How We Calculate Livability Scores

DwellCheck uses a transparent, data-driven approach to evaluate NYC addresses. We believe you should know exactly how scores are calculated.

an aerial view of a city with tall buildings
Photo by Zoshua Colah on Unsplash

Scoring Algorithm

Every address starts with a baseline score of 100. We then apply penalties for negative factors (violations, complaints, crime) and bonuses for positive factors (transit access, parks, amenities).

Final scores are clamped between 0-100:

Excellent
90-100
Good
75-89
Fair
60-74
Concerns
0-59

Scoring Categories

Building Health

High Weight

Analyzes HPD violations, DOB violations, heat/hot water complaints, bedbug reports, and elevator issues at the specific building.

Impact: Up to -30 points for severe violations

Safety

High Weight

Evaluates NYPD crime data, arrests, and safety-related 311 complaints within walking distance of the address.

Impact: Up to -25 points for high crime areas

Nuisance

Medium Weight

Measures noise complaints, trash issues, rodent sightings, and construction permits in the immediate area.

Impact: Up to -20 points for persistent issues

Commute/Transit

Medium Weight

Calculates proximity to subway stations and bus stops, with bonuses for multiple transit options.

Impact: +3 to +8 points for excellent transit

Amenities

Medium Weight

Evaluates access to parks, grocery stores, restaurants, and other conveniences.

Impact: +3 to +10 points for walkable amenities

Environment/Wellness

Low Weight

Assesses sensory environment including quiet zones, third places, and street vitality.

Impact: +5 to -15 points based on factors

Data Sources

All data comes from official NYC government sources through the NYC Open Data Portal and the US Census Bureau.

Building Health

HPD Violations
Housing violations from NYC Housing Preservation & Development
Dataset: wvxf-dwi5
HPD Complaints
Tenant complaints filed with HPD
Dataset: uwyv-629c
DOB Violations
Building code violations from Dept of Buildings
Dataset: 3h2n-5cm9
Bedbug Reports
Annual bedbug infestation filings
Dataset: wz6d-d3jb

Safety

NYPD Complaints (Current)
Crime complaints, 2025+
Dataset: 5uac-w243
NYPD Complaints (Historic)
Crime complaints, ≤2024
Dataset: qgea-i56i
NYPD Shooting Incidents (Current)
Shooting incidents, 2025+
Dataset: 5ucz-vwe8
NYPD Shooting Incidents (Historic)
Shooting incidents, ≤2024 (with statistical_murder_flag)
Dataset: 833y-fsy8
NYPD Arrests (Current)
Arrests, 2025+
Dataset: uip8-fykc
NYPD Arrests (Historic)
Arrests, ≤2024
Dataset: 8h9b-rp9u

Nuisance

311 Service Requests
Noise, trash, rodent, and other complaints
Dataset: erm2-nwe9
DOB Permits (incl. After-Hours Variance)
Construction permits; after-hours variance flagged separately as g76y-dcqj
Dataset: ic3t-wcy2

Transit

Subway Entrances
MTA subway entrance locations (NY State Open Data)
Dataset: i9wp-a4ja
Bus Stops
MTA bus stop locations via GTFS feeds
Dataset: various

Amenities

Parks Properties
NYC Parks Department properties
Dataset: enfh-gkve
Retail Food Stores
NY State licensed food stores (statewide; filtered to NYC by ZIP)
Dataset: 9a8c-vfzj
Recognized Shop Healthy Stores
Smaller curated bodega/grocery list (city-recognized)
Dataset: ud4g-9x9z
School Locations
NYC public school locations
Dataset: r2nx-nhxe

Environment

FDNY Firehouses
Fire station locations (noise factor)
Dataset: hc8x-tcnd
Air Quality Surveillance
NYC Community Air Survey (NYCCAS)
Dataset: c3uy-2p5r
Street Trees
Tree canopy density via 2015 NYC Street Tree Census
Dataset: uvpi-gqnh
Flood Zones
FEMA-derived flood hazard maps
Dataset: dpc8-z3jc

Demographics

US Census ACS
American Community Survey 5-year estimates
Dataset: census-api

Confidence Score

Each report includes a confidence percentage that indicates how much data was successfully retrieved. A 100% confidence means all 11 scoring modules returned data. Lower confidence may occur if:

  • NYC Open Data APIs are temporarily unavailable
  • The address is very new and not yet in all databases
  • Rate limiting affects data retrieval

We recommend treating low-confidence scores with appropriate skepticism and re-checking later.

Limitations & Disclaimers

  • Not real estate advice: DwellCheck is an informational tool. Always visit addresses in person and consult professionals before making housing decisions.
  • Data lag: Government data may be days to weeks old. Recent incidents may not be reflected.
  • Address precision: Some data sources use approximate locations. We use building-level (BBL) data where available.
  • Subjective factors: Scores cannot capture personal preferences, neighbor quality, or future changes.
  • No guarantee: A high score does not guarantee a positive living experience, and a low score does not mean an address should be avoided.

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