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This dataset contains property sales data, including information such as PropertyID, property type (e.g., Commercial or Residential), tax keys, property addresses, architectural styles, exterior wall materials, number of stories, year built, room counts, finished square footage, units (e.g., apartments), bedroom and bathroom counts, lot sizes, sale dates, and sale prices. Explore this dataset to gain insights into real estate trends and property characteristics.
| Field Name | Description | Type |
|---|---|---|
| PropertyID | A unique identifier for each property. | text |
| PropType | The type of property (e.g., Commercial or Residential). | text |
| taxkey | The tax key associated with the property. | text |
| Address | The address of the property. | text |
| CondoProject | Information about whether the property is part of a condominium | text |
| project (NaN indicates missing data). | ||
| District | The district number for the property. | text |
| nbhd | The neighborhood number for the property. | text |
| Style | The architectural style of the property. | text |
| Extwall | The type of exterior wall material used. | text |
| Stories | The number of stories in the building. | text |
| Year_Built | The year the property was built. | text |
| Rooms | The number of rooms in the property. | text |
| FinishedSqft | The total square footage of finished space in the property. | text |
| Units | The number of units in the property | text |
| (e.g., apartments in a multifamily building). | ||
| Bdrms | The number of bedrooms in the property. | text |
| Fbath | The number of full bathrooms in the property. | text |
| Hbath | The number of half bathrooms in the property. | text |
| Lotsize | The size of the lot associated with the property. | text |
| Sale_date | The date when the property was sold. | text |
| Sale_price | The sale price of the property. | text |
Data.milwaukee.gov, (2023). Property Sales Data. [online] Available at: https://data.milwaukee.gov [Accessed 9th October 2023].
Open Definition. (n.d.). Creative Commons Attribution 4.0 International Public License (CC BY 4.0). [online] Available at: http://www.opendefinition.org/licenses/cc-by [Accessed 9th October 2023].
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TwitterSuccess.ai’s Commercial Real Estate Data and B2B Contact Data for Global Real Estate Professionals is a comprehensive dataset designed to connect businesses with industry leaders in real estate worldwide. With over 170M verified profiles, including work emails and direct phone numbers, this solution ensures precise outreach to agents, brokers, property developers, and key decision-makers in the real estate sector.
Utilizing advanced AI-driven validation, our data is continuously updated to maintain 99% accuracy, offering actionable insights that empower targeted marketing, streamlined sales strategies, and efficient recruitment efforts. Whether you’re engaging with top real estate executives or sourcing local property experts, Success.ai provides reliable and compliant data tailored to your needs.
Key Features of Success.ai’s Real Estate Professional Contact Data
AI-Powered Validation: All profiles are verified using cutting-edge AI to ensure up-to-date accuracy. Real-Time Updates: Our database is refreshed continuously to reflect the most current information. Global Compliance: Fully aligned with GDPR, CCPA, and other regional regulations for ethical data use.
API Integration: Directly integrate data into your CRM or project management systems for seamless workflows. Custom Flat Files: Receive detailed datasets customized to your specifications, ready for immediate application.
Why Choose Success.ai for Real Estate Contact Data?
Best Price Guarantee Enjoy competitive pricing that delivers exceptional value for verified, comprehensive contact data.
Precision Targeting for Real Estate Professionals Our dataset equips you to connect directly with real estate decision-makers, minimizing misdirected efforts and improving ROI.
Strategic Use Cases
Lead Generation: Target qualified real estate agents and brokers to expand your network. Sales Outreach: Engage with property developers and executives to close high-value deals. Marketing Campaigns: Drive targeted campaigns tailored to real estate markets and demographics. Recruitment: Identify and attract top talent in real estate for your growing team. Market Research: Access firmographic and demographic data for in-depth industry analysis.
Data Highlights 170M+ Verified Professional Profiles 50M Work Emails 30M Company Profiles 700M Global Professional Profiles
Powerful APIs for Enhanced Functionality
Enrichment API Ensure your contact database remains relevant and up-to-date with real-time enrichment. Ideal for businesses seeking to maintain competitive agility in dynamic markets.
Lead Generation API Boost your lead generation with verified contact details for real estate professionals, supporting up to 860,000 API calls per day for robust scalability.
Targeted Outreach for New Projects Connect with property developers and brokers to pitch your services or collaborate on upcoming projects.
Real Estate Marketing Campaigns Execute personalized marketing campaigns targeting agents and clients in residential, commercial, or industrial sectors.
Enhanced Sales Strategies Shorten sales cycles by directly engaging with decision-makers and key stakeholders.
Recruitment and Talent Acquisition Access profiles of highly skilled professionals to strengthen your real estate team.
Market Analysis and Intelligence Leverage firmographic and demographic insights to identify trends and optimize business strategies.
Success.ai’s B2B Contact Data for Global Real Estate Professionals delivers the tools you need to connect with the right people at the right time, driving efficiency and success in your business operations. From agents and brokers to property developers and executiv...
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This dataset comprises detailed real estate listings scraped from Realtor.com, providing a snapshot of various property types across Chicago. It includes 2,000 entries with information on property characteristics such as type, size, age, price, and features. This dataset was ethically collected using an API provided by Apify, ensuring all data scraping adhered to ethical standards.
This dataset is ideal for a variety of data science applications, including but not limited to: - Predictive Modeling: Forecast property prices based on various features like location, size, and age. - Market Analysis: Understand trends in real estate, including the types of properties being sold, pricing trends, and the influence of property features on market value. - Natural Language Processing: Analyze the textual descriptions provided for each listing to extract additional features or perform sentiment analysis. - Anomaly Detection: Identify unusual listings or potential outliers in the data, which could indicate errors in data collection or unique investment opportunities.
This dataset was responsibly and ethically mined, adhering to all legal standards of data collection. The use of Apify's API ensures that the data collection process respects privacy and the platform's terms of service.
We thank Realtor.com for maintaining a comprehensive and accessible database, and Apify for providing the tools necessary for ethical data scraping. Their contributions have been invaluable in the creation of this dataset. Credits to Dall E3 for thumbnail image.
This dataset is provided for non-commercial and educational purposes only. Users are encouraged to use this data to enhance learning, contribute to academic or personal projects, and develop skills in data science and real estate market analysis.
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TwitterThis table contains property sales information including sale date, price, and amounts for properties within Fairfax County. There is a one to many relationship to the parcel data. Refer to this document for descriptions of the data in the table.
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TwitterThe Office of Policy and Management maintains a listing of all real estate sales with a sales price of $2,000 or greater that occur between October 1 and September 30 of each year. For each sale record, the file includes: town, property address, date of sale, property type (residential, apartment, commercial, industrial or vacant land), sales price, and property assessment. Data are collected in accordance with Connecticut General Statutes, section 10-261a and 10-261b: https://www.cga.ct.gov/current/pub/chap_172.htm#sec_10-261a and https://www.cga.ct.gov/current/pub/chap_172.htm#sec_10-261b. Annual real estate sales are reported by grand list year (October 1 through September 30 each year). For instance, sales from 2018 GL are from 10/01/2018 through 9/30/2019. Some municipalities may not report data for certain years because when a municipality implements a revaluation, they are not required to submit sales data for the twelve months following implementation.
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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.
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:
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.
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In 2023, the UK Real Estate Market reached a value of USD 816.7 million, and it is projected to surge to USD 919.0 million by 2030.
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The Real Estate Marketing Automation Software market has witnessed significant evolution over the past few years, driven by the ever-growing need for efficiency and effectiveness in real estate marketing strategies. This technology enables real estate professionals-agents, brokers, and firms-to streamline their mark
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This dataset features original charted data visualizations derived from NBREA reports covering the Greater Saint John, New Brunswick real estate market. It includes monthly insights from January 2024 to present: sales activity, new listings, dollar volume, average prices, sales-to-new-listings ratio, and months of inventory.
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Explore the Redfin USA Properties Dataset, available in CSV format. This extensive dataset provides valuable insights into the U.S. real estate market, including detailed property listings, prices, property types, and more across various states and cities. Perfect for those looking to conduct in-depth market analysis, real estate investment research, or financial forecasting.
Key Features:
Who Can Benefit From This Dataset:
Download the Redfin USA Properties Dataset to access essential information on the U.S. housing market, ideal for professionals in real estate, finance, and data analytics. Unlock key insights to make informed decisions in a dynamic market environment.
Looking for deeper insights or a custom data pull from Redfin?
Send a request with just one click and explore detailed property listings, price trends, and housing data.
🔗 Request Redfin Real Estate Data
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The real estate lead generation software market is experiencing robust growth, driven by increasing adoption of digital marketing strategies by real estate agents and brokers. The market's value is estimated to be in the hundreds of millions of dollars in 2025, reflecting a significant expansion since 2019. This growth is fueled by several key factors: the rising preference for online property searches, the need for efficient lead management to improve conversion rates, and the increasing sophistication of CRM (Customer Relationship Management) systems integrated with lead generation tools. Competition is intense, with established players like Infusionsoft, Pardot, and Marketo vying for market share alongside niche players specializing in real estate like Real Geeks, BoomTown!, and Zillow Premier Agent. The market is segmented by software features (e.g., CRM, email marketing, social media integration), pricing models (subscription-based, one-time purchase), and deployment methods (cloud-based, on-premise). While the market shows strong potential, challenges remain, including the high cost of implementation for some solutions, the need for ongoing training and support, and the potential for data security breaches. The market is expected to maintain a healthy Compound Annual Growth Rate (CAGR) throughout the forecast period (2025-2033), propelled by ongoing technological advancements and the continued shift towards digital interactions in the real estate industry. Further growth will be influenced by evolving consumer preferences, regulatory changes impacting data privacy, and the emergence of innovative lead generation techniques such as AI-powered chatbots and personalized marketing campaigns. Companies are constantly innovating to offer more integrated solutions that streamline workflows and improve agent productivity. Geographic expansion, particularly in emerging markets with growing internet penetration, also presents a significant opportunity for market expansion. The competitive landscape will continue to evolve, with mergers and acquisitions likely as larger companies seek to consolidate their market presence. Success in this market will depend on offering robust, user-friendly software with strong customer support and a proven track record of generating qualified leads. The ability to integrate with other real estate platforms and provide insightful data analytics will be key differentiators for market leaders.
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TwitterNorth America was home to the largest listed real estate market in 2024. The aggregate market size of the listed commercial real estate market in Canada and the United States amounted to *** trillion U.S. dollars as of December 2024. Listed real estate refers to real estate companies that are quoted on stock exchanges and receive income from real estate assets.
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The Information Technology (IT) sector in the real estate market has rapidly transformed how industry stakeholders conduct business, making it a critical area of focus for professionals and investors alike. IT solutions have streamlined property management, enhanced customer experience, and facilitated a more effici
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The Housing Data Extracted from Homes.com (USA) dataset is a comprehensive collection of 2 million real estate listings sourced from Homes.com, one of the leading real estate platforms in the United States. This dataset offers detailed insights into the U.S. housing market, making it an invaluable resource for real estate professionals, investors, researchers, and analysts.
The dataset contains extensive property details, including location, price, property type (single-family homes, condos, apartments), number of bedrooms and bathrooms, square footage, lot size, year built, and availability status. Organized in CSV format, it provides users with easy access to structured data for analyzing trends, developing investment strategies, or building real estate applications.
Key Features:
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Real Estate CMA Software Market size was valued at USD 5.1 Billion in 2024 and is projected to reach USD 8.62 Billion by 2031, growing at a CAGR of 7.1% during the forecasted period 2024 to 2031
Global Real Estate CMA Software Market Drivers
Growing Need for Data-driven Decision-Making: Real estate agents are depending more and more on analytics and data to help them make wise choices. With the use of CMA software, which offers thorough data analysis and insights into comparable sales, market trends, and property values, agents and brokers can more successfully negotiate transactions, set listing prices, and evaluate properties with accuracy.
Requirement for a Competitive Advantage: In the current competitive real estate market, brokerages and agents look for solutions that set them apart from rivals and improve the value they offer to clients. Agents can dazzle customers and acquire more listings by using the sophisticated features of CMA software to create professional-looking comparative market assessments, customisable presentations, and interactive reports.
Growing Significance of Engaging Clients: Gaining trust, cultivating relationships, and closing deals in the real estate sector depend on offering clients individualized and engaging experiences. Through visually appealing presentations, interactive maps, and dynamic charts that provide market data and property information in an engaging and understandable manner, agents may effectively engage clients with the help of CMA software.
Simplifying the Listing Presentation Process: Real estate marketing and client acquisition heavily depend on the preparation and delivery of listing presentations. With the help of CMA software, agents can rapidly create professional-looking reports, add branding elements, and show prospective sellers the features, amenities, and market comparisons of their properties. The process of making bespoke listing presentations is also made more efficient and automated.
Integration with Various Data Sources: To obtain thorough and current market data, CMA software integrates with a variety of data sources, such as MLS (Multiple Listing Service) databases, property tax records, public documents, and third-party data providers. The accuracy and reliability of CMAs are increased by this integration, which gives agents access to reliable property information, historical sales data, area demographics, and market statistics.
Efficiency and Time Savings: CMA software saves agents time and effort while creating market studies by automating repetitive operations including data collecting, analysis, and report preparation. CMA software increases efficiency by optimizing workflow procedures and decreasing manual data input, freeing up agents to concentrate more on interacting with clients, generating leads, and completing sales.
Use of sophisticated Technologies: The real estate sector is changing as a result of the use of sophisticated technologies including machine learning (ML), artificial intelligence (AI), and predictive analytics. CMA software helps agents predict market trends, pricing swings, and changes in property worth by using AI and ML algorithms to scan massive information, spot patterns, and produce predictive insights.
Remote Work and Virtual Collaboration: The COVID-19 epidemic has hastened the trend toward remote work and virtual collaboration, which has raised demand for digital solutions that facilitate communication and cooperation from a distance. Agents can make virtual listing presentations, electronically communicate information with clients, and work in real-time team collaborations regardless of their physical locations thanks to CMA software.
Accuracy and Regulatory Compliance: Real estate transactions must adhere to a number of rules and regulations, such as ethical norms, disclosure legislation, and fair housing laws. By offering precise and impartial market evaluations and assisting agents in avoiding the possible legal ramifications of overpricing or underpricing properties, CMA software helps them maintain compliance.
Globalization and Market Expansion: The need for CMA software with international capabilities is driven by the growth of real estate brokerages into new geographic areas and the globalization of real estate markets. Agents can serve clients in a variety of global marketplaces thanks to multilingual support, currency conversion, and localization tools, which facilitate cross-border transactions and global expansion strategies.
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The Artificial Intelligence (AI) in Real Estate market is rapidly transforming the landscape of property buying, selling, and management, utilizing advanced algorithms and data analytics to enhance decision-making processes and customer experiences. As of 2023, the estimated market size for AI in real estate stands
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TwitterIn the second quarter of 2025, the real estate index in Poland amounted to ***** points, which was an improvement of **** points compared to the first quarter of 2025.
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TwitterZillow 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)
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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.
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This dataset contains property sales data, including information such as PropertyID, property type (e.g., Commercial or Residential), tax keys, property addresses, architectural styles, exterior wall materials, number of stories, year built, room counts, finished square footage, units (e.g., apartments), bedroom and bathroom counts, lot sizes, sale dates, and sale prices. Explore this dataset to gain insights into real estate trends and property characteristics.
| Field Name | Description | Type |
|---|---|---|
| PropertyID | A unique identifier for each property. | text |
| PropType | The type of property (e.g., Commercial or Residential). | text |
| taxkey | The tax key associated with the property. | text |
| Address | The address of the property. | text |
| CondoProject | Information about whether the property is part of a condominium | text |
| project (NaN indicates missing data). | ||
| District | The district number for the property. | text |
| nbhd | The neighborhood number for the property. | text |
| Style | The architectural style of the property. | text |
| Extwall | The type of exterior wall material used. | text |
| Stories | The number of stories in the building. | text |
| Year_Built | The year the property was built. | text |
| Rooms | The number of rooms in the property. | text |
| FinishedSqft | The total square footage of finished space in the property. | text |
| Units | The number of units in the property | text |
| (e.g., apartments in a multifamily building). | ||
| Bdrms | The number of bedrooms in the property. | text |
| Fbath | The number of full bathrooms in the property. | text |
| Hbath | The number of half bathrooms in the property. | text |
| Lotsize | The size of the lot associated with the property. | text |
| Sale_date | The date when the property was sold. | text |
| Sale_price | The sale price of the property. | text |
Data.milwaukee.gov, (2023). Property Sales Data. [online] Available at: https://data.milwaukee.gov [Accessed 9th October 2023].
Open Definition. (n.d.). Creative Commons Attribution 4.0 International Public License (CC BY 4.0). [online] Available at: http://www.opendefinition.org/licenses/cc-by [Accessed 9th October 2023].