Rental is Australia’s emerging tenure. Each year the proportion of Australians who rent increases, many of us will rent for life, and for the first time in generations there are now more renters than home owners. Though the rental sector is home to almost one-third of all Australians, researchers and policy-makers know little about conditions in this growing market because there is currently no systematic or reliable data. This project provides researchers and policy stakeholders with an essential database on Australia’s rental housing conditions. This data infrastructure will provide the knowledge base for national and international research and allow better urban, economic and social policy development. Building on The 2016 Australian Housing Conditions Dataset, in 2020 we collected data on the housing conditions of 15,000 rental households, covering all Australian states and territories. The project is funded by the Australian Research Council and The University of Adelaide, in partnership with the University of South Australia, the University of Melbourne, Swinburne University of Technology, Curtin University and Western Sydney University and is led by Professor Emma Baker at the University of Adelaide. The Australian Housing and Urban Research Institute provided funding for the focussed COVID-19 Module.

Datasets present number of dwellings available for rental purposes for Public Housing (PH) and State Owned and Managed Indigenous Housing (SOMIH) across Local Government Areas (LGAs) in South Australia as at 30 June. PH and SOMIH refers to dwellings owned and managed by the SA Housing Authority. These rentals are accessed by those on low income and/or with special needs. Strategies have been employed to mitigate the risk of releasing any identifying data, which may occur in smaller areas. Data specifications of measures and data quality statements for these files are maintained by the Australian Institute of Health and Welfare (AIHW) and available in their metadata online registry (METEOR), see https://meteor.aihw.gov.au/content/711016 and https://meteor.aihw.gov.au/content/749351 . Properties set aside for administrative purposes and those which will not be available for rental purposes are excluded (e.g. recently purchased or in the process of being sold).

Context

There's a story behind every dataset and here's your opportunity to share yours.

ACTIVITIES

Follow the process below to develop a model that can be used by real estate companies and real estate agents to predict the price of a house.

1. Business Understanding -Conduct a literature review to understand the factors that determine the price of houses globally and locally. -Based on the dataset provided, formulate a business question to be answered through the analysis.

2. Data Understanding -The data in the dataset provided was collected through webs scrapping. Conduct further reading to understand the process of web scrapping, how it is conducted (methods and tools) and any ethical challenges related to it.

3. Data Preparation -Conduct a detailed exploratory analysis on the dataset. -Prepare the dataset for modeling -Identify the technique relevant for answering the business question stated above. -Ensure that the dataset meets all the assumptions of the technique identified. -Conduct preliminary feature selection by identifying the set of features that are likely to provide a model with good performance.

4. Modeling -Split the dataset into two; training set and validation set. With justifications, decide on the ratio of the training set to the validation set. -Generate the required model

5. Evaluation -Interpret the model in terms of its goodness of fit in predicting the price of houses. -Assume that the model is not good enough and then conduct further feature engineering or use any other model tuning strategies at your disposal to generate additional two instances of the model. -Settle on the best model instance and then re-interpret.

6. Implementation -Think of how the model can be implemented and used by real estate firms and agents. -Identify possible challenges of applying the model. -Recommendations on how the model can be improved in future

Percentage of overcrowded houses at the Statistical Area level 2 (SA2) level representing the measure of housing utilization based on a comparison of the number of bedrooms in a dwelling with a series of household demographics, such as the number of usual residents, their relationship to each other, age and sex; calculated for the total number of occupied private dwellings for which the housing suitability could be determined. The criteria are based on the Canadian National Occupancy Standard.

Data collected as part of the City of Melbourne's Census of Land Use and Employment (CLUE). The data covers the period 2002-2023. The dwelling data is based on the Council's property rates database, using a simplified classification schema of Residential Apartment, House/Townhouse and Student Apartment. The count of dwellings per residential building is shown.

For more information about CLUE see http://www.melbourne.vic.gov.au/clue

Houses Permits in Australia decreased to 9027 in August from 9264 in July of 2025. This dataset includes a chart with historical data for Australia Houses Permits.

Graph and download economic data for Real Residential Property Prices for Australia (QAUR628BIS) from Q1 1970 to Q2 2025 about Australia, residential, HPI, housing, real, price index, indexes, and price.

Home inspection dataset for Gemini Long Context kaggle comp

There are three sections to this dataset:

1) Building standards This is a copy of construction codes from: https://ncc.abcb.gov.au/ It describes the standards to which Australian residential homes should be constructed and is a valuable resource for anyone looking to assess a home. In Australia this is the minimum standard for new homes.

2) Examples This is a set of "task examples" designed for in-context learning. It is a set of images of houses and corresponding professional assessment (that I have paid experts for)

3) User data Here is a set of images / videos from the house I am looking to evaluate

In general, the idea is that we use Gemini's long context window to effectively evaluate the User data against the building standards, using the examples to demonstrate to the LLM how we want the assessment to work

Summary data showing a count of properties for each street and suburb in the City of Onkaparinga

Housing Affordability Reports describes the extent and general nature of local housing needs by: South Australia, Metropolitan Adelaide, Greater Adelaide and Local Government Areas. Reports from 2018 and 2013 are available.

Summary details for each election year for the House of Representatives elections since 1901. This data includes electoral system characteristics, seats in chamber, number of enrolled voters, ballots cast, rate of voter turnout and rate of informal voting for Western Australia.

Quarterly median house prices for metropolitan Adelaide by suburb

Comprehensive dataset containing 62 verified Group home businesses in Australia with complete contact information, ratings, reviews, and location data.

This layer shows the third component of housing intensification for Melbourne 2011. SPSS was used to run the factor analysis. Raw data was downloaded from ABS, SA1, 2011 Factor 3 is dominated by multi-family households with children -this group is dominated by young children and family households with children living and multi-family households in separate houses. Migrants in this group has arrived in Australia from more than 30 years ago to nearly 2 decades ago (1961-2000) mostly from Southern Eastern Europe, and a fewer proportion from North Africa Middle East and Sub Saharan Africa. This group is engaged in lower occupational employment (machine operators and labourer occupations) with low income rate (A$400-799 weekly). Majority of this group is not in labour force. There are also aged group over 55 in this group who seems to be migrated more than 60 years ago (1941-1960) and owns their houses. Component 3 explains 6.0 eigenvalues of variance (%12 of total variation).

CPI Housing Utilities in Australia increased to 153.30 points in the second quarter of 2025 from 151.50 points in the first quarter of 2025. This dataset provides - Australia Cpi Housing Utilities- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Australia Standardised Price-Income Ratio: sa data was reported at 149.268 Ratio in Dec 2024. This records a decrease from the previous number of 152.371 Ratio for Sep 2024. Australia Standardised Price-Income Ratio: sa data is updated quarterly, averaging 82.643 Ratio from Mar 1970 (Median) to Dec 2024, with 220 observations. The data reached an all-time high of 153.422 Ratio in Jun 2024 and a record low of 62.554 Ratio in Sep 1983. Australia Standardised Price-Income Ratio: sa data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Australia – Table AU.OECD.AHPI: House Price Index: Seasonally Adjusted: OECD Member: Quarterly. Nominal house prices divided by nominal disposable income per head. Net household disposable income is used. The population data come from the OECD national accounts database. The long-term average is calculated over the whole period available when the indicator begins after 1980 or after 1980 if the indicator is longer. This value is used as a reference value. The ratio is calculated by dividing the indicator source on this long-term average, and indexed to a reference value equal to 100.

Provides the number and value of dwelling units approved by sector (public/private) and by state, number and value of new houses, new other residential dwelling units approved by type of building, and the number and value of non-residential building jobs approved by type of building (i.e. by function such as 'retail and wholesale trade', 'offices') and value ranges. State data includes the number of private sector houses approved; number and value of new other residential dwellings by type of building such as flats, units or apartments in a building of one or two storeys; number and value of non-residential building jobs by type of building and sector; and for Greater Capital City Statistical Areas, the total number of dwelling units approved broken down by Houses, Dwellings Excluding Houses and Total Dwelling Units. Seasonally adjusted and trend estimates by state are included for the number of dwelling units and value of building approved. The quarterly value of building approved is shown in chain volume measure terms. Small geographic area data cubes are presented for Statistical Areas Level 2 and Local Government Areas. Small area data cubes will be released in an "Additional information" release five business days after the main publication.

Key information about House Prices Growth

• Australia house prices grew 2.9% YoY in Dec 2024, following an increase of 5.8% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Sep 2004 to Dec 2024, with an average growth rate of 5.8%.
• House price data reached an all-time high of 24.1% in Dec 2021 and a record low of -6.1% in Mar 2019.

Point-of-interest (POI) is defined as a physical entity (such as a business) in a geo location (point) which may be (of interest).

We strive to provide the most accurate, complete and up to date point of interest datasets for all countries of the world. The Australian POI Dataset is one of our worldwide POI datasets with over 98% coverage.

This is our process flow:

Our machine learning systems continuously crawl for new POI data
Our geoparsing and geocoding calculates their geo locations
Our categorization systems cleanup and standardize the datasets
Our data pipeline API publishes the datasets on our data store

POI Data is in a constant flux - especially so during times of drastic change such as the Covid-19 pandemic.

Every minute worldwide on an average day over 200 businesses will move, over 600 new businesses will open their doors and over 400 businesses will cease to exist.

In today's interconnected world, of the approximately 200 million POIs worldwide, over 94% have a public online presence. As a new POI comes into existence its information will appear very quickly in location based social networks (LBSNs), other social media, pictures, websites, blogs, press releases. Soon after that, our state-of-the-art POI Information retrieval system will pick it up.

We offer our customers perpetual data licenses for any dataset representing this ever changing information, downloaded at any given point in time. This makes our company's licensing model unique in the current Data as a Service - DaaS Industry. Our customers don't have to delete our data after the expiration of a certain "Term", regardless of whether the data was purchased as a one time snapshot, or via a recurring payment plan on our data update pipeline.

The main differentiators between us vs the competition are our flexible licensing terms and our data freshness.

The core attribute coverage for Australia is as follows:

Poi Field Data Coverage (%) poi_name 100 brand 13 poi_tel 49 formatted_address 100 main_category 94 latitude 100 longitude 100 neighborhood 3 source_url 55 email 10 opening_hours 41 building_footprint 60

The dataset may be viewed online at https://store.poidata.xyz/au and a data sample may be downloaded at https://store.poidata.xyz/datafiles/au_sample.csv

This dataset contains Housing Affordability Supply and Demand Data broken down by very low, low and moderate income brackets. This dataset relates to section 4, Housing Stress, of the Affordability master reports produced by the SA Housing Authority. Each master report covers one Local Government Area and is entitled Housing Affordability Demand and Supply by Local Government Area. Explanatory Notes: Data sourced from the Australian Bureau of Statistics (ABS), Census for Population and Housing …Show full descriptionThis dataset contains Housing Affordability Supply and Demand Data broken down by very low, low and moderate income brackets. This dataset relates to section 4, Housing Stress, of the Affordability master reports produced by the SA Housing Authority. Each master report covers one Local Government Area and is entitled Housing Affordability Demand and Supply by Local Government Area. Explanatory Notes: Data sourced from the Australian Bureau of Statistics (ABS), Census for Population and Housing and it is updated every 5 years in line with the ABS Census. The nature of the income imputation means that the reported proportion may significantly overstate the true proportion. Census housing stress data is best used in comparing results over Censuses (ie did it increase or decrease in an area) rather than using it to ascertain what proportion of households were in rental stress. Income bands are based on household income. High income households can also experience rental stress. These households are included in the total but not identified separately. Data is representative of households in very low, low and moderate income brackets. Please note that there are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. Field Definitions: LGA Name: 2011 Local Government Areas are an ABS approximation of officially gazetted LGAs as defined by each State and Territory Local Government Department. The boundaries produced for LGAs are constructed from allocations of whole Mesh Blocks and reviewed annually. Tenure Type: This is a consolidation of the census tenure and landlord types. The following definitions have been used: Rented: Private and not stated, this is comprised of rented dwellings (excluding rent free) where the Landlord type is a Real Estate Agent, Person not in the same household or where the Landlord type is not stated Rented: Other, this is comprised of rented dwellings (excluding rent free) where the Landlord type is Employer (Govt or other), Housing cooperative,community,church group, or Residential park (incl caravan parks and marinas) Rented: TOTAL, this is comprised of the sum of Rented: Public, Rented: Private and not stated, and Rented: Other landlord. Please note that this field should be excluded when summing the total households Other tenure types: this is comprised of dwellings that are owned outright, occupied rent free, occupied under a life tenure scheme, other tenure types and tenure type not stated. Total Households: this is comprised of the sum of Being purchased (incl rent,buy), Rented: TOTAL and Other tenure types. Total - Includes all South Australian households. Source: The data was downloaded from data.sa.gov.au and spatialised by the Adelaide Data Hub using the ABS 2011 Local Government Areas dataset. Copyright attribution: Government of South Australia - SA Housing Authority, (2014): . Accessed from AURIN Portal on 12/3/2020. Licence type: Creative Commons Attribution 4.0 International (CC BY 4.0)