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Record W2803067502 · doi:10.5931/djim.v14i0.7871

Mortgage Lending and Borrowing Regulatory Changes

2018· article· en· W2803067502 on OpenAlexaffvenueabout
Haydn McInroy

Bibliographic record

VenueDalhousie Journal of Interdisciplinary Management · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsDalhousie University
FundersAustralian Government
KeywordsDebtMortgage insuranceDebt service coverage ratioGovernment (linguistics)LoanBusinessFinanceFinancial systemSecondary mortgage marketFinancial crisisSubprime mortgage crisisLoan-to-value ratioEconomicsExternal debt

Abstract

fetched live from OpenAlex

Prices in Vancouver and Toronto housing markets are forcing a tightening of mortgage lending across Canada by the Office of the Superintendent of Financial Institutions (OSFI). This form of risk mitigation is carried out through increased stress testing of mortgage borrower income levels along with more strictly enforced loan-to-value requirements used to regulate lending practices by Federally Regulated Financial Institutions (FRFIs).The goal of the tightened mortgage lending is due to concerns surrounding the increasingly alarming debt problem as Canadian debt to income levels doubled in the past two decades, along with historical drops in Debt-Service ratios of Canadians during economic downturn. With Canadian debt to income levels at an all-time high, coupled with the facts that the current Canadian rates are higher than those of the US prior to the 2008 Financial Crisis, and the high level of mortgage debt that makes up the overall debt of Canadians, tightening of mortgage regulation is something that the Government deems necessary.Written in the format of a Memorandum to a Deputy Minister in the Federal Canadian Government, this paper examines and addresses key issues related to the increased mortgage regulation being carried out by OSFI and the Federal Government.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.367
Threshold uncertainty score0.457

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.018
GPT teacher head0.318
Teacher spread0.300 · 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 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

Citations0
Published2018
Admission routes3
Has abstractyes

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