Empirical Study of the Causes of Valuation Variance and Inaccuracy in Nigeria
Bibliographic record
Abstract
The question about accuracy and consistency in valuation practice has been subject of interest to the academicians, professionals and users of valuations not only in Nigeria or developing countries alone but also in the developed countries of the world such as UK, USA, Canada and Australia. Property valuation is the core of Estate Surveying and Valuation profession. Thus its accuracy and/or reliability is critical to the valuers, their clients and the stakeholders of valuation in general. Earlier studies have confirmed the existence of inaccuracy and inconsistency of valuations in these countries including Nigeria. However, there has not been much study into the factors contributing into inaccuracy and variance in the valuation estimates by the Nigerian valuers. Hence, this study aims at examining the causes of inaccuracy and variance in the country’s valuation practice. To achieve the objective of the study, the use of survey method in conjunction with personal interviews with some members of the Nigerian Institution of Estate Surveyors and Valuers in practice and academic institutions in some universities and polytechnics Lagos metropolis were used for the purpose gathering data for the study. The data collected were analyzed with the aid of simple statistical tools. The findings revealed divergent causes of valuation inaccuracy and variance ranging from dearth of market data to lack of adequate training and experience on the part of the valuers and many more. The paper concluded by recommending the establishment of functional property data bank and promotion of the use of valuation standards by the Nigerian Institution of Estate Surveyors and Valuers and Estate Surveyors and Valuers Registration Board of Nigeria the bodies responsible for the regulation of valuation practice in Nigeria.
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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 | 0.013 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".