To our knowledge, analysis on this topic has been very limited. Most certainly, our study is the first of its kind using Australian data.
Over the period that was analysed, it was found that inner Melbourne property transactions make up no more than 10 per cent of total settled properties in the state, while metro Melbourne, outer Melbourne and regional Victoria account for 28, 34 and 29 per cent of all settled sales. Across vintages, properties in inner Melbourne make up a smaller share of the early data vintages relative to the numbers reported in the final data vintages. Our data analysis also found that higher value houses make up a larger share of the early data vintages. However, the share of properties settling by location and type appears to stay the same.
On the impact of settlement lag on HPI values, our analysis shows that early estimates of price growth for inner Melbourne houses require the largest upward revisions, compared to other property types. The required growth rate adjustment can be as much as 3 percentage points, which is four times the rate difference found for other property types. This large gap appears to persist well into the seventh vintage (6 months ahead). Metro and outer Melbourne houses also need some upward adjustment, but only by some 0.9 percentage points, at most. This difference also dissipates more quickly.
Price growth estimates for units show a different trend altogether, particularly for metro Melbourne units. We find that early vintage data for units or apartments in metro Melbourne indicate a price growth that is 1.2 percentage points higher than price growth estimates using final vintage data. This falls quickly though, to just around a 0.5 percentage points difference by the second vintage (1 month ahead) and disappears altogether by the fifth vintage (4 months ahead).
Our findings strongly confirm the validity of our data approach to HPI projections. While the first vintage only contains about 60 per cent of total property transactions on average, our results indicate that it contains sufficient information for early estimates, such as for DTF’s HPI through-the-year growth rate, to not change significantly. The average size of revisions relative to final vintage estimates are less than 1 percentage point.
While this is a reassuring result, it could be worthwhile revisiting this analysis at a future date, with additional vintages and under different property market and economic conditions. This analysis covers only a short period of time, with data vintages captured during a pandemic that was disruptive for the property market.
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