Challenges of constructing commercial property price (and associated) indicators
Notice bibliographique
Résumé
While several countries, G20 and non-G20 alike, have built and are continually expanding their experience in housing price statistics, the availability of data from official institutions – NCBs or government (such as NSIs) – on commercial property is scarce. If available at all, the published information is mostly to be considered of experimental nature and comes at varying frequencies from monthly to annual and with very different length of the time series. As a result of increasing user demand for commercial property price indicators (CPPIs), the European Commission (Eurostat) and the European Central Bank (ECB) established a joint expert group (JEG) to explore the further development of commercial property price and associated indicators. In order to ascertain which data sources exist both within the EU and internationally, the JEG jointly approached the central bank and administration of each EU Member State and, via the Bank for International Settlements (BIS), selected members of the G20. The JEG examined nine variables (prices, rents, yields, vacancies, 'building and construction', transactions) related to the physical commercial property market based on the Recommendation on closing real estate data gaps by the European Systemic Risk Board (ESRB), which is addressed at macroprudential authorities. It conducted a stocktaking exercise covering all EU Member States plus G20 members Australia, Brazil, Canada, Japan, Saudi Arabia and the United States (with replies from all countries except the US). It had in-depth discussions with user groups (from the ESRB, ECB, Commission as well as external experts) and identified various existing data sources split into NCB / government or private. This talk would present, and invite to discuss, the work of the JEG on CPPIs. The JEG concludes that since data sources for some of the indicators are absent, international consensus on appropriate methods is lacking, and resources at national level in general, as well as experts in this domain in particular are scarce, the collection of data is technically difficult and in its infancy both in the EU and around the world. The stock-taking exercise also revealed that there are no 'quick wins' that would allow comparable and reliable data to be supplied. Not least because of this, the short to medium-term solution is likely to rely on the already available price and associated indicators from private sources. The report proposes concrete milestones for the way forward.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».