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.

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).

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.

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; ...).

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.

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.

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

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

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).

🇬🇧 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.

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).

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

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).

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)

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.

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.

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.

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.

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.

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.