House Sales Indicators - A New Dataset Able to Capture Housing Market Developments in Europe
Bibliographic record
Abstract
Among the housing statistics published by Eurostat, housing price statistics are available for more than a decade. However, house sales statistics in number and value of transactions are publicly available only since summer 2020 capturing quarterly information for many European countries at least since 2015. Housing statistics are key for policy makers but also for households due to their economic and social importance. In the surveillance of the COVID 19 impact on economic activities, housing prices did not yet show a huge impact of the economic downturn in many countries. In contrast, house sales indicated a slowdown of housing market activities. Consequently, these indicators are essential for analyzing crisis developments. During the ERES conference begin June 2021 the data for all the quarters in 2020 will be available in the Eurostat database encompassing house sales indicators for 24 European countries. Every quarter, Eurostat publishes the following indicators: house prices with a breakdown for new and existing dwellings, owner occupiers housing price indices, numbers and volumes of house sales. These indicators support not only medium and long-term analyses of this key sector of the economy but in addition allow monitoring crisis effects. The presentation provides a preliminary analysis for European countries and will guide to find the requested information on the Eurostat website which is free of charge and can be consulted on https://ec.europa.eu/eurostat/.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.013 |
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".