FEATURES OF REAL ESTATE ASSESSMENT: FOREIGN EXPERIENCE
Bibliographic record
Abstract
The purpose of the article is to study the features of real estate valuation abroad. The article analyzes the experience of real estate valuation in foreign countries. It was found that the Center of Lithuanian Registers operates in Lithuania, which includes the register of immovable property and cadastre, the register of legal entities, the register of addresses and registers of residents. In Georgia, all registers are united in the National Public Register Agency. In Sweden, a general assessment is carried out every six years with the purpose of adjustments if necessary, a simplified general assessment is carried out three years after the general assessment, a special assessment is carried out annually to assess newly built properties. In Slovenia, an infrastructure has been created for more efficient management of land resources and mass valuation of real estate, and a register of sales prices is in place to ensure the collection of information about deals on the real estate market. In Poland, mass assessment is a set of procedures for determining the cadastral value of property for tax purposes. In Switzerland, the cadastral accounting of real estate is carried out at the international and cantonal levels. In France, residential buildings are taxed separately from industrial and commercial objects. The annual rental value is used to estimate the value of commercial buildings. In Australia, Denmark, Sweden and Indonesia, the method of determining the estimated value is based on the comparison of sales prices, in Switzerland, Canada and the Netherlands – on the capitalization of income from the permanent use of real estate. Ukrainian legislation on real estate valuation is analyzed. Its features during the war period are highlighted. It is indicated that access to the State Register of Property Rights to immovable property has been restored, and separate norms and rules have been established regarding registration actions. It has been found that in Ukraine such methodical approaches to assessment as income (profitable), expenditure (property), comparative (market) have been established. Further research should be aimed at revealing the role of economic analysis in the land management system after the end of martial law.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".