Increasingly, the best analysis relies on data. Lots of it. Last November, when we released our median prices, we searched and made calculations from a table of over 35 million sales, and 40 columns.
While we used to be able to do a lot of our data analysis in Excel, this is no longer the case - the largest spreadsheet will only handle a touch over one million rows.
That one table, though our largest, only comprises a tiny fraction of what we need to provide the commentary that we do.
Data at a street and suburb level comes from our database of all residential sales in Australia.
But we also rely on ABS data for things like housing approvals and industry-wide lending, APRA for how our banks and bank accounts are holding up, Corelogic for suburb and major city analyses, and, my personal favourite, nonfungible.com, to see if a virtual property next to Snoop Dogg’s really is a steal at $US40,000.
You’ve probably heard of the phrase garbage in, garbage out. While we sometimes ignore this wisdom regarding our diets (I recall a time at uni when I ate Maccas for dinner for a straight week), it’s incredibly important to adhere to this principle with our data.
Doing so is critical and is the role of both the analyst, and the data engineer. I, as the former, owe a lot of the accuracy of our reports to the latter, found in our digital team.
These are the sources, and the relevant concerns, of your Economics team. As our operations continue to generate more data, we look forward to harnessing this data, and continuing to communicate interesting trends.
Rest assured that now I eat all my veggies every night too!
