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

We have developed this file to be tied to our Consumer and B2B Database so additional data fields can be applied to the owners. Each record is ranked by confidence and only the highest quality data is used.

Note - all Consumer packages can include necessary PII (address, email, phone, DOB, etc.) for merging, linking, and activation of the data.

BIGDBM Privacy Policy: https://bigdbm.com/privacy.html

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)

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.

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

In the first half of 2024, over ** percent of the commercial property sales in New Zealand were attributed to private purchasers. In the same period, just around ***** percent of the sales volume went to corporations.

In 2022, the volume of commercial real estate transactions reached *** billion U.S. dollars, up from *** billion U.S. dollars in 2020. One of the reasons for the surge was the pandemic and the release of pent-up demand as the economy reopened. A real estate transaction refers to the process of passing the rights in a property unit from the seller to the buyer in return for an agreed upon sum. Effect of 2007-2008 credit crisis The U.S. real estate market reached its peak in 2007, just before the 2007-2008 credit crisis when the property market collapsed. The value of commercial property returns dropped between 2007 and 2009. Since 2010, the market has steadily recovered, and the volume of transactions climbed until 2015, and has levelled out since then. Types of commercial real estate The change in overall transaction volume is most likely impacted by the type of commercial properties which are more attractive to investors in a particular period. For instance, the interest in multifamily housing investment opportunities went down in the same period that interest in hotel investment opportunities went up.

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.

Update Frequency: Yearly

Access to Residential, Condominium, Commercial, Apartment properties and vacant land sales history data.

To download XML and JSON files, click the CSV option below and click the down arrow next to the Download button in the upper right on its page.

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

 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.

Commercial property prices in the U.S. plateaued in 2024 after declining in 2023. Between 2014 and 2021, commercial real estate prices nearly doubled, with the index reaching ***** index points. Following a slowdown in the market, the index declined, falling to ***** index points. Despite the correction, this indicated an increase of almost ** percent in prices since 2010, which was the baseline year for the index. How have prices of different property types developed over the past years? After more than a decade of uninterrupted growth, office real estate prices started to decline in 2022, reflecting a decline in occupier demand and a tougher lending environment. Industrial real estate prices, which have grown rapidly over the past few years, also experienced a correction in late 2022. Retail real estate prices displayed most resilience amid the difficult economic environment, with the equal weighed repeat sales index remaining stable. How much is invested in new commercial properties? The value of commercial real estate construction has been on the rise since 2010 in the United States. This trend mirrors the recovery seen across all economic sectors after the 2007-2009 recession. However, investment volumes in commercial property vary by type, with private office space, warehouses, and retails reading the pack.

The Commercial Real Estate (CRE) industry is exhibiting significant variations across markets, with persistently high office vacancy rates juxtaposed against thriving prime office spaces. Hard hit by the widespread adoption of remote and hybrid work models, the overall office vacancy rate rose to 20.4% in Q4 2024 from the pre-pandemic rate of 16.8%. However, leasing volumes for prime office spaces are set to climb, providing opportunities for seasoned investors. On the other hand, the multifamily sector is gaining from a prominent move towards renting, primarily driven by housing affordability concerns and changing lifestyle preferences. This has increased demand for multifamily properties and opportunities to convert underutilized properties, such as offices, into residential rentals. The industrial real estate segment is also evolving, with the boom in e-commerce necessitating the development of strategically located warehouses for quick fulfillment and last-mile delivery. Industry revenue has gained at a CAGR of 0.8% to reach $1.4 trillion through the end of 2025, including a 0.4% climb in 2025 alone. The industry is grappling with multiple challenges, including high interest rates, wide buyer-seller expectation gaps and significant disparities in demand across different geographies and asset types. The Federal Reserve's persistent high-interest-rate environment creates refinancing hurdles for properties purchased during the low-rate period of 2020-2021. Because of remote working trends, office delinquency rates are predicted to climb from 11.0% in late 2024 to 14.0% by 2026, leading to a job market increasingly concentrated in certain urban centers. Through the end of 2030, the CRE industry is expected to stabilize as the construction pipeline shrinks, reducing new supply and, in turn, rebalancing supply and demand dynamics. With this adjustment, occupancy rates are likely to improve, and rents may observe gradual growth. The data center segment is set to witness accelerating demand propelled by the rapid expansion of artificial intelligence, cloud computing and the Internet of Things. Likewise, mixed-use properties are poised to gain popularity, driven by the growing appeal of flexible spaces that accommodate diverse businesses and residents. This new demand, coupled with the retiring baby boomer generation's preference for leisure-centric locales, is expected to push the transformation of traditional shopping plazas towards destination centers, offering continued opportunities for savvy CRE investors. Industry revenue will expand at a CAGR of 1.9% to reach $1.6 trillion in 2030.

SafeGraph Places provides baseline location information for every record in the SafeGraph product suite via the Places schema and polygon information when applicable via the Geometry schema. The current scope of a place is defined as any location humans can visit with the exception of single-family homes. This definition encompasses a diverse set of places ranging from restaurants, grocery stores, and malls; to parks, hospitals, museums, offices, and industrial parks. Premium sets of Places include apartment buildings, Parking Lots, and Point POIs (such as ATMs or transit stations).

SafeGraph Places is a point of interest (POI) data offering with varying coverage and properties depending on the country. Note that address conventions and formatting vary across countries. SafeGraph has coalesced these fields into the Places schema.

SafeGraph provides clean and accurate geospatial datasets on 51M+ physical places/points of interest (POI) globally. Hundreds of industry leaders like Mapbox, Verizon, Clear Channel, and Esri already rely on SafeGraph POI data to unlock business insights and drive innovation.

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 16.13922 in April of 2006 and a record low of -30.24535 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 July of 2025.

New Home Sales in China increased to 38849 CNY Hundred Million in June from 30119 CNY Hundred Million in May of 2025. This dataset provides - China Sales Value of Commercial Residential Buildings- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The number of commercial real estate transactions in the UK plummented in 2020 due to the coronavirus pandemic, followed by a swift recovery in the following years. In 2024, the number of non-residential property sales over 40,000 British pounds completed amounted to *******. In the UK, England is responsible for the majority of completed non-residential property transactions.

Graph and download economic data for Rental and Leasing of Commercial Space for Lessors of Real Estate, All Establishments, Employer Firms (LORERALOCSA45311) from 2015 to 2022 about lessors, employer firms, leases, rent, real estate, establishments, commercial, and USA.

Commercial properties for sale by the City of Detroit.