MCP server

Microburbs Australian Property Data Official

au.com.microburbs/property-data

Checked 25 Oct 2026, 20:29 UTC · Connected to it and listed its tools

Australian property and suburb data: valuations, sales, rents, schools, risk and zoning.

Website

At a glance

Type

MCP server

Publisher

microburbs.com.au (verified by the MCP Registry)

Status

Working at last check

Tools

174

Token cost

About 21,120 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

25 Oct 2026, 20:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http property-data https://api.microburbs.com.au/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.property-data]
url = "https://api.microburbs.com.au/mcp"

Gemini CLI

{"mcpServers":{"property-data":{"httpUrl":"https://api.microburbs.com.au/mcp"}}}

Cursor

{"mcpServers":{"property-data":{"url":"https://api.microburbs.com.au/mcp"}}}

VS Code

{"servers":{"property-data":{"type":"http","url":"https://api.microburbs.com.au/mcp"}}}

Antigravity

agy mcp add property-data https://api.microburbs.com.au/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"property-data","server_url":"https://api.microburbs.com.au/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"property-data","server_url":"https://api.microburbs.com.au/mcp"}

Tools

• area_stats_fields ~294 tokens
  Discover which statistical fields exist. Free (0c). Fuzzy, token-based matching covers table topics and clean column labels as well as terse field codes. Common natural-language plurals and Census synonyms are handled, so the verbatim `query=couples with children` discovers the exact 2021 ABS family
• area_stats_regions ~198 tokens
  List the regions that exist at a level — every suburb, LGA, postcode, etc. Free (0c). Use it to discover the exact region names to pass to /values. **Free.**
• area_stats_values ~294 tokens
  Statistical field values for one region. Pass `fields` — 1 to 5 field codes (from /fields) — to fetch. At SAL level, `mode=sa1` switches from the suburb-aggregate value to a per-SA1 breakdown — every SA1 that falls inside the suburb, each with its own values. **Price: 3¢ per call.**
• geocode_address ~149 tokens
  Resolve a freeform address string to candidate G-NAF identifiers. Use this before calling any ``/v1/properties/{gnaf_id}/...`` route — those endpoints require a GNAF in the path. Returns up to 10 candidates, ranked by Meilisearch score. **Price: 1¢ per call.**
• geocode_suburb ~139 tokens
  Autocomplete a partial suburb search string against the ABS SAL list. Use this before calling ``/v1/suburbs/{name}/...`` — those endpoints require a canonical SAL name in the path. **Free.**
• lga_list ~108 tokens
  All Australian LGAs. Optional `?state=` filter (e.g. 'NSW'). Returns `{lga, state}` per row. Flat cost. **Price: 3¢ per call.**
• lga_profile ~78 tokens
  LGA's parent chain — state + GCCSA. LGAs aren't strictly nested in SA4 so the upward chain stops there. **Price: 3¢ per call.**
• mbs_list ~124 tokens
  Mesh-block codes inside a suburb. ``sal`` is required for now — nationwide / by-LGA / by-SA3 listings will land later when there's a story for the large payloads (a single LGA can hit a few thousand mesh blocks). **Price: 3¢ per call.**
• mbs_profile ~84 tokens
  The mesh block's upward ABS chain — SA1, SA2/3/4, GCCSA, state — plus the parallel admin chain (LGA, suburb (SAL), postcode). **Price: 3¢ per call.**
• me ~50 tokens
  Return the calling account and its current prepaid credit balance. **Free.**
• ping ~84 tokens
  Returns immediately if the API is up. No authentication required and not metered — safe to poll from a load balancer, uptime monitor, or status dashboard without affecting your usage. **Free.**
• properties_amenities_nearby ~129 tokens
  Curated amenities (supermarkets, green spaces, restaurants/cafes) within `radius_m` of the property, bucketed and nearest-first. **Price: 10¢ per call.**
• properties_basics_all ~130 tokens
  The physical facts about a dwelling in one call: bedrooms, bathrooms, parking spaces, dwelling type (house, unit, townhouse and so on) and land area in square metres. Priced as the sum of the five individual calls. Ask for a single field instead if that is all you need. **Price: 23¢ per call.**
• properties_basics_bath_count ~66 tokens
  Bathroom count from the latest matching listing. **Price: 5¢ per call.**
• properties_basics_bed_count ~66 tokens
  Bedroom count from the latest matching listing. **Price: 5¢ per call.**
• properties_basics_dwelling_type ~66 tokens
  Dwelling type (house, unit, townhouse, ...). **Price: 5¢ per call.**
• properties_basics_land_area_sqm ~70 tokens
  Parcel land area in square metres from the cadastral feed. **Price: 3¢ per call.**
• properties_basics_parking_count ~68 tokens
  Parking-space count from the latest matching listing. **Price: 5¢ per call.**
• properties_comparables_all ~137 tokens
  The evidence a valuer or agent would use to price this property: a CMA comparable set, the single closest comparable sale, and recent sales nearby. Use it to answer "what did similar homes around here actually sell for", and to show the working behind a valuation rather than a bare number. **Price
• properties_comparables_cma_comp_set ~75 tokens
  Top comparable sales used to triangulate the subject property's value. **Price: 60¢ per call.**
• properties_comparables_cma_single_comp ~73 tokens
  Single best comparable from the CMA set (highest dist_score). **Price: 10¢ per call.**
• properties_comparables_recent_sales_nearby ~76 tokens
  Recent comparable sales in a small radius around the subject parcel. **Price: 4¢ per call.**
• properties_context_mesh_blocks ~166 tokens
  Mesh-block polygons within 1 km of the property (GeoJSON), each with avg annual growth, investor counts, modal street and median sold price. **Price: 30¢ per call.**
• properties_context_nearby_for_sale ~122 tokens
  Currently for-sale listings within `radius_km` of the property (newest first, max 30) with AVM and rent-yield comparison. **Price: 20¢ per call.**
• properties_context_street_bedroom_series ~88 tokens
  The subject's bedroom band (bed count from listing history) and the suburb's 10-year price series for that band. **Price: 15¢ per call.**
• properties_development_all ~142 tokens
  What the planning scheme allows on this block: its zoning code and every planning overlay that touches it (heritage, flood, bushfire, height and so on, as published by the council). Answers "can I build on it, and what is restricted". Development applications in the surrounding area are a suburb-le
• properties_development_zoning_code ~95 tokens
  Primary zoning at the property's point — code (R2, B4, ...) when the council ships one, plus the human-readable name and the broader category. **Price: 3¢ per call.**
• properties_development_zoning_overlays ~71 tokens
  Development overlays intersecting the property's parcel. **Price: 5¢ per call.**
• properties_history_all ~158 tokens
  Everything we hold on what this property has sold and rented for: the full sale history and the full rent history, each with its most recent record called out separately. This is the expensive one in the Property - History family, because it returns complete series rather than a latest figure. For
• properties_neighbours ~85 tokens
  Adjoining-parcel neighbours, enriched with address + property type + bed/bath/garage from the latest matching listing. **Price: 30¢ per call.**
• properties_profile ~124 tokens
  Resolve a GNAF to its full ABS Statistical Geography Standard hierarchy (Mesh Block → SA1 → SA2 → SA3 → SA4 → GCCSA → State) plus the parallel admin chain (LGA, suburb (SAL), postcode). Call this once to get every ABS key the rest of the data layers index on. **Price: 3¢ per call.**
• properties_rent_history ~63 tokens
  All For Rent records, most-recent first. **Price: 30¢ per call.**
• properties_rent_history_latest ~61 tokens
  Most-recent For Rent record. **Price: 10¢ per call.**
• properties_risks_all ~198 tokens
  Every property-level risk overlay in one call. Flat 30¢ — may grow without increasing the bundle price as new overlays are added. With ``?geojson=true`` each risk that has a polygon overlay also carries its 1km-radius-clipped `geojson` FeatureCollection + `geojson_meta`. **Price: 48¢ per call.**
• properties_risks_bushfire ~162 tokens
  Bushfire-prone-area overlays within 1 km. Read `on_property` — a returned row means "found nearby", not "this property is in a bushfire zone". **Price: 15¢ per call.**
• properties_risks_environment ~69 tokens
  Environmental-risk overlay (contamination, salinity, ...). **Price: 3¢ per call.**
• properties_risks_erosion ~159 tokens
  Coastal / soil erosion overlays within 1 km. Read `on_property` — a returned row means "found nearby", not "this property is eroding". **Price: 3¢ per call.**
• properties_risks_flood ~156 tokens
  Modelled flood overlays within 1 km. Read `on_property` — a returned row means "found nearby", not "this property floods". **Price: 10¢ per call.**
• properties_risks_heritage ~160 tokens
  Heritage-listing overlays within 1 km. Read `on_property` — a returned row means "found nearby", not "this property is heritage-listed". **Price: 3¢ per call.**
• properties_risks_historical_flood ~93 tokens
  Recorded historical flood extents within 1 km. Read `on_property` — a returned row means "found nearby", not "this property flooded". **Price: 3¢ per call.**
• properties_risks_hv_power_line ~67 tokens
  High-voltage power-line easement near the parcel. **Price: 2¢ per call.**
• properties_risks_landslide ~160 tokens
  Landslide-risk overlays within 1 km. Read `on_property` — a returned row means "found nearby", not "this property is at landslide risk". **Price: 3¢ per call.**
• properties_risks_mobile_black_spot ~69 tokens
  Mobile-coverage black-spot overlay near the parcel. **Price: 2¢ per call.**
• properties_risks_public_housing ~68 tokens
  Public-housing concentration overlay near the parcel. **Price: 4¢ per call.**
• properties_sale_history ~65 tokens
  All Sold + For Sale records, most-recent first. **Price: 30¢ per call.**
• properties_sale_history_latest ~64 tokens
  Most-recent Sold or For Sale record. **Price: 10¢ per call.**
• properties_schools_all ~143 tokens
  Schools for this address: the ones nearby with their distances, and how each ranks on NAPLAN alongside sector, level and attendance. The priciest bundle in the Property family, because it joins the school register, the ranking table and distance calculations. Call `/schools/nearby` alone if ranking
• properties_schools_nearby ~70 tokens
  Schools within ~5km of the property, with rank + student counts. **Price: 5¢ per call.**
• properties_schools_ranking ~74 tokens
  Schools within ~5km of the property, ranked by NAPLAN / socioeconomic score. **Price: 100¢ per call.**
• properties_street_profile ~165 tokens
  The property's own street: current median price, 2/4/8-year price forecasts, realised growth, sale/rental turnover, renter share and houses/units on the street — plus the suburb-wide medians to judge it against and the street's rank on 4-year forecast growth. 404s when the street has no forecast ro
• properties_surroundings_listing_history_signals ~79 tokens
  Latest sold/rent dates and likely-owner-status signal per nearby property. **Price: 20¢ per call.**
• properties_surroundings_parcels ~74 tokens
  Cadastral parcel polygons (GeoJSON) for properties around the subject. **Price: 25¢ per call.**
• properties_surroundings_properties ~76 tokens
  Every GNAF property within 200 m of the subject, with lat/lng and mesh block. **Price: 15¢ per call.**
• properties_surroundings_public_housing ~76 tokens
  Properties around the subject flagged by the public-housing classifier. **Price: 15¢ per call.**
• properties_surroundings_tenure ~73 tokens
  ABS Census 2021 tenure shares for each mesh block around the subject. **Price: 15¢ per call.**
• properties_transport_nearby ~131 tokens
  Nearest train / tram / bus / ferry stop to the property, per mode, with distance. Widen `radius_m` for the nearest station beyond 1km. **Price: 5¢ per call.**
• properties_valuation_agent_quoting ~118 tokens
  Quoting accuracy of the property's current listing agency — cohort, median guide-vs-sold deviation, underquote rate and its 5 most-recent tracked sales. Only while the property is listed for sale with a tracked agency. **Price: 50¢ per call.**
• properties_valuation_all ~177 tokens
  Our automated valuation for one property: the point estimate, the 80% range around it, and a 0-100 confidence score derived from how wide that range is. Today this bundles the single `/valuation/avm` call, so it costs the same; it exists so an agent can ask for "everything valuation" without tracki
• properties_valuation_avm ~89 tokens
  Microburbs ML AVM for a single GNAF — point estimate, 80% range, plus a confidence score (0–100) derived from the interval width. **Price: 10¢ per call.**
• properties_valuation_land_values ~92 tokens
  Council land valuations for the parcel — val_2021..val_2025 series with YoY change and $/sqm, plus lot area and zoning description. **Price: 15¢ per call.**
• properties_valuation_negative_gearing ~102 tokens
  Negative-gearing stat at the finest available level: per-property cashflow scenarios (80% + 105% LVR), falling back to mesh-block then suburb investor-exposure. **Price: 20¢ per call.**
• properties_valuation_value_series ~88 tokens
  Modelled property value over time (monthly for the last year, annual before that). Houses only — units have no series. **Price: 30¢ per call.**
• sa4_list ~100 tokens
  All Australian SA4s. Optional `?state=` filter. Returns `{sa4, state}` per row. Flat cost. **Price: 3¢ per call.**
• sa4_profile ~58 tokens
  SA4's parent chain — GCCSA + state. **Price: 3¢ per call.**
• suburbs_crime_breakdown ~77 tokens
  Per-crime-type rates (predicted incidents per 100,000 residents per year) for the suburb vs national medians.
• suburbs_crime_by_mesh_block ~102 tokens
  Total crime rate for every mesh block in the suburb, keyed by ABS mesh-block code. Values are predicted incidents per 100,000 residents per year (see the payload's `unit` / `definition` keys).
• suburbs_crime_summary ~80 tokens
  Suburb and national total-crime medians (predicted incidents per 100,000 residents per year) with a vs-national verdict.
• suburbs_demographics_age ~56 tokens
  Population by age bracket and gender.
• suburbs_demographics_all ~122 tokens
  Who lives in the suburb, from the census: population by age bracket and gender, and households by income bracket. Two calls bundled, priced as the sum. Tenure, employment, family make-up, schooling and the rest of the Suburb - Demographics family are separate.
• suburbs_demographics_ancestry_map ~84 tokens
  The dominant ancestry (and its share) in each of the suburb's microburbs, plus suburb-level ancestry shares. Pilot coverage.
• suburbs_demographics_business ~101 tokens
  ABN industry-mix pattern (Diverse / Professional-services-heavy / Mining-exposed / Low-commercial residential) plus the POI categories unusually common in the suburb, with named examples.
• suburbs_demographics_cohorts ~92 tokens
  How the suburb ranks inside its SA4 region and against its touching neighbours on renter share, welfare dependency, overseas-born parents and public housing.
• suburbs_demographics_cultural_integration ~86 tokens
  Suburb cultural-integration composite (0-100) with national percentile and tier, plus the per-microburb scores behind it.
• suburbs_demographics_income ~58 tokens
  Household income breakdown by bracket.
• suburbs_demographics_income_map ~87 tokens
  Median weekly household income for every microburb in the suburb, plus the suburb median, national percentile and highest/lowest pockets.
• suburbs_demographics_population_history ~130 tokens
  Total suburb population is `current_population` (ABS Census 2021), alongside suburb census points (2011/2016/2021) and the LGA's yearly population series with a 10-year modelled projection tail. Use this route for questions such as "what is the total population of Clarkson?
• suburbs_demographics_private_school ~82 tokens
  Share of residents attending Catholic / Independent schools — suburb rollup plus the per-microburb shares behind it.
• suburbs_demographics_single_parents ~84 tokens
  Single-parent-family share for the suburb against the pinned national median, with the associated capital-growth impact.
• suburbs_demographics_tenure ~146 tokens
  Explicit total owner-occupation rate plus owned-outright / mortgaged / renting / public-housing shares — a suburb rollup and the per-microburb downscaled Census shares. `owner_occupied_pct` is the requested headline; it equals owned outright plus mortgaged and is null if either is absent. `census_ye
• suburbs_demographics_underclass_drag ~121 tokens
  Welfare-dependency and public-housing shares with the suburb's underclass tier (High / Mid / Low) and the capital-growth impact associated with that tier. Regional and neighbour ranks for the same two metrics are served by `/demographics/cohorts`.
• suburbs_demographics_unemployment ~86 tokens
  Labour-force-weighted unemployment rate for the suburb against the pinned national median, plus the per-microburb shares behind it.
• suburbs_demographics_voting ~84 tokens
  Projected first-preference shares per party, aggregated across the suburb's microburbs (booth-derived model — not an actual vote).
• suburbs_development_all ~117 tokens
  What is being built in the suburb: the count of development applications on record, and the list itself with each application's address, type and status. Read it as supply coming down the pipeline, and as a signal of where a suburb is changing.
• suburbs_development_applications ~71 tokens
  Recent development applications (up to 100, newest first) with category counts.
• suburbs_development_building_controls ~72 tokens
  Height-of-building and floor-space-ratio control polygons with band summaries.
• suburbs_development_construction_activity ~72 tokens
  Satellite-detected construction change per mesh block plus a suburb rollup.
• suburbs_development_council_intelligence ~105 tokens
  Council capex / project-pipeline signals extracted from the LGA's meeting minutes — each with stage, dollar value, location and time horizon — plus a rollup of the total pipeline in dollars.
• suburbs_development_council_rates ~66 tokens
  Average annual residential council rate for the suburb's LGA.
• suburbs_development_da_count ~63 tokens
  Count of recent development applications in the suburb.
• suburbs_development_da_list ~62 tokens
  Recent development applications lodged in the suburb.
• suburbs_development_density ~67 tokens
  Dwellings and people per km², plus the land area and counts behind them.
• suburbs_development_profile ~64 tokens
  Dwelling-mix scalars — apartment share and average lot size.
• suburbs_development_supply ~70 tokens
  New dwellings (last 24 months) split units vs houses per mesh block plus a rollup.
• suburbs_development_zoning_map ~71 tokens
  Zoning polygons clipped to the suburb, with per-class and parent-group area shares.
• suburbs_ethnicity_all ~119 tokens
  Cultural make-up of the suburb from the census: ancestry breakdown and country of birth, each as a share of residents. Useful for understanding who a suburb's buyers and renters are. The map version, which splits these by mesh block, is a separate call.
• suburbs_ethnicity_ancestry ~58 tokens
  Ancestry breakdown from the ABS census.
• suburbs_ethnicity_birthplace ~60 tokens
  Country-of-birth breakdown from the ABS census.
• suburbs_finder_count ~664 tokens
  How many suburbs match the filter, without paying per row. 5c. Use this to size a result before searching: /search is capped at 25 rows per call, so `total` here tells you how many `offset` pages you need. **Price: 5¢ per call.**
• suburbs_finder_fields ~183 tokens
  Every field you can filter / sort on, with its label and unit. 0c. Houses and units are separate keys: the `unit`-prefixed ones (`unitMedianPrice`, `unitGrossYield`, `unitMedianRent`, …) are the apartment market, the unprefixed market keys are houses, and both can be filtered in the same query. Uni
• suburbs_finder_regions ~62 tokens
  SA4 regions grouped by state — the values accepted by `states` / `regions` on search. 0c. **Free.**
• suburbs_finder_search ~1678 tokens
  Boolean-AND range filter over suburb metrics, single-column sort, paginated. Every row carries its `rank` — position in the ranked result (1 = top of the sort, out of `meta.total`). When sorting by a median or a growth, the actual value is also shown for the first 5 and last 5 rows of the page; for
• suburbs_forecast_sale ~62 tokens
  1-year sale-price growth forecast (low / most-likely / high).
• suburbs_hero_neighbours ~64 tokens
  Nearest neighbouring suburbs with distance in km, closest first.
• suburbs_hero_price_growth_12mo ~64 tokens
  12-month sale-price growth by property type (house / unit).
• suburbs_hero_summary ~66 tokens
  State, SA3/SA4, postcode, population, dwellings and centroid for the suburb.
• suburbs_lifestyle_cbd ~185 tokens
  Straight-line distance to the nearest city and to the nearest capital, in km. Both, because they answer different questions and are often nowhere near each other: Belmont North is 14 km from Newcastle and 108 km from Sydney. `nearest_city` is usually what a buyer means by "how far is town". The to
• suburbs_lifestyle_landmarks ~71 tokens
  Nearest beach and hospital with straight-line distances (null = none within ~50 km).
• suburbs_lifestyle_livability ~75 tokens
  Livability scores (0–100) per dimension, aggregated from mesh-block scores, with the SA4 benchmark.
• suburbs_lifestyle_livability_map ~150 tokens
  All seven livability dimensions — affluence, lifestyle, hip, convenience, family, safety, tranquility — scored 0-100 for every microburb (mesh block) in the suburb, each with its human band. Averaging a dimension across the microburbs reproduces the suburb figure at ``/lifestyle/livability``. Values
• suburbs_lifestyle_narrative ~66 tokens
  One plain-English sentence per livability dimension for the suburb.
• suburbs_lifestyle_poi_impacts ~74 tokens
  What the absence of petrol stations, fast food and bottle shops near sold properties was worth.
• suburbs_lifestyle_pois ~416 tokens
  Every named venue / amenity in the suburb as GeoJSON Point features, plus a per-category count. Each point carries `name`, a broad `category` (Restaurant / Cafe, Shopping, Health, Entertainment, Sport & Fitness, Education, …) and a finer `subtype` (e.g. `pub`, `bar`, `cafe`, `restaurant`, `gym`, `s
• suburbs_lifestyle_summary ~70 tokens
  POI category breakdown, terrain scalars, nearest train/shop and beach/hospital flags.
• suburbs_list ~620 tokens
  List Australian suburbs (SALs). Always returns `{suburb, state}` per row so SAL-name collisions across states are unambiguous. With no filter you get the first 1000 suburbs alphabetically (browser-renderable preview). Pass any of the filters to narrow: state / lga / sa4 / sa3 / postcode / q (name s
• suburbs_listings_for_rent ~281 tokens
  Rental listings live right now in the suburb, with address, weekly rent, beds/baths, coordinates and an `off_market` flag per listing. Same paging and filters as for-sale.
• suburbs_listings_for_sale ~354 tokens
  Current for-sale listings in the suburb, with address, price, beds/baths, coordinates and an `off_market` flag per listing. `total` is the full number on the market (computed before `limit`), so a count is accurate even when the rows are capped. Raise `limit` for more listings; set `verbose=true` f
• suburbs_market_affordability ~65 tokens
  Microburbs affordability index for the suburb's postcode area.
• suburbs_market_all ~223 tokens
  The eight headline market indicators in one call: median sale price and median rent (each with its full history), 5-year price growth, gross yield, days on market, and transaction volume. **This is a core set, not every market endpoint.** The `Suburb - Market` family has 29 endpoints; this bundles
• suburbs_market_bedroom_stats ~72 tokens
  House/unit price, sales volume, median bath/car/land, and stock mix per bedroom count.
• suburbs_market_built_year ~67 tokens
  Estimated build-year profile — suburb aggregate + per-mesh-block medians.
• suburbs_market_comparable_suburbs ~66 tokens
  Same-SA3 comparable-suburb candidates (canonical SAL names).
• suburbs_market_days_on_market ~60 tokens
  Median days on market for current listings.
• suburbs_market_days_on_market_benchmark ~80 tokens
  Median days on market across the suburb's greater region (GCCSA) — compare with /market/days-on-market.
• suburbs_market_growth_drivers ~136 tokens
  Per-indicator capital-growth attribution for the suburb. Each row pairs the suburb's own value for an indicator with the capital-growth differential our matched studies attribute to it, in percentage points. Covers supply & demand pressure, market signals, environmental risks and demographic compos
• suburbs_market_investor_concentration ~68 tokens
  Rental (investor) share per mesh block, with a suburb-wide rollup.
• suburbs_market_land_values ~72 tokens
  Suburb land-value medians ($/sqm, lot size, land share of price) + per-mesh-block $/sqm.
• suburbs_market_median_price_map ~107 tokens
  Median house price for every microburb (mesh block) in the suburb, with its modal street name, plus the dearest and cheapest pockets. Values only — the matching polygons come from ``/shapes/mesh-blocks``.
• suburbs_market_median_rent ~58 tokens
  Latest median weekly rent for the suburb.
• suburbs_market_median_rent_series ~130 tokens
  Full median weekly-rent observation history for the suburb, including 2024 and 2025 where the suburb has coverage. Older observations are generally month-end and recent observations can be weekly; this is not a uniformly quarterly series. Values are advertised median AUD per week.
• suburbs_market_median_sale_price ~60 tokens
  Latest median sale price for the suburb.
• suburbs_market_median_sale_price_series ~132 tokens
  Full median sale-price observation history for the suburb. Well-covered suburbs extend back 20+ years; coverage and observation frequency vary. This is the raw historical series needed to calculate a stated 20-year return, not a precomputed "consistent growth" score or a forecast.
• suburbs_market_months_of_supply ~87 tokens
  How many months the current for-sale stock would take to clear at the suburb's current sales rate. Under 2 months is a seller's market.
• suburbs_market_mortgage_stress ~69 tokens
  Mortgage-stress score (0-100) for the suburb, with per-mesh-block breakdown.
• suburbs_market_price_growth ~91 tokens
  Past compound sale-price growth over 1 / 3 / 5 / 10 years, by property type (decimal). The 3y / 10y figures are the same ones the Suburb Finder ranks on.
• suburbs_market_price_growth_5y ~60 tokens
  5-year compound sale-price growth (decimal).
• suburbs_market_recent_price_movement ~88 tokens
  The last four weekly smart-median observations — house price, unit price and house rent — each with its week-on-week percentage change.
• suburbs_market_rent_map ~110 tokens
  Median advertised weekly house rent for every microburb (mesh block) in the suburb, with its modal street name, plus the dearest and cheapest pockets. Values only — the matching polygons come from ``/shapes/mesh-blocks``.
• suburbs_market_repeat_sales ~67 tokens
  Realised resale growth — summary + individual buy/sell pairs (max 500).
• suburbs_market_stock_on_market ~79 tokens
  Properties currently listed for sale in the suburb, split house/unit, with the suburb's rank among its peers.
• suburbs_market_strata_levies ~62 tokens
  Median estimated annual strata levy for the suburb.
• suburbs_market_supply_pressure ~70 tokens
  For-sale listings per 100 dwellings (90 days) — per mesh block + suburb rollup.
• suburbs_market_transaction_volume ~61 tokens
  Recent sale-transaction count for the suburb.
• suburbs_market_vacancy_map ~65 tokens
  Calibrated rental-vacancy rate for each mesh block in the suburb.
• suburbs_market_vacancy_rate ~89 tokens
  Suburb-wide rental vacancy rate by property type, as a decimal fraction (0.00842 = 0.84%). Use `/market/vacancy-map` for the per-mesh-block surface.
• suburbs_market_vacancy_rate_series ~67 tokens
  Full rental-vacancy history by property type, decimal fractions.
• suburbs_market_yield_map ~120 tokens
  Gross rental yield for every microburb (mesh block) in the suburb — weekly rent x 52 over median price — with both inputs returned so the number is checkable, plus the best- and worst-yielding pockets. Values only; polygons come from ``/shapes/mesh-blocks``.
• suburbs_market_yield_pct ~64 tokens
  Gross rental yield = median rent annualised / median sale price.
• suburbs_market_yield_pct_series ~67 tokens
  Full gross-rental-yield history by property type, decimal fractions.
• suburbs_profile ~95 tokens
  The suburb's upward ABS admin chain — LGA, SA2/3/4, GCCSA, state, postcode. Use ``/v1/suburbs/list`` to discover canonical SAL names (and filter by state / LGA / SA4 / postcode).
• suburbs_risks_all ~235 tokens
  Natural-hazard exposure for the suburb: how much of its area sits inside a designated bushfire-prone area and inside a modelled flood overlay, each with a plain risk band and the number of distinct hazard polygons. Pass `geojson=true` to get the hazard shapes themselves, clipped to the suburb bound
• suburbs_risks_bushfire ~238 tokens
  Share of the suburb AREA covered by bushfire-prone designation. Area, not properties — and the two differ a lot. Belmont North is 45.72% by area but 23.62% by property count, because designated land is not evenly built on. If you want "what share of homes here are affected", which is the figure our
• suburbs_risks_capital_growth_impact ~100 tokens
  Matched-study capital-growth impact (%/yr) for bushfire, flood, contamination, mine subsidence, aircraft noise and surface acid sulfate, scaled by the share of properties affected.
• suburbs_risks_counts ~71 tokens
  Counts of contamination points, heritage items and erosion-prone polygons inside the suburb.
• suburbs_risks_extras ~79 tokens
  Landslide / storm-tide coverage, EPA notices, landlord-insurance medians and crime benchmark, with per-microburb detail.
• suburbs_risks_flood ~247 tokens
  Share of the suburb AREA covered by modelled flood overlays. Area, not properties. For the share of DWELLINGS affected — the figure our own reports headline — use `GET /v1/suburbs/{suburb_name}/risks/property-hazard-counts`. To answer **where** the flood zone is, pass `geojson=true`. The response
• suburbs_risks_heritage_share ~64 tokens
  Share of the suburb's properties inside a heritage polygon.
• suburbs_risks_hv_power_lines ~81 tokens
  Whether high-voltage transmission lines cross the suburb, how many, and the highest voltage class (overhead lines only).
• suburbs_risks_property_hazard_counts ~88 tokens
  Counts and shares of the suburb's properties flagged bushfire / flood / mine-subsidence / contamination / aircraft-noise / acid-sulfate.
• suburbs_risks_slope ~67 tokens
  Median / 90th-percentile property gradient and share of steep (>10°) properties.
• suburbs_sales_recent ~191 tokens
  Properties sold in the suburb over the last 3 years, with beds/baths/land size, sale price and date, and coordinates. Paginated: `total` reports how many sales are available for the suburb (counted before `limit`/`offset`), so you can page through the full feed. Flat price per call regardless of pa
• suburbs_sales_summary ~66 tokens
  Number of sales and median sold price in the suburb over the last 3 years.
• suburbs_schools_all ~124 tokens
  Schools for a suburb: which public catchments cover it, and which schools sit nearby regardless of zone, with NAPLAN rank, sector, level and attendance for each. Catchments answer "where would my child be guaranteed a place"; nearby answers "what else is within reach".
• suburbs_schools_catchment ~61 tokens
  Public-school catchment polygons covering the suburb.
• suburbs_schools_nearby ~63 tokens
  Schools serving / near the suburb, with rank + attendance data.
• suburbs_shapes_boundary ~142 tokens
  Simplified SAL boundary polygon (GeoJSON Feature) plus centroid.
• suburbs_shapes_busy_roads ~149 tokens
  Busy-road line segments clipped to the suburb — the report's road-noise exposure proxy.
• suburbs_shapes_easements ~148 tokens
  Registered easement geometries intersecting the suburb (GeoJSON FeatureCollection).
• suburbs_shapes_hazards ~202 tokens
  Hazard polygons for one layer, clipped to the suburb boundary (empty collection = hazard not present).
• suburbs_shapes_mesh_blocks ~153 tokens
  ABS 2021 mesh-block polygons covering the suburb (GeoJSON FeatureCollection, MB code per feature).
• suburbs_shapes_neighbours ~154 tokens
  Neighbouring suburbs as a GeoJSON FeatureCollection — each with its boundary polygon and distance in km.
• suburbs_shapes_noise_heatmap ~150 tokens
  Precomputed noise colour regions clipped to the suburb, plus cell-centre heat points.
• suburbs_shapes_public_housing_sa1 ~153 tokens
  SA1 polygons in the suburb with their 2021 census public-housing share (only SA1s above 0%).
• suburbs_similar ~61 tokens
  Top-10 suburbs ranked closest by Microburbs' similarity model.
• suburbs_street_forecasts ~198 tokens
  2/4/8-year price forecasts for every street in the suburb, anchored to the real sold median. Unpaged by default. Large suburbs are large — Point Cook has 943 streets (~1.4 MB) — so pass `limit`/`offset` when you don't need the lot. `total` reports how many exist. Flat price per call regardless of p

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