Sanitized data for "Building-level exposed asset values for Germany''

Building-level exposed asset values for Ahrweiler county in western Germany This repository contains the following three building-level exposure datasets resulting from three corresponding asset value estimation models presented in [paper citation]: LoD1+EUROSTAT model results LoD1+BEAM model results EHRE model results Addtionally, a benchmark dataset of 844 sample buildings from the Ahrweiler region is provided here including detailed information on building construction types, associated economic sectors, and asset value estimations to support the comparative evaluation of economic sector classification results and building asset values derived from the other three exposure models. All models evaluate structural fixed asset values of buildings and their classifications across economic sectors, providing harmonized and reproducible data designed for comprehensive risk analysis and disaster management. The datasets provided here are sanitized from precise geographic information about the building location in order to preserve privacy. The corresponding geographic information can be provided upon request from: Geometry data for “Building-level exposed asset values for Germany”. Zenodo. All tables in here link to a table of the same name with suffix _geom in the repository stated above. Each corresponding table pair includes unique identifiers in column "id" which can be used to link back the geometry and location of each building. In the following, details of all three full model extent datasets as well as the benchmark dataset are given. Content and column name explanations are given in the "column_explanations.xlsx" table down below. LoD1+EUROSTAT model results Key Features Underlying input dataset: EUROSTAT - Statistical Office of the European Union. Source: https://ec.europa.eu/eurostat/web/main/data/database Economic sectors: classified by NACE economic sectors: Agriculture (A) Production (B-E) Construction (F) Market service (G-J) Corporate services (K-N) Non-market services (O-U) Cost basis: Replacement costs in current prices referenced to 2018. Files & data format eurostat-based_model_full_extent_results_sanitized.csv Provides a tabular representation of model outputs, including building asset values and classifications. LoD1+BEAM model results Key Features Underlying input dataset: BEAM (Basic European Assets Map) provided by Copernicus. Source: https://mapping.emergency.copernicus.eu/activations/EMSN076/ Economic sectors: Residential Agricultural Service Industry Cost basis: Depreciated construction costs (for residential assets); net asset values (for all other sectors) referenced to 2018. Files & data format beam-based_model_full_extent_results_sanitized.csv Provides a tabular representation of model outputs, including building asset values and classifications. EHRE model results Key Features Underlying input dataset: EHRE (European High-Resolution Exposure) provided by Cecilia Nievas. Source: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5022908 Economic sectors: Residential Commercial Industrial Cost basis: Replacement costs referenced to 2020. Files & data format ehre-based_model_full_extent_results_sanitized.csv Provides a tabular representation of model outputs, including building asset values and classifications. Benchmark dataset of 844 sample buildings from the Ahrweiler region Overview Precise information on building use type and construction type of all sample buildings was collected by visually inspecting sample buildings in Google Earth, Google Streetview, Mapillary, and the online real-estate market place ImmoScout24. This information was linked to standard reconstruction costs for 24 representative building construction types derived from the book Baukosten Gebäude Neubau 2021 (translates as “Construction costs for new buildings 2021”) published by the Baukosteninformationszentrum Deutscher Architektenkammern GmbH (translates as "Building Cost Information Center of German Chambers of Architects"). These sample buildings serve as a basis for the building-by-building comparison of sector classification results and estimated individual building asset values. Key Features Sample Dataset: A collection of 844 buildings, with equal representation of residential and non-residential structures. Building Information: Comprehensive classification based on building construction type and associated economic sectors. Regional Context: Focused on the Ahrweiler region, leveraging diverse building characteristics typical of the area. Asset Valuation: Asset values estimated using statistical average construction costs, adapted to regional factors. Underlying input dataset: BKI book Baukosten Gebäude Neubau 2021 (translates as “Construction costs for new buildings 2021”) published by the Baukosteninformationszentrum Deutscher Architektenkammern GmbH (translates as "Building Cost Information Center of German Chambers of Architects"). Source: https://bki.de/ Economic sectors: Residential Service Industrial Ambiguous Cost basis: Standardized construction costs referenced to 2021. Files & data format benchmark_dataset_sanitized.csv Provides 844 sample buildings including benchmark classification and asset values stemming from BKI, as well as results from the LoD1+EUROSTAT and LoD1+BEAM models. benchmark_dataset_ehre_sanitized.csv Provides 699 sample buildings including benchmark classification and asset values stemming from BKI, as well as results from the EHRE model. Note: transferring results from the EHRE model involved some spatial mismatches of buildings because EHRE is based on OSM building locations instead of LoD1. Therefore, this dataset includes only 699 sample buildings. For detailed information refer to the corresponding publication below. Benchmark dataset creation process 1. Selection of Sample Areas Four sample areas manually defined to ensure representativeness. Buildings selected to include diverse types typical of the Ahrweiler region. 2. Building Classification Visual inspection of buildings using Google Earth, Google Street View, Mapillary, and ImmoScout24. Assignment of buildings to 24 representative types. Sub-classification of residential buildings using characteristics like storeys, detachment, and presence of basements. 3. Economic Sector Assignment Classification into sectors: residential, industrial, service, and ambiguous. 4. Asset Value Estimation Standard construction cost values (EUR/m³) applied to individual building volumes. Adjustments made using regionalization factors for Rhineland-Palatinate. 5. Transfer of Asset Values from other Models Results derived from the other three exposure models named the LoD1+EUROSTAT model, the LoD1+BEAM model, and the EHRE model were transfered to this dataset, which evaluate asset values and classifications across economic sectors, providing data necessary for comprehensive risk analysis and disaster management. Applications Comparative evaluation of sector classifications. Analysis of building asset valuation methods. Flood risk analyses. Tools and Software For asset value models: Model construction was performed in a PostgreSQL Database. All scripts incl. explanations are given in the following gitlab repository: https://git.gfz-potsdam.de/hydro/flood_damage/exposure_ahr_models For benchmark dataset: QGIS 3.34.2 for dataset construction. Online tools such as Google Earth, Google Street View, Mapillary, and ImmoScout24 for visual inspection. Contact For inquiries or further information, please contact Aaron Buhrmann at [e-mail] Related Publication This model construction procedure is described and analyzed in detail in the following publication: [Buhrmann], "Building-level exposed asset values for Germany", Journal Name, Volume(Issue), Page Numbers, Year. DOI: [Insert Paper DOI], URL: https://www.overleaf.com/project/673e452c3b542e585b964d02 Please refer to this publication for a comprehensive explanation of the methods and analysis related to the models.