Assessing the Impact of Climate-Related Risks on Canadian Real Estate Investment Trusts: Insights and Implications for Investors
Bibliographic record
Abstract
This study aims to analyze the impact of physical climate risks on the Canadian real estate market. Building upon the framework established by Duprey et al. (2021), we define and utilize a Multi-hazard Exposure Average Index (MHE) to measure the intensity and frequency of natural disaster exposure for each of the 1658 Forward Sortation Areas (FSA) in Canada. We examine the effects of the Average MHE on the operating and equity performance of Canadian Real Estate Investment Trusts (REITs). Our findings reveal that REITs with properties facing heightened exposure to climate change physical risks report lower rental revenues and operating expenses. Additionally, our analysis indicates no significant relationship between the exposure of property portfolios to physical climate risks and abnormal stock returns, suggesting that the effects of climate risks are already integrated into market valuations. We further develop our study by exploring the interactions between the MHE Average Index and the main property types within REIT portfolios, where we observe statistically significant effects. This paper contributes to the understanding of how environmental factors are reshaping the financial dynamics of Canadian real estate investments, highlighting the importance of considering climate risks in investment decisions and property management.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".