Certifierade kontorsfastigheter i Milano: En analys av marknadsprestanda och investeringsbeteende
Bibliographic record
Abstract
The purpose of this study is to examine how ESG certification affects investment behavior and valuation within the office real estate market in Milan. The study addresses three central research questions: How do sustainable buildings influence transaction volumes and investor interest? How can non-certified buildings be adapted to meet current sustainability standards? And how does improved energy efficiency contribute to increased property values? By combining qualitative methods, through semi-structured interviews with industry professionals, and quantitative analysis of transaction data, the study finds that ESG-certified office buildings generally sell at higher prices and are more attractive, particularly to foreign investors. Certifications such as LEED and BREEAM serve as quality indicators and help reduce investment risks by enhancing energy efficiency and future-proofing assets. The study also shows that transitioning from so-called “brown” to “green” buildings is both technically and economically challenging, yet increasingly necessary due to rising regulatory demands such as those introduced in the EU’s ESRS E1 standard. At the same time, the findings suggest that upgrading older buildings requires both technical and strategic efforts to meet current demands for energy efficiency and climate performance. Overall, the study indicates that sustainability certification has become a strategic factor in Milan’s office market, with growing importance for both valuation and investment decisions. The results reflect a broader market shift in which environmental performance and energy efficiency play an increasingly central role in determining a building’s attractiveness, suggesting that ESG considerations will be essential for the future of real estate investment.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".