Housing Affordability Supply and Demand Data. \r \r Number of South Australian households paying more than 30% of their household income on housing (rent or mortgage) broken down by very low, low and moderate income brackets.\r \r 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’. \r \r The Demand for Supply for LGA reports are available online at: https://data.sa.gov.au/data/dataset/housing-affordability-demand-and-supply-by-local-government-area\r \r Explanatory Notes:\r \r 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. \r \r 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.\r \r Income bands are based on household income.\r \r 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.\r \r 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.

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.

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

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

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.

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

This dataset presents information from 2016 at the household level; the percentage of households within each Index of Household Advantage and Disadvantage (IHAD) quartile for Statistical Area Level 3 (SA3) 2016 boundaries.

The IHAD is an experimental analytical index developed by the Australian Bureau of Statistics (ABS) that provides a summary measure of relative socio-economic advantage and disadvantage for households. It utilises information from the 2016 Census of Population and Housing.

IHAD quartiles: All households are ordered from lowest to highest disadvantage, the lowest 25% of households are given a quartile number of 1, the next lowest 25% of households are given a quartile number of 2 and so on, up to the highest 25% of households which are given a quartile number of 4. This means that households are divided up into four groups, depending on their score.

This data is ABS data (catalogue number: 4198.0) used with permission from the Australian Bureau of Statistics.

For more information please visit the Australian Bureau of Statistics.

Please note:

• AURIN has generated this dataset through aggregating the original SA1 level data (with calculated number of households/quartile) to SA3 level.

• The number of occupied private dwellings, and number of households in each of the IHAD quartiles for each SA3 were calculated by aggregating the values of each of those specified columns from the SA1 dataset. Percentages of households in each of the IHAD quartiles were calculated for each SA3 from these aggregated totals.

• A household is defined as one or more persons, at least one of whom is at least 15 years of age, usually resident in the same private dwelling. All occupants of a dwelling form a household. For Census purposes, the total number of households is equal to the total number of occupied private dwellings (Census of Population and Housing: Census Dictionary, 2016 cat. no. 2901.0).

• IHAD output has been confidentialised to meet ABS requirements. In line with standard ABS procedures to minimise the risk of identifying individuals, a technique has been applied to randomly adjust cell values of the output tables. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals.

This information was complied from the Australian Bureau of Statistics in Partial fullfilment of Coursework for the Master of Data Science taught at UNSW

Household income and wealth Australia, Building Activity Australia, Affordable Housing Database, National and Regional House Price Indices, Population Projections, Lending Indicators

Household income and wealth Australia ->https://www.abs.gov.au/statistics/economy/finance/household-income-and-wealth-australia/latest-release, Affordable Housing Database ->http://www.oecd.org/social/affordable-housing-database.htm, National and Regional House Price Indices ->https://stats.oecd.org/Index.aspx?DataSetCode=RHPI_TARGET, Population Projections ->https://stats.oecd.org/Index.aspx?DataSetCode=POPPROJ, Lending Indicators ->https://www.abs.gov.au/statistics/economy/finance/lending-indicators/apr-2021

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

Sales Data contains information about the sale of freehold and leasehold properties within Western Australia. This dataset is derived from; information from Transfer of Land documents registered at Landgate subject to the Transfer of Land Act 1943 for each of the last 3 sales of the property, and known property attribute information at the time of last sale, that was gathered subject to the Valuation of Land Act 1977. This dataset reflects information about the last three sales of property dating back to 1988. As the information is gathered from 2 different sources of stored data, that have been captured to service the requirements of independent Legislation, data contained in this dataset is subject to anomalies and may not necessarily meet the intended purpose of the user. © Western Australian Land Information Authority. Use of Landgate data is subject to Personal Use License terms and conditions unless otherwise authorised under approved License terms and conditions.

Datasets present number of household members residing in 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 .

Residential Property Attribute data provides the most current building attributes available for residential properties as captured within Landgate's Valuation Database. Attribute information is captured as part of the Valuation process and is maintained via a range of sources including building and sub division approval notifications. This data set should not be confused with Sales Evidence data which is based on property attributes as at the time of last sale. This dataset has been spatially enabled by linking cadastral land parcel polygons, sourced from Landgatge's Spatial Cadastral Database (SCDB), to the Residential Property Attribute data sourced from the Valuation database. Customers wishing to access this data set should contact Landgate on +61 (0)8 9273 7683 or email customerexperience@landgate.wa.gov.au © Western Australian Land Information Authority (Landgate). Use of Landgate data is subject to Personal Use License terms and conditions unless otherwise authorised under approved License terms and conditions. Changes will be applied to this dataset resulting from the implementation of the Community Titles Act 2018 please refer to the Data Dictionary below.

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.

SA1 based data for Dwelling Structure, in General Community Profile (GCP), 2016 Census. Count of Private dwellings and Persons in occupied private dwellings. Count of all persons enumerated in the dwelling on Census Night, including visitors from within Australia. Excludes usual residents who were temporarily absent on Census Night. Excludes 'Visitors only' and 'Other non-classifiable' households. The data is by SA1 2016 boundaries. Periodicity: 5-Yearly. Note: 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. For more information visit the data source: http://www.abs.gov.au/census.

Housing Affordability Supply and Demand Data. Number of South Australian households paying more than 50% of their household income on housing (rent or mortgage) 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’. The Demand for Supply for LGA reports are available online at: https://data.sa.gov.au/data/dataset/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.

This dataset represents residential property data in Australia. It is designed for academic and analytical purposes to study how various factors influence the selling price of properties. The dataset mimics realistic property characteristics based on Australian market trends.

Each record in the dataset represents one sold property after 1 August 2025. The data contains numerical and categorical variables suitable for conducting descriptive statistics, ANOVA, and correlation analysis as required in the assignment.

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.

Description: A US-based housing company named Surprise Housing has decided to enter the Australian market. The company uses data analytics to purchase houses at a price below their actual values and flip them on at a higher price. For the same purpose, the company has collected a data set from the sale of houses in Australia. The data is provided in the CSV file below.

The company is looking at prospective properties to buy to enter the market. You are required to build a regression model using regularization in order to predict the actual value of the prospective properties and decide whether to invest in them or not.

The company wants to know:

Which variables are significant in predicting the price of a house, and

How well those variables describe the price of a house.

Also, determine the optimal value of lambda for ridge and lasso regression.

Business Goal

You are required to model the price of houses with the available independent variables. This model will then be used by the management to understand how exactly the prices vary with the variables. They can accordingly manipulate the strategy of the firm and concentrate on areas that will yield high returns. Further, the model will be a good way for management to understand the pricing dynamics of a new market.

New Home Sales in Australia decreased to 4239 Units in August from 4290 Units in July of 2025. This dataset provides the latest reported value for - Australia New Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Important Notice\r

\r This dataset is not being updated currently due to data migration work at IP Australia. We are sorry for the inconvenience and we will update this page once the migration is complete.\r \r The Intellectual Property Government Open Live Data (IPGOLD) includes over 100 years of Intellectual Property (IP) rights administered by IP Australia comprising patents, trade marks, designs and plant breeder's rights. The data is highly detailed, including information on each aspect of the application process from application through to granting of IP rights. We have published a paper to accompany IPGOLD which describes the data and illustrates its use, as well as a technical paper on the firm matching.\r \r IPGOLD is inherently the same data as the IPGOD data set, with a weekly update instead of the annual snapshot available in IPGOD. Many of the scripts of IPGOLD are still being developed and tested. As such IPGOLD should be considered a Beta release.