The Effects of Mortgage Rate Regulation in the Russian Federation
Bibliographic record
Abstract
The main purpose of the present research is to define the impact of changes that took place in the monetary and credit policy of the Russian Federation. The changes touched upon the regulatory issues of mortgage lending which have influenced some aspects of life of the population, including the affordability factor of square meter housing prices in the Russian Federation. The article analyzes the money incomes of the population in the RF, including the main source of average salary, rates, volume and number of credits granted in the period from 2019 to the third quarter of 2021. This period reflects the current situation and takes into account the factor of economic consequences occurred during the pandemics, that is characterized by crisis in the economy. The research describes general trends in mortgage lending in the Russian Federation with the account of some factors. It also provides some solutions for the problems encountered, including the increase in the demand for housing and consequently growing real estate prices. The reduction in rates was aimed to increase affordability of housing, but in practice it had the opposite effect, when the rate cut led to increase in real estate prices. Based on the research findings, we can conclude that money and credit policy in the Russian Federation has failed in terms of mortgage rate regulation and that it is necessary to review the rate in the short-term.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".