Christchurch, New Zealand The accuracy of MUREAU residential market forecasts
Bibliographic record
Abstract
market, expert panels Abstract: Several years ago the Massey University Real Estate Analysis Unit (MUREAU) established the “Real Estate Outlook Survey ” series, incorporating quarterly forecasts for various sectors of the New Zealand real estate market. The series attempt to bridge the gap between historic market information and forecasting future real estate market behaviour; to improve the range of information available to the property industry and the public so that people may be in a better position to report, make decisions, generate other analyses etc. at various points in time during the course of property ownership, lease, mortgage etc. Forecasts are based on confidential questionnaires completed each quarter by panels of property “experts”. The focus of this paper is the Auckland residential property market forecasts. It reviews the accuracy of quarterly property market forecasts by comparing the Auckland panelists ’ forecasts to market indicators. The review is intended to help readers gain an enhanced appreciation for the forecasts, and secondly and equally importantly to provide forecast participants with feedback to help improve judgmental accuracy of future predictions. 1.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".