Research Trends and Directions on Real Estate Investment Trusts’ Performance Risks
Bibliographic record
Abstract
The status of real estate investment trusts (REITs) rose in investment decisions and research since 2008, after the global financial crisis (GFC) and the surge in REITs. However, the sector is still in its infancy in most emerging markets and African countries. The current study examines the literature on the performance of REITs and the related risks using bibliometric and content analyses. The study’s objectives were to determine the research trends on the topic since 2008, the prominent authors, countries, and sources, the knowledge trend and themes associated with the existing research to date, and future or new directions for research. Materials from 2008 to 2022 indexed in the Scopus database were retrieved and visualised using VOSviewer software. The findings revealed that publications were mostly in Australia, Italy, Singapore, and Canada. The co-authorship links were dominant among the Australian authors. The themes that emerged were centred around REITs’ portfolio measurement, risk management in diversified portfolios, capital structure, efficiency measurement, corporate governance, portfolio risk assessment, portfolio construction, and asset allocation strategies. The findings are envisaged to be beneficial in informing further research directions on the subject. The performance threats are also highlighted for industry stakeholders’ decision-making and strategic planning around REITs’ sustainability.
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.021 | 0.048 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".