Urban public housing wait times, wait list and allocations, Northern Territory, 2018 to 2020
liveSummary figures for urban public housing in Darwin and Casuarina, Palmerston, Alice Springs, Katherine, Nhulunbuy and Tennant Creek, released twice a year from 31 December 2018 to 31 December 2020. Each release gives the estimated wait time by bedrooms, the applicants on the wait list and the homes allocated in the preceding twelve months. The publisher's three tables in each file are read as one table, one row per cell.
Also called: Northern Territory public housing waiting list, Darwin public housing wait times, Alice Springs public housing wait list, NT urban public housing allocations.
- What is in each row?
- One cell of the publisher's summary tables. Each release has three tables, the estimated wait time, the applicants on the wait list and the homes allocated, each by urban region and number of bedrooms. The value is a band of years for a wait time and a count for the wait list and allocations.
- Which licence applies?
- The portal states Creative Commons Attribution without a version. The Northern Territory Department of Corporate and Digital Development confirmed in writing on 2 October 2026 that, under the Northern Territory Government's Creative Commons Policy, this series is released under CC BY 4.0.
- What period does an allocations count cover?
- The twelve months to the release date. A December release counts the calendar year and a June release the financial year, so the two overlap by six months.
- What do the asterisks and the Total rows mean?
- An asterisk on a wait time marks one the publisher calculated by hand because a region has few homes and little turnover. The Total region and Total column are the publisher's own totals and are kept as published, so a sum over every row counts each applicant or allocation twice.
- Why does the series stop in 2020?
- The publisher released the summary twice a year from December 2018 to December 2020 and has released none on the portal since.
Made for agents
Connect your AI agent
Any agent that connects to publicdata.au/mcp can find this dataset, read its 5 fields, count and filter across all 370 rows and diff its versions. Every answer names the version and carries Housing, Local Government and Community Development's attribution. No key.
Then ask it: What does Urban public housing wait times, wait list and allocations, Northern Territory, 2018 to 2020 hold, and what changed in the newest version?
claude mcp add --transport http publicdata https://publicdata.au/mcp One 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/mcp Streamable 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=value&metric=count&as_at=eq.2020-12-31&table=eq.Wait%20Times&bedrooms=eq.1%20bedroom
Regions by the estimated wait for a one-bedroom home in the newest release: 4 6 to 8 years, 2 4 to 6 years.
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 keeps its content.
One workbook with a records sheet of 370 typed rows, a fields sheet and a publicdata sheet with the provenance. Dates are real Excel dates. Opens in Excel, Numbers, LibreOffice and Sheets.
UTF-8 without a byte-order mark, RFC 4180 quoting, one header row using the normalised field names. Opens in Excel, Numbers and Sheets.
One object with a publicdata header (publisher, licence, attribution, version, source hash), a fields list and a records array of 370 objects with typed values. Blank cells are null.
Columnar and typed. The smallest download and the fastest to query. Reads directly in DuckDB, pandas, Polars, Arrow and Spark. The provenance header is in the file metadata.
One database with a records table, a fields table and a publicdata metadata table. Open it with any SQLite client.
One DuckDB database with a records table of typed columns, a fields table and a publicdata metadata table. It attaches read-only over HTTPS, so DuckDB, Python and R can query it without downloading it.
The first line is the provenance header, then one JSON object per line. For streaming and for tools that read line by line.
Arrow IPC file (Feather v2), zstd compressed, with the provenance in the schema metadata. Memory-maps in pyarrow, Polars, Arrow JS and R without parsing.
The same CSV, gzipped, at about a tenth of the size. curl, pandas, DuckDB and R read it directly.
One JSON file per value of table, 3 files. The example is by/table/allocations.json. Every file carries the same header and a partition note.
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/nt-urban-public-housing-wait-list/latest/ redirects to the newest version.
https://publicdata.au/d/nt-urban-public-housing-wait-list/latest/data.csv In 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/nt-urban-public-housing-wait-list/latest/data.csv In 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("nt-urban-public-housing-wait-list")
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("nt-urban-public-housing-wait-list")
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/nt-urban-public-housing-wait-list/v/2021-03-31/data.duckdb' AS nt_urban_public_housing_wait_list (READ_ONLY);
SELECT table, count(*) FROM nt_urban_public_housing_wait_list.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/nt-urban-public-housing-wait-list/aggregate?group=value&metric=count&as_at=eq.2020-12-31&table=eq.Wait%20Times&bedrooms=eq.1%20bedroom");
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 |
|---|---|---|---|
| as_at | date | (file) | The date of the release. |
| table | string | Section | Wait Times, Wait List or Allocations. |
| region | string | Region | |
| bedrooms | string | Column | As the publisher heads the column. Wait times give 3 bedroom, and the wait list and allocations 3+ bedroom and a Total. |
| value | string | Value | A band of years for a wait time, such as 4 to 6 years, and a count for the wait list and allocations. |
Download this table as a data dictionary (Excel), or read it as a Frictionless Table Schema at schema.json. The key is as_at, table, region, bedrooms.
A sample of 10 rows
The Territory totals in each release, oldest first, of applicants on the wait list and homes allocated in the twelve months before. A blank cell is shown as null.
| as_at | table | region | bedrooms | value |
|---|---|---|---|---|
| 2018-12-31 | Wait List | Total | Total | 4201 |
| 2018-12-31 | Allocations | Total | Total | 656 |
| 2019-06-30 | Wait List | Total | Total | 4353 |
| 2019-06-30 | Allocations | Total | Total | 783 |
| 2019-12-31 | Wait List | Total | Total | 4589 |
| 2019-12-31 | Allocations | Total | Total | 778 |
| 2020-06-30 | Wait List | Total | Total | 4773 |
| 2020-06-30 | Allocations | Total | Total | 645 |
| 2020-12-31 | Wait List | Total | Total | 5049 |
| 2020-12-31 | Allocations | Total | Total | 597 |
The first row as JSON
As it appears in data.json.
GET https://publicdata.au/d/nt-urban-public-housing-wait-list/latest/data.json
{
"as_at": "2020-12-31",
"table": "Allocations",
"region": "Alice Springs",
"bedrooms": "1 bedroom",
"value": "29"
}
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 that like and ilike ignore case in the ASCII letters A to Z. 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, ignoring case in the ASCII letters A to Z. |
| ilike.*text* | The same as like. Both ignore case in ASCII letters only, so É and é differ. |
| 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. When absent, rows come in the order of the file they are read from. For the newest versions that is the version's own data.parquet, which keeps the publisher's order unless the dataset sorted it. Any other version is read from the Parquet file the answer names in file, and when that file is sorted the answer names its sort in order, which ties follow too. |
| 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 version, and that answer changes when the publisher releases again. Put versions/<date>/ before rows or aggregate for an answer from that version alone. Every version with a table can be queried, and /api/v1/datasets/<slug>/versions lists them. A query on a version the database does not hold is read from its Parquet file, and one that would read too much of it is refused with DuckDB SQL that answers it from the file. 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. Its row tools and the query API answer every version through one query path, so a tool's answer and the query URL it cites give the same rows. A query that would read too much of a file is refused with DuckDB SQL that answers it from the file. It also has search and fetch, the two tools ChatGPT's deep research and company knowledge call by name, which run search_datasets and read a dataset's record in the shapes those features expect. 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 dataset with a table 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.
Smaller files
The same rows split by a field, one JSON file each. Each file carries the same provenance header.
By table (3 files)
by/table/index.json lists every file with its row count.
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.
- 2021-03-31 370 rows5 fieldsutf-8e17d6b00c731
The publisher offers the series as one file per period. These bytes are every row of every file as one table, ordered by every column, with a row that appears in two files kept once; the manifest counts the files and the repeated rows.
versions.json · changes.json · history.tar.zst (6.0 KB, every version's Parquet and manifest)
Questions
How do I download Urban public housing wait times, wait list and allocations, Northern Territory as a CSV file?
Open https://publicdata.au/d/nt-urban-public-housing-wait-list/latest/data.csv. It redirects to the newest dated version, which is https://publicdata.au/d/nt-urban-public-housing-wait-list/v/2021-03-31/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 Urban public housing wait times, wait list and allocations, Northern Territory in Excel?
Yes. https://publicdata.au/d/nt-urban-public-housing-wait-list/v/2021-03-31/data.xlsx is a workbook with the 370 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/nt-urban-public-housing-wait-list/v/2021-03-31/data.csv.gz is the CSV at about a tenth of the size.
What years does Urban public housing wait times, wait list and allocations, Northern Territory cover?
The current version covers the years since 2018. Each release from Housing, Local Government and Community Development becomes a new dated version here, and earlier versions stay online.
How often is Urban public housing wait times, wait list and allocations, Northern Territory updated?
Housing, Local Government and Community Development no longer updates it. This site checks the portal every week and adds a dated version when the file changes.
Can I use Urban public housing wait times, wait list and allocations, Northern Territory commercially?
Yes. CC BY 4.0 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 Urban public housing wait times, wait list and allocations, Northern Territory?
No. The publisher is Department of Housing, Local Government and Community Development, and its page is https://data.nt.gov.au/dataset/urban-public-housing-wait-times-wait-list-and-allocations-december-2020. National Digital converted the publisher's file to other formats. It renamed and typed the columns and kept only those the schema lists. National Digital made these changes, and the publisher is not responsible for them. The original sits beside every version as source.csv with its SHA-256, so the two can be compared.
How do I get only the rows for one table?
Every version has one JSON file per value of table, 3 files in the current version, listed with row counts at https://publicdata.au/d/nt-urban-public-housing-wait-list/v/2021-03-31/by/table/index.json. For example https://publicdata.au/d/nt-urban-public-housing-wait-list/v/2021-03-31/by/table/allocations.json holds the 140 rows where table is Allocations.
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.
The licence requires the publisher's attribution. We ask that you also name publicdata.au and link to the version you used.
Department of Housing, Local Government and Community Development, Northern Territory Government, Urban Public Housing Wait Times, Wait List and Allocations, sourced 5 October 2026, https://data.nt.gov.au/, licensed under CC BY 4.0. Serialised and versioned by National Digital at publicdata.au, https://publicdata.au/d/nt-urban-public-housing-wait-list/v/2021-03-31/
The attribution alone is inside every file. Harvard is the author-date form a report or a brief asks for. Paste the HTML on a web page so the links carry.
Department of Housing, Local Government and Community Development states the licence, CC BY 4.0, on its own page, which was read and recorded here on 5 October 2026.
Every fetch reads the licence again and stops when it has changed. The fetch that made version 2021-03-31 read it from data.nt.gov.au on 5 October 2026.
- Hugging Faceload_dataset("National-Digital/nt-urban-public-housing-wait-list")
- Kagglewith a starter notebook
- ZenodoDOI 10.5281/zenodo.23173837
Each copy carries the same attribution and links back to this page.
- datapackage.jsonFrictionless data package pointing at the latest version
- schema.jsonTable Schema for every field
- schema.xlsxthe same field list as an Excel data dictionary
- versions.jsonevery version with date, rows, fields and hash
- changes.jsondiff for every consecutive pair
- manifest.jsonwhere the bytes came from and when
- Query APIfiltered rows and counts without a download
- openapi.jsonOpenAPI 3.1 for this dataset's query paths
- explore/charts and dashboards in the browser, shareable and embeddable
- index.mdthis page as Markdown
This page carries a schema.org Dataset record and is listed in /.well-known/ard.json and /catalog.json.
Department of Housing, Local Government and Community Development publishes this table on data.nt.gov.au. Questions about the content go to the publisher. Questions about the files here go to corrections.
Fetched 5 October 2026. Source SHA-256 e17d6b00c7311ecb…
This page is built from register/nt-urban-public-housing-wait-list.yaml on GitHub. Anyone can propose a fix to it, such as a clearer description or a field the publisher renamed, as a pull request.
Report a file that differs from the publisher's · How to contribute