How we score your matches

Every property gets a single 1–100 fit score, built from your own stated wants — not a generic listing rank. Here's exactly what goes into it.

It's weighted to what you told us matters

Your criteria become weighted components. A must-have (for a homebuyer: a yard for the dog, proximity to where you live and work; for an investor: cap rate and cash flow) carries more weight than a nice-to-have. Each property is graded on every component, and the weighted blend is the score. Tap the score pill on any match to see that property's actual component breakdown.

Partial credit, not pass/fail

A home that's a little over budget or a touch small isn't thrown out — it's graded down proportionally. That's why scores spread across a range instead of clustering at one number.

"Almost matches" still surface

Properties that just miss your threshold are flagged as close calls rather than hidden, so a near-perfect fit a few points under the line still reaches you.

Beyond the basic filters

We layer in signals a plain MLS search misses — proximity to the specific places in your life, realistic all-in monthly cost (rate, taxes, insurance, PMI, HOA), and a yard a listing filter would overlook.

What we never do

• We never invent a reason after the fact — the breakdown shows only the real components the engine computed.
• We never use AI-generated photos of a property — every photo is the real MLS listing photo.
• We flag modeled figures (cap rate, rent, taxes) as "to verify" — they're a first pass, not a promise.