Calibrating Macroprudential Policies for the Canadian Mortgage Market
Bibliographic record
Abstract
Macroprudential regulation has been on the rise since the 2007–09 global financial crisis. In Canada, the primary policy tools that have been employed in this regard are related to the residential housing market – namely, changes in mortgage loan-to-value ratios and loan maturity requirements. In this Commentary, we use an analytical model to forecast the probability of a state of low financial stability in the Canadian economy and recommend when policy action might be taken in light of its costs and benefits. We project a low probability of low financial stability in Canada that rises gradually through year-end 2020, but remains low. This might seem odd given recent events around COVID-19. However, there are two things for readers to keep in mind. First, COVID-19 is a black swan event occurring in the real economy, one that does not originate in financial markets, making it difficult for financial regulators and policymakers to anticipate and model in advance. This is critical, as the goal of our paper is to provide a modeling tool to do just that. Second, once we have entered a downturn, financial regulators will not tighten a policy to head off financial instability. They will, in fact, do the opposite, by loosening policy rules to try and stimulate the economy. Canada is an interesting case with respect to financial stability concerns and policies. Although the Canadian economy was able to stave off many of the negative effects of the last financial crisis, it continues to have growing levels of household debt. As a result, after loosening housing-related macroprudential policies in the lead-up to the crisis, policymakers have spent much of the past decade tightening these same policies. Despite work analyzing the effects of housing-related macroprudential policies, there has been very little focus on advising policymakers about when to implement them. Any such advice naturally begins with identifying occasions when financial stability concerns are prominent and likely to remain so, which we refer to as “low financial stability states.” The model identifies three such episodes in Canada between 1990 and the middle of 2019: the early 1990s recession, the mid-1990s government budget rebalancing and the 2008 financial crisis. The four Financial Stability Indicators (FSIs) in our model specifications are the house-priceto-rent ratio, the price-to-income ratio, the debt-servicing ratio and the household-credit-to-GDP ratio. We then use the model to forecast the probability of entering another such episode over a two-year policy horizon. The model provides an answer to the question of whether the probability of entering and staying in a low financial stability state is high enough to go ahead with the policy, given the cost of implementation. Our analysis suggests that, as of the second quarter of 2019, and abstracting from the black swan COVID-19 event, the probability of a lengthy period of low financial stability is low, extending to late 2020.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".