Suburb hotness index
/v1/suburbs/{suburb_name}/market/suburb-hotness5¢ per callModelled 0-100 score for how hot each suburb's house and unit market is against the rest of its state, monthly for the last two years, with the four parts behind the latest score. Modelled estimates.
Coverage: About 4,300 suburbs have a house score and 1,700 a unit score in at least one of the last 24 months (about 2,300 and 800 in the latest month); suburbs with fewer than 10 sales of that type in the latest 3 months, and states with fewer than 30 such suburbs, are not scored. Monthly, November 2024 to October 2026. Current to 2026-10-01. Full description, field list and method: /v1/datasets/suburb-hotness.
Exact suburb identifier required. Exact official suburb name (SAL), e.g. Burwood (NSW) — many suburbs carry a state suffix. If starting from free text or an unverified bare name, first call GET /v1/geocode/suburb?q=<text>, then use the exact data[].area_name it returns. Suburb data endpoints do not guess a state or typo-correct names.
Authorization
Request
The only values you send. Fields under Responses below are what comes back — you never pass those.
Path parameters
suburb_namestringrequiredExact official suburb name (SAL), e.g. `Burwood (NSW)` — many suburbs carry a state suffix. If starting from free text or an unverified bare name, first call `GET /v1/geocode/suburb?q=<text>`, then use the exact `data[].area_name` it returns. Suburb data endpoints do not guess a state or typo-correct names.
Query parameters
fieldsstringoptionalComma-separated subset of: hotness_house, hotness_unit, price_momentum_house, sales_volume_change_house, rent_growth_house, rent_vs_price_house, price_momentum_unit, sales_volume_change_unit, rent_growth_unit, rent_vs_price_unit. Omit for all.
fromstringoptionalFirst period to include: 2020, 2020-Q3, 2020-07 or 2020-07-01.
tostringoptionalLast period to include, same formats as `from`.
Responses
Successful Response
datasetstringalways returnedDataset slug; describe it with /v1/datasets/{slug}.
levelstringalways returnedsuburb, lga or block.
areastringalways returnedCanonical name (or mesh-block code) of the area answered.
period_typestringalways returnedsnapshot, year, financial_year, quarter or month.
as_ofstring · datealways returnedDate the dataset is current to. Values are never withheld for age; check this instead.
modelledbooleanalways returnedTrue when the figures are estimates from a model rather than counts or measurements.
fieldsarray of DatasetFieldSeriesalways returned/v1/suburbs/{suburb_name}/market/suburb-hotnesscurl --request GET \
--url 'https://api.microburbs.com.au/v1/suburbs/Belmont%20North/market/suburb-hotness' \
--header 'Authorization: Bearer test'{
"data": {
"dataset": "suburb-hotness",
"level": "suburb",
"area": "St Kilda (Vic.)",
"period_type": "month",
"as_of": "2026-10-01",
"modelled": true,
"fields": [
{
"field": "hotness_house",
"name": "Hotness score, houses",
"unit": "score (0-100)",
"values": [
{
"period": "2026-08",
"period_start": "2026-08-01",
"value": 85.8
},
{
"period": "2026-09",
"period_start": "2026-09-01",
"value": 82.3
},
{
"period": "2026-10",
"period_start": "2026-10-01",
"value": 74
}
]
},
{
"field": "hotness_unit",
"name": "Hotness score, units",
"unit": "score (0-100)",
"values": [
{
"period": "2026-08",
"period_start": "2026-08-01",
"value": 90.2
},
{
"period": "2026-09",
"period_start": "2026-09-01",
"value": 80
},
{
"period": "2026-10",
"period_start": "2026-10-01",
"value": 69.7
}
]
},
{
"field": "price_momentum_house",
"name": "Price momentum, houses",
"unit": "percentile (0-100)",
"values": [
{
"period": "2026-10",
"period_start": "2026-10-01",
"value": 18.7
}
]
}
]
}
}About this dataset
About 4,300 suburbs have a house score and 1,700 a unit score in at least one of the last 24 months (about 2,300 and 800 in the latest month); suburbs with fewer than 10 sales of that type in the latest 3 months, and states with fewer than 30 such suburbs, are not scored. Monthly, November 2024 to October 2026. Current to 2026-10-01; refreshed monthly. These figures are modelled estimates.
What it is
A modelled score from 0 to 100 for each suburb and dwelling type (houses and units), saying how hot the local market is compared with other suburbs in the same state. 100 means the hottest suburb in the state that month, 50 the middle, 0 the coolest. The score is given for each of the last 24 months, and the four parts behind the latest score are given as percentiles.
What it shows
Per suburb: a house score and a unit score for each month, and for the latest month a percentile (0 to 100, within the state) for each part: price momentum (12-month change in median asking price), sales volume change (sales in the latest 3 months against a year earlier), rent growth (12-month change in median asking rent) and rent against price (rent growth minus price growth). Only suburbs with at least 10 sales of that dwelling type in the latest 3 months are scored, and only where the state has at least 30 such suburbs, so a few thousand suburbs a month are covered rather than all of them.
Why use it
Buyers, investors and agents want a quick read on where demand is building, in one number that can be sorted and tracked. Because it is a percentile within the state it answers a simple question, 'is this suburb hotter or cooler than its peers', and the monthly history shows whether it is warming or cooling. It does not tell you the price level or give a forecast for one home.
How it is built
This is a model, built and tested before release. Each month, using only information available at that month, we rank every suburb in its state on the four parts above, average the ranks with fixed weights, and re-rank the result from 0 to 100. The weights were fitted on 2014 to 2020 and then tested on 2021 to 2025 without being changed. For houses the weights are rent growth 23% and rent against price 77%; for units, sales volume change 12%, rent growth 16% and rent against price 72%. Price momentum gets weight zero, because past price growth did not predict future growth: suburbs whose prices had run hardest tended to lag afterwards. So this is a measure of tenant and buyer demand outrunning prices, not a chaser of recent price rises. In the test on 2021 to 2025, suburbs scoring in the top tenth went on to grow about 6.3 percentage points (houses) and 7.8 points (units) more over the next 12 months than the bottom tenth; the top fifth beat their state's middle suburb 61% (houses) and 63% (units) of the time, against 50% by chance, and the rank correlation with next-year growth was 0.20 and 0.21, positive at every one of 18 test dates, where shuffled scores never passed 0.02. Growth here means the change in median asking price of listings, not repeat-sale growth. A correlation of 0.2 is a useful tilt across many suburbs, not a forecast for one. Days on market, stock on market and vacancy are not inputs because we do not hold their history.
Fields
| Field | Unit | Description |
|---|---|---|
hotness_houseHotness score, houses | score (0-100) | How hot the house market is in the suburb compared with other suburbs in its state, 0 (coolest) to 100 (hottest). Blends rent growth, rent against price growth and sales activity, with weights fitted on past data. Suburbs with fewer than 10 house sales in the latest 3 months are not scored. |
hotness_unitHotness score, units | score (0-100) | The same score for units and apartments, ranked against other suburbs in the state. Suburbs with fewer than 10 unit sales in the latest 3 months are not scored. |
price_momentum_housePrice momentum, houses | percentile (0-100) | Where the suburb's 12-month change in median asking price ranks among suburbs in its state, 0 (weakest) to 100 (strongest). Shown for context only: in the back-test, strong past price growth did not predict strong next-year growth, so it carries no weight in the score. Latest month only. Dwelling type: houses. |
sales_volume_change_houseSales volume change, houses | percentile (0-100) | Where the change in sales over the last 12 months (sales in the latest 3 months against the same 3 months a year earlier) ranks among suburbs in its state, 0 (biggest fall) to 100 (biggest rise). Latest month only. Dwelling type: houses. |
rent_growth_houseRent growth, houses | percentile (0-100) | Where the 12-month change in median asking rent ranks among suburbs in its state, 0 (weakest) to 100 (strongest). Latest month only. Dwelling type: houses. |
rent_vs_price_houseRent against price, houses | percentile (0-100) | Where rent growth minus price growth over 12 months ranks among suburbs in its state. High means rents are rising faster than prices, so tenant demand is running ahead of what buyers are paying. This is the largest part of the score. Latest month only. Dwelling type: houses. |
price_momentum_unitPrice momentum, units | percentile (0-100) | Where the suburb's 12-month change in median asking price ranks among suburbs in its state, 0 (weakest) to 100 (strongest). Shown for context only: in the back-test, strong past price growth did not predict strong next-year growth, so it carries no weight in the score. Latest month only. Dwelling type: units. |
sales_volume_change_unitSales volume change, units | percentile (0-100) | Where the change in sales over the last 12 months (sales in the latest 3 months against the same 3 months a year earlier) ranks among suburbs in its state, 0 (biggest fall) to 100 (biggest rise). Latest month only. Dwelling type: units. |
rent_growth_unitRent growth, units | percentile (0-100) | Where the 12-month change in median asking rent ranks among suburbs in its state, 0 (weakest) to 100 (strongest). Latest month only. Dwelling type: units. |
rent_vs_price_unitRent against price, units | percentile (0-100) | Where rent growth minus price growth over 12 months ranks among suburbs in its state. High means rents are rising faster than prices, so tenant demand is running ahead of what buyers are paying. This is the largest part of the score. Latest month only. Dwelling type: units. |