Detecting bubbles in the Finnish housing markets 2006-2023
Bibliographic record
Abstract
The research motivation for this study derives from the importance of understanding housing markets as it has a great effect on people’s lives, their financials and the macroeconomy. Throughout the history, housing markets have also been an important factor in a variety of economic crises. Therefore, a housing bubble detection study contributes a lot in order to analyze the housing markets and detect possible price bubble scenarios. \nThe housing prices from the Finnish capital area and from Finland are collected from the secondary housing price data from the first quarter of 2006 to the second quarter of 2023. OLS regression analysis is then performed with real housing prices as the dependent variable and disposable aggregate income and unemployment rates as independent variables. From the fitted values, it is assessed whether the determined bubble thresholds are exceeded during the sample period. The results from the regression analysis are compared to the results received from the ratio analysis conducted with the price-to-rent ratio and price-to-income ratio using the same data that the regression analysis to again evaluate whether the determined bubble thresholds are exceeded. \nThe results indicate that the hypotheses about a housing market bubble between 2020 and 2022 for the Finnish housing markets and for the Finnish capital area housing markets are somewhat confirmed with small variations in the actual bubble periods between the different research methods. The results from the OLS-regression model, price-to-rent ratio and price-to-income ratio indicate that there is notable bubble activity from 2019 to 2022 depending on the research method.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".