100+ datasets found
  1. NZ Properties: Building Age

    • data.linz.govt.nz
    • geodata.nz
    csv, dbf (dbase iii) +4
    Updated Aug 1, 2023
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    Land Information New Zealand (2023). NZ Properties: Building Age [Dataset]. https://data.linz.govt.nz/table/105617-nz-properties-building-age/
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    mapinfo mif, mapinfo tab, geopackage / sqlite, csv, geodatabase, dbf (dbase iii)Available download formats
    Dataset updated
    Aug 1, 2023
    Dataset authored and provided by
    Land Information New Zealandhttps://www.linz.govt.nz/
    License

    https://data.linz.govt.nz/license/attribution-4-0-international/https://data.linz.govt.nz/license/attribution-4-0-international/

    Area covered
    New Zealand
    Description

    This is the look-up table for Building Age and is part of the set of District Valuation Roll (DVR) data.

    The Building Age look-up table is used by the NZ Properties: National District Valuation Roll table.

    Look-up tables are provided to make it easier to interpret coded DVR attributes and are given as reference data, pre-populated with fixed values defined in the Rating Valuations Rules 2008.

    More information Please refer to the NZ Properties Data Dictionary for detailed metadata and information about this table.

  2. e

    Building age of residential development (environmental atlas)

    • data.europa.eu
    wfs
    Updated Mar 15, 2025
    + more versions
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    (2025). Building age of residential development (environmental atlas) [Dataset]. https://data.europa.eu/data/datasets/ad4eca4b-1205-371c-b6e3-57f001228995
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    wfsAvailable download formats
    Dataset updated
    Mar 15, 2025
    Description

    Block and block area-related allocation of predominant building age classes (decades) of residential buildings at the level of the base map 1: 5,000 (ISU5, spatial reference environmental atlas 2010).

  3. a

    Map of Building Age - PDF

    • agzone-auburnme.opendata.arcgis.com
    • hub.arcgis.com
    Updated Jan 22, 2019
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    AccessAuburn (2019). Map of Building Age - PDF [Dataset]. https://agzone-auburnme.opendata.arcgis.com/documents/c72c911cfbb144a2bb824126626cd5da
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    Dataset updated
    Jan 22, 2019
    Dataset authored and provided by
    AccessAuburn
    Description

    PDF maps of building ages. Created 3/2018 by Spatial Alternatives for The Ad-Hoc Committee on Auburn's Agriculture and Natural Resource. Building ages from Auburn assessing data.

  4. Average age measures of non-residential capital stock by industry, by asset,...

    • www150.statcan.gc.ca
    • open.canada.ca
    • +1more
    Updated Nov 14, 2024
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    Government of Canada, Statistics Canada (2024). Average age measures of non-residential capital stock by industry, by asset, Canada, provinces and territories [Dataset]. http://doi.org/10.25318/3410016601-eng
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    Dataset updated
    Nov 14, 2024
    Dataset provided by
    Government of Canadahttp://www.gg.ca/
    Area covered
    Canada
    Description

    This table contains 42560 series, with data for years 2009 - 2014 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (14 items: Canada; Newfoundland and Labrador; Prince Edward Island; Nova Scotia; ...); Price (2 items: Original prices; Current prices); Age (2 items: Average age; Remaining useful service life ratio); Industry (20 items: Total all industries; Agriculture, forestry, fishing and hunting; Mining, quarrying and oil and gas extraction; Utilities; ...); Assets (38 items: Total non-residential; Building; Industrial buildings; Office buildings; ...).

  5. Building Age

    • filming-esrica-uc.opendata.arcgis.com
    • open-data-esrica-uc.hub.arcgis.com
    Updated Aug 15, 2017
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    Esri Canada - Technical Marketing (2017). Building Age [Dataset]. https://filming-esrica-uc.opendata.arcgis.com/datasets/building-age
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    Dataset updated
    Aug 15, 2017
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Esri Canada - Technical Marketing
    Area covered
    Description

    The age of most buildings in the City (year it was built) as well as some historical data such as the Building Name, Developer / Builder, Architect / Designer and year the building has been moved if relevant and available.

  6. E

    age of buildings in McCauley

    • data.edmonton.ca
    application/rdfxml +5
    Updated Sep 6, 2018
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    City of Edmonton (2018). age of buildings in McCauley [Dataset]. https://data.edmonton.ca/City-Administration/age-of-buildings-in-McCauley/fhnx-5in9
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    csv, xml, application/rdfxml, json, tsv, application/rssxmlAvailable download formats
    Dataset updated
    Sep 6, 2018
    Authors
    City of Edmonton
    Description

    The information is collected for property assessment interpretation purposes only. The City of Edmonton does not warrant or guarantee the completeness and accuracy of the information presented.

    The City of Edmonton does not assume responsibility nor accept any liability arising from any use of the information other than for property assessment interpretation.

    This dataset is information of properties within the City of Edmonton. It is effective from January 1st, 2017 until December 31st, 2017.

  7. a

    Building Ages 2018

    • hub.arcgis.com
    • data-staug.opendata.arcgis.com
    Updated Jun 5, 2019
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    City of St. Augustine (2019). Building Ages 2018 [Dataset]. https://hub.arcgis.com/documents/ecec24c1715d40f6a1d03378a876bb68
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    Dataset updated
    Jun 5, 2019
    Dataset authored and provided by
    City of St. Augustine
    Area covered
    Description

    Building Ages 2018 City of St. Augustine - building ages sourced from the St. Johns County property appraiser parcel data.

  8. r

    Building information

    • researchdata.edu.au
    • data.melbourne.vic.gov.au
    • +1more
    Updated Mar 7, 2023
    + more versions
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    data.vic.gov.au (2023). Building information [Dataset]. https://researchdata.edu.au/building-information/2296017
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    Dataset updated
    Mar 7, 2023
    Dataset provided by
    data.vic.gov.au
    Description

    Data collected as part of the City of Melbourne's Census of Land Use and Employment (CLUE). The data covers the period 2002-2022. It shows selected building attributes including location, construction year, refurbished year, number of floors above ground, predominant space use, bicycle/shower facilities and building accessibility. Building accessibility data is collected to track accessibility for internal City of Melbourne purposes. This data is provided as a community service by the City of Melbourne. It is not and does not purport to be a complete guide. There may be errors or omissions. Data is liable to change. The City of Melbourne accepts no responsibility in respect of any claim arising from use or reliance upon this data.

    For more information about CLUE see http://www.melbourne.vic.gov.au/clue

  9. Vacancies by Age of Building for Colorado 2016

    • data.wu.ac.at
    • data.colorado.gov
    csv, json, xml
    Updated Jun 12, 2018
    + more versions
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    DOLA - Department of Local Affairs (2018). Vacancies by Age of Building for Colorado 2016 [Dataset]. https://data.wu.ac.at/schema/data_colorado_gov/MnAyay1pcTdm
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    csv, json, xmlAvailable download formats
    Dataset updated
    Jun 12, 2018
    Dataset provided by
    Colorado Department of Local Affairshttp://colorado.gov/dola
    License

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

    Description

    Average rents and vacancies distributed by age of building structure for regions across Colorado dating back to 2006 as defined by the Colorado Department of Local Affairs Housing Division (DOLA).

  10. g

    Building

    • gimi9.com
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    Building [Dataset]. https://gimi9.com/dataset/uk_building2
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    License

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

    Description

    🇬🇧 영국 English Buildings that consist of multiple adjoining building parts. When contained in a Land Use Site, adjoining building parts will be represented by a single feature. The latest data schema version includes attributes such as the number of floors, building age, construction material, basement presence, building description, building use, address counts, and connectivity.

  11. Z

    Model America - data for every U.S. building

    • data.niaid.nih.gov
    • zenodo.org
    Updated Mar 25, 2024
    + more versions
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    New, Joshua (2024). Model America - data for every U.S. building [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_6908188
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    Dataset updated
    Mar 25, 2024
    Dataset provided by
    New, Joshua
    Adams, Mark
    Bass, Brett
    Berres, Andy
    Clinton, Nicholas
    Area covered
    United States
    Description

    DATA HAS BEEN MIGRATED TO https://data.ess-dive.lbl.gov/view/doi:10.15485/2283980

    The 5-year goal of the “Model America” concept was to generate a model of every building in the United States. This data repository delivers on that goal with "Model America v1".

    Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (bit.ly/AutoBEM).

    There were 125,715,609 buildings detected in the United States. Of this number, 122,146,671 (97.2%) buildings resulted in a successful generation and simulation of a building energy model. This dataset includes the full 125 million buildings. Future updates may include additional buildings, data improvements, or other algorithmic model enhancements in "Model America v2".

    Data, separated by state - minimalist list of each building (rows) for the following fields (columns)

    ID - unique building ID

    Footprint2D - lat/lon vertices of building footprint

    State_Abbrev - Abbreviation for the from which building is located

    Area - estimate of total conditioned floor area (ft2)

    Area2D - footprint area (ft2)

    CZ - ASHRAE Climate Zone designation

    Height - building height (ft)

    NumFloors - number of floors (above-grade)

    WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, 40% for commercial buildings)

    CZ - US climate zone designation

    BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards

    Standard - building vintage (determined by building age)

    This data is made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL) Laboratory Directed Research and Development (LDRD), U.S. Dept. of Energy’s (DOE) Building Technologies Office (BTO), Office of Electricity (OE), Biological and Environmental Research (BER), and National Nuclear Security Administration (NNSA).

  12. a

    Building Age Distribution

    • data-staug.opendata.arcgis.com
    Updated Mar 27, 2019
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    City of St. Augustine (2019). Building Age Distribution [Dataset]. https://data-staug.opendata.arcgis.com/datasets/77f74627d4634c428f292483064f98b1
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    Dataset updated
    Mar 27, 2019
    Dataset authored and provided by
    City of St. Augustine
    Area covered
    Description

    Building Age Distribution based on the University of Florida's GeoPlan layer dissolved by year/decade for St Johns County

  13. Data from: Objectively measured external building quality, Census housing...

    • catalog.data.gov
    Updated Mar 15, 2025
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    U.S. EPA Office of Research and Development (ORD) (2025). Objectively measured external building quality, Census housing vacancies and age, and serum metals in an adult cohort in Detroit, Michigan [Dataset]. https://catalog.data.gov/dataset/objectively-measured-external-building-quality-census-housing-vacancies-and-age-and-serum-
    Explore at:
    Dataset updated
    Mar 15, 2025
    Dataset provided by
    United States Environmental Protection Agencyhttp://www.epa.gov/
    Area covered
    Michigan, Detroit
    Description

    The data is tabular data containing information on residential history, neighborhood built environment, individual-level economic and demographic information, and measured serum metals. This dataset is not publicly accessible because: The data is not owned by the EPA and contains protected information in the form of residential history and thus cannot be uploaded into ScienceHub. It can be accessed through the following means: The data can be accessed by contacting Dr. Chantel Martin. Format: Data is tabular data containing information on residential history, neighborhood built environment, individual-level economic and demographic information, and measured serum metals concentrations. This dataset is associated with the following publication: Lodge, E., C. Martin, R.C. Fry, A. White, C. Ward-Caviness, S. Martin, and A. Aiello. Objectively measured external building quality, Census housing vacancies and age, and serum metals in an adult cohort in Detroit, Michigan. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 177-186, (2023).

  14. Building age map, Vienna, around 1920

    • zenodo.org
    • data.niaid.nih.gov
    zip
    Updated Jan 30, 2022
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    Ferdinand Reimer; Ferdinand Reimer; Ulrich Kral; Ulrich Kral; Emre Can Sönmez; Emre Can Sönmez; Friedrich Hauer; Friedrich Hauer; Severin Hohensinner; Severin Hohensinner; Hanna Wolfinger; Klara Stuppacher; Andreas Danzinger; Ingeborg Hengl; Ingeborg Hengl; Lupina Prospero; Sarah Prunner; Helmut Rechberger; Helmut Rechberger; Hanna Wolfinger; Klara Stuppacher; Andreas Danzinger; Lupina Prospero; Sarah Prunner (2022). Building age map, Vienna, around 1920 [Dataset]. http://doi.org/10.5281/zenodo.3715200
    Explore at:
    zipAvailable download formats
    Dataset updated
    Jan 30, 2022
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Ferdinand Reimer; Ferdinand Reimer; Ulrich Kral; Ulrich Kral; Emre Can Sönmez; Emre Can Sönmez; Friedrich Hauer; Friedrich Hauer; Severin Hohensinner; Severin Hohensinner; Hanna Wolfinger; Klara Stuppacher; Andreas Danzinger; Ingeborg Hengl; Ingeborg Hengl; Lupina Prospero; Sarah Prunner; Helmut Rechberger; Helmut Rechberger; Hanna Wolfinger; Klara Stuppacher; Andreas Danzinger; Lupina Prospero; Sarah Prunner
    License

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

    Area covered
    Vienna
    Description

    This data respository includes the following datasets:

    1. Building stock map 1920 (BSM_1920.shp) and its attribute table (BSM_1920_attribute_table.csv)
    2. Areas out of scope 1920 (AOOS_1920.shp)
    3. Scope of analog building age map 1920 (SABAM_1920.shp)
  15. C

    Non-residential building construction years Wuppertal

    • ckan.mobidatalab.eu
    Updated Jan 20, 2021
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    Geoportal (2021). Non-residential building construction years Wuppertal [Dataset]. https://ckan.mobidatalab.eu/dataset/non-residential-building-years-of-construction-wuppertal
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    http://publications.europa.eu/resource/authority/file-type/shp, http://publications.europa.eu/resource/authority/file-type/wms_srvcAvailable download formats
    Dataset updated
    Jan 20, 2021
    Dataset provided by
    Geoportal
    License

    Data licence Germany – Attribution – Version 2.0https://www.govdata.de/dl-de/by-2-0
    License information was derived automatically

    Area covered
    Wuppertal
    Description

    The data set is created by merging the non-residential buildings from the building file of the Wuppertal statistical office with the addresses of the real estate cadastre, which is carried out once a year. The year in which the building was built is also classified into 11 age groups, these are mostly 10-year intervals. The resulting data set models the buildings with the house number coordinates of the real estate cadastre as punctiform objects. The attributes include i.a. the address (street name and house number), the year the building was built and the age group from the above classification. The building file is based on the results of the 1987 census, it is continuously updated using the statistical survey forms from the building application documents and status reports from the building permit process regarding approval, start of construction and completion of the building. In 2015, the building file was systematically improved through comparisons with other data sources (2011 census, GWG data, etc.). The annual intersection with the addresses of the real estate cadastre will take place in the first half of the year from 2017 onwards. The intersection results are provided in ESRI Shapefile, KML, GeoJSON and CSV formats as open data under the CC BY 4.0 license.

  16. t

    Building years Wuppertal

    • service.tib.eu
    Updated Feb 4, 2025
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    (2025). Building years Wuppertal [Dataset]. https://service.tib.eu/ldmservice/dataset/govdata_77d02b01-2106-47be-bdf4-b104c27a60fd
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    Dataset updated
    Feb 4, 2025
    Area covered
    Wuppertal
    Description

    The data set is created by intersecting the residential buildings from the building file of the Wuppertal Statistical Office with the addresses of the property cadastre once a year. Here, the building construction year is also classified into 11 age groups, most of which are 10-year intervals. The resulting data set models the buildings with the house number coordinates of the property cadastre as point-shaped objects. The attributes include, inter alia, the address (street name and house number), the year of construction of the building and the age group from the above-mentioned classification. The building file is based on the results of the 1987 census and is continuously updated via the statistical survey sheets from the building application documents and status reports from the building permit process on the approval, start of construction and completion of the building. In 2015, the building file was systematically improved by comparing it with other data sources (Census 2011, GWG data, etc.). The annual intersection with the addresses of the property cadastre will take place from 2017 onwards in the first half of each year. The intersection results are provided in the formats ESRI-Shapefile, KML, GeoJSON and CSV as open data under the CC BY 4.0 license.

  17. g

    Non-residential building years Wuppertal | gimi9.com

    • gimi9.com
    Updated Jan 23, 2022
    + more versions
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    (2022). Non-residential building years Wuppertal | gimi9.com [Dataset]. https://gimi9.com/dataset/eu_c312b259-a7a5-41ff-a208-c819503282e8_1/
    Explore at:
    Dataset updated
    Jan 23, 2022
    License

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

    Area covered
    Wuppertal
    Description

    The data set is created by a 1x per year intersection of the non-residential buildings from the building file of the Wuppertal Statistical Office with the addresses of the real estate register. There is also a classification of the building construction year in 11 ages, most of which are 10-year intervals. The resulting data set models the buildings with the house number coordinates of the property register as point-shaped objects. The attributes include, among other things, the address (road name and house number), the building year and the age level from the above classification. The building file is based on the results of the 1987 census, it is continuously updated via the statistical survey sheets from the building application documents and status reports from the building approval process on the approval, start of construction and completion of the building. In 2015, the building file was systematically improved by comparisons with other data sources (Zensus 2011, data from the GWG, etc.). The annual intersection with the addresses of the property register takes place from 2017 onwards in the first half of each year. The intersection results are provided in the formats ESRI-Shapefile, KML, GeoJSON and CSV as open data under the CC BY 4.0 license.

  18. NYC's average infrastructure age 2014

    • statista.com
    Updated Jan 19, 2022
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    Statista (2022). NYC's average infrastructure age 2014 [Dataset]. https://www.statista.com/statistics/724427/new-york-average-age-of-infrastructure/
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    Dataset updated
    Jan 19, 2022
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2014
    Area covered
    New York, United States
    Description

    This statistic displays the average age, in years, of New York City infrastructure. The figures were broken down according to the type of infrastructure. On average, buildings in the city of New York are 53 years old as of 2014. It is estimated that it will cost a minimum of 47.3 billion U.S. dollars to repair and/or replace the existing infrastructure in New York City.

  19. l

    Block level energy consumption (modelled on building attributes) - 2016...

    • devweb.dga.links.com.au
    • data.melbourne.vic.gov.au
    • +1more
    csv, geojson, json +3
    Updated May 5, 2025
    + more versions
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    City of Melbourne (2025). Block level energy consumption (modelled on building attributes) - 2016 projection - retrofit scenario [Dataset]. https://devweb.dga.links.com.au/data/dataset/block-level-energy-consumption-modelled-on-building-attributes-2016-projection-retrofit-scenari
    Explore at:
    json, xls, shp, geojson, csv, kmlAvailable download formats
    Dataset updated
    May 5, 2025
    Dataset authored and provided by
    City of Melbourne
    Description

    This dataset should be read alongside other energy consumption datasets on the City of Melbourne open data platform as well as the following report:

    http://imap.vic.gov.au/uploads/Meeting%20Agendas/2014%20August/Att%207a_IMAP_Energy_Map_-CSIRO-Energy_Use_2011-2026_Report-2014June30-Final_pdf_11.2MB.pdf

    The dataset outlines modelled energy consumption across the City of Melbourne municipality. It is not energy consumption data captured by a meter, but modelled data based on building attributes such as building age, floor area etc. This data was provided by the CSIRO as a result of a study commissioned by IMAP Councils. The study was governed by a Grant Agreement between Councils and the CSIRO, which stated an intent for the data to be published. This specific dataset is presented at a block level scale. It includes both commercial and residential buildings and is a 2016 projection, relative to a 2011 baseline, based on a scenario of buildings being retrofitted. It does not include the industrial sector.

  20. d

    Building Footprints

    • catalog.data.gov
    • opendata.dc.gov
    • +1more
    Updated Feb 4, 2025
    + more versions
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    Office of the Chief Technology Officer (2025). Building Footprints [Dataset]. https://catalog.data.gov/dataset/building-footprints-d97ff
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    Dataset updated
    Feb 4, 2025
    Dataset provided by
    Office of the Chief Technology Officer
    Description

    Building structures include parking garages, ruins, monuments, and buildings under construction along with residential, commercial, industrial, apartment, townhouses, duplexes, etc. Buildings equal to or larger than 9.29 square meters (100 square feet) are captured. Buildings are delineated around the roof line showing the building "footprint." Roof breaks and rooflines, such as between individual residences in row houses or separate spaces in office structures, are captured to partition building footprints. This includes capturing all sheds, garages, or other non-addressable buildings over 100 square feet throughout the city. Atriums, courtyards, and other “holes” in buildings created as part of demarcating the building outline are not part of the building capture. This includes construction trailers greater than 100 square feet. Memorials are delineated around a roof line showing the building "footprint."Bleachers are delineated around the base of connected sets of bleachers. Parking Garages are delineated at the perimeter of the parking garage including ramps. Parking garages sharing a common boundary with linear features must have the common segment captured once. A parking garage is only attributed as such if there is rooftop parking. Not all rooftop parking is a parking garage, however. There are structures that only have rooftop parking but serve as a business. Those are captured as buildings. Fountains are delineated around the base of fountain structures.

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Land Information New Zealand (2023). NZ Properties: Building Age [Dataset]. https://data.linz.govt.nz/table/105617-nz-properties-building-age/
Organization logo

NZ Properties: Building Age

Explore at:
mapinfo mif, mapinfo tab, geopackage / sqlite, csv, geodatabase, dbf (dbase iii)Available download formats
Dataset updated
Aug 1, 2023
Dataset authored and provided by
Land Information New Zealandhttps://www.linz.govt.nz/
License

https://data.linz.govt.nz/license/attribution-4-0-international/https://data.linz.govt.nz/license/attribution-4-0-international/

Area covered
New Zealand
Description

This is the look-up table for Building Age and is part of the set of District Valuation Roll (DVR) data.

The Building Age look-up table is used by the NZ Properties: National District Valuation Roll table.

Look-up tables are provided to make it easier to interpret coded DVR attributes and are given as reference data, pre-populated with fixed values defined in the Rating Valuations Rules 2008.

More information Please refer to the NZ Properties Data Dictionary for detailed metadata and information about this table.

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