Donuka offers a simple, reliable property data solution to power innovation and create seamless business solutions for companies of all sizes. Our data covers more than 37 million properties spread out across the U.S. that can be accessed in bulk-file format or through our APIs.

We offer access to data ONLY in selected states and counties

DATA SOURCES:

1. ONLY state sources (city/county/state administration, federal agencies, ministries, etc.). We DO NOT use unverified databases
2. Over 2300 sources. We use even the smallest sources, because they contain valuable data. This allows us to provide our users with the most complete data

DATA RELEVANCE:

1. Our data is updated daily, weekly, monthly depending on the sources
2. We collect, process and store all data, regardless of their relevance. Historical data is also valuable

DATA TYPES:

1. Specifications
2. Owners
3. Permits
4. Sales
5. Inspections
6. Violations
7. Assessed values
8. Taxes
9. Risks
10. Foreclosures
11. Property Tax Liens
12. Deed Restrictions

NUMBERS:

1. 2300+ data sources in total
2. 4 billion records (listed in the "data types" block above) in total
3. 2 million new records every day

DATA USAGE:

1. Property check, investigation (even the smallest events are stored in our database)
2. Prospecting (more than 100 parameters to find the required records)
3. Tracking (our data allows us to track any changes)

Graph and download economic data for Commercial Real Estate Prices for United States (COMREPUSQ159N) from Q1 2005 to Q3 2024 about real estate, commercial, rate, and USA.

Commercial valuation data collected and maintained by the Cook County Assessor's Office, from 2021 to present. The office uses this data primarily for valuation and reporting. This dataset consolidates the individual Excel workbooks available on the Assessor's website into a single shared format. Properties are valued using similar valuation methods within each model group, per township, per year (in the year the township is reassessed). This dataset has been cleaned minimally, only enough to fit the source Excel workbooks together - because models are updated for each township in the year it is reassessed, users should expect inconsistencies within columns across time and townships. When working with Parcel Index Numbers (PINs) make sure to zero-pad them to 14 digits. Some datasets may lose leading zeros for PINs when downloaded. This data is property-level. Each 14-digit key PIN represents one commercial property. Commercial properties can and often do encompass multiple PINs. Additional notes: Current property class codes, their levels of assessment, and descriptions can be found on the Assessor's website. Note that class codes details can change across time. Data will be updated yearly, once the Assessor has finished mailing first pass values. If users need more up-to-date information they can access it through the Assessor's website. The Assessor's Office reassesses roughly one third of the county (a triad) each year. For commercial valuations, this means each year of data only contain the triad that was reassessed that year. Which triads and their constituent townships have been reassessed recently as well the year of their reassessment can be found in the Assessor's assessment calendar. One KeyPIN is one Commercial Entity. Each KeyPIN (entity) can be comprised of one single PIN (parcel), or multiple PINs as designated in the pins column. Additionally, each KeyPIN might have multiple rows if it is associated with different class codes or model groups. This can occur because many of Cook County's parcels have multiple class codes associated with them if they have multiple uses (such as residential and commercial). Users should not expect this data to be unique by any combination of available columns. Commercial properties are calculated by first determining a property’s use (office, retail, apartments, industrial, etc.), then the property is grouped with similar or like-kind property types. Next, income generated by the property such as rent or incidental income streams like parking or advertising signage is examined. Next, market-level vacancy based on location and property type is examined. In addition, new construction that has not yet been leased is also considered. Finally, expenses such as property taxes, insurance, repair and maintenance costs, property management fees, and service expenditures for professional services are examined. Once a snapshot of a property’s income statement is captured based on market data, a standard valuation metric called a “capitalization rate” to convert income to value is applied. This data was used to produce initial valuations mailed to property owners. It does not incorporate any subsequent changes to a property’s class, characteristics, valuation, or assessed value from appeals.Township codes can be found in the legend of this map. For more information on the sourcing of attached data and the preparation of this datase

The US Commercial Property/Real Estate file has 30 million+ non-residential properties which include property characteristics, site details, purchase details, tax details, and ownership information.

Please note the data below is a subset of the data available on the register and only relates to the Dublin area. For more information on the data, contact the PSRA at info@psr.ie or visit the site, http://psr.ie/en/PSRA/Pages/home. The Commercial Leases Register is produced by the Property Services Regulatory Authority (PSRA) pursuant to the Property Services (Regulation) Act 2011 (the Act). The Register includes the following information in respect of all commercial leases entered into since 1 January 2010: The Address of the Commercial Property the Subject of the Lease; The Date of the Lease of the Property; The Term of Years of the Lease, and The Rent Payable in Respect of the Property. All of the above-mentioned information is available free of charge. With regard to commercial leases entered into on or after 3 April 2012, the Act imposes an obligation on the tenants of such properties, to furnish the following additional information to the PSRA: The Commencement Date of the Terms of the Lease; Any Capital Contribution Paid in respect of the Property; The Frequency of Rent Reviews; Liability for Rates, Insurance, Service Charges and Repairs; Particulars of Rent-free Periods, Fitting-out Time, Fit-out Allowances and Capital Considerations, and Particulars of any Break Clause in the Lease. The additional information is available directly from the Property Services Regulatory Authority website and can be accessed through the following url: https://propertypriceregister.ie/Website/NPSRA/pprweb-com.nsf/page/ppr-home-en

Commercial Real Estate Prices for United States was -10.47280 % Chg. from Yr. Ago in July of 2024, according to the United States Federal Reserve. Historically, Commercial Real Estate Prices for United States reached a record high of 15.91993 in April of 2006 and a record low of -30.40094 in October of 2009. Trading Economics provides the current actual value, an historical data chart and related indicators for Commercial Real Estate Prices for United States - last updated from the United States Federal Reserve on May of 2025.

Germany Commercial Property Market Index: 127 Cities: Average City Centre Office Rent data was reported at 114.360 1990=100 in 2019. This records an increase from the previous number of 107.870 1990=100 for 2018. Germany Commercial Property Market Index: 127 Cities: Average City Centre Office Rent data is updated yearly, averaging 89.865 1990=100 from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 114.360 1990=100 in 2019 and a record low of 79.810 1990=100 in 2005. Germany Commercial Property Market Index: 127 Cities: Average City Centre Office Rent data remains active status in CEIC and is reported by Bulwiengesa AG. The data is categorized under Global Database’s Germany – Table DE.EB004: Property Market Index.

This table contains information about commercial properties including number of stories, elevators, exterior wall type, floor type and roof type for commercial 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.

Graph and download economic data for Delinquency Rate on Commercial Real Estate Loans (Excluding Farmland), Booked in Domestic Offices, All Commercial Banks (DRCRELEXFACBS) from Q1 1991 to Q1 2025 about farmland, domestic offices, delinquencies, real estate, commercial, domestic, loans, banks, depository institutions, rate, and USA.

Real Estate Investment: Commercial Building: Anhui data was reported at 35,627.570 RMB mn in 2023. This records a decrease from the previous number of 51,792.600 RMB mn for 2022. Real Estate Investment: Commercial Building: Anhui data is updated yearly, averaging 26,422.390 RMB mn from Dec 1995 (Median) to 2023, with 28 observations. The data reached an all-time high of 102,586.200 RMB mn in 2015 and a record low of 626.370 RMB mn in 1995. Real Estate Investment: Commercial Building: Anhui 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: Commercial Building.

Germany Commercial Property Market Index: 127 Cities data was reported at 127.680 1990=100 in 2019. This records an increase from the previous number of 123.710 1990=100 for 2018. Germany Commercial Property Market Index: 127 Cities data is updated yearly, averaging 101.500 1990=100 from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 127.680 1990=100 in 2019 and a record low of 93.330 1990=100 in 2004. Germany Commercial Property Market Index: 127 Cities data remains active status in CEIC and is reported by Bulwiengesa AG. The data is categorized under Global Database’s Germany – Table DE.EB004: Property Market Index.

Property NSW keeps a Register of Commercial Office Space for Lease, which is intended for information only, and is subject to change and onsite verification. Property NSW does not warrant the accuracy of the information. The rental stated in the register is subject to contract, and reflective of the current market conditions, or the current rent for the premises in the case of premises leased by the Government and available on a sub-lease.

Information captured by the register includes: • Office space address • Type of space • Net lettable area (m2)

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...

Commercial rents services price index (CRSPI) by North American Industry Classification System (NAICS). Monthly data are available from January 2006 for the total index and from January 2019 for all other indexes. The table presents data for the most recent reference period and the last five periods. The base period for the index is (2019=100).

 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 data Dwelling file Entrance data containing data from appraisers' visits; Other Buildings and Yard Improvements Sales File Tax 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.

Snapshot img

Commercial Real Estate Market Size 2025-2029

The commercial real estate market size is forecast to increase by USD 427.3 billion, at a CAGR of 4.6% between 2024 and 2029.

The market is experiencing significant growth, fueled by increasing marketing initiatives and the rising emphasis on remote work and online shopping. This trend is transforming the commercial real estate landscape, with a shift towards adaptive spaces that cater to the evolving needs of businesses and consumers. The increasing adoption of marketing strategies, such as digital marketing and experiential retail, is driving demand for commercial properties that can effectively showcase brands and create memorable customer experiences. Additionally, the shift towards remote work and online shopping is leading to a surge in demand for data centers, logistics facilities, and flexible office spaces.
However, this market is not without challenges. The rapid pace of technological advancements and changing consumer preferences pose significant obstacles for commercial real estate developers and investors. The need to adapt to these shifts and stay competitive requires a deep understanding of market trends and the ability to pivot quickly. Furthermore, regulatory changes and economic instability can also impact the market's growth trajectory. To capitalize on the opportunities and navigate the challenges effectively, companies must stay informed about the latest market trends and consumer preferences. Investing in technology and innovation, while also maintaining flexibility and adaptability, will be key to success in the evolving the market.

What will be the Size of the Commercial Real Estate Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, with dynamic market activities unfolding across various sectors. Environmental impact assessments are increasingly crucial in property development, shaping the design and construction process. Tenant representation plays a pivotal role in securing suitable spaces for businesses, while 3D modeling facilitates effective space planning and data visualization. Due diligence is an ongoing process, ensuring compliance with legal and regulatory requirements. Property tax assessments, vacancy rates, and property management are essential components of commercial real estate investment strategies. Distressed properties present opportunities for joint ventures and strategic investments, while interior design and machine learning contribute to enhancing tenant experience and optimizing building performance.

Investment properties, industrial properties, and urban planning strategies benefit from big data analytics and virtual tours, enabling informed decision-making. Commercial mortgages and brokerage services facilitate the buying and selling of properties, while occupancy costs and building codes ensure operational efficiency and safety. The market is a complex, ever-changing landscape, with continuous market dynamics shaping its various sectors. From environmental impact assessments to tenant representation, property management, and investment strategies, the integration of various components is essential for success in this dynamic industry.

How is this Commercial Real Estate Industry segmented?

The commercial real estate industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

End-user

  Offices
  Retail
  Leisure
  Others


Channel

  Rental
  Lease
  Sales


Transaction Type

  Commercial Leasing
  Property Sales
  Property Management


Service Type

  Brokerage Services
  Property Development
  Valuation Consulting
  Facilities Management


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  Middle East and Africa

    Egypt
    KSA
    Oman
    UAE


  APAC

    China
    India
    Japan


  South America

    Argentina
    Brazil


  Rest of World (ROW)

By End-user Insights

The offices segment is estimated to witness significant growth during the forecast period.

The U.S. commercial real estate market is undergoing major shifts, particularly in the office segment, driven by flexible work models, evolving corporate needs, and technological advancements. Businesses now favor adaptable, tech-enabled spaces to attract talent, fueling demand for co-working hubs like Regus and WeWork. Industry leaders such as Google and Amazon are redefining office design to boost collaboration and satisfaction.

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The Offices segment was valued at USD 476.50 billion in 2019 and showed a gradual increase during th

This metadata document provides details of the data used for the dissertation: “Improving Commercial Property Price Statistics”. The study explores data related and methodological challenges in the construction of price statistics for commercial real estate.

Short abstract of the dissertation

Since the financial crisis of 2008, National Statistical Institutes (NSIs) have worked to develop commercial real estate (CRE) indicators for official statistics. These indicators are considered essential in financial stability monitoring and may help contain the consequences of future crises or even prevent future crises. However, progress at NSIs to develop these indicators has been slow due to challenges like low observation numbers and high heterogeneity. This dissertation addresses these challenges by exploring data issues and suggesting methodological improvements.

The first three studies focus on data challenges regarding share deals and portfolio sales. Both are real estate trading constructions that are specific to CRE. The results show that share deals and portfolio sales significantly differ from the rest of the market. Therefore, under specific circumstances, CRE indicators could benefit from including these trading types. The final two studies focus on methodological challenges regarding index construction methods and the role of sustainability in real estate pricing. The results show that, by combining established techniques, it is possible to construct price indices that meet official statistics’ standards. Furthermore, the results uncover a complex relationship between sustainability and prices: while energy efficiency generally involves price premiums, others aspects like health and environment display a discount for low sustainable properties.

Overall, this dissertation contributes to the legislative framework that is currently being developed for EU countries to publish official statistics for commercial real estate and adds to the academic discussion by presenting innovative techniques for data analyses and index construction.

Data sources

The following data sources were used:

1. Bussiness Register (Statistics Netherlands)
2. Transactions linked to the Register of Adresses and Buildings (BAG)
3. Linking table buildings and companies (Dutch Land Registry Office)
4. Property Transfer Tax data (Dutch Tax Authorities)
5. Building sustainability scores (W/E advisors)Commercial real estate transactions (Dutch Land Registry Office)
6. Commercial real estate transactions (Dutch Land Registry Office)

Processing methodology

1. The data is originally stored in an SQL database and is processed with SQL and R code (version 4.2). In the code, the name of the table is tbl_SPE_2_ABR_Bedrijfsinfo. The data is used for deriving company transfers by comparing ownership states of various periods. The first period that an ownership differs of the same company indicates an ownership transfer.
2. The data is originally stored in an SQL database and is processed with SQL and R code (version 4.2). In the code, the name of the table is tbl_SPE_6_ABR_CompleetMicro. The data is used for calcuting the size of real estate share deals and estimating price developments by applying appropriate filters and counting the output.
3. The data is originally stored in an SQL database and is processed with R code (version 4.2). In the code, the name of the table is SPE_KADASTER. The data is used for finding real estate information that corresponds to company transfers by linking the company register (ABR) to the real estate register (BAG).
4. The data is originally stored in an SQL database and is processed with R code (version 4.2). In the code, the name of the table is tbl_SPE_3_OVB_Bedrijfsinfo. The data is used for deriving real estate share deals by linking this table (Kadaster) to the real estate register (BAG).
5. The data is originally stored in an SQL database and is processed with R code (version 4.2). In the code, the name of the table is duurzaamheid_input_regressie2. The data is used for finding the relationship between sustainabilty measures and real estate transaction prices by linking sustainabilty scores from a consultancy (WE) to transaction prices (Cadastre) and running regression analyses.
6. The data is originally stored in an SQL database and is processed with R code (version 4.2). In the code, the name of the table is tbl_OV20_pand. The data is used for 4 purposes (separate studies).

• (1) Chapter 3: Determining the price effect of portfolio sale by running regression analyses
• (2) Chapter 4: Developing methods to include portfolio sales in CPPI calcutions by using auxilary data of the real estate properties.
• (3) Chapter 5: Developing a price index method for small domains by using these data to test the outcomes
• (4) Chapter 6: Determining the relationship between sustatinability by running regression analyses

Data restrictions

As part of the CBS law, sharing micro-data outside of the CBS-environment is prohibited. Furthermore, CBS manages the data, but in some cases other parties are still formal owners of the data. The 2 other parties are The Land Registry Office and WE consultancy. Ownership and intellectual property rights are managed in contracts with both owners. It was agreed upon that the data can only be used for the purpose of the PhD study and that the microdata will never be externally disseminated. The data is still owned by them and the intellectual property rights of the analyses belong to me. An intended use of the microdata should be approved by both Statistics Netherlands and the formal data owner. Because of the above, no data can be publicly shared.

If one intends to do research on these data, an application for data use can be requested at CBS. CBS will charge costs for anonymising the data and providing a closed environment to work with the data. More information on this can be found at: https://www.cbs.nl/en-gb/our-services/customised-services-microdata/microdata-conducting-your-own-research

Contact information

Author: Farley Ishaak

Statistics Netherlands | Henri Faasdreef 312 | P.O. Box 24500 | 2490 HA The Hague

TU Delft | Delft University of Technology | Faculty of Architecture and the Built Environment

Department of Management in the Built Environment | P.O. Box 5043 | 2600 GA Delft

M +31 6 46307974 | ff.ishaak@cbs.nl | f.f.ishaak@tudelft.nl

Germany Commercial Property Market Index: WG: 49 Cities: Average Retail Rent: Suburban data was reported at 166.760 1975=100 in 2019. This records an increase from the previous number of 166.010 1975=100 for 2018. Germany Commercial Property Market Index: WG: 49 Cities: Average Retail Rent: Suburban data is updated yearly, averaging 143.580 1975=100 from Dec 1975 (Median) to 2019, with 45 observations. The data reached an all-time high of 192.090 1975=100 in 1993 and a record low of 100.000 1975=100 in 1975. Germany Commercial Property Market Index: WG: 49 Cities: Average Retail Rent: Suburban data remains active status in CEIC and is reported by Bulwiengesa AG. The data is categorized under Global Database’s Germany – Table DE.EB004: Property Market Index.

The vacancy rate of office real estate in the United States was higher than of any other property type in 2025. In the first quarter of the year, approximately ** percent of office real estate was vacant, compared to **** percent of multifamily. Shopping centers and industrial property had the lowest vacancy rates, at *** percent and ***** percent, respectively.

Graph and download economic data for Real Estate Loans: Commercial Real Estate Loans, All Commercial Banks (CREACBM027NBOG) from Jun 2004 to May 2025 about real estate, commercial, loans, banks, depository institutions, and USA.