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

The table Historical Property 08 is part of the dataset Cotality Smart Data Platform: Historical Property, available at https://stanford.redivis.com/datasets/e9sx-cn4k3cyva. It contains 149059118 rows across 220 variables.

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

Comercial Property

## Overview

Comercial Property is a dataset for classification tasks - it contains Properties annotations for 942 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Real Property parcel characteristics for Allegheny County, PA. Includes information pertaining to land, values, sales, abatements, and building characteristics (if residential) by parcel. Disclaimer: Parcel information is provided from the Office of Property Assessments in Allegheny County. Content and availability are subject to change. Please review the Data Dictionary for details on included fields before each use. Property characteristics and values change due to a variety of factors such as court rulings, municipality permit processing and subdivision plans. Consequently the assessment system parcel data is continually changing. Please take the dynamic nature of this information into consideration before using it. Excludes name and contact information for property owners, as required by Ordinance 3478-07.

Orientation

The first two items listed below are slightly different versions of the most current property-assessments records. The first is optimized for faster download but has 1) a few fields (including PROPERTY_ZIP and MUNICODE) as integers instead of strings and 2) the date columns in two different formats. The second item downloads more slowly, is optimized for API queries, and has all dates in a standard YYYY-MM-DD format. Further down you can find useful links, documentation, and then archived versions of property assessments files.

HUD’s Multifamily Housing property portfolio consist primarily of rental housing properties with five or more dwelling units such as apartments or town houses, but can also include nursing homes, hospitals, elderly housing, mobile home parks, retirement service centers, and occasionally vacant land. HUD provides subsidies and grants to property owners and developers in an effort to promote the development and preservation of affordable rental units for low-income populations, and those with special needs such as the elderly, and disabled. The portfolio can be broken down into two basic categories: insured, and assisted. The three largest assistance programs for Multifamily Housing are Section 8 Project Based Assistance, Section 202 Supportive Housing for the Elderly, and Section 811 Supportive Housing for Persons with Disabilities. The Multifamily property locations represent the approximate location of the property. The locations of individual buildings associated with each property are not depicted here. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes: ‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) ‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) ‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) ‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) ‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) ‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) Null - Could not be geocoded (does not appear on the map) For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. In an effort to protect Personally Identifiable Information (PII), the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10. To learn more about Multifamily Housing visit: https://www.hud.gov/program_offices/housing/mfh, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_HUD Assisted Multifamily Properties Date of Coverage: 06/2025

Average house prices are derived from data supplied by the mortgage lending agencies on loans approved by them rather than loans paid. In comparing house prices figures from one period to another, account should be taken of the fact that changes in the mix of houses (incl apartments) will affect the average figures. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. National and Other Areas figure changed for 2015Q4 on 27/6/15 as revised data received from Local Authorities (includes houses and apartments measured in €) .hidden { display: none }

This data set is a listing of all property sales by NORA through the following disposition channels. - Auction: Properties put up for auction and sold to the highest bidder. - Development: Properties offered to development partners at a discounted rate to support the development of affordable housing. - Lot Next Door: Properties sold to adjacent parcel owners, with discount opportunities for eligible participants. - Alternative Land Use: Properties sold for development of green space and community gardens. Note: this dataset contains duplicate addresses, which likely represent reversions or quitclaims that NORA sold again.

Graph and download economic data for Real Residential Property Prices for United States (QUSR628BIS) from Q1 1970 to Q2 2025 about residential, HPI, housing, real, price index, indexes, price, and USA.

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

This dataset represents real estate assessment and sales data that is updated on a quarterly basis by the Real Estate Assessor’s Office. This dataset contains information for properties in the city including: Acreage, Square footage, GPIN, Street Address, year built, current land value, current improvement values, and current total value. The information is obtained from Real Estate Assessor’s Office ProVal records database.

This records the location and type of Royal Borough's property assets

Dubai, UAE Real Estate Market Dataset

👨‍💻 Author: Azhar Saleem

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Dataset Description

This comprehensive dataset provides an exhaustive snapshot of property listings for sale across the United Arab Emirates, including major cities like Dubai, Abu Dhabi, and Al Ain. Sourced from Bayut.com, this dataset serves as an invaluable resource for Data Scientists, Real Estate Analysts, Urban Planners, and Developers keen on exploring real estate market dynamics, price fluctuations, and development trends in the UAE.

Dataset Overview

The dataset contains over 41,000 entries, each representing a unique property for sale. It includes detailed information such as:

• Price: Listing price of the property in AED.
• Type: Specifies the property type, such as Apartment, Townhouse, etc.
• Beds: Number of bedrooms, with '0' indicating a studio flat.
• Baths: Number of bathrooms.
• Address: Full address of the property, providing insights into its precise location.
• Furnishing: Indicates whether the property is furnished or unfurnished.
• Completion Status: Current status of the property (Ready, Off-Plan).
• Building Name, Area Name, City: Provide contextual location details.
• Year of Completion: Year when the property was completed or is expected to be completed.
• Total Floors, Parking Spaces, Building Area: Key features of the property's building.
• Latitude, Longitude: Geographic coordinates for more refined location analysis.
• Purpose: Intended purpose of the listing, consistently noted as 'For Sale'.

Usage

This dataset is ideal for a variety of applications, including:

• Market Analysis: Analyze trends in property prices and types across different regions.
• Predictive Modeling: Develop machine learning models to predict property prices or to classify types of properties based on their features.
• Urban Development Studies: Examine property distribution and characteristics to inform urban planning and development strategies.
• Comparative Analysis: Compare properties across different cities and districts to identify investment opportunities or to study market behavior.

Data Accessibility

This dataset is publicly available and well-suited for anyone interested in conducting detailed analyses of the UAE real estate market, from academic researchers to industry professionals.

Feel free to dive into this dataset to unlock comprehensive insights into the vibrant and diverse property market of the UAE, supporting a wide range of real estate, economic, and geographic studies.

The table Historical Property 15 is part of the dataset Cotality Smart Data Platform: Historical Property, available at https://stanford.redivis.com/datasets/e9sx-cn4k3cyva. It contains 127093775 rows across 220 variables.

Explore property insights effortlessly with APISCRAPY's services – Realtor Property Data, Realtor Data, and Realtor API. Access publicly available property listings and Property Owner Data seamlessly. Our platform is easy to integrate, making property data access simple and efficient.

This dataset represents real property information within a parcel of land in the City of Baltimore. The data dictionary for this dataset can be accessed by visiting the following link.: Data Dictionary For Real Property Information | Open Baltimore (baltimorecity.gov). Data is updated on a weekly basis. To leave feedback or ask a question about this dataset, please fill out the following form: Real Property Information feedback form.

An export of data from FRPP, a database of Federal Real Property Profile for Department of Defense entities.

The Oklahoma Real Property Asset Report is published annually in compliance with the Oklahoma State Government Asset Reduction and Cost Savings Program found in Title 62 O.S. §908. The act requires the Office of Management and Enterprise Services (OMES) to compile and maintain a comprehensive inventory of all real property owned and leased by the state. All data contained in this report was self-reported by each state agency, board, commission, or public trust having the State of Oklahoma as a beneficiary.

According to Cognitive Market Research, the global Property Management market size will be USD 27812.8 million in 2025. It will expand at a compound annual growth rate (CAGR) of 8.80% from 2025 to 2033.

North America held the major market share for more than 40% of the global revenue with a market size of USD 10290.74 million in 2025 and will grow at a compound annual growth rate (CAGR) of 6.6% from 2025 to 2033.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 8065.71 million.
APAC held a market share of around 23% of the global revenue with a market size of USD 6675.07 million in 2025 and will grow at a compound annual growth rate (CAGR) of 10.8% from 2025 to 2033.
South America has a market share of more than 5% of the global revenue with a market size of USD 1056.89 million in 2025 and will grow at a compound annual growth rate (CAGR) of 7.8% from 2025 to 2033.
The Middle East had a market share of around 2% of the global revenue and was estimated at a market size of USD 1112.51 million in 2025. It will grow at a compound annual growth rate (CAGR) of 8.1% from 2025 to 2033.
Africa had a market share of around 1% of the global revenue and was estimated at a market size of USD 611.88 million in 2025 and will grow at a compound annual growth rate (CAGR) of 8.5% from 2025 to 2033.
On-premises category is the fastest growing segment of the Property Management industry

Market Dynamics of Property Management Market

Key Drivers for Property Management Market

Technological Advancements and Automation to Boost Market Growth

The integration of advanced technologies such as Property Management Software (PMS), Internet of Things (IoT), and automation systems is a key driver for the property management market. These technologies streamline operations like lease management, tenant communication, and maintenance scheduling. Automated systems enable property managers to provide better services, ensuring efficiency and improving the tenant experience. For instance, PMS allows for real-time tracking of rental payments, maintenance requests, and communication between tenants and landlords. The rise of IoT enables the implementation of smart building solutions, offering energy efficiency and enhanced security. As more property managers adopt these technologies, operational costs are reduced, tenant satisfaction improves, and the overall management process becomes more seamless.

Growing Urbanization and Real Estate Development To Boost Market Growth

Rapid urbanization and increased real estate development are significant driving forces behind the property management market. As more people move to urban centres for work and lifestyle opportunities, the demand for residential, commercial, and mixed-use properties increases. This surge in population and development leads to a higher need for efficient property management to handle the complexities of large residential complexes, office spaces, and retail properties. With real estate developers focusing on building modern infrastructures, property managers are required to oversee these assets, ensuring everything from tenant relations to property maintenance is handled effectively. Furthermore, urbanization results in higher property values, which incentivizes both individual property owners and businesses to invest in professional property management services.

Restraint Factor for the Property Management Market

High Operational Costs, Will Limit Market Growth

A significant restraining factor in the property management market is the high operational costs associated with maintaining and managing properties. Property managers are often required to deal with expensive maintenance, repairs, insurance, and legal fees. This financial burden can be exacerbated by the need for continuous staff training, property inspections, and compliance with local regulations. The costs associated with technological tools and software for property management also add to the operational expenses. In some cases, property managers may need to pass these increased costs onto tenants, which could lead to reduced demand for rental properties, particularly in competitive or price-sensitive markets.

Key Trends for Property Management Market

Adoption of Cloud-Based and Mobile Property Management Platforms

A notable trend influencing the property management sector is the swift adoption of cloud-base...

The investment prospects for all property types in the United States, except for single-family housing, was expected to improve in 2025, according to a 2024 survey among industry experts. Industrial real estate observed the best prospects, with a score of 3.67 on a scale between one (abysmal) and five (excellent). Conversely, offices had the poorest score, at 2.79.