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Record W4401219365 · doi:10.1108/ijhma-05-2024-0062

Navigating real estate purchase decisions: an interplay of influential factors

2024· article· en· W4401219365 on OpenAlexaff
Asha Jaisy Sam, Benny Godwin J. Davidson, Jossy P. George, Peter V. Muttungal

Bibliographic record

VenueInternational Journal of Housing Markets and Analysis · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsUniversity of the Fraser Valley
Fundersnot available
KeywordsReal estateBusinessResidential real estateFinancial economicsActuarial scienceReal estate investment trustFinanceEconomics

Abstract

fetched live from OpenAlex

Purpose The purpose of this study is to investigate the relationship between social trends, peer influence, personal attitudes regarding real estate purchase decisions, perception of long-term property value and the mediating effect of hedging in influencing property and real estate purchases. Design/methodology/approach Using a combination of quantitative surveys, this study aims to provide a comprehensive knowledge of the factors influencing real estate buying decisions. Data were obtained from 399 young consumers in four Indian cities. Using structural equation modeling, the suggested conceptual framework is examined. Findings The study’s findings suggest that attitude plays an important role in influencing real estate purchase decisions. Young adults also tend to look for long-term gains or value when purchasing a home. Developing durable products for the customers is the best way to grow business, according to the results. Originality/value To the best of the authors’ knowledge, this is the first paper that examines the role of sentimental, personal and financial factors in real estate purchase decisions. The study provides insights into how these factors interact and affect the decisions of consumers in real estate. The authors hope that the findings will be useful for real estate professionals to better tailor their services to meet the needs of their customers.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.022
GPT teacher head0.303
Teacher spread0.281 · 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 designOther design
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
Published2024
Admission routes1
Has abstractyes

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