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Record W4409256960 · doi:10.31235/osf.io/mjbv8_v1

Assisting first homebuyers: an international policy review

2022· preprint· en· W4409256960 on OpenAlexaboutno aff
Hal Pawson, Chris Martin, Julie Lawson, Stephen Whelan, Fatemeh Aminpour

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsnot available
FundersAustralian Housing and Urban Research InstituteAustralian Government
KeywordsBusiness

Abstract

fetched live from OpenAlex

This research reviewed first homebuyer (FHB) assistance programs in Australia and seven comparator countries: Canada, Finland, Germany, Ireland, the Netherlands, Singapore and the UK. It considered to what degree such assistance are effective in expanding access to home ownership to those whose entry would be otherwise delayed or impossible, or in making more affordable and less risky the cost of home ownership.Current Australian first homebuyer assistance measures primarily act to bring forward first home purchase for households already close to doing so, rather than opening home-ownership access to households otherwise excluded. In doing so, these measures add to demand and hence to house prices. An ‘effective’ FHB-assistance mechanism or instrument is one that can be credited with ‘additionality’, as it makes first home ownership possible for people who would be otherwise excluded or—in fact far more likely—significantly accelerates access to owner-occupation. An ‘efficient’ initiative is one that is effective at an acceptably modest unit cost and with minimum administrative complexity.FHB interventions can be demand-side or supply side measures. A demand-side intervention involves a benefit directly received by the consumer, effectively boosting FHB-purchasing power, and include homebuyer grants and tax concessions, low-deposit mortgage products and shared equity arrangements). Supply-side interventions directly relate to the provision or use of housing; this covers the disposal of government-owned assets, funding channelled through property developers or suppliers, and regulatory instruments that affect housing production or use of housing assets.The eight countries assessed have substantial diversity in terms of policy approaches to supporting home ownership; supply-side approaches are more common in a number of comparator countries. Australia stands out as it is overwhelmingly reliant on demand-side instruments and lacks a strategic framework.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.010
Science and technology studies0.0010.002
Scholarly communication0.0050.005
Open science0.0020.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0120.001

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.

Opus teacher head0.070
GPT teacher head0.293
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations0
Published2022
Admission routes1
Has abstractyes

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