Estonian housing market: affordability problem and regulatory framework
Bibliographic record
Abstract
The Estonian economy has quickly adjusted to new circumstances brought about by the global crisis. Weak domestic demand due to the decrease in incomes and worsening situation in the labor market is still the main source of negative impact on the economy. But the Estonian economy began to grow again in the fourth quarter of 2009 and already in 2010 the economic growth was stronger than expected. The recovery is mostly based on exports. Since January 2011 Estonia is the 17th member of EMU. The development of the housing market was rapidly growing until 2007: demand exceeds supply for housing; bank finance allocated to real estate was very high comparison to total lending etc. The end of the housing boom shows that there is a need for more attention to the housing affordability problem. Situation in the real estate market changed in 2008 and it seems to be more stabilized in 2011, but the questions about the affordability of housing still remain. This article seeks answers how to define housing affordability for Estonian housing market. Also describes the regulatory framework and policy decisions made by the government. It is difficult to measure regulatory frameworks impact to the housing market, but are the Estonian households able to afford the housing? The article seeks the answer on how to evaluate the current situation and prognosticate potential development trends.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".