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Record W2063096822 · doi:10.1177/002795011423000105

Housing Finance in Canada: Looking Back to Move Forward

2014· article· en· W2063096822 on OpenAlexaffabout
Lawrence Schembri

Bibliographic record

VenueNational Institute Economic Review · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsBank of Canada
Fundersnot available
KeywordsUnderwritingMortgage insuranceIncentiveFinanceFinancial crisisBusinessGovernment (linguistics)DebtMortgage underwritingFinancial systemSubprime mortgage crisisSecondary mortgage marketPrivate sectorStructured financeShared appreciation mortgageLoan-to-value ratioEconomicsInsurance policyMarket economyGeneral insuranceMacroeconomicsEconomic growth

Abstract

fetched live from OpenAlex

The Canadian system of housing finance proved to be resilient and efficient during the global financial crisis and its aftermath. The system's effectiveness is the result of a rigorous prudential regulatory and supervisory regime coupled with targeted government guarantees of mortgage insurance and securitisation products. In the post-crisis period, household debt levels and house prices have risen, owing, in part, to accommodative monetary conditions necessary to support the economic recovery. These vulnerabilities were mitigated by tightening macroprudential policy, specifically mortgage insurance rules, and strengthening mortgage-underwriting standards. Looking ahead, the housing finance framework needs to be adjusted and strengthened by rebalancing the risk exposures away from the government towards the private sector participants in the housing finance market. Although some measures have already been taken for this purpose, more adjustments may be needed to create the right incentives and achieve a sustainable rebalancing in risk exposures. Measures should also be considered to promote a liquid private-label mortgage securitisation market in Canada.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.949
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.036
GPT teacher head0.247
Teacher spread0.211 · 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.

Study designNot applicable
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

Citations13
Published2014
Admission routes2
Has abstractyes

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