The World Settlement Footprint (WSF) 3D provides detailed quantification of the average height, total volume, total area and the fraction of buildings at 90 m resolution at a global scale.

  It is generated using a modified version of the World Settlement Footprint human settlements mask derived from Sentinel-1 and Sentinel-2 satellite imagery in combination with digital elevation data and radar imagery collected by the TanDEM-X mission. 
  The framework includes three basic workflows: i) the estimation of the mean building height based on an analysis of height differences along potential building edges, ii) the determination of building fraction and total building area within each 90 m cell, and iii) the combination of the height information and building area in order to determine the average height and total built-up volume at 90 m gridding. 
  In addition, global height information on skyscrapers and high-rise buildings provided by the Emporis database is integrated into the processing framework, to improve the WSF 3D Building Height and subsequently the Building Volume Layer.

  A comprehensive validation campaign has been performed to assess the accuracy of the dataset quantitatively by using VHR 3D building models from 19 globally distributed regions (~86,000 km2) as reference data. 

  The WSF 3D standard layers are provided in the format of Lempel-Ziv-Welch (LZW)-compressed GeoTiff files, with each file - or image tile - covering an area of 1 x 1 ° geographical lat/lon at a geometric resolution of 2.8 arcsec (~ 90 m at the equator). Following the system established by the TDX-DEM mission, the latitude resolution is decreased in multiple steps when moving towards the poles to compensate for the reduced circumference of the Earth.

Luxembourg Houses Sold: Avg Size: Flats: Old data was reported at 81.965 sq m in Jun 2018. This records an increase from the previous number of 80.403 sq m for Mar 2018. Luxembourg Houses Sold: Avg Size: Flats: Old data is updated quarterly, averaging 80.778 sq m from Mar 2007 (Median) to Jun 2018, with 46 observations. The data reached an all-time high of 83.794 sq m in Dec 2010 and a record low of 76.956 sq m in Jun 2008. Luxembourg Houses Sold: Avg Size: Flats: Old data remains active status in CEIC and is reported by The Portal of Statistics of Luxembourg. The data is categorized under Global Database’s Luxembourg – Table LU.EB001: House Price Index and Houses Sold.

Grid-based building morphological parameters with global coverage at 30-arc second spatial resolution are currently available in GeoTIFF format. Provided datasets contains three-building morphological parameters (the mean building height Have, plan area density PAD and frontal area density FAD) and two-aerodynamic parameters (aerodynamic roughness length z0 and zero-place displacement d) and sky-view factor (svf).The building morphological datasets were estimated from the global databases such as population, nighttime light, impervious surface area and gross domestic products. Two aerodynamic parameters and sky-view factors are calculated using the empirical equations discussed by Kanda et al. (2013) and Kanda et al. (2005), respectively.1. Raster files: (parameter name)_2013.tifFormat: GeoTIFFProjection: WGS 1984 World Mercator projectionSpatial resolution: 30-arc secondData list: Have_2013.tif, PAD_2013.tif, FAD_2013.tif, d_2013.tif, z0_2013.tif, svf_2013.tif2. Building Original DataFormat: Microsoft Excel WorkbookOriginal_building_data.xlsx contains observed building morphological parameters calculated from three- and two-dimensional building databases, and global databases (impervious surface area ISA and population density adjusted by nighttime light PopdenVIIRS) at each grid code.Validation_analysis.xlsx contains building morphological parameters calculated from three-dimensional building database (observed) and parameters estimated from global databases (predicted) at one-km spatial resolution in Berlin, Singapore and Osaka.Additional_validation_UScities.xlsx contains building morphological parameters at one-km resolution by NUDAPT database (observed) and estimated from global databases (predicted) for 42 US cities. We used this data in the Supplementary Discussion. Megacities_statistic.xlsx contains GDPcity, the maximum, minimum, mean value and standard deviation of each predicted building morphological parameters at 37 megacities. 3. Source CodeProgramming language 1: Python site package in ArcGIS v10.3.1Calculate_parameters.py contains code for calculating observed building morphological parameters from grid-based two- and three-dimensional building database input. We recommend using this script after using the Split By Attributing Tools to convert a fishnet building footprint map into multiple grids.Modifying_population_by_nightlight.py contains code for adjusted population density by nighttime light at each grid.Programming language 2: Python v2.7Converting_grids.py contains code for converting grid-based population density adjusted by nighttime light into a global map. This source code is used after running Modifying_population_by_nightlight.py.

Cambodia Floor Area: Average sq m per Person: Urban data was reported at 14.700 sq m in 2021. This records a decrease from the previous number of 15.000 sq m for 2020. Cambodia Floor Area: Average sq m per Person: Urban data is updated yearly, averaging 14.850 sq m from Jun 2020 (Median) to 2021, with 2 observations. The data reached an all-time high of 15.000 sq m in 2020 and a record low of 14.700 sq m in 2021. Cambodia Floor Area: Average sq m per Person: Urban data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Cambodia – Table KH.H005: Occupied Dwellings: Floor Area.

Average Household Size: Urban data was reported at 3.280 Person in 2018. This records a decrease from the previous number of 3.330 Person for 2017. Average Household Size: Urban data is updated yearly, averaging 3.760 Person from Mar 2002 (Median) to 2018, with 17 observations. The data reached an all-time high of 4.530 Person in 2002 and a record low of 3.280 Person in 2018. Average Household Size: Urban data remains active status in CEIC and is reported by Central Bank of the Islamic Republic of Iran. The data is categorized under Global Database’s Iran – Table IR.H002: Average Household Size: Urban.

--- DATASET OVERVIEW --- This dataset captures detailed information about each vacation rental property listing, providing insights that help users understand property distribution, characteristics, management styles, and guest preferences across different regions. With extensive global coverage and regular weekly updates, this dataset offers in-depth snapshots of vacation rental supply traits at scale.

The data is sourced directly from major OTA platforms using advanced data collection methodologies that ensure high accuracy and reliability. Each property listing is tracked over time, enabling users to observe changes in supply, amenity offerings, and host practices.

--- KEY DATA ELEMENTS --- Our dataset includes the following core performance metrics for each property: - Property Identifiers: Unique identifiers for each property with OTA-specific IDs - Geographic Information: Location data including neighborhood, city, region, and country - Listing Characteristics: Property type, bedroom count, bathroom count, in-service dates. - Amenity Inventory: Comprehensive list of available amenities, including essential facilities, luxury features, and safety equipment. - Host Information: Host details, host types, superhost status, and portfolio size - Guest Reviews: Review counts, average ratings, detailed category ratings (cleanliness, communication, etc.), and review timestamps - Property Rules: House rules, minimum stay requirements, cancellation policies, and check-in/check-out procedures

--- USE CASES --- Market Research and Competitive Analysis: VR professionals and market analysts can use this dataset to conduct detailed analyses of vacation rental supply across different markets. The data enables identification of property distribution patterns, amenity trends, pricing strategies, and host behaviors. This information provides critical insights for understanding market dynamics, competitive positioning, and emerging trends in the short-term rental sector.

Property Management Optimization: Property managers can leverage this dataset to benchmark their properties against competitors in the same geographic area. By analyzing listing characteristics, amenity offerings and guest reviews of similar properties, managers can identify optimization opportunities for their own portfolio. The dataset helps identify competitive advantages, potential service gaps, and management optimization strategies to improve property performance.

Investment Decision Support: Real estate investors focused on the vacation rental sector can utilize this dataset to identify investment opportunities in specific markets. The property-level data provides insights into high-performing property types, optimal locations, and amenity configurations that drive guest satisfaction and revenue. This information enables data-driven investment decisions based on actual market performance rather than anecdotal evidence.

Academic and Policy Research: Researchers studying the impact of short-term rentals on housing markets, urban development, and tourism trends can use this dataset to conduct quantitative analyses. The comprehensive data supports research on property distribution patterns and the relationship between short-term rentals and housing affordability in different markets.

Travel Industry Analysis: Travel industry analysts can leverage this dataset to understand accommodation trends, property traits, and supply and demand across different destinations. This information provides context for broader tourism analysis and helps identify connections between vacation rental supply and destination popularity.

--- ADDITIONAL DATASET INFORMATION --- Delivery Details: • Delivery Frequency: weekly | monthly | quarterly | annually • Delivery Method: scheduled file loads • File Formats: csv | parquet • Large File Format: partitioned parquet • Delivery Channels: Google Cloud | Amazon S3 | Azure Blob • Data Refreshes: weekly

Dataset Options: • Coverage: Global (most countries) • Historic Data: N/A • Future Looking Data: N/A • Point-in-Time: N/A • Aggregation and Filtering Options: • Area/Market • Time Scales (weekly, monthly) • Listing Source • Property Characteristics (property types, bedroom counts, amenities, etc.) • Management Practices (professionally managed, by owner)

Contact us to learn about all options.

--- DATA QUALITY AND PROCESSING --- Our data collection and processing methodology ensures high-quality data with comprehensive coverage of the vacation rental market. Regular quality assurance processes verify data accuracy, completeness, and consistency.

The dataset undergoes continuous enhancement through advanced data enrichment techniques, including property categorization, geographic normalization, and time series alignment. This processing ensures that users receive clean, structured data ready for immediate analysis without extensive preprocess...

Property Price: YTD Avg: Beijing data was reported at 28,360.916 RMB/sq m in Mar 2025. This records a decrease from the previous number of 36,835.882 RMB/sq m for Feb 2025. Property Price: YTD Avg: Beijing data is updated monthly, averaging 19,466.029 RMB/sq m from Jan 2003 (Median) to Mar 2025, with 267 observations. The data reached an all-time high of 42,343.603 RMB/sq m in Jun 2021 and a record low of 4,515.769 RMB/sq m in Feb 2004. Property Price: YTD Avg: Beijing data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Price – Table CN.PD: NBS: Property Price: Monthly.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

Luxembourg Houses Sold: Avg Size: Flats: New data was reported at 82.712 sq m in Jun 2018. This records a decrease from the previous number of 85.688 sq m for Mar 2018. Luxembourg Houses Sold: Avg Size: Flats: New data is updated quarterly, averaging 81.890 sq m from Mar 2007 (Median) to Jun 2018, with 46 observations. The data reached an all-time high of 86.670 sq m in Sep 2015 and a record low of 75.721 sq m in Sep 2016. Luxembourg Houses Sold: Avg Size: Flats: New data remains active status in CEIC and is reported by The Portal of Statistics of Luxembourg. The data is categorized under Global Database’s Luxembourg – Table LU.EB001: House Price Index and Houses Sold.

--- DATASET OVERVIEW --- This dataset delivers critical market intelligence including occupancy rates, average daily rates, revenue per available rental, booking pace, and seasonal demand patterns across different geographic areas. With daily updates, AI-driven forward projections, and four years of historical data, it offers property managers, investors, and market analysts the essential benchmarks needed to understand market performance, identify emerging trends, and develop data-driven strategies in the rapidly evolving vacation rental sector.

The data is sourced from major OTA platforms and processed through advanced aggregation methodologies that ensure statistical validity while preserving crucial market signals. Our proprietary algorithms enhance the raw data with sophisticated trend analysis and forward-looking projections, enabling users to anticipate future market conditions with increased confidence.

--- KEY DATA ELEMENTS --- Our dataset includes the following core performance metrics for each property: - Property Groups: Group by property type, bedroom counts, key amenities groups - Geographic Identifiers: Multiple geographic levels (vacation area, vacation region, county, etc) - Temporal Dimensions: Daily, weekly, monthly, and quarterly performance metrics - Occupancy Metrics: Market-wide occupancy rates and booking pace indicators - Pricing Metrics: Average daily rates (ADR), revenue per available rental night (RevPAR), and price trends - Booking Pattern Indicators: Average lead time, length of stay, and booking frequency - Seasonality Metrics: Seasonal demand patterns and year-over-year comparisons - Demand Forecasts: Forward-looking projections for occupancy and pricing trends - Historical Pacing: Snapshots into how stay date ranges developed for tracking pacing trends - Forward Looking Trends: Area KPIs 180-365 days into the future

--- USE CASES --- Market Performance Benchmarking: Property managers and owners can benchmark their individual property or portfolio performance against market-wide metrics. By comparing property-specific occupancy rates, ADR, and RevPAR against market averages for similar property types, managers can assess relative performance and identify areas for improvement. These benchmarks provide crucial context for performance evaluation and goal setting.

Investment Decision Support: Real estate investors and portfolio managers can use market-level performance data to identify attractive investment opportunities across different geographic areas. The comprehensive market metrics reveal high-performing areas, emerging markets, and potential investment risks based on actual performance data rather than anecdotal evidence. This information supports data-driven acquisition strategies and portfolio diversification decisions.

Demand Forecasting and Planning: Revenue managers and property operators can leverage the historical performance patterns and forward-looking projections to anticipate demand fluctuations and plan accordingly. The seasonal patterns, booking pace indicators, and AI-enhanced forecasts enable proactive rate adjustments, marketing timing, and operational planning to maximize revenue opportunities during high-demand periods.

Market Entry Analysis: Companies considering entering new vacation rental markets can utilize this dataset to understand market dynamics, competitive intensity, and performance expectations before committing resources. The comprehensive market metrics reduce market entry risk by providing clear visibility into potential revenue opportunities, seasonal patterns, and overall market health.

Performance Attribution Analysis: Market analysts can use this dataset to understand the drivers behind performance variations across different markets and time periods. By analyzing how market composition, seasonality, and external factors influence overall performance, analysts can identify the underlying causes of performance trends and develop more accurate forecasting models.

Economic Impact Assessment: Economic development organizations and tourism authorities can leverage this dataset to quantify the economic contribution of the vacation rental sector. The market-wide revenue metrics, occupancy patterns, and supply growth indicators provide valuable inputs for economic impact studies and policy development related to the short-term rental industry.

--- ADDITIONAL DATASET INFORMATION --- Delivery Details: • Delivery Frequency: daily | weekly | monthly | quarterly | annually • Delivery Method: scheduled file loads • File Formats: csv | parquet • Large File Format: partitioned parquet • Delivery Channels: Google Cloud | Amazon S3 | Azure Blob • Data Refreshes: daily

Dataset Options: • Coverage: Global (most countries) • Historic Data: Available (2021 for most areas) • Future Looking Data: Available (Current date + 180-365 days) • Point-in-Time: Available (with weekly as of dates) • Aggreg...

Average Household Size data was reported at 3.700 Person in 2015. This records a decrease from the previous number of 3.900 Person for 2010. Average Household Size data is updated yearly, averaging 3.950 Person from Dec 2000 (Median) to 2015, with 4 observations. The data reached an all-time high of 4.300 Person in 2000 and a record low of 3.700 Person in 2015. Average Household Size data remains active status in CEIC and is reported by National Institute of Statistics and Geography. The data is categorized under Global Database’s Mexico – Table MX.H014: Average Household Size.

In my thesis ‘Modelling impacts of habitat loss and fragmentation on mammal species’, my aim was to quantify species-specific impacts on land-use change across a large number of mammal species. To achieve this aim, I wanted to (1) obtain and validate species-specific data relevant for quantifying these impacts across a large number of mammal species and (2) quantify ecologically meaningful indicators of habitat loss and fragmentation. Chapters 2-4 of my thesis focus on the first subgoal, chapters 5-6 focus on the second subgoal. In this repository, data accompanying chapters 4 and 5 were published.In chapter 4: ‘Global environmental drivers of home range size in terrestrial and marine mammal species’, the relationship between home range size (HRS) and environmental variables was quantified, accounting for species traits and their interactions with environmental variables for terrestrial and marine mammals. A novel, comprehensive dataset of 2,800 HRS estimates from 586 terrestrial and 27 marine mammal was used. The results indicated that HRS of terrestrial mammals was strongly related to both species traits (body mass, diet, and locomotion type) and environmental conditions (human disturbance, productivity, seasonality). HRS of marine mammals was only related to environmental conditions (mean sea surface temperature and standard deviation of sea surface temperature). In this repository the following data were published that were used to perform the analyses:• Home_range_data: Excelsheet with the subset of home range sizes from the HomeRange database (Broekman et al., 2023) included in the analysis with associated data on the values of species traits and environmental variables.• Data_sources: List of references for the home range data included in this study.• Chapter4_AppendixS2: Results of model selection for terrestrial and marine mammals.In chapter 5: ‘Impacts of existing and planned roads on terrestrial mammal habitat in New Guinea’, the Equivalent Connected Area (ECA) was estimated of habitat for 139 terrestrial mammal species with >90% of their habitat area in New Guinea. The ECA was calculated in three different situations: (1) no roads (baseline situation), (2) existing roads (current situation), and (3) existing and planned roads (future situation). Habitat fragmentation effects of roads were then quantified for each species by comparing the ECA in situations 2 and 3 to the ECA in situation 1. On average across the species, the ECA in the current situation equals 89% (SD = 12%) of the baseline ECA values (i.e., a situation without roads) and the lowest remaining ECA was found for Shawmayer’s coccymys (Coccymys shawmayeri, 53%). The average remaining ECA decreases to 71% (SD = 20%) of the baseline ECA values in the future situation. Further, the future remaining ECA drops to below 50% of the baseline for 28 species and the lowest remaining ECA was found for the montane soft-furred paramelomys (Paramelomys mollis, 36%). In this repository the following data were published that were used to perform the analyses:• Roads_new_Guinea: shapefile with the existing and planned roads in New Guinea. These road data were either obtained from the Global Roads Inventory Project (GRIP) database (Meijer et al., 2018) or digitized from the maps published by Alamgir et al. (2019) and Sloan et al. (2019). Attribute data for each road includes an unique road identity number, road type, road surface, road width, and whether the road is an existing road or planned road.• Refined_range_[species_name]: species-specific raster files (.tif) in WGS84 projection at ~100m resolution indicating whether a grid cell is part of the refined species range (= 1).• Trait_data_and_results: Excelsheet with species-specific trait data, road crossing probabilities, and ECA values.• Chapter5_AppendixS1: see Trait_data_and_resultsReferencesAlamgir M., Sloan S., Campbell M.J., Engert J., Kiele R., Porolak G., Mutton T., Brenier A., Ibisch P.L., Laurance W.F. (2019) Infrastructure expansion challenges sustainable development in Papua New Guinea. Plos One, 14, 20.Broekman M.J.E., Hoeks S., Freriks R., Langendoen M.M., Runge K.M., Savenco E., ter Harmsel R., Huijbregts M.A., TuckerM.A. (2023) HomeRange: A global database of mammalian home ranges. Global Ecology and Biogeography, 32, 198-205.Meijer J.R., Huijbregts M.A.J., Schotten K., Schipper A.M. (2018) Global patterns of current and future road infrastructure. Environmental Research Letters, 13, 10.Sloan S., Campbell M.J., Alamgir M., Engert J., Ishida F.Y., Senn N., Huther J., Laurance W.F. (2019) Hidden challenges for conservation and development along the Trans-Papuan economic corridor. Environmental Science & Policy, 92, 98-106.

Property Price: YTD Avg: Shanghai data was reported at 39,575.041 RMB/sq m in Mar 2025. This records an increase from the previous number of 38,438.579 RMB/sq m for Feb 2025. Property Price: YTD Avg: Shanghai data is updated monthly, averaging 16,245.712 RMB/sq m from Jan 2003 (Median) to Mar 2025, with 267 observations. The data reached an all-time high of 49,301.406 RMB/sq m in Feb 2021 and a record low of 3,659.000 RMB/sq m in Feb 2003. Property Price: YTD Avg: Shanghai data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Price – Table CN.PD: NBS: Property Price: Monthly.

Jordan Average Households Size data was reported at 4.800 Person in 2017. This stayed constant from the previous number of 4.800 Person for 2016. Jordan Average Households Size data is updated yearly, averaging 5.400 Person from Dec 2000 (Median) to 2017, with 18 observations. The data reached an all-time high of 5.800 Person in 2002 and a record low of 4.700 Person in 2015. Jordan Average Households Size data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Jordan – Table JO.H003: Total Households and Average Size of Households.

Average Household Size data was reported at 3.600 Person in 2017. This stayed constant from the previous number of 3.600 Person for 2016. Average Household Size data is updated yearly, averaging 4.150 Person from Dec 1990 (Median) to 2017, with 24 observations. The data reached an all-time high of 4.700 Person in 1990 and a record low of 3.500 Person in 2015. Average Household Size data remains active status in CEIC and is reported by National Statistics Office of Mongolia. The data is categorized under Global Database’s Mongolia – Table MN.H006: Average Household Size.

Cambodia Floor Area: per Household: Rural: Average sq m data was reported at 52.400 sq m in 2021. This records an increase from the previous number of 51.500 sq m for 2020. Cambodia Floor Area: per Household: Rural: Average sq m data is updated yearly, averaging 51.950 sq m from Jun 2020 (Median) to 2021, with 2 observations. The data reached an all-time high of 52.400 sq m in 2021 and a record low of 51.500 sq m in 2020. Cambodia Floor Area: per Household: Rural: Average sq m data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Cambodia – Table KH.H005: Occupied Dwellings: Floor Area.

Cambodia Floor Area: per Household: Urban: Average sq m data was reported at 55.300 sq m in 2021. This records a decrease from the previous number of 57.300 sq m for 2020. Cambodia Floor Area: per Household: Urban: Average sq m data is updated yearly, averaging 56.300 sq m from Jun 2020 (Median) to 2021, with 2 observations. The data reached an all-time high of 57.300 sq m in 2020 and a record low of 55.300 sq m in 2021. Cambodia Floor Area: per Household: Urban: Average sq m data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Cambodia – Table KH.H005: Occupied Dwellings: Floor Area.

Myanmar Average Household Size: Yangon data was reported at 4.100 Person in 2017. Myanmar Average Household Size: Yangon data is updated yearly, averaging 4.100 Person from Dec 2017 (Median) to 2017, with 1 observations. Myanmar Average Household Size: Yangon data remains active status in CEIC and is reported by Central Statistical Organization. The data is categorized under Global Database’s Myanmar – Table MM.H001: Living Conditions Survey: Average Household Size.

Brazil Construction Cost data was reported at 1,818.640 BRL/sq m in Apr 2025. This records an increase from the previous number of 1,810.250 BRL/sq m for Mar 2025. Brazil Construction Cost data is updated monthly, averaging 854.130 BRL/sq m from Mar 1986 (Median) to Apr 2025, with 470 observations. The data reached an all-time high of 14,030,088.660 BRL/sq m in Jul 1993 and a record low of 187.160 BRL/sq m in Jan 1989. Brazil Construction Cost data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Global Database’s Brazil – Table BR.EA004: Construction Cost: Average: by Region and State. Average costs at the level of Federation Units: Costs are estimated by the weighted average of the costs of residential projects with a normal finishing standard; For this calculation, the weight (relative importance) of each project is considered in the most populous municipality of each geographic area.

China Population: Average Household Size data was reported at 2.800 Person in 2023. This records an increase from the previous number of 2.760 Person for 2022. China Population: Average Household Size data is updated yearly, averaging 3.150 Person from Dec 1982 (Median) to 2023, with 31 observations. The data reached an all-time high of 4.430 Person in 1982 and a record low of 2.620 Person in 2020. China Population: Average Household Size data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: No of Person per Household.