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Enregistrement W3097363137 · doi:10.5539/ibr.v13n11p114

Evidence From Data Analysis, Fifteen Developed Countries and the United States Home Prices Increase Between 1990 to 2006 Result of Advancement In Technology, Worldwide Economic Collapse and Great Recession Result of False Information by Media and Economic Policy Failures: Walters Real Estate Bubble Impossibility Price Transparency Theory, Real Estate Bubble Is Impossible, An End to Economic Policies Based on False Information

2020· article· en· W3097363137 sur OpenAlexvenueaboutno aff
Eddison T. Walters

Notice bibliographique

RevueInternational Business Research · 2020
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueHousing Market and Economics
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésReal estateFinancial crisisBlameRecessionEconomicsDeveloping countryEconomic bubbleEconomic collapseGlobal recessionSubprime mortgage crisisFinancial systemFinanceMacroeconomicsEconomic growthPolitical sciencePolitics

Résumé

récupéré en direct d'OpenAlex

Based on the findings of the current study, policymakers must take a hard look at the media and themselves, because the world can no longer blame the subprime mortgage industry for causing the Global Financial Crisis of 2007 and 2008. The public must demand answers from the media and policymakers explaining how an economic crisis that could have been avoided resulted in the collapse of the global economy. The lack of evidence supporting the theory of a financial bubble and a real estate bubble called for further investigation of factors leading to the Global Financial Crisis of 2007 and 2008. Evidence presented from data analysis in Walters (2018) suggested no financial bubble existed in developed or developing countries around the world, preceding the Global Financial Crisis of 2007 and 2008. Based on data analysis in Walters (2018) the evidence also suggested, the lasting effect of economic policies in response to the Global Financial Crisis of 2007 and 2008 for both developed and developing countries around the world, had no significant impact on the financial sector but pointed to a lack of economic growth. The findings raised significant questions about the existence of a real estate bubble in both developed and developing countries. Evidence from data analysis presented in Walters and Djokic (2019) suggested the existence of a real estate bubble in the United States real estate market preceding the Global Financial Crisis of 2007 and 2008 was a false conclusion. Data analysis in Walters (2019) resulted in, 0.989 Adjusted R-square, 194.041 Mean Dependent Variable, 5.908 Square Error of Regression, 488.726 Sum-of- Square Residual, and 0.00000 Probability (F-statistic), for correlation between the independent variable representing advancement in technology, and the dependent variable representing home purchase price in the United States preceding the Global Financial Crisis of 2007 and 2008. The findings in Walters (2019) concluded the rapid increase in home purchase price in the United States real estate market, was due to increased demand for homes from the adaptation of advancement in technology in the real estate and mortgage industries. The current study expanded the investigation of the growth in home purchase price to fifteen developed countries around the world, building on the findings of previous research by the current researcher. The researcher in the current study concluded, the existence of significant and near-perfect correlation in many cases, between the dependent variable representing growth in home purchase price, and the independent variable representing advancement in technology. The analysis was based on data analyzed from fifteen developed countries around the world, which was collected between 1990 and 2006. The data analysis included home purchase price data from, Canada, United Kingdom, Denmark, Finland, France, Italy, New Zealand, Sweden, Netherlands, Australia, Ireland, Belgium, Norway, Spain, and Portugal. Data preceding the Global Financial Crisis of 2007 and 2008 were analyzed in the current study. The researcher in the current study concluded the existence of overwhelming evidence suggesting advancement in technology was responsible for the rapid increase in home prices in developed countries around the world preceding the Global Financial Crisis of 2007 and 2008. The result of data analysis in the current study provided further confirmation of the accuracy of former Federal Reserve Board Chairmen, Alan Greenspan and Ben Bernanke 2005 assessment which concluded, the occurrence of a real estate bubble developing was impossible due to the Efficient Market Hypothesis, before reversing course subsequent their assertion in 2005 (Belke & Wiedmann, 2005; Starr,2012). The result of the current study provided additional evidence supporting Eddison Walters Risk Expectation Theory of The Global Financial Crisis of 2007 and 2008. The result from data analysis also confirmed the need for the adaptation of Eddison Walters Modern Economic Analysis Theory. As a result of the findings in the current study, the researcher concluded the development of a real estate bubble is impossible where there exists real estate price transparency, as is the case in most developed and developing countries. The researcher presented Walters Real Estate Bubble Impossibility Price Transparency Theory based on the findings. False information of a real estate bubble and predictions of a real estate crash disseminated through the mainstream media and social media can be a destructive force with a disastrous effect on the economy around the world. The failure by the media to hold themselves and policymakers to a higher standard resulted in the Global Financial Crisis of 2007 and 2008. The result of the failure by the media was a worldwide economic crisis and the Great Recession that followed the Global Financial Crisis of 2007 and 2008. Lessons learned from the Global Financial Crisis of 2007 and 2008 can assist in preventing another economic crisis in the future.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,007
score de la tête « metaresearch » (Gemma)0,044
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,047
Score d'incertitude au seuil0,093

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0070,044
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0050,012
Études des sciences et des technologies0,0010,001
Communication savante0,0020,002
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0070,002

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.

Tête enseignante Opus0,049
Tête enseignante GPT0,329
Écart entre enseignants0,280 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2020
Routes d'admission2
Résumé présentoui

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