The summary statistics by North American Industry Classification System (NAICS) which include: operating revenue (dollars x 1,000,000), operating expenses (dollars x 1,000,000), salaries wages and benefits (dollars x 1,000,000), and operating profit margin (by percent), of real estate agents, brokers and appraisers (NAICS 53121) & offices of real estate appraisers (NAICS 53132), annual, for five years of data.

The number of members of the National Association of Realtors (NAR) in 2023 declined for the first time since 2012. This trend also reflects the recovery of the property market after the financial crisis of 2007-2009, as the volume of home sales began to climb from 2011. The NAR is a North American trade association for real estate workers formed in 1908 and currently based in Chicago, Illinois. In 2022, the association had nearly *** million members.Employment in the real estate sector The upward in NRA membership is mirrored in overall employment in the real estate sector in the United States. In 2023, *** million people were employed in the sector, which indicates that the majority of workers are members of the NAR. Employees in the real estate, rental, and leasing industry in the U.S. earned slightly above the average wage in the country. Membership growth ties in with growth in home sales The growth in NAR membership also correlates with the growth of residential property sales. For instance, the number of new houses sold in the U.S. has been on the rise since 2011. American adults as a whole have been steady in their view that homeownership is an important part of the American Dream. However, the share of American Millennials – those born between 1981 and 1996 - who view homeownership as important has been fluctuating since 2010. This adds an element of uncertainty to the future of the housing market because millennials are in their mid-twenties and thirties, which is widely viewed as the best time to buy a home from a home equity perspective.

In the 2024 survey, ** percent of home buyers used a real estate agent when searching for a home in the United States. People between 35 and 44 were least likely to use a real estate agent, while 70- to 78-year-olds were most likely to do so.

Graph and download economic data for Employed full time: Wage and salary workers: Real estate brokers and sales agents occupations: 16 years and over (LEU0254498300A) from 2000 to 2025 about agents, brokers, occupation, full-time, real estate, salaries, workers, 16 years +, wages, sales, employment, and USA.

Graph and download economic data for Employed full time: Wage and salary workers: Real estate brokers and sales agents occupations: 16 years and over: Men (LEU0254605100A) from 2000 to 2025 about agents, brokers, occupation, real estate, full-time, males, salaries, workers, 16 years +, wages, sales, employment, and USA.

📝 Dataset Description: This synthetic dataset contains 3,000 residential property listings modeled after real U.S. house sales data (in a Zillow-style format). It is designed for use in real estate analysis, machine learning, data visualization, and web scraping practice.

Each row represents a unique property and includes 16 key features commonly used by real estate agents, investors, and analysts. The data spans multiple U.S. states and cities, with realistic values for price, square footage, bedroom/bathroom count, property type, and more.

✅ Included Fields: Price – Listing price (in USD)

Address, City, State, Zipcode – U.S. formatted property location

Bedrooms, Bathrooms, Area (Sqft) – Core home specs

Lot Size, Year Built, Days on Market

Property Type, MLS ID, Listing Agent, Status

Listing URL – Mock Zillow-style property link

⚙️ Use Cases: Exploratory data analysis (EDA)

Regression/classification model training

Feature engineering and preprocessing

Real estate dashboards and web app mockups

Practice with BeautifulSoup, Pandas, or Power BI

This dataset provides a comprehensive view of real estate agent performance, detailing property transactions, client engagement, and brokerage affiliations. It enables analysis of sales metrics, agent productivity, and client satisfaction, supporting data-driven decisions for brokerages and real estate organizations.

The summary statistics by North American Industry Classification System (NAICS) which include: operating revenue (dollars x 1,000,000), operating expenses (dollars x 1,000,000), salaries wages and benefits (dollars x 1,000,000), and operating profit margin (by percent), of real estate agents, brokers and appraisers (NAICS 53121) & offices of real estate appraisers (NAICS 53132), annual, for five years of data.

In the fiscal year 2024, there were more than *** million registered real estate transaction agents in Japan. Registered real estate agents have passed a state examination and registered their qualifications with prefectural governments. While ******** registered real estate agents had obtained a license required to work as a real estate agent, around ******* of them were actually engaged in real estate brokerage.

Around **** million real estate transaction agents were registered in Japan by the end of the fiscal year 2023. More than ** thousand real estate agents newly registered that year.Persons who have passed an examination for real estate agents and have at least two years of practical work experience or, alternatively, take a practical training course can register their qualifications with prefectural governments.

Zillow reigns supreme in the U.S. real estate website landscape, attracting a staggering ***** million monthly visits in 2025. 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 2025, 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 decreased between 2021 and 2023. A probable cause for the decline was 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 real and QuinStreet experiencing significant stock price increases in 2024. 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.

For over 7 years we've been providing real estate licensee data for residential real estate, commercial real estate, and investors with active licenses.

We provide Name, Brokerage, License and Contact details for all the licensees, standardized to a common set of values.

Common use cases include: - Compliance use cases ensuring agents and brokers maintain an active license - Viewing market agent movement - Assessing Agent Count by Brokerage across States - Marketing to agents and brokers by specialization - Recruiting new agents - Monitoring agent growth

Record counts by state are as follows:

AK ~2,900 AL ~23,700 AR ~14,000 AZ ~62,700 CA ~406,400 CO ~42,900 CT ~26,000 DC ~13,500 DE ~7,100 FL ~323,800 GA ~87,400 HI ~13,300 IA ~9,400 ID ~13,400 IL ~64,300 IN ~25,400 KS ~13,900 KY ~16,700 LA ~18,900 MA ~54,700 MD ~45,300 ME ~7,200 MI ~59,200 MN ~28,600 MO ~42,500 MS ~10,000 MT ~7,000 NC ~81,700 ND ~2,800 NE ~7,700 NH ~10,900 NJ ~93,000 NM ~9,600 NV ~26,700 NY ~127,300 OH ~43,200 OK ~18,900 OR ~22,600 PA ~56,700 RI ~8,900 SC ~47,100 SD ~3,500 TN ~42,500 TX ~177,800 UT ~29,700 VA ~57,600 VT ~2,400 WA ~42,300 WI ~28,900 WV ~4,900 WY ~3,300

Graph and download economic data for All Employees: Financial Activities: Offices of Real Estate Agents and Brokers in New York (SMU36000005553120001SA) from Jan 1990 to Dec 2025 about agents, brokers, real estate, NY, employment, and USA.

Real estate is a dynamic and ever-evolving industry that relies heavily on data to make informed decisions. One of the fundamental aspects of this industry is real estate listing data. This data encompasses detailed information about properties that are available for sale or rent in a given market. It plays a pivotal role in assisting buyers, sellers, real estate professionals, and investors in making well-informed choices. In this data brief, we will provide an overview of what real estate listing data is and highlight five key industry use cases.

Real Estate Listings Data Includes:

• Property Location
• 50+ Property and Building Characteristics
• School District Information
• List Date
• Listing Price - Maximum, Minimum, Sold Price
• Listing Status
• Number of Days on Market
• Listing Agent and Office

The industry expenses expressed in percentage of the total operating expenses, for real estate agents, brokers (North American Industry Classification System 53121), annual, two years of data.

This dataset compiles information about real estate agents operating in Mumbai and its neighboring areas. It includes details such as the agent's name, location, rating, sales and rental statistics, operating areas, and the types of deals they are involved in (buying, selling, renting, or providing PG accommodations). The dataset offers insights into the diverse real estate landscape, covering different neighborhoods and regions within and around Mumbai. It serves as a valuable resource for understanding the real estate market and the key players in the industry in this geographical context.

Comprehensive list containing 10 verified Real estate agent businesses in LI with latest contact information, ratings, reviews, and location data.

Dataset Overview

Welcome to the New York Real Estate Data 2026!

This dataset contains over 8,200 active property listings scraped in Q1 2026, covering one of the most dynamic and complex real estate markets in the world. It includes a vast array of property types—from multi-million dollar luxury single-family homes and dense multi-family units to completely vacant commercial and residential land.

True to the real world, this data is beautifully messy. I have deliberately left the dataset in its raw, unpolished state. You will find that land listings naturally lack bedrooms, bathrooms, and square footage, resulting in heavy structural missing values (NaNs). This is not an accident; it is an invitation for you to practice real data science.

Data Science Applications

This dataset is engineered to push your data wrangling and modeling skills to the next level:

• Advanced Imputation & Wrangling: With sub_type missing 77% of the time, garage missing 50%, and sqft missing 13%, simple mean imputation or dropping rows won't cut it. You must use conditional logic (e.g., if type == 'land', then beds = 0) to salvage the dataset.
• Natural Language Processing (NLP): The text column contains the full, unedited promotional descriptions written by New York real estate agents. Extract keywords using TF-IDF or BERT to discover how terms like "pre-war," "gut-renovated," or "development opportunity" impact the final price.
• Predictive Modeling: Train robust regression models (XGBoost, LightGBM, Random Forest) to predict the listPrice target variable across highly skewed, multi-modal distributions.

Column Descriptors

Provenance & Methodology

• Source: All property records and descriptions were extracted directly from public-facing real estate listings on Realtor in Q1 2026.
• Methodology: Data collection was engineered using the Apify API framework to systematically scrape property attributes and agent descriptions across the New York market. The extraction pipeline was deliberately configured to preserve the raw, unstructured nature of the original listings to provide an authentic, educational data wrangling challenge.
• Data Privacy & PII Scrubbing: An automated regex-based redaction engine was applied to the text column to hunt down and scrub critical Personally Identifiable Information (PII) such as phone numbers, email addresses, and standard street addresses (replaced with [Redacted Entity]). Disclaimer: Because this relies on raw human-written text, some residual PII (such as unique agent names or non-standard formatting) may still exist. Please handle the text data responsibly and ethically.

Acknowledgements

• Source Material: The raw real estate listing data was sourced directly from Realtor.
• Artwork: The dataset cover image was generated using Google Nano Banana.
• Intended Use: Please note that this dataset is provided strictly for educational purposes, portfolio building, and non-commercial work.

The statistical report on the establishment and changes of real estate appraisers in the 105th year. (Taishan District)

The Real Estate Software for Builders and Real Estate Agents market has become an essential component in streamlining operations, enhancing customer engagement, and driving sales within the industry. With the increasing complexity of real estate transactions and the growing demand for digital solutions, software des