Substanssiarvoalennukset eurooppalaisissa listatuissa kiinteistöyhtiöissä
Bibliographic record
Abstract
Net asset value (NAV) discounts in listed real estate are a well-documented phenomenon. NAV discounts reflect persistent gaps between company market price and the value of the underlying property portfolio. This thesis examines how firm characteristics and economic policy uncertainty influence those discounts from 2015 to 2024 in Europe. The analysis uses quarterly panel data on 56 firms from five European countries and estimates five regression models: Pooled OLS with country fixed effects, and four fixed effects panels that add firm fixed effects, time fixed effects, quarter by sector fixed effects, and finally an EPU by sector interaction. Key firm variables include size, leverage, past returns, volatility, and REIT status. Economic policy uncertainty is measured using a country level economic policy uncertainty index (EPU). Results confirm that listed real estate firms in Europe trade at a discount to net asset value over time. Discounts vary by property sector and deepen when policy uncertainty rises. The effect of economic policy uncertainty is significant but economically modest. Additionally, sectors differ in their sensitivity to EPU movements, with some showing stronger discount responses than others.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".