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<scp>C</scp>anada's Housing Bubble Story: Mortgage Securitization, the State, and the Global Financial Crisis

2012· article· en· W2133407940 on OpenAlexaff
Alan Walks

Bibliographic record

VenueInternational Journal of Urban and Regional Research · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSecuritizationFinancial systemFinancial crisisBailoutBusinessSubprime mortgage crisisReal estateState (computer science)FinanceFinancial marketEconomics

Abstract

fetched live from OpenAlex

Abstract C anada's experience during and after the financial crisis appears to distinguish it from its international peers. C anadian real estate sales and values experienced record increases since the global financial crisis emerged in 2008, rather than declines, and C anada did not witness any bank failures. The dominant trope concerning C anada's financial and housing markets is that they are sound, prudent, appropriately regulated and ‘boring but effective’. It is widely assumed that C anadian banks did not need, nor receive, a ‘bailout’, that mortgage lending standards remained high, and that the securitization of mortgages was not widespread. The truth, however, does not accord with this mainstream view. In fact, the C anadian financial and housing markets reveal marked similarities with their international peers. C anada's banks needed, and received, a substantial ‘bailout’, while federal policies before and after the financial crisis resulted in the massive growth of mortgage securitization and record household indebtedness. This article documents the growth of C anada's housing bubble, the history of mortgage securitization, and of government policies implemented before and after the crisis. Instead of making the C anadian financial and housing sectors more resilient and sustainable, the outcomes of state responses are best understood as regressively redistributive.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.648
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.050
GPT teacher head0.293
Teacher spread0.244 · 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

Citations82
Published2012
Admission routes1
Has abstractyes

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