Identifying rent pressures [on housing market] in your neighbourhood: a newmodel of Irish regional rent indicators. ESRI WP567, June 2017
Bibliographic record
Abstract
Since 2013, researchers in the Economic and Social Research Institute (ESRI) have compiled a hedonic rental index for the Residential Tenancies Board (RTB). The indicator estimates a standardised rental index on a national, Dublin and outside of Dublin basis based on the 950,000 rental properties registered with the RTB. The provision in late 2016 of detailed geographical identifiers has enabled an alternative series of indicators to be estimated. In particular, hedonic rental indicators for 137 local electoral areas (LEAs) are now available on a quarterly basis from 2007 quarter 3 to 2016 quarter 4. By providing a more accurate assessment of regional trends in rental supply and demand, the indicators should enable a more precise implementation of policies in the rental market. They should also serve as a proxy for measuring underlying economic activity in these regions on an ongoing basis.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".