Is Rising Residential Land Prices a Consequence of Domestic or Foreign Land Demand? Evidence From Mauritius Island
Bibliographic record
Abstract
Some emerging island economies have been fostering foreign direct investments in the real estate market. Given the rise in real estate demand under such contexts, this must have entailed a rise in land demand, and subsequently, land prices could have been affected. This study assesses if the rise in land demand caused by domestic and/or foreign land demand has been influencing residential land prices in Mauritius. To undertake the research, annual data was collected from the year 2000 to 2019 and a structural time series approach was used. It was found that unobserved components, namely the trend level and slope, as well as the cycles were significant in explaining land prices. It was concluded that fluctuations in land prices are significantly explained through latent variables, such as regulations in the land market, fiscal policies concerning mortgage loans, and speculative land buying among others. It was also found that foreign real estate investment (FREI) used as a measure of foreign land demand did not significantly influence land prices. To further probe the factors affecting land prices in Mauritius, domestic demand-side factors were considered and it was found that income, population, unemployment, and real construction costs were significant in explaining land prices.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".