Bibliographic record
Abstract
This study presents a first summary of the housing finance results of the OeNB’s Household Survey on Housing Wealth in Austria. 22% of Austrian households have taken out debt to finance housing. The probability of holding such debt is significantly higher for younger and higher-income households than for others. High-income households are much more likely to have a variable rate loan or a foreign currency loan, but at the same time they also have lower loan-to-value (LTV) ratios than the other groups. Regional differences – or more specifically, a west-east pattern – were identified regarding the type and amount of debt incurred: Austrian households in the western provinces tend to have higher debt and higher LTV ratios than those in the eastern provinces. Housing assistance funds and alternative forms of financing, such as inheritances or inter vivos gifts (money), play quite a significant role in housing finance. Austrian households use their property mainly for residential purposes rather than as an investment instrument: Of the households with outstanding housing loans, 74% used (at least part of) the money to purchase their primary residence, 12% used it to finance the deposit they had to make for their housing association apartment and 17% purchased a second home. 52% of the households that took out a loan to purchase a second property use it for residential or similar purposes, while 26% of them offer it for rent and the remaining households (roughly one-quarter) use it as a store of value. These facts and the existence of a strongly subsidized rental market seem to have contributed to the rather low ownership ratio and the moderate development of Austrian real estate and rental prices by international standards. The differences identified in the structure of housing finance of Austrian households suggest that the impact of monetary policy on wealth (and hence on household consumption and investment) will also differ markedly.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".