Category Archives: data analysis

There isn’t one labour market

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I’ve heard it said (of the US) that there is no national property market, but rather than local property markets are relatively independent of each other. I guess this was more true before 2007 than it is today.

Property in one area is a weak substitute for another property in a nearby area, and an awful substitute for property far away.  Supply and demand is determined at least partially independently in each area.

I’m suggesting that in South Africa, we don’t have a single labour market. We have several different labour markets, with fairly independent supply and demand for labour in each, and with each labour in each market being a limited subtitute for labour in neighbourhood markets and with limited portability between markets.

Supply and Demand for Labour

Supply and Demand for Labour (2007 Census data)

Continue reading →

How not to lose money in Make a Million

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I have a clear strategy for how not to lose money playing the Make a Million competition. As I explain it, you may come up with some smart tactics to win the competition and enhance your returns, but you’re on you’re own there.

So, how does one not lose money with the Make a Million competition?

Don’t enter.


You are overwhelmingly like to lose money if you enter this competition. I’ve said this before, and I’ve been right before. I’m right again.

There’s also the little idea that the  structure of the Make a Million competition increases risks of  financial meltdown

Let’s look at some hard statistics to show what I mean.

Telling statistics (what they don’t show)

In the MaM presentation, the organisers include some interesting statistics about number of trades, trading activity and many other metrics.

They don’t show average returns or performance.

So let’s look at some of the numbers:

Raw return data (excluding prize money) based on 2009 MaM competition.

Average Return -11.49%
Expected Loss R 1,149
Median Return -15.06%
Mode Return -9.12%
Probability of breaking even 25.00%
Probability of earning less than 10% 83.00%
Probability of doubling money 1.78%
Probability of winning 0.20%

Suddenly the competition doesn’t look so great, does it?  (This isn’t the first time, here is my analysis of the Comedy and Tragedy that was the 2008 Make a Million competition.) Continue reading →

Implied Pension Return Assumptions and the Equity Risk Premium

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When companies value pension obligations and required contribution rates, they make assumptions about the expected future investment returns. (Accounting standards require market-based rates reflecting fixed interest returns, but that’s a separate point).

So what assumptions are pension funds making? The WSJ has an interesting article showing that the average US pension fund is assuming future returns of approximately 8%. To put that in perspective, yields on 30 year T-bonds in the US are about 3.9%, 10-year yields are below 3% and inflation is currently about nothing. This is a huge real return and suggests that many of these pension funds may be underfunded.

It’s also interesting to work out what Equity Risk Premiums these valuation assumptions imply. FinanceClippings makes  some educated guesses at likely portfolio construction, and estimates assumed ERPs of nearly 8%. For reasons I’ve described before, an 8% ERP is madness.

My own calculations

FinanceClippings assumes a simple portfolio mix of 50% equities and 50% government bonds in this calculation, and assumes the average yield will be consistent with 30-year assumptions. I would differ slightly here. If we are looking at an overall portfolio, I would expect some investment grade corporate bonds and property in the mix too. These assets could be expected to earn 1% to 2% over risk-free over time (after adjusting for expected default loss on the corporate bonds). These return assumptions may seem low to some, but this is another area where it’s easy to overestimate the possible returns based on inappropriate periods of data. Continue reading →

Interactive house price data (including South Africa)

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The Economist has a brilliant interactive chart showing nominal house price growth across a range of countries, including South Africa.

It’s clear, as we already know, that South African house price increases have been dramatic. Somehow though, seeing it on a graph with a range of other countries brings it into sharper focus. Our property market has been manic.

Property investment – the value of data over opinions

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Lightstone have a trick up their sleeves. Their raison d’être is collecting, analysing, understanding and packaging data for themselves and others to use to understand past, current and future property valuations.

Their housing price index is more robust (and more independent) than those of the banks based off their own data and target markets. Rather than consider only the average price of houses sold in that particular month (which is a function of house price growth / decline but also how the type, condition, size and location of the houses sold that month differ from the prior month and year) they consider repeat sales where the same property has been bought and sold more than once.

This data is combined or “chain-linked” to provide a continuous measure of house price inflation over time.

House Price Inflation 2010

House Price Inflation 2010 source: lightstone.co.za

The result of all of this data, best-in-class methodology and analysis? When Lightstone says “opportunities abound in local market” I actually listen. Since their business model is to sell information, I’m more likely to trust what they say.

CPI at 3.7% for July 2010

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From Stats SA

The headline inflation rate in July 2010 (i.e. the Consumer Price Index for all urban areas in July 2010 compared with that at July 2009) was 3,7%

The official inflation rate (i.e. the percentage change in the CPI for all urban areas in July 2010 compared with that in July 2009) was 3,7% at July 2010. This rate was 0,5 of a percentage point lower than the corresponding annual rate of 4,2% in June 2010 (i.e. the Consumer Price Index for all urban areas in June 2010 compared with that in June 2009).

From June 2010 to July 2010 the Consumer Price Index for all urban increased by 0,6%

CPI Headline July 2010 = 3,7%

So this is close to the bottom of our 3% to 6% inflation targeting range. Economic growth is struggling, unemployment is high, but we haven’t reduced interest rates? Something here is a little odd.

I’ll put another $100 in Kiva, to be “microlent” to businesses and people across the world, if the next monetary policy committee meeting doesn’t cut interest rates.

More on cars and colour

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In researching my previous post on accurately measuring the risks associated with vehicle crimes based  on colour, I stumbled across another colour related risk measure.

Red cars, supposedly, attract more than their fair share of traffic fines.

Turns out this is incorrect.  Snopes.com has (as usual) an excellent article on red cars, including references to research showing red cars are not more likely to be fined than other vehicles. Unfortunately, the underlying research isn’t available online (as far as I could find).