Enhance Commercial Real Estate Analysis
with Big Data Location Analytics
Webinar | August 22, 2018 | 9:00 a.m. PT, 12:00 p.m. ET
You and your team of commercial real estate (CRE) analysts and/or portfolio managers are under pressure to adapt to market uncertainty and to respond to heightened regulatory scrutiny while generating more revenue. But it is virtually impossible to make confident, forward-looking investment decisions using stale data on traditional data platforms and disparate scoring systems.
On Wednesday, August 22nd, 9:00 am PT, 12:00 pm ET, join JJ Medina and Paul Lashmet to learn how to optimize CRE analysis through big data location intelligence and native visual analytics. They will build off earlier published material to demonstrate how you can respond to CRE market trends. Learn how to keep pace with changing market factors, including how to:
- Enhance portfolio analysis by quickly aggregating, geospatially organizing, and enriching your property data.
- Ensure data timeliness so that your firm can quickly react to market uncertainties and market changes.
- Drill down into multiple or single view of properties and visualize changes in crime, demographics, geo-behaviors, location desirability, and other factors that may impact vacancy rates and NOI.
- Analyze neighborhood and commercial boundaries to more accurately assess the repurposing of commercial properties.
More about our presenters:
Managing Director, Enterprise Software Solutions for Financial Services
Practice Lead and Advisor for Financial Services
J.J. Medina is a Managing Director in the Pitney Bowes Financial Services Solutions group, where he is responsible for the development and delivery of contextual single view, location intelligence, and analytics solutions. These solutions are designed to help clients achieve critical growth, significant productivity gains, and navigate regulatory imperatives with ease.
For the past 16 years, JJ has worked alongside commercial and retail banking practitioners to improve how banks measure operational, credit, and market risk. With a background in data quality, credit risk management, and analytics, JJ brings a unique blend of business process improvement and technology experience to clients, including expertise in areas such as - commercial real estate location analytics, credit risk monitoring and administration, and financial crimes and compliance.
Most recently his projects have been focused on bridging the gap between data science and business operations by operationalizing data to enhance Commercial Real Estate (CRE) analysis through Big Data location analytics; and implementing enterprise property data hubs to optimize underwriting and improve the customer experience.
Paul Lashmet is practice lead and advisor for financial services at Arcadia Data, a company that provides visual big data analytics software that empowers business users to glean meaningful and real-time business insights from high-volume and varied data in a timely, secure, and collaborative way. Paul writes extensively about the practical applications of emerging and innovative technologies to regulatory compliance. Previously, he led programs at HSBC, Deutsche Bank, and Fannie Mae.