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Has the COVID-19 Pandemic Changed People’s Attitude about Where to Live? Some Preliminary Answers from a Study of the Atlanta Housing Market

2021· dissertation· en· W7000776490 sur OpenAlexaboutno aff

Notice bibliographique

RevueSMARTech Repository (Georgia Institute of Technology) · 2021
Typedissertation
Langueen
DomaineSocial Sciences
ThématiqueUrban, Neighborhood, and Segregation Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésAtlantaQuarter (Canadian coin)UnemploymentSocial distancePandemicPublic housingWork (physics)VitalityCrunch
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

In March 2020, the national lockdowns and social distancing mandates to contain the COVID-19 pandemic in the US abruptly disrupted all aspects of urban life, requiring people to conduct daily activities including work, shopping, learning, schooling, and socializing, from home using online tools. These lockdowns and stay-at-home orders sharply increased unemployment and hindered active transactions in the housing market in the second quarter of 2020 (Liu & Su, 2021). While the high unemployment rate was a severe economic and social concern affecting housing demand, monetary easing and low interest rates increased liquidity and the flow of money into the housing market (Zhao, 2020).\n\nA growing body of work started to examine the overall vitality of the housing market in response to the disruptions caused by the pandemic (D’Lima et al., 2020; Liu & Su, 2021; Yoruk, 2020; Zhao, 2020). In addition, reports in popular media have highlighted trends in cities like New York and San Francisco, where many households were giving up expensive central city residences for low-density suburban houses with large yards. This finding implied that cities were losing their appeal given the reduction in the need for commuting in a work-from-home culture and the desire for security and open space in a low-density environment in the suburbs. Despite this type of anecdotal evidence, we know very little about how the preferences for housing in different locations are changing in response to the COVID-19 pandemic.\n\nThis study explores whether and how the pandemic affected the housing preferences in the Atlanta single-family housing market. The focus goes to locational characteristics such as the accessibility to the rail transit system, accessibility to freeway systems, and walkability. The housing market participants’ attitudes toward the different travel modes can be revealed with the price effects of the accessibility-related locational characteristics. The impact of whether a house is in the inner city, inner-ring suburb, or outer-ring suburb on housing prices is also examined. \n\nA few main findings are derived from comparing the descriptive statistics and hedonic price models for 2018, 2019, and 2020. First, a steep drop in the number of transactions in the second quarter of 2020 was followed by an increase in the number of transactions and housing prices. The observed boom in the Atlanta single-family housing market aligns with the arguments of Zhao (2020) and Liu and Su (2021) that the lowered mortgage rate caused the influx of money to the housing markets across the US. Second, the positive price effect of parcel size and a pool increased in 2020 while that of square footage decreased. Third, the recently increasing preference for the inner city over the suburban area was restrained in 2020, which might have resulted from the diminished advantage of staying near the city center for job accessibility. Fourth, the pandemic did not substantially change the capitalization effect of the accessibility to a MARTA rail station and freeway.\n\nA few suggestions are made for future studies. First, the endeavor to further clarify the underlying reasons for the observations from this study would be necessary, which hedonic price models alone cannot do. Conducting a customized survey is one way to reveal the existence of and reasons for the changes in the attitudes, lifestyle, and travel patterns of diverse market participants covering both the supply and demand sides. Second, investigating the parts of the housing market that are not examined in this study will bring a comprehensive and detailed understanding of the housing market and the changes the market went through. The houses for rent and the houses other than detached single-family houses are not included in this study. Moreover, the transactions of the newly constructed houses are not usually in the FMLS data even though they take up a significant proportion of the transactions in the Atlanta region. Third, the analyses with some submarket segmentation using such criteria as the housing price, number of rooms, and location are expected to bring useful policy implications enabling detailed and customized solutions to the issues that planners are tackling.

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)0,005
score de la tête « metaresearch » (Gemma)0,010
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: Empirique
Score de désaccord entre enseignants0,101
Score d'incertitude au seuil0,200

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,010
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0040,003
Communication savante0,0050,007
Science ouverte0,0010,003
Intégrité de la recherche0,0030,005
Charge utile insuffisante (le modèle a refusé de juger)0,0080,001

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 Opus0,035
Tête enseignante GPT0,298
Écart entre enseignants0,263 · 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

Citations1
Publié2021
Routes d'admission1
Résumé présentoui

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