Konut Özelliklerinin Konut Fiyatlarına Etkisinin Kantil Regresyon Yöntemi ile İncelenmesi: İzmit Örneği
Bibliographic record
Abstract
House is an good composed of different housing attributes and summation of these attributes determines the price of an house. Hedonic Price model frequently preferred to price the heterogeneous goods is used to determine impact of housing attributes on house prices in this study. The dataset gathered in July – August 2020 in Izmit housing market is analyzed by utilizing the quantile regression method. Results obtained from this study mentions that open swimming pool, built-in kitchen, middle flat, elevator, number of bath, size of an house, tram, being in building complex, closed garage and under floor heating system variables have positive effect on the houses in the all three price groups. Besides, sea view has an negatif impact on houses in the middle and low price categories.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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; both teacher heads agree on what is shown here.
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".