In July 2024, industrial was the only real estate sector where listed companies traded at a premium to their net asset value (NAV). Conversely, listed companies focused on specialty real estate traded at a discount to NAV amounting of over ** percent. That reveals an overwhelmingly negative sentiment among investors and suggests that the underlying assets may be overpriced. Across the major European markets, Sweden was the only country where companies traded at a premium to NAV. Despite that pessimistic picture, the average discount to NAV in the listed property market in Europe has slightly improved since November 2022.

In July 2024, listed real estate companies in the office sector traded at an average discount to their net asset value (NAV) of ***** percent. That reveals a generally negative sentiment among investors and suggests that the underlying assets may be overpriced. Other property types also exhibited wide gaps between stock prices and asset values, with the discount to NAV being the highest for residential real estate. Across the major European markets, Sweden was the only country where companies traded at a premium to NAV. Despite that pessimistic picture, the average discount to NAV in the listed property market in Europe has slightly improved since November 2022.

In July 2024, listed real estate companies in Spain traded at an average discount to their net asset value (NAV) of **** percent. That reveals that investors believed that the underlying assets may be overvalued, and the industry is facing challenges. Despite the negative sentiment, the market saw an improvement from September 2022, the discount to NAV was the highest, at **** percent. The narrowing of the gap between stock prices and NAV reveals an early sign of a recovery in the listed real estate market. This trend aligns with the broader European context, where most countries observed a discount to NAV. Across the different property types, residential showed the highest discount to NAV.

Real Estate / Housing Dataset

This dataset is web scrapped from a real estate website, collecting all the necessary infos on the resale and new properties. It has around 14000+ rows of data having properties from various Indian cities like Chennai, Mumbai, Bangalore, Delhi, Pune, Kolkata and Hyderabad. Columns:

Name: Property Name, Property Title: Property Ad Title, Price: Property Price Location: Property Located Locality and Region Total Area: Total SQFT of the property Price Per SQFT: Price of Per SQFT of the property Description: Small paragraph about the property Baths: Number of baths in the property Balcony: Whether the Property has balcony or not

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

List of approved management companies in relation to real estate investment trust

Embark on a comprehensive exploration of Mumbai's vibrant real estate market with our meticulously curated dataset, comprising over 12,685 entries. This extensive collection encapsulates a diverse array of properties, ranging from residential to commercial, providing invaluable insights into the dynamic landscape of Mumbai's real estate sector. Whether you are a property enthusiast, data analyst, or investor, this dataset offers a rich tapestry of information, empowering you to make informed decisions in this bustling metropolis.

Dataset Highlights: The dataset encompasses an extensive array of columns, each revealing intricate details about the properties. From essential information like possession status, floor details, and pricing to more nuanced aspects such as developer details, amenities, and property uniqueness, every facet of a property transaction is meticulously documented. The dataset also features data related to maintenance, booking amounts, covered and carpet areas, and specific features like electricity and water status.

Granular Property Information: Explore nuances such as the type of property, ownership details, and the number of bedrooms and bathrooms. Uncover insights into the furnishing status, parking facilities, and the direction a property faces. The dataset delves into the transaction type, offering a glimpse into the variety of property dealings within the city. From luxury flats to standard apartments, the dataset captures the essence of Mumbai's diverse real estate offerings.

Geographic Insights: For those interested in the geographical distribution of properties, the dataset includes information on landmarks, area names, and the city itself. This geographical granularity allows users to analyze property trends across different regions of Mumbai.

Amenities and Beyond: In addition to property-specific details, the dataset includes an exhaustive list of amenities. Whether you're interested in proximity to schools, shopping centers, or specific luxury features like a swimming pool or a private terrace, this dataset provides a holistic view of the lifestyle offerings associated with each property.

Data Integrity: Carefully curated and verified, this dataset ensures data integrity, offering a reliable foundation for in-depth analyses and modeling. With information sourced meticulously, users can trust the accuracy of each entry.

Empower Your Real Estate Insights: Whether you're a real estate professional, a prospective homebuyer, or an investor seeking opportunities in Mumbai, this dataset serves as an invaluable resource. Gain a holistic understanding of the city's real estate dynamics, identify emerging trends, and make well-informed decisions with the Mumbai Real Estate Properties Dataset.

Important Points: - Data was being collected for over 10 months, since this is real estate-based data, prices can fluctuate a little bit based upon various worldwide scenarios occurred. - This dataset contains real projects ongoing and developed across Mumbai. Don't depend on prices given by us while buying any property mentioned in dataset since these are collected from various sources, prices can fluctuate a bit in real-life buying scenarios. Although there won't be big differences in price. We tried our best while dealing with collection of data in order to ensure credibility. - For amenities, we have used 0 and 1 where 0 stands for false and 1 for true. This indicates whether that property has that particular amenity or not. - NA means Not Available, the particular data was not available during collection.

Thank You

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.

The global Real Estate & Property Management Services market is booming, projected to reach $1.2 trillion by 2025 and grow at a 7% CAGR through 2033. Discover key market trends, drivers, restraints, and leading companies shaping this dynamic industry. Explore regional market share and growth forecasts.

This dataset offers a holistic view of a real estate agency's operations. It is divided into five Excel sheets, each representing a key component of the business:

🏘️ Properties Details of available properties.

Columns:

• PropertyID: Unique identifier for each property

• PropertyType: Type of property (e.g., Villa, Retail, Warehouse)

• Location: City where the property is located

• Size_sqm: Size of the property in square meters

• PriceUSD: Listing price in USD

👤 Clients Information about potential buyers or renters.

Columns:

• ClientID: Unique identifier for each client

• Name: Client's full name

• Email: Contact email address

• Phone: Contact phone number

• PreferredLocation: Desired location for property

• BudgetUSD: Budget in USD

🧑‍💼 Agents Details of the real estate agents.

Columns:

• AgentID: Unique identifier for each agent

• Name: Agent's name

• Email: Contact email

• Phone: Phone number

• YearsExperience: Number of years in real estate

• PropertiesSold: Total properties sold

💰 Sales Transactional records of property sales.

Columns:

• SaleID: Unique transaction identifier

• PropertyID: Linked property

• ClientID: Buyer client

• AgentID: Responsible agent

• SaleDate: Date of transaction

• SalePriceUSD: Final sale price

📅 Visits Records of client visits to properties.

Columns:

• VisitID: Unique identifier for the visit

• ClientID: Visiting client

• PropertyID: Visited property

• VisitDate: Date of the visit

• InterestLevel: Client's interest (e.g., High, Medium, Low)

This dataset is ideal for projects involving predictive modeling, real estate price estimation, agent performance tracking, client segmentation, and sales funnel analysis. Its clean structure and multiple relational tables make it suitable for machine learning, business intelligence dashboards, and educational use.

Success.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

• Comprehensive Industry Coverage Gain direct access to verified profiles of real estate professionals across the globe, including:

1. Real Estate Agents: Professionals facilitating property sales and purchases.
2. Brokers: Key intermediaries managing transactions between buyers and sellers.
3. Property Developers: Decision-makers shaping residential, commercial, and industrial projects.
4. Real Estate Executives: Leaders overseeing multi-regional operations and business strategies.
5. Architects & Consultants: Experts driving design and project feasibility.

• Verified and Continuously Updated 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.

• Customizable Data Delivery Tailor your data access to align with your operational goals:

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.

• Use Cases for Real Estate Contact Data

1. Targeted Outreach for New Projects Connect with property developers and brokers to pitch your services or collaborate on upcoming projects.

2. Real Estate Marketing Campaigns Execute personalized marketing campaigns targeting agents and clients in residential, commercial, or industrial sectors.

3. Enhanced Sales Strategies Shorten sales cycles by directly engaging with decision-makers and key stakeholders.

4. Recruitment and Talent Acquisition Access profiles of highly skilled professionals to strengthen your real estate team.

5. Market Analysis and Intelligence Leverage firmographic and demographic insights to identify trends and optimize business strategies.

• What Makes Us Stand Out? >> Unmatched Data Accuracy: Our AI-driven validation ensures 99% accuracy for all contact details. >> Comprehensive Global Reach: Covering professionals across diverse real estate markets worldwide. >> Flexible Delivery Options: Access data in formats that seamlessly fit your existing systems. >> Ethical and Compliant Data Practices: Adherence to global standards for secure and responsible data use.

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...

Egypt Number of Listed Companies: EGX: Real Estate data was reported at 30.000 Unit in Oct 2018. This stayed constant from the previous number of 30.000 Unit for Sep 2018. Egypt Number of Listed Companies: EGX: Real Estate data is updated monthly, averaging 30.000 Unit from Apr 2004 (Median) to Oct 2018, with 174 observations. The data reached an all-time high of 89.000 Unit in Apr 2004 and a record low of 26.000 Unit in Jun 2011. Egypt Number of Listed Companies: EGX: Real Estate data remains active status in CEIC and is reported by The Egyptian Exchange. The data is categorized under Global Database’s Egypt – Table EG.Z013: The Egyptian Exchange: Number of Listed Companies.

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 agent email list is a list of agents’ email addresses. First of all, it helps you contact many agents fast. You can use it for sales, offers, or news. The directory may have names, emails, phone numbers, company names, and locations. Moreover, A real estate agent is a licensed professional who represents buyers or sellers in real estate transactions. Their primary role is to act as an intermediary, helping clients navigate the complex process of buying, selling, or renting property. It can also show what they sell—homes, land, or offices. Many people use this list. Builders use it to share new property deals. Loan companies use it to offer home loans.

Real estate agent email list helps your business grow. You can send offers, job posts, or updates with one click. It saves time and money. The list must follow email laws. Agents must agree to get emails. This is called “opt-in.” If not, emails can go to spam or break the law. You can buy a real estate agent email address. Some lists are free, but paid ones are better. They are checked and updated often. This saves time and avoids mistakes. Businessmen can build their own list or get one from a trusted company. Some lists are large and cover many cities. Others are small and local. In brief, a real estate agent email list is a smart tool. It helps with sales, hiring, and digital marketing. Real estate agent email database will help your company earn a huge return on investment (ROI). Therefore, we deliver to people an accurate contact number that will add benefits in many ways. However, we provide you with a CSV or Excel file for use in CRM. So, as a seller, a Marketer can do digital marketing effortlessly. Thus, we give you 95% valid and active contacts while following proper GDPR guidelines. Likewise, it will allow to promote products if you take this database from the List to Data. Besides, we take a fair amount, so buy it right now.

Buyers' origin - Real estate companies corresponds to the dataset describing the origin of the buyers (legal persons engaged in real estate activities as defined in section L of the NACE-BEL 2008 nomenclature) of real estate located in Belgium according to the municipality of their headquarters for legal persons settled in Belgium and according to the country or territory of their headquarters for foreign legal persons (headquarter at the date of the deed). This dataset is made up of seven classes. The first class shows, at national level, for each type of property, the total number of parcels, the number of parcels acquired by buyers from each Belgian municipality and the number of parcels acquired by buyers from each country or territory. The number of parcels takes into account the shares actually acquired. The second class shows this information at the level of the three regions. The following classes do the same at the level of provinces, arrondissements, municipalities, land register divisions and statistical sectors. The dataset is freely downloadable, in the form of zipped CSV files.

This data contains active Real Estate Salesperson and Broker Licenses from New York State Department of State (DOS). Each line will be either an individual or business licensee which holds business address and license number information. If the license type is an individual, the business name that the individual works for will be listed.

Owners' origin - Real estate companies corresponds to the dataset describing the origin of the legal persons engaged in real estate activities (section L of the NACE-BEL 2008 nomenclature) that are holders of real rights over immovable properties located in Belgium according to the municipality of their headquarters for legal persons settled in Belgium and according to the country or territory of their headquarters for foreign legal persons. This dataset is made up of seven classes. The first class shows, at national level, for each type of property, the total number of parcels, the number of parcels held by holders from each Belgian municipality and the number of parcels held by holders from each country or territory. The number of parcels takes into account the shares actually held. The second class shows this information at the level of the three regions. The following classes do the same at the level of provinces, arrondissements, municipalities, land register divisions and statistical sectors. The dataset is freely downloadable, in the form of zipped CSV files.

Abstract

Title: Cotality Multiple Listing Service (MLS)

A multiple listing service (MLS) is an exchange where real estate brokers share information about properties they are selling. Other real estate brokers review the listings, and are compensated if they can identify a buyer for a property. Multiple listing services promote cooperation and mutual benefit for real estate brokers representing buyers and sellers. The Cotality Multiple Listing Service data contains listings from 135 real estate boards utilizing Cotality's multiple listing service software. The data was produced in August 2024.

Formerly known as CoreLogic Multiple Listing Service (MLS).

Methodology

The data consists of listings from 135 real estate boards that use Cotality listing software. The data DOES NOT cover listings from all real estate boards in the United States. The National Association of Realtors maintains the most complete and up-to-date list of real estate boards; however, this information is only available to members of the National Association of Realtors.

For more information about how the data was prepared for Redivis, please see Cotality 2024 GitLab.

Usage

Quick Search (QS) contains the most recent listing data (as of August 2024). In order to see the entire listing history of a property/record, you will need to search the Quick History (QH) table on the SysPropertyID, which is a unique key for a listing across multiple listing boards. You can use the variable FA_PostDate to see when updates occurred. Updates include name changes, price changes, etc.

During upload to Data Farm, a small number of invalid records were dropped from the Quick History (QH) table. For more information, see Cotality 2024 GitLab. To access the complete data (including invalid records), please see Bulk Data Access instructions, below.

Bulk Data Access

Data access is required to view this section.

Premiere Property Group is a real estate company that specializes in residential and commercial properties. The company's website provides information on a wide range of properties, including vacant land, single-family homes, and multi-unit dwellings. With a strong focus on customer satisfaction, Premiere Property Group's team of experienced agents and brokers work closely with clients to understand their unique needs and preferences.

Premiere Property Group's website offers a wealth of information for those looking to buy, sell, or rent properties. The company's extensive property listings are regularly updated to reflect the latest market trends and developments. By partnering with Premiere Property Group, clients can gain insights into the local real estate market, receive expert advice, and navigate the buying and selling process with confidence.

North 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.