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Ben Kingsley Blog post by Ben Kingsley

Smart Decisions Boil Down to Smart Numbers

Who knows what a correlation coefficient measurement is?

Answer: A very sophisticated measure that determines the degree to which two random linear variables movements are associated.

The correlation coefficient is calculated as:

The calculation will result in a figure between -1 or +1.  A zero result indicates no linear relationship between the two variables.   A figure close to +1 means there is a strong correlation and a figure close to -1 means there is a negative correlation.

Please don’t think for a second that I am the next ‘Good Will Hunting’ far from it,  in fact and for the record this type of calculation is out of my depth, but understanding what it can do for me is really the point of my comment this month.

Let me share with you what I mean.  In my previous career part of my job within the tourism company I worked for was to lobby the airlines to get more flights to our destination – in this example it was Ayers Rock Airport.  Put simply the remoteness of the airport meant that if we didn’t get more flights in, then our resorts would not have people in them.  Through the knowledge and understanding of one of our managers this formula was discovered as a measure that might allow us to convince the airlines to put more flights on.  So we set about trying to prove two random variables had a correlation coefficient that was positive.  In our case we had often believed that the international arrivals into Cairns were reflective of the travel demand for Ayers Rock.  I.e. International tourist wants to see Uluru, the Great Barrier Reef and usually Sydney.  So by applying this formula we got a 0.98% positive correlation.  When this was presented as part a report to the airlines in resulted in them putting on more flights when peak international arrivals occurred.  This increase was estimated at driving millions of additional dollars in tourist to Ayers Rock Resort and the surrounding area.  And I doubt that without this compelling data that the airlines would have believed our theory.

Now I realise households are not going to be a position to use sophisticated formulas such as this to make household spending decision, but the reason for writing this piece is really to highlight that through my personal observations that many households use little or any analysis of the numbers – ‘their money’ before making their decisions.

Let me give you a property investment example.  It is so often when people are sold into buying a new investment unit off the plan based on a very smooth presentation and certain macro data collected to support their sales pitch. However the reality is that certain types of properties in certain types of area all perform differently.  So imagine if you are being sold on the suburb data that they present to you saying that over the last 10 years property prices in the area have grown over 8% per year?.  You initially think – not bad, but your failure to investigate that other new units like the one you are being sold have only grown in value by 4% per year.  Naturally, they are not going to tell you that data, because it would mean they wouldn’t sell any apartments.  The end result of someone making a decision on poor data and poor analysis is lost wealth and a bad investment.

Naturally in our business we are always looking at better understanding the numbers, as smart decisions about money and wealth boil down to smart numbers – knowing the numbers that count will result in better decision making and over the long term a better wealth position for you and your household.

Knowledge is empowering – if you act on it!

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