This is a collection of CSV files that contain assessment data. The files in this extract are: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 dataDwelling fileEntrance data containing data from appraisers' visits;Other Buildings and Yard ImprovementsSales FileTax 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.

This is a comprehensive collection of tax and assessment data extracted at a specific time. The data is in CSV format. A data dictionary (pdf) and the current tax rate book (pdf) are also included.

This is a comprehensive collection of tax and assessment data extracted at a specific time. The data is in CSV format. A data dictionary (pdf) and the current tax rate book (pdf) are also included.

This is a comprehensive collection of tax and assessment data extracted at a specific time. The data is in CSV format. A data dictionary (pdf) and the current tax rate book (pdf) are also included.

This is a comprehensive collection of tax and assessment data extracted at a specific time. The data is in CSV format. A data dictionary (pdf) and the current tax rate book (pdf) are also included.

What you get:

Upvote! The database contains +40,000 records on US Gross Rent & Geo Locations. The field description of the database is documented in the attached pdf file. To access, all 325,272 records on a scale roughly equivalent to a neighborhood (census tract) see link below and make sure to upvote. Upvote right now, please. Enjoy!

Get the full free database with coupon code: FreeDatabase, See directions at the bottom of the description... And make sure to upvote :) coupon ends at 2:00 pm 8-23-2017

Gross Rent & Geographic Statistics:

• Mean Gross Rent (double)
• Median Gross Rent (double)
• Standard Deviation of Gross Rent (double)
• Number of Samples (double)
• Square area of land at location (double)
• Square area of water at location (double)

Geographic Location:

• Longitude (double)
• Latitude (double)
• State Name (character)
• State abbreviated (character)
• State_Code (character)
• County Name (character)
• City Name (character)
• Name of city, town, village or CPD (character)
• Primary, Defines if the location is a track and block group.
• Zip Code (character)
• Area Code (character)

Abstract

The data set originally developed for real estate and business investment research. Income is a vital element when determining both quality and socioeconomic features of a given geographic location. The following data was derived from over +36,000 files and covers 348,893 location records.

License

Only proper citing is required please see the documentation for details. Have Fun!!!

Golden Oak Research Group, LLC. “U.S. Income Database Kaggle”. Publication: 5, August 2017. Accessed, day, month year.

For any questions, you may reach us at research_development@goldenoakresearch.com. For immediate assistance, you may reach me on at 585-626-2965

please note: it is my personal number and email is preferred

Check our data's accuracy: Census Fact Checker

Access all 325,272 location for Free Database Coupon Code:

Don't settle. Go big and win big. Optimize your potential**. Access all gross rent records and more on a scale roughly equivalent to a neighborhood, see link below:

• Website: Golden Oak Research make sure to upvote

A small startup with big dreams, giving the every day, up and coming data scientist professional grade data at affordable prices It's what we do.

IntroductionLinking free-text addresses to unique identifiers in a structural address database [the Ordnance Survey unique property reference number (UPRN) in the United Kingdom (UK)] is a necessary step for downstream geospatial analysis in many digital health systems, e.g., for identification of care home residents, understanding housing transitions in later life, and informing decision making on geographical health and social care resource distribution. However, there is a lack of open-source tools for this task with performance validated in a test data set.MethodsIn this article, we propose a generalisable solution (A Framework for Linking free-text Addresses to Ordnance Survey UPRN database, FLAP) based on a machine learning–based matching classifier coupled with a fuzzy aligning algorithm for feature generation with better performance than existing tools. The framework is implemented in Python as an Open Source tool (available at Link). We tested the framework in a real-world scenario of linking individual’s (n=771,588) addresses recorded as free text in the Community Health Index (CHI) of National Health Service (NHS) Tayside and NHS Fife to the Unique Property Reference Number database (UPRN DB).ResultsWe achieved an adjusted matching accuracy of 0.992 in a test data set randomly sampled (n=3,876) from NHS Tayside and NHS Fife CHI addresses. FLAP showed robustness against input variations including typographical errors, alternative formats, and partially incorrect information. It has also improved usability compared to existing solutions allowing the use of a customised threshold of matching confidence and selection of top n candidate records. The use of machine learning also provides better adaptability of the tool to new data and enables continuous improvement.DiscussionIn conclusion, we have developed a framework, FLAP, for linking free-text UK addresses to the UPRN DB with good performance and usability in a real-world task.

Metadata The ‘Requests for land values’ database, or DVF, lists all sales of land over the last five years, in mainland France and in the overseas departments and territories — except in Mayotte and Alsace-Moselle. The properties concerned can be built (apartment and house) or unbuilt (plots and farms). The data are produced by Bercy, i.e. by the Directorate-General for Public Finance. They come from the deeds registered with notaries and the information contained in the cadastre. Legal framework: The DVF database does not contain personal data, such as the name of the seller or the buyer of a good. It contains only information on transactions: type of property sold, area, selling price and so on. As these data can be cross-checked with other data already online, the Directorate-General for Public Finance recalls that the use of data from the DVF database cannot have the purpose or effect of allowing the re-identification of data subjects, nor should it be indexed on online search engines. Consult the general conditions of use: https://static.data.gouv.fr/resources/request-de-valeurs-foncieres/20190419-091643/conditions-generales-dutilisation.pdf Fields code_service_ch: not provided reference_document: not entered articles_cgi1: not entered articles_cgi2: not entered articles_cgi3: not entered articles_cgi4: not entered articles_cgi5: No_provision: Each provision of a document has a number. Only the provisions concerning transfers for consideration are returned to the file. The provisions concerning transfers free of charge are removed from the register by the application. The disposition numbers used do not therefore necessarily follow the numerical order date_mutation: Date of signature of nature_mutation document: Sale, sale in the future state of completion, sale of building land, tendering, expropriation or exchange of land value: This is the price or valuation declared in the context of a transfer for consideration. It can correspond to several properties. The details are not traced in the information system no_voie: Number in track b_t_q: Repetition index type_of_way: Track type (example: Street, Avenue,...) code_voie: Track code: Wording of the code_postal route: Common postal code: Wording of the commune code_departement: Common_code department code: Common code prefix_of_section: Prefix of cadastral section section: Cadastral section no_plan: Cadastral plan no_volume: Cadastral volume A condominium lot consists of a private part (apartment, cellar, etc.) and a share of the common part (tenths). Only the first 5 lots are mentioned. If the number of lots exceeds 5, they will not be returned. 1st lot surface_carrez_du_1er_lot: surface area CARREZ of the 1st lot 2nd_lot: 2nd lot surface_carrez_du_2eme_lot: surface area CARREZ of the 2nd lot 3rd_lot: 3rd lot surface_carrez_du_3eme_lot: CARREZ surface area of the third lot, fourth lot: 4th lot surface_carrez_du_4eme_lot: surface area CARREZ of the 4th lot 5th_lot: 5th lot surface_carrez_du_5eme_lot: surface area CARREZ of the 5th lot number_of_lots: Total number of lots per layout code_type_local: Local type code type_local 1: House, 2: apartment, 3: dependency (isolated), 4: Industrial and commercial premises or similar identifier_local: This is the number that identifies each room. The local is a tax concept of built property. The file includes one line per number (per local) with the corresponding real area surface_reelle_bati next to it: The real area is attached to the local identifier. This is the sum of the actual surface area of the premises and the surface areas of the outbuildings (see real estate lexicon) number_pieces_principal: Number of main nature_culture parts: Nature of culture nature_culture_speciale: Nature of special crop surface_terrain: Building land capacity: indicates the presence of racks (non-zero local_type) nb_line: Number of lines of the transaction (number of lines on grouping of a single value of the department code set, common code, date of transfer, nature of transfer, land value, no_disposition) id_parcelle: PCI-type parcel identifier

Snapshot img

Metaverse Real Estate Market Size 2024-2028

The metaverse real estate market size is forecast to increase by USD 11.58 billion, at a CAGR of 73.6% between 2023 and 2028.

The market is an evolving digital landscape, characterized by the fusion of mixed reality and cryptocurrency. This market is witnessing significant traction due to the increasing adoption of blockchain technology for secure virtual transactions. The Metaverse offers a new frontier for real estate investments, providing unique opportunities for businesses and individuals alike. The market's dynamics are shaped by several factors. One of the most intriguing aspects is the uncertainty surrounding the pricing of virtual properties. While some virtual real estate parcels fetch high prices, others remain undervalued. This volatility can be attributed to the novelty and evolving nature of the market.
Moreover, the Metaverse's potential applications extend beyond gaming and entertainment. Industries such as education, healthcare, and retail are exploring the Metaverse for innovative solutions. For instance, educational institutions are using virtual campuses to provide immersive learning experiences, while healthcare providers are leveraging virtual environments for telemedicine and patient engagement. Despite the market's uncertainty, the market's growth trajectory is promising. According to recent estimates, the number of active users in the Metaverse is projected to reach 23.3 million by 2025, indicating a significant increase from the current user base. This trend is expected to drive demand for virtual real estate, leading to potential investment opportunities.
The market presents a unique investment opportunity, characterized by its fusion of mixed reality and cryptocurrency, the adoption of blockchain technology, and the potential for diverse applications across various sectors. The market's dynamics are shaped by factors such as pricing uncertainty and the evolving nature of the Metaverse. Despite these challenges, the market's growth trajectory is promising, with increasing user adoption and the potential for innovative applications driving demand for virtual real estate.

Major Market Trends & Insights

North America dominated the market and accounted for a 78% growth during the forecast period.
The market is expected to grow significantly in Second Largest Region as well over the forecast period.
By the End-user, the Enterprises sub-segment was valued at USD 225.80 billion in 2022
By the Type, the Virtual Land sub-segment accounted for the largest market revenue share in 2022

Market Size & Forecast

Market Opportunities: USD 1.25 billion
Future Opportunities: USD USD 11.58 billion 
CAGR : 73.6%
North America: Largest market in 2022

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

Get Key Insights on Market Forecast (PDF) Request Free Sample

Metaverse real estate represents a significant and expanding sector within the digital economy. According to recent estimates, the market for metaverse property currently accounts for over 1% of total digital asset transactions. Looking ahead, industry experts project a compound annual growth rate of approximately 25% over the next five years. Considerable investment activity characterizes this dynamic market. For instance, virtual land parcels in popular metaverses have seen substantial price increases, with some plots selling for millions of dollars. In comparison, the average price for a residential property in the United States was around USD350,000 as of 2021.
This discrepancy underscores the significant potential for returns in metaverse real estate. Moreover, the market encompasses a diverse range of offerings. These include virtual world economies, digital identity verification, data storage solutions, user interface design, and blockchain security audits, among others. As the market continues to evolve, the integration of payment gateway services, ownership verification, and digital asset management solutions is expected to further streamline transactions and enhance user experience. Transaction fees and content moderation policies are essential considerations for investors. While fees vary between platforms, they can impact potential returns. Additionally, adherence to data privacy compliance and legal frameworks is crucial to mitigate risks and maintain a positive user experience.
In summary, the market represents a burgeoning sector with significant growth potential. Investment opportunities span a wide range of offerings, from virtual land sales to platform integration services. As the market continues to mature, regulatory compliance and user experience enhancements will play increasingly important roles.

How is this Metaverse Real Estate Industry segmented?

The metaverse real estate industry research report provides comprehensive data (region-wise segment

August 2025

This is a comprehensive collection of personal property tax and assessment data extracted at a specific time. The data is in CSV format. A data dictionary (pdf) and the current tax rate book (pdf) are also included.

To access parcel information:Enter an address or zoom in by using the +/- tools or your mouse scroll wheel. Parcels will draw when zoomed in.Click on a parcel to display a popup with information about that parcel.Click the "Basemap" button to display background aerial imagery.From the "Layers" button you can turn map features on and off.Complete Help (PDF)Parcel Legend:Full Map LegendAbout this ViewerThis viewer displays land property boundaries from assessor parcel maps across Massachusetts. Each parcel is linked to selected descriptive information from assessor databases. Data for all 351 cities and towns are the standardized "Level 3" tax parcels served by MassGIS. More details ...Read about and download parcel dataUpdatesV 1.1: Added 'Layers' tab. (2018)V 1.2: Reformatted popup to use HTML table for columns and made address larger. (Jan 2019)V 1.3: Added 'Download Parcel Data by City/Town' option to list of layers. This box is checked off by default but when activated a user can identify anywhere and download data for that entire city/town, except Boston. (March 14, 2019)V 1.4: Data for Boston is included in the "Level 3" standardized parcels layer. (August 10, 2020)V 1.4 MassGIS, EOTSS 2021

This is a collection of CSV files that contain assessment data. The files in this extract are:

Primary Account file containing primary owner information; Detail file containing asset assessment information; Additional Address file containing any additional addresses that exist for an account; Assessment file containing assessed value-related data;

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.

Downloadable PDF reports containing market value forecasts and data trends by region.

Output formatted extracts from ALKIS, property map with further information Rhineland-Palatinate (RP32), output forms: analogue or print-prepared (PDF). In the property register, data of a factual and legal nature must be provided on all properties (lots and buildings), including data on the owners and hereditary builders of the plots. The property register consists in particular of the property map and the property description. The property map is the scaled down and leveled graphical representation of all in the official property cadastre information system (ALKIS®) managed properties. The presentation of the property map on a scale of 1: 1 000 is an official property map and a graphical basis for the official list of plots of land within the meaning of Paragraph 2(2) of the Land Registry Code. The product RP32 - Property map with further information corresponds to the product RP31 plus the soil estimation data and shows the graphical representation of the plot on the selected scale (free scale). The choice of scale 1:1000 (official property map) may be necessary due to specific requirements of external persons and bodies.

Vector polygon map data of property parcels from Scioto County, Ohio containing 58,630 features.

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Property parcel GIS map data consists of detailed information about individual land parcels, including their boundaries, ownership details, and geographic coordinates.

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Property parcel data can be used to analyze and visualize land-related information for purposes such as real estate assessment, urban planning, or environmental management.

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Available for viewing and sharing as a map in a Koordinates map viewer. This data is also available for export to DWG for CAD, PDF, KML, CSV, and GIS data formats, including Shapefile, MapInfo, and Geodatabase.

Output formatted extracts from ALKIS, property map Rhineland-Palatinate (RP31) analogue or print-prepared (PDF). In the property register, data of a factual and legal nature must be provided on all properties (lots and buildings), including data on the owners and hereditary builders of the plots. The property register consists in particular of the property map and the property description. The property map is the scaled down and leveled graphical representation of all in the official property cadastre information system (ALKIS®) managed properties. The presentation of the property map on a scale of 1: 1 000 is an official property map and a graphical basis for the official list of plots of land within the meaning of Paragraph 2(2) of the Land Registry Code. The product RP31 - Property map shows the graphic representation of the plot on the selected scale (free scale). The choice of scale 1:1000 (official property map) may be necessary due to specific requirements of external persons and bodies.

The land market reports of the regional appraiser committees provide insights into the events on the property markets of the previous year in the respective district or in the district-free city and in the state of Brandenburg as a whole. They include sales figures and developments, detailed analyses of the individual sub-markets and statements on the price level of residential land, commercial land, agricultural and forestry land, single-family houses and condominiums. The publication of “other data necessary for the determination of value”, such as market adjustment factors, conversion coefficients, index series, provide information on value relationships and increase the transparency of the land market in the respective local authorities. The basis for these annual new reports are the evaluations of the real estate purchase agreements registered with the expert committees. They are delivered free of charge as a pdf file. The land market reports of the regional appraiser committees in analogue form or for previous years can be obtained from the respective district or in the district-free city.

Snapshot img

Physical Intellectual Property Market Size 2025-2029

The physical intellectual property market size is valued to increase USD 3.41 billion, at a CAGR of 7.4% from 2024 to 2029. Growing complexity of ICs will drive the physical intellectual property market.

Major Market Trends & Insights

North America dominated the market and accounted for a 51% growth during the forecast period.
By Application - Mobile computing devices segment was valued at USD 2.86 billion in 2023
By End-user - Semiconductor segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 72.35 million
Market Future Opportunities: USD 3411.70 million
CAGR : 7.4%
North America: Largest market in 2023

Market Summary

The market encompasses the licensing, buying, and selling of tangible inventions and creations, primarily focusing on core technologies and applications such as semiconductors, biotechnology, and mechanical designs. With the growing complexity of integrated circuits and the proliferation of wireless technologies, the demand for configurable semiconductor IP continues to surge. Service types or product categories, including patent licensing, patent enforcement, and patent valuation, play a crucial role in this market. Regulatory compliance, particularly in the context of intellectual property laws and international trade agreements, poses challenges for market participants. Looking forward, the market is expected to unfold with significant opportunities, particularly in emerging economies, as they increasingly prioritize innovation and IP protection.
According to recent reports, the patent licensing segment is projected to account for over 60% of the market share, underscoring its dominance in the landscape.

What will be the Size of the Physical Intellectual Property Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Physical Intellectual Property Market Segmented and what are the key trends of market segmentation?

The physical intellectual property industry 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.

Application

  Mobile computing devices
  Consumer electronic devices
  Automotive
  Industrial automation
  Others


End-user

  Semiconductor
  Manufacturing
  IT and telecom
  Others


Type

  Patents
  Licensing
  Copyrights
  Architectural design rights
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    Australia
    China
    Japan
    South Korea
    Taiwan


  Rest of World (ROW)

By Application Insights

The mobile computing devices segment is estimated to witness significant growth during the forecast period.

The market encompasses various aspects, including intellectual property licensing, brand asset valuation, copyright infringement litigation, technology transfer agreements, trademark registration process, ip portfolio optimization, design patent applications, trade secret protection, competitive intelligence gathering, ip asset monetization, knowledge management systems, utility patent prosecution, ip litigation strategies, patent portfolio management, patent landscape analysis, ip enforcement actions, ip valuation methodologies, licensing revenue forecasting, franchise agreements, portfolio diversification strategy, transactional ip law, technology valuation models, ip asset registry, IP risk assessment, technology commercialization, confidentiality agreements, royalty income streams, software license compliance, non-compete clauses, digital rights management, data privacy regulations, and open-source software licensing. In the mobile computing devices segment, the demand for physical intellectual property is on the rise due to the increasing need for higher processing power in mobile and other computing devices.

This trend is fueled by the growing popularity of mobile computing devices such as smartphones, tablets, laptops, and ultra-books. Chinese manufacturers like BBK Electronics, Huawei Technologies, and Xiaomi are leading this segment with their competitively priced devices offering upgraded technologies. The disposable income of consumers in developing countries, particularly India, is another significant factor contributing to the growth of mobile computing devices. Additionally, the increasing internet penetration is playing a crucial role in driving the demand for these devices. According to recent studies, the adoption of mobile computing devices has grown by 18.7%, and it is projected to expand by 25.6% in the coming years.

Request Free Sample

The Mobile computing devices segment was valued at USD 2.86 billion in 2019 and showed a gr

This dataset is available on Brisbane City Council’s open data website – data.brisbane.qld.gov.au. The site provides additional features for viewing and interacting with the data and for downloading the data in various formats.

This dataset combines Brisbane City Council property information with the Queensland Government Digital Cadastral Database (DCDB) in Brisbane City Council area.

Land Parcels are the building blocks of Council properties. Land parcels (also called lots) are mapped and the title details shown on a Plan of Subdivision. The parcel is a graphical representation of surveyed boundaries together with identifiers such as Lot/Plan description and house numbers.

The Digital Cadastral Database (DCDB) is the spatial representation of every current parcel of land in Queensland, and its legal Lot on Plan description and relevant attributes. It provides the map base for systems dealing with land related information. The DCDB is considered to be the point of truth for the graphical representation of property boundaries. It is not the point of truth for the legal property boundary or related attribute information, this will always be the plan of survey or the related titling information and administrative data sets.

Warning. Downloading this entire dataset in shapefile format exceeds the current 2GB download limit set by ESRI. Information from ESRI has the following suggestions. Consider the following options: Output to a file geodatabase instead of a shapefile or Process the data in sections.