Written by Stefan Kostarelis
Last month, the Australian Bureau of Statistics (ABS) released some preliminary findings from the 2016 Census.
According to the ABS, the “average” South Australian is a 40-year old female of English ancestry whose parents were both born in Australia. She is married and lives in a (mortgaged) home with three bedrooms and two motor vehicles.
But these details only scratch the surface of what will be revealed when the full 2016 Census results are released on June 27th.
As the saying goes “knowledge is power” and this is no doubt true in the real estate industry. “Back in the old days”, agents benefited from asymmetrical knowledge of the market: in other words, they knew things that their prospects didn’t.
Today’s consumer is now more educated, prepared and connected than ever before, thanks to all that the internet has to offer. Buyers can search recent sales prices through their banking app, search agent reviews on agent ranking sites, and give instant (and cutting) feedback very, very publicly on social media.
Now more than ever, agents have to be on the cutting edge. Drilling down into the full 2016 Census data is an example of something that may yield greater knowledge of potential clients and suburbs. So what kind of things can agents learn from the Census, and where can they learn it?
On the ABS website, data sets are broken down into two main groups. The first is “groups of people”, which concerns “persons, families and dwellings”. The second group is “communities, areas and locations” and this includes data about specific geographic regions.
For the most part, the ABS suggests the use of something called “QuickStats“, a feature that allows you to quickly search and access basic information on certain people and areas, without getting too statistically complex.
For example, I plugged my suburb – Mile End – into the 2011 QuickStats Search engine and found plenty of information that should be of interest to real estate agents.
At a glance, I can see that there were 4,413 people in Mile End in 2011 and 2,055 dwellings. The average people per household was 2.3, the median weekly household income was $1,132, and median monthly mortgage repayments were $1,800. Meanwhile, the median weekly rent was $264, and the average number of motor vehicles per dwelling were 1.4.
Looking deeper, I can learn more about my suburb. The data tells me about the median age, how well educated people were and whether or not they had children. Putting it all together, I can build personas for individuals in the area.
For example, I might conclude that people living in Mile End tend to be young professionals who were recently married and had just started a family. I can then adjust my marketing material accordingly, and post more pictures of smiling thirty-somethings taking Poochie for a walk in the local park with their kids.
It sounds cynical, but this is what digital marketing in the modern era is all about: using big data to create personalised campaigns that get the right messages to the right people.
The possibilities for prospecting are endless. I might also notice that there is an abundance of divorcees aged over 65 in the area who could be looking to sell. Or maybe their kids have left the nest, and they want to downsize. From there, I could set up a direct mail campaign that targets them based on their particular life situation.
You can also use the data to analyse cultural trends in the area. For example, I can see that Mile End has had an increase of Chinese buyers in recent years. In the 2011 QuickStats, China was the third most common country of origin (after Australia and Greece) at 3.8%. This was a marked increase on the 2.3% of Chinese present in the 2006 Quickstats. And I expect that number to be even greater in the 2016 results. We’ll find out on June 27th if I am right.
These are just several examples of how real estate agents can analyse Census data to learn more about a particular area or to create buyer/seller personas. Other uses might include creating personas based on occupation, income or dwelling type. In the age of big data, agents must stay ahead of the curve and keep up to date with the latest trends and information.
For a full schedule of Census 2016 data releases, visit this link.