Housing Tenure Choice and Housing Expenditures in the Czech Republic
Bibliographic record
Abstract
Choosing between rented housing and homeownership, the so called housing tenure choice, is a key decision made by each household. Therefore housing economists often seek an answer to the question which factors have an impact on this decision. The paper investigates potential tenure choice determinants using probit regression model based on the sample data. Results of the analysis, making use of the investigation of EU-SILC in the CR, showed that tenure choice is affected by the factors similar to those in other countries – household income, marital status of the household head and household size (persons per household). By contrast, the influence of other demographic characteristics, such as gender and age of head of the household has not been confirmed. The econometric model has also made it possible to evaluate potential impact of these factors on housing related expenses of households. In addition to the logical influence of household income, tenure choice decisions are significantly influenced by household size and residence in Prague, particularly in the rented housing sector.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".