An analysis on the effect of the COVID-19 pandemic on the housing demand in Finland
Bibliographic record
Abstract
This empirical work studies the effect of municipality characteristics on the demand for housing in Finland during the COVID-19 pandemic. It analyses how home prices, rents, the number of sales and the number of tenancy agreements have been affected by the pre-pandemic density, house and rent prices, households' average earned income, degree of urbanization and percentage of commuters. The purpose is to elaborate on whether some municipality characteristics have had a negative or positive effect on housing demand in order to infer whether this could possibly result in the settlement of a new spatial equilibrium. The rental market and real estate market data was collected from the first quarter of 2019 to the third quarter of 2021. It is found that density, income, the share of commuters and the degree of urbanization have had a positive effect on house prices and rent prices, whereas they affected negatively the number of tenancy agreements. The number of sales were affected positively by both income and pre-pandemic rent and house prices. The results conflict with the existing studies of US and UK ZIP-code level analysis, which find that there has been a significant decrease in demand in dense areas. The differences are attributed to demographic and social dissimilarities between the countries and the different severity degrees of the COVID-19 pandemic.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.104 | 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".