Notice bibliographique
Résumé
This expansive issue highlights the global focus of housing research from Australia, Finland, India, Italy, Malaysia, Northern Ireland, South Korea and the UK.All 13 papers have completed a rigorous double-blind refereeing process and provide unique insights into housing markets in both developed and developing countries.This issue also includes a research analysis examining new housing in the USA, Australia, Canada and France.The first paper provides a unique analysis of landmark residential buildings in Italy over the period between 2007 and 2013.The study evaluates the performance of landmark residential buildings in comparison to other residential investments and considers the usefulness of a diversification strategy.It also considers the relevance of the modern portfolio theory and evaluates the usefulness of a diversification strategy.The results confirm a landmark residential building can be a good investment, especially for risk-seeking investors.The second paper from Malaysia seeks to identify attributes which affect buyer behaviour for residential property.Based on a survey approach, the study examined the demographic characteristics and used the analytical heirachy process.The findings provided a valuable insight into criteria influencing buyer decisions and will assist stakeholders to identify and better understand the relevant demand drivers.The third paper from Malaysia examines the performance of different subsectors in the real estate market including residential property.The quarterly data from between 2002 and 2014 are analysed in three different phases and examined using Sharpe's index to investigate how each sector performed, relative to each other.The findings confirmed the residential property sector maintained its ranking position as the best subsector for every risk analysis.The fourth paper analyses housing density in South Korea, with the focus placed on the development of apartments in Seoul.The study investigated the potential for a price premium and consumer demand for higher density housing and examined the relationship between housing density and sale prices.The findings confirmed that households are inclined to live in populated areas but do not always prefer higher density.Insights were also provided regarding the preferences of households towards housing attributes, including floor level, floor area ratio, building coverage ratio, central heating and parking spaces.The fifth paper investigates the consumer preferences for housing attributes in India.The data related to Delhi and the National Capital Region with the survey findings were analysed using a cross-tabulation approach.The findings confirmed the Indian community is conservative and will not over-spend or over-commit beyond their accepted level of income.Also, housing preferences in this study were predominantly influenced by demographic characteristics including age, household composition, income and current housing status.The sixth paper from Australia analyses the long-term relationship between house prices and demographic variables.Based on 48 demographic variables between 1996 and 2011 for over 180 individual suburbs, the methodology used principal component analysis (PCA) to identify high-loading attributes and the strength of the association with house prices.Founded on the proven social area analysis framework, the findings showed that between 70-77 per cent of the IJHMA 9,4
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».