Loan pre-approvals by banks can be confusing. They aren't a rubber stamp to buy whatever you want, but some buyers are being a little too cautious.
I’m on a low data diet this year. I’m cutting down on my consumption of house price indices, I’m saying no to an extra serve of building approvals numbers and I’m not digging into the latest affordability metric.
I’ll be maintaining a Zen-like aloofness to the rise and fall of auction clearance rates, remaining quiet when the Reserve Bank doesn’t move the cash rate (again) and I’ll only extrapolate the results of The Block insofar as its impact on reality TV viewing numbers.
You see this summer I hardly consumed any data. And it felt good. Quiet. What I did see tended to be of a longer-range, more reflective nature that’s more prevalent in end-of-year coverage: how had the property market performed over the last 12 months, comparisons with years gone past and measured analysis of the major economic drivers of these results. Rather than experiencing events portrayed as a seismograph recording an eternal earthquake, I saw the bigger picture and the fundamentals. I rode the trend line and it was smooth.
This time-out from the usual frenzy gave me perspective. It was a reminder that most of the ‘breaking news’ on property turns out to be white noise. Alas that doesn’t stop most of us getting caught up in it at the time. We agonise about whether decisions made or those pending should be reviewed in light of some new but ultimately valueless item.
The way we place too much weight on new information is of course no revelation, but it is a lesson that is always a struggle to apply in the face of the data fury that is day-to-day life.
But like carbs and fat there is good data and bad data. I’m resolved to stick with good data; the sources that will illuminate not obfuscate.
Capital growth numbers stay in the diet. But not daily or even monthly indexes. How often in 2014 did we see a downturn in prices one month leading to claims the property market was headed for the doldrums, only for it to rebound the next month? Every second month. The data providers need to reflect on the value of information that suggests values of homes regularly gyrate one or two percent a month. They don’t. Stick with quarterly data.
But committing to the slow data movement is not enough; be wary of inferring that a market-wide change in prices will mean a similar result applies to your investments. It rarely does. I’m afraid the only guaranteed way to establish a property’s current price and performance is to sell it on the open market and compare that to what it was previously bought for. Everything else is an estimate. The least worst next option is to identify similar properties that sold in recent times and to use their sale prices to make an informed judgement of what your property is worth.
Rental yields are in. They are a good yardstick for setting the rent. If other landlords in your area are earning around 4% gross rental income, then don’t get greedy and try to earn 4.5%. You’ll struggle to get tenants.
Definitely keep an eye on vacancy rates. Overall these remain low – which is great for investors. But avoid areas where vacancy rates are unusually high. There may be an over-supply of new properties on the market or demand may been slashed by the loss of a major employer in the area.
Population and migration data published by the Australian Bureau of Statistics are worthy additions to your diet. They are the ultimate slow data nutrition as you really need to zoom out to a year-on-year view to see meaningful trends. And guess what? Suddenly you can see powerful, fundamental forces at work. Some of our cities’ populations are growing by 2% a year, far in excess of property supply growth. The conclusions are then obvious.
Percentage changes to suburb median values are out as are percentage changes to median rental values. Both are the equivalent of fast food. First, there aren’t that many property transactions in most of our suburbs so the sample sizes are invariably small. Second, the numbers can be skewed widely by differences in the composition of property type in each sample period.
So reduce your data dependency in 2015. It will lead to clearer thinking and less noise. Richard Wakelin