News Analysis 25
And despite the HIA release focusing primarily on the more upbeat seasonally-adjusted data, Dale said the general story about housing approvals had “changed very little”. “We are forecasting a moderate recovery in new home building. We are not expecting anything particularly buoyant, and remain quite subdued about the prospects for NSW and Queensland markets in the short term,” he said. He agreed that there was room for a lot of volatility in the NSW figure, given that it is driven by a “very unpredictable housing unit segment and a unique Sydney housing market”. Giving an expert opinion on the numbers, Melbourne Business School associate professor Sally Wood said she agreed with Gadiel’s criticism of seasonally-adjusted data but said two points needed to be made. Firstly, as a trend is just a ‘smoothed’ version of seasonally-adjusted figures, any inferences drawn from the trend series (for example, the UTA claim that Australia-wide the trend estimate for home approvals rose 2.2% in April 2009, with revised figures now reporting a positive trend for three consecutive months) would clearly depend on what type of smoothing was used to produce the series. Secondly, she said using these trends to predict the future is difficult because of the error associated with such forecasts. And of course, one should not discount the feel-good impact of positive data. In defence of reporting the NSW seasonally-adjusted data, Dale said: “It’s so rare to see any kind of positive number on NSW, its nice to report one”.
Harley Dale
Seasonally adjusted vs trend data
According to Sally Wood, a couple of issues need to be kept in mind when examining seasonally adjusted and trend data: • Accounting for seasonality is important when making inferences. For example, suppose historical figures show that house sales in April are significantly higher than those in May. Suppose further that sales in May this year were the same as those in April: then, on a seasonally-adjusted basis, sales have increased. Without correcting for predictable seasonal fluctuations this increase in sales would not be detected. • For a trend estimate to be valid you must make sure the observed data supports the assumptions underlying the model used to estimate the trend. If these assumptions are not supported by the data then inferences drawn from the estimated trend are invalid. • Most, but not all, trend estimates focus on fitting the observed data well. However, the estimate of the fitted data may be a very poor estimate of future data and therefore not useful for forecasting.
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