Understanding and Mitigating Direct Investment Risk in the Indian Real Estate Market
Bibliographic record
Abstract
This paper seeks to extend the findings regarding factors that affect Canadian propensity to undertake direct investment abroad by examining the perception of risk factors that may hinder direct investment in the Indian real estate market. This study utilized survey research (a non-experimental field study design). 226 Canadian investors were surveyed and reported their perceptions of various risk factors regarding investing in the Indian real estate market. The findings suggest that perceptions of political and legal nature, corruption, confiscation, and economic risk can hinder investments and may lead to capital losses on investments in the Indian real estate market. We also found that investors’ foreign direct investment behavior does not differ based on their age and the level of education. This paper discusses several techniques by which investors can mitigate foreign direct investment risk in India. It also points out how real estate investors can implement these techniques and the challenges that they might face through this implementation process. Finally, some suggestions to overcome these challenges are provided.
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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 | 0.001 | 0.001 |
| 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".