Do green buildings capture higher market valuations and lower vacancy rates? A Canadian case study of LEED and BOMA-BEST properties
Bibliographic record
Abstract
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 ofχ2tests. 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,χ2tests 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".