Exploring housing market and urban densification during COVID-19 in Turkey
Bibliographic record
Abstract
The paper explores the housing market, urban densification, and government policy interventions due to COVID-19 in Turkey. From 1980 to 2019, the share of urban population in Turkey increased from 43.78% to 75.14% (UN DESA, 2018) and simultaneously the housing production has been increased more than 30% at the same period and it has planned to build or reconstruct about 13 million housing units including 1 million housing units per year from 2020 (Housing Development Administration of Turkey, 2020). However, COVID-19 has radically changed Turkey's real estate market, more specifically, housing market. Based on secondary data and information, the study has found that there has been a sharp decrease occurred during the month of April and May in 2020 due to curfew and other related COVID-19 controlled measures. After government interventions such as lowering interest rates in public banks, online land registry applications, government stimulus packages etc. a sharp increase happened from June and the third quarter of 2020; even after out looking 10 lowest densely populated provinces, 10 highest densely populated provinces in Turkey and districts in Istanbul. Focusing impact on the housing market in three different quarters (Q1, Q2 and Q3) of 2020 in the studied areas, it has found that there is no significant relationship between housing sales with respect to population density but government policy intervention during COVID-19 plays a very significant role in increasing housing demand.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".