Human mobility and commercial real estate: Evidence from REIT operating performance
Bibliographic record
Abstract
Abstract The pandemic triggered a structural shift in the ways we work and live, and consequently altered human mobility. This study reveals how human mobility affects commercial real estate performance. We first use a machine learning model to determine the local factors that best predict human mobility. We next analyze the impact of predicted mobility on the operating performance of US Real Estate Investment Trusts (REITs), controlling for other pandemic‐related factors. Our findings demonstrate that REITs with more exposure to mobility reduction report lower net operating income (NOI) during the pandemic, and importantly, the impact also extends to the postpandemic period. We find that the mobility reduction effect is more apparent in the office (through rental revenue) and retail (through operating expenses) sectors. We further demonstrate the sensitivity of adjusted funds from operations (AFFO) to predicted mobility has led to negative stock market reactions, more particularly for office REITs. Overall, our findings reveal that commercial real estate cash flows during the pandemic are more impacted by human mobility when compared to pandemic‐specific factors such as COVID‐19 cases and government interventions. Moreover, we extend our findings to the postpandemic period and show that human mobility has become a strong and persistent predictor of REIT performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".