Best Fit
All known gates pass and the path has the strongest group of positive signals. Only one card receives this label.
How DSCR Loan Central finds your financing match.
The matcher uses visible path gates, positive signals, and explicit unknown states. It does not use hidden approval logic or fake precision.
The matching process
1
Collect only self-reported inputs
Property, goal, income, challenges, rent, credit band, equity, experience, and entity plan.
2
Apply category gates
Property use and stated facts remove DSCR paths that clearly do not fit.
3
Count transparent signals
Each positive reason is tied to a specific answer.
4
Explain the result
The output shows fit, confirmation questions, trade-offs, and missing information.
The inputs we use
Property and use
Residential rental use and unit count determine which DSCR paths are available.
Loan purpose
Purchase, rate-and-term, and cash-out carry different published leverage limits.
Rent evidence
A signed lease, documented short-term rental history, or a market-rent estimate. Lenders weigh these very differently.
Rent versus payment
The self-reported coverage band, which is the centre of DSCR underwriting.
Nothing is verified
Every answer is self-reported and never independently checked.
Credit and equity bands
Used for ordering and cautions, not hard eligibility claims.
Experience and entity plan
Optional context that changes cautions and explanations.
How a DSCR path becomes a match
Strong Fit
Gates pass and at least two positive signals align.
Worth Comparing
The option has one positive signal or is a standard alternative to the top result.
More Information Needed
A missing or Not sure answer prevents a confident placement.
Less Likely Fit
A stated answer fails a path gate. The reason remains visible.
Methodology approval state: Approved for publication.
Why we do not show a percentage score
A number such as 94% suggests measured predictive accuracy. The current method is a rules-based educational classifier, so it uses plain-language tiers and shows the reasons instead.
That lets a user disagree with an input or reason, and it avoids disguising lender-specific underwriting as mathematical authority.
Common questions
Does the matcher use a percentage score?
No. Percentage precision would imply an approved statistical model. The current system uses transparent category gates and positive signals.
What happens when I choose Not sure?
The affected category moves to More Information Needed. Unknown information never becomes yes, no, or zero.
Can a commercial relationship change my ranking?
No. No lender pays to appear here, to rank higher, or to have an option removed. Match evaluation runs only on the DSCR path rules described on this page.
How are ties handled?
Only one result can carry Best Fit. Ties use the number of fired signals and a deterministic category order. The underlying methodology still requires formal approval before launch.
Does this tool underwrite loans?
No. It does not verify documents, pull credit, price loans, or make approval decisions.
Run your own scenario
The matcher exposes the same gates and explanations described on this page.