Value Maximizing Decisions in the Real Estate Market: Real Options Valuation Approach
Bibliographic record
Abstract
The real estate market of EU countries has undergone a severe global financial crisis 2008–2009, recovered successfully later, and now experiencing significant uncertainty due to the COVID-19 pandemic event. Significant volatility of the real estate business is once again evident, just as it was following the global financial crisis. The paper aims to provide a case study of a real estate project by giving insight into the Latvian real estate project that had been experiencing similar economic uncertainty, to demonstrate hybrid real options valuation (ROV) method to adapt real estate investments to changing circumstances and to develop the decision-making solution to similar EU real estate problems during the pandemic. The paper provides the “step-by-step” ROV application’s methodology in real estate development projects. The presented methodology is a powerful managerial risk management tool for the executives of similar real estate development projects in the EU countries struggling to make investment decisions in the pandemic and post-pandemic period. Since any estimation includes assumptions, ROV results should be interpreted and perceived as approximations only. The future works can provide robust ROV analyses and interpretations regarding the demand for real estate, showing quantitatively how competition can impact strategic investment decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".