The Office of Policy and Management maintains a listing of all real estate sales with a sales price of $2,000 or greater that occur between October 1 and September 30 of each year. For each sale record, the file includes: town, property address, date of sale, property type (residential, apartment, commercial, industrial or vacant land), sales price, and property assessment. Data are collected in accordance with Connecticut General Statutes, section 10-261a and 10-261b: https://www.cga.ct.gov/current/pub/chap_172.htm#sec_10-261a and https://www.cga.ct.gov/current/pub/chap_172.htm#sec_10-261b. Annual real estate sales are reported by grand list year (October 1 through September 30 each year). For instance, sales from 2018 GL are from 10/01/2018 through 9/30/2019. Some municipalities may not report data for certain years because when a municipality implements a revaluation, they are not required to submit sales data for the twelve months following implementation.

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

GSA, the nation's largest public real estate organization, provides workspace for over one million federal workers. These employees, along with government property, are housed in space owned by the federal government and in leased properties including buildings, land, antenna sites, etc. across the country.

What is IPGOD?\r

The Intellectual Property Government Open Data (IPGOD) includes over 100 years of registry data on all intellectual property (IP) rights administered by IP Australia. It also has derived information about the applicants who filed these IP rights, to allow for research and analysis at the regional, business and individual level. This is the 2019 release of IPGOD.\r \r \r

How do I use IPGOD?\r

IPGOD is large, with millions of data points across up to 40 tables, making them too large to open with Microsoft Excel. Furthermore, analysis often requires information from separate tables which would need specialised software for merging. We recommend that advanced users interact with the IPGOD data using the right tools with enough memory and compute power. This includes a wide range of programming and statistical software such as Tableau, Power BI, Stata, SAS, R, Python, and Scalar.\r \r \r

IP Data Platform\r

IP Australia is also providing free trials to a cloud-based analytics platform with the capabilities to enable working with large intellectual property datasets, such as the IPGOD, through the web browser, without any installation of software. IP Data Platform\r \r

References\r

\r The following pages can help you gain the understanding of the intellectual property administration and processes in Australia to help your analysis on the dataset.\r \r * Patents\r * Trade Marks\r * Designs\r * Plant Breeder’s Rights\r \r \r

Updates\r

\r

Tables and columns\r

\r Due to the changes in our systems, some tables have been affected.\r \r * We have added IPGOD 225 and IPGOD 325 to the dataset!\r * The IPGOD 206 table is not available this year.\r * Many tables have been re-built, and as a result may have different columns or different possible values. Please check the data dictionary for each table before use.\r \r

Data quality improvements\r

\r Data quality has been improved across all tables.\r \r * Null values are simply empty rather than '31/12/9999'.\r * All date columns are now in ISO format 'yyyy-mm-dd'.\r * All indicator columns have been converted to Boolean data type (True/False) rather than Yes/No, Y/N, or 1/0.\r * All tables are encoded in UTF-8.\r * All tables use the backslash \ as the escape character.\r * The applicant name cleaning and matching algorithms have been updated. We believe that this year's method improves the accuracy of the matches. Please note that the "ipa_id" generated in IPGOD 2019 will not match with those in previous releases of IPGOD.

Development of the National Register for Social Housing (NROSH) was started by the Department for Communities and Local Government (DCLG) in 2004. NROSH aimed to be a database of all social housing properties in England, with a range of details captured on each property. NROSH was transferred to the Tenant Services Authority, the social housing regulator, in April 2010 and was discontinued in May 2011. Ownership of the latest NROSH dataset passed from the TSA to the Homes and Communities Agency (HCA) when responsibility for social housing regulation passed to the Regulation Committee of the HCA in April 2012. In addition to being out of date, the records submitted by social landlords to NROSH are of varying quantity and quality with many incomplete, inaccurate or missing records. The database may also contain a number of duplicate entries. Two datasets are available. One is the latest NROSH database held by the HCA as at May 2011. This release contains a large subset of the full NROSH dataset (48 from 201 fields in total; for 4,826,417 unique property records). The data in this release does not include those fields where data could enable specific identification of vulnerable people or other sensitive personal data. It also excludes fields where a minimum completion threshold is not met (generally fields where less than 25% of records have data). There are still issues of quality, incomplete data, and potential duplication of records in the data that accompanies this release that HCA is not able to resolve. Additional information, including data that falls below the minimum quality thresholds for this release, may be requested from the HCA (Referrals & Regulatory Enquiries Team, mail@homesandcommunities.co.uk). The 48 fields included in this release are summarised and described in the two tables accompanying this metadata. The data is contained in five compressed single CSV files: NROSH Data Extract Part 1; - 2; - 3; -4 and -5. Due to the large volume of records, analysis will require database software (MS Excel will not support analysis). Also available is a snapshot of the NROSH database held by DCLG as at March 2010. The data is that which was reported by social landlords in line with the system specifications and includes a selected set of fields on property address, type of accommodation, form of structure, number of rooms and bedspaces are included.

This dataset provides information about the number of properties, residents, and average property values for Excel Court cross streets in Sheridan, OR.

The study highlights the model's effectiveness in forecasting prices for second-hand houses, particularly in rapidly growing urban areas like Guangzhou. This research provides accurate and reliable insights for real estate investors considering second-hand housing markets in China.The above attachments contain the data set and the final Python code for the research work

The dataset titled "Measures supporting affordable housing development" is a comprehensive collection of data pertaining to the affordability and development of housing. It is categorized under the domain of Housing and is tagged with keywords such as Affordability, Affordable Housing, Construction, Development, Finance, Housing Potential, Indicator, and property boundary. The dataset is available in PDF and Excel formats and was published on May 29, 2021. The data spans from January 1, 2019, to December 31, 2021, and covers OECD member countries. The dataset is open for access with certain usage limitations set by the OECD. The dataset is owned and published by the OECD and can be contacted via phone or fax for access-related queries. The dataset is in English and was last accessed on November 26, 2023. It does not contain data about individuals or identifiable individuals. The dataset is updated annually and covers OECD member countries in terms of geospatial resolution. It consists of 67 rows, 15 columns, and 516 data cells. The dataset is owned by The Organisation for Economic Co-operation and Development (OECD) and provides an overview of measures to property developers to finance the construction of new affordable housing. The dataset does not specify a license and was created on November 26, 2023, and last modified on March 28, 2025.

Annual house price data based on a sub-sample of the Regulated Mortgage Survey.

A. SUMMARY The Department of Public Health and the Mayor’s Office of Housing and Community Development, with support from the Planning Department, created these 41 neighborhoods by grouping 2010 Census tracts, using common real estate and residents’ definitions for the purpose of providing consistency in the analysis and reporting of socio-economic, demographic, and environmental data, and data on City-funded programs and services. These neighborhoods are not codified in Planning Code nor Administrative Code, although this map is referenced in Planning Code Section 415 as the “American Community Survey Neighborhood Profile Boundaries Map. Note: These are NOT statistical boundaries as they are not controlled for population size. This is also NOT an official map of neighborhood boundaries in SF but an aggregation of Census tracts and should be used in conjunction with other spatial boundaries for decision making. B. HOW THE DATASET IS CREATED This dataset is produced by assigning Census tracts to neighborhoods based on existing neighborhood definitions used by Planning and MOHCD. A qualitative assessment is made to identify the appropriate neighborhood for a given tract based on understanding of population distribution and significant landmarks. Once all tracts have been assigned a neighborhood, the tracts are dissolved to produce this dataset, Analysis Neighborhoods. C. UPDATE PROCESS This dataset is static. Changes to the analysis neighborhood boundaries will be evaluated as needed by the Analysis Neighborhood working group led by DataSF and the Planning department and includes staff from various other city departments. Contact us for any questions. D. HOW TO USE THIS DATASET Downloading this dataset and opening it in Excel may cause some of the data values to be lost or not display properly (particularly the Analysis Neighborhood column). For a simple list of Analysis Neighborhoods without geographic coordinates, click here: https://data.sfgov.org/resource/xfcw-9evu.csv?$select=nhood E. RELATED DATASETS 2020 Census tracts assigned a neighborhood 2010 Census tracts assigned a neighborhood

The research objective of this dataset is to compute and analyze the piezoelectric properties of a specific class of Metal-Organic Frameworks i.e, Zeolitic Imidazalote Frameworks (ZIFs). All the calculations were done by the DFT method and the software used is CRYSTAL17. With regard to that, this dataset contains 1) Representative input files of CRYSTAL17 that can be used to reproduce the calculations 2) The output files of all the calculations with the piezoelectric and mechanical properties of the ZIFs 3) Actual data in excel file with the different properties of ZIFs that are used for plotting in the reference publication 4) Detailed analysis of internal strain piezoelectric constant for all ZIFs in the study in an excel file.

This dataset provides information on Tempe's subsidized housing program. Tempe has a fixed number of Housing Choice Vouchers (HCVs) based on our HUD contract, which represents the maximum number of families that the Housing Authority could assist. Congress and HUD do not fund the program to assist all of the families we are allotted to assist. We can only assist the number of families we have the budget to assist. HUD provides an initial funding amount based on what they anticipate they will allocate to housing assistance payments. The actual amount of funding received is subject to change depending on Federal Budget priorities, Congressional approval and many other factors. Expenditures are reported monthly, as HUD requires expenses to be posted in the month they were incurred rather than the month the expense was paid. The performance measure dashboard is available at 3.05 Subsidized Housing.Additional InformationSource: Manually maintained data, Housing Pro and QuickbooksContact: Irma Hollamby CainContact Phone: 480-858-2264Data Source Type: ExcelPreparation Method: Monthly values are calculated by determining the month each of the expenditures was for and retroactively accruing the funding use to the appropriate period. There are multiple, multistep excel worksheets that are used to balance between the specialty Housing Software, City Financial System and the HUD mandated reporting system. Additionally, it is important to note that Funding is allocated by Congress on the Federal Fiscal Year (October - September), the City operates on a Fiscal Year (July - June) and HUD provides funding on the Housing Authority in Calendar Year (January - December) funding increments. Therefore, the City must cross balance between three funding years.Publish Frequency: AnnuallyPublish Method: ManualData Dictionary

The dataset titled "Units Under Construction: By Dwelling Type" falls under the domain of Housing and is tagged with keywords such as Housing Market and Housing Potential. It is available in CSV format and was published on April 6, 2023. The dataset spans the time period from January 1, 2022, to December 31, 2022, and covers the geographical area of Canada. The dataset is open for access and its location is provided. The owner, author, and contact email for the dataset are all associated with the Canada Mortgage and Housing Corporation (CMHC). The dataset was accessed on July 9, 2023, and is in English. The dataset does not contain data about individuals, identifiable individuals, or Indigenous communities. It is the 2022 version of the dataset and the temporal resolution is annual. The geospatial resolution is city-wise. The dataset is machine-readable, indicating good data quality. The dataset is owned by the Canada Mortgage and Housing Corporation and provides an annual count of housing units currently under construction in Canadian urban centers with a population of at least 10,000. The dataset is organized by type of dwelling to provide industry professionals with an overview of new construction across Canada. The license for the dataset is not specified. The resources available in the dataset include an Excel file named 'units-under-construction-dwelling-type-2022-en.xlsx'. The metadata for the dataset was created on July 9, 2023, and was last modified on April 8, 2025.

The Residential School Locations Dataset [IRS_Locations.csv] contains the locations (latitude and longitude) of Residential Schools and student hostels operated by the federal government in Canada. All the residential schools and hostels that are listed in the Indian Residential School Settlement Agreement are included in this dataset, as well as several Industrial schools and residential schools that were not part of the IRRSA. This version of the dataset doesn’t include the five schools under the Newfoundland and Labrador Residential Schools Settlement Agreement. The original school location data was created by the Truth and Reconciliation Commission, and was provided to the researcher (Rosa Orlandini) by the National Centre for Truth and Reconciliation in April 2017. The dataset was created by Rosa Orlandini, and builds upon and enhances the previous work of the Truth and Reconcilation Commission, Morgan Hite (creator of the Atlas of Indian Residential Schools in Canada that was produced for the Tk'emlups First Nation and Justice for Day Scholar's Initiative, and Stephanie Pyne (project lead for the Residential Schools Interactive Map). Each individual school location in this dataset is attributed either to RSIM, Morgan Hite, NCTR or Rosa Orlandini. Many schools/hostels had several locations throughout the history of the institution. If the school/hostel moved from its’ original location to another property, then the school is considered to have two unique locations in this dataset,the original location and the new location. For example, Lejac Indian Residential School had two locations while it was operating, Stuart Lake and Fraser Lake. If a new school building was constructed on the same property as the original school building, it isn't considered to be a new location, as is the case of Girouard Indian Residential School.When the precise location is known, the coordinates of the main building are provided, and when the precise location of the building isn’t known, an approximate location is provided. For each residential school institution location, the following information is provided: official names, alternative name, dates of operation, religious affiliation, latitude and longitude coordinates, community location, Indigenous community name, contributor (of the location coordinates), school/institution photo (when available), location point precision, type of school (hostel or residential school) and list of references used to determine the location of the main buildings or sites.

This is a preliminary version of a new open data asset and will be updated later this year once the Assessor's Office has finished reassessing commercial properties, then once annually. Use accordingly.

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 modelgroup, 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.

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 dataset, see the Assessor's Standard Operating Procedures for Open Data on GitHub.

Read about the Assessor's 2023 Open Data Refresh.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Results of Kenya's 6th National Census i.e The 2019 Kenya Population and Housing Census Volume I, II, III, and IV reports.

A log of dataset alerts open, monitored or resolved on the open data portal. Alerts can include issues as well as deprecation or discontinuation notices.

This dataset includes Statistics Canada table 46-10-0026-01, titled “Immigrant status and selected admission categories for residential property owners in the census metropolitan areas of Toronto and Vancouver”. The table has been edited to include only geographies from British Columbia. The table is available in CSV and Excel Workbook format. Definitions and notes are included at the bottom of the spreadsheet. This data set was collected as part of the Canadian Housing Statistics Program by Statistics Canada. Geographies: Vancouver Census Metropolitan Area

The dataset titled "Severe housing deprivation" falls under the domain of Housing and is tagged with keywords such as Affordability, Homelessness, and Sanitation among others. It is available in PDF and Excel formats and was published on August 7, 2022. The dataset spans a time period from January 1, 2010, to December 31, 2020, and covers OECD member countries. It is an open access dataset, however, the OECD has set certain limits on its use, particularly prohibiting the posting of its PDF files on any internet sites. The dataset is owned and published by the OECD and was last accessed on November 21, 2023. The dataset is in English and contains a persistent identifier but not a globally unique identifier. It does not contain data about individuals or identifiable individuals. The dataset is the latest version updated on August 7, 2022, and has an annual temporal resolution. It contains 300 rows, 43 columns, and 12,900 data cells. The dataset is owned by The Organisation for Economic Co-operation and Development (OECD) and provides a comprehensive analysis of various factors influencing the quality of housing, with a focus on severe housing deprivation. The dataset does not specify a license and includes resources like 'Severe housing deprivation'. The metadata for this dataset was created on November 22, 2023, and last modified on April 7, 2025.

The American Community Survey (ACS) is a nationwide survey conducted by the U.S. Census Bureau that is designed to provide communities a fresh look at how they are changing. It is a critical element in the Census Bureau's reengineered decennial census program, incorporating the detailed socioeconomic and housing questions that were previously asked on the decennial census long form into the ACS questionnaire. The ACS now collects and produces this detailed population and housing information every year instead of every ten years. Data are collected on an on-going basis throughout the year and are released each year for large geographic areas, those with 65,000 persons or more. However, sample sizes are not large enough for annual releases that cover smaller areas, those with less than 65,000 persons. Data that are suitable for areas with 20,000 to 65,000 persons are accumulated over three years and termed a three-year period estimate, the first of which was for the 2005-2007 period. Data that are suitable for areas with less than 20,000 persons are accumulated over five years and termed a five-year period estimate, the first of which was for the 2005-2009 period. The data in this series of RGIS Clearinghouse tables are for all New Mexico counties and are based on the 2005-2009 ACS Five-Year Period Estimates collected between January 2005 and December 2009. These data tables are a summary of all major housing topics published through the ACS, providing information about the condition of housing, and illuminating various financial characteristics of the housing stock. Major topics include housing occupancy, year structure built, rooms and bedrooms, housing tenure (owners and renters), year householder moved into unit, vehicles available, type of house heating fuel, units without complete plumbing and kitchen facilities or without telephone service, occupants per room, home value, mortgage status, monthly owner costs, owner costs as a percentage of household income, gross rent, and gross rent as a percentage of household income. Percentages are shown along with numeric estimates for most data items. Because the data are based on a sample the Census Bureau also provides information about the magnitude of sampling error. Consequently, the estimated margin of error (MOE) is shown next to each data item. Each housing topic is covered in a separate file in both Excel and CSV formats. These files, along with file-specific descriptions (in Word and text formats) are available in a single zip file.