Housing Price and Interest Rate Hike: A Tale of Five Cities in Australia
Bibliographic record
Abstract
Australian housing prices are reported to be overvalued and unaffordable for the past two decades. Many researchers and practitioners have attributed the persistent growth in housing prices to the prolonged period of low borrowing costs. However, due to inflationary pressure, the Central Bank has raised its cash rate consecutively in recent months. This paper aims to examine whether interest rate rises affect housing price in different parts of Australia. Evidence generated from the analysis reported bipolar results between the large and smaller cities, whereby housing prices in Sydney and Melbourne show a significant negative relationship with interest rate changes while Brisbane and the Gold Coast and Perth and Adelaide, respectively, are showing negative but insignificant results during the study period. Short-run trend projections on housing prices indicate that Sydney, Melbourne, Brisbane and the Gold Coast are on a downward trend while Adelaide and Perth will maintain its current momentum before plateauing out later next year. Likewise, control variables, such as oil prices, inflation rate and stock market performance, are found to be related to housing prices in larger cities only. These findings have implications on housing policy, house purchase decisions and investment portfolio management strategy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".