Comparison of International Real Estate Valuation Standards
Bibliographic record
Abstract
In a globalizing world, real estate investors have great interest in uniform valuation standards to allow comparison of outcomes across countries. Transparency, reliable reports, sound procedures and comparability are core values for investors, valuations and valuers. This is particularly evident during financial crises and in fraud cases. Introduction to and compliance with established valuation standards could improve the reliability of the investments and the consistency and presentation of information and encourage greater transparency in reporting to investors and the market in general. As far as (real estate) valuation is concerned over time three major valuation standards have been developed: the International Valuation Standards (IVS by IVS Council), the European Valuation Standards (EVS by TEGoVA) and the Uniform Standards of Professional Appraisal Practice (USPAP, USA/Canada by The Appraisal Foundation TAP). Those standards are regularly amended because of expansion, interpretation, changes in regulation or law as well as jurisdiction. There are similarities between the standards, but also important differences. The procedures followed and the reported outcome of valuations may therefore differ. Especially, if investors have a globally diversified portfolio, one should be aware of the valuation differences between regions. For purposes of (financial) reporting, it is important to map these similarities and differences between those standards. This research will be carried out in close cooperation with the standard setters as much as possible. The objectives of the research are: To identify, clarify and explain differences and similarities on a conceptual and practical level between the recently published IVS 2017 and EVS 2016 as well as between IVS2017 and USPAP 2018/2019. To formulate topics and recommendations for IVSC and TEGoVA and TAP to be discussed The aim of the research will be to make stakeholders of valuation standards aware about the consequences of the differences at the moment.
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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 | 0.020 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".