Nationwide mass appraisal modeling in China: feasibility analysis for scalability given ad valorem property tax reform
Bibliographic record
Abstract
The Chinese government has stated its intention of introducing an annual property tax since 2003, but, while selecting six pilot cities for experimenting with the viability of a mass appraisal system rollout, has not yet adopted this policy. The Shenzhen Center for Assessment and Development of Real Estate was founded to facilitate the process of piloting the viability of property taxes — an initiative that coincided with the Lincoln Institute of Land Policy’s initial involvement in China in 2003 (with the International Property Tax Institute [IPTI], ESRI Canada, and others) — and to provide expertise in topics ranging from property tax and municipal finance to public land management and land expropriation. The long-standing intention to roll out property tax, allied with significant capacity building, begs the questions, why has there not been more progress to ate, and are there any fundamental barriers to policy adoption? This paper seeks to contribute to understanding this issue by assessing the feasibility of creating computerassisted mass appraisal (CAMA) and utomated valuation models (AVMs) in China and their respective capability to conform to IAAO valuation standards, with implications for scalability across national and regional markets.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".