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Neighbourhood Liveability Scores: Building Your Own Index Using AI
Once the maths is in place, scaling to every neighbourhood you service is simply more rows and richer conversations with every client.
Home / AI for Market Research & Analysis / Neighbourhood Liveability Scores: Building Your Own Index Using AI

Author – Ken Hobson.

Neighbourhood Liveability Scores: Why They Matter

A liveability score tells buyers, renters and investors—at a glance—how easy and pleasant it is to live in a particular pocket. When you build your own index you can:

  • Win listings by showing vendors objective proof of their suburb’s strengths.

  • Attract buyers with maps that highlight walkability, green space and cafés, not just price.

  • Spot growth areas early by tracking micro-improvements in transport, schools or safety.

Platforms such as the Australian Urban Observatory already map 42 indicators across nine domains, proving that rich data is readily available for local use.


Step 1: Choose Your Liveability Pillars

Start with 6–8 themes that matter most to your customers. Typical pillars include:

  1. Walkability & cycling – footpaths, crossings, bike lanes.

  2. Public transport – distance to stops, service frequency.

  3. Green & open space – parks, beaches, sport fields.

  4. Essential services – supermarkets, schools, health care.

  5. Safety – recorded crime rates.

  6. Affordability – median rent or price-to-income ratios.

  7. Community vibe – sentiment from social posts or reviews.

Keep the list short so agents can explain it in one sentence.


Step 2: Gather the Right Data

PillarFree SourcesNotes
WalkabilityOpenStreetMap; AUO walkability layerFootpath & crossing counts
TransportTransport for NSW GTFS feeds; API.NSWReal-time bus/train headways (Data NSW, api.nsw.gov.au)
Green spaceState spatial portals; AUO open-space indexInclude off-leash areas (Australian Urban Observatory)
ServicesGoogle Places API; ABS business countsFilter by POI type
SafetyNSW Bureau of Crime Statistics open dataFive-year trend smooths spikes
AffordabilityABS SEIFA & median income tablesSocio-economic context (Australian Bureau of Statistics)

Tip: register once with each portal, then automate downloads with Python.


Step 3: Clean & Combine with AI

  • Geocode everything to a common mesh block or SA1 code.

  • Fill gaps using machine-learning imputation (e.g., K-NN on nearby blocks).

  • Classify amenities: feed raw POI names into a large-language-model to label “café”, “GP”, “child-care” or “other”—no manual tagging needed.

  • Detect sentiment: run social-media posts through an LLM to score positive or negative neighbourhood mentions.


Step 4: Score Each Pillar

  1. Normalise every metric to 0–100 (higher = better).

  2. Weight pillars:

    • Expert method – ask local planners to assign percentages.

    • Data-driven – fit a Random Forest predicting past capital-growth and let feature-importance set the weights.

  3. Validate with hold-out suburbs to ensure the score “feels right” on the ground.


Step 5: Build the Composite Index

Add the weighted pillar scores to create a single liveability number. Where scores cluster, consider using k-means to group neighbourhoods into Gold, Silver and Bronze tiers for simpler storytelling.


Step 6: Visualise & Share

  • Heat-maps in QGIS, Kepler.gl or Power BI.

  • Interactive dashboards embedded in your website or listing presentations.

  • One-page suburb sheets that sit alongside CMA reports.

The Australian Urban Observatory shows how colour-coded maps instantly communicate strengths and gaps.


Step 7: Keep It Fresh

  • Schedule weekly API calls to pull new transport, crime and review data.

  • Use anomaly-detection models to alert you when a suburb’s score changes by more than 5 points.

  • Publish quarterly updates so vendors see you as the market’s data authority.


Handy AI & Data Tools 

ToolSnapshot
GeoPandas + ShapelyPython libraries that merge spatial files and calculate distances, perfect for walkability buffers.
OpenAI GPT-4oClassifies unstructured text reviews into positive, neutral, negative sentiment within seconds.
Google Earth EngineCloud platform for satellite imagery; measure tree-canopy cover or heat-island effects block-by-block.
Scikit-learnOffers PCA, Random Forest and k-means algorithms to weight pillars and segment liveability tiers.
Kepler.glBrowser-based geospatial visualiser that drags-and-drops CSVs to produce slick, shareable heat-maps.
FME FlowLow-code ETL that automates daily data pulls and pushes cleaned layers to dashboards.
Power BI ServicePublishes interactive scorecards you can embed in cloud brochures or email vendors each week.

Practical Tips for Agents

  • Use scores in prospecting letters: “Your block ranks 88/100 for green space—see how that supports price growth.”

  • Match buyers to streets: filter properties by minimum liveability score that meets their lifestyle brief.

  • Justify vendor-paid marketing: a high score highlights premium appeal and supports larger campaigns.


Ethical & Privacy Checks

  • Aggregate to SA1 or larger—never expose single-house data.

  • Publish your weightings so consumers understand how scores are made.

  • Review for unintended bias; for example, purely price-based affordability can penalise lower-income areas unfairly.


Ready to Begin?

Start small: pick three suburbs, collect free datasets, and draft a pilot index. Once the maths is in place, scaling to every neighbourhood you service is simply more rows and richer conversations with every client.

 

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