THE MODEL OF PREFERENTIAL PROVISION OF LAND PLOTS FOR INDIVIDUAL HOUSING CONSTRUCTION
Bibliographic record
Abstract
Целью данной статьи является разработка предложений, способствующих обеспечить доступность жилья для граждан Российской Федерации. Автором проанализирована динамика средних значений показателей цен за кв. м квартир в новостройках, представлены факторы, служащие причиной резкого и продолжительного роста цен на жилье. Сделан вывод о том, что жилье становится все менее доступным для приобретения: прирост цен на жилье опережает прирост доходов населения, ситуация усугубляется инфляционными процессами, повышением цен на других рынках экономических благ. Автором в процессе достижения цели исследования проведен критический анализ литературы, посвященной льготным мерам в отношении обеспечения граждан жильем. Рассмотрены такие меры поддержки как налоговые льготы на недвижимость, социальное жилье и льготная ипотека. Сделан вывод о недостаточности подобных мер в современных условиях. В процессе исследования предложена авторская модель льготного предоставления земельных участков под индивидуальную жилищную застройку. Обозначены предпосылки к созданию данной льготной программы, предложен новый вид разрешенного использования земельных участков, сформированы требования к участникам программы и объектам недвижимости, выделены возможные мультипликативные эффекты. Социальные эффекты предложенных мероприятий поддержки отражаются в повышении доступности базовых потребностей граждан в жилье, повышении качества жизни, с учетом отрицательных последствий урбанизации, а также поддержке инициатив граждан в развитии строительства и территорий страны. Предполагается, что подобные меры поспособствуют решению проблем доступности жилья, а также развитию территорий. The purpose of this article is to develop proposals that will help ensure the availability of housing for citizens of the Russian Federation. The author analyzes the dynamics of average values of price indicators per quarter. m apartments in new buildings, the factors that cause a sharp and prolonged increase in housing prices are presented. It is concluded that housing is becoming less affordable for purchase: the increase in housing prices outstrips the increase in household incomes, the situation is aggravated by inflationary processes, price increases in other markets of economic benefits. In the process of achieving the research goal, the author conducted a critical analysis of the literature on preferential measures in relation to housing provision for citizens. Such support measures as tax incentives for real estate, social housing and preferential mortgages are considered. The conclusion is made about the insufficiency of such measures in modern conditions. In the course of the research, the author's model of preferential provision of land plots for individual housing development is proposed. The prerequisites for the creation of this preferential program are outlined, a new type of permitted use of land plots is proposed, requirements for program participants and real estate objects are formed, possible multiplicative effects are highlighted. The social effects of the proposed support measures are reflected in increasing the accessibility of basic housing needs of citizens, improving the quality of life, taking into account the negative consequences of urbanization, as well as supporting citizens' initiatives in the development of construction and the country's territories. It is assumed that such measures will contribute to solving the problems of housing affordability, as well as the development of territories.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.004 |
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".