Evaluating Financial Feasibility Studies Based on Real Estate Developers’ Requirements
Bibliographic record
Abstract
A financial feasibility study has been identified as a ‘critical success factor’ of construction projects that can cause these projects to fail, if not executed or communicated correctly. Yet, research indicates that the aforementioned feasibilities are not executed well, inconsistent, neglected, and problematic. They greatly lack best practice items. An audience analysis was conducted through semi-structured interviews with 23 real estate developers in the private sector. This analysis presented the developers’ needs and requirements for the documents. A total of 23 requirements were identified. From these requirements, an evaluation tool was created and 18 financial feasibility studies were evaluated. The average score of all 18 feasibilities is 62%. Three feasibilities achieved a below-average score in both categories. Six (33.33%) of the feasibilities managed to score above average in both categories, while the average of the respective categories are 68% and 50%. The data indicates that an audience analyses lacks in the industry, leading to poor communication that does not fulfil the needs of real estate developers.
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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 | 0.113 | 0.257 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".