Innovate Or Die

I was in a meeting last week when a colleague made the dramatic proclamation: ‘you either innovate or die’. As much as I joked about how seemingly extreme the sentiment was, the more I thought about it, the more I came to realise the validity of the statement.

Rather than adopting an ‘if it ain’t broke, don’t fix it’ mentality, try an ‘is this the best it can be?’ mentality. It would be a rather boring existence if you genuinely believed you’ve reached the peak of your potential, be it personally or in business. Before I end up sounding too much like Tony Robbins, I’ll get to my point.

The research team at Blue Wealth recently ran some numbers to quantify the factors that make property markets tick, an innovation outside of academic research. It doesn’t take an economics degree to know price growth is largely a function of supply and demand; the question we asked was: what factors drive price growth and, more importantly, to what degree do they do so.

To quantify the question, we ran a regression of price growth by capital city against the following factors:

  1. Private capital spend – reflective of business sentiment
  2. Sales volumes
  3. New dwelling funding

A regression predicts a relationship between an independent factor, price growth in this case, and one or more dependent variables (1 to 3 above). That is, price growth depends on, among other things, private spend, sales volumes and new dwelling funding. If you’re not familiar with the purpose of regression, here’s a simple example: you could use it to determine a relationship between the test results of students in a class (independent variable) and the number of hours students studied for the test (dependent variable). The result would be an equation of the form:

Test results = (m)*study time + b

where ‘m’ is the weight assigned to the dependent variables (the relationship isn’t perfect). You’d assume that even with zero study time, the inadequately prepared student would still achieve a score above zero, hence why we include the value ‘b’.

 

Our regression resulted in an equation like the one below:

Price growth = (m1)*private spend + (m2)*sales volumes + (m3)*new dwelling funding + b

 

Around 75% (R – squared for all you stats nerds) of the variation in price growth in our capital cities can be accounted for by the three factors above – a compellingly high percentage given emotion plays a role in property prices. Increasing the model’s predictive power, we found that positive changes in sales volumes typically lead (i.e. happen before) price growth by about 12 months. Price growth in turn results in higher levels of buyer activity (more demand) and so the cycle continues.

Why is this important? As a growth focused investor, understanding the factors that drive price growth in property markets will help you make informed decisions. For a property developer, an acquisitions philosophy dictated by demand and its likely future trajectory makes commercial sense.

What can you do differently today to get a better result tomorrow?


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