Bibliographic record
Abstract
Real estate has long been a popular form of investment in many countries. With recent years seeing an increased level of global interest in indirect investment in this asset class through real estate investment trusts (REITs), opportunities in China markets are now grabbing much attention. China is being tipped as the area likely to offer the best returns over the coming years. This article traces the development and growth of REITs in US, where there are lessons to be learnt, so that subsequent efforts to introduce REITS’ status, requirements, types and advantages. Further more, benefits, key evaluating elements, risks and obstacles were discussed respectively in order to promote practice and application of REITs in China and avoid risks in applying process to create a reasonable framework and structure. Key Words: REIT, Development, Benefit, Evaluation, Risk, Obstacle Resume: L’immobilier est depuis lontemps une forme d’investissement populaire dans beaucoup de pays. Dans les dernieres annees avec le constat d’un niveau eleve de l’interet global des investissements directs dans cette industrie a travers le REITs(Real Estate Investment Trusts), les opportunites sur le marche chinois tirent maintenant beaucoup plus d’attention. La Chine est consideree comme la region susceptible de rapporter le plus dans les prochaines annees. L’article present decrit le developpement du REITs aux Etats-Unis qui nous donne des lecons et indique des efforts a faire pour introduire le REITs en ce qui concerne son statut, ses conditons, ses types et ses avantages. De plus, les benefices, elements cles d’evaluation, risques et obstacle seront etudies respectivement dans le but de promouvoir la pratique et l’application du REITs en Chine et d’eviter les risques dans le processus d’application afin de creer un cadre raisonnable. Mots-Cles: REIT, developpement, benefice, evaluation, risque, obstacle
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".