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Enregistrement W7133003304

Thesis on the Canadian Housing Market

2024· dissertation· W7133003304 sur OpenAlexaffabout
Wanlin Chen

Notice bibliographique

RevueTSpace · 2024
Typedissertation
Langue
DomaineEconomics, Econometrics and Finance
ThématiqueHousing Market and Economics
Établissements canadiensUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésReal estateCounterfactual thinkingImmigrationInvestment (military)Public policyProperty valueValue (mathematics)Property market
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

The housing market in Canada has experienced notable price growths over the past two decades, especially in gateway cities such as Toronto between 2013 and 2017, which was characterized by rapid property value appreciation and high sales turnovers. The rapidly rising property prices have contributed to a housing crisis. To address the affordability issue, it is critical to understand the underlying causes of the exuberance. This thesis seeks to contribute to such knowledge. Chapter 1 provides a scoping review of academic and policy studies that examine the determining factors of the Canadian residential real estate price movements between 2000 and 2019. Using a text-mining search and processing algorithm on relevant articles, it shows that the fundamentals, policies and regulations, foreign ownership and immigration are among the top research priorities in relevant Canadian literature. These topics reflect the historical and socioeconomic environment in Canada. Chapter 1 further reviews these studies in detail and identifies research gaps that are addressed in Chapter 2 and Chapter 3 respectively. Despite the strong public and policy interests in foreign ownership, quantitative estimates of its influences have been limited, especially for the period of 2013 - 2017 when cross-boarder purchases were widely perceived as one of the main drivers of the sky-rocketing housing prices in some regions. Chapter 2 fills this gap by quantifying the influences of increased foreign real estate investment demand and low interest rates on the Toronto housing market. I use a general equilibrium model which is calibrated to Toronto statistical moments, and perform counterfactual exercises by feeding into the model the June 2013 - April 2017 levels of foreign investment shock and interest rate shock respectively. The results show foreign investment influx at the time raised home prices by 2.2% - 4.9%, whereas lower interest rates increased home prices by 9.0% - 20.8%. Between the two, the low interest rate effects dominated the foreign investment effects, contributing to over 80% of the composite influence. My findings suggest that foreign investment was not a major contributing factor to surging property prices in Toronto during the time examined, contrary to mainstream beliefs. In comparison, low interest rates accounted for a substantial fraction of the observed price appreciation. Furthermore, the results suggest that historically low interest rates worsen homeownership inequality by generating cross-sectional heterogeneous effects: while high-income households take advantage of low interest rates and drive up property demand, low-income households are disproportionately priced out of homeownership. Meanwhile, although there is evidence that expectations and sentiment are important in home price movements and they warrant attentions from policy makers and researchers, there lacks a measure of housing market sentiment that has a sufficiently long time series and available at the sub-provincial level. To address this gap, I construct a text-based sentiment index to approximate housing market expectations, which measures the relative tone in regional newspaper articles that focuses on residential real estate. I examine the relationship between the sentiment index and local home price movements and investigate whether the COVID-19 pandemic had an impact on this relationship. The results show that the media sentiment has statistically significant but short- lived predictive effects on future home price growth, while it is also influenced by cumulative price appreciations in the recent past. However, this relationship has significantly weakened between 2020 and 2022.

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)0,001
score de la tête « metaresearch » (Gemma)0,003
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: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,063
Score d'incertitude au seuil0,396

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0020,004
Études des sciences et des technologies0,0050,003
Communication savante0,0050,002
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0630,004

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 Opus0,036
Tête enseignante GPT0,255
Écart entre enseignants0,219 · 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

Citations0
Publié2024
Routes d'admission2
Résumé présentoui

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