Assessing the Effects of Housing Policy Measures on New Lending in Australia
Bibliographic record
Abstract
In 2014, policymakers in Australia judged that the rapid increase in the share of housing lending to investors posed a growing risk to household balance sheets. At the time, housing prices were rising rapidly and there was a concern that investor activity could be amplifying the upswing in housing prices and construction activity, in turn raising the prospect of a sharp unwinding in the future. Moreover, strong growth in investor lending was occurring at a time when housing debt more broadly was rising considerably faster than incomes, off an already-high base. This was judged to pose a downside risk for economic activity because highly indebted households could sharply reduce their consumption in the event of falls in incomes or housing prices. As a result, regulatory measures were implemented over several years which sought to address these risks. These measures targeted housing lending, rather than housing prices. The most high-profile and measurable of these policies were two benchmarks introduced by the Australian Prudential Regulation Authority (APRA): the first of these (announced in December 2014) sought to limit the rate of new investor lending growth and the second (announced in March 2017) to limit the share of new interest-only lending. Both benchmarks were applied at the institution level. This paper uses empirical methods to identify the effect of these two policy measures on new housing lending. The approach follows that suggested by the Bank for International Settlements (BIS) for individual country teams to replicate across a range of Asia-Pacific countries (Cantu et al (2019)). It advocates the use of a bank-level dynamic panel regression to exploit bank-level variation, while controlling for bank-specific factors as well as macroeconomic factors that can affect banks’ lending decisions. Dummy variables are used to identify the policy impact on new lending. In addition to assessing the effect of the policies on the flow of total new housing lending, I replicate the procedure separately for loans to owner-occupiers and to investors. This breakdown of lending type is interesting because the policy measures implemented in Australia were motivated principally by the growth in lending to riskier lending types, such as investors, rather than owner-occupiers. I find that the benchmarks had the effect of reducing the flow of new lending; this effect is statistically significant in some specifications. I also find that the benchmarks had a much larger, negative and statistically significant effect on the growth rate of lending to investors, which was the type of lending explicitly targeted by the first benchmark. Full Publication: Measuring the Effectiveness of Macroprudential Policies Using Supervisory Bank-Level Data
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".