Navigating real estate purchase decisions: an interplay of influential factors
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".