Housing in Victoria: an interactive website providing data on housing and housing affordability indicators at the local level in Victoria
Bibliographic record
Abstract
This website provides data on housing and housing affordability indicators in Victoria, Australia, at four geographical levels: (1) major areas; (2) regions; (3) local government areas; and (4) suburbs. The purpose of the website is to provide useful and accessible data to inform the identification of affordable housing needs and targets at the regional and local government levels via consistent data sets that enable comparability over time and spatially. The website is designed to be used by any individual or organisation interested in being informed about housing affordability. Its target users includes Local Government, State Government, housing associations, other housing organisations and agencies, peak bodies, researchers, residents, developers, students and consultants. It was a joint initiative of the Cities of Melbourne, Yarra, Stonnington and Port Phillip under the Inner Regional Housing Statement and the Inner Melbourne Action Plan, with Port Phillip being the lead Council for this initiative. It was funded by a $100,000 grant from the Victorian Department of Planning & Community Development's Local Area Planning Support Program. The lead consultant was Swinburne Institute for Social Research, with Swinburne's Information Technology Innovations Group being sub-contracted to create the website. The website presents housing and demographic data in three forms: (1) Tables; (2) Graphs; and (3) Maps. Table and graph data include: housing affordability indicators; data on house sales and prices; housing and tenure information; and demographic information. Map data includes: sales by price segment; affordability and available stock; threshold income; ratio of housing costs to household income; private rental affordability for low income households; and SEIFA (Socio-Economic Indexes for Areas).
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".