Vliv sdílené ekonomiky na ceny nemovitostí v Praze
Bibliographic record
Abstract
This thesis employs a hedonic regression to measure the impact of Airbnb, the digital platform for short term rentals, on residential prices in Prague. The model is based on the unique transaction dataset of all apartment sales from the first quarter of 2014 to the third quarter of 2018 in Prague. Also, Airbnb listings dataset is used and other datasets containing Prague city data enabling involvement of the property specifications and several neighborhood characteristics influencing the sale price in the model. The main variable of interest included in the regression is Airbnb activity, proxied by the number of Airbnb listings within 300 m of the property at the time of the sale. The results show that a 1% increase in Airbnb activity leads to a 0.0423% increase in sale prices. Moreover, in the city center, the estimated impact is almost twice as high, a 1% increase in Airbnb activity leads to a 0.0816% increase in sale prices. The third hypothesis tested in this thesis shows that the impact of Airbnb has increased in 2017 and 2018. All the estimated results slightly vary, depending on the proxy for Airbnb activity. Nevertheless, estimates in all regressions are statistically significant.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".