Approaches to the land evaluation and practice of their application in developed countries
Bibliographic record
Abstract
Wise land use is an essential basis for steady economic growth. In many developing countries, land usage policy is one of the most troubled areas. An important issue of land usage policies is the process of land value appraisal. It is necessary to study the experience of developed countries in order to make recommendations for the ones which are still developing. Due to standard practice, property is evaluated using three main methodological approaches: comparable sales, cost analysis and income analysis. Techniques of one approach can be used as well as combination of the methods. The choice of method depends on the object of evaluation, available information, type of property usage and others. During mass valuation of real estate comparable sales and income analysis approaches should be preferred in case of assessing the value of multi-family housing when sufficient data on sales and income is available. Approach, based on a comparison of sales, is best suited to evaluate the single-family housing. The cost analysis approach is a good complement to these approaches. As a primary approach it should be applied in the event when there is no sufficient data on sales transactions. The income approach is not suitable for mass estimates of individual single-family housing, as most of this type of property is not rented. Method of determining the value of the land in the developed countries commonly are based either on a comparison of sales, which implies the study of market transactions prices of real estate (Australia, Denmark, Sweden, Indonesia and Japan), or capitalization of income from the potential of the best and most profitable use of real property (some Swiss cantons, some real estate in Denmark and Sweden), or the cost analysis method, which is based on calculating the costs that would be required for a full reconstruction of the property (Indonesia, Japan and South Korea - for buildings), or a combination of all three these methods (U.S., Canada, the Netherlands). Analyzing the experience of the developed countries on land evaluation it can be said that all three main methodological approaches are used. But the practical application of a method of evaluation depends on the type of the object, available information as well as the degree of the market development.
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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 | 0.027 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".