New York City and Miami metropolitan areas have greatest risk of
Atlantic Coast contains 57% of the total homes at risk of storm
IRVINE, Calif.–(BUSINESS WIRE)–CoreLogic® (NYSE: CLGX), a leading global property
information, analytics and data-enabled solutions provider, today
released its 2019
CoreLogic Storm Surge report, which shows more than 7.3 million
single- and multifamily homes along the Gulf and Atlantic Coasts have
the potential for storm surge damage, with a total estimated
reconstruction cost value (RCV) of nearly $1.8 trillion. Early
predictions from the National Oceanic and Atmospheric Administration
(NOAA) indicate a near-normal year for the 2019 Atlantic hurricane
The CoreLogic Storm Surge report provides an annual evaluation of the
number of homes in the United States that are vulnerable to storm surge
in the Gulf of Mexico and Atlantic Basin, which includes every state
from Texas to Maine, approximately 3,700 miles. The report also includes
associated RCV of these properties, which is calculated using the
combined cost of construction materials as well as equipment and labor.
The analysis examines risk across 19 states and 85 Core-Based
Statistical Areas (CBSA). This is the first year the Storm Surge report
analysis includes multifamily structures, which encompass apartments,
condominiums and multi-unit dwellings.
“It is essential to understand and evaluate the total hazard exposure of
properties at risk of storm surge prior to a hurricane event, so
insurers can better protect and restore property owners from financial
catastrophe,” said Dr. Tom Jeffery, senior hazard scientist at
CoreLogic. “Damage from storm surge and inland flooding has proven to be
far more destructive than wind in recent years, so we cannot rely on the
hurricane category alone to give us a sense of the potential loss. A
Category 5 hurricane in an area with few structures may be far less
devastating than a Category 1 hurricane in a densely populated area.”
The Atlantic Coast contains 57% of the total homes at risk of storm
surge flooding and 62.7% of the total RCV. The region has more than
4.1 million homes at risk of storm surge with an RCV of over $1.1
Conversely, the Gulf Coast contains 43% of the homes at risk and 37.3%
of the total RCV. The region has nearly 3.1 million homes at risk with
over $668 billion in potential exposure to total destruction damage.
Florida, Louisiana, New York and Texas have the greatest number of
homes at risk of storm surge.
Florida has the most exposure to storm surge flooding, with more than
2.9 million homes at risk. The state also has the highest RCV at over
Louisiana has the second most exposure to storm surge flooding, with
more than 847,000 at-risk homes and the third highest RCV at over $202
In New York, the density of the residential population near the coast
makes it extremely vulnerable to flooding despite less frequent
hurricane events. New York ranks third in the number of homes at risk
(over 564,000) and second in RCV (over $240 billion).
Texas ranks fourth with more than 561,000 at-risk homes. Texas has the
fifth-highest RCV with more than $113 billion.
CoreLogic looked at 85 Core-Based Statistical Areas (CBSA) to determine
the metropolitan areas with the greatest number of homes exposed to and
the highest RCV from storm surge flooding.
The New York, Newark and Jersey City metro area has the greatest risk
of storm surge with just over 831,000 homes at risk and RCV of over
$330 billion. Although this number of homes at risk is similar to that
of the Miami metro area, the RCV for these homes is double Miami’s
metro area RCV.
The Miami, Florida metro area that includes Miami, Fort Lauderdale and
West Palm Beach, Florida follows the New York metro area with more
than 827,000 homes at risk and an RCV of $166 billion.
Because of the density of residences in large metro areas, the top 15
CBSAs account for 67.5% of the total number of homes at risk and 68.9%
of the total RCV for storm surge risk in the United States. This
underscores the importance of considering location of future storms
when assessing the potential for catastrophic damage.
Important notes regarding definitions:
Single-family and multifamily homes are provided in separate charts and
categorized by level of exposure to storm surge hazard from Categories 1
through 5 hurricanes. RCV figures represent the cost to completely
rebuild a property in case of damage assuming the worst-case scenario at
100% destruction. For more information about this data and what to
expect from CoreLogic hurricane season reporting, contact us at firstname.lastname@example.org
to get access to a recorded pre-season media-only webinar.
Note: These numbers are cumulative. A home being affected by a
Category 1 storm would accordingly also be affected by a Category 5—so
the highest Category represents the aggregate total.
The analysis in the 2019 CoreLogic Storm Surge Report encompasses
single-family residential structures less than four stories, including
mobile homes, duplexes, manufactured homes and cabins (among other
non-traditional home types). And, for the first time, the report also
encompasses multifamily structures, which include apartments,
condominiums and multi-unit dwellings. It is important to note that the
inclusion of high-rise residential units such as those listed above may
skew both the numbers associated with storm surge risk. This is because
lower-level units are most likely to be affected, whereas the units
above the second floor will rarely, if ever, experience storm surge
Year-over-year changes between the number of homes at risk and the RCV
can be the result of several variables, including new home construction,
improved public records, enhanced modeling techniques, fluctuation in
labor, equipment and material costs and even a potential rise in sea
level. Indeed, this year’s addition of new data in the form of
multifamily structures has increased the total number of structures at
risk. For that reason, direct year over year comparisons should be
warily considered. To estimate the value of property exposure of
single-family residences, CoreLogic uses its RCV methodology, which
estimates the cost to rebuild the home in the event of a total loss and
is not to be confused with property market values or new construction
cost estimation. Reconstruction cost estimates more accurately reflect
the actual cost of damage or destruction of residential buildings that
would occur from hurricane-driven storm surge, since they include the
cost of materials, equipment and labor needed to rebuild. These
estimates also factor in geographical pricing differences (although
actual land values are not included in the estimates). The values in
this report are based on 100% percent (or “total”), destruction of the
residential structure. Depending on the amount of surge water from a
given storm, there may be less than 100% damage to the residence, which
would result in a lower realized RCV.
To evaluate storm surge risk at the local level, CoreLogic uses the
designation of Core-Based Statistical Areas (CBSAs), which are often
referred to as metropolitan areas (>50,000 people), or micropolitan
The high-resolution, granular modeling for underwriting individual risk
allows enhanced understanding of the risk landscape and damage
potentials. CoreLogic offers high-resolution solutions with a view of
hazard and vulnerability consistent with the latest science for more
realistic risk differentiation. The high-resolution storm surge modeling
using 10m digital elevation model (DEM) and parcel-based geocoding
precision from PxPoint facilitates a realistic view of the
The probabilistic CoreLogic North Atlantic Hurricane Model, which can be
accessed in the catastrophe modeling platform RQE®, is
powered with unparalleled property data from CoreLogic. The combination
of high-quality data and detailed modeling provides realistic and
credible view of the potential risk to make informed business decisions,
understand risk and accelerate recovery.
CoreLogic (NYSE: CLGX), the leading provider of property insights and
solutions, promotes a healthy housing market and thriving communities.
Through its enhanced property data solutions, services and technologies,
CoreLogic enables real estate professionals, financial institutions,
insurance carriers, government agencies and other housing market
participants to help millions of people find, acquire and protect their
homes. For more information, please visit www.corelogic.com.
CORELOGIC, the CoreLogic logo, RQE and PXPoint are trademarks of
CoreLogic, Inc. and/or its subsidiaries. All other trademarks are the
property of their respective owners.
INK Communications for CoreLogic
Strainprint™ Technologies Welcomes Organic Medical Cannabis Producer to Growing List of Subscribers
Strainprint Technologies Ltd, the leader in cannabis data and analytics is pleased to expand their nation-wide coverage with the addition of Stewart Farms.
Stewart Farms is a late-stage applicant headquartered in Alberta that is building a 100,000 sq. ft. vertical aquaponics farm in Saint Stephens, New Brunswick. They will be utilizing automation, vertical farming, and land-based aquaculture to produce medical grade organic cannabis for both recreational and medical markets in Canada. All of their products will be free of herbicides, pesticides and synthetic nutrients. At full capacity, they will reach more than 10,000 kgs per year of organically farmed cannabis and more than 200,000 kgs of organic tilapia.
Stewart Farms’ long term vision is to deliver tools and products to support future customers and patients during their health and wellness journeys. “Through our partnership with Strainprint we gain direct access to their incomparable data and analytics tools. This will aid us in educating a wider audience on the medical benefits and best practices of cannabis-based medicine,” said Tanner Stewart, Co-founder & CEO of Stewart Farms. “We know our customers are looking to consume more than dried cannabis. We know Canadians of all legal ages are trying to sort through their personal engagement with cannabis as a medicine. What we want to know, on an ongoing basis, is what is and is not working for people. Strainprint’s patient-led data services will give us the insight needed to create custom products and further understand the benefits of our existing products. Finally, our goal is to partner with companies and teams that truly have people’s best interest at heart. Caring about a patient’s success is what makes up the core of the Strainprint team. We couldn’t be happier to move forward with them at our side.”
Stewart Farms will access the Strainprint Analytics web platform, the most sophisticated cannabis analytics platform available to improve product development. Strainprint Analytics is built on top of the largest and most granular scientific data set of its kind in the world, with more than 1.3 million anonymized, patient reported medical cannabis outcomes and more than 65 million data points on strain efficacy.
“We’re thrilled to provide our real-time, crowd sourced data to an environmentally conscious company that uses innovative, cutting edge farming technology to produce top-tier medical cannabis,” said Strainprint CEO, Andrew Muroff. “Our organizations are equally committed to improving lives using cannabis therapy, and our shared core motivation is to provide guidance and support to help cannabis patients achieve their health goals.”
SOURCE Strainprint Technologies Ltd.
Field Trip Ventures Inc. Retains KCSA Strategic Communications as Public Relations Counsel
Greenery Map, the world’s first and only cannabis search engine to allow users to search for cannabis products based on their desired mood, medicinal use, and method of consumption, is now offering users – both businesses and consumers – the opportunity to register and connect to the app in an entirely mobile way. Previously, businesses were required to access GreeneryMap.com from a desktop in order to manage accounts and connect to the inventory API system, but that road block has been removed, allowing for an entirely mobile experience through the Greenery Map app.
App users turn to Greenery Map to help them decide on the strain of Cannabis and product that is right for them depending on their desired mood or medicinal effect. Following the matching, consumers are given a detailed history and description of the strain so that they can make educated purchases, cutting down on time that budtenders need to spend with patrons and speeding up the purchase process. Consumers can then see all of the ways to purchase said product, including the nearby dispensaries that have it in stock, online options, and even delivery options through the app. Greenery Map fully integrates with a dispensary’s inventory API system to update in real-time and give consumers real-time information on dispensary stock for the item they are looking for. The Greenery Map system also provides cannabis businesses in both the B2B and B2C sectors a private database to share and sell information and products. For Cannabis businesses, Greenery Map also offers the opportunity to open bank and merchant accounts through their partnership with PayHouse Consulting Group.
SOURCE Greenery Map
WeBank, IBM and Other Organizations Jointly Held the 1st International Workshop on Federated Machine Learning in conjunction with IJCAI 2019
Once a concept, AI is now ushering in a key stage of application. What’s the solution to the data silos among businesses? Given the enhanced regulation on data at home and abroad, what’s the solution to data privacy and security concerns? What’s the status quo of Federated Machine Learning and how to establish an ecosystem for FML in the future?
WeBank, IBM and other organizations jointly held the 1st International Workshop on Federated Machine Learning for User Privacy and Data Confidentiality (FML’19) in conjunction with the 28th International Joint Conference on Artificial Intelligence (IJCAI-19) on Aug. 12, 2019, to further discussion on these issues.
President of IJCAI, Chair of FML Steering Committee, Chief AI Officer of WeBank Professor Qiang Yang delivered opening remarks at the workshop. Dr. Shahrokh Daijavad from IBM and Dr. Jakub Konečný from Google presented keynote addresses. In the panel discussion, top scholars from WeBank, Bar-Ilan University, IBM, Squirrel AI, Google, Huawei, Clustar, Sinovation Ventures and many other renowned enterprises and universities shared and discussed their findings and experience in FML as an emerging AI technology.
This workshop received 40 papers, of which 12 were presented during the workshop, 19 presented via poster. Awards include Best Theory Paper Award, Best Application Paper Award, Best Student Paper Award, Best Presentation Award. Selected high quality papers will be invited for publication in a special issue in the IEEE Intelligent Systems journal. All these attracted numerous scholars to engage in discussions and join efforts for building the FML ecosystem.
Experts from IBM and Google Share Groundbreaking Findings with a Focus on the Theory and Application of FML
Privacy and security are becoming a key concern in our digital age. On 25th May last year, the implementation of General Data Protection Regulation (GDPR) by the EU, the toughest Act on data privacy protection, stressed that user data collection must be open and transparent. A series of laws and regulations from China and overseas also pose new challenges to the traditional way of handling data and model for cooperation. Seeking ways for AI to adapt to this new reality became top priority, a demand that led to this workshop on FML.
A wealth of solutions and breakthroughs were shared by Dr. Shahrokh Daijavad from IBM and Dr. Jakub Konečný from Google in their speech on FML.
Besides how FML can help tackle challenges in the business world, Dr. Shahrokh Daijavad also shared the concept of Fusion AI, which means to train models on widely distributed data sets, but fuse them to produce one equivalent to what centralized training would yield. “Unlike traditional machine learning, in Fusion AI, model parameters are shared and data is not transferred, which makes Fusion AI model better than models that moving data centrally.” Given the widely distributed data, the development of Fusion AI and FML became ever important and imminent.
“FML enables machine learning engineers and data scientists to work productively with decentralized data with privacy by default,” said Dr. Jakub Konečný from Google. He also shared with us how FML works and its use cases at Google. In the case of Gboard, as on-device data is privacy sensitive or large or is more relevant than server-side proxy data, and labels can be inferred naturally via user interaction, the application of Federated RNN compared to prior n-gram model can increase the accuracy of next-word prediction by 24%, and the click rate of prediction strip by 10%.
Major Figure Panelists Discuss the Way Ahead for FML
The moderator of the panel discussion, AI Principal Scientist of WeBank Dr. Lixin Fan joined panelists including Professor Benny Pinkas from Bar-Ilan University, Dr. Shahrokh Daijavad of IBM Academy of Technology, Chief Architect of Squirrel AI Dr. Richard Tong, Research Scientist of Google Dr. Jakub Konečný, Dr. Baofeng Zhang from CTO Office of CBG Software in Huawei, Executive VP of Clustar Dr. Junxue Zhang, VP of AI Institute in Sinovation Ventures Dr. Ji Feng and other experts in a host of in-depth exchanges with attendees, to shed light on the way ahead for FML.
Experts shared thoughts in the panel discussion on questions including but not limited to: How to meet the security and compliance requirements? Is there a way to extend the value of data while observing user privacy and data security? Given the classic trade-off between data regulation and development of AI, how to achieve the long-term goal of establishing a stable and win-win business ecosystem?
List of Award-Winners
Best Theory Paper Award, Best Application Paper Award, Best Student Paper Award and Best Presentation Award selected by all attendees were announced at the closing of the workshop.
Best Theory Paper Award:
Preserving User Privacy for Machine Learning: Local Differential Privacy or Federated Machine Learning? By Huadi Zheng, Haibo Hu & Ziyang Han;
Best Application Paper Award:
FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare. By Yiqiang Chen, Jindong Wang, Chaohui Yu, Wen Gao & Xin Qin;
Best Student Paper Award:
Quantifying the Performance of Federated Transfer Learning. By Qinghe Jing, Weiyan Wang, Junxue Zhang, Han Tian & Kai Chen;
Best Presentation Award:
Federated Generative Privacy. By Aleksei Triastcyn and Boi Faltings.
President of IJCAI, Chief AI Officer of WeBank Professor Qiang Yang, Chief Architect of Squirrel AI Dr. Richard Tong, andVP of AI Institute in Sinovation Ventures Dr. Ji Feng presented the awards.
“The mission of this International Federated Machine Learning Workshop is to facilitate further understanding in the academia, business community as well as legal and regulatory institutions by promoting the establishment of FML ecosystem in the hope that more businesses will join and build a platform for students aspired to work in FML to find research teams that suit them,” said Professor Qiang Yang.
Held Aug. 10-16, 2019 in Macao, China, IJCAI-19 is one of the leading International Academic Conference on AI, attracting over 3000 AI research personnel and experts. The 1st International Workshop on Federated Machine Learning (FML’19) was a highlight for experts joining this event. Visionaries in the academia and industrial sector expressed the willingness to be part of the effort for academic research, application of FML in the future, and the development and boom of AI ecosystem.
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