The dataset "aus_real_estate.csv" encapsulates comprehensive real estate information pertaining to Australia, showcasing diverse attributes essential for property assessment and market analysis. This dataset, comprising 5000 entries across 10 distinct columns, offers a detailed portrayal of various residential properties in cities across Australia.

The dataset encompasses crucial factors influencing property valuation and purchase decisions. The 'Price' column represents the property's cost, spanning a range between $100,000 and $2,000,000. Attributes such as 'Bedrooms' and 'Bathrooms' highlight the accommodation specifics, varying from one to five bedrooms and one to three bathrooms, respectively. 'SqFt' denotes the square footage of the properties, varying between 800 and 4000 square feet, elucidating their size and spatial dimensions.

The 'City' column encompasses major Australian urban centers, including Sydney, Melbourne, Brisbane, Perth, and Adelaide, delineating the geographical distribution of the properties. 'State' further categorizes the locations into New South Wales (NSW), Victoria (VIC), Queensland (QLD), Western Australia (WA), and South Australia (SA).

The dataset encapsulates temporal information through the 'Year_Built' attribute, spanning from 1950 to 2023, providing insights into the age and vintage of the properties. Moreover, property types are delineated within the 'Type' column, encompassing variations such as 'Apartment,' 'House,' and 'Townhouse.' The binary 'Garage' column signifies the presence (1) or absence (0) of a garage, while 'Lot_Area' provides an understanding of the land area, ranging from 1000 to 10,000 square feet.

This dataset offers a comprehensive outlook into the Australian real estate landscape, facilitating multifaceted analyses encompassing property valuation, market trends, and regional preferences. Its diverse attributes make it a valuable resource for researchers, analysts, and stakeholders within the real estate domain, enabling robust investigations and informed decision-making processes regarding property investments and market dynamics in Australia.

Housing Index in Australia increased to 183.90 points in the fourth quarter of 2021 from 175.60 points in the third quarter of 2021. This dataset provides the latest reported value for - Australia House Price Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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.

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.

Introduction

This Australian English Call Center Speech Dataset for the Real Estate industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for English -speaking Real Estate customers. With over 40 hours of unscripted, real-world audio, this dataset captures authentic conversations between customers and real estate agents ideal for building robust ASR models.

Curated by FutureBeeAI, this dataset equips voice AI developers, real estate tech platforms, and NLP researchers with the data needed to create high-accuracy, production-ready models for property-focused use cases.

Speech Data

The dataset features 40 hours of dual-channel call center recordings between native Australian English speakers. Captured in realistic real estate consultation and support contexts, these conversations span a wide array of property-related topics from inquiries to investment advice offering deep domain coverage for AI model development.

Topic Diversity

This speech corpus includes both inbound and outbound calls, featuring positive, neutral, and negative outcomes across a wide range of real estate scenarios.

Such domain-rich variety ensures model generalization across common real estate support conversations.

Transcription

All recordings are accompanied by precise, manually verified transcriptions in JSON format.

These transcriptions streamline ASR and NLP development for English real estate voice applications.

Metadata

Detailed metadata accompanies each participant and conversation:

This enables smart filtering, dialect-focused model training, and structured dataset exploration.

Usage and Applications

This dataset is ideal for voice AI and NLP systems built for the real estate sector:

A CSV list of all properties within the City of Playford.

Dataset

This dataset was created by Jaime

Released under Data files © Original Authors

Contents

Residential Property Prices in Australia increased 3.47 percent in June of 2025 over the same month in the previous year. This dataset includes a chart with historical data for Australia Residential Property Prices.

This dataset is available on Brisbane City Council’s open data website – data.brisbane.qld.gov.au. The site provides additional features for viewing and interacting with the data and for downloading the data in various formats.
This dataset combines Brisbane City Council property information with the Queensland Government Digital Cadastral Database (DCDB) in Brisbane City Council area. Land Parcels are the building blocks of Council properties. Land parcels (also called lots) are mapped and the title details shown on a Plan of Subdivision. The parcel is a graphical representation of surveyed boundaries together with identifiers such as Lot/Plan description and house numbers. The Digital Cadastral Database (DCDB) is the spatial representation of every current parcel of land in Queensland, and its legal Lot on Plan description and relevant attributes. It provides the map base for systems dealing with land related information. The DCDB is considered to be the point of truth for the graphical representation of property boundaries. It is not the point of truth for the legal property boundary or related attribute information, this will always be the plan of survey or the related titling information and administrative data sets. Warning. Downloading this entire dataset in shapefile format exceeds the current 2GB download limit set by ESRI. Information from ESRI has the following suggestions. Consider the following options: Output to a file geodatabase instead of a shapefile or Process the data in sections.

Proprietary property trends data and analytics for Australia and NZ from Corelogic.

Comprehensive dataset containing 108 verified Real estate school businesses in Australia with complete contact information, ratings, reviews, and location data.

Original maps and plans created by real estate firms from 1850s to mid 1900s from State Library of Queensland collection collections.

This dataset includes descriptions, geographic coordinates and links for 1259 digitised maps.

Estate maps can give information about land subdivisions, including how the land was subdivided, when it was first auctioned, who the surveyors were and who sold the land. They are useful for investigating the history of urban land areas.

The maps are predominantly from Brisbane but also cover some regional areas of Queensland such as the Gold and Sunshine Coasts.

Additional information about this collection is available at: https://www.slq.qld.gov.au/blog/real-estate-map-collection-treasure-collection-john-oxley-library

Also available in State Library’s library catalogue http://onesearch.slq.qld.gov.au

Explore State Library’s events, programs and services at https://slq.qld.gov.au

Provides locations and land boundaries / cadastre of each property within the Adelaide City Council area. Note only contains site designated as common property for Strata and community properties.

This property app provides interactive map layers and datasets offering detailed information about land lots, properties, and their associated attributes within the city boundaries. This tool is designed to support residents, developers, planners, real estate professionals, and council staff by providing reliable, up-to-date information for property-related enquiries, planning, and decision-making. Show full description

What is IPGOD?

The Intellectual Property Government Open Data (IPGOD) includes over 100 years of registry data on all intellectual property (IP) rights administered by IP Australia. It also has derived information about the applicants who filed these IP rights, to allow for research and analysis at the regional, business and individual level. This is the 2019 release of IPGOD.

How do I use IPGOD?

IPGOD is large, with millions of data points across up to 40 tables, making them too large to open with Microsoft Excel. Furthermore, analysis often requires information from separate tables which would need specialised software for merging. We recommend that advanced users interact with the IPGOD data using the right tools with enough memory and compute power. This includes a wide range of programming and statistical software such as Tableau, Power BI, Stata, SAS, R, Python, and Scalar.

IP Data Platform

IP Australia is also providing free trials to a cloud-based analytics platform with the capabilities to enable working with large intellectual property datasets, such as the IPGOD, through the web browser, without any installation of software. IP Data Platform

References

The following pages can help you gain the understanding of the intellectual property administration and processes in Australia to help your analysis on the dataset.

• Patents
• Trade Marks
• Designs
• Plant Breeder’s Rights

Updates

Tables and columns

Due to the changes in our systems, some tables have been affected.

• We have added IPGOD 225 and IPGOD 325 to the dataset!
• The IPGOD 206 table is not available this year.
• Many tables have been re-built, and as a result may have different columns or different possible values. Please check the data dictionary for each table before use.

Data quality improvements

Data quality has been improved across all tables.

• Null values are simply empty rather than '31/12/9999'.
• All date columns are now in ISO format 'yyyy-mm-dd'.
• All indicator columns have been converted to Boolean data type (True/False) rather than Yes/No, Y/N, or 1/0.
• All tables are encoded in UTF-8.
• All tables use the backslash \ as the escape character.
• The applicant name cleaning and matching algorithms have been updated. We believe that this year's method improves the accuracy of the matches. Please note that the "ipa_id" generated in IPGOD 2019 will not match with those in previous releases of IPGOD.

Property management of the state's social housing assets including modifications and maintenance.

If you are thinking of using a real estate agent or property manager, customers should first check that they have a valid licence. This API has been developed to enable real time browsing, verification and obtaining of NSW government property industry licensing information for real estate agents, strata agents, property managers, stock and station agents and more.

Comprehensive dataset containing 23 verified Real estate rental businesses in Australia with complete contact information, ratings, reviews, and location data.

CoreLogic Dwelling Prices MoM in Australia increased to 0.90 percent in September from 0.80 percent in August of 2025. This dataset includes a chart with historical data for Australia CoreLogic Dwelling Prices MoM.