Zillow reigns supreme in the U.S. real estate website landscape, attracting a staggering ***** million monthly visits in 2024. This figure dwarfs its closest competitor, Realtor.com, which garnered less than half of Zillow's traffic. Online platforms are extremely popular, with the majority of homebuyers using a mobile device during the buying process. The rise of Zillow Founded in 2006, the Seattle-headquartered proptech Zillow has steadily grown over the years, establishing itself as the most popular U.S. real estate website. In 2023, the listing platform recorded about *** million unique monthly users across its mobile applications and website. Despite holding an undisputed position as a market leader, Zillow's revenue has decreased since 2021. A probable cause for the decline is the plummeting of housing transactions and the negative housing sentiment. Performance and trends in the proptech market The proptech market has shown remarkable performance, with companies like Opendoor and Redfin experiencing significant stock price increase in 2023. This growth is particularly notable in the residential brokerage segment. Meanwhile, major players in proptech fundraising, such as Fifth Wall and Hidden Hill Capital, have raised billions in direct investment, further fueling the sector's development. As technology continues to reshape the real estate industry, online platforms like Zillow are likely to play an increasingly crucial role in how people search for and purchase homes. (1477916, 1251604)

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.

Extract detailed property data points — address, URL, prices, floor space, overview, parking, agents, and more — from any real estate listings. The Rankings data contains the ranking of properties as they come in the SERPs of different property listing sites. Furthermore, with our real estate agents' data, you can directly get in touch with the real estate agents/brokers via email or phone numbers.

A. Usecase/Applications possible with the data:

1. Property pricing - accurate property data for real estate valuation. Gather information about properties and their valuations from Federal, State, or County level websites. Monitor the real estate market across the country and decide the best time to buy or sell based on data

2. Secure your real estate investment - Monitor foreclosures and auctions to identify investment opportunities. Identify areas within special economic and opportunity zones such as QOZs - cross-map that with commercial or residential listings to identify leads. Ensure the safety of your investments, property, and personnel by analyzing crime data prior to investing.

3. Identify hot, emerging markets - Gather data about rent, demographic, and population data to expand retail and e-commerce businesses. Helps you drive better investment decisions.

4. Profile a building’s retrofit history - a building permit is required before the start of any construction activity of a building, such as changing the building structure, remodeling, or installing new equipment. Moreover, many large cities provide public datasets of building permits in history. Use building permits to profile a city’s building retrofit history.

5. Study market changes - New construction data helps measure and evaluate the size, composition, and changes occurring within the housing and construction sectors.

6. Finding leads - Property records can reveal a wealth of information, such as how long an owner has currently lived in a home. US Census Bureau data and City-Data.com provide profiles of towns and city neighborhoods as well as demographic statistics. This data is available for free and can help agents increase their expertise in their communities and get a feel for the local market.

7. Searching for Targeted Leads - Focusing on small, niche areas of the real estate market can sometimes be the most efficient method of finding leads. For example, targeting high-end home sellers may take longer to develop a lead, but the payoff could be greater. Or, you may have a special interest or background in a certain type of home that would improve your chances of connecting with potential sellers. In these cases, focused data searches may help you find the best leads and develop relationships with future sellers.

How does it work?

• Analyze sample data
• Customize parameters to suit your needs
• Add to your projects
• Contact support for further customization

Real estate datasets from various websites cover all major real estate data points including: property type, size, location, price, bedrooms, baths, address, history, images, and much more. Popular use cases include: forecast housing demand, analyze price fluctuations, improve customer satisfaction, see past prices to monitor market trends, and more.

The Easiest Way to Collect Data from the Internet Download anything you see on the internet into spreadsheets within a few clicks using our ready-made web crawlers or a few lines of code using our APIs

We have made it as simple as possible to collect data from websites

Easy to Use Crawlers Amazon Product Details and Pricing Scraper Amazon Product Details and Pricing Scraper Get product information, pricing, FBA, best seller rank, and much more from Amazon.

Google Maps Search Results Google Maps Search Results Get details like place name, phone number, address, website, ratings, and open hours from Google Maps or Google Places search results.

Twitter Scraper Twitter Scraper Get tweets, Twitter handle, content, number of replies, number of retweets, and more. All you need to provide is a URL to a profile, hashtag, or an advance search URL from Twitter.

Amazon Product Reviews and Ratings Amazon Product Reviews and Ratings Get customer reviews for any product on Amazon and get details like product name, brand, reviews and ratings, and more from Amazon.

Google Reviews Scraper Google Reviews Scraper Scrape Google reviews and get details like business or location name, address, review, ratings, and more for business and places.

Walmart Product Details & Pricing Walmart Product Details & Pricing Get the product name, pricing, number of ratings, reviews, product images, URL other product-related data from Walmart.

Amazon Search Results Scraper Amazon Search Results Scraper Get product search rank, pricing, availability, best seller rank, and much more from Amazon.

Amazon Best Sellers Amazon Best Sellers Get the bestseller rank, product name, pricing, number of ratings, rating, product images, and more from any Amazon Bestseller List.

Google Search Scraper Google Search Scraper Scrape Google search results and get details like search rank, paid and organic results, knowledge graph, related search results, and more.

Walmart Product Reviews & Ratings Walmart Product Reviews & Ratings Get customer reviews for any product on Walmart.com and get details like product name, brand, reviews, and ratings.

Scrape Emails and Contact Details Scrape Emails and Contact Details Get emails, addresses, contact numbers, social media links from any website.

Walmart Search Results Scraper Walmart Search Results Scraper Get Product details such as pricing, availability, reviews, ratings, and more from Walmart search results and categories.

Glassdoor Job Listings Glassdoor Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Glassdoor.

Indeed Job Listings Indeed Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Indeed.

LinkedIn Jobs Scraper Premium LinkedIn Jobs Scraper Scrape job listings on LinkedIn and extract job details such as job title, job description, location, company name, number of reviews, and more.

Redfin Scraper Premium Redfin Scraper Scrape real estate listings from Redfin. Extract property details such as address, price, mortgage, redfin estimate, broker name and more.

Yelp Business Details Scraper Yelp Business Details Scraper Scrape business details from Yelp such as phone number, address, website, and more from Yelp search and business details page.

Zillow Scraper Premium Zillow Scraper Scrape real estate listings from Zillow. Extract property details such as address, price, Broker, broker name and more.

Amazon product offers and third party sellers Amazon product offers and third party sellers Get product pricing, delivery details, FBA, seller details, and much more from the Amazon offer listing page.

Realtor Scraper Premium Realtor Scraper Scrape real estate listings from Realtor.com. Extract property details such as Address, Price, Area, Broker and more.

Target Product Details & Pricing Target Product Details & Pricing Get product details from search results and category pages such as pricing, availability, rating, reviews, and 20+ data points from Target.

Trulia Scraper Premium Trulia Scraper Scrape real estate listings from Trulia. Extract property details such as Address, Price, Area, Mortgage and more.

Amazon Customer FAQs Amazon Customer FAQs Get FAQs for any product on Amazon and get details like the question, answer, answered user name, and more.

Yellow Pages Scraper Yellow Pages Scraper Get details like business name, phone number, address, website, ratings, and more from Yellow Pages search results.

AI-Generated Real Estate Listing Market Outlook

According to our latest research, the AI-generated real estate listing market size reached USD 1.57 billion globally in 2024, reflecting a robust surge in adoption across multiple verticals. The market is projected to grow at a CAGR of 18.9% from 2025 to 2033, with the total market size expected to reach USD 7.28 billion by the end of the forecast period. This growth trajectory is primarily fueled by the increasing demand for automation, enhanced property marketing, and the need for data-driven insights to optimize real estate transactions. As per the latest research, the integration of AI technologies in property listing platforms is transforming how properties are marketed, discovered, and managed, providing unprecedented value to real estate stakeholders globally.

The rapid expansion of the AI-generated real estate listing market is underpinned by several compelling growth factors. Chief among these is the rising need for efficiency and accuracy in property listing creation and management. Traditional real estate listing processes are time-consuming and prone to human error, often resulting in incomplete or inconsistent property data. AI-powered solutions automate the generation of property descriptions, image enhancements, and even virtual staging, enabling real estate professionals to deliver high-quality listings at scale. This automation not only reduces operational costs but also accelerates the time-to-market for new listings, allowing agencies and agents to respond swiftly to changing market dynamics and consumer preferences.

Another major driver for the AI-generated real estate listing market is the increasing consumer expectation for personalized and engaging property search experiences. Modern buyers and renters demand comprehensive, visually appealing, and accurate property information that AI can deliver through advanced natural language processing (NLP) and computer vision technologies. AI-generated listings can tailor content based on user behavior, search history, and demographic data, resulting in higher engagement rates and improved conversion metrics. Furthermore, the integration of AI with virtual tours, chatbots, and predictive analytics enhances the overall customer journey, making property discovery more intuitive and satisfying for end-users.

The proliferation of digital platforms and the shift towards online property transactions have further accelerated the adoption of AI-generated real estate listing solutions. As the real estate industry becomes increasingly digitalized, stakeholders are leveraging AI to gain a competitive edge by offering differentiated services and richer content. The ability of AI to analyze vast datasets, identify emerging trends, and generate actionable insights is particularly valuable in highly competitive markets. Additionally, regulatory changes and the growing emphasis on transparency and compliance are prompting agencies to adopt AI tools that ensure listings are accurate, up-to-date, and legally compliant. These factors collectively create a fertile environment for sustained growth in the AI-generated real estate listing market.

From a regional perspective, North America currently leads the global AI-generated real estate listing market, accounting for the largest share in 2024. The region’s dominance is attributed to the high rate of technology adoption, significant investments in AI research, and a mature real estate ecosystem. Europe and Asia Pacific are also witnessing rapid growth, with countries like the United Kingdom, Germany, China, and India emerging as key markets. The Asia Pacific region, in particular, is expected to register the highest CAGR during the forecast period, driven by urbanization, a booming property sector, and increasing digital transformation initiatives. Latin America and the Middle East & Africa, while still nascent, are poised for substantial growth as internet penetration and real estate investments continue to rise.

Component Analysis

The AI-generated real estate listing market can be segmented by component into software and services, each playing a critical role in the ecosystem. The software segment comprises AI-powered platforms, listing engines, and data analytics tools that automate the creation and optimization of property listings. These solutions leverage advanced algorithms for natural language generation, image recognition, and content personalization, enabling real estat

Extract detailed property data points — address, URL, prices, floor space, overview, parking, agents, and more — from any real estate listings. The Rankings data contains the ranking of properties as they come in the SERPs of different property listing sites. Furthermore, with our real estate agents' data, you can directly get in touch with the real estate agents/brokers via email or phone numbers.

A. Usecase/Applications possible with the data:

1. Property pricing - accurate property data for real estate valuation. Gather information about properties and their valuations from Federal, State, or County level websites. Monitor the real estate market across the country and decide the best time to buy or sell based on data

2. Secure your real estate investment - Monitor foreclosures and auctions to identify investment opportunities. Identify areas within special economic and opportunity zones such as QOZs - cross-map that with commercial or residential listings to identify leads. Ensure the safety of your investments, property, and personnel by analyzing crime data prior to investing.

3. Identify hot, emerging markets - Gather data about rent, demographic, and population data to expand retail and e-commerce businesses. Helps you drive better investment decisions.

4. Profile a building’s retrofit history - a building permit is required before the start of any construction activity of a building, such as changing the building structure, remodeling, or installing new equipment. Moreover, many large cities provide public datasets of building permits in history. Use building permits to profile a city’s building retrofit history.

5. Study market changes - New construction data helps measure and evaluate the size, composition, and changes occurring within the housing and construction sectors.

6. Finding leads - Property records can reveal a wealth of information, such as how long an owner has currently lived in a home. US Census Bureau data and City-Data.com provide profiles of towns and city neighborhoods as well as demographic statistics. This data is available for free and can help agents increase their expertise in their communities and get a feel for the local market.

7. Searching for Targeted Leads - Focusing on small, niche areas of the real estate market can sometimes be the most efficient method of finding leads. For example, targeting high-end home sellers may take longer to develop a lead, but the payoff could be greater. Or, you may have a special interest or background in a certain type of home that would improve your chances of connecting with potential sellers. In these cases, focused data searches may help you find the best leads and develop relationships with future sellers.

How does it work?

• Analyze sample data
• Customize parameters to suit your needs
• Add to your projects
• Contact support for further customization

The Multiple Listing Service (MLS) Software market, valued at $876.7 million in 2025, is projected to experience robust growth, driven by increasing adoption of cloud-based solutions among real estate professionals and a growing demand for efficient property management tools. The 6.2% CAGR indicates a steady expansion throughout the forecast period (2025-2033). Key drivers include the need for enhanced data management, improved collaboration among agents, and the rising popularity of online property searches. The market is segmented by deployment type (cloud-based and on-premises) and user type (large enterprises and SMEs), with cloud-based solutions witnessing significant traction due to scalability and cost-effectiveness. Growth is further fueled by technological advancements, such as integration with mobile applications and artificial intelligence for improved lead generation and property valuation. While market restraints may include initial investment costs for software implementation and the need for ongoing maintenance, the overall market outlook remains positive, driven by increasing digitization within the real estate sector. The competitive landscape includes established players like Zillow, Trulia, and Realtor.com, along with several regional and niche players catering to specific market segments. Geographical expansion, particularly in developing economies with burgeoning real estate markets, will also contribute significantly to market growth. The North American market currently holds a significant share, owing to high technological adoption and a well-established real estate infrastructure. However, regions like Asia Pacific and Europe are also exhibiting promising growth potential, driven by rising internet penetration and increasing smartphone usage. The large number of real estate companies listed indicates a high degree of market competition, fostering innovation and driving down costs for end-users. The long forecast period (2019-2033) provides ample opportunity for market expansion and consolidation, with both established players and emerging startups vying for market share. Future growth will depend upon continued technological innovation, strategic partnerships, and effective marketing strategies targeting key user segments.

The Multiple Listing Service (MLS) Listing Software market is experiencing robust growth, driven by increasing adoption of technology in the real estate sector and the growing preference for cloud-based solutions. The market, estimated at $5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $14 billion by 2033. This expansion is fueled by several key factors. Firstly, the rising demand for efficient property management tools among large enterprises and small-to-medium enterprises (SMEs) is significantly boosting market growth. Secondly, the increasing preference for cloud-based MLS listing software offers scalability, cost-effectiveness, and enhanced accessibility, further driving market expansion. The trend towards mobile optimization and integration with other real estate platforms also contributes to the market's upward trajectory. However, the market faces certain restraints, including the high initial investment costs associated with implementing new software and the need for ongoing training and technical support. The competitive landscape is marked by established players such as Zillow, Realtor.com, and Rightmove, alongside several regional and niche players. The segmentation of the market reveals significant opportunities within both the application (large enterprises vs. SMEs) and type (cloud-based vs. web-based) categories. Large enterprises are expected to dominate the market share due to their higher investment capacity and greater need for sophisticated functionalities. However, the SME segment is also demonstrating substantial growth potential, propelled by the increasing affordability and accessibility of cloud-based solutions. Geographically, North America currently holds the largest market share, followed by Europe and Asia-Pacific. However, emerging economies in Asia-Pacific and the Middle East & Africa are expected to witness significant growth in the coming years, driven by increasing internet penetration and rising real estate activity. The forecast period (2025-2033) promises substantial growth, especially for companies focusing on innovative features, superior user experience, and robust customer support. Competition will intensify as technology evolves and new players enter the market.

Carbon-Smart Real Estate Listing Market Outlook

According to our latest research, the global Carbon-Smart Real Estate Listing market size reached $2.1 billion in 2024, reflecting robust momentum driven by increasing environmental regulations and a growing demand for sustainable property solutions. The market is projected to grow at a CAGR of 17.5% from 2025 to 2033, reaching a forecasted market size of $10.7 billion by 2033. This expansion is primarily fueled by the tightening of climate policies, rising investor interest in ESG-compliant assets, and the digital transformation of the real estate sector, which is increasingly integrating carbon intelligence into property transactions and management.

One of the primary growth factors propelling the Carbon-Smart Real Estate Listing market is the rapid evolution of environmental regulations across major economies. Governments and regulatory bodies are imposing stricter guidelines on building emissions, energy efficiency, and sustainability disclosures. As a result, real estate stakeholders are compelled to adopt carbon-smart technologies and listing platforms that enable transparent reporting and compliance. This regulatory push is particularly pronounced in the European Union, North America, and select Asia Pacific markets, where carbon neutrality targets are being aggressively pursued. The growing prevalence of green building codes and mandatory carbon tracking is making carbon-smart listings not just a value-add but a necessity for market participation.

In addition to regulatory drivers, shifting consumer and investor preferences are playing a substantial role in shaping market dynamics. Modern tenants, buyers, and institutional investors are increasingly prioritizing properties with credible sustainability credentials. This trend is catalyzed by a broader societal shift towards environmental responsibility and the recognition that sustainable properties offer long-term value, lower operational costs, and reduced risk of obsolescence. Carbon-smart real estate listings, which provide granular data on energy efficiency, carbon footprints, and green certifications, are thus becoming a critical differentiator in both residential and commercial markets. The ability to showcase a property’s sustainability profile is emerging as a key factor in property selection, investment decisions, and rental negotiations.

Technological advancements are also significantly accelerating the adoption of carbon-smart real estate solutions. The integration of IoT sensors, AI-driven analytics, blockchain for transparent certification, and cloud-based platforms has enabled the collection, analysis, and dissemination of property-level sustainability data at an unprecedented scale. These innovations are making it easier for real estate agencies, property owners, and investors to access, verify, and act upon carbon-related information. Furthermore, the rise of proptech startups and partnerships between technology providers and established real estate firms are fostering a dynamic ecosystem that supports continuous product innovation and market penetration. As digital infrastructure becomes more sophisticated, the barriers to entry for carbon-smart listings are diminishing, further driving market growth.

From a regional perspective, the market exhibits strong growth in North America and Europe, where policy frameworks and capital market pressures are most mature. The Asia Pacific region is rapidly catching up, spurred by urbanization, regulatory reforms, and the increasing adoption of green building standards in markets like China, Japan, and Australia. Latin America and the Middle East & Africa, while currently representing smaller shares, are expected to witness accelerated adoption as sustainability awareness spreads and international investors demand higher transparency. Regional disparities in market maturity are largely dictated by differences in regulatory environments, technological readiness, and the pace of urban development.

Property Type Analysis

The Carbon-Smart Real Estate Listing market by property type is segmented into Residential, Commercial, Industrial, and Mixed-Use properties, each demonstrating unique adoption dynamics and growth trajectories. The residential segment is currently the largest, underpinned by growing consumer demand for sustainable homes and increasing regulatory mandates on energy efficiency in new and existing housing stock. Homebuyers and ren

Note:- Only publicly available data can be worked upon

APISCRAPY collects and organizes data from Zillow's massive database, whether it's property characteristics, market trends, pricing histories, or more. Because of APISCRAPY's first-rate data extraction services, tracking property values, examining neighborhood trends, and monitoring housing market variations become a straightforward and efficient process.

APISCRAPY's Zillow real estate data scraping service offers numerous advantages for individuals and businesses seeking valuable insights into the real estate market. Here are key benefits associated with their advanced data extraction technology:

1. Real-time Zillow Real Estate Data: Users can access real-time data from Zillow, providing timely updates on property listings, market dynamics, and other critical factors. This real-time information is invaluable for making informed decisions in a fast-paced real estate environment.

2. Data Customization: APISCRAPY allows users to customize the data extraction process, tailoring it to their specific needs. This flexibility ensures that the extracted Zillow real estate data aligns precisely with the user's requirements.

3. Precision and Accuracy: The advanced algorithms utilized by APISCRAPY enhance the precision and accuracy of the extracted Zillow real estate data. This reliability is crucial for making well-informed decisions related to property investments and market trends.

4. Efficient Data Extraction: APISCRAPY's technology streamlines the data extraction process, saving users time and effort. The efficiency of the extraction workflow ensures that users can access the desired Zillow real estate data without unnecessary delays.

5. User-friendly Interface: APISCRAPY provides a user-friendly interface, making it accessible for individuals and businesses to navigate and utilize the Zillow real estate data scraping service with ease.

APISCRAPY provides real-time real estate market data drawn from Zillow, ensuring that consumers have access to the most up-to-date and comprehensive real estate insights available. Our real-time real estate market data services aren't simply a game changer in today's dynamic real estate landscape; they're an absolute requirement.

Our dedication to offering high-quality real estate data extraction services is based on the utilization of Zillow Real Estate Data. APISCRAPY's integration of Zillow Real Estate Data sets it different from the competition, whether you're a seasoned real estate professional or a homeowner wanting to sell, buy, or invest.

APISCRAPY's data extraction is a key element, and it is an automated and smooth procedure that is at the heart of the platform's operation. Our platform gathers Zillow real estate data quickly and offers it in an easily consumable format with the click of a button.

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This statistic presents the real estate websites that proved most popular among people who hunt for properties to purchase in the United Kingdom in 2015. One ****** of respondents said they would use all three websites: Rightmove, Zoopla and OnTheMarket. However, OnTheMarket only had *** percent of respondents reporting they would use the site alone.

ImmobilienScout24 is the largest real estate internet platform in Germany. Properties for private as well as commercial use are offered on the website. However, the data only cover residential properties. The dataset covers most characteristics collected on the platform like price, size and characteristics of the housing unit but also automatically generated items like the duration of the advertisement spell.

Based on the RWI-GEO-RED data that base on the data provided by ImmobilienScout24 hedonic housing price indices are estimated. The indices are on the grid level, district/county and municipality level. We conduct a hedonic price regression that covers characteristics of the object as well as regional fixed effects. The hedonic regression is estimated separately for houses for sale as well as apartments for rent and for sale. We also offer a combined index which combines the individual housing types into one index. There are three different specifications: First, the overall time development from 01/2008 to 11/2023 on grid level given yearly and quaterly; Second, cross-regional differences for each year separately and time development within one region from 01/2018 to 11/2023 (municipality, district and grid level); third, the time-region fixed effect between 2008 and 2023, which is used to determine the price changes for all three region types to the base year of 2008 or year-quarter 2008-Q1. RWI-GEO-REDX Other The data is based on the data set RWI-GEO-RED, that collects all offers for private housing on ImmobilienScout24 between January 2008 and November 2023. ImmobilienScout24 is the largest listing website for real estate in Germany. The price indices are estimated labor market region, district and municipality level The data is based on the data set RWI-GEO-RED, that collects all offers for private housing on ImmobilienScout24 between January 2008 and November 2023. ImmobilienScout24 is the largest listing website for real estate in Germany. The price indices are estimated labor market region, district and municipality level. Stratified random sampling

ImmobilienScout24 is the largest real estate internet platform in Germany. Properties for private as well as commercial use are offered on the website. However, the data only cover residential properties. The dataset covers most characteristics collected on the platform like price, size and characteristics of the housing unit but also automatically generated items like the duration of the advertisement spell. ImmobilienScout24 ist die größte Immobilien-Internetplattform in Deutschland. Auf der Website werden sowohl Immobilien zur privaten als auch zur gewerblichen Nutzung angeboten. Die Daten umfassen jedoch nur Wohnimmobilien. Der Datensatz umfasst die meisten auf der Plattform erhobenen Merkmale wie z.B. Preis, Größe und Ausstattung der Wohneinheit, aber auch generierte Informationen wie die Dauer der Inseratverfügbarkeit. RWI-GEO-RED Computer-based observation All houses for sale, all apartments for sale and all apartments for rent at ImmobilienScout24

Graph and download economic data for Housing Inventory: Pending Listing Count Year-Over-Year in Big Spring, TX (CBSA) (PENLISCOUYY13700) from Jul 2017 to Jun 2025 about Big Spring, pending, listing, TX, and USA.

The average transaction price of new housing in Europe was the highest in Norway, whereas existing homes were the most expensive in Austria. Since there is no central body that collects and tracks transaction activity or house prices across the whole continent or the European Union, not all countries are included. To compile the ranking, the source weighed the transaction prices of residential properties in the most important cities in each country based on data from their national offices. For example, in Germany, the cities included were Munich, Hamburg, Frankfurt, and Berlin. House prices have been soaring, with Sweden topping the ranking Considering the RHPI of houses in Europe (the price index in real terms, which measures price changes of single-family properties adjusted for the impact of inflation), however, the picture changes. Sweden, Luxembourg and Norway top this ranking, meaning residential property prices have surged the most in these countries. Real values were calculated using the so-called Personal Consumption Expenditure Deflator (PCE), This PCE uses both consumer prices as well as consumer expenditures, like medical and health care expenses paid by employers. It is meant to show how expensive housing is compared to the way of living in a country. Home ownership highest in Eastern Europe The home ownership rate in Europe varied from country to country. In 2020, roughly half of all homes in Germany were owner-occupied whereas home ownership was at nearly ** percent in Romania or around ** percent in Slovakia and Lithuania. These numbers were considerably higher than in France or Italy, where homeowners made up ** percent and ** percent of their respective populations.For more information on the topic of property in Europe, visit the following pages as a starting point for your research: real estate investments in Europe and residential real estate in Europe.

Dataset Overview

This dataset provides a detailed snapshot of real estate properties listed in Dubai, UAE, as of August 2024. The dataset includes over 5,000 listings scraped using the Apify API from Propertyfinder and various other real estate websites in the UAE. The data includes key details such as the number of bedrooms and bathrooms, price, location, size, and whether the listing is verified. All personal identifiers, such as agent names and contact details, have been ethically removed.

Data Science Applications

Given the size and structure of this dataset, it is ideal for the following data science applications:

• Price Prediction Models: Predicting the price of properties based on features like location, size, and furnishing status.
• Market Analysis: Understanding trends in the Dubai real estate market by analyzing price distributions, property types, and locations.
• Recommendation Systems: Developing systems to recommend properties based on user preferences (e.g., number of bedrooms, budget).
• Sentiment Analysis: Extracting and analyzing sentiments from the property descriptions to gauge the market's tone.

This dataset provides a practical foundation for both beginners and experts in data science, allowing for the exploration of real estate trends, development of predictive models, and implementation of machine learning algorithms.

# Column Descriptors

• title: The listing's title, summarizing the key selling points of the property.
• displayAddress: The public address of the property, including the community and city.
• bathrooms: The number of bathrooms available in the property.
• bedrooms: The number of bedrooms available in the property.
• addedOn: The timestamp indicating when the property was added to the listing platform.
• type: Specifies whether the property is residential, commercial, etc.
• price: The listed price of the property in AED.
• verified: A boolean value indicating whether the listing has been verified by the platform.
• priceDuration: Indicates if the property is listed for sale or rent.
• sizeMin: The minimum size of the property in square feet.
• furnishing: Describes whether the property is furnished, unfurnished, or partially furnished.
• description: A more detailed narrative about the property, including additional features and selling points.

# Ethically Mined Data

This dataset was ethically scraped using the Apify API, ensuring compliance with data privacy standards. All personal data such as agent names, phone numbers, and any other sensitive information have been omitted from this dataset to ensure privacy and ethical use. The data is intended solely for educational purposes and should not be used for commercial activities.

# Acknowledgements

This dataset was made possible thanks to the following:

• Apify: For providing the API to ethically scrape the data.
• Propertyfinder and various other real estate websites in the UAE for the original listings.
• Kaggle: For providing the platform to share and analyze this dataset.

-**Photo by** : Francesca Tosolini on Unsplash

Use the Data Responsibly

Please ensure that this dataset is used responsibly, with respect to privacy and data ethics. This data is provided for educational purposes.

The Multiple Listing Service (MLS) Software market is experiencing robust growth, driven by increasing adoption of technology in the real estate sector and a rising demand for efficient property listing and management solutions. The market's expansion is fueled by several key factors, including the growing preference for online property searches, the need for streamlined data management among real estate agents and brokers, and the increasing integration of advanced features such as virtual tours and 3D modeling within MLS platforms. The market is segmented by software type (cloud-based, on-premise), deployment mode (web-based, mobile-based), and user type (real estate agents, brokers, developers). Major players like Zillow, Crexi, and CoStar Group are constantly innovating and expanding their offerings to cater to the evolving needs of the market. The competitive landscape is characterized by a mix of established players and emerging startups, resulting in a dynamic and rapidly evolving market. While initial investment costs can be a barrier for some smaller firms, the long-term benefits of enhanced efficiency and increased market reach are compelling drivers of adoption. The market's growth is expected to continue at a healthy rate through 2033, supported by ongoing technological advancements and the continued digitization of the real estate industry. The historical period (2019-2024) shows a steady upward trend, likely reflecting the accelerating digital transformation within real estate. We can project a reasonable market size for 2025 based on this trend and the indicated CAGR (let's assume, for example, a CAGR of 15% for illustration). The segmentation allows for targeted marketing strategies, with cloud-based solutions likely dominating due to scalability and accessibility. Furthermore, integration with other real estate technologies, such as CRM and marketing automation tools, is becoming crucial for competitive advantage. Regional variations will likely reflect differences in technology adoption rates and the maturity of the real estate markets in each region. Restraints might include data security concerns, the need for consistent user training, and the integration challenges associated with legacy systems. However, the overall market outlook remains positive, indicating substantial growth opportunities for both established players and new entrants.

Based on the RWI-GEO-RED data that base on the data provided by ImmobilienScout24 hedonic housing price indices are estimated. The indices are on the grid level, LMR, district/county and municipality level. We conduct a hedonic price regression that covers characteristics of the object as well as regional fixed effects. The hedonic regression is estimated separately for houses for sale as well as apartments for rent and for sale. We also offer a combined index which combines the individual housing types into one index. There are three different specifications: First, the overall time development from 01/2008 to 05/2024 on grid level given yearly and quaterly; Second, cross-regional differences for each year separately and time development within one region from 01/2018 to 05/2024 (municipality, district, LMR, and grid level); third, the time-region fixed effect between 2008 and 2024, which is used to determine the price changes for all three region types to the base year of 2008. RWI-GEO-REDX Other The data is based on the data set RWI-GEO-RED, that collects all offers for private housing on ImmobilienScout24 between January 2008 and May 2024. ImmobilienScout24 is the largest listing website for real estate in Germany. The price indices are estimated labor market region, district and municipality level