International listed real estate returns: evidence from the global financial crisis
Bibliographic record
Abstract
Purpose The purpose of this paper is to analyze and revisit the risk and performance of publicly traded real estate companies from 14 countries over the period 2000–2015, marked by the unprecedented Global Financial Crisis, in presence of errors-in-variables (EIV) and illiquidity (measured by serial correlation, following Getmanskyet al.(2004)). Design/methodology/approach The authors extend the seminal work of Bondet al.(2003), and shed a new light on the relative performance of listed real estate before and after the GFC. First, the authors suggest the use of various asset pricing models (APM) including the Fama and French (2015) five-factor APM with global and country-level factors. Second, the authors implement unbiased estimators to correct for the econometric bias induced by EIV in APM. Third, the authors deal with the impact of illiquidity (measured by serial correlation) on the risk properties of international securitized real estate returns. Findings The findings show that post-GFC, a radical change in international listed real estate risk factors has resulted in more homogeneous markets internationally and less diversification opportunities for international investors. Practical implications The authors suggest the use of robust linear APM (including the Fama and French (2015) five-factor APM) to analyze the risk and performance of publicly traded real estate companies from 14 countries over the period 2000–2015. Originality/value The authors analyze and revisit the risk and performance of publicly traded real estate companies from 14 countries over the period 2000–2015, marked by the unprecedented Global Financial Crisis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".