Historical Patterns and Recent Impacts of Chinese Investors on United States Real Estate
Bibliographic record
Abstract
Since supplanting Canada in 2014, Chinese investors have been the lead foreign buyers of U.S. real estate, concentrating their purchases in urban areas with higher Chinese populations like California. The reasons for investment include prestige, freedom from capital confiscation, and safe, diversified opportunities from abroad simply being more lucrative and available than in their home country, where the market is eroding. Interestingly, since 2019, Chinese investors have sold a net 23.6 billion dollars of U.S. commercial real estate, a stark contrast to past acquisitions between 2013 to 2018 where they were net buyers of almost 52 billion dollars worth of properties. A similar trend appears in the residential real estate segment too. In both 2017 and 2018, Chinese buyers purchased over 40, 000 U.S. residential properties which were halved in 2019 and steadily declined to only 6, 700 in the past year. This turnaround in Chinese investment can be attributed to a deteriorating relationship between the U.S. and China during the Trump Presidency, financial distress in China, and new Chinese government regulations prohibiting outbound investments. Additionally, while Chinese investment is a small share of U.S. real estate (~1.5% at its peak), it has outsized impacts on market valuations of home prices in U.S. zip codes with higher populations of foreign-born Chinese, increasing property prices and exacerbating the issue of housing affordability in these areas. This paper investigates the rapid growth and decline of Chinese investment in U.S. real estate and its effect on U.S. home prices in certain demographics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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 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".