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Enregistrement W2770367236 · doi:10.1108/sasbe-03-2017-0008

Do green buildings capture higher market valuations and lower vacancy rates? A Canadian case study of LEED and BOMA-BEST properties

2017· article· en· W2770367236 sur OpenAlex
Farhan Rahman, Ian Rowlands, Olaf Weber

Pourquoi ce travail est dans la base

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affAu moins un auteur déclare une institution canadienne dans l'instantané OpenAlex épinglé.
aboutLe titre ou le résumé porte un signal canadien du lexique géographique.

Notice bibliographique

RevueSmart and Sustainable Built Environment · 2017
Typearticle
Langueen
DomaineEngineering
ThématiqueSustainable Building Design and Assessment
Établissements canadiensUniversity of Waterloo
Organismes subventionnairesnon disponible
Mots-clésValuation (finance)Real estateContext (archaeology)Green buildingSustainabilityEconomicsBusinessEngineeringFinanceArchitectural engineeringGeography

Résumé

récupéré en direct d'OpenAlex

Purpose It is becoming increasingly clear that as the pressures of climate change increase around the world, all nations must strive to lower their carbon footprint through conservation. If the growth trend of green building and infrastructure construction is to be continued and improved upon, then evidence must be collected as to the benefits they bring about, and the level of support they enjoy in the market. The purpose of this paper is to shed light on the economic performance of green buildings by evaluating whether LEED for Homes and BOMA-BEST properties capture higher market valuations and lower vacancy rates. These types of research questions have not been investigated to a great deal in the Canadian context. The primary analysis concerning municipal market valuation of green buildings was conducted using robust ordinary least squares and logistic regression models. Commercial vacancy rates were compared through the use of χ 2 tests. Our analysis did not lead to conclusive evidence that there exists a “green” premium in the real estate market with respect to municipal market valuations. The authors argue that this may largely be due to municipal appraisal methods that currently do not incorporate sustainability factors. As such, they may not adequately reflect market tastes and trends. Furthermore, while the vacancy rates of green commercial buildings were, on the whole, lower than their non-green counterparts, the differences were not statistically significant. Given these results, the authors propose a set of research activities that the academic community should pursue. Design/methodology/approach Statistical techniques are utilized test whether green certification (LEED/BOMA-BEST) leads to higher municipal valuation for both commercial and residential green properties, using regression analysis. Furthermore, χ 2 tests are conducted to evaluate whether certification leads to lower vacancy rates for commercial properties. Findings In terms of valuation, certification does not exert (on average) a positive role in terms of higher valuations for both commercial and residential properties. However, with respect to vacancy rates, there is a tendency towards lower vacancy rates for green properties, but the relationship is not statistically significant. Research limitations/implications The next set of research needs to gather greater amount of data with respect to how municipal evaluations are performed since the results are counter-intuitive. Greater tracking of the financial performance of green buildings should be conducted and made available for both public and private bodies. Particularly, rental and sale prices of green buildings need to be tracked in an organized manner. Practical implications The valuation techniques utilized by the municipal authorities need revision as green properties are being assessed without appropriate guidance from educational institutions. Furthermore, the limited amount of “green” valuation techniques in existence may not be applied. Originality/value This is the first Canadian-based research looking into the valuation of green certification using rigorous quantitative statistical techniques and original and publicly available data. Furthermore, it holds important lessons for municipal authorities with respect to green building valuation beyond Canada as the limitations of current practice go mostly likely beyond the North American context.

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.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
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 enseignants0,272
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

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

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.

Tête enseignante Opus0,019
Tête enseignante GPT0,235
Écart entre enseignants0,216 · 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