Who Owns Offshore Real Estate? Evidence from Dubai
Bibliographic record
Abstract
This paper analyzes a unique micro-dataset capturing the ownership of about 800,000 properties in Dubai. We use this dataset to document patterns in cross-border real estate investments, a blind spot in the analysis of financial globalization. We obtain four main findings. First, offshore real estate in Dubai is large: at least $146 billion in foreign wealth is invested in the Dubai property market. This is twice as much as real estate held in London by foreigners through shell companies. Second, geographical proximity and historic ties are key determinants of foreign investments in Dubai. About 20% of offshore Dubai real estate is owned by investors from India and 10% by investors from the United Kingdom; other large investing countries include Pakistan, Gulf countries, Iran, Canada, Russia, and the United States. These patterns hold when focusing on the most affluent neighborhoods, with the main difference that Indian investments become relatively smaller and Russian investments larger.Third, a number of conflict-ridden countries and autocracies have large holdings in Dubai relative to the size of their economy, equivalent to 5%–10% of their GDP. This suggests that the official net foreign asset position of a number of low income economies is significantly under-estimated. Last, by matching properties owned by Norwegians to administrative tax records in Norway, we find that the probability to own offshore real estate rises with wealth, including within the very top of the wealth distribution. About 70% of Dubai properties owned by Norwegian taxpayers were not reported for tax purposes in 2019. These results suggest that the lack of cross-border exchange of information on real estate ownership is a significant issue for tax enforcement.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".