2013-02: The GST and mortgage costs: Australian evidence (Working paper)
Bibliographic record
Abstract
Purpose - In July 2000, Australia implemented a new tax system of goods and services tax (GST). Since then Australia has experienced significant rises in mortgage costs and sharp decline in housing affordability. This paper aims to empirically examine and quantitatively measure the changes of mortgage yield spreads in the pre- and post-GST periods. Design/methodology/approach - Using unique longitudinal data of banks and mortgage corporations in Australia, we perform t-tests and multivariate regression analysis to examine the GST effects on both nominal and effective mortgage yield spreads. We also perform a robustness test by using unbalanced panel data analysis. Findings - We found that all the lenders significantly increased their mortgage charges in the post-GST periods. For example, the increase by the lenders is found to be, on average, 51.4 basis points. Furthermore, we found the lenders started to increase the mortgage yield spreads before and continued to increase the spreads after the implementation of the GST, indicating that the rise in mortgage costs was not a one-off surge in the quarter when the GST was implemented. Originality/value - This study is the first research internationally that examines the impact of the GST on mortgage costs. The findings of the study provide insights into mortgage costs and should have significant financial, economic and policy implications and wider economic relevance.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".