100+ datasets found
  1. d

    Assessor - Commercial Valuation Data

    • catalog.data.gov
    • datacatalog.cookcountyil.gov
    • +1more
    Updated Apr 12, 2025
    + more versions
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    datacatalog.cookcountyil.gov (2025). Assessor - Commercial Valuation Data [Dataset]. https://catalog.data.gov/dataset/assessor-commercial-valuation-data
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    Dataset updated
    Apr 12, 2025
    Dataset provided by
    datacatalog.cookcountyil.gov
    Description

    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

  2. g

    NIST Property Data Summaries for Advanced Materials - SRD 150

    • gimi9.com
    • datasets.ai
    • +3more
    Updated Jun 22, 2015
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    (2015). NIST Property Data Summaries for Advanced Materials - SRD 150 [Dataset]. https://gimi9.com/dataset/data-gov_nist-property-data-summaries-for-advanced-materials-srd-150-8c83a
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    Dataset updated
    Jun 22, 2015
    Description

    Property Data Summaries are collections of property values derived from surveys of published data. These collections typically focus on either one material or one particular property. Studies of specific materials typically include thermal, mechanical, structural, and chemical properties, while studies of particular properties survey one property across many materials. The property values may be typical, evaluated, or validated. Values described as typical are derived from values for nominally similar materials.

  3. a

    Personal Property Data Extract EOY18

    • hub.arcgis.com
    • data2-stlcogis.opendata.arcgis.com
    • +1more
    Updated Mar 15, 2019
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    Saint Louis County GIS Service Center (2019). Personal Property Data Extract EOY18 [Dataset]. https://hub.arcgis.com/datasets/8f6dadc43954471189bfd7347113ea6a
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    Dataset updated
    Mar 15, 2019
    Dataset authored and provided by
    Saint Louis County GIS Service Center
    Description

    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.

  4. F

    State Tax Collections: T01 Property Taxes for the United States

    • fred.stlouisfed.org
    json
    Updated Jun 12, 2025
    + more versions
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    (2025). State Tax Collections: T01 Property Taxes for the United States [Dataset]. https://fred.stlouisfed.org/series/QTAXT01QTAXCAT3USNO
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    jsonAvailable download formats
    Dataset updated
    Jun 12, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Area covered
    United States
    Description

    Graph and download economic data for State Tax Collections: T01 Property Taxes for the United States (QTAXT01QTAXCAT3USNO) from Q1 1994 to Q1 2025 about collection, tax, and USA.

  5. Real Estate Data South Carolina 2025

    • kaggle.com
    Updated Jul 8, 2025
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    Kanchana1990 (2025). Real Estate Data South Carolina 2025 [Dataset]. http://doi.org/10.34740/kaggle/ds/7823602
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jul 8, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Kanchana1990
    License

    Open Data Commons Attribution License (ODC-By) v1.0https://www.opendatacommons.org/licenses/by/1.0/
    License information was derived automatically

    Area covered
    South Carolina
    Description

    South Carolina Real Estate Dataset 2025

    Dataset Overview

    This comprehensive real estate dataset contains over 5,000 property listings from South Carolina, collected in 2025 from Realtor.com using apify api. The dataset captures diverse property types including single-family homes, condominiums, land parcels, townhomes, and other residential properties. This dataset provides a rich snapshot of South Carolina's real estate market suitable for predictive modeling, market analysis, and investment research.

    Data Science Applications

    • Price Prediction Models: Build regression models (Random Forest, XGBoost, Neural Networks) to predict property values based on size, location, bedrooms, and age
    • Property Type Classification: Develop multi-class classifiers to categorize properties based on physical characteristics
    • Market Segmentation: Apply clustering algorithms (K-means, DBSCAN) to identify distinct property segments and price brackets
    • Time Series Analysis: Analyze construction trends and property age distributions to forecast future development patterns
    • Investment Opportunity Detection: Create anomaly detection models to identify undervalued properties or outliers
    • Feature Engineering: Generate derived features like price per square foot, bathroom-to-bedroom ratios for enhanced model performance

    Column Descriptors

    • type: Primary property category (single_family, condos, land, townhomes, multi_family, farm)
    • sub_type: Detailed property classification (condo, townhouse, co_op)
    • sqft: Property size in square feet
    • baths: Number of bathrooms (decimal values indicate half baths)
    • beds: Number of bedrooms
    • stories: Number of floors/stories in the property
    • year_built: Construction year of the property
    • listPrice: Property listing price in USD

    Ethically Obtained Data

    This dataset was ethically scraped from publicly available listings on Realtor.com and is provided strictly for educational and learning purposes only. The data collection complied with ethical web scraping practices and contains only publicly accessible information. Users should utilize this dataset exclusively for academic research, educational projects, and learning data science techniques. Any commercial use is strictly prohibited.

  6. O

    Property Information

    • data.brla.gov
    • catalog.data.gov
    • +2more
    application/rdfxml +5
    Updated Jul 14, 2025
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    City-Parish Planning Commission (2025). Property Information [Dataset]. https://data.brla.gov/w/re5c-hrw9/default
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    csv, application/rssxml, tsv, json, xml, application/rdfxmlAvailable download formats
    Dataset updated
    Jul 14, 2025
    Dataset authored and provided by
    City-Parish Planning Commission
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Description

    This dataset is a combination of attribute information from the master address table and the lot or property records table. The address points are created within a building footprint and in the case where there is no building, then the point is the center of the lot. The address information comes from a variety of sources including final subdivision plats, building permits, E-911 master street address guide (MSAG) database, Polk City Directory, and field data collection.

  7. s

    Personal Property Data Extract EOY19

    • data.stlouisco.com
    • datav3-stlcogis.opendata.arcgis.com
    • +2more
    Updated Feb 25, 2020
    + more versions
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    Saint Louis County GIS Service Center (2020). Personal Property Data Extract EOY19 [Dataset]. https://data.stlouisco.com/datasets/44b454338d9449f68dd64cd3c92343a8
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    Dataset updated
    Feb 25, 2020
    Dataset authored and provided by
    Saint Louis County GIS Service Center
    Description

    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.

  8. AI-Assisted Real Estate Valuation Market Research Report 2033

    • growthmarketreports.com
    csv, pdf, pptx
    Updated Jun 28, 2025
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    Growth Market Reports (2025). AI-Assisted Real Estate Valuation Market Research Report 2033 [Dataset]. https://growthmarketreports.com/report/ai-assisted-real-estate-valuation-market
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    pdf, pptx, csvAvailable download formats
    Dataset updated
    Jun 28, 2025
    Dataset authored and provided by
    Growth Market Reports
    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    AI-Assisted Real Estate Valuation Market Outlook



    According to our latest research, the AI-Assisted Real Estate Valuation market size reached USD 1.68 billion in 2024, with a robust compound annual growth rate (CAGR) of 14.7% projected through the forecast period. By 2033, the market is anticipated to achieve a value of approximately USD 5.22 billion, driven by escalating digital transformation across the real estate sector and increasing adoption of advanced analytics for property valuation. The market’s expansion is underpinned by the growing need for accurate, transparent, and efficient valuation processes, which are critical for decision-making among real estate professionals, investors, and regulatory bodies.




    One of the primary growth factors fueling the AI-Assisted Real Estate Valuation market is the rapid digitalization of the real estate industry. As property markets become increasingly dynamic and complex, traditional methods of valuation are often unable to keep pace with the volume and diversity of data generated. AI-powered valuation tools leverage machine learning algorithms and big data analytics to process vast datasets, including historical sales, location-based trends, and market fluctuations, thereby delivering more precise and timely property valuations. This technological advancement not only enhances the accuracy of appraisals but also reduces the time and operational costs associated with manual processes, making AI solutions highly attractive for real estate agencies and financial institutions.




    Another significant driver is the rising demand for transparency and compliance in property transactions. Regulatory bodies and financial institutions are placing greater emphasis on standardized and auditable valuation methodologies to mitigate risks associated with property investments and lending. AI-assisted platforms offer traceable, data-driven insights that align with regulatory requirements and foster trust among stakeholders. The ability of AI systems to continuously learn and adapt to changing market conditions further strengthens their value proposition, ensuring that valuations remain relevant and reliable even in volatile market environments. This shift towards data-driven decision-making is expected to accelerate the adoption of AI-assisted valuation tools globally.




    The integration of AI with emerging technologies such as Geographic Information Systems (GIS), Internet of Things (IoT), and blockchain is also propelling market growth. These integrations enable real-time data collection and analysis, automate property inspections, and secure transaction records, thereby streamlining the entire valuation process. In addition, the proliferation of cloud-based platforms has democratized access to sophisticated AI tools, enabling small and medium-sized enterprises (SMEs) and individual appraisers to leverage advanced analytics without significant upfront investments in infrastructure. As a result, the AI-Assisted Real Estate Valuation market is witnessing increased participation from diverse end-user segments, further amplifying its growth trajectory.




    Regionally, North America leads the market, owing to the early adoption of AI technologies, a mature real estate ecosystem, and supportive regulatory frameworks. Europe follows closely, driven by stringent compliance standards and a high degree of digital literacy among market participants. The Asia Pacific region is emerging as a high-growth market, fueled by rapid urbanization, expanding property markets, and increasing investments in PropTech. Latin America and the Middle East & Africa are also exhibiting steady growth, albeit from a smaller base, as digital transformation initiatives gain momentum in these regions. Overall, the global landscape is characterized by a strong emphasis on innovation, data security, and scalability, which are expected to shape market dynamics through 2033.





    Component Analysis



    The AI-Assisted Real Estate Valuation market by component is segmented into

  9. a

    Personal Property Data Extract CERT19

    • dataold-stlcogis.opendata.arcgis.com
    • data-stlcogis.opendata.arcgis.com
    • +5more
    Updated Jul 3, 2019
    + more versions
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    Saint Louis County GIS Service Center (2019). Personal Property Data Extract CERT19 [Dataset]. https://dataold-stlcogis.opendata.arcgis.com/datasets/a9a31f5b261b44f8b92b030cb2adecea
    Explore at:
    Dataset updated
    Jul 3, 2019
    Dataset authored and provided by
    Saint Louis County GIS Service Center
    Description

    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.

  10. d

    Realtor.com Dataset | Property Listings | MLS Data | Real Estate Data |...

    • datarade.ai
    .json, .csv, .txt
    Updated Oct 4, 2023
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    CrawlBee (2023). Realtor.com Dataset | Property Listings | MLS Data | Real Estate Data | Residential Data | Realtime Real Estate Market Data [Dataset]. https://datarade.ai/data-products/crawlbee-realtor-com-dataset-property-listings-mls-dat-crawlbee
    Explore at:
    .json, .csv, .txtAvailable download formats
    Dataset updated
    Oct 4, 2023
    Dataset authored and provided by
    CrawlBee
    Area covered
    United States of America
    Description

    Our Realtor.com (Multiple Listing Service) dataset represents one of the most exhaustive collections of real estate data available to the industry. It consolidates data from over 500 MLS aggregators across various regions, providing an unparalleled view of the property market.

    Features:

    Property Listings: Each listing provides comprehensive details about a property. This includes its physical address, number of bedrooms and bathrooms, square footage, lot size, type of property (e.g., single-family home, condo, townhome), and more.

    Photographs and Virtual Tours: Visuals are crucial in the property market. Most listings are accompanied by high-quality photographs and, in many cases, virtual or 3D tours that allow potential buyers to explore properties remotely.

    Pricing Information: Listings provide asking prices, and the dataset frequently updates to reflect price changes. Historical price data, which includes initial listing prices and any subsequent reductions or increases, is also available.

    Transaction Histories: For sold properties, the dataset provides information about the date of sale, the sale price, and any discrepancies between the listing and sale prices.

    Agent and Broker Information: Each listing typically has associated details about the property's real estate professional. This might include their name, contact details, and affiliated brokerage.

    Open House Schedules: Open house dates and times are listed for properties that are actively being shown to potential buyers.

    1. Analytical Insights:

    Market Trends: By analyzing the dataset over time, one can glean insights into market dynamics, such as the rate of price appreciation or depreciation in certain areas, the average time properties stay on the market, and seasonality effects.

    Neighborhood Data: With comprehensive geographical data, it becomes possible to understand neighborhood-specific trends. This is invaluable for potential buyers or real estate investors looking to identify burgeoning markets.

    Price Comparisons: Realtors and potential buyers can benchmark properties against similar listings in the same area to determine if a property is priced appropriately.

    1. Utility:

    For Industry Professionals and Analysts: Beyond buyers and sellers, the dataset is a trove of information for real estate agents, brokers, analysts, and investors. They can harness this data to craft strategies, predict market movements, and serve their clients better.

  11. P

    Property Valuation Software Report

    • archivemarketresearch.com
    doc, pdf, ppt
    Updated Jun 6, 2025
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    Archive Market Research (2025). Property Valuation Software Report [Dataset]. https://www.archivemarketresearch.com/reports/property-valuation-software-563744
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    ppt, pdf, docAvailable download formats
    Dataset updated
    Jun 6, 2025
    Dataset authored and provided by
    Archive Market Research
    License

    https://www.archivemarketresearch.com/privacy-policyhttps://www.archivemarketresearch.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global Property Valuation Software market is experiencing robust growth, driven by increasing demand for efficient and accurate property assessments across diverse sectors. The market size in 2025 is estimated at $2.5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth is fueled by several key factors, including the rising adoption of cloud-based solutions, the increasing need for automation in property valuation processes to reduce costs and improve efficiency, and the growing sophistication of algorithms enabling more precise valuations. Furthermore, stringent regulatory requirements related to property transactions and the integration of advanced analytics within the software are pushing the market forward. Key players like Evalo, ValuePRO Software, and MRI Software are driving innovation through the development of feature-rich platforms offering advanced functionalities, such as automated data collection, AI-powered valuation models, and robust reporting capabilities. The market segmentation is diverse, encompassing various software types catering to distinct property categories (residential, commercial, industrial) and user needs. The market is witnessing a shift towards integrated platforms that offer a comprehensive suite of functionalities, consolidating various valuation tools into a single ecosystem. While the market faces challenges such as high initial investment costs and the need for skilled professionals to operate the software effectively, the long-term benefits of increased efficiency and reduced operational costs are outweighing these limitations. The ongoing technological advancements, coupled with the increasing adoption of PropTech solutions across the globe, are poised to propel the Property Valuation Software market towards substantial growth in the coming years, particularly in regions with thriving real estate sectors.

  12. d

    National Property Administration's Statistics on Valuation Cases of...

    • data.gov.tw
    csv
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    National Property Administration,Ministry of Finance, National Property Administration's Statistics on Valuation Cases of State-Owned Land Over the Years [Dataset]. https://data.gov.tw/en/datasets/24133
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    csvAvailable download formats
    Dataset provided by
    National Property Administrationhttps://www.fnp.gov.tw/fnpen
    Authors
    National Property Administration,Ministry of Finance
    License

    https://data.gov.tw/licensehttps://data.gov.tw/license

    Description

    90-103 years (data collection suspended)..........

  13. M

    Vital Signs: List Rents – by property

    • open-data-demo.mtc.ca.gov
    • data.bayareametro.gov
    application/rdfxml +5
    Updated Dec 8, 2016
    + more versions
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    real Answers (2016). Vital Signs: List Rents – by property [Dataset]. https://open-data-demo.mtc.ca.gov/dataset/Vital-Signs-List-Rents-by-property/wfp9-cb9q/about
    Explore at:
    application/rssxml, csv, tsv, xml, json, application/rdfxmlAvailable download formats
    Dataset updated
    Dec 8, 2016
    Dataset authored and provided by
    real Answers
    Description

    VITAL SIGNS INDICATOR List Rents (EC9)

    FULL MEASURE NAME List Rents

    LAST UPDATED October 2016

    DESCRIPTION List rent refers to the advertised rents for available rental housing and serves as a measure of housing costs for new households moving into a neighborhood, city, county or region.

    DATA SOURCE real Answers (1994 – 2015) no link

    Zillow Metro Median Listing Price All Homes (2010-2016) http://www.zillow.com/research/data/

    CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

    METHODOLOGY NOTES (across all datasets for this indicator) List rents data reflects median rent prices advertised for available apartments rather than median rent payments; more information is available in the indicator definition above. Regional and local geographies rely on data collected by real Answers, a research organization and database publisher specializing in the multifamily housing market. real Answers focuses on collecting longitudinal data for individual rental properties through quarterly surveys. For the Bay Area, their database is comprised of properties with 40 to 3,000+ housing units. Median list prices most likely have an upward bias due to the exclusion of smaller properties. The bias may be most extreme in geographies where large rental properties represent a small portion of the overall rental market. A map of the individual properties surveyed is included in the Local Focus section.

    Individual properties surveyed provided lower- and upper-bound ranges for the various types of housing available (studio, 1 bedroom, 2 bedroom, etc.). Median lower- and upper-bound prices are determined across all housing types for the regional and county geographies. The median list price represented in Vital Signs is the average of the median lower- and upper-bound prices for the region and counties. Median upper-bound prices are determined across all housing types for the city geographies. The median list price represented in Vital Signs is the median upper-bound price for cities. For simplicity, only the mean list rent is displayed for the individual properties. The metro areas geography rely upon Zillow data, which is the median price for rentals listed through www.zillow.com during the month. Like the real Answers data, Zillow's median list prices most likely have an upward bias since small properties are underrepresented in Zillow's listings. The metro area data for the Bay Area cannot be compared to the regional Bay Area data. Due to afore mentioned data limitations, this data is suitable for analyzing the change in list rents over time but not necessarily comparisons of absolute list rents. Metro area boundaries reflects today’s metro area definitions by county for consistency, rather than historical metro area boundaries.

    Due to the limited number of rental properties surveyed, city-level data is unavailable for Atherton, Belvedere, Brisbane, Calistoga, Clayton, Cloverdale, Cotati, Fairfax, Half Moon Bay, Healdsburg, Hillsborough, Los Altos Hills, Monte Sereno, Moranga, Oakley, Orinda, Portola Valley, Rio Vista, Ross, San Anselmo, San Carlos, Saratoga, Sebastopol, Windsor, Woodside, and Yountville.

    Inflation-adjusted data are presented to illustrate how rents have grown relative to overall price increases; that said, the use of the Consumer Price Index does create some challenges given the fact that housing represents a major chunk of consumer goods bundle used to calculate CPI. This reflects a methodological tradeoff between precision and accuracy and is a common concern when working with any commodity that is a major component of CPI itself. Percent change in inflation-adjusted median is calculated with respect to the median price from the fourth quarter or December of the base year.

  14. a

    Personal Property Data Extract Billing 2020

    • data-stlcogis.opendata.arcgis.com
    • data.stlouisco.com
    • +3more
    Updated Oct 23, 2020
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    Saint Louis County GIS Service Center (2020). Personal Property Data Extract Billing 2020 [Dataset]. https://data-stlcogis.opendata.arcgis.com/datasets/6032b4cf06fc48fea5246c30fbd24f13
    Explore at:
    Dataset updated
    Oct 23, 2020
    Dataset authored and provided by
    Saint Louis County GIS Service Center
    Description

    This is a collection of CSV files that contain personal property assessment data. 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.

  15. P

    Property Viewing Software Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated May 15, 2025
    + more versions
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    Data Insights Market (2025). Property Viewing Software Report [Dataset]. https://www.datainsightsmarket.com/reports/property-viewing-software-538339
    Explore at:
    doc, pdf, pptAvailable download formats
    Dataset updated
    May 15, 2025
    Dataset authored and provided by
    Data Insights Market
    License

    https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The property viewing software market is experiencing robust growth, driven by increasing demand for efficient property management solutions and the widespread adoption of technology within the real estate sector. The market's expansion is fueled by several key factors. Firstly, the shift towards online property viewings, accelerated by the recent pandemic, has created a significant demand for user-friendly software that streamlines the entire process, from scheduling appointments to collecting feedback. Secondly, the rising popularity of cloud-based solutions offers accessibility and scalability, making them attractive to businesses of all sizes. This is further complemented by the increasing integration of smart home technologies, which enhances the overall property viewing experience and facilitates data collection for improved decision-making. While the on-premises segment still holds a considerable market share, cloud-based solutions are projected to witness faster growth due to their cost-effectiveness and flexibility. Competition within the market is fierce, with established players and emerging startups continuously innovating to offer enhanced features and functionalities. This includes features like virtual tours, 3D modeling, and automated communication tools. Geographical expansion, particularly within developing economies with burgeoning real estate markets, presents significant opportunities for market growth. However, challenges remain, including the need for robust cybersecurity measures to protect sensitive data and the ongoing requirement for user training and technical support to ensure seamless adoption and usage. Despite potential restraints such as initial investment costs and the need for continuous updates to remain competitive, the long-term outlook for the property viewing software market remains positive. The continued rise of e-commerce in the real estate industry, coupled with the growing preference for digital solutions, points to a consistently expanding market. The segmentation by application (residential, tenant) further showcases the versatility and wide applicability of this software, catering to both individual property owners and large-scale property management companies. As technological advancements continue, we can anticipate the integration of even more sophisticated features, like AI-powered analytics and predictive modeling, which will further optimize the property viewing process and enhance the overall user experience. This will ultimately drive further market expansion and adoption in the coming years.

  16. V

    Venezuela Central Government: Expenditure: Non Recurrent: Loan Repayment:...

    • ceicdata.com
    + more versions
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    CEICdata.com, Venezuela Central Government: Expenditure: Non Recurrent: Loan Repayment: Domestic: Acquisition of Real Estate Property [Dataset]. https://www.ceicdata.com/en/venezuela/central-government-expenditure-quarterly/central-government-expenditure-non-recurrent-loan-repayment-domestic-acquisition-of-real-estate-property
    Explore at:
    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2007 - Sep 1, 2010
    Area covered
    Venezuela
    Variables measured
    Operating Statement
    Description

    Venezuela Central Government: Expenditure: Non Recurrent: Loan Repayment: Domestic: Acquisition of Real Estate Property data was reported at 0.000 VEF th in Sep 2010. This stayed constant from the previous number of 0.000 VEF th for Jun 2010. Venezuela Central Government: Expenditure: Non Recurrent: Loan Repayment: Domestic: Acquisition of Real Estate Property data is updated quarterly, averaging 0.000 VEF th from Mar 1998 (Median) to Sep 2010, with 51 observations. The data reached an all-time high of 33,994.880 VEF th in Sep 2001 and a record low of 0.000 VEF th in Sep 2010. Venezuela Central Government: Expenditure: Non Recurrent: Loan Repayment: Domestic: Acquisition of Real Estate Property data remains active status in CEIC and is reported by Ministry of Economy, Finance and Public Banking. The data is categorized under Global Database’s Venezuela – Table VE.F005: Central Government: Expenditure: Quarterly.

  17. F

    State Tax Collections: T01 Property Taxes for Massachusetts

    • fred.stlouisfed.org
    json
    Updated Jun 12, 2025
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    (2025). State Tax Collections: T01 Property Taxes for Massachusetts [Dataset]. https://fred.stlouisfed.org/series/QTAXT01QTAXCAT3MANO
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    jsonAvailable download formats
    Dataset updated
    Jun 12, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Area covered
    Massachusetts
    Description

    Graph and download economic data for State Tax Collections: T01 Property Taxes for Massachusetts (QTAXT01QTAXCAT3MANO) from Q1 1994 to Q1 2025 about collection, MA, tax, and USA.

  18. o

    Zoopla properties listing information dataset

    • opendatabay.com
    .undefined
    Updated May 25, 2025
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    Bright Data (2025). Zoopla properties listing information dataset [Dataset]. https://www.opendatabay.com/data/premium/9e626c7a-38e8-446e-bf9b-1c9a3d71154a
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    .undefinedAvailable download formats
    Dataset updated
    May 25, 2025
    Dataset authored and provided by
    Bright Data
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    E-commerce & Online Transactions
    Description

    Zoopla Properties Listing dataset to explore detailed property information, including pricing, location, and features. Popular use cases include real estate market analysis, property valuation, and investment research.

    Use our Zoopla Properties Listing Information dataset to explore detailed property listings, including property details, pricing, location, and market trends across various regions. This dataset provides valuable insights into property valuations, consumer preferences, and real estate dynamics, enabling businesses and researchers to make data-driven decisions.

    Tailored for real estate professionals, investors, and market analysts, this dataset supports market trend analysis, property valuation assessments, and investment strategy development. Whether you're evaluating property investments, tracking market conditions, or conducting competitive analysis, the Zoopla Properties Listing Information dataset is a key resource for navigating the real estate landscape.

    Dataset Features

    • url: The original listing URL on Zoopla.
    • property_type: Type of property (e.g., Flat, Detached, Terraced).
    • property_title: Title or headline of the listing.
    • address: Full postal address of the property.
    • google_map_location: Geographical coordinates (latitude, longitude).
    • virtual_tour: Link to a virtual walkthrough or 360° tour.
    • street_view: Link to the Google Street View of the property.
    • url_property: Zoopla-specific property page URL.
    • currency: Currency in which the property is priced.
    • deposit: Security deposit required (typically for rentals).
    • letting_arrangements: Letting details (e.g., short-term, long-term).
    • breadcrumbs: Category breadcrumbs for location and type navigation.
    • availability: Availability status (e.g., Available now, Under offer).
    • commonhold_details: Information about commonhold ownership.
    • service_charge: Annual service charge (for leasehold properties).
    • ground_rent: Annual ground rent cost.
    • time_remaining_on_lease: Lease duration remaining in years.
    • ecp_rating: Energy Performance Certificate rating.
    • council_tax_band: Council tax band.
    • price_per_size: Price per square meter or foot.
    • tenure: Tenure type (Freehold, Leasehold, etc.).
    • tags: Descriptive tags (e.g., New build, Chain-free).
    • features: List of property features (e.g., garden, garage, en-suite).
    • property_images: URLs to property photos.
    • additional_links: Other related links (e.g., brochures, agents).
    • listing_history: Changes in price, listing dates, and status over time.
    • agent_details: Information about the listing agent or agency.
    • points_ofInterest: Nearby landmarks or facilities (schools, transport).
    • bedrooms Number of bedrooms.
    • price: Listed price of the property.
    • bathrooms: Number of bathrooms.
    • receptions: Number of reception rooms (living, dining, etc.).
    • country_code: Country code of the listing (e.g., GB for UK).
    • energy_performance_certificate: Detailed EPC documentation or summary.
    • floor_plans: URL or data related to property floor plans.
    • description: Detailed property description from the listing.
    • price_per_time: Price frequency for rentals (e.g., per week, per month).
    • property_size: Area of the property (in sq ft or sq m).
    • market_stats_last_12_months: Market stats for the area over the past year.
    • market_stats_renta_opportunities: Data on rental yields and opportunities.
    • market_stats_recent_sales_nearby: Sales history for nearby properties.
    • market_stats_rental_activity: Local rental activity trends.
    • uprn: Unique Property Reference Number for UK properties.
    • listing_label: Label/category of the listing.

    Distribution

    • Data Volume: 44 Columns and 95.92K Rows
    • Format: CSV

    Usage

    This dataset is ideal for a variety of high-impact applications:

    • Property Valuation Models: Train ML models to estimate market value using features like size, location, and amenities.
    • Real Estate Market Analysis: Identify pricing trends, demand patterns, and neighbourhood growth over time.
    • Investment Research: Analyse rental yields, price per square foot, and historical price changes for investment opportunities.
    • Recommendation Systems: Develop intelligent recommendation engines for property buyers and renters.
    • Urban Planning & Policy Making: Use location and infrastructure data to guide city development.
    • Sentiment & Description Analysis: NLP-driven insights from listing descriptions and agent narratives.

    Coverage

    • Geographic Coverage: Global
    • Time Range: Ongoing collection; historical data may span multiple years

    License

    CUSTOM

    Please review the respective licenses below:

    1. Data Provider's License
      -
  19. HUD Data: Low-Income Housing Tax Credit (LIHTC): Property Level Data

    • datalumos.org
    Updated Feb 13, 2025
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    United States Department of Housing and Urban Development (2025). HUD Data: Low-Income Housing Tax Credit (LIHTC): Property Level Data [Dataset]. http://doi.org/10.3886/E219323V1
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    Dataset updated
    Feb 13, 2025
    Dataset authored and provided by
    United States Department of Housing and Urban Developmenthttp://www.hud.gov/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    1987 - 2022
    Area covered
    United States of America
    Description

    HUD's LIHTC database contains information on 53,032 projects and 3.65 million housing units placed in service between 1987 and 2022. Data for properties placed in service in 2023 will be collected in the fall of 2024 and added to this database in the spring of 2025. The database includes project address, number of units and low-income units, number of bedrooms, year the credit was allocated, year the project was placed in service, whether the project was new construction or rehab, type of credit provided, and other sources of project financing. The database has been geocoded, enabling researchers to look at the geographical distribution and neighborhood characteristics of tax credit projects. It may also help show how incentives to locate projects in low-income areas and other underserved markets are working. With the continued support of the national LIHTC database, HUD hopes to enable researchers to learn more about the effects of the tax credit program.Summary of filesIn the zip file:LIHTC Data Dictionary 2022.PDF - The data dictionary for the LIHTC database (multiple address data use same formats) in Adobe Acrobat.LIHTCPUB.ACCDB - The LIHTC Database in MS Access format. This file also includes building addresses from HUD’s LIHTC tenant data collection.LIHTCPUB.CSV - The LIHTC Database in CSV format.missing data.PDF - Percent of Projects with Missing Data by Variable and Year Placed in Service

  20. Annual Housing Survey, 1978 [United States]: SMSA File

    • icpsr.umich.edu
    ascii, sas, spss +1
    Updated Aug 22, 2008
    + more versions
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    United States. Bureau of the Census (2008). Annual Housing Survey, 1978 [United States]: SMSA File [Dataset]. http://doi.org/10.3886/ICPSR09017.v1
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    spss, ascii, stata, sasAvailable download formats
    Dataset updated
    Aug 22, 2008
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    Authors
    United States. Bureau of the Census
    License

    https://www.icpsr.umich.edu/web/ICPSR/studies/9017/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/9017/terms

    Time period covered
    1978
    Area covered
    Indiana, San Bernardino, New York (state), Hampton, San Diego, Missouri, New Jersey, Newport News, Kansas, Clifton
    Description

    This data collection contains data focusing on housing characteristics from 15 Standard Metropolitan Statistical Areas (SMSAs). Data include year the structure was built, type and number of living quarters, occupancy status, presence of commercial establishments on the property, presence of a garage, and property value. Additional data focus on kitchen and plumbing facilities, type of heating fuel used, source of water, sewage disposal, and heating and air conditioning equipment. Information about housing expenses includes mortgage or rent payments, utility costs, garbage collection fees, property insurance, and real estate taxes as well as repairs, additions, or alterations to the property. Similar data are provided for housing units previously occupied by respondents who had recently moved. Indicators of housing and neighborhood quality are also supplied. Housing quality variables include privacy of bedrooms, condition of kitchen facilities, basement or roof leakage, presence of cracks or holes in walls, ceilings, or floor, reliability of plumbing and heating equipment, and concealed electrical wiring. The presence of storm doors and windows and insulation was also noted. Neighborhood information is provided on the presence of and objection to noise, traffic, odors, trash and litter, abandoned structures, rundown housing, commercial or industrial activity, and the adequacy of services, including public transportation, schools, shopping, and police and fire protection. In addition to housing characteristics, demographic data for household members are provided, including sex, age, race, income, marital status, and household relationship. Additional data are available for the household head, including Hispanic origin, length of residence, and travel-to-work information.

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datacatalog.cookcountyil.gov (2025). Assessor - Commercial Valuation Data [Dataset]. https://catalog.data.gov/dataset/assessor-commercial-valuation-data

Assessor - Commercial Valuation Data

Explore at:
Dataset updated
Apr 12, 2025
Dataset provided by
datacatalog.cookcountyil.gov
Description

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

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