Home > Uncategorized > All-States Calculated Median Vendor Price Index Chart; and Considering Compositional Bias

All-States Calculated Median Vendor Price Index Chart; and Considering Compositional Bias


In accordance with the last post, I have put this one together for all the Australian states combined, for the Calculated Median Index (as opposed to the Weighted Average Index, from the last post):

Generally, for statistical distributions that are skewed (significantly “non-normal”) like these data sets are, median representations tend to be better than means/averages, as they are less subject to changes in the composition of the data  itself; hence, medians tend to be less volatile and, thus, more “reliable” as a measure of the central tendency of the data set in question…

* Side Note: … even if, for data sets like these, I am a believer that: visual representations are an absolute must, at a minimum; and, for these sorts of skewed data sets, simply stating a lone median, or mean, etc, is a poor way of communicating the nature of the data – standard deviations (etc) should also be stated to assist in providing the “user” with a better idea of how well the median (etc) actually represents the data set … *

And here are some examples of how skewed these data sets are – for NSW, VIC, QLD (as examples); notice how different the shapes of the distributions are, even for these 3 major states…

It is interesting that NSW and QLD have some similarities (with NSW still being flatter and more broadly distributed), but VIC is tending more and more towards strong centralisation in the listed $300K-400K bracket – is this due to the surge of units coming onto the market in Melbourne, which we have heard so much about?

I’m not sure, but the $300K-$350K bracket has done nothing but increase its fraction of listing since I started recording the data in April 2011. Is it also the First Home Owner bracket for outer Melbourne? Are they finding things particularly stressful, and are now getting out? It’s hard to say.

Another point to consider is whether the composition of the Vendor Listings are having a  leading effect on the sales trends – perhaps leading by 1-3 months? It’s a working hypothesis of mine, anyway, and I’ll look at ways of testing it over the coming months.

However, in light of this idea, I am wondering if the large surge (increase) in the NSW Calculated Median Vendor (Listing) Price Index (first chart) will cause an increase in subsequent sales index figures? That is, will the large swing to the upside in the weight of the distribution have a flow-on effect to seeing a rise (or mitigation?) of sales-indices in the 2-3 or so months following the August “turn around”? Subsequently, will the significant and rapid decline in the Vendor Price Index(s) (reflecting listings/supply price composition) following October then pre-empt similar sales index declines? We will see, I guess…

Is what I call “compositional bias” an issue? I am not talking about the bias of a collector, user or operator; I am talking about the actual makeup of the listings effecting subsequent sales results. Surely it is a significant consideration, since we are just not talking about changing buying habits of the property-buying population for a fixed supply profile – the supply/listings profile seems to change as dynamically as buying habits and preferences, as is suggested even by just the three listings profile time trends for NSW, VIC and QLD, above.

To expand a little more on the concept, this compositional bias notion suggests that changes in price-composition/structure of the listings available will influence the subsequent sales results. Honestly, it seems to make sense to me, and should work both ways: to either cause compositionally-biased decreases in sales prices, or increases; for, the more (greater supply) of a certain-priced property there is, the more likely it is that a property in that bracket will sell, and thus “drag” indexed-sales results in its own direction.

Additionally, it might temper/minimise larger trends either way – for example, compositional swings to the downside in a bull market could temper indexed-sales rises; and, similarly, compositional swings to the upside could temper indexed-sales falls in a bear market; and large compositional swings either way could see actual short-term counter-trend movements in sales-indices – for example, indexed rises in a longer-term trend bear-market, and what might possibly happen for the NSW market recently (or soon to be), as discussed above.

I am not saying that this is always a significant case; but I am saying that compositional bias should be considered when contemplating sales-indices values and trends, as it is part of the bigger picture…and a bigger picture is a better picture…and that it could be considered part of a predictive model.

Unfortunately, the major data providers do not make this sort of information readily available – if at all, or only for a significant price to a privileged few? – so, until they (and/or the govt data providers?) get their bums into gear and provide the populace with more information in this largely data-opaque industry, you the reader, are left with little ‘ol me, and my spreadsheets and charts to provide you with such information and insight!

The sooner it changes, the better, right? 😉

Pick up your act data providers! There is a lot of hungry and interested people who want more diverse and transparent information for this industry. Surely there is a competitive motivator here somewhere?

Until next time,



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