Real estate price polarization projected to increase until 2030 in Germany
Bibliographic record
Abstract
Demographic projections for Germany indicate a drop in the population of many regions by 2030. This is likely to have an impact on the real estate market. Our report presents the result of a model calculation of asking prices for residential real estate in Germany up to 2030 based on market data from empirica-systeme GmbH and a population projection from the Bertelsmann Foundation. Depending on the model specifications, it appears that real estate price polarization will increase by 2030. As with all model calculations, the results are subject to uncertainty. In the scenario presented here, we strictly focus on the demographic effect on real estate prices. According to our projections, in one-third of all rural districts (Landkreise) and urban districts (kreisfreie Städte), the market value of condominiums will fall by over 25 percent. This will also be the case for single- and two-family homes in one-quarter of all districts. Some regions in eastern Germany will be hit particularly hard by this development. In and around urban centers, however, the trend of rising prices is expected to continue. Our findings also show that the polarization of real estate prices might cause the inequality of wealth in Germany to rise slightly.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".