Geographic Scope and Real Estate Firm Performance during the COVID-19 Pandemic
Bibliographic record
Abstract
This study examines the effect of geographic scope in mitigating the adverse impact of the COVID-19 pandemic in the real estate sector. Utilizing the Chinese setting over the two-month period in 2020 from the beginning of the outbreak to the successful containment of the spread of virus, we show that while the pandemic has negatively impacted real estate firm returns, firms with broader geographic scope and more geographically diversified property allocations have managed to better endure the crisis. We further find that firms with higher leverage report lower returns during the pandemic irrespective of their geographic scope, but larger firms can lessen the adverse impact of the pandemic only if they have adopted a more diversified strategy. Overall, our study provides novel evidence on the benefit of diversification by demonstrating the importance of geographic scope and diversification at times of crises. Specifically, we show corporate diversification could be especially useful to mitigate the negative stock market reactions resulting from the pandemic. Moreover, diversification could even become essential for larger firms that are expected by the market to be more diversified.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".