Understanding the exposure of business characteristics like its location, sector, size, age, and owner demographics and structure to building damages from an earthquake can inform business continuity planning and broader community and regional programs and policies for business recovery. A baseline analysis was performed for the HayWired scenario (Wein, Haveman and others, 2019) using business characteristic data from the National Establishment Time Series (NETS) which showed that location and sector are strong differentiators of disruptive building damage risk, while minority ownership, branch ownership structure, and low revenue are other above average differentiators. The HayWired analysis was limited by damage information at the census tract scale for occupancy classes (per the Federal Emergency Management Agency Hazus software). In this study, we expand the analysis to multiple earthquake scenarios and apply machine learning techniques to generate building level assessments instead of census tract level. Association of business characteristics with building damages by location increases the analysis resolution providing more nuanced understanding and analyzing across multiple scenarios shows the variability of business characteristic exposure to different earthquakes for the region and across communities. Anne Wein, Jon Haveman, Cynthia Kroll, Jeff Peters. 2019. Characteristics of Businesses disrupted by Building Damages from the HayWired Scenario Mainshock, in press.