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Record W2792118621 · doi:10.1080/09599916.2018.1436582

Time to completion in the Lagos commercial real estate market: an examination of institutional effects

2018· article· en· W2792118621 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Property Research · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsToronto Metropolitan University
FundersCommonwealth Scholarship CommissionWorld Bank Group
KeywordsReal estateDatabase transactionBusinessReal estate investment trustTransaction costInvestment (military)FinanceProperty managementProperty marketComputer scienceDatabase

Abstract

fetched live from OpenAlex

This study explores how institutions affect the process of investment and the time it takes to buy and sell commercial property in Lagos, Nigeria. We isolate institutional factors that impact transaction efficiency and provide a snapshot of the process with average transaction times for the largest commercial real estate market in the most populous country in Africa. This study adopts a qualitative approach and relies on information collected from semi-structured interviews with 36 senior level individuals active in the Lagos commercial real estate market. Among our findings, we note the commercial real estate transaction process is divided into seven distinct stages and the average time to complete an acquisition across all stages (all property types) is 306 days. Title registration/perfection stage takes the longest time (around 132 days) and represents a significant risk to investors. We argue this is a consequence of imperfections in the formal institutions of title registration.

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 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)0.011
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.925
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.119
GPT teacher head0.324
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