Average age of public housing dwellings by suburb and bedrooms, Western Australia
liveOne row per Western Australian suburb or town with public housing, from the Department of Housing's 2015 file. Each row gives the average age in years of the department's dwellings there with 0, 1, 2, 3, 4, 5, 6, 7, 8 and 10 bedrooms, and the average age of all its dwellings in the suburb, both to two decimal places and rounded to the year.
Also called: WA Public Housing Dwelling Age by Suburb by Bedroom, Dwelling Suburb Age By Bedroom, Average age of public housing in WA suburbs.
- What is in each row?
- One Western Australian suburb or town, with the average age in years of the public housing dwellings there that have no separate bedroom, one bedroom, and so on up to ten bedrooms, and the average age across all of them, also given rounded to a whole year.
- Why are many cells blank?
- A blank cell means the suburb had no public housing dwelling with that number of bedrooms. There is no column for nine bedrooms in the publisher's file.
- When is it from?
- The Department of Housing published the file in June 2015 and has not updated it. The ages are as the department calculated them then.
Made for agents
Connect your AI agent
Any agent that connects to publicdata.au/mcp can find this dataset, read its 13 fields, count and filter across all 430 rows and diff its versions. Every answer names the version and carries Department of Housing WA's attribution. No key.
Then ask it: What does Average age of public housing dwellings by suburb and bedrooms, Western Australia hold, and what changed in the newest version?
claude mcp add --transport http publicdata https://publicdata.au/mcpOne command. No key, no account, no sign-up.
Settings, then Connectors, then Add custom connector, and paste https://publicdata.au/mcp.
On the web and in the desktop app.
https://publicdata.au/mcpStreamable HTTP. Cursor, VS Code or any client that connects to a remote MCP server.
Using https://publicdata.au/llms.txt, how many people died on Queensland roads in 2025, by month?For an assistant that reads the web and has no connector.
A dashboard over every row. Click to filter, add panels, share a link or embed a frame.
Query APIFilter and count from a URLaggregate?group=suburb&metric=avg.total_average_age
Average dwelling age (years) by suburb: 106 NORTHBRIDGE, 66 BYFORD, 65.2 KENSINGTON, 62 SWANBOURNE.
Download it as Excel, CSV, JSON and 6 more formats
Pick a format and a version. Excel is picked first because it opens in the tools most offices have. The URL is yours to keep. A dated version never changes.
In your own tools
Use it in Excel, R, Python and more
Excel and Power BI read the CSV from its address and refresh from it. The R and Python packages take any dataset on this site by its slug, so a new dataset needs no new release. The DuckDB file attaches read-only over HTTPS, and a query reads only the blocks it touches.
The dated URL in the code never changes. https://publicdata.au/d/wa-public-housing-dwelling-age-by-suburb/latest/ redirects to the newest version.
https://publicdata.au/d/wa-public-housing-dwelling-age-by-suburb/latest/data.csvIn Excel choose Data, then From Web, and paste this address. Excel keeps it, so Refresh All reads the newest version. A sheet holds about a million rows; past that, load the query to the Data Model.
https://publicdata.au/d/wa-public-housing-dwelling-age-by-suburb/latest/data.csvIn Power BI Desktop choose Get data, then Web, paste this address and choose Anonymous when asked how to sign in. A scheduled refresh reads the same address, so the report follows each new version.
library(publicdataau)
df <- pd_read("wa-public-housing-dwelling-age-by-suburb")
pd_attribution(df)install.packages("publicdataau"). pd_read() fetches the version's Parquet file; pd_rows() and pd_aggregate() ask the query API instead, and pd_connect() attaches the DuckDB file.
import publicdata_au as pd_au
df = pd_au.read("wa-public-housing-dwelling-age-by-suburb")
df.attrs["publicdata"]["attribution"]pip install "publicdata-au[pandas]". read() fetches the version's Parquet file; rows() and aggregate() ask the query API, and connect() attaches the DuckDB file.
INSTALL httpfs; LOAD httpfs;
ATTACH 'https://publicdata.au/d/wa-public-housing-dwelling-age-by-suburb/v/2015-06-29/data.duckdb' AS wa_public_housing_dwelling_age_by_suburb (READ_ONLY);
SELECT suburb, count(*) FROM wa_public_housing_dwelling_age_by_suburb.records GROUP BY 1 ORDER BY 2 DESC;The DuckDB file attaches read-only over HTTPS and only the blocks a query touches are read. Parquet works the same way: FROM read_parquet(url).
const res = await fetch("https://publicdata.au/api/v1/datasets/wa-public-housing-dwelling-age-by-suburb/aggregate?group=suburb&metric=avg.total_average_age");
const { rows, publicdata } = await res.json();
console.log(rows, publicdata.attribution);The query API answers a page on any site as well as Node, with no key. It returns the rows and the attribution the licence asks for.
What is in it
Column names are made snake_case and the publisher's original header is kept beside each one. Blank cells are null. Nothing is added, removed, ranked or summarised.
| Field | Type | Publisher's header | Note |
|---|---|---|---|
| suburb | string | Suburb | |
| bed_0 | number | 0 bed | |
| bed_1 | number | 1 bed | |
| bed_2 | number | 2 bed | |
| bed_3 | number | 3 bed | |
| bed_4 | number | 4 bed | |
| bed_5 | number | 5 bed | |
| bed_6 | number | 6 bed | |
| bed_7 | number | 7 bed | |
| bed_8 | number | 8 bed | |
| bed_10 | number | 10 bed | |
| total_average_age | number | Total Average Age | |
| total_average_age_rounded_to_year | integer | Total Average Age rounded to year |
Download this table as a data dictionary (Excel), or read it as a Frictionless Table Schema at schema.json. The key is suburb.
A sample of 10 rows
The ten suburbs and towns whose public housing was oldest on average, oldest first. A blank cell is shown as null.
| suburb | bed_0 | bed_1 | bed_2 | bed_3 | bed_4 | bed_5 | bed_6 | bed_7 | bed_8 | bed_10 | total_average_age | total_average_age_rounded_to_year |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| NORTHBRIDGE | null | 110 | null | 94 | null | null | null | null | null | null | 106 | 106 |
| BYFORD | null | null | null | 66 | null | null | null | null | null | null | 66 | 66 |
| KENSINGTON | null | 64.47 | 61.16 | 66.98 | 67.06 | 68 | null | null | null | null | 65.18 | 65 |
| SWANBOURNE | null | null | 60 | 66 | null | null | null | null | null | null | 62 | 62 |
| SOUTH PERTH | 54 | 53.42 | 47.3 | 69 | 69 | null | null | null | null | null | 53.29 | 53 |
| SERPENTINE | null | null | 53 | null | null | null | null | null | null | null | 53 | 53 |
| JARRAHDALE | null | null | 51 | 51 | null | null | null | null | null | null | 51 | 51 |
| SUBIACO | 43.25 | 56.51 | 44.13 | 36 | null | null | null | null | null | null | 50.64 | 51 |
| CUBALLING | null | null | 50 | null | null | null | null | null | null | null | 50 | 50 |
| MARTIN | null | null | null | 50 | null | null | null | null | null | null | 50 | 50 |
The first row as JSON
As it appears in data.json.
GET https://publicdata.au/d/wa-public-housing-dwelling-age-by-suburb/latest/data.json
{
"suburb": "ALBANY",
"bed_0": 37.0,
"bed_1": 21.67,
"bed_2": 19.33,
"bed_3": 24.51,
"bed_4": 23.51,
"bed_5": 11.0,
"bed_6": 11.5,
"bed_7": null,
"bed_8": null,
"bed_10": null,
"total_average_age": 21.99,
"total_average_age_rounded_to_year": 22
}
Query it without downloading
The query API returns only the rows you ask for, as JSON, NDJSON or CSV, from the same typed rows as the files. It needs no key. Build a query here and run it, then copy the URL or the code into your own work.
How filters work
Each filter is field=operator.value in the query string, and every filter must match. A number or date field compares as a number or date, and a text field compares as text. Values are case-sensitive except with ilike. Prefix not. to negate a filter, as not.eq.value.
| Operator | What it matches |
|---|---|
| eq.value | Equal to the value. |
| neq.value | Not equal to the value. A blank cell does not match. |
| gt.value | Greater than the value. |
| gte.value | Greater than or equal to the value. |
| lt.value | Less than the value. |
| lte.value | Less than or equal to the value. |
| like.*text* | Matches a pattern where * stands for any run of characters. Case-sensitive. |
| ilike.*text* | The same as like, ignoring case. |
| in.(a,b,c) | Equal to any value in the list. A value cannot contain a comma. |
| is.null | Blank in the source, or suppressed by the publisher. |
| Parameter | What it does |
|---|---|
| select | Fields to return, comma-separated. Every field when absent. |
| order | field.asc or field.desc, comma-separated. The publisher's row order when absent. |
| limit | Rows per page, 1 to 10,000. 100 when absent. |
| offset | Rows to skip. The next URL in each answer sets it for you. |
| group | On aggregate, fields to group by, comma-separated. |
| metric | On aggregate, count, sum.field, avg.field, min.field or max.field, comma-separated. count when absent. |
| format | json, ndjson or csv. JSON carries the provenance header, and the others carry it in response headers. |
Without a version the API answers from the newest loaded version, and that answer changes when the publisher releases again. Put versions/<date>/ before rows or aggregate for an answer that never changes. /api/v1/datasets/<slug>/versions lists the loaded versions. The 1 newest version is loaded; versions lists them.
The API allows 60 requests in 10 seconds from one address. Above that it answers 429 for 10 seconds with a Retry-After header, a RateLimit-Policy header and a JSON body that gives the limit. Every API answer carries the same RateLimit-Policy. A client should wait the Retry-After seconds, or read the files, which have no limit. openapi.json describes this dataset's query paths for client generators and agents.
https://publicdata.au/mcp is a remote MCP server over Streamable HTTP with the same tools, defined in the same place, so a tool added to the pages is added here too. It needs no key and no account. The row tools are held to the query API's limit of 60 queries in 10 seconds from one address, and each call is two queries because it also counts the matching rows. Add it to Claude Code with claude mcp add --transport http publicdata https://publicdata.au/mcp, add the URL in Claude as a custom connector, or give it to any client that connects to remote MCP servers. Each queryable dataset is also a resource at https://publicdata.au/d/<slug>/fields.json, which lists its fields with their types, the publisher's descriptions, their ranges and the values they hold, so an agent can read a dataset's shape before it writes a query.
What changed
One version for every release the publisher has made since this site started following the dataset. The date is the day the file changed on the portal.
- 2015-06-29 430 rows13 fieldsutf-8-sig0e656ee541ee
versions.json · changes.json · history.tar.zst (16 KB, every version's Parquet and manifest)
Questions
How do I download Average age of public housing dwellings by suburb and bedrooms as a CSV file?
Open https://publicdata.au/d/wa-public-housing-dwelling-age-by-suburb/latest/data.csv. It redirects to the newest dated version, which is https://publicdata.au/d/wa-public-housing-dwelling-age-by-suburb/v/2015-06-29/data.csv today. The same path serves Excel, JSON, Parquet, SQLite, DuckDB, NDJSON, Arrow. A dated URL never changes, so use it when the file must stay the same.
Can I open Average age of public housing dwellings by suburb and bedrooms in Excel?
Yes. https://publicdata.au/d/wa-public-housing-dwelling-age-by-suburb/v/2015-06-29/data.xlsx is a workbook with the 430 rows on a records sheet, the field list on a second sheet and the provenance on a third. The CSV also opens in Excel. https://publicdata.au/d/wa-public-housing-dwelling-age-by-suburb/v/2015-06-29/data.csv.gz is the CSV at about a tenth of the size.
How often is Average age of public housing dwellings by suburb and bedrooms updated?
Department of Housing WA no longer updates it. This site checks the portal every week and adds a dated version when the file changes.
Can I use Average age of public housing dwellings by suburb and bedrooms commercially?
Yes. CC BY 3.0 AU allows commercial use, redistribution and derived works as long as the attribution is kept. The attribution string is in this page's side column and inside every file.
Is this the official source for Average age of public housing dwellings by suburb and bedrooms?
No. The publisher is Department of Housing, Western Australia, and its page is https://data.gov.au/data/dataset/dwelling-suburb-age-by-bedroom. This site republishes the publisher's file without changing its content. The original sits beside every version as source.csv with its SHA-256, so the two can be compared.
What this site did to the data. Cells were typed, headers were renamed and the encoding was made UTF-8. Rows were left alone. The publisher's file sits beside every version as source.csv so the change can be checked.