Lies, Damned Lies, and Statistics! – Melbourne’s Median House Price Increases 1.4 per cent in 2018

Data from the Real Estate Institute of Victoria (REIV) last week showed that Melbourne house prices have been steady over the last year.

In the 2018 calendar year, the median house price has increased by 1.4% to $826,500 and the unit price increased by 1.8% to $597,000.

How can this be so when every other property data source proclaims that property values have reduced in the last 12 months?

To be fair to the REIV, they did record that the median house price dropped by 3.7 per cent to $796,500 in the final quarter.

Understandably they did not want to focus on the negative results as part of their public relations media release. They are, after all, the peak body for real estate agents who earn an income from you being positive about the property market.

When we extrapolate the result over the last quarter, a 3.7% reduction over a full year, it gets ugly!

This equates to a downright plunge in values by 14.8 per cent when annnualised. Not such a pretty picture!

Yet, this number is taken from the same set of numbers as the positive result over the last twelve months!

So according to REIV, property values went up in Melbourne over the last 12 months, but backwards in the last quarter at a rapid rate of knots if annualised.

These numbers provide a helicopter view of a macro market.

Macro market numbers are often titillating, but can be deceiving, especially with a boisterous headline that provides click bait as illustrated above.

Having set the scene for how statistics can be manipulated to suit a purpose, I want to focus on how the various property data companies actually provide differing methodologies when calculating the ubiquitous median property value.

Why is the median value calculated differently?

There are many ways to skin the cat that is the median property value.

For example, the REIV uses ‘median prices’ which can be crude, but which is how most of the population assume all property median values are calculated.

Unfortunately, calculating median property values is far from being this simple!

Core Logic, which is the most recognisable and respected property index, uses a historical hedonic regression method which produces the patented Core Logic Daily Home Value Index.

The method is a tongue twister and it sounds like a brain frying exercise to try an understand it!

Essentially, this means they do not simply look at the value of properties sold and determine a median average as the REIV does, it factors in compositional change by obtaining information such as bedrooms, bathrooms, land size, suburbs and they access real time data via real estate agents from as many property sale values as they can locate.

Other methods include the ‘stratified median price method’ which the Australian Bureau of Statistics uses. Residex uses an index that only records repeat sales of the same properties over time known as the Case-Shiller technique.

It is debatable which method is superior (although Core Logics appears to be the most thorough).

Why get so technical?

The reason complex formulas are used is because questions need to be asked when determining median values such as:

  • How do you factor in a renovation, which is not really an increase in the property’s intrinsic value?
  • How do you incorporate brand new properties which are often inflated due to the ‘shiny newness’ and can give an illusion of growth in values of a location which is often not the case for a first-time sale?

Further there is no legislation that says all property sales data needs to be recorded as part of an index, like the share market for example. Property is not considered a financial product and property advice is unregulated (unfortunately). This means that companies need to chase the property sales information from agents and vendors who are not required to disclose the information to these parties.

What do we make of this?

Property data is murky.

Property statistics, especially when relating to a macro market such as the country or a city, or even a suburb, should be taken with a grain of salt and not considered a bible for making decisions.

We do not purchase a market, we purchase a property. And each individual property is different (less so when buying high density apartments), as is each street, pocket within a suburb and suburb within a city.

This means that we are still a hell of a long way from property data being the panacea of all property decisions.

Ultimately, that will never occur.

Human behavior through buyers, renters, economies and government policies will rule for a little while yet. At least until the bots take over and, presumably, they will not have egos and will all be happy to live in the same type of shelter to protect themselves from the elements.

Until that day, it is important to understand that ALL property decisions and selections are subjective.

Human behavior cannot be measured by a straight-line formula or set of data that will provide you with a precise outcome that will guarantee a financial win.

Understanding this is paramount to overcome paralysis by analysis. Decisions take us forward.

You should always aim to optimise the success of your property decisions, as they align with your personal financial situation and long-term Property Plan and goals.

If are not clear on your financial and lifestyle goals, start there before making a property decision.

You can tip the scales of success in your favour through detailed, clear analysis of your finances, setting your long-term plans and goals, then aligning these with your specific individual property decisions.

If you would like to understand more as to how property data is calculated, below are links to two articles on how property index and median values are determined.

One by a friend of Property Planning Australia’s, Tim Riley, called the ‘The fundamentals of house price information’.

The other called ‘House prices for dummies’ by Christopher Joye, who is one of my must reads and a leader in Australia when it comes to investment strategy.

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