The Effects of Economic Measures on House Prices in Turkey During the Covid-19
Bibliographic record
Abstract
The demand for housing in Turkey has been increasing in recent years to meet the need for shelter as well as an investment good. Especially during Covid-19, housing prices continued to increase in many regions of Turkey, similar to the development in many other countries around the globe. The Turkish government conducted various measures to counter the severe economic consequences of Covid-19, and several of these measures have had direct and indirect impacts on the housing markets, too. This study analyzes the effect of selected economic measures applied during the pandemic on Turkey's housing prices. Due to data availability, this study focuses on the main housing markets in Turkey: Istanbul, Ankara, and Izmir, which account for more than one quarter of the Turkish population. The conducted economic measures and other macroeconomic factors are assessed over the Central Bank Money Supply, Consumer Price Index, Unemployment Rate, and Housing Loan Interest Rate variables to analyze potential effects. Hereby, residential real estate prices are evaluated on a regional level to show the potential diverging impacts of the respective measures on local housing markets. Descriptive statistics and stationarity levels of the variables used in the study are examined with the Dickey-Fuller (ADF) test. In the study, a Vector Auto-Regressive (VAR) model is used to analyze the various variables’ impact on housing markets in Tukey. While already several studies analyzed the impact of economic measures on house prices in different countries, this study uniquely assesses the impact of such measures in Turkey during Covid-19. The study also evaluates the impact of multiple macroeconomic factors on house prices, providing a more comprehensive understanding of the factors that cause fluctuations in house prices in Turkey. The study can guide policy decisions and investment strategies by providing insights into the impact of economic measures and other macroeconomic factors on house prices.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".