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Record 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 on OpenAlex
Farhan Rahman, Ian Rowlands, Olaf Weber

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueSmart and Sustainable Built Environment · 2017
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsValuation (finance)Real estateContext (archaeology)Green buildingSustainabilityEconomicsBusinessEngineeringFinanceArchitectural engineeringGeography

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.235
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it