Real estate and land property automated valuations systems: a taxonomy and conceptual model
Bibliographic record
Abstract
Purpose - Automated valuation models have been in use at least for the last fifty years in both academia and practice, while a proper definition was coined only in the last decade. Automated valuation models is a very mature topic that has recently reemerged as very important with the rise of digital infrastructure. Therefore, this paper provides needed analysis and synthesis of the accumulated body of knowledge, and proposes a conceptual framework adapted to reemerging trend. Design/methodology/approach - This imply two-sided contribution of this paper, a taxonomy and a conceptual framework. In order to address properly a broad notion of automated valuation models’ use, this paper introduces automated valuation system as a term and its taxonomy based on key facets, properties and measurements. Proposed taxonomy is non-hierarchical because all automated valuation systems have the same importance and each one has these facets. Furthermore, conceptual model represents the relationships between the facets. The conceptual model for automated valuation system is based on the visualized decision support system consisting of decision, end user, interface data and model. Both taxonomy and conceptual model came into being after literature review that included a bit more than one hundred references. Findings - The overview of facets, their properties and their dummy measurement is discussed only with examples that would be sufficient to illustrate their regularities. Examples are selected as the most cited articles for each of the newly introduced automated valuation approaches. As mentioned, all indicated facets are visualized in a conceptual model that is again an adapted version of the most visuals example of a decision support systems. Research limitations/implications – As mentioned, taxonomy and conceptual model are built upon although relatively broad but selective choice on more than one hundred references. Perhaps a systematic literature review process could additional validate the proposed taxonomy and conceptual model. Practical implications - Assuring the credibility of an automated valuation model that is based purely on comparing the predictive accuracy of method ‘a’ versus method ‘b’ has become a common practice. Therefore, discussion of the use of the proposed automated valuation has been push forward. In addition, as a domain of price estimates has been far surpassed any unique discipline, term that is more generic would be appropriate to accommodate future research coming from multitude of disciplines. Originality/value – By knowledge of the authors this is the first paper that develops taxonomy and conceptual model of automated valuation systems.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".