Zillow.com was the most-visited real estate website worldwide in 2024, with an average of ************* visits per month during the measured period. Leboncoin.fr ranked second, with ***** million monthly visits, while Carigslist.org ranked third, with ***** million average accesses.

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Context:

My dataset is a valuable collection of real estate information sourced from REALTING.com, an international affiliate sales system known for facilitating safe and convenient property transactions worldwide. REALTING.com has a strong foundation, with its founders boasting approximately 20 years of experience in creating information technologies for the real estate market. This dataset offers insights into various properties across the globe, making it a valuable resource for real estate market analysis, property valuation, and trend prediction.

Content:

The dataset contains information on a diverse range of properties, each represented by a row of data. Here are the key columns and their contents:

• Title: A brief description or name of the property listing.
• Country: The country where the property is located.
• Location: The specific address or location of the property within the country.
• Building Construction Year: The year in which the building was constructed.
• Building Total Floors: The total number of floors or stories in the building.
• Apartment Floor: The floor on which the apartment is situated within the building.
• Apartment Rooms: The total number of rooms in the apartment.
• Apartment Bedrooms: The number of bedrooms in the apartment.
• Apartment Bathrooms: The number of bathrooms in the apartment.
• Apartment Total Area: The total area of the apartment in square meters.
• Apartment Living Area: The living area of the apartment in square meters.
• Price in USD: The price of the property listed in United States Dollars (USD).
• Image: References or links to images associated with the property listing.
• URL: Web links to the full property listing or more detailed information.

This dataset is rich in real estate-related information, making it suitable for various analytical tasks such as market research, property comparison, geographical analysis, and more. The dataset's global scope and diverse property attributes provide a comprehensive view of the international real estate market, offering ample opportunities for data-driven insights and decision-making.

A complete list of live websites using the Great Real Estate technology, compiled through global website indexing conducted by WebTechSurvey.

Monaco was the leading city in terms of most expensive luxury real estate worldwide in 2024. One million dollars could only buy 19 square meters of luxury property there. In London, the same amount of money could purchase 34 square meters of luxury real estate. In Tokyo, one million dollars was enough to buy 58 square meters of prime real estate in 2024. Luxury real estate – additional information Real estate is considered one of the best long-term investments, and it certainly is one of the major investments one might make during a lifetime. As far as luxury real estate is concerned, though, only the most affluent individuals or prominent real estate companies can afford to invest in prime properties in the world’s most attractive locations.

In 2024, China ranked first by real estate transaction value in the 'Residential Real Estate Transactions' segment of the real estate market among the 20 countries presented in the ranking. China's real estate transaction value amounted to ************* U.S. dollars, while the United States and France, the second and third countries, had records amounting to ************* U.S. dollars and ************** U.S. dollars, respectively.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Residential Real Estate Transactions.

Portugal, Canada, and the United States were the countries with the highest house price to income ratio in 2024. In all three countries, the index exceeded 130 index points, while the average for all OECD countries stood at 116.2 index points. The index measures the development of housing affordability and is calculated by dividing nominal house price by nominal disposable income per head, with 2015 set as a base year when the index amounted to 100. An index value of 120, for example, would mean that house price growth has outpaced income growth by 20 percent since 2015. How have house prices worldwide changed since the COVID-19 pandemic? House prices started to rise gradually after the global financial crisis (2007–2008), but this trend accelerated with the pandemic. The countries with advanced economies, which usually have mature housing markets, experienced stronger growth than countries with emerging economies. Real house price growth (accounting for inflation) peaked in 2022 and has since lost some of the gain. Although, many countries experienced a decline in house prices, the global house price index shows that property prices in 2023 were still substantially higher than before COVID-19. Renting vs. buying In the past, house prices have grown faster than rents. However, the home affordability has been declining notably, with a direct impact on rental prices. As people struggle to buy a property of their own, they often turn to rental accommodation. This has resulted in a growing demand for rental apartments and soaring rental prices.

Introduction

This Canadian 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 30 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 30 hours of dual-channel call center recordings between native Canadian 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:

Introduction

This Egyptian Arabic 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 Arabic -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 Egyptian Arabic 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 Arabic 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:

Australian Housing Prices

Location, Size, Price, Etc

By Jeff [source]

About this dataset

This dataset contains information on 1000 properties in Australia, including location, size, price, and other details

More Datasets

For more datasets, click here.

Featured Notebooks

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How to use the dataset

If you're looking for a dataset on Australian housing data, this is a great option. This dataset contains information on over 1000 properties in Australia, including location, size, price, and other details. With this data, you can answer questions like What is the average price of a home in Australia?, What are the most popular type of homes in Australia?, and more

Research Ideas

• This dataset can be used to predict hosing prices in Australia.
• This dataset can be used to find relationships between housing prices and location.
• This dataset can be used to find relationships between housing prices and features such as size, number of bedrooms, and number of bathrooms

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: RealEstateAU_1000_Samples.csv | Column name | Description | |:--------------------|:---------------------------------------------------------------------------------------| | breadcrumb | A breadcrumb is a text trail that shows the user's location within a website. (String) | | category_name | The name of the category that the listing belongs to. (String) | | property_type | The type of property being listed. (String) | | building_size | The size of the property's building, in square meters. (Numeric) | | land_size | The size of the property's land, in square meters. (Numeric) | | preferred_size | The preferred size of the property, in square meters. (Numeric) | | open_date | The date that the property was first listed for sale. (Date) | | listing_agency | The agency that is listing the property. (String) | | price | The listing price of the property. (Numeric) | | location_number | The number that corresponds to the property's location. (Numeric) | | location_type | The type of location that the property is in. (String) | | location_name | The name of the location that the property is in. (String) | | address | The property's address. (String) | | address_1 | The first line of the property's address. (String) | | city | The city that the property is located in. (String) | | state | The state that the property is located in. (String) | | zip_code | The zip code that the property is located in. (String) | | phone | The listing agent's phone number. (String) | | latitude | The property's latitude. (Numeric) | | longitude | The property's longitude. (Numeric) | | product_depth | The depth of the product. (Numeric) | | bedroom_count | The number of bedrooms in the property. (Numeric) | | bathroom_count | The number of bathrooms in the property. (Numeric) | | parking_count | The number of parking spaces in the property. (Numeric) | | RunDate | The date that the listing was last updated. (Date) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Jeff.

Introduction

This Danish 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 Danish -speaking Real Estate customers. With over 30 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 30 hours of dual-channel call center recordings between native Danish 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 Danish 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:

Convert websites into useful data Fully managed enterprise-grade web scraping service Many of the world's largest companies trust ScrapeHero to transform billions of web pages into actionable data. Our Data as a Service provides high-quality structured data to improve business outcomes and enable intelligent decision making

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Large Scale Web Crawling for Price and Product Monitoring - eCommerce, Grocery, Home improvement, Shipping, Inventory, Realtime, Advertising, Sponsored Content - ANYTHING you see on ANY website.

Amazon, Walmart, Target, Home Depot, Lowes, Publix, Safeway, Albertsons, DoorDash, Grubhub, Yelp, Zillow, Trulia, Realtor, Twitter, McDonalds, Starbucks, Permits, Indeed, Glassdoor, Best Buy, Wayfair - any website.

Travel, Airline and Hotel Data Real Estate and Housing Data Brand Monitoring Human Capital Management Alternative Data Location Intelligence Training Data for Artificial Intelligence and Machine Learning Realtime and Custom APIs Distribution Channel Monitoring Sales Leads - Data Enrichment Job Monitoring Business Intelligence and so many more use cases

We provide data to almost EVERY industry and some of the BIGGEST GLOBAL COMPANIES

About this Dataset

Contains the contact information of commerical realtors around the world. A sample listing is also included for each realtor. All information was verified on 11/18/17. Whether for web or app develoment, marketing lists, or identifying the competition, this a hard to come by contact lists. The lists was developed by hiring freelancers on UpWork and then verified with an email campaign. We stand by our lists and will replace any bounced emails for free!

Category

Lead Generation

Keywords

realestate,realtors,commercial,global,contact

Row Count

1142

Price

$99.99

Best World Real Estates Limited Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Indonesia BLS: LoanDisbursementUnderTarget:ES:RealEstate,Leasing&CompService data was reported at 17.500 % in Jun 2019. This records a decrease from the previous number of 22.500 % for Mar 2019. Indonesia BLS: LoanDisbursementUnderTarget:ES:RealEstate,Leasing&CompService data is updated quarterly, averaging 14.100 % from Sep 2010 (Median) to Jun 2019, with 34 observations. The data reached an all-time high of 40.000 % in Sep 2018 and a record low of 1.900 % in Sep 2010. Indonesia BLS: LoanDisbursementUnderTarget:ES:RealEstate,Leasing&CompService data remains active status in CEIC and is reported by Bank of Indonesia. The data is categorized under Global Database’s Indonesia – Table ID.SD001: Banking Survey.

Explore the historical Whois records related to world-web-realestate.com (Domain). Get insights into ownership history and changes over time.

Introduction

This Philippines 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 30 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 30 hours of dual-channel call center recordings between native Philippines 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:

The richest person involved in the global real estate industry as of November 28, 2024, was the American businessman Dan Gilbert, who is also the owner of the NBA's Cleveland Cavaliers and a philanthropist, had an estimated net worth of 30.5 billion U.S. dollars. Lee Shau Kee, who is the majority owner of Henderson Land Development, came in second with an estimated net worth of approximately 24.4 billion U.S. dollars.

Real Estate Email List is a premium mailing database for your needs. Most importantly, the list is the most popular site in the world. It is the largest data provider. Besides, the list is verified by human checks and automated software. You get new connections instantly. In addition, our expert team builds a qualified email list and checks the accuracy levels from millions of sources. The list is 95% accurate for giving the best results. Moreover, the dataset provides authentic service. This service can help you grow your business in a short time. Also, the leads link is ready for instant download. Furthermore, we give weekly updates and a bounce-back guarantee with Excel and CSV files. The leads give more information about your services. If you want a specific real estate email list, tell us. We make it for you properly. We provide new data for free to replace missing data.

Real Estate Email List provides a free sample for marketing campaigns. You can create any custom order with your desired areas. The leads ensure that you never get inactive email data. After visiting our website, List to Data, contact us. You can purchase this email list to make your business more competitive. The dataset is profitable. In conclusion, you can get instant results for your products and services. Real Estate Email Database gives you verified and updated contact details. Also, it helps you connect with property owners, agents, and investors directly. In fact, this dataset includes names, phone numbers, email addresses, and postal details. Therefore, you can reach the right people in the real estate market quickly. So, you get high-quality leads that can help you grow your business. Likewise, it covers both residential and commercial real estate sectors. As a result, you can target your audience more effectively. Real Estate Email Database is fresh and regularly updated. This way, your campaigns always reach active contacts. Also, the affordable price makes it suitable for businesses of any size.

Therefore, you can boost sales without spending too much. Furthermore, this Email database supports various marketing goals. For example, you can promote property listings, offer investment deals, or build long-term client relationships. Finally, choose our database to enjoy better leads, higher ROI, and steady business growth.

Japan FW(15-64): FM: AWHW: NAI: RealEstate & GoodsRental&Leasing(RE & GRL) data was reported at 5.000 Hour tt in Oct 2025. This records a decrease from the previous number of 19.000 Hour tt for Sep 2025. Japan FW(15-64): FM: AWHW: NAI: RealEstate & GoodsRental&Leasing(RE & GRL) data is updated monthly, averaging 25.000 Hour tt from Jan 2011 (Median) to Oct 2025, with 172 observations. The data reached an all-time high of 51.000 Hour tt in Jul 2016 and a record low of 2.000 Hour tt in May 2019. Japan FW(15-64): FM: AWHW: NAI: RealEstate & GoodsRental&Leasing(RE & GRL) data remains active status in CEIC and is reported by Statistical Bureau. The data is categorized under Global Database’s Japan – Table JP.G: Labour Force Survey: Aggregate Weekly Hours of Work: Family Worker: JSIC 12th Rev.

Hong Kong Underemployed: Fin & Ins, RealEstate, Prof & Buss Serv: Age 40 and O data was reported at 2,200.000 Person in Jun 2018. This records a decrease from the previous number of 2,400.000 Person for Mar 2018. Hong Kong Underemployed: Fin & Ins, RealEstate, Prof & Buss Serv: Age 40 and O data is updated quarterly, averaging 3,250.000 Person from Mar 2008 (Median) to Jun 2018, with 42 observations. The data reached an all-time high of 5,200.000 Person in Jun 2010 and a record low of 1,300.000 Person in Dec 2017. Hong Kong Underemployed: Fin & Ins, RealEstate, Prof & Buss Serv: Age 40 and O data remains active status in CEIC and is reported by Census and Statistics Department. The data is categorized under Global Database’s Hong Kong SAR – Table HK.G064: Underemployment: GHS: RPA: by Industry HSIC 2.0 and Age.