Towards a Methodology to Determine the Size of the Commercial Real Estate Market
Bibliographic record
Abstract
In most developed countries, no current complete inventory of the size of the commercial real estate (in terms of sq ft or value) market exists. While the investment is real estate is large, it is not a category tracked by most governments, except possibly in terms of ìflowî through building permit data. The localized nature of the real estate market makes collection of data regarding the size of the market more difficult. This study discusses the difficulties involved in finding data rated to physical size and value of assets in various categories of commercial space in Canada (hotels, multi-residential, seniors housing, office, retail and industrial). It examines existing data tracked by commercial brokerage, consulting and other private sector companies and assesses the comparability of the data among different suppliers. It also examines the possibility of using market value assessment data as a proxy for value estimates for the size of the market and how these values compare to those derived from market data on average prices or rents per sq ft. The resulting numbers are also compared to the proxy estimate for value based on GDP developed by Liang and Gordon (2003) to assess the validity of their relatively simple method in this market.
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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 | 0.018 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".