Bibliographic record
Abstract
Purpose – This paper aims to investigate Lithuanian house price changes. Its twin motivations are the importance of information on future house price movements to sector stakeholders and the limited number of related Lithuanian property market studies. Design/methodology/approach – The study employs ARIMA modelling approach. It assesses whether past is a good predictor of the future. It then examines issues relating to an application of this univariate time-series modelling technique in a forecasting context. Findings – As the results of the study suggest, ARIMA is a useful technique to assess broad market price changes. Government and central bank can use ARIMA modelling approach to forecast national house price inflation. Developers can employ this methodology to drive successful house-building programme. Investor can incorporate forecasts from ARIMA models into investment strategy for timing purposes. Research limitations/implications – Certainly, there are number of limitations attached to this particular modelling approach. Firm predictions about house price movements are also a challenge, as well as more research needs to be done in establishing a dynamic interrelationship between macro variables and the Lithuanian housing market. Originality/value – Although the research focused on Lithuania, the findings extend to global housing market. ARIMA house price modelling provides insights for a spectrum of stakeholders. The use of this modelling approach can be employed to improve monetary policy oversight, facilitate planning for infrastructure or social housing as a countercyclical policy and mitigate risk for investors. What is more, a greater appreciation of Lithuania housing market can act as a bellwether for real estate markets in other trade-exposed small country economies.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".