Residential Real Estate in Europe: An Exploration of Common Risk Factors
Bibliographic record
Abstract
Abstract We conduct an exploratory analysis using proxy measures of cross-sectional returns and rental yields in residential real estate. Asset pricing models predict that expected returns should exhibit some sensitivity to one or several fundamental variables that represent a common source of undiversifiable risk. Residential real estate, just like works of art and collectibles, is unique because it represents both an investment vehicle and a durable consumption good. Its pricing and returns should thus reflect both the benefits from portfolio diversification and the effect of supply and demand. In this paper, we investigate the variation in proxy returns and proxy rental yields across 34 major European cities, using a handful of independent variables that should account for the influence of market risk, inflation, and liquidity. In spite of obvious limitations stemming from our sample, we find that the explanatory power of our model is unusually high for a cross-sectional data analysis. Some of our findings concur with other studies showing that in spite of strong segmentation, real estate markets respond to the same structural risk factors. A good portion of our results, however, is hard to explain and interpret. Either we need to take into account cultural differences between Eastern and Western Europe as part of a behavioral approach, or we have to concede that we have been misled by the mismatch in the level of aggregation and the crude estimation of the dependent variables.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".