Havn

Methodology

The market, plainly explained.

Every score Havn shows is produced by the formula on this page — nothing hidden, nothing subjective. This page renders from the same scoring module that computes every score shown anywhere in the app, so this explanation can never drift from what you actually see on a market page.

The composite score

The buy score is a weighted sum of four sub-scores, each on a 0–100 scale where higher favors buyers and lower favors sellers.

Buy Score = Rate × 35% + Inventory × 30% + DOM Trend × 20% + Price Cuts × 15%

Model v1, effective 2026-07-20.

Sub-score formulas

Mortgage rate

weight 35%

100 − percentile rank of the current 30yr rate within its trailing 24-month range

worked example: 37.5 / 10030yr fixed rate: 6.58%24-month range: 5.62%–7.41%

Inventory

weight 30%

clamp((months of supply − 2) / 5 × 100, 0, 100)

worked example: 36 / 100Months of supply: 3.8

Days-on-market trend

weight 20%

clamp(50 + clamp((current avg DOM − 90-day avg DOM) / 90-day avg DOM, −50%, +50%) × 100, 0, 100)

worked example: 66.7 / 100Current avg DOM: 35 daysTrailing 90-day avg DOM: 30 days

Price cuts

weight 15%

clamp(price-cut % / 30 × 100, 0, 100)

worked example: 41.7 / 100Active listings with a price cut: 12.5%

Worked example

Illustrative — computed from representative sample data, not a live metro. As of 2026-07-20.

44leans seller favor
Mortgage rate37.5× 35%13.1
Inventory36× 30%10.8
Days-on-market trend66.7× 20%13.3
Price cuts41.7× 15%6.3

The plain-English summary

The headline sentence shown with every score (e.g. on the homepage or a market page) isn't written per metro or generated freely — it's built from a fixed set of phrases, one pair per sub-score, describing what a high or low value means. The two sub-scores furthest from neutral (50) in the breakdown above are picked, and their phrases are joined. It never says anything the numbers above don't already show, and when the two biggest drivers pull in opposite directions, the sentence says so rather than forcing a one-sided story.

Homes are sitting on the market longer than usual, and inventory remains tight.

Generated from the worked example above — for real metros, this describes today's actual data.

Score bands

RangeReading
030strongly favors sellers
3145leans seller favor
4655neutral / balanced
5670leans buyer favor
71100strongly favors buyers

What's not in the model yet

The rate sub-score currently reflects only where today's rate sits within its trailing 24-month range. A momentum adjustment — whether a fast recent move up or down should shift the score further — is deliberately left out for now: the direction it should push the score isn't settled without backtested data, and an honest formula beats a decorative one. It's reserved for a future model version once it's been validated against historical outcomes.