Assessing Housing Price Bubbles through an Affordability and Profitability Ratio Analysis. The case of Greek Residential Market
Bibliographic record
Abstract
This research paper investigates housing price bubbles within the context of the residential real estate market in Greece, employing alternative analytical techniques and presenting novel econometric-based findings. The study concentrates on the temporal span from the first quarter of 1997 to the third quarter of 2022. Affordability and profitability indicators are employed to identify and evaluate episodes of either under- or overvaluation. Valuation is assessed by measuring the deviation of the price-to-rent ratio from its long-term average and trend, revealing an overvaluation of approximately 20% since the second quarter of 2019. Furthermore, the time-series properties of the dataset are subjected to rigorous econometric analysis, and specific housing-related hypotheses are empirically tested. Cointegration analysis yields varying implications for valuation depending on the chosen methodology and metric; nevertheless, the results collectively point to an overvaluation in terms of the price-to-income ratio. Moreover, among the four Right-Tailed Augmented Dickey-Fuller techniques employed, the Supremum Augmented Dickey-Fuller method identifies the presence of housing bubbles concerning the price-torent ratio. These bubbles are precisely dated using the Generalized Supremum Augmented Dickey-Fuller technique. The robustness of these findings is verified through the application of the Hodrick-Prescott Filter to real house prices. Remarkably, the most recent housing bubble episode, out of the four detected, persists until the final quarter of the sample period (2022Q3). This observation raises significant questions regarding its potential impact on the broader economy and necessitates a discussion on appropriate policy measures that should be considered.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".