Análise macroeconômica dos impactos da pandemia do COVID-19 no mercado imobiliário em Curitiba-PR no ano de 2020
Bibliographic record
Abstract
The year 2020 was characterized by developments resulting from the Pandemic caused by the Corona Virus. Different sectors of the economy and different Brazilian regions reacted divergently to the Pandemic. The present work sought to analyze the macroeconomic impacts caused by the COVID-19 Pandemic on the real estate market in the city of Curitiba-PR. The economic and macroeconomic indicators GDP, IPCA, INPC, IGP-M and unemployment rate were analyzed. Such data were correlated with the number of launches and sales of properties and the value of the square meter in the city of Curitiba-PR. Through the analysis and crossing of data, it was found that both the Brazilian and Curitiba economy, as well as the real estate market, suffered fluctuations during the year 2020, showing a retraction in the first half and an increase in the second half. Brazilian Gross Domestic Product (GDP) followed the retraction and rise in the number of sales and launches in the city of Curitiba-PR. The decrease in Selic and the increase in the IGP-M, with consequent ease of credit and increase in rental prices, were in line with the ease of financing by the consumer and the growth in the number of sales and launches registered in the city. The value of the square meter registered in the capital of Paraná in 2020 was above inflation, indicative of real appreciation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".