What is the SR?
The SR stands for Statistical Reliability. The SR is a score out of 100 for the statistical reliability of theDSR.The higher the SR, the more reliable the data that was used to score the property market with a DSR figure.
Why is the SR important?
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.
Is the SR reliable?
There are up to 9 different aspects of reliability considered in the calculation of the SR. Some examples include:
- Volume
- Volatility
- Supporting stats
- Currency
The calculations involved in determining an SR are usually more complicated than the calculationsto determine the statistics themselves.What is a good SR?
The followingcontext rulershows 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 ratherthan having the provider decide on your behalf.
In April 2016 extra care was applied to the SR calculations for thinly traded markets such asregional 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 usWhy 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 theMarket Matcherto ensure it only finds markets with sufficiently reliable data.
FAQs
Reliability refers to the extent to which a scale produces consistent results, if the measurements are repeated a number of times.
What are the 4 types of reliability? ›
The reliability is categorized into four main types which involve:
- Test-retest reliability.
- Interrater reliability.
- Parallel forms reliability.
- Internal consistency.
Are there sources that are always reliable? Government sources, educational institutions, and policy institutes are generally good sources to rely on. Private parties or personal web sites, organizations that may not be objective, and unknown sources should be scrutinized carefully before using statistics from them.
What is a good reliability statistic? ›
Between 0.9 and 0.8: good reliability. Between 0.8 and 0.7: acceptable reliability. Between 0.7 and 0.6: questionable reliability. Between 0.6 and 0.5: poor reliability.
How do you interpret reliability in statistics? ›
How do you interpret reliability coefficients? The reliability of a test is indicated by a reliability coefficient. It is denoted by the letter "r," and is expressed as a number ranging between 0 and 1.00, with r = 0 indicating no reliability, and r = 1.00 indicating perfect reliability.
How to assess reliability? ›
Four major ways of assessing reliability are test-retest, parallel test, internal consistency, and inter-rater reliability. In theory, reliability refers to the true score variance to the observed score variance. Reliability is majorly an empirical issue concentrated on the performance of an empirical measure.
How to evaluate reliability of data? ›
The assessment will typically measure three different aspects of data reliability:
- Validity – is the data correctly formatted and stored in the right way?
- Completeness – does the dataset include values for all the fields required by your system?
- Uniqueness – is the data free from duplicates and dummy entries?
9 Factors that influence data reliability
- Data source.
- Comprehensive coverage.
- Data collection methods.
- Data integrity.
- Time sensitivity.
- Consistency and repeatability.
- Data cleaning and preprocessing.
- Standardized metrics.
Accuracy refers to how close a measurement is to the true or accepted value. In the analogy, it's how close the arrows come to hitting the bullseye (represents the true or accepted value). Reliability refers to the consistency of a set of measurements. Reliable data can be reproduced under the same conditions.
What is high reliability statistics? ›
A measure is said to have a high reliability if it produces similar results under consistent conditions: "It is the characteristic of a set of test scores that relates to the amount of random error from the measurement process that might be embedded in the scores.
In general, a test-retest correlation of +. 80 or greater is considered to indicate good reliability. Again, high test-retest correlations make sense when the construct being measured is assumed to be consistent over time, which is the case for intelligence, self-esteem, and the Big Five personality dimensions.
What is an acceptable reliability value? ›
A general accepted rule is that α of 0.6-0.7 indicates an acceptable level of reliability, and 0.8 or greater a very good level. However, values higher than 0.95 are not necessarily good, since they might be an indication of redundance (Hulin, Netemeyer, and Cudeck, 2001).
What is the simplest definition of reliability? ›
If you look up the root word of reliability, the definition is “Consistently good in quality or performance; able to be trusted. A person or thing with trustworthy qualities. It also means dependable, well-founded, authentic, valid, genuine, trustworthy, committed, unfailing, infallible, and constant.
What is reliability vs validity? ›
Reliability refers to the consistency of a measure (whether the results can be reproduced under the same conditions). Validity refers to the accuracy of a measure (whether the results really do represent what they are supposed to measure).
Which is the best definition of reliability? ›
Reliability is defined as the probability that a product, system, or service will perform its intended function adequately for a specified period of time, or will operate in a defined environment without failure.
How is reliability calculated? ›
The system reliability formula builds on the failure rate metric. Once you know the failure rate of each component part of an asset, you can use that to calculate the overall reliability of the entire system. The formula looks like this: R=(1−F1) ∗(1−F2) ∗(1−F3) ∗(1−F4) …
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