Mortgage regulation as a quick fix for the financial crisis: standardised lending and risky borrowing in Canada and the Netherlands
Bibliographic record
Abstract
Although the role of the housing sector in the unfolding of the 2007-08 Global Financial Crisis has been studied extensively, the post-crisis nexus between housing and finance has not received equal attention. Grounded in a comparative case study between Canada and the Netherlands, this article adds situated knowledge from mortgage market professionals. It discusses the state interventions for regulating mortgage markets that were pursued by each national government during and after the crisis. Our analysis shows that in both cases state interventions contributed to restoring the investment value of mortgage products and failed to de-link housing from global speculative financial practices. Standardised lending regulations targeting the ‘average man’ were put in place. These contributed to further excluding non-prime households from mortgage markets, and drove them into risky practices, such as borrowing outside regulated markets. In addition, the new regulatory regimes forced households that retained access to mortgage markets to become highly leveraged and exposed to increased risks in future crises scenarios. We argue that the policies put in place as a response to the crisis in Canada and the Netherlands, ultimately led to a shift in risk-taking from lenders to current and prospective mortgage holders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".