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Release Notes (Dec 2021)
Release Notes (Dec 2021)

December 2021 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.


Dashboard Updates

Now you can create and edit your own dashboards in CoreExplore! Gain immediate insights into your data by creating robust visualizations, time-series charts, and much more at the click of a button.

Build new dashboards from Cherre Foundation data or any internal or partner datasets we’ve connected for you. This feature can be enabled for CoreExplore subscribers upon request, so contact your sales rep to receive access and training.

Create Dashboards

  1. Click “Create Dashboard” from home or “New” in the Dashboards panel

  2. Name your dashboard

  3. Add widgets using the “Add Tile” button

In create mode, build new visualization tiles from available data sets and customize the dashboard layout.

Data Connections

RCA Market Trends

We’re happy to announce that RCA Market Trends is now available as a Cherre Lightning Connection! Cherre Lightning Connections enable customers to add unique datasets to their Cherre Data analytics warehouse within hours instead of weeks.

This dataset includes metro, regional, and national level aggregates for key capital market metrics, as well as the number of sale transactions, total sales volume, total square feet sold, average price per square foot, and average cap rate. Historical data goes back to 2001 and is available for eight property sectors.

RCA Market Trends is a powerful tool for understanding capital market dynamics, market selection, underwriting, property valuation, and benchmarking.

For further information, including a data dictionary, see the RCA Market Trends profile inside the Lightning Connections landing page.

Trepp CRE Income Statement Aggregates

Trepp CRE Income Statement Aggregates is now available as a Cherre Lightning Connection. This dataset provides aggregated metrics on property financials by property type, market, and state, including benchmarks for revenue, operating expenses, and taxes. Coverage includes office, industrial, multifamily, retail, lodging, and mixed-use property types with historical data available from 2016 onward.

This dataset enables CRE professionals to benchmark operating performance for existing portfolios and provides critical underwriting assumptions for acquisitions teams.

For further information, including a data dictionary, see the Trepp CRE Income Statement Aggregates profile inside the Lightning Connections landing page.

Trepp Loan Universe

The Trepp Loan Universe dataset is also now available as a Cherre Lightning Connection. Using Trepp’s capital market quality commercial real estate loan and property data, clients can access the largest available catalog of securitized loans with accompanying information. The breadth of this dataset makes it ideal for dealmakers, underwriters, and asset and portfolio managers to find loan and property deals, track the market, benchmark CRE loan and property performance, and manage risk.

With an extensive loan library that stretches back to 1998, Trepp offers a single source of high-quality, easy-to-use data for powerful analysis to support regulatory reporting, benchmarking, and forecasting.

For further information, including a data dictionary, see the Trepp Loan Universe profile inside the Lightning Connections landing page.

Trepp CMBS Financials

Trepp detailed CMBS financials data is also available as a Lightning Connection. The Trepp Data Feed file layouts were developed to give CMBS investors, traders and researchers a single source for maintaining a historical CMBS database. This dataset enables CRE professionals to benchmark the operating performance of existing portfolios and populates critical underwriting assumptions for acquisitions. It features granular details of operating expenses, leasing commissions, capital expenditures, and tenant improvements at the property level for all US CMBS properties.

For further information, including a data dictionary, see the Trepp CMBS Financials profile inside the Lightning Connections landing page.

Cherre API

New Boundary Objects Added to Cherre Foundation

New boundary objects are now available in Cherre’s Foundation Data that enable customers to join datasets and visualize data using the polygons and centroids returned. Many partner datasets (e.g. Unacast, RCA Market Trends, Trepp CRE Income Statement Aggregates) contain geographic information that can be seamlessly connected to Cherre Foundation Data through boundary objects so that you can understand demographic trends, track market performance, and much more.

The newly available objects in the API are:

  1. usa_state_boundary

  2. usa_county_boundary

  3. usa_census_tract_boundary

  4. usa_msa_census_boundary

These are in addition to the existing boundary objects:

  1. usa_neighborhood_boundary

  2. usa_school_boundary

  3. usa_zip_code_boundary

  4. usa_census_places_boundary

USA State Boundaries

Inside usa_state_boundary you can retrieve the state’s FIPS code, geographic boundary, and interior point geometry.

Here is a query that pulls data for New York state:

query StateBoundaries {
usa_state_boundary(where: { state: { _eq: "NY" } }) {

USA County Boundaries

Not only does usa_county_boundary contain the county’s geometric boundary, but it also contains information about the county’s land and water area.

Here is a query that pulls all the counties in New York and orders them by county name alphabetically:

query CountyBoundary {
where: { state: { _eq: "NY" } }
order_by: { county_name: asc }
) {

USA Census Tract

usa_census_tract_boundary is very similar in structure to the usa_county_boundary.

Rather than pull data directly from usa_census_tract_boundary, you can see here how we have connected the tax_assessor object to usa_census_tract_boundary, so that we could see the information from usa_census_tract_boundary for that boundary. This demonstrates how geospatial data is connected through relationships and how we can enrich property data.

query CensusTractBoundary {
tax_assessor(where: { tax_assessor_id: { _eq: 674610448 } }) {
usa_census_tract_boundary__geo_id_pk {

USA MSA Boundaries

usa_msa_census_boundary contains fields with the MSA name, whether it is a Metro or Micropolitan area, and the geometric boundary of the MSA.

Rather than filter for results using one of the text fields like msa_name, with geometry objects we can also filter for results using PostGIS function with a point or polygon to retrieve the data we are interested in.

query MSABoundary($point: geography!) {
usa_msa_census_boundary(where: { geometry: { _st_intersects: $point } }) {
lsad # Legal/Statistical Area Description
"point": { "type": "Point",
"coordinates": [ -73.974343, 40.7950997]

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