Real estate data collection and analytics costs can total in the millions of dollars. That’s why in 2016, L.D. Salmanson founded Cherre, a startup that leverages AI to cost-effectively resolve property data from disparate public and private sources. After raising $9 million in October 2018, the New York-based company today announced that it’s snagged $16 million in venture and debt funding led by Intel Capital, with participation from Navitas Capital, Carthona Capital, Zigg Capital, Dreamit Ventures, and Silicon Valley Bank.
“Last year was marked by incredible growth for the entire market,” said Salmanson, who noted that this latest capital infusion brings Cherre’s total raised to $25 million. “This massive industry migration towards fully integrated data systems is just starting, and we’re incredibly proud to be leading the charge. We look forward to continue working with our most demanding clients on their mission-critical data needs.”
Cherre’s technology is modeled after high-frequency trading platforms, and as such, it continuously organizes and updates internal and external databases. With the help of machine learning models that programmatically identify and index real estate data, it provides relevant info on demand in a clean and readable format.
Cherre’s software-as-a-service (SaaS) suite consists of several core components: CoreConnect, CoreAugment, CoreExplore, and CorePredict. The first three fall under the category of data management — they integrate with new and existing on-premises and cloud stores to provide location, transportation, social, and demographic information via APIs, which get compiled into dashboard visualizations. Explore has thousands of filters including custom queries, which support programmatic overlays and help to identify property owners behind corporations.
As for CorePredict, it taps AI supervised by asset managers and underwriters to source and evaluate new opportunities. In addition to automated asset evaluation and recommendation, the tool handles automated risk analysis and mitigation, as well as return on investment modeling and real-time forecasting.
Cherre has one of the largest real estate knowledge graphs in the world, according to Salmanson, with over 315 unique data sets and two billion data points pertaining to 177 million properties and 84 million companies. Its predictive models take into account property characteristics like amenities, materials, and zoning; community and demographics such as population, languages, and schools; recorder and deed transaction information; lien and mortgages; geospatial information; boundaries; tax and assessment; and valuations derived from over 450 million transactions across 100 million properties.
“The global real estate industry is undergoing a transformation, catalyzed by massive data flows and the application of artificial intelligence. Despite its substantial impact on the global economy, this sector is still in its infancy when it comes to data-centric investing and underwriting decisions,” said Intel Capital vice president Trina Van Pelt. “We see Cherre as critical infrastructure to accelerate the future of this industry. Our customer diligence repeatedly indicated Cherre’s AI-enabled platform was a foundational pillar — a data system of record for large enterprises across the real estate segment. We’re excited to help accelerate Cherre’s global growth trajectory.”
Cherre might not be the first in the $8.5 trillion real estate market with sophisticated AI-powered analytics tools — properties startup Compass recently raised $400 million for its big data platform, following hot on the heels of Skyline‘s $18 million round and other raises by HomeLight, Reonomy, and Geophy. But it has managed to attract a few notable Fortune 500 clients, including The Real Estate Board of NY (REBNY), Keller Williams NYC, Stratus Data Systems, August Partners, and Platinum Properties.
Author: Kyle Wiggers.
Source: Venturebeat
