The dynamics of house price in Vietnam
Bibliographic record
Abstract
We have assessed the housing market of Vietnam, with particular apartment prices in 10 urban districts of Hanoi, the capital of Vietnam. Significant determinants of house price include construction cost, income per capita, urban rent expense, and lending rate. Our findings show statistically significant and dynamic determinant effects on the apartment prices of 10 urban districts of Ha Noi. There are signs of volatility clustering in the GARCH effect at nine districts’ apartment prices, all with magnified effects. Amongst the ordinary least square (OLS), robust least squares (RLS) and bootstrap technique, RLS presents more significant fundamentals with higher Rs squared than the OLS and bootstrap on individual districts and overall Hanoi apartment prices. We find signs of price bubbles in the first quarter of 2015. While this quantitative analysis is limited to the north of Vietnam, the findings also provide insights into other significant centres of Vietnam. It also provides a basis for apartment price forecasts to the stakeholders in the housing market of Vietnam, investment decision-making and portfolio management for both household investors and mortgage investors. The study outcomes can be used to forecast the volatility dynamics of the expanded types of dwellings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".