The Impact of the COVID-19 Pandemic on the Private Rental Housing Market in Poland: What Do Experts Say and What Do Actual Data Show?
Bibliographic record
Abstract
The aim of the article is to determine the impact of the COVID-19 pandemic on the level of housing rents using the example of the City of Krakow. This study is based on objective data on rental prices and subjective information obtained from real estate agents using a questionnaire survey. The research revealed that the first wave of the COVID-19 pandemic actually led to a 6-7% decrease in prices in the rental market in Krakow, while at the same time the surveyed real estate agents had estimated that rents would drop by about 13%. With the second wave of the pandemic, it is possible to see that its immediate impact, i.e. between the third and fourth quarter of 2020, has led to a further 6.25% drop in rents. It should be noted that the latter decrease was very accurately predicted, both by the survey respondents and by the econometric models used. Finally, the results of the analysis also indicated that the worsening of the pandemic in the last quarter of 2020 will have a significant impact on rent levels in Krakow for all of next year. Regardless of how the economy develops, rental prices are forecast to fall further in 2021q1. However, in the subsequent quarters of 2021, rents are projected to increase, but ultimately their level will not return to pre-pandemic values even in 2021q4. The latter is likely to happen only in the second half of 2022.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".