Analysis of the various factors impact on apartment and dwelling house prices in the Pomurska region
Bibliographic record
Abstract
This degree dissertation presents basic terms in the field of the real estate market. Beside \ntheoretical terms, the paper encloses a real estate market analysis in the third quarter of the \nyear 2009 in the Pomurska region. According to data on the real estate market portals, the \nlocal communities for tourism and areas of administration centres have carried out basic \nstatistical analyses, the difference between arithmetic means, correlation analysis and the \nanalysis of factors affecting the prices of apartments and dwelling houses. Included factors for \napartments are their size, age and floor. As for the dwelling houses, there are the size of a \nhouse, the size of a surface area and the hypothetical surface area. The results of this degree \ndissertation are the evaluated impacts of factors on the apartment and dwelling house prices. \nThe highest impact on a square metre has the age of the apartment. It is followed by the size \nand floor. The highest impact on house dwelling prices have the age and size of a house, \nfollowed by the surface area size and hypothetical surface area. Only this way calculated \nimpacts of the factors on the real estate values can be used as adjustment factors to evaluate \nthe market value of the apartments and dwelling houses in the Pomurska region.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".