Assessing financial stability risks from the real estate market in Italy
Bibliographic record
Abstract
We provide an analytical framework for assessing financial stability risks arising from the real estate sector in Italy. This framework consists of two blocks: three complementary early warning models (EWMs) and a broad set of indicators related to the real estate market, to credit and to households. We focus separately on households and on firms engaged in construction, management and investment services in the real estate sector. Since in Italy there have been no real estate-related systemic banking crises, as vulnerability indicator we consider a continuous indicator represented by the ratio between the annual flow of bad debts related to the real estate sector and banks� capital and reserves. We contribute to the recent literature on EWMs by implementing a Bayesian Model Averaging (BMA) based on linear regression models with a continuous dependent variable of vulnerability and an ordered logit model with a discrete dependent variable of vulnerability classes. Both models exhibit good predictive abilities. Based on the BMA projections for the period from the third quarter of 2015 to the second quarter of 2016, banking vulnerability related to the real estate sector is expected to gradually decline.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".