Climate risks and their implications for commercial property valuations
Bibliographic record
Abstract
Purpose The authors outline a framework that captures the channels through which physical climate risks could affect cash flows and pricing of income-producing real estate. This facilitates detailed consideration of how the future performance of real estate investments could be affected by such risks. Design/methodology/approach This is a literature-based investigation that draws on work commissioned by UNEP-FI (Clayton et al. , 2021a, b). It extends this work to consider in more detail the channels through which climate risks may impact property performance and the implications for the valuation community. Findings Recent empirical studies have identified more instances where pricing is reflecting both current and anticipated climate risks. Market valuations cannot properly incorporate climate risk without clear evidence that it is priced by market participants, but valuers can advise clients on the potential for future impacts. Research limitations/implications While inferences can be made from studies of residential real estate, more research on commercial real estate pricing and climate risk is required to assist valuers and their clients, as well as other stakeholders in the real estate market. Practical implications Differences between a Market Value and an Investment Value context are considered, and how valuers could and should account for climate risk in each setting is discussed with reference to existing professional standards and guidance. Originality/value The article synthesises a wide range of literature to produce a framework for the channels by which real estate values could be influenced by climate risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".