A breakdown of the proprietary engine behind the CRE industry’s most advanced refi simulations.
In commercial real estate, refinance opportunities have traditionally been identified through spreadsheets, guesswork, scattered records, and outdated CMBS-style snapshots.
This approach falls apart in today’s environment, where maturities exceed $700B, interest rates shift rapidly, and borrower behavior is increasingly nuanced.
LoanBase was built because brokers and lenders need precision, not projections.
Our refinance valuation engine analyzes thousands of daily data points and models refinance outcomes with the same sophistication as institutional capital markets teams – but with speed and simplicity any originator can use.
The result is a valuation system that doesn’t just reflect the past.
It anticipates the next best move.
Where LoanBase Gets Its Data
Our system aggregates and verifies information from over 55+ independent and institutional-grade sources, including:
- National title and deed repositories
- Public and private loan registries
- Lender funding activity datasets
- Historical amortization and rate curve data
- Skip-trace ownership and identity layers
- NOI and rent growth datasets from institutional providers
- Market-level cap rate, absorption, and supply data
The power of LoanBase lies not in any one dataset – but in the way we cross-reference, validate, and unify them into a single actionable output.
This produces a refinance model that mirrors real-world underwriting.
How LoanBase Runs Its Refinance Simulation
Once the system collects the required inputs, it runs a refined multi-step calculation:
Step 1: Establishing Debt & Property Baseline
Using title and mortgage datasets, we extract key loan and property attributes:
- Original interest rate
- Maturity date
- Principal balance
- Borrower entity
- Lender name
- Property type and size
- Recorded encumbrances
This forms the foundation of the refinance profile.
Step 2: Market-Based Property Valuation
LoanBase applies a dynamic valuation model by:
- Updating cap rates with current market spreads
- Applying NOI growth assumptions (standard 3% trend, adjusted for volatility)
- Incorporating rent, vacancy, and absorption rates at the submarket level
- Adjusting for asset class variations such as multifamily, mixed-use, retail, or industrial
This lets us estimate a true-to-market valuation, not a static appraisal.
Step 3: Refinance Outcome Prediction
Next, the engine determines if a refinance is viable by simulating:
- Updated interest rate ranges
- New amortization periods
- DSCR thresholds
- LTV constraints
- Maximum loan proceeds
- Prepayment penalty impacts
- Estimated borrower cash-out potential
Deals that show 20%+ positive cash-out are flagged as early-refi candidates. This allows brokers to prioritize high-impact opportunities.
Step 4: Lender & Quote Matching
LoanBase then pulls from the largest lender activity dataset in CRE, identifying lenders who are currently:
- Funding in that market
- Active in the specific asset class
- Pricing competitively
- Closing similar transactions
Instant indicative quotes are displayed directly in the deal.
This is where data becomes deal flow – brokers can go from discovery to outreach to lender match in minutes.
Why This Matters in Today’s CRE Market
CRE debt markets have become more fragmented, opaque, and time-sensitive than ever.
A mature valuation engine allows originators to:
- Know which loans are real opportunities
- Understand the borrower’s position before calling
- Go into conversations with a data-backed narrative
- Move faster than competitors who still rely on public listings
LoanBase turns each opportunity into an investable, actionable refinance story.
That’s how brokers win the next $700B in maturities.
