CANADA’S COMMERCIAL REAL ESTATE MARKETS PRIMED FOR GROWTH Highlights
Bibliographic record
Abstract
• The run up in residential housing activity and the ensuing overvaluation incorporated in prices has led headlines to concentrate solely on the residential side of the real estate market. Chugging along more silently in the background has been a commercial market which has once again found its footing after being hit hard by the 2008-09 recession. In 2011, Canadian commercial real estate put forth an impressive showing to return to pre-recession volume and activity levels – over $21 billion assets changed hands. After the stellar showing last year, we expect transaction volume to moderate in 2012. That being said, supportive factors will still be present – favourable borrowing environment, corporate profits advancing by 4.3 % this year, and healthy cash reserves for firms. The wild card to the second half of 2012 will be consumer and business confidence given the current risk-filled economic climate. With demand elevated post-recession and many developers waiting to see if global risks abate, most property classes (office, retail and industrial) remain fairly tight. To help ease this pressure, we anticipate a new construction cycle will take place over 2013-14. Demand for high-quality commercial office space continues to outpace supply in Calgary and Edmonton. This trend will persist into 2013 as there are only a handful of office projects currently under
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.013 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.058 | 0.003 |
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".