Influential factors analysis on LEED building markets in U.S. East Coast cities by using Support Vector Regression

Abstract

Abstract Building industry is closely related to current energy and environmental issues. Several green building codes and rating systems addressing the problems have been developed. Leadership in Energy and Environmental Design (LEED) rating system is recognized as one of the effective and widely adopted commercial building standards. LEED buildings were investigated in several green city and green building studies but only used as instances in static matrices. These studies were not able to answer the question why a particular city favors LEED. However, in this paper, three commonly used machine learning algorithms – Linear Regression, Locally Weighted Regression and Support Vector Regression (SVR) – are compared and SVR is used to investigate, discover and evaluate the variables that could influence LEED building markets in U.S. East Coast cities. Machine learning models are first created and optimized with the features of city geography, demography, economy, higher education and policy. Then SVR model identifies the key factors by dynamic self-training and model-tuning using the dataset. Via optimization, the correlation coefficient between the model's prediction and actual value is 0.79. The result suggests that population and policy can be important factors for developing LEED buildings. It is also interesting that higher education institutions, especially accredited architecture schools could also be driving forces for LEED commercial building markets in East Coast cities.