Stapledon thesis puts house prices at $83k, which results in it taking about 275 years of disposable income to purchace a house!

By contrast, in 2006, TDI was $158 trillion, population was 21 million => $7500 disposable income per capita. Houses were $357k, meaning 47 years to purchase a house.

What is the obvious factor I am missing? I suspect the TDI numbers are not inflation/CPI adjusted, but this notion seems too silly to seriously contemplate…

Thanks

Table 4.6: Occupied and unoccupied dwellings by dwelling structure, 2006

I don’t claim to be an expert on the property market–and in many ways got dragged into this debate because of the ancillary role that asset markets have in my analysis of the economy. I’ve had a quick skim of your post on your blog http://www.stubbornmule.net/, and I appreciate what I have read (I’d read in more detail, but I’m on holidays right now).

That particular claim could be technically wrong, but there are aspects to the Australian data that are incongruent with the usual “undersupply” argument that is used to sustain the proposition that Australian house prices will always rise. The key one I see is the ratio of new population to new residences. If there was an oversupply, then this ratio would be running above the current people per residence ratio of about 2.8. In fact it was running well below at from memory 1.7 population increase per new residence.

This and other aspects of the data–including the consistently very high non-occupied residence ratio–deserves to be highlighted. As an amateur in property–but an expert in economics–I could well have made some false hypotheses as to why some discrepancies exist. But they are there nonetheless.

I find this hard to believe. Quite apart from the fact that rental incomes exceed the costs of property management (you can outsource it after all), if you keep your property off the market, claiming a tax deduction is in fact illegal. According to the tax office you cannot claim deductions for “rental properties that are not genuinely available for rent”.

Have you studied the effect of marriage failure / single parents and so on, on demand for housing? Going back 15-20 years I suspect that this was quite an insignificant contributor to demand, but today it is very common to have very small households. Coupled to this is the skewed incomes of the households with single parents.

Unlike the older generation (especially public servants) todays generation have no job security and it is not uncommon to have 1 partner working interstate. All of this contributes to an abnormally high demand for accommodation. I know of several people who have worked very hard for years as casuals and treated like ****, and have now taken out massive loans which they will happily tell you they can’t afford. They say they are no different to the companies they work for and if they go bankrupt then so what! They never lied to the banks and we’re encouraged to borrow. (Better to go out big time! At least they too can benefit from the “goodtimes” )

Whatever model is used, to me it seems that just as important as the accuracy of the data inputs is the level of understanding of it.

In regards to tools like “housing statistics”, it seems to me that the data types are incomes, rents, ad prices. But noone’s allowed to understand it, because, protection of privacy gets you at every turn. To model/understand anything meaningful the identities of those that it applies to appear to be the crucial level of understanding to appreciate any predictive capacity. Who is getting what incomes ad who is buying what houses?

If policymakers (which is a pretty embarrassing term in this day and age) want to make decisions responding to evidence (ie meaningful decisions) then they really must be aware of the families/singles/married/males/females/educated/working/immigrant/foreign entities that are active in the system. Are predator-prey models up to this task?

Just as with the effects of debt. If I was to ever try to understand it and look seriously at the data, my first question would be who is carrying it.

Is there any statistical data gathering that can help with this question?

I reckon it would be good to have stats on total hours paid and total amount paid per month. This way we could track those that don’t register for benefits and the scaling back of overtime and casual hours etc. Anyone know if this kind of data is available?