The SR stands for Statistical Reliability. The SR is a score out of 100 for the statistical reliability
The higher the SR, the more reliable the data that was used to score the property market with a DSR figure.
Data is not always available for every property market. There is almost always some data. But rarely is there ever a complete list of stats with no omissions.
You may see a high DSR for a market and jump into further research only to find that a number of the stats were not all that accurate. The SR can save you the trouble. If data is insufficient, the SR will be low. In these cases, you may prefer look elsewhere. Other investors might like to delve deeper hoping the market is an uncut gem.
Although the DSR data website shows lots of stats, these are just the end product of a lot more data munging behind the scenes. For example, the vacancy rate calculations use about a dozen different figures from a number of different data sources. A lot of data is acquired to calculate just one statistic.
There are up to 9 different aspects of reliability considered in the calculation of the SR. Some examples include:
The following context ruler shows the SR for Newtown units was 84 in January 2015. The chart also shows a range of possible, likely and typical SR values.
As you can see from the Context Ruler, an SR of 55 was pretty normal in January 2015 around Australia.
In some thinly traded markets there may be insufficient information to publish a reliable figure. Many other data providers choose not to publish anything at all if some arbitrary threshold of unreliability is exceeded. But who gets to choose where that threshold lies?
At DSR Data the policy is to publish everything, but to publish it along with a Statistical Reliability figure (SR or SR+ for short).
This policy of publishing a reliability figure allows members to at least examine some data, but it places the reliability of that data in context for them. The choice of how to make use of the data is placed back in the lap of the consumer rather than having the provider decide on your behalf.
In April 2016 extra care was applied to the SR calculations for thinly traded markets such as regional areas or unit markets in suburbs dominated by houses. This extra care placed tighter requirements on markets making it harder to attain a high SR.
The following context ruler highlights the change. This is the SR for houses in Reservoir. This is a large market with ample trades to provide sufficient information for high reliability. Thus the SR was 84 out of 100 which is exceptional.
But note that the average and median SR are both seen to the left of the ruler in the pink section. You'd expect to see these somewhere in the middle of the ruler where red and green merge.
Since the vast majority of localities exist outside state capitals and are thinly traded, the average and median SRs country-wide are now much lower than they have been in the past. This is not because of reduced reliability of the data but because of an improvement in how SR is calculated.
Following is the context ruler for a thinly traded market - units in Chandler QLD.
At the time of writing some people familiar with Chandler might question whether there were any units in that suburb. If you think there is data for a market that doesn't exist, please check this FAQ before contacting us Why is there data for units when there are no units?.
Note that the SR is only 25 out of 100. This means the demand to supply ratio (DSR) for this market cannot be trusted. It may be a very high DSR or a low DSR, but whatever it is, you cannot rely on it. Further research would be needed of a manual nature to confirm the demand relative to supply.
Note however, that the benchmark that members should compare against for a reliable DSR is still around the 50 mark. This is found in the brown-olive section of the ruler. Make sure that before basing any investment decision on the DSR, you firstly check the SR. The SR should be above 50 if you're to rely on the DSR.
You should add the SR in your criteria for the Market Matcher to ensure it only finds markets with sufficiently reliable data.