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Vertical Integration & Performance in Residential Real Estate

2023· article· en· W4383219108 on OpenAlexaff
Grant Alexander Wilson, Jason Jogia, Gabriel Millard

Bibliographic record

VenueCritical Housing Analysis · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Retail Behavior Studies
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsVertical integrationReal estateRentingProperty (philosophy)Property managementBusinessHorizontal integrationRelevance (law)Real estate developmentEmpirical researchCorporate Real EstateValue (mathematics)Real propertyIndustrial organizationMarketingFinanceEngineeringComputer sciencePolitical scienceCivil engineering

Abstract

fetched live from OpenAlex

Vertical integration is a growth strategy whereby a firm engages in multiple stages of the value chain. Although the benefits of vertical integration are well documented, few studies have examined its relevance in real estate. In response to this lack of research, this paper explores tenant perceptions of property managers’ vertical integration and effectiveness. The results of this international study show the benefits of vertical integration extend to residential real estate, such that renters are more trusting, loyal, committed, and desirable when they perceive their property manager as vertically integrated. This paper also uncovers a concering finding that many tenants are living in unaffordable rental accomodations, requiring further research. This study contributes to a large body of vertical integration literature and extends the empirical examinations to real estate and property management.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.049
GPT teacher head0.321
Teacher spread0.272 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2023
Admission routes1
Has abstractyes

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