Bibliographic record
Abstract
Abstract The total value apportionment between land and building components remains an international issue both in theory and in practice. There are several concepts and methods of value separation, each leading to approximate estimations and therefore to divergent opinions about their reliability. In this paper, we present an alternative method of value apportionment based on Shapley’s scheme of values, well recognized in the coalitional game theory. The practicality of this method is verified using observed prices of 14,715 residential properties sold during the year 2019 over all the 27 districts in Montreal (Canada). This unique data comes with detailed information about the essential attributes of the land and the building components. The empirical results of the method presented in this work are in line with practical expectations of total and separate values, either taken case-by-case or in aggregation per district. They are indeed encouraging when compared to the results of two other independent methods (i.e., the city evaluations and the OLS predictions) for the same properties. The results are interesting not only regarding the separation of value but also in several other related aspects. For instance, land values are often close to or even higher than the building values. This shows a phenomenon of building depreciation and land value appreciation. Some districts seem to favor the quality of the building, others being influenced by the location and quality of the land. Interestingly, in contrast to what is believed in practice, a good quality parcel of land does not necessarily have a good quality building according to the results.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".