Mortgage Fraud, the Land Titles Act and Due Diligence: The Rabi v. Rosu Decision
Bibliographic record
Abstract
Real estate fraud has become a very real threat in Ontario. A product of an increasingly common crime, identity theft, real estate fraud occurs when homeowners' titles are fraudulently transferred, and mortgages are registered against those titles, all without the homeowners' knowledge or consent. By the time a homeowner discovers the occurrence of the fraudulent purchase, the fraudster has run off with the mortgage funds, leaving two innocent parties, the homeowner and the mortgagee, with a mortgage charge on the property that must be shouldered by one of them. That is precisely what happened in Rabi v. Rosu. While Rabi v. Rosu is a trial court judgment, it is significant for three reasons. First, it circumvented the application of a very similar Ontario Court of Appeal decision, CIBC Mortgages Inc. v. Chan that, under the doctrine of precedent, should have been followed. Second, it determined that the common law will prevail under the Land Titles Act unless a different legislative intention is indicated and that the theory of deferred indefeasibility governs in the Act. Finally, the decision in Rabi v. Rosu imported a concept of due diligence into the Land Titles Act, the implications of which will require money lenders to double check every mortgage transaction on which they sign off in the future. This article discusses the importance of Rabi v. Rosu in the area of mortgage fraud and outlines the most recent amendments to the Land Titles Act.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.015 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".