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4BED3BATH
BTC
Market Guide

Neighborhood Score Template

A weighted score template for comparing neighborhoods before buying a 4-bed 3-bath home.

Scoring Template

Use 1-5 ratings and weighted scores for:

  • School quality trajectory
  • Commute reliability
  • Safety trend direction
  • Service proximity
  • Price-to-value fit

Decision Rule

Reject neighborhoods below your minimum weighted threshold, even if the home itself looks attractive.

Default Weight Distribution (Adjust Based on Household Priorities)
Schools
30% default
Commute
20% default
Safety
20% default
Price-to-Value
20% default
Services
10% default

Worked Scoring Example — Two-Neighborhood Comparison

DimensionWeightNeighborhood A ScoreWeightedNeighborhood B ScoreWeighted
School quality trajectory30%41.2030.90
Commute reliability20%30.6040.80
Safety trend direction20%40.8030.60
Service proximity10%30.3040.40
Price-to-value fit20%30.6040.80
Total100%3.503.50

When two neighborhoods tie, the tiebreaker is trend direction: which dimensions are improving versus declining for each neighborhood? An improving 3.5 is often a stronger long-term position than a flat or declining 4.0.

Textbook Field Notes

Scoring Template Lab
Instructor Note: A tied weighted score is useful information, not a problem. It tells you to look at trend direction rather than snapshot data, and lets you make your final decision on evidence rather than on emotion or coin flip.

Breakout Exercise: Neighborhood Final Four

Score four candidate neighborhoods using the full template with your household-specific weights applied. Eliminate the two lowest weighted total scores. For the remaining two, compare trend direction dimension by dimension. Make your final selection with a one-sentence written rationale per dimension that changed your thinking. This produces a decision you can defend — and revisit if conditions change.

  • Update your dimension weights whenever household needs shift — a new child, a job change, or a transition to remote work can materially change which dimensions matter most.
  • Version the template with a date and your current weight assumptions — compare to your previous version to catch assumption drift.
  • Never score a neighborhood without completing a physical visit at rush hour on a workday and again on a weekend morning — one-time-of-day observations miss important patterns.
Weight Calibration: Adjust the "price-to-value fit" dimension weight upward if you are near your payment ceiling. For buyers with limited financial slack, this dimension should receive more weight than the default 20%.

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Cross References