Mortgage Lending to Individuals in Russia during the Financial Crisis
Bibliographic record
Abstract
In this paper, the authors gave an overview of the main models of mortgage lending, and presented the results of their comparative analysis. The experience of mortgage lending in developed countries during the financial crisis of 2008 is considered. It is noted that a two-level model of mortgage lending in Russia is currently used, which allows the state to control the mortgage lending market, to realize refinancing of commercial banks issuing mortgage loans using securitization. The authors considered mortgage lending in Russia during the financial crises of 2008 and 2014, and its qualitative analysis was carried out. The results of the analysis allowed the authors to conclude that the state, represented by the Agency for Housing Mortgage Lending (AHML), played a crucial role in supporting mortgage lending in Russia during financial crises. It is shown that the government subsidized the interest rate on mortgage lending, which allowed not only to stabilize its volumes after the financial crisis of 2014, but also to ensure growth. The authors used the regression model for analyzing the statistical data of mortgage lending, which made it possible to identify factors that significantly influenced the volume of mortgage lending in Russia in the period under review. The results of the study have been summarized, specific recommendations aimed at further development of mortgage lending in Russia have been prepared.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".