This is a sample dataset. To access the full version or request any custom dataset tailored to your needs, contact DataHive at contact@datahive.ai. This free trial dataset contains high-resolution Matterport panoramic image data extracted from 500 residential properties located in the US. Each property is paired with structured metadata - including geolocation, square footage, pricing, and structural details - and between dozens and over a thousand 360° interior panoramas. These images provide… See the full description on the dataset page: https://huggingface.co/datasets/datahiveai/Matterport-Panoramic-Property-Dataset.

This dataset contains property tax information for the calendar year 2023. Taxes are calculated from property values, and billed in Fiscal Year 2024, which runs from July 1, 2023 to June 30, 2024. The properties include Residential, Commercial and Publicly Held parcels and structures.

Graph and download economic data for Real Residential Property Prices for United States (QUSR628BIS) from Q1 1970 to Q1 2025 about residential, HPI, housing, real, price index, indexes, price, and USA.

Abstract

The Owner Transfer and Mortgage data covers over 450 million properties, and includes over 50 years of sales history. The tables were generated in June 2024, and cover all U.S. states, the U.S. Virgin Islands, Guam, and Washington, D.C.

The Owner Transfer data provides historical information about property sales and ownership-related transactions, including full, nominal, and quitclaim transactions (involving a change in title/ownership). It contains comprehensive property and transaction information, such as property characteristics, current ownership, transaction history, title company, cash purchase/foreclosure/resale/short sale indicators, and buyer information.

The Mortgage data provides historical information at the mortgage level, including purchase, refinance, equity, as well as details associated with each transaction, such as lender, loan amount, loan date, interest rate, etc. Mortgage details include mortgage amount, type of loan (conventional, FHA, VHA), mortgage rate type, mortgage purpose (cash out first, consolidation, standalone subordinate), mortgage ARM features, and mortgage indicators such as fixed-rate, conforming loan, construction loan, and private party. The Mortgage data also includes subordinate mortgage types, rate details, and lender details (NMLS ID, Loan Company, Loan Officers).

The CoreLogic Smart Data Platform (SDP) Owner Transfer and Mortgage data was formerly known as the CoreLogic Deed data. The CoreLogic Deed data contained both owner transfer and mortgage information. In the CoreLogic Smart Data Platform (SDP), this data was separated into two tables: Owner Transfer and Mortgage. Between the two tables, the CoreLogic Smart Data Platform (SDP) Owner Transfer and Mortgage data contains almost all of the variables that were included in the CoreLogic Deed data. Further, each CoreLogic Smart Data Platform (SDP) table is augmented with additional owner transfer and mortgage characteristics.

Methodology

In the United States, parcel data is public record information that describes a division of land (also referred to as "property" or "real estate"). Each parcel is given a unique identifier called an Assessor’s Parcel Number or APN. The two principal types of records maintained by county government agencies for each parcel of land are deed and property tax records. When a real estate transaction takes place (e.g. a change in ownership), a property deed must be signed by both the buyer and seller. The deed will then be filed with the County Recorder’s offices, sometimes called the County Clerk-Recorder or other similar title. Property tax records are maintained by County Tax Assessor’s offices; they show the amount of taxes assessed on a parcel and include a detailed description of any structures or buildings on the parcel, including year built, square footages, building type, amenities like a pool, etc. There is not a uniform format for storing parcel data across the thousands of counties and county equivalents in the U.S.; laws and regulations governing real estate/property sales vary by state. Counties and county equivalents also have inconsistent approaches to archiving historical parcel data.

To fill researchers’ needs for uniform parcel data, CoreLogic collects, cleans, and normalizes public records that they collect from U.S. County Assessor and Recorder offices. CoreLogic augments this data with information gathered from other public and non-public sources (e.g., loan issuers, real estate agents, landlords, etc.). The Stanford Libraries has purchased bulk extracts from CoreLogic’s parcel data, including mortgage, owner transfer, pre-foreclosure, and historical and contemporary tax assessment data. Data is bundled into pipe-delimited text files, which are uploaded to Data Farm (Redivis) for preview, extraction and analysis.

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

Usage

The Property, Mortgage, Owner Transfer, Historical Property and Pre-Foreclosure data can be linked on the CLIP, a unique identification number assigned to each property.

Mortgage records can be linked to a transaction using the MORTGAGE_COMPOSITE_TRANSACTION_ID.

For more information about included variables, please see:

• core_logic_sdp_owner_transfer_data_dictionary_2024.txt
• core_logic_sdp_mortgage_data_dictionary_2024.txt
• Mortgage_v3.xlsx
• Owner Transfer_v3.xlsx

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For a count of records per FIPS code, please see core_logic_sdp_owner_transfer_counts_2024.txt and core_logic_sdp_mortgage_counts_2024.txt.

For more information about how the CoreLogic Smart Data Platform: Owner Transfer and Mortgage data compares to legacy data, please see core_logic_legacy_content_mapping.pdf.

Bulk Data Access

Data access is required to view this section.

The Zoopla Dataset provides a detailed repository of information covering property listings available on the Zoopla platform. Tailored to support businesses, researchers, and analysts in the real estate sector, this dataset delivers valuable insights into market trends, property valuations, and consumer preferences within the real estate market.

With key attributes such as property details, pricing data, location information, and listing history, users can conduct thorough analyses to refine property investment strategies, assess market demand, and identify emerging trends.

Whether you're a real estate agent seeking to enhance your property listings, a researcher investigating trends in the housing market, or an analyst aiming to refine investment strategies, the Zoopla Dataset serves as an essential resource for unlocking opportunities and driving success in the competitive landscape of real estate

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 ****** square meter property from AXA for an undisclosed price. The most expensive sale was Digital Frankfurt I. The valuation of the site was *** million U.S. dollars and Digital Core REIT obtained **** percent from Digital Realty.

Transfer of ownership of industrial property

Residential Property Attribute data provides the most current building attributes available for residential properties as captured within Landgate's Valuation Database. Attribute information is captured as part of the Valuation process and is maintained via a range of sources including building and sub division approval notifications. This data set should not be confused with Sales Evidence data which is based on property attributes as at the time of last sale. This dataset has been spatially enabled by linking cadastral land parcel polygons, sourced from Landgatge's Spatial Cadastral Database (SCDB), to the Residential Property Attribute data sourced from the Valuation database. Customers wishing to access this data set should contact Landgate on +61 (0)8 9273 7683 or email businesssolutions@landgate.wa.gov.au © Western Australian Land Information Authority (Landgate). Use of Landgate data is subject to Personal Use License terms and conditions unless otherwise authorised under approved License terms and conditions. Changes will be applied to this dataset resulting from the implementation of the Community Titles Act 2018 please refer to the Data Dictionary below. Show full description

This City Owned Property data has been compiled from deeds, maps, assessor records, and other public records on file in the City of Hartford. The intent of this data layer is to depict a graphical representation of real property information relative to the planimetric features for the City of Hartford and is subject to change as a more accurate survey may disclose.

Webmap of Allegheny municipalities and parcel data. Zoom for a clickable parcel map with owner name, property photograph, and link to the County Real Estate website for property sales information.

List of all State of Oklahoma-owned and underutilized properties that are available for auction.

Santa Cruz Property Insights is a premier real estate marketplace, offering an extensive range of listings and data on residential and commercial properties in the Santa Cruz area. The company's vast database provides valuable information for potential buyers, sellers, and real estate professionals alike, making it an indispensable resource for anyone involved in the local market.

With a focus on providing accurate and up-to-date information, Santa Cruz Property Insights has established itself as a trusted authority in the real estate industry. From property listings to market trends and analysis, the company's comprehensive data sets enable users to make informed decisions and navigate the complex landscape of real estate with confidence.

This dataset provides information about the median City property tax as a percentage of median family income (SD23 measure GTW.A.1). The Travis County Appraisal District (TCAD) property value file, the annually adopted City property tax rate, and median income data from the U.S. Department of Housing and Urban Development (HUD) all contribute to the data supporting this measure.

This data can be used to help understand trends of affordability and the cost of city services over time in Austin.

View more details and insights related to this dataset on the story page: https://data.austintexas.gov/stories/s/7kz2-s6y2

This dataset represents real property information within a parcel of land in the City of Baltimore.

Residential property values by type of property for Canada, provinces and territories, annual data from 2005 to today.

This dataset is a combination of attribute information from the master address table and the lot or property records table. The address points are created within a building footprint and in the case where there is no building, then the point is the center of the lot. The address information comes from a variety of sources including final subdivision plats, building permits, E-911 master street address guide (MSAG) database, Polk City Directory, and field data collection.

According to Cognitive Market Research, the Global Real Estate Property Software market size will be USD 10654.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 13.30% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 4261.68 million in 2024 and will grow at a compound annual growth rate (CAGR) of 11.5% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 3196.26 million in 2024 and will grow at a compound annual growth rate (CAGR) of 11.8% from 2024 to 2031.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 2450.47 million in 2024 and will grow at a compound annual growth rate (CAGR) of 15.3% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 532.71 million in 2024 and will grow at a compound annual growth rate (CAGR) of 12.7% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 213.08 million in 2024 and will grow at a compound annual growth rate (CAGR) of 13.0% from 2024 to 2031.
Customer Relationship Management Software held the highest Real Estate Property Software market revenue share in 2024.

Market Dynamics of Real Estate Property Software Market

Key Drivers for Real Estate Property Software Market

Increasing Demand for Cloud-based Solutions to Boost Market Growth 

The increasing demand for cloud-based solutions is a key driver in the growth of real estate property management software. Cloud technology offers scalability, flexibility, and cost-efficiency, enabling property managers to streamline operations, automate tasks, and improve overall service delivery. With the shift towards digital transformation in the real estate sector, cloud-based platforms help enhance collaboration, allow remote access, and centralize data management. This drives increased adoption of Software-as-a-Service (SaaS) models, which improve efficiency, reduce operational costs, and provide better customer experiences, making cloud solutions a crucial element for market growth in property management software.

Growing Demand for SaaS-based Property Management Software to Propel Market Growth 

The increasing adoption of Software-as-a-Service (SaaS) platforms is a significant driver in the growth of property management software. SaaS solutions offer scalability, flexibility, and cost-efficiency, enabling property managers to streamline operations, automate tasks, and enhance tenant experiences. These cloud-based platforms provide centralized access to various functionalities, including accounting, maintenance tracking, and tenant communication, facilitating real-time data access and improved decision-making. As the real estate industry embraces digital transformation, the demand for SaaS-based property management software continues to rise, contributing to the market's expansion. For instance, in February 2025, Summer pivoted to a Software-as-a-Service (SaaS) model by launching SummerOS, an asset management platform designed specifically for short-term rental operators, enabling data-driven decisions and improved property performance. (Source:https://shorttermrentalz.com/news/summer-saas-pivot-summeros-launch/)

Key Restraint for the Real Estate Property Software Market

Budget Constraints to Hamper Market Growth

Budget constraints pose a significant challenge to the growth of the property management software market. High implementation costs, including software licensing, hardware infrastructure, data migration, and employee training, can deter small and medium-sized enterprises from adopting these solutions. Additionally, the complexity of integrating new software with existing systems and the need for customized configurations can add to the establishment costs, further discouraging potential buyers. These financial barriers hinder the widespread adoption of property management software, limiting its market expansion. For instance, in April 2025, San Diego proposed a ban on rent-pricing algorithms used by landlords, claiming that these systems inflate rents through collusion, which may hamper the use of property management software in the city. (Source:https://w...

This statistic shows the average property price in the United States in 2011, by property type. Damaged REOs cost an average of 102,149 U.S. dollars in the U.S. that year. The abbreviation REO stands for real estate owned properties.

This dataset contains all property in the City of Pittsburgh that is delinquent on property taxes for the given year.