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Record W3125786627 · doi:10.5430/ijba.v3n4p54

An Examination of the Tenancy Agreement as a Shield in Property Management in Nigeria

2012· article· en· W3125786627 on OpenAlexvenueno aff
Dabara I. Daniel, Olatoye Ojo, Okorie Augustina

Bibliographic record

VenueInternational Journal of Business Administration · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Issues in South Africa
Canadian institutionsnot available
Fundersnot available
KeywordsLeasehold estateRentingBusinessLandlordFinanceLaw and economicsEconomicsLawPolitical science

Abstract

fetched live from OpenAlex

The purpose of this study is to critically examine tenancy agreement as a shield in property management in Nigeria. Renting is an essential component of a healthy housing system of a nation. It is observed that for most tenants, signing their tenancy agreement will be their largest financial commitment during the year; hence it is an issue not to be taken lightly. The rental housing sector in Nigeria is bedeviled with acute shortage of housing units (said to be between 14-17million units) this in turn impacts negatively on the sale and rental markets. The study revealed that tenancy agreement gives protection to the stakeholders in the rental housing sector only on paper. This is because many rental agreements between landlords and tenants in Nigeria are personal and informal in nature, concluded outside of any government regulatory framework or formal legal system. This informality and lack of official documentation makes going to court an extremely impractical way of dealing with landlord-tenant conflicts. The provisions of the rent control and recovery of premises laws in Nigeria have been held more in disobedience than in obedience for many years. For effective regulation of the rental property market in Nigeria, it was recommended among others, that a strategic approach to developing a workable rental housing policy should first acknowledge the rental arrangements which already exist and then find flexible, realistic ways to regulate and enforce them.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score0.174

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.027
GPT teacher head0.337
Teacher spread0.310 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations9
Published2012
Admission routes1
Has abstractyes

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