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Release Notes (Sep 2022)
Release Notes (Sep 2022)

September 2022 product release notes

Kevin Mattice avatar
Written by Kevin Mattice
Updated over a week ago

About Cherre

Cherre is the leader in real estate data and insight. We connect decision makers to accurate property and market information, and help them make faster, smarter decisions. By providing a unique “single source of truth,” Cherre empowers customers to evaluate opportunities and trends faster and more accurately, while saving millions of dollars in manual data collection and analytics costs.

CoreExplore

Deal Sourcing & Pipeline Integration

We’re pleased to announce the release of robust deal sourcing and pipeline management features in CoreExplore Search! CRE owners can now integrate their existing deal pipeline platforms (e.g. DealCloud, DealPath, Altrio) into CoreExplore Search. Acquisitions teams can improve operational efficiency and deal volume by utilizing our interactive, map-based search to source deal targets, create new deals, and review their pipelines, while feeding the deal data to their acquisitions platforms.

Highlights include:

  • Integrate with any deal platform (e.g. DealCloud, DealPath, Altrio)

  • Identify acquisition targets through robust filter & mapping features

  • Source and create deals from Cherre’s enriched 145M property inventory

  • Integrate proprietary or 3rd party data sources - lease comps, sale comps, market fundamentals - on detail pages or as map layers to contextualize deal targets

Review your acquisitions pipeline through an interactive, map-based interface

Source, create, and edit individual & portfolio deals directly from CoreExplore Search

If you’re interested in the CoreExplore Search deal pipeline integration, call your sales or customer service rep to ask about access!

Google Place API Integration

We’ve integrated Google Place Autocomplete API into the location lookup filter in CoreExplore Search, giving you a faster, easier, and more accurate way to search for commercial or residential property addresses.

Lookup filter enhancements include:

  1. Viewing up to 3 address matches based on Google’s extensive address inventory

  2. Quickly seeing address matches in the autocomplete after typing in few characters

  3. Select new autocomplete result types, such as “Streets” and “Intersections,” to position the map to your ideal geography

Access the location lookup filter on the CoreExplore homepage or in the map search

CRE Loan Screener

We’ve launched a new set of out-of-the-box dashboards for CRE Lenders, which leverage data from Cherre Foundation and various third-party data providers. These dashboards will enable a Customer Outreach Team to view all loans and transactions in one place with the ability to filter down to specific Maturity Dates, Interest Rates, Loan Balances, and other metrics that meet your specific criteria.

Profile markets based on the active loans that meet your search criteria

Target owners of properties within the markets you decide to go after

Cherre Foundation Data

Cherre Lot Boundaries

Cherre Lot Boundaries is a new dataset within our foundation data layer that contains unique geographic boundaries of tax parcels. Lot boundaries solve the problem of duplicate data caused by overlapping parcels. As a Cherre client, you can now leverage lot boundaries to geospatially join addresses, buildings, transactions, loans, and other key datasets.

For example, the property shown below located at 300 NORTH STATE STREET, CHICAGO, IL 60654 is a high-rise condo building in Chicago with two towers, where each condo is its own tax parcel. This address has a total of 895 overlapping tax parcels.

Previously, if you would like to connect ownership data to transactions for the building geospatially using tax parcel boundaries, each of the 895 tax parcels located at this property would have duplicated sales transactions from a provider such as RCA. With our new lot boundaries object, this condo building is now represented using a single boundary – giving you a single result when connecting to RCA sales. This makes connecting geospatially at the property-level a more friendly experience.

Cherre Owner Unmasking

We have made a number of improvements to the quality and user experience of our Owner Unmasking offering. These are all available to you today in production!

Accuracy

When compared to our benchmark from July, we have improved our accuracy by 11.2% overall and 8.4% for large portfolios (total assessed value > $10M). Breaking down these results by asset type, we have improved our large portfolio accuracy 11% for office properties, 13.3% for multi-family properties, and 9.6% for residential properties.

Noise Reduction

When reviewing the unmasked owners of a property, we recognize that showing too many potential entities can cause confusion and slow you down in outreach. Therefore, we have removed some of the additional entities identified in an attempt to make it easier for you to get in touch with the correct owner. In the current production data, you will find:

  • We have reduced the number of properties with more than 8 unmasked owners from 1.7 million to 0 (100% Reduction).

  • We have reduced the number of properties with more than 6 unmasked owners from 2.8 million to 0.9 million (68% Reduction).

  • We have reduced the number of properties with more than 5 unmasked owners from 3.9 million to 2.1 million (46% Reduction).

Coverage

We were able to unmask an additional 1.1 million properties by adding additional datasets and improving our algorithm behind the scenes!

Data Connections

Contact Information Integration

We are excited to announce the launch of PiPl and ZoomInfo as Cherre Lightning Connections! Cherre clients can now leverage contact information from these industry-leading providers to accurately and instantly get contact information for the right people and companies to kick off a conversation.

The emails and phone numbers will soon be integrated with Cherre Owner Unmasking and the SFR Data Kit to make it easier to contact the owners of assets. Today, Cherre clients can pull contact information for owners through the Cherre API or CoreExplore Search and in the very near future through Cherre’s Dashboards.

Zoominfo data integration within CoreExplore

If you’re interested in getting access to either dataset through Cherre, contact your sales or customer service rep.

Data Kits

New Dashboards, Deciles, and Polygons

Detailed Investor Dashboards

To add to the existing suite of SFR Data Kit portfolio dashboards, the new Portfolio Details dashboards are intended to allow users to filter down investors in markets based on their target buy box. Whether your use case is to acquire portfolios, disposition, or finance, these dashboards lets you find the investors and their target holdings.

Identify portfolios leveraging search criteria across building info, crime, and income

Distressed SFR Dashboard

These dashboards surface SFRs based on actions like Lis Pendens, Default, or Foreclosure as well as the ownership relationships to better allow clients to source distressed SFR deals.

Target properties for leaseback based on property details and distress situations

Market Cluster / Segment Dashboards and API - General and Granular

We have broken down 60 different SFR and some STR markets into submarkets based on price and price derivative. Testing with these geographical boundaries applied to an AVM model showed a 10 percentage point increase in explainability while simplifying markets into easier-to -understand geographies. These polygons can be used for AVM builds, for comparables, and for identifying the changes in markets over time. These Cherre-produced polygons are also available at different levels of granularity and available to all Data Kit clients.

Market polygons by MSA, year and granularity

If you’re not already using the SFR Data Kit as part of our Alpha program, call your sales or customer service rep to ask about access!

Cherre API

Listing Data Procurement Guide

With the rising popularity of single family residences in the investment space, gaining -access to high quality residential listing data is a must-have for many of our clients. Unfortunately, with over 500 Multiple Listing Services in the United States, figuring out how to get a data feed can be a daunting task. That’s why we’ve created the Listing Data Procurement Guide as a resource for our clients to understand the various sources of listing data and how to acquire them.

Listing Data Model

What happens once you acquire several listing data feeds? Unfortunately not all sources of listing data (especially Multiple Listing Services) have a consistent data model. The Cherre Listing Data Model is our solution to standardize various MLS listing data sources into a single schema. The Listing Data Model is compatible with the real estate industry’s standard data dictionary and serves as the foundation for onboarding our client’s MLS feeds. To demo the model, we’ve loaded the Austin Board of REALTORs reference dataset and it will be available for client demonstration per request by the end of the month.

Address Service Now Available in BigQuery API

Cherre’s Address Standardization service is now available in Cherre’s GraphQL API backed by BigQuery. This represents a significant step towards feature parity between Cherre’s two APIs*.

Sample query:

query addressStandardizationDemo {
  address(address: "8161 Seaton Pl, MONTGOMERY Alabama") {
    one_line_address
  }
}

Response:

{
  "data": {
    "address": {
      "one_line_address": "8161 SEATON PLACE, MONTGOMERY, AL 36116"
    }
  }
}

If you are interested in learning more about the BigQuery-backed API (currently in Alpha), please contact your Sales or Customer Success Manager.

Connection Services

Cherre Address

Cherre’s address inventory has been updated, increasing the number of connections that can be made between datasets. Clients will see up to a 6% increase in match rates across datasets.

Every address in our inventory has also been re-geocoded increasing the geocoding accuracy of all of our addresses within the inventory.

In the API, you will now find address components available for every standardized address and be able to filter results by those address components. In the example below, you can filter for all of the tax assessor id’s where the mailing address is the same as the zip code of the property address.

query addressComponents {  
   address(address: "300 S Biscayne Blvd, Miami, FL 33131") {               
   cherre_address__address{      
      street_address      
      city      
      state      
      zip      
      tax_assessor__mailing_address(where:    {   cherre_address__mailing_address: {zip: {_eq: "33131"}}}) {        
      tax_assessor_id        
cherre_address__mailing_address{          
      zip        
            }      
         }    
      }  
   }
}

Coming Up Next

Adding Portfolio Deals in CoreExplore Search

We’re continuing to expand our integration with deal pipeline management platforms by delivering the ability to add, edit, and view portfolio or multi-property details within CoreExplore Search. Users will be able to easily create a geographically diverse portfolio deal and track it across the acquisitions lifecycle.

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