Strategically Measuring Quality of Existing Building Stock
Bibliographic record
Abstract
Dutch housing corporations generally have two methods of assessing the strategic value of existing housing stock. The first is by calculating the financial return on investments with a life expectancy of fifty to sixty years. The second method is to balance the technical quality against maintenance and renovation planning. A Dutch housing corporation needed a integrated method, so in a single case study, a new method was developed based on research on how to monitor the technical and financial assets better. Four problems were detected: (1) the existing strategy did not seem to be resilient to future changes, (2) there was no instrument for measuring progress, (3) there was no way to translate strategic data to individual estates and (4) there was no instrument for monitoring the results of improvements set off against the strategic goals. With one integrated tool to fix these four problems, an integrated approach to a closed asset management strategy and policy would be available. Such a tool would make it possible to make adjustments to the strategy based on facts gained by measuring the results of former adjustments to the strategy. The goal of this paper is to present the research supporting the design of a new model. The result, the so called Return Matrix, is a fully elaborated model. It supports the management team in decision making about strategy (five years) and vision (twenty years) development. It creates insight into and support for the outcome of the strategy among policy professionals, staff and colleagues. And finally, it creates understanding among the tenants, it s understood and supported by the civil servants and gets approval and agreement of cooperation from the municipal executives. With the knowledge gained by this study, it will not be difficult to compose the instrument for other cases too.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".