How we calculate town fit.

What the score is

The Town Fit Score™ is a numerical reading of how well a Hudson Valley town matches the answers you gave. It is not a ranking of towns in the abstract. It is the result of running your seven answers against a published matrix of weighted signals, then anchoring the output to the maximum score your specific answer set could achieve.

The same town can score very differently for two different buyers. That is the point. We don't believe in a single "best" Hudson Valley town — we believe in matching real households to the places that fit how they actually live.

We never claim a magic algorithm. We claim a calibrated, documented, expert-curated framework.

The seven axes

Each quiz question maps to one axis. The seven axes are deliberately small in number — research on decision quality consistently shows that six to nine criteria is the sweet spot for honest reflection without analysis fatigue.

Use case

Whether you're relocating, buying a second home, selling, or still exploring — each opens a different decision tree.

Commute

How much train access to NYC matters. The strongest signal in the entire matrix when you commute multiple days a week.

Rhythm

Walkable village, quiet country with a town center, or real privacy. The single best predictor of daily quality of life.

Housing

Historic village home, renovated farmhouse, modern build, or land-first. Shapes what inventory you actually consider.

Privacy

From visible neighbors to off-grid. A budget item as much as a lifestyle one.

Outdoors

Daily trails, weekend hikes, or walks and parks. Sets the cadence of how you use the valley.

Budget

Premium, mid-market disciplined, or value-first. Changes the inventory you see — not the towns we recommend.

The 14 signals

Every launch town is rated on the same 14 signals. The signals are calibrated quarterly by editorial review against on-the-ground inventory, train schedules, walk audits, and recent transactions.

Train access

Frequency and reliability of NYC rail service from the town — Metro-North, Amtrak, or none.

Walkability

Density and continuity of village or downtown streets within a typical 12-minute walk radius.

Privacy / acreage

Average lot scale and visual buffer from neighbors in the typical inventory.

Second-home fit

How well the town supports weekend or part-time use — services, dining, low-maintenance posture.

Full-time fit

How well the town supports year-round daily life — schools, healthcare, weekday rhythm.

Design-forward

Presence of contemporary architecture, design culture, galleries, and creative industries.

Outdoor access

Proximity to trails, water, climbing, and public outdoor space.

Water access

Direct or near access to the Hudson River, lakes, swimming, or marinas.

Culture

Density of music, arts, performance, and cultural programming.

Dining

Density of restaurants the town's residents actually eat at, not just visitor traps.

Family fit

Practical signals for families — playgrounds, community programs, school context (NOT rankings).

Retiree fit

Walkability + healthcare proximity + community rhythm for retirees and downshifters.

Remote-work fit

Broadband, third-places, daytime energy, and the ability to work without commuting.

Budget posture

Price posture in the typical inventory — high = premium, medium = mid-market, low = value-first.

How alignment works

For each rule in the matrix, your answer triggers an alignment check against the town's signal value. Alignment is a number between 0 and 1. A "strong" reading at full alignment credits the full rule weight; "moderate" credits partial; "limited" or "none" credits nothing.

Example: if you answered "Essential — I commute multiple days" for the commute axis, a town with strong train access earns the full 4-point weight; a town with moderate access earns 0 on that rule. The rule then explains itself in your results breakdown: "Train access reads strong — matches your essential-commute answer."

Normalization

The displayed percentage is honest. For each (user, town) pair we compute the raw points and the ceiling — the maximum points a hypothetical town could earn against your specific answer set. The display percentage is round((raw / ceiling) × 100).

This is intentional. The most common shortcut — "the top town in this set scores 100%" — flatters the result but tells you nothing about absolute fit. Our normalization tells you how close the town came to being a perfect match for the life you described.

Versioning and audit

Every completed quiz is tagged with the methodology version that scored it. When we recalibrate the matrix, historical results stay reproducible. The current matrix is v1.1.0, last calibrated 2026-05-30. The matrix itself is open in this project's source at src/lib/townFit/weights.v1.ts.

Trust posture

We publish this methodology because it has to be auditable. Buyers making seven-figure decisions deserve to see the assumptions, not just the output. If you disagree with a specific weight or alignment, that disagreement is a conversation we want to have — not one we want to hide behind a black-box score.

— The Editorial Desk