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Record W3006673296 · doi:10.15396/eres2019_158

A negotiator's tool: An Affordable Housing Calculator for voluntary agreements

2019· article· en· W3006673296 on OpenAlexaff
Georgia Warren‐Myers, Katrina Raynor, Matthew Palm

Bibliographic record

Venue26th Annual European Real Estate Society Conference · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAffordable housingBusinessNegotiationLegislationGovernment (linguistics)Context (archaeology)SubsidyLocal governmentIncentiveEconomic growthFinanceEconomicsPublic administrationPolitical science

Abstract

fetched live from OpenAlex

Rapid increases in housing costs, stagnant wage growth and limited government funding have created a housing affordability crisis in many cities, in particular in capital cities in Australia. Unlike elsewhere in the world where affordable housing contributions are secured through inclusionary zoning or other planning processes, the Australian context is largely devoid of any mandatory requirements for affordable housing provision in new development. Recent changes to legislation in Victoria have enabled planners to negotiate with developers to secure voluntary affordable housing contributions by offering alternative incentives. However, the lack of financial literacy and understanding of development feasibility and the effects of affordable housing provision on development viability and profit is likely to limit the success of this change. This paper reports on the conceptual framework and development of an Affordable Housing Negotiation Calculator to assist in educating local and state government representatives, community housing providers and developers about affordable housing provision and its effects on development feasibility. It is hoped this tool will enable those decision-makers to better negotiate positive outcomes for an increase in affordable housing while communicating the factors that impact on development feasibility.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.832
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.029
GPT teacher head0.233
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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