A direct, private CRE fund
Private CRE deals no one else has access to.
We use massive data pipelines and automation to be first to a deal, create relationships with developers, and close faster than the competition using our in-house underwriting platform.
Germantown Apartments
Philadelphia, PA
Performing
Loan Size
$33.1M
Loan Type
Ground Up
We were responsible for the senior debt of the capital stack. The total LTC was 80% with an exit LTV of less than 60%. The team has an A+ team with a solid track record and personal liquidity to support this $45mm deal.
* View our full deal history on the results page
How we're first
Automated data pipelines get us to deals first.
We’ve built automated pipelines at scale that ingest public and private data from permits, deeds, and other filings, which we process using machine learning. We reach out early in the development process to build a relationship with the sponsor.
Records ingested
350,000
Records available
50,000,000
Capital deployed
$752M
Addressable market
$47B
Scrapers or API
Data ingestion
Collect data from public & private sources of permits, deeds, and other filings.
Pipeline
Normalization
Aggregate the incoming data into a unified structure for all the different objects types and their relationships: deals, properties, contacts, companies, etc.
Scrapers, API, and Apps
Enrichment
Use supplemental data to enrich the entity in the graph with the goal of better understanding the project, owners, and opportunity.
App
Our platform
All the data is presented on our platform, easily editable, and allow our team to explore potential deals in one place.
Database
Internal data store
We keep all this newly parsed and processed data in a new unified WelcomeLend database.
API
CRM
We also push all our data to a CRM (Hubspot) to help our sales team automate and track their early-stage interactions.
How we're the fastest
We underwrite 3x to minimize risk potential.
We start every deal by letting our system score a deal's risk by running borrower, project, and market factors through our model. If the deal passes our automated underwriting, we introduce our in-house underwriting experts and subsequently a 3rd party team to review and validate. This gives us a few major advantages:
- Faster initial underwriting than competition
- Less human bias in risk scoring
- Expert underwriters for nuanced matters
- 3rd party partners performing additional review
01. Automated analysis
The first risk assessment is done automatically by our platform with a focus on high-level viability of the project and the sponsor's history.
Safe
02. Internal underwriters
After a deal has undergone automated evaluation, we introduce an in-house team member to review and dig into nuanced factors that can't automatically evaluated.
Safe
03. External review
Our final risk assessment is performed by a collection of 3rd party partners that provide us with a final review to point out undiscovered holes.
Very safe
Sponsor experience
Best-in-class borrower experience.
We’ve created an entire platform to modernize the lending and borrowering. This allows us to move significantly faster and with fewer hangups than the competition.
Collaborative tasks
Assign documents, signatures, questions, and more to the sponsor team or limited access 3rd parties.
Document management
Documents are uploaded and auto assigned to responsible parties for processing, revisions, clarification, or collaborative feedback.
OM generator
Design and publish a beautiful offering memorandum in minutes.
Financing calculator
Build the entire financial model for a deal in a self-balancing calculator, including sources & uses, income, expenses, debt service, comps, completed values, and much more.
Reporting
Understand the bigger picture of performance across multiple deals and your team through its entire history.
Our results
We’ve deployed over $750M into 50+ deals with zero in lender losses.
Our automations and platform have enabled us to rapidly scale and assess more deals than our competition, while keeping our risk profile low. Like, never lost a dime low.