The 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.

What Makes Our Data Unique?

Inmuebles24’s Mexico Real Estate Listings Data offers an unparalleled level of detail and accuracy in the real estate sector. With over 100,000 meticulously curated property listings, this dataset is designed to provide users with the most comprehensive view of the Mexican real estate market. Each listing includes detailed metadata such as property type, location, pricing, and contact information, along with additional attributes like the number of bedrooms, bathrooms, and available amenities. Our data is enriched with precise geolocation coordinates, allowing for advanced spatial analysis and mapping applications.

Our dataset stands out for its up-to-date nature, with listings scraped and refreshed regularly to ensure that buyers and analysts always have access to the latest market trends. This dynamic approach to data curation means that users can trust the data for making informed decisions, whether they are monitoring market trends, conducting investment research, or developing real estate strategies.

How Is the Data Generally Sourced?

The data is sourced directly from Inmuebles24, one of Mexico's leading real estate marketplaces. We employ a robust web scraping infrastructure that captures the full breadth of listings available on the platform. Our scraping technology is designed to extract data efficiently, ensuring that we capture every relevant detail from the listings, including images, descriptions, pricing, and metadata. Each entry is validated and cleaned to remove any duplicates or outdated information, ensuring that the dataset is both comprehensive and reliable.

Primary Use-Cases and Verticals

This Data Product is particularly valuable across several key verticals:

Real Estate Investment Analysis: Investors can leverage this dataset to identify lucrative opportunities by analyzing property prices, location attributes, and market trends.

Market Research and Trends: Researchers can use the data to track the evolution of the real estate market in Mexico, identifying shifts in pricing, demand, and supply across various regions.

Property Development: Developers can assess the market landscape, understanding where new developments might meet the most demand based on the attributes and locations of current listings.

Urban Planning: Government and city planners can utilize the geolocation data to analyze urban sprawl, housing density, and other critical metrics for sustainable development.

Real Estate Marketing: Marketers and real estate agents can tailor their strategies based on detailed insights into the types of properties available, pricing trends, and consumer preferences.

How Does This Data Product Fit into Our Broader Data Offering?

This Mexico Real Estate Listings Data Product is part of our broader commitment to providing high-quality, actionable data across various sectors and geographies. Inmuebles24’s real estate data complements our extensive portfolio of data products that cater to industries such as financial services, marketing, and location-based services. By integrating this dataset with other data offerings, users can derive even deeper insights. For example, combining real estate data with consumer behavior data could unlock new dimensions of market research, enabling a more holistic approach to understanding market dynamics.

Our broader data offering is built around the principle of providing end-to-end data solutions that empower businesses to make data-driven decisions with confidence. Whether you’re a real estate investor, a market researcher, or a developer, our data products are designed to meet your needs with precision and reliability

Overview: This dataset was collected and curated to support research on predicting real estate prices using machine learning algorithms, specifically Support Vector Regression (SVR) and Gradient Boosting Machine (GBM). The dataset includes comprehensive information on residential properties, enabling the development and evaluation of predictive models for accurate and transparent real estate appraisals.Data Source: The data was sourced from Department of Lands and Survey real estate listings.Features: The dataset contains the following key attributes for each property:Area (in square meters): The total living area of the property.Floor Number: The floor on which the property is located.Location: Geographic coordinates or city/region where the property is situated.Type of Apartment: The classification of the property, such as studio, one-bedroom, two-bedroom, etc.Number of Bathrooms: The total number of bathrooms in the property.Number of Bedrooms: The total number of bedrooms in the property.Property Age (in years): The number of years since the property was constructed.Property Condition: A categorical variable indicating the condition of the property (e.g., new, good, fair, needs renovation).Proximity to Amenities: The distance to nearby amenities such as schools, hospitals, shopping centers, and public transportation.Market Price (target variable): The actual sale price or listed price of the property.Data Preprocessing:Normalization: Numeric features such as area and proximity to amenities were normalized to ensure consistency and improve model performance.Categorical Encoding: Categorical features like property condition and type of apartment were encoded using one-hot encoding or label encoding, depending on the specific model requirements.Missing Values: Missing data points were handled using appropriate imputation techniques or by excluding records with significant missing information.Usage: This dataset was utilized to train and test machine learning models, aiming to predict the market price of residential properties based on the provided attributes. The models developed using this dataset demonstrated improved accuracy and transparency over traditional appraisal methods.Dataset Availability: The dataset is available for public use under the [CC BY 4.0]. Users are encouraged to cite the related publication when using the data in their research or applications.Citation: If you use this dataset in your research, please cite the following publication:[Real Estate Decision-Making: Precision in Price Prediction through Advanced Machine Learning Algorithms].

This dataset is created by the Town of Cary GIS Group. It contains data from the western portion of Wake County, the eastern portion of Chatham County, and the southern portion of Durham County. It has been modified from the original sources to act as one layer for use by the Town of Cary. This file is updated once a month from the respective sources. Please refer to each Counties' data for the latest information:

Wake County

Durham County

Chatham County

Portfolios, development pipelines and property transactions for over 900 public real estate companies.

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

This 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.

Explore the Redfin Canada Real Estate Data, last extracted in June 2022 and available in CSV format. This robust dataset contains over 100,000 records, offering detailed insights into the Canadian housing market.

It includes comprehensive data on property listings, prices, square footage, and more across various cities and provinces.

Ideal for real estate analysis, market trend research, and investment planning, this dataset is a valuable resource for professionals seeking in-depth understanding of the Canadian real estate landscape.

This is a comprehensive collection of tax and assessment data extracted at a specific time. The data is in CSV format. A data dictionary (pdf) and the current tax rate book (pdf) are also included.

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...

What Makes Our Data Unique? We do not buy and resell other provider's data. We aggregate our housing data, which we source ourselves, to ensure the highest quality.

Our real estate data encompasses a wide range of comprehensive information on homeowners and properties.

• Current listings as well as properties for rent.
• Current and past property owners
• Current and past building permits
• Property attributes such as number of rooms, square footage, roof type, and many more.
• Zoning details
• Businesses at location
• Forclosure details
• Current and historical property values
• Tax details

Use cases and verticals.

• Developers looking to create applications around real estate data tailored towards real estate investors or agents.
• Agents or investors looking to acquire new leads within a geographical territory.
• Market analysts looking to uncover trends within the housing sector.

This is a collection of CSV files that contain assessment data. The files in this extract are:Primary Parcel file containing primary owner and land information;Addn file containing drawing vectors for dwelling records;Additional Address file containing any additional addresses that exist for a parcel;Assessment file containing assessed value-related data;Appraisal file containing appraised value-related data;Commercial file containing primary commercial data;Commercial Apt containing commercial apartment data;Commercial Interior Exterior dataDwelling fileEntrance data containing data from appraisers' visits;Other Buildings and Yard ImprovementsSales FileTax Rate File for the current billing cycle by taxing district authority and property class; and,Tax Payments File containing tax charges and payments for current billing cycle.In addition to the CSV files, the following are included:Data Dictionary PDF; and,St Louis County Rate Book for the current tax billing cycle.

This dataset contains data on all Real Property parcels that have sold since 2013 in Allegheny County, PA.

Before doing any market analysis on property sales, check the sales validation codes. Many property "sales" are not considered a valid representation of the true market value of the property. For example, when multiple lots are together on one deed with one price they are generally coded as invalid ("H") because the sale price for each parcel ID number indicates the total price paid for a group of parcels, not just for one parcel. See the Sales Validation Codes Dictionary for a complete explanation of valid and invalid sale codes.

Sales Transactions Disclaimer: Sales information is provided from the Allegheny County Department of Administrative Services, Real Estate Division. Content and validation codes are subject to change. Please review the Data Dictionary for details on included fields before each use. Property owners are not required by law to record a deed at the time of sale. Consequently the assessment system may not contain a complete sales history for every property and every sale. You may do a deed search at http://www.alleghenycounty.us/re/index.aspx directly for the most updated information. Note: Ordinance 3478-07 prohibits public access to search assessment records by owner name. It was signed by the Chief Executive in 2007.

In 2023 and the first half of 2024, the largest property sale in the data center real estate market in Europe was DATA4 Paris-Saclay in Paris. In April 2023, Brookfield bought the 47,300 square meter property from AXA for an undisclosed price. The most expensive sale was Digital Frankfurt I. The valuation of the site was 270 million U.S. dollars and Digital Core REIT obtained 24.9 percent from Digital Realty.

Graph and download economic data for Housing Inventory: Median Days on Market in the United States (MEDDAYONMARUS) from Jul 2016 to Feb 2025 about median and USA.

The Ministry of Justice provides official and private entities, as well as investors, with weekly, monthly, quarterly, semi-annual and annual real estate bulletins.

These bulletins are issued by the Real Estate Registration Department, and they show the real estate price transaction activity in the State.

They are the only official sources of real estate trading movement in the State, the reading and analysis of trading indicators, and their upward or downward movement.

The Ministry is keen that these bulletins accurately express the correct trading movement in the market so as not to lead to wrong investment decisions, whether in individual transactions between citizens, or in financial transactions between traders and businessmen.

This dataset has been published by the Office of the Real Estate Assessor of the City of Virginia Beach and data.virginiabeach.gov. The mission of data.virginiabeach.gov is to provide timely and accurate City information to increase government transparency and access to useful and well organized data by the general public, non-governmental organizations, and City of Virginia Beach employees.

Real Estate Across the United States (REXUS) is the primary tool used by PBS to track and manage the government's real property assets and to store inventory data, building data, customer data, and lease information. STAR manages aspects of real property space management, including identification of all building space and daily management of 22,000 assignments for all property to its client Federal agencies. This data set contains PBS building inventory that consists of both owned and leased buildings with active and excess status.

China Real Estate Investment data was reported at 12,026,351.460 RMB mn in 2018. This records an increase from the previous number of 10,979,852.880 RMB mn for 2017. China Real Estate Investment data is updated yearly, averaging 779,092.230 RMB mn from Dec 1986 (Median) to 2018, with 33 observations. The data reached an all-time high of 12,026,351.460 RMB mn in 2018 and a record low of 10,100.000 RMB mn in 1986. China Real Estate Investment data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Real Estate Sector – Table CN.RKA: Real Estate Investment: Summary.

This dataset was generated for analyzing the economic impacts of subway networks on housing prices in metropolitan areas. The provision of transit networks and accompanying improvement in accessibility induce various impacts and we focused on the economic impacts realized through housing prices. As a proxy of housing price, we consider the price of condominiums, the dominant housing type in South Korea. Although our focus is transit accessibility and housing prices, the presented dataset is applicable to other studies. In particular, it provides a wide range of variables closely related to housing price, including housing properties, local amenities, local demographic characteristics, and control variables for the seasonality. Many of these variables were scientifically generated by our research team. Various distance variables were constructed in a geographic information system environment based on public data and they are useful not only for exploring environmental impacts on housing prices, but also for other statistical analyses in regard to real estate and social science research. The four metropolitan areas covered by the data—Busan, Daegu, Daejeon, and Gwangju—are independent of the transit systems of Greater Seoul, providing accurate information on the metropolitan structure separate from the capital city.