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Record W2141605637 · doi:10.5430/ijfr.v4n1p54

The Impact of the Goods and Services Tax on Mortgage Costs: Evidence from Australian Mortgage Corporations

2012· article· en· W2141605637 on OpenAlexvenueno aff
Allen Huang, Benjamín Liu

Bibliographic record

VenueInternational Journal of Financial Research · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsMortgage insuranceSecondary mortgage marketCollateralized mortgage obligationShared appreciation mortgageMortgage underwritingYield (engineering)BusinessEconomicsMonetary economicsFinanceFinancial systemInsurance policy

Abstract

fetched live from OpenAlex

Australia has seen significant rises in mortgage costs and sharp declines in housing affordability in the past decade or so, which corresponds with the introduction of the Goods and Services Tax (GST) in July 2000. To what extent the GST has impacted mortgage costs is the research question. This study investigates the GST impact on the mortgage costs of Australian mortgage corporations. Using data of mortgage corporations operating in Australia, we perform t-tests and multivariate regression analysis to examine the GST effects on mortgage yield spreads. The empirical results clearly indicate that mortgage corporations increased their mortgage charges in the post-GST periods significantly beyond the magnitude of the GST. Furthermore, the lenders started to increase the yield spreads before and continued to increase the spreads after the implementation of the GST, indicating the rise in mortgage costs was not a one-off surge. The findings offer insights into mortgage costs and have significant policy implications and wider economic relevance.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.140
GPT teacher head0.388
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2012
Admission routes1
Has abstractyes

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