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)

Doorda's UK Commercial Real Estate Data provides a comprehensive database of over 6 million commercial locations sourced from 20 data sources, offering unparalleled insights for business intelligence and analytics purposes.

Volume and stats: - 6M Commercial locations with internals - 1.7M Named Commercial Occupants - 1.4M Non-Domestic Energy Performance Inspections

Our Commercial Real Estate Data offers a multitude of use cases: - Market Analysis - Competitor Analysis - Lead Generation - Risk Management - Location Planning

The key benefits of leveraging our Commercial Real Estate Property Data include: - Data Accuracy - Informed Decision-Making - Competitive Advantage - Efficiency - Single Source

Covering a wide range of industries and sectors, our data empowers organisations to make informed decisions, uncover market trends, and gain a competitive edge in the UK market.

The Property Intelligence Platform market is experiencing robust growth, driven by increasing demand for data-driven decision-making in the real estate sector. Technological advancements, such as AI and machine learning, are enhancing the capabilities of these platforms, providing more accurate and insightful property data analysis. This allows real estate professionals to make informed decisions regarding investments, valuations, risk assessment, and portfolio management. The market size in 2025 is estimated at $5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is fueled by several factors, including the increasing adoption of cloud-based solutions, the growing need for efficient property management, and the expansion of the global real estate market. The rise of PropTech and the integration of various data sources, such as public records, transactional data, and market analytics, are further contributing to this expansion. The competitive landscape is highly fragmented, with a mix of established players and emerging startups. Key players like Yardi, VTS, and CoreLogic are leveraging their existing market presence and expertise to maintain their market share. However, agile startups are innovating with advanced analytical tools and specialized solutions, catering to niche market segments. Geographical expansion, particularly in emerging economies with rapidly growing real estate sectors, presents significant opportunities for both established and new entrants. The market's future growth will likely be shaped by the ongoing integration of data analytics, the development of more sophisticated predictive models, and the increasing adoption of these platforms by smaller real estate firms. The continued focus on enhancing data security and privacy will also play a crucial role in shaping the market's trajectory.

Explore Doorda's UK Residential Real Estate Data, offering insights into 34M+ Addresses sourced from 20 data sources. Unlock business intelligence and analytics capabilities.

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

data source (https://www.federalreserve.gov/apps/ContactUs/feedback.aspx?refurl=/releases/h8/%). For questions on FRED functionality, please contact us here (https://fred.stlouisfed.org/contactus/).

The Property Intelligence Platform market is experiencing robust growth, driven by increasing demand for data-driven decision-making in the real estate sector. The market's expansion is fueled by several key factors: the rising adoption of cloud-based solutions offering scalability and accessibility; the increasing need for sophisticated analytics to optimize investment strategies amongst both SMEs and large enterprises; and the proliferation of readily available data sources enriching the insights generated by these platforms. Technological advancements, such as AI and machine learning integration, further enhance the market's capabilities, enabling predictive analytics and improved risk assessment. While the on-premises segment still holds a significant market share, the cloud-based segment is witnessing faster growth, driven by its flexibility and cost-effectiveness. Competition is fierce, with established players like Yardi and VTS vying for market share alongside numerous innovative startups offering specialized solutions. Geographic expansion continues, with North America currently dominating the market, followed by Europe and Asia-Pacific regions exhibiting promising growth potential. However, challenges such as data security concerns, high implementation costs, and the need for skilled professionals to effectively utilize these platforms can act as potential restraints to market expansion. Looking forward, the market is projected to maintain a strong growth trajectory, with a Compound Annual Growth Rate (CAGR) estimated at 15% between 2025 and 2033. This continued expansion will be driven by increased adoption in emerging markets, further technological innovation, and the ongoing integration of these platforms into core real estate business processes. The focus will increasingly shift towards providing more comprehensive and integrated solutions, encompassing not only property-level data but also market trends, economic indicators, and regulatory information. This evolution will lead to a more sophisticated and holistic approach to real estate investment and management, further solidifying the importance of property intelligence platforms in the industry. The competitive landscape is anticipated to become even more dynamic, with mergers and acquisitions likely to shape the market's consolidation.

The Commercial Building Inventories provide modeled data on commercial building type, vintage, and area for each U.S. city and county. Please note this data is modeled and more precise data may be available through county assessors or other sources. Commercial building stock data is estimated using CoStar Realty Information, Inc. building stock data. This data is part of a suite of state and local energy profile data available at the "State and Local Energy Profile Data Suite" link below and builds on Cities-LEAP energy modeling, available at the "EERE Cities-LEAP Page" link below. Examples of how to use the data to inform energy planning can be found at the "Example Uses" link below.

Revenue for the Commercial Real Estate industry in China is expected to decrease at an annualized 6.5% over the five years through 2025, with strict control on real estate sector and the effects of the COVID-19 epidemic since 2020.Since August 2020, the Government has proposed three debt indicators for real estate development and management companies through which the company's financial health can be rated. This new policy has exacerbated the company's debt pressure, making it unable to repay old debts by borrowing new debt. Some real estate companies faced a liquidity crisis.In 2022, the city's lockdown and laying-off caused by COVID-19 epidemic led to the pressure of delaying the delivery of commercial real estate. The industry's newly constructed and completed areas decreased significantly throughout the year. In addition, the epidemic has impacted sales in the real estate development and management industry, and some sales offices have been forced to close temporarily. In 2022, the newly constructed area of office buildings decreased by 39.1%, the newly constructed area of commercial-used buildings decreased by 42.0%, and the completed area dropped by 22.8% and 22.0% respectively.Industry revenue is forecast to recover at an annualized 1.4% over the five years through 2030. The industry's growth is anticipated to stabilize over the period, as the government continues to strengthen controls on real estate. The industry is projected to further expand into second- and third-tier cities, like Chengdu, Shenyang, and Xi'an, as firms seek to gain market share in regional centers over the next five years. Several city complex projects are planned to be built in these cities over the five years through 2030.

Source ID: FL075035503.Q

For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx).

With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx).

In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL075035503&t=) provided by the source.

About Dataset

Dataset Overview This dataset provides a snapshot of real estate transactions in London for 2024. It includes key property details such as the number of bedrooms, bathrooms, living space size, lot size, and transaction price. Additionally, it incorporates information about property features like waterfront views, renovation history, and construction quality. Designed for educational and research purposes, the dataset offers insights into London's real estate market trends and serves as a valuable resource for data analysis and machine learning applications.

Data Science Applications This dataset is ideal for students, researchers, and professionals seeking to apply data science techniques to real-world real estate data. Potential applications include:

Exploratory Data Analysis (EDA): Investigate price trends, property characteristics, and geographical distribution of transactions. Price Prediction Models: Develop machine learning models to predict property prices based on features like size, location, and condition. Trend Analysis: Analyze historical and geographical trends in property prices and features. Geospatial Analysis: Map properties based on latitude and longitude to identify hotspots or underserved areas.

Column Descriptions

Ethically Mined Data This dataset was ethically sourced from publicly available property listings. It does not include any Personally Identifiable Information (PII) or data that could infringe on individual privacy. All information represents public details about properties for sale in London.

Acknowledgements

Data Source: Real estate data provided from publicly accessible resources. Image Credit: Unsplash for real estate-themed visuals. Use this dataset responsibly for educational and analytical purposes!

The US property management industry, a significant segment of the broader real estate market, is experiencing steady growth, driven by several key factors. The increasing urbanization and population growth, particularly in major metropolitan areas, fuels the demand for rental properties and professional property management services. The rise of institutional investors in the multifamily sector further contributes to this demand, as these investors often outsource management to specialized firms. Technological advancements, such as property management software and online tenant portals, are streamlining operations and improving efficiency, leading to increased market penetration and attracting new players. Furthermore, the evolving preferences of renters, who increasingly value convenience and amenities, necessitate sophisticated property management solutions, fostering industry expansion. The commercial sector also contributes significantly, with businesses relying on professional managers for office buildings, retail spaces, and industrial properties. While the industry faces challenges, such as fluctuating interest rates impacting investment decisions and potential labor shortages within the property management sector, the long-term outlook remains positive. The diverse service offerings within the industry—from marketing and property evaluation to tenant services and maintenance—provide resilience against economic downturns. The fragmentation of the market presents opportunities for both established players and new entrants, with mergers and acquisitions potentially reshaping the competitive landscape in the coming years. The increasing focus on sustainable and environmentally friendly practices also presents a growth avenue, as property managers adapt their strategies to meet evolving tenant expectations and regulatory requirements. Considering the provided global market size of $81.52 billion (assuming “Million” is a typo and should be “Billion”) and a CAGR of 3.94%, a reasonable estimate for the US market share, given its prominence in the global real estate market, would place it in the range of $40-50 billion in 2025. This estimate is further supported by the presence of large US-based property management companies listed in the provided data. Recent developments include: February 2024: Wood Partners, the 4th-largest real estate developer in the United States, sold its property management business for its 38,000+ units in 17 states to Greystar (Charleston, South Carolina), the country's largest apartment management company., November 2023: RealPage Inc. acquired On-site Managers Inc. for approximately USD 250 million in cash. On-Site is an on-demand leasing platform for multifamily property management and renters that integrates leads from all sources and converts them to signed leases for the multifamily industry and the single-family housing industry. RealPage will continue to support the on-site platform and plans to integrate the best of its on-site platforms in the future. Clients on both platforms will continue to benefit from future improvements without disruption.. Key drivers for this market are: Increasing Demand from the Commercial Segment is Driving the Market, Increasing Disposable Income of Consumers is Driving the market. Potential restraints include: Increasing Demand from the Commercial Segment is Driving the Market, Increasing Disposable Income of Consumers is Driving the market. Notable trends are: Demand from the Residential Sector is Supporting the Market.

Source ID: FL010000386.Q

For more information about the Flow of Funds tables, see the Financial Accounts Guide (https://www.federalreserve.gov/apps/fof/Default.aspx).

With each quarterly release, the source may make major data and structural revisions to the series and tables. These changes are available in the Release Highlights (https://www.federalreserve.gov/apps/fof/FOFHighlight.aspx).

In the Financial Accounts, the source identifies each series by a string of patterned letters and numbers. For a detailed description, including how this series is constructed, see the series analyzer (https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL010000386&t=) provided by the source.

The Commercial Vacancy All Properties dataset shows a variety of current and historical data points regarding the commercial real estate availability, vacancy, and absorption across the entire City of Mesa for all commercial properties. This dataset was collected from a third-party source, CoStar, which is a commercial real estate database. CoStar is widely accepted as the trusted, industry standard for commercial real estate data, and while the City of Mesa believes this information to be accurate, we do not claim to have verified every and all information provided. If you require further explanation of some of the real estate terms used in the dataset, please visit the CoStar Terms Glossary below, which explains each term in greater detail. CoStar Terms Glossary: https://www.costar.com/about/support/costar-glossary

China Real Estate Inv: YoY: Year to Date: Commercial Building data was reported at -9.400 % in Mar 2025. This records an increase from the previous number of -12.300 % for Feb 2025. China Real Estate Inv: YoY: Year to Date: Commercial Building data is updated monthly, averaging 17.500 % from Jan 1999 (Median) to Mar 2025, with 315 observations. The data reached an all-time high of 64.900 % in Feb 2004 and a record low of -25.600 % in Feb 2020. China Real Estate Inv: YoY: Year to Date: Commercial Building data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under Global Database’s China – Table CN.RKA: Real Estate Investment: Monthly: Summary. Source annotated growth rate is calculated on a comparable basis since March 2023.

Data source is the Office of Tax and Revenue’s Computer-Assisted Mass Appraisal (CAMA) system. The CAMA system is used by the Assessment Division (AD) within the Real Property Tax Administration to value real estate for ad valorem real property tax purposes.The intent of this data is to provide a sale history for active properties listed among the District of Columbia’s real property tax assessment roll. This data is updated daily. The AD constantly maintains sale data, adding new data and updating existing data. Daily updates represent a “snapshot” at the time the data was extracted from the CAMA system, and data is always subject to change.

Snapshot img

US Real Estate Property Management Software Market Size 2025-2029

The us real estate property management software market size is forecast to increase by USD 447.3 million, at a CAGR of 6.1% between 2024 and 2029.

The Real Estate Property Management Software Market in the US is experiencing significant growth, driven by the increasing emphasis on customer-centric business processes. Property management companies are recognizing the value of streamlined operations and enhanced tenant experiences, leading to a surge in demand for advanced software solutions. Moreover, the adoption of big data analytics is transforming the industry, enabling data-driven decision-making and improved operational efficiency. However, the market faces challenges as well. The threat of open-source real estate property management software is growing, with some organizations opting for cost-effective alternatives. This trend could put pressure on established players to innovate and differentiate their offerings, ensuring they maintain a competitive edge. To capitalize on opportunities and navigate challenges effectively, companies must focus on delivering superior customer service, leveraging data insights, and continuously improving their technology offerings.

What will be the size of the US Real Estate Property Management Software 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 real estate property management market in the US is witnessing significant advancements, driven by the integration of smart home technologies and data backup solutions. Energy efficiency is a top priority, with regulatory compliance and property insurance companies encouraging the adoption of green building standards and sustainability certifications. Tenant screening services are utilizing background checks, credit history reports, and biometric authentication for thorough vetting processes. Artificial intelligence (AI) and machine learning are revolutionizing property management through predictive analytics, workflow optimization, and eviction prevention. Virtual tours and 3D modeling enable remote property inspections, while data visualization tools provide valuable insights for property investment analysis. Cloud security and mobile device management are essential for secure data access and management. Property risk management is a growing concern, with disaster recovery plans and property liability insurance playing crucial roles. Property management training and lease negotiation strategies are also key components in maintaining tenant retention. In summary, the US real estate property management market is undergoing a digital transformation, focusing on energy efficiency, regulatory compliance, tenant screening, and advanced technologies such as AI, data visualization, and predictive analytics. These trends are shaping the future of property management, offering increased efficiency, security, and profitability for businesses.

How is this market segmented?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. TypeIntegrated softwareStandalone softwareDeploymentCloud basedOn premisesApplicationResidentialCommercialIndustrialSectorLarge enterpriseSMEsIndividualsGeographyNorth AmericaUS

By Type Insights

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

Real estate property management software in the US integrates various applications to streamline operations for single-family homes, vacation rentals, student housing, and commercial properties. This software includes property marketing automation for tenant communication and listing platforms, occupancy management for rent collection and lease management, property accounting for financial reporting and automated payment processing, and property data analytics for value optimization and market trends. Compliance management ensures legal requirements, while property inspections and maintenance management maintain property conditions. API integration enables tenant screening and property investor collaboration. Cloud-based platforms offer accessibility and data security. Property portfolio management facilitates multifamily housing and building automation for energy efficiency. Insurance management and access control enhance security systems. Real estate agents and property managers can utilize these integrated features to effectively manage their property businesses.

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The Integrated software segment was valued at USD 659.20 million in 2019 and showed a gradual increase during the forecast period.

Market D

New version 2.0.0 with majors change

For free and complete informations concerning CASSMIR datasets, please visit our website (in French).

The CASSMIR database (Contribution to the Spatial and Sociological Analysis of Residential Real Estate Markets) is a spatial and population datasets on housing property market of the Parisian metropolitan area, from 1996 to 2018. The indicators in the CASSMIR database cover four "thematic areas of investigation" : prices, socio-demographic profile of buyers and sellers, purchasing regimes and types of property transfers and types of real estate. These indicators characterize spatial units at three scales (communal level, 1km grid and 200m grid) and population groups of buyers and sellers declined according to social, generational and gender criteria. The delivery of the database follows a series of matching and aggregation of individual data from two original databases : a database on real estate transactions (BIEN database) and a database on first-time buyer investments (PTZ database). CASSMIR delivers aggregated data (with nearly 350 variables) in open access for non-commercial use.

This repository consists of sevenfiles.

"CASSMIR_SpatialDataBase" is a Geopackage file, it lists all the data aggregated to spatial units of reference. It is composed of three layers that correspond to the geographical scale of aggregation: at a communal level, a grid of one kilometer on each side and a grid of two hundred meters on each side.

"CASSMIR_GroupesPopDataBase" is a .csv file, it lists all the data aggregated to population groups of reference. There are three types of population groups : groups referenced by the social position of the buyers/sellers (social group), groups referenced by the age group to which the buyers/sellers belong (generational group), groups referenced by the sex of the buyers/sellers (gender group).

Two metadata files (.csv) lists the metadata of the indicators made available. They are systematically structured as follows :

• Id_var: the identifier of the variable contained in "CASSMIR_SpatialDataBase" or "CASSMIR_GroupesPopDataBase"
• Unite d'observation des variables descriptives : descriptive units of observation (Prices, buyers, sellers...)
• Type d'information : precision on the type of information
• Label : Description of the contents of the variable
• Indicator_Group: The group of indicators to which the variable relates (prices, socio-demographics indicators of buyers and sellers...)
• Unit : The unit of measurement of the variable
• Spatial_Availability : A precision on the availability of the variable in the spatial database (communes, 1 km grid and 200m grid)
• GroupesPop_Availability : A precision on the availability of the variable in the population groupes database (Social, generational , gender)
• Data_Source: The main origin of the data (INSEE, BIEN and/or PTZ)
• Remarques : possible remarks on the construction of the variable

"BIENSampleForTest" and "PTZSampleForTest" are two .txt files which restore a sample of individual data from each of the original databases. All data were anonymized and the values randomized. These two files are specifically dedicated to reproducing the different stages of processing that lead to the production of the CASSMIR files ("CASSMIR_SpatialDataBase" or "CASSMIR_GroupesPopDataBase") and cannot be used in any other way.

"LEXIQUE" is a glossary of terms used to name the variables (.csv).

The creation of the database was funded by the National Reseach Agency (ANR WIsDHoM https://anr.fr/Projet-ANR-18-CE41-0004).

All CASSMIR documentation (in French) and R codes are accessible via the Gitlab repository at the following address : https://gitlab.huma-num.fr/tlecorre/cassmir.git

METADATA :

• Licence

This dataset is registered under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. You are free to copy, distribute, transmit, and adapt the data, provided that you give credit to the CASSMIR data base and specify the original source of the data. If you modify or use the data in other derivative works, you may distribute them only under the same license. You may not make commercial use of this database, nor may you use it for any purpose other than scientific research.

• Citation standard

- Figures: (CC - CASSMIR database, indicator(s) constructed from XXX data)

- Bibliography : Productions that use the CASSMIR database must reference the dataset and the data paper.

Dataset: Le Corre T., 2020, CASSMIR (Version 2.0.0) [Data set], Zenodo. http://doi.org/10.5281/zenodo.4497219

Data paper: Thibault Le Corre, « Une base de données pour étudier vingt années de dynamiques du marché immobilier résidentiel en Île-de-France », Cybergeo: European Journal of Geography [En ligne], Data papers, article No.992, mis en ligne le 09 août 2021. URL : http://journals.openedition.org/cybergeo/37430 ; DOI : https://doi.org/10.4000/cybergeo.37430

• Data paper title

"Une base de données pour étudier vingt années de dynamiques du marché immobilier en Île-de-France"

• Author

Thibault Le Corre

• Keywords

Housing market, data base, Île-de-France, spatio-temporal dynamics

• Related Publication

DOI : https://doi.org/10.4000/cybergeo.37430

• Language

French

• Time Period Covered

The time period covered by the indicators in the database depends on the data sources used, respectively:
For data from BIEN: 1996, 1999, 2003-2012, 2015, 2018
For data from PTZ: 1996-2016

• Kind of data

Nature of data submitted

• vector: Vector data

• grid: Data mesh

• code: programming code (see the website or GitLab of the project)

• Data Sources

Base BIEN

Base PTZ

• Geographical Coverage

Île-de-France region

• Geographical Unit

Municipalities and grid mesh elements (1km side grid and 200 side grid) concerned by real estate transactions

• Geographic Bounding Box

Reference Coordinate System (RCS): EPSG 2154 RGF93/Lambert 93.

- Xmin : 586421.7
- Xmax : 741205.6
- Ymin : 6780020
- Ymax : 6905324

• Type of article

Data Paper

The Commercial Vacancy Multi-Family (MF) dataset shows a variety of current and historical data points regarding the commercial real estate availability, vacancy, and absorption across the entire City of Mesa for all MF properties. This dataset was collected from a third-party source, CoStar, which is a commercial real estate database. CoStar is widely accepted as the trusted, industry standard for commercial real estate data, and while the City of Mesa believes this information to be accurate, we do not claim to have verified every and all information provided. If you require further explanation of some of the real estate terms used in the dataset, please visit the CoStar Terms Glossary below, which explains each term in greater detail. CoStar Terms Glossary: https://www.costar.com/about/support/costar-glossary

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset hosted by the City of New York. The city has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York City using Kaggle and all of the data sources available through the City of New York organization page!

• Update Frequency: This dataset is updated quarterly.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Photo by Ricardo Gomez Angel on Unsplash