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.

1. Customers File (customers.csv)

• Description: This file contains information about clients involved in real estate transactions. It includes personal details such as name, surname, birth date, gender, and country, along with transaction-specific information like the purpose of the deal and the satisfaction level.
• Key Columns:
  • customerid: Unique identifier for the customer.
  • entity: Type of client, whether an individual or a company.
  • name and surname: First and last name of the customer.
  • birth_date: Customer's date of birth.
  • sex: Gender of the customer (Male/Female).
  • country and state: The country and state the customer is associated with.
  • purpose: Purpose of the transaction (e.g., Home purchase or Investment).
  • deal_satisfaction: Customer's satisfaction level with the transaction, ranging from 1 to 5.
  • mortgage: Indicates whether the transaction involved a mortgage (Yes/No).
  • source: How the customer was acquired (e.g., Website or Agency).

2. Properties File (properties.csv)

• Description: This file contains information about the properties sold, including building details, property type, area, price, and sale status.
• Key Columns:
  • id: Unique identifier for the property.
  • building: Number of the building where the property is located.
  • date_sale: The date when the property was sold.
  • type: Type of property (e.g., Apartment).
  • property#: The property number within the building.
  • area: Area of the property in square feet.
  • price: Sale price of the property.
  • status: Status of the sale (e.g., Sold).
  • customerid: The unique identifier of the customer associated with the property.

Suggested Analysis and Tasks

1 Customer Insights: - Customer Segmentation: Group customers based on demographics, purpose, or deal satisfaction to understand different customer profiles. - Satisfaction Analysis: Investigate what factors (e.g., property price, area, or mortgage involvement) influence customer satisfaction levels. - Source Effectiveness: Analyze which acquisition sources (e.g., website or agency) yield the highest deal satisfaction.

2 Property Market Analysis: - Price Trends: Analyze how property prices vary over time or by location to identify market trends. - Demand Analysis: Determine which types of properties (e.g., apartments vs. houses) are most popular based on sales data. - Area vs. Price: Explore the relationship between property area and price to develop pricing models or evaluate property value.

3 Predictive Modeling: - Price Prediction: Build models to predict property prices based on features like area, type, and location. - Satisfaction Prediction: Create models to predict customer satisfaction using transaction details and demographics. - Likelihood of Sale: Develop a model to predict the likelihood of a property being sold based on its attributes and market conditions.

4 Geographical Analysis: - Heatmaps: Create heatmaps to visualize property sales and identify high-demand areas. - Country and State Trends: Examine how real estate trends differ between countries and states.

5 Mortgage Impact Study: - Mortgage vs. Non-Mortgage Analysis: Compare transactions that involved a mortgage to those that didn’t to study the impact on price, satisfaction, and deal closure speed.

6 Time Series Analysis: - Sales Over Time: Analyze property sales over different periods to identify seasonal trends or patterns. - Customer Birth Date Analysis: Study any correlations between customers’ birth years and their purchasing behavior.

Main Dataset

I scrapped data from 99acres using their (kind of) hidden API. I scrapped almost 10,000+ data using my scrapper app see here.

DESCRIPTION OF THE DATA

• Contains details of properties of Gurgaon, Hyderabad, Mumbai, Kolkata cities of India.
• All datasets of different cities contains almost 10K properties.
• In some datasets, some columns are not available. Sorry!!
• Target column: PRICE

Data Usage

This dataset can be used for various real estate-related tasks, including:

• Property price prediction.
• Market analysis to identify trends and patterns.
• Identifying popular property types and locations.
• Evaluating the impact of property attributes on price.

EXPLANATION OF EACH COLUMNS

NOTE: Not all the columns are important for you so first try to understand your problem statement and then filter this dataset accordingly.

• AGE: The age of the property in years.
• ALT_TAG: An alternative tag or description.
• AMENITIES: Describes the amenities available with the property.
• AREA: The area of the property.
• BALCONY_NUM: The number of balconies in the property.
• BATHROOM_NUM: The number of bathrooms in the property.
• BEDROOM_NUM: The number of bedrooms in the property.
• BROKERAGE: Information about the brokerage or agency associated with the property listing.
• BUILDING_ID: An integer identifier for the building.
• BUILDING_NAME: The name of the building.
• BUILTUP_SQFT: The total built-up area of the property in square feet.
• CARPET_SQFT: The total carpet area of the property in square feet.
• CITY_ID: An identifier for the city in which the property is located.
• CITY: The city where the property is located.
• CLASS_HEADING: A heading for the property class.
• CLASS_LABEL: A label representing the property class.
• CLASS: A classification label for the property.
• COMMON_FURNISHING_ATTRIBUTES: Attributes related to the furnishings and amenities commonly found in the property.
• CONTACT_COMPANY_NAME: The name of the company or agency responsible for the property listing.
• CONTACT_NAME: The name of the contact person associated with the property listing.
• DEALER_PHOTO_URL: URL to a photo or image associated with the property dealer.
• DESCRIPTION: A description of the property listing.
• EXPIRY_DATE: The date when the listing expires.
• FACING: Indicates the direction the property is facing.
• FEATURES: Describes the features of the property.
• FLOOR_NUM: The floor number of the property.
• FORMATTED_LANDMARK_DETAILS: Details of nearby landmarks.
• FORMATTED: Formatted information related to the property.
• FSL_Data: Data related to the property, possibly specific to a particular real estate agency.
• FURNISH: Indicates whether the property is furnished.
• FURNISHING_ATTRIBUTES: Attributes describing the level of furnishing in the property.
• GROUP_NAME: The name of the group or organization to which the property may belong.
• LISTING: Information about the property listing, possibly including its status and other details.
• LOCALITY_WO_CITY: The locality name without the city information.
• LOCALITY: The specific locality or neighborhood where the property is situated.
• location: Additional location information.
• MAP_DETAILS: Contains latitude and longitude information.
• MAX_AREA_SQFT: The maximum area of the property in square feet.
• MAX_PRICE: The maximum price of the property.
• MEDIUM_PHOTO_URL: URL to a medium-sized photo or image of the property.
• metadata: Additional metadata or information about the dataset.
• MIN_AREA_SQFT: The minimum area of the property in square feet.
• MIN_PRICE: The minimum price of the property.
• OWNTYPE: An integer representing the ownership type.
• PD_URL: URL to additional property details.
• PHOTO_URL: URL to photos or images associated with the property.
• POSTING_DATE: The date when the property listing was posted.
• PREFERENCE: Indicates the preference type for the property listing (e.g., "S" for sale).
• PRICE_PER_UNIT_AREA: The price per unit area of the property.
• PRICE_SQFT: The price per square foot of the property.
• PRICE: The price of the property. This is target column for ML.
• PRIMARY_TAGS: Primary tags or labels.
• PRODUCT_TYPE: The type of product listing.
• profile: Profile information related to the property or listing.
• PROJ_ID: An integer identifier for the project.
• PROP_DETAILS_URL: URL to detailed property information.
• PROP_HEADING: A heading or title for the property.
• PROP_ID: A ...

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.

https://cdn.vectorstock.com/i/preview-1x/58/33/shwedish-town-silhouette-vector-9305833.webp">

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.

The Property Group is a leading real estate organization that provides expert guidance throughout the home buying and selling process. With a strong presence in Little Rock, Arkansas, the company has established itself as a trusted partner for individuals and families seeking to buy, sell, or rent properties. The Property Group's expert agents are well-versed in local market trends, ensuring that clients receive tailored solutions to their unique needs.

Through their user-friendly website, The Property Group offers a range of resources and tools for homebuyers, including exclusive property listings, neighborhood information, and real-time market reports. Whether buying or selling a home, clients can rely on the company's dedicated professionals to navigate the complex process with ease. With a focus on transparency, efficiency, and personalized attention, The Property Group has earned a reputation as a top choice for those seeking a seamless and stress-free real estate experience.

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

AI-Generated Real Estate Listing Market Outlook

According to our latest research, the global AI-Generated Real Estate Listing market size reached USD 1.42 billion in 2024, reflecting robust momentum in the adoption of artificial intelligence within the real estate sector. The market is projected to grow at a CAGR of 18.9% during the forecast period, with the value expected to reach USD 7.32 billion by 2033. This remarkable growth is primarily driven by the increasing demand for automation in property listing processes, enhanced customer experiences, and the need for efficient data-driven decision-making in real estate transactions. As per our comprehensive analysis, the market is witnessing rapid technological advancements and broader acceptance across various end-user segments, setting the stage for significant expansion over the next decade.

One of the primary growth factors fueling the AI-Generated Real Estate Listing market is the accelerating digital transformation within the real estate industry. Real estate agencies, property developers, and individual agents are increasingly leveraging artificial intelligence to automate the creation, curation, and management of property listings. AI-powered solutions can analyze vast amounts of data, generate compelling descriptions, and provide personalized recommendations, thereby reducing manual workload and enhancing the accuracy of listings. This automation not only improves operational efficiency but also allows real estate professionals to focus on higher-value activities such as client engagement and strategic decision-making. The integration of AI with existing real estate platforms is further streamlining workflows and offering a seamless experience for both sellers and buyers.

Another significant driver is the rising consumer expectation for personalized and immersive property search experiences. Modern homebuyers and renters demand highly detailed, visually appealing, and tailored property listings that go beyond traditional text-based descriptions. AI-generated listings can automatically incorporate high-quality images, virtual tours, and dynamic content based on user preferences and behavior analytics. This personalized approach increases user engagement, boosts conversion rates, and enhances overall satisfaction. Real estate platforms utilizing AI are able to match properties more effectively with potential buyers, minimize time-on-market, and optimize pricing strategies, thereby creating a competitive advantage for early adopters in the industry.

The proliferation of cloud computing and advancements in natural language processing (NLP) and computer vision technologies have also played a pivotal role in market growth. Cloud-based AI solutions offer scalability, flexibility, and cost-effectiveness, making them accessible to a broader range of real estate stakeholders, from large agencies to individual agents. Enhanced NLP algorithms enable the automatic generation of contextually relevant and grammatically accurate property descriptions, while computer vision assists in categorizing and enhancing property images. These technological innovations are not only improving the quality and consistency of listings but are also enabling the integration of real-time market insights and predictive analytics, further empowering users to make informed decisions.

From a regional perspective, North America remains the dominant market for AI-Generated Real Estate Listing solutions, accounting for the largest revenue share in 2024. The region's advanced technological infrastructure, high internet penetration, and strong presence of leading proptech companies have accelerated the adoption of AI-driven tools. Europe is also witnessing substantial growth, driven by increasing investments in digital real estate platforms and a rising focus on sustainability and smart city initiatives. The Asia Pacific region is expected to experience the fastest CAGR during the forecast period, fueled by rapid urbanization, growing real estate markets, and government initiatives supporting digital transformation. Latin America and the Middle East & Africa are gradually catching up as awareness and adoption of AI technologies expand across these regions.

The introduction of the AI-Powered Rental Price Index is a groundbreaking development in the real estate sector. This innov

Graph and download economic data for Housing Inventory: Active Listing Count in the United States (ACTLISCOUUS) from Jul 2016 to Oct 2025 about active listing, listing, and USA.

Irambati is a leading real estate company that provides property listings and market insights to individuals seeking to buy, sell, or rent properties in India. As a prominent player in the Indian real estate market, Irambati's website features a vast array of data on residential and commercial properties, including prices, locations, and amenities.

The company's data repository includes a wide range of property types, from apartments to independent houses, and covers major cities and towns across India. Irambati's expertise in the Indian real estate market, combined with its vast database of property listings, makes it an invaluable resource for anyone seeking to make an informed decision about their property needs.

Our extensive database contains approximately 800,000 active rental property listings from across the United States. Updated daily, this comprehensive collection provides real estate professionals, investors, and property managers with valuable market intelligence and business opportunities. Database Contents

Property Addresses: Complete location data including street address, city, state, ZIP code Listing Dates: Original listing date and most recent update date Availability Status: Currently available, pending, or recently rented properties Geographic Coverage: Properties spanning all 50 states and major metropolitan areas

Applications & Uses

Market Analysis: Track rental pricing trends across different regions and property types Investment Research: Identify high-opportunity markets with favorable rental conditions Lead Generation: Connect with property owners potentially needing management services Competitive Intelligence: Monitor listing volumes, vacancy rates, and market saturation Business Development: Target specific neighborhoods or property categories for expansion

File Format & Delivery

Organized in easy-to-use CSV format for seamless integration with data analysis tools Accessible through secure download portal or API connection Daily updates ensure you're working with the most current market information Custom filtering options available to narrow results by location, date range, or other criteria

Data Quality

Rigorous validation processes to ensure address accuracy Duplicate listing detection and removal Regular verification of active status Standardized format for consistent analysis

Subscription Benefits

Access to historical listing archives for trend analysis Advanced search capabilities to target specific property characteristics Regular market reports summarizing key trends and opportunities Custom data exports tailored to your specific business needs

AK ~ 1,342 listings AL ~ 6,636 listings AR ~ 4,024 listings AZ ~ 25,782 listings CA ~ 102,833 listings CO ~ 14,333 listings CT ~ 10,515 listings DC ~ 1,988 listings DE ~ 1,528 listings FL ~ 152,258 listings GA ~ 28,248 listings HI ~ 3,447 listings IA ~ 4,557 listings ID ~ 3,426 listings IL ~ 42,642 listings IN ~ 8,634 listings KS ~ 3,263 listings KY ~ 5,166 listings LA ~ 11,522 listings MA ~ 53,624 listings MD ~ 12,124 listings ME ~ 1,754 listings MI ~ 12,040 listings MN ~ 7,242 listings MO ~ 10,766 listings MS ~ 2,633 listings MT ~ 1,953 listings NC ~ 22,708 listings ND ~ 1,268 listings NE ~ 1,847 listings NH ~ 2,672 listings NJ ~ 31,286 listings NM ~ 2,084 listings NV ~ 13,111 listings NY ~ 94,790 listings OH ~ 15,843 listings OK ~ 5,676 listings OR ~ 8,086 listings PA ~ 37,701 listings RI ~ 4,345 listings SC ~ 8,018 listings SD ~ 1,018 listings TN ~ 15,983 listings TX ~ 132,620 listings UT ~ 3,798 listings VA ~ 14,087 listings VT ~ 946 listings WA ~ 15,039 listings WI ~ 7,393 listings WV ~ 1,681 listings WY ~ 730 listings

Grand Total ~ 977,010 listings

Real estate websites emerged as the most popular resource among homebuyers reviewing mortgage financing options in 2024. Approximately 58 percent of respondents shared that they used websites such as Zillow, RE/MAX or Realtor.com when looking at finance options. Referrals and search engines also played a crucial role, according to over half of respondents.

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.

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

The Multiple Listing Service (MLS) Software market is poised for robust expansion, projected to reach a substantial market size of approximately $5,500 million by 2025, with a compelling Compound Annual Growth Rate (CAGR) of 12.5% anticipated throughout the forecast period from 2025 to 2033. This significant growth is fueled by the increasing digitization of real estate transactions and the rising demand for efficient property listing and management solutions among real estate professionals. The market is experiencing a strong surge driven by the need for streamlined workflows, enhanced data accuracy, and improved client engagement tools within the real estate industry. Cloud-based solutions are dominating the market due to their scalability, accessibility, and cost-effectiveness, offering a distinct advantage over traditional on-premises systems. The competitive landscape is characterized by the presence of key players like Zillow, Crexi, News Corp, and CoStar Group, who are continually innovating to offer comprehensive features such as advanced search functionalities, virtual tour integrations, and robust CRM capabilities. The market is segmented by application into Large Enterprises and Small and Medium-sized Enterprises (SMEs), with both segments demonstrating a growing appetite for advanced MLS software to gain a competitive edge. Geographically, North America, particularly the United States, remains a dominant region, while the Asia Pacific region is expected to witness the fastest growth due to its burgeoning real estate markets and increasing adoption of technology. Despite the positive outlook, challenges such as data security concerns and the initial cost of implementation for smaller entities could pose some restraints, but the overwhelming benefits of enhanced productivity and market reach are expected to outweigh these hurdles.

Dataset Overview

This dataset provides a snapshot of properties listed for sale in London, sourced from the Rightmove website. It includes various property details such as the number of bedrooms, bathrooms, type of property, and price. The dataset is designed for educational purposes, offering insights into real estate trends and allowing data science enthusiasts to apply their skills in the context of property analysis.

Data Science Applications

This dataset is a valuable resource for students and researchers to practice various data science and analytics techniques. Potential applications include: - Exploratory Data Analysis (EDA): Understanding property distribution across London, price trends, and property types. - Price Prediction Models: Building machine learning models to estimate property prices based on available features. - Real Estate Trend Analysis: Analyzing trends in London’s real estate market, such as price fluctuations or differences in property features by neighborhood. - Text Analysis: Using the property descriptions for natural language processing (NLP) to extract keywords or sentiment related to property value or appeal.

Column Descriptions

• addedOn: Date when the property listing was added or updated on the website.
• title: Brief listing title describing the property, typically including the number of bedrooms and the location.
• descriptionHtml: Detailed description of the property, including features and potentially some promotional language.
• propertyType: Type of property, such as House, Terraced, or Detached.
• sizeSqFeetMax: Maximum size of the property in square feet, if provided.
• bedrooms: Number of bedrooms in the property.
• bathrooms: Number of bathrooms in the property.
• listingUpdateReason: Reason for updating the listing (e.g., new listing, price reduction).
• price: Price at which the property is listed for sale.

Ethically Mined Data

This dataset was ethically mined from a publicly accessible website using the APIFY API. All data in this dataset reflects publicly available information about properties listed for sale, with no Personally Identifiable Information (PII) included. The dataset does not include any data that could infringe on individual privacy.

Acknowledgements

• Data Source: Rightmove for providing publicly accessible real estate listings.
• Image Credit: Photo by Douglas Sheppard on Unsplash.

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

Real Estate & Property Software Market Outlook

The global real estate and property software market is experiencing a robust growth trajectory, with a market size valued at approximately USD 9.5 billion in 2023. It is projected to reach an impressive USD 19.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.3% over the forecast period. This significant growth can be attributed to the increasing digitization within the real estate sector, fueled by advancements in technology that are reshaping how real estate professionals and property managers conduct their operations. The integration of sophisticated software solutions offers numerous benefits including improved efficiency, enhanced customer experiences, and streamlined operations, which are critical growth factors driving this market forward.

A major growth factor for the real estate and property software market is the heightened demand for automation and digital transformation in real estate operations. As the industry transitions from traditional methods to more innovative solutions, software platforms are becoming central to managing property listings, tenant information, and financial transactions with greater accuracy and speed. This transition is driven by the need for real-time data analytics, which can provide insights into market trends and consumer behaviours. Property managers and real estate agents are increasingly reliant on these insights to make data-driven decisions that enhance profitability and operational efficiency. Furthermore, the rise of smart technologies and the Internet of Things (IoT) is encouraging the adoption of property management software, making it indispensable in modern real estate practices.

Another critical driver of market growth is the surging investment in real estate technology startups. Venture capital firms and investors are recognizing the potential of innovative real estate software solutions to disrupt traditional business models. As a result, substantial investments are being funneled into startups that are developing cutting-edge solutions to address various aspects of real estate management, from virtual reality tours and blockchain-based transactions to AI-driven customer service platforms. These investments are not only accelerating product development but are also expanding the reach of these technologies globally, thereby fostering market growth. This influx of capital is expected to continue, supporting new advancements and wider adoption of real estate and property software.

Moreover, the increased focus on sustainability and energy efficiency within the real estate sector is propelling the demand for software solutions that can monitor and optimize energy consumption in buildings. Property software now includes functionalities that enable the tracking of energy use, emissions, and sustainability metrics, aligning with the growing environmental awareness and regulatory pressures. As governments and organizations worldwide commit to reducing carbon footprints, the reliance on technology to meet these targets becomes more pronounced. Software solutions that aid in achieving energy efficiency and sustainability goals are thus becoming increasingly sought after, contributing significantly to the market's expansion.

The integration of Multiple Listing Service (MLS) Listing Software is becoming increasingly pivotal in the real estate industry. This software facilitates the seamless sharing of property listings among real estate professionals, enhancing collaboration and transparency. By providing a centralized database of listings, MLS software enables agents to access comprehensive property information, including pricing, location, and features, all in real-time. This not only streamlines the property search process for buyers but also aids sellers in reaching a wider audience. As the demand for efficient property transactions grows, MLS Listing Software is set to play a crucial role in modernizing real estate operations, offering a competitive edge to those who adopt it.

Regionally, North America holds a substantial share of the real estate and property software market, driven by a high adoption rate of advanced technologies and the presence of major market players. However, the Asia Pacific region is expected to witness the highest growth rate over the forecast period, with a CAGR of approximately 9.5%. This can be attributed to the rapid urbanization and industrialization in countries such as China and India, where there is a burgeoning demand for automated property management

About Dataset

Context

The dataset consists of data that was scraped from Graana.com website. It is Pakistani top leading property buy and sell platform. Content

Geography: Pakistan

Time period: 2022

Unit of analysis: Real states Data Analysis

Dataset: The dataset contains detailed information online data available on Graana.com website . It contains propertyid,locationid,pageurl propertytype,price,location,city,provincename,latitude,longitude baths,area,purpose,bedrooms,dateadded.

Variables: The dataset contain id,purpose,type,price,size,size_unit,user_id,listing_type, bed,bath,status,custom_title,lat,lon,geotagged_by,platform,created_at,system_user_name,user_name,area_name, area_marla_size,city_name,linksubtype,link

File Type: CSV