Bibliographic record
Abstract
Among the statistics published by Eurostat are housing price statistics but also many other housing related statistics. These statistics are key for policy makers but also for households due to their economic, social and environmental importance. European statistics on house prices are part of the Macroeconomic Imbalance Procedure (MIP), which is a surveillance mechanism that aims to identify potential macroeconomic risks early on, prevent the emergence of harmful macroeconomic imbalances and correct the imbalances that are already in place. In the surveillance of the COVID 19 impact on economic activities housing prices play a role as well. Every quarter, Eurostat publishes indices on: house prices with a breakdown for new and existing dwellings, owner occupiers housing price indices, numbers and volumes of house sales. Beyond these indices, Eurostat also publishes many economic, social and environmental statistics related to housing allowing analysis of trends and correlations as well as comparisons between countries and European averages. In particular, they aim at providing answers to questions on: How do we live? With indicators on (a) tenure status (owner or tenant), dwelling type (house, flat) by degree of urbanisation, (b) the size of housing, (c) the quality of housing and (d) the environmental impact of housing. What does housing cost? With indicators on (a) the evolution of house prices and rents and (b) housing affordability. What is the importance of the construction sector in the economy and land use? An overview of these statistics can be found in the following online publication released for the first time in December 2020: https://ec.europa.eu/eurostat/cache/digpub/housing The presentation will guide to find the relevant 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.021 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.031 |
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".