Challenges of constructing commercial property price (and associated) indicators
Bibliographic record
Abstract
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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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 | 0.101 | 0.295 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.024 | 0.041 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".