An Empirical Investigation on the European Housing Market Prices
Bibliographic record
Abstract
Although the housing market prices and trends have been the object of a great deal of \nstudies in the last decade, since the 2007-2008 financial crisis, a unifying and commonly accepted \ninterpretation for them is still missing. In this paper we introduce an empirical and heuristic \napproach to analyze the price of the European housing market relative to the stock market, \nconsistent with a general equilibrium approach, on the basis of a set of theoretically relevant \nvariables. We perform panel data estimates (with GMM-DIF) of the relative price of the real \nestates for the 15 countries that were members of the EU on the 1st of January 1995, using annual \ndata from 1993 to 2015. We follow, in this regard, the “general-to-specific” approach and GMMdiff estimating methodology. Our results show that the relative price of the real estates is not only \naffected by the fundamentals, but also displays a strong influence of autoregressive and “selfsustaining” mechanism in the relative 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".