Bibliographic record
Abstract
The purpose of this study is to investigate and analyze the perception of direct investment in overseas real estate. The research method combines theoretical research and case study. In the case study, 100 real estate-related workers were surveyed on their perceptions of overseas real estate direct investment, and this was statistically analyzed. Considering the results of the analysis, As a result of the analysis, first, 94% were positive about the need for overseas real estate direct investment, and only 4% were negative. Second, as for preferred investment destination countries, “developed countries such as the United States, Canada, and Europe” were high at 41% and “developing countries such as Vietnam, the Philippines, and Malaysia” were high at 32%. Third, 91% were positive about Japanese real estate investment. Fourth, as for the motives for overseas real estate direct investment, “domestic strong regulation-oriented real estate policy” was found to be high at 56%, and “opaque future of Korean society” was found to be high at 25%. Fifth, in terms of investment intention, “stable rate of return” was 28%, “cheap ownership tax and capital gains tax” was 27%, and “certainty of ownership guarantee” was 23%. Sixth, as obstacles to direct investment in overseas real estate, “problem of overseas real estate management” was found to be high at 53%, and “ignorance of overseas real estate market” was found to be high at 29%. Considering the results of this analysis, the government suggests that a policy to support direct overseas real estate direct investment is needed to reduce investment risk and ensure stable investment for overseas real estate direct investors.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".