Housing finance sees a slight rise, with first home buyers jumping to 18%

Richard Whitten 17 January 2018 NEWS

Finance figures on blue background.

First home buyers are the real growth story in the latest ABS figures for November 2017, which show modest rises in housing finance after drops in October.

Growth in Australian housing finance was relatively flat in November, with small rises in various types of mortgage lending coming after a sluggish October.

The latest figures, released monthly by the Australian Bureau of Statistics, show the number of first home buyers rose to 18%, up from 17.6% the previous month.

Over that time, the average loan size for first home buyers rose to $327,100. The overall average loan size for owner occupiers wasn't too far behind, at $388,900.

In seasonally adjusted terms, the number of financial housing commitments rose in NSW (2.0%), QLD (3.1%), the ACT (8.6%), VIC (0.5%), SA (1.5%) and TAS (0.7%).

Western Australia and the Northern Territory saw declines of 3.5% and 8.5% respectively.

Key statistics from November 2017

Here's a breakdown of the main movements in housing finance.

  • The total value of dwelling commitments rose 2.3% in November 2017.
  • The value of owner-occupied finance rose 2.7% in November.
  • Finance from banks rose 2.2% over the month, while finance from non-bank lenders rose 1.3%, having risen by 3.6% the previous month.
  • Refinancers are up 1.5% for the month, after a slight dip in October.
  • Construction lending rose 2.0% after a 1.6% fall in October.
  • Purchase of new dwellings rose 2.6% in November, having dropped 2.1% in October.

The rise in first home buyer lending is unsurprising and has been reported for several months now.

It is interesting to note the rise in non-bank lending, an ongoing trend in several recent industry reports.

A note on the statistics

The ABS publishes its housing finance statistics monthly. The figures are presented as original numbers, trend estimates and seasonally adjusted numbers. The data reported in this article uses the seasonally adjusted numbers, which smooth out the data in order to eliminate seasonal patterns (public holidays, retail sales periods, etc) that affect the underlying reality represented by the figures.

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