Christchurch, New Zealand The accuracy of MUREAU residential market forecasts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it