Our US Home Ownership Data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.

Our comprehensive data enrichment solution includes various data sets that can help you address gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences. 1. Geography - City, State, ZIP, County, CBSA, Census Tract, etc. 2. Demographics - Gender, Age Group, Marital Status, Language etc. 3. Financial - Income Range, Credit Rating Range, Credit Type, Net worth Range, etc 4. Persona - Consumer type, Communication preferences, Family type, etc 5. Interests - Content, Brands, Shopping, Hobbies, Lifestyle etc. 6. Household - Number of Children, Number of Adults, IP Address, etc. 7. Behaviours - Brand Affinity, App Usage, Web Browsing etc. 8. Firmographics - Industry, Company, Occupation, Revenue, etc 9. Retail Purchase - Store, Category, Brand, SKU, Quantity, Price etc. 10. Auto - Car Make, Model, Type, Year, etc. 11. Housing - Home type, Home value, Renter/Owner, Year Built etc.

Consumer Graph Schema & Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings:

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly).

Consumer Graph Use Cases: 360-Degree Customer View: Get a comprehensive image of customers by the means of internal and external data aggregation. Data Enrichment: Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment Fraud Detection: Use multiple digital (web and mobile) identities to verify real users and detect anomalies or fraudulent activity. Advertising & Marketing: Understand audience demographics, interests, lifestyle, hobbies, and behaviors to build targeted marketing campaigns.

What Makes Our Data Unique? We do not buy and resell other provider's data. We aggregate our housing data, which we source ourselves, to ensure the highest quality.

Our real estate data encompasses a wide range of comprehensive information on homeowners and properties.

• Current listings as well as properties for rent.
• Current and past property owners
• Current and past building permits
• Property attributes such as number of rooms, square footage, roof type, and many more.
• Zoning details
• Businesses at location
• Forclosure details
• Current and historical property values
• Tax details

Use cases and verticals.

• Developers looking to create applications around real estate data tailored towards real estate investors or agents.
• Agents or investors looking to acquire new leads within a geographical territory.
• Market analysts looking to uncover trends within the housing sector.

New version 2.0.0 with majors change

For free and complete informations concerning CASSMIR datasets, please visit our website (in French).

The CASSMIR database (Contribution to the Spatial and Sociological Analysis of Residential Real Estate Markets) is a spatial and population datasets on housing property market of the Parisian metropolitan area, from 1996 to 2018. The indicators in the CASSMIR database cover four "thematic areas of investigation" : prices, socio-demographic profile of buyers and sellers, purchasing regimes and types of property transfers and types of real estate. These indicators characterize spatial units at three scales (communal level, 1km grid and 200m grid) and population groups of buyers and sellers declined according to social, generational and gender criteria. The delivery of the database follows a series of matching and aggregation of individual data from two original databases : a database on real estate transactions (BIEN database) and a database on first-time buyer investments (PTZ database). CASSMIR delivers aggregated data (with nearly 350 variables) in open access for non-commercial use.

This repository consists of sevenfiles.

"CASSMIR_SpatialDataBase" is a Geopackage file, it lists all the data aggregated to spatial units of reference. It is composed of three layers that correspond to the geographical scale of aggregation: at a communal level, a grid of one kilometer on each side and a grid of two hundred meters on each side.

"CASSMIR_GroupesPopDataBase" is a .csv file, it lists all the data aggregated to population groups of reference. There are three types of population groups : groups referenced by the social position of the buyers/sellers (social group), groups referenced by the age group to which the buyers/sellers belong (generational group), groups referenced by the sex of the buyers/sellers (gender group).

Two metadata files (.csv) lists the metadata of the indicators made available. They are systematically structured as follows :

• Id_var: the identifier of the variable contained in "CASSMIR_SpatialDataBase" or "CASSMIR_GroupesPopDataBase"
• Unite d'observation des variables descriptives : descriptive units of observation (Prices, buyers, sellers...)
• Type d'information : precision on the type of information
• Label : Description of the contents of the variable
• Indicator_Group: The group of indicators to which the variable relates (prices, socio-demographics indicators of buyers and sellers...)
• Unit : The unit of measurement of the variable
• Spatial_Availability : A precision on the availability of the variable in the spatial database (communes, 1 km grid and 200m grid)
• GroupesPop_Availability : A precision on the availability of the variable in the population groupes database (Social, generational , gender)
• Data_Source: The main origin of the data (INSEE, BIEN and/or PTZ)
• Remarques : possible remarks on the construction of the variable

"BIENSampleForTest" and "PTZSampleForTest" are two .txt files which restore a sample of individual data from each of the original databases. All data were anonymized and the values randomized. These two files are specifically dedicated to reproducing the different stages of processing that lead to the production of the CASSMIR files ("CASSMIR_SpatialDataBase" or "CASSMIR_GroupesPopDataBase") and cannot be used in any other way.

"LEXIQUE" is a glossary of terms used to name the variables (.csv).

The creation of the database was funded by the National Reseach Agency (ANR WIsDHoM https://anr.fr/Projet-ANR-18-CE41-0004).

All CASSMIR documentation (in French) and R codes are accessible via the Gitlab repository at the following address : https://gitlab.huma-num.fr/tlecorre/cassmir.git

METADATA :

• Licence

This dataset is registered under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. You are free to copy, distribute, transmit, and adapt the data, provided that you give credit to the CASSMIR data base and specify the original source of the data. If you modify or use the data in other derivative works, you may distribute them only under the same license. You may not make commercial use of this database, nor may you use it for any purpose other than scientific research.

• Citation standard

- Figures: (CC - CASSMIR database, indicator(s) constructed from XXX data)

- Bibliography : Productions that use the CASSMIR database must reference the dataset and the data paper.

Dataset: Le Corre T., 2020, CASSMIR (Version 2.0.0) [Data set], Zenodo. http://doi.org/10.5281/zenodo.4497219

Data paper: Thibault Le Corre, « Une base de données pour étudier vingt années de dynamiques du marché immobilier résidentiel en Île-de-France », Cybergeo: European Journal of Geography [En ligne], Data papers, article No.992, mis en ligne le 09 août 2021. URL : http://journals.openedition.org/cybergeo/37430 ; DOI : https://doi.org/10.4000/cybergeo.37430

• Data paper title

"Une base de données pour étudier vingt années de dynamiques du marché immobilier en Île-de-France"

• Author

Thibault Le Corre

• Keywords

Housing market, data base, Île-de-France, spatio-temporal dynamics

• Related Publication

DOI : https://doi.org/10.4000/cybergeo.37430

• Language

French

• Time Period Covered

The time period covered by the indicators in the database depends on the data sources used, respectively:
For data from BIEN: 1996, 1999, 2003-2012, 2015, 2018
For data from PTZ: 1996-2016

• Kind of data

Nature of data submitted

• vector: Vector data

• grid: Data mesh

• code: programming code (see the website or GitLab of the project)

• Data Sources

Base BIEN

Base PTZ

• Geographical Coverage

Île-de-France region

• Geographical Unit

Municipalities and grid mesh elements (1km side grid and 200 side grid) concerned by real estate transactions

• Geographic Bounding Box

Reference Coordinate System (RCS): EPSG 2154 RGF93/Lambert 93.

- Xmin : 586421.7
- Xmax : 741205.6
- Ymin : 6780020
- Ymax : 6905324

• Type of article

Data Paper

Our Realtor.com (Multiple Listing Service) dataset represents one of the most exhaustive collections of real estate data available to the industry. It consolidates data from over 500 MLS aggregators across various regions, providing an unparalleled view of the property market.

Features:

Property Listings: Each listing provides comprehensive details about a property. This includes its physical address, number of bedrooms and bathrooms, square footage, lot size, type of property (e.g., single-family home, condo, townhome), and more.

Photographs and Virtual Tours: Visuals are crucial in the property market. Most listings are accompanied by high-quality photographs and, in many cases, virtual or 3D tours that allow potential buyers to explore properties remotely.

Pricing Information: Listings provide asking prices, and the dataset frequently updates to reflect price changes. Historical price data, which includes initial listing prices and any subsequent reductions or increases, is also available.

Transaction Histories: For sold properties, the dataset provides information about the date of sale, the sale price, and any discrepancies between the listing and sale prices.

Agent and Broker Information: Each listing typically has associated details about the property's real estate professional. This might include their name, contact details, and affiliated brokerage.

Open House Schedules: Open house dates and times are listed for properties that are actively being shown to potential buyers.

1. Analytical Insights:

Market Trends: By analyzing the dataset over time, one can glean insights into market dynamics, such as the rate of price appreciation or depreciation in certain areas, the average time properties stay on the market, and seasonality effects.

Neighborhood Data: With comprehensive geographical data, it becomes possible to understand neighborhood-specific trends. This is invaluable for potential buyers or real estate investors looking to identify burgeoning markets.

Price Comparisons: Realtors and potential buyers can benchmark properties against similar listings in the same area to determine if a property is priced appropriately.

1. Utility:

For Industry Professionals and Analysts: Beyond buyers and sellers, the dataset is a trove of information for real estate agents, brokers, analysts, and investors. They can harness this data to craft strategies, predict market movements, and serve their clients better.

Russia Credit Institutions: Assets: FA: ow Real Estate Temporary Not Used in Main Activity data was reported at 255,017,848.000 RUB th in Dec 2018. This records a decrease from the previous number of 257,515,691.000 RUB th for Nov 2018. Russia Credit Institutions: Assets: FA: ow Real Estate Temporary Not Used in Main Activity data is updated monthly, averaging 112,276,956.500 RUB th from Jan 2012 (Median) to Dec 2018, with 84 observations. The data reached an all-time high of 271,429,484.000 RUB th in Feb 2018 and a record low of 64,821,009.000 RUB th in Dec 2013. Russia Credit Institutions: Assets: FA: ow Real Estate Temporary Not Used in Main Activity data remains active status in CEIC and is reported by The Central Bank of the Russian Federation. The data is categorized under Russia Premium Database’s Monetary and Banking Statistics – Table RU.KAK002: Aggregate Balance: Credit Institutions.

Table of INEBase Buildings mainly or exclusively for housing and no. of real estate properties by municipality (with more than 2,000 inhabitants), state of the building and year of construction (aggregate). Population and Housing Censuses

BatchData provides access to 150+ million residential and commercial properties and property owners, covering 99+% of the us population. Enrich records, build lists, or power real estate websites and application based on:

• Property Type
• Property Owner Info
• Building Characteristics
• MLS Listing Details
• Foreclosure Information
• Distress Factors
• Mortgage Details
• Household Demographics
• Ownership/Vacancy Status
• Home Equity
• Real Estate Valuation
• Property Liens
• Transfer of Sale, Probate, Inherited
• Much more!

BatchData is both a data and technology company, offering multiple self-service platforms, APIs and professional services solutions to meet your specific data needs. Whether you're looking for residential real estate data, commercial real estate data, property listing and transaction data, we've got you covered!

BatchData is the most comprehensive aggregator of US property and homeowner information, known for accuracy and completeness of records. BatchService can also provides homeowner and agency contact information for residential and commercial properties, including cell phone number and emails.

This pie chart displays books per publication date using the aggregation count. The data is filtered where the book publisher is Real Estate Education Co.. The data is about books.

Table of INEBase Buildings mainly or exclusively for housing and no. of real estate properties by municipality (with more than 2,000 inhabitants), state of the building and year of construction (aggregate). Population and Housing Censuses

Introduction

This dataset contains detailed data on 42,207 apartments (242,257 rooms) in 3,093 buildings including their geometries, room typology as well as their visual, acoustical, topological and daylight characteristics.

Procurement

The data is sourced from commercial clients of Archilyse AG specializing on the digitization and analysis of buildings. The existing building plans of clients are converted into a geo-referenced, semantically annotated representation and undergo a manual Q/A process to ensure accuracy of the data and to ensure a maximum 5%-deviation in the apartments' areas (validated with a median deviation of 1.2%).

Geometries

The dataset contains a file geometries.csv which contains the geometries of all areas, walls, railings, columns, windows, doors and features (sinks, bathtubs, etc.) of an apartment.

In total the datasets contains the 2D geometry of ~1.2 million separators (walls, railings), ~550,000 openings (windows, doors), ca. 400,000 areas (rooms, bathrooms, kitchens, etc.) and ~240,000 features (sinks, toilets, bathtubs, etc.).

Each row contains:

entity_type: The entity type (area, separator, opening, feature)

entity_subtype: The entity’s sub type (e.g. WALL)

geometry: The element’s geometry as a WKT geometry. The geometry is given in the site’s local coordinate system. I.e. the position between elements of the same site are correct in respect to each other. The +y direction points northwards, the +x direction points eastwards.

area_id: The ID of the area in which the element is spatially contained (for features)

unit_id: The ID of the unit in which the element is spatially contained (for features, areas)

apartment_id: The ID of the apartment (for features, areas)

floor_id: The ID of the floor

building_id: The ID of the building

site_id: The ID of the site

An example:

    column





    entity_type
    area


    entity_subtype
    ROOM


    geometry
    POLYGON ((-2.10406 4.02039…


    site_id
    127


    building_id
    164


    floor_id
    12864


    apartment_id
    d4438f2129b30290845ce7eef98a5ba7


    unit_id
    76643


    area_id
    684674

Simulations

Beside the geometrical model, we also provide simulation data on the visual, acoustic, solar, layout and connectivity-related characteristics of the apartments. The file simulations.csv contains the simulation data aggregated on a per-area basis. Each row contains the identifier columns area_id, unit_id, apartment_id, floor_id, building_id, site_id as defined above as well as 367 simulation columns. Each simulation column is formatted as:

_

For instance. the column view_buildings_median describes the amount of building surface that can be seen from any point in a given room. The aggregation methods vary per simulation category and are described in detail below.

Layout

The layout features represent simple features based on the geometry and composition of a room, the dataset provides the following information in an unaggregated form.

Area Basics / Geometry

    dimension
    description




    layout_area_type
    The area’s area type


    layout_net_area
    The area’s share of the apartment’s net area (e.g. 0 for a balcony)


    layout_area
    The area’s actual area


    layout_perimeter
    The area’s perimeter


    layout_compactness
    The area’s compactness (the Polsby–Popper score)


    layout_room_count
    The area’s share to the apartment’s room count


    layout_is_navigable
    True if the area is navigable by a wheelchair

Area Features

    dimension
    description




    layout_has_sink
    True if the area has a sink


    layout_has_shower
    True if the area has a shower


    layout_has_bathtub
    True if the area has a bathtub


    layout_has_toilet
    True if the area has a toilet


    layout_has_stairs
    True if the area has stairs


    layout_has_entrance_door
    True if the area is directly leading to an exit of the apartment

Area Windows / Doors

    dimension
    description




    layout_number_of_doors
    The number of doors directly leading to the area


    layout_number_of_windows
    The number of windows of the area


    layout_door_perimeter
    The sum of all door lengths directly leading to the area


    layout_window_perimeter
    The sum of all window lengths of the area

Area Walls / Railings

    dimension
    description




    layout_open_perimeter
    The sum of all of the areas boundaries that are neither walls nor railings


    layout_railing_perimeter
    The sum of all of the areas boundaries that are railings


    layout_mean_walllengths
    The mean length of the area’s sides


    layout_std_walllengths
    The standard deviation of the lengths of the area’s sides

Area Adjecency

    dimension
    description




    layout_connects_to_bathroom
    True if the area connects to a bathroom


    layout_connects_to_private_outdoor
    True if the area connects to an outside area that is private to the apartment

View

The views from an object help to understand the impact of the surroundings on the object. The view simulation calculates the visible amount of buildings, greenery, water etc. on each individual hexagon from the analyzed object. The values are expressed in steradians (sr) and represent the amount a certain object category occupies in the spherical field of view.

Each of the following dimension is provided using the room-wise aggregations min, max, mean, std, median, p20 and p80. For instance, the column view_greenery_p20 describes the amount of greenery that can be seen from at least 20% of the positions in the area.

    dimension
    description




    view_buildings
    The amount of visible buildings


    view_greenery
    The amount of visible greenery


    view_ground
    The amount of visible ground


    view_isovist
    The amount of visible isovist


    view_mountains_class_2
    The amount of visible mountains of UN mountain class 2


    view_mountains_class_3
    The amount of visible mountains of UN mountain class 3


    view_mountains_class_4
    The amount of visible mountains of UN mountain class 4


    view_mountains_class_5
    The amount of visible mountains of UN mountain class 5


    view_mountains_class_6
    The amount of visible mountains of UN mountain class 6


    view_railway_tracks
    The amount of visible railway_tracks


    view_site
    The amount of visible site


    view_sky
    The amount of visible sky


    view_tertiary_streets
    The amount of visible tertiary_streets


    view_secondary_streets
    The amount of visible secondary_streets


    view_primary_streets
    The amount of visible primary_streets


    view_pedestrians
    The amount of visible pedestrians


    view_highways
    The amount of visible highways


    view_water
    The amount of visible water

Sun

Sun simulations help to understand the impact of the solar radiation on the object. The outcome of the sun simulations helps to identify surfaces that have great solar potential. Sun simulations are defined by the amount of sun radiation on each individual hexagon from the analyzed object. The sun simulation not only includes direct sun but also considers scattered light. The sun simulation values are given in Kilolux (klx). Simulations are performed for the days of summer solstice, winter solstice and vernal equinox.

Each of the following dimension is provided using the room-wise aggregations min, max, mean, std, median, p20 and p80. For instance, column sun_201806211200_median describes the median amount of direct daylight received on the positions in the area.

Vernal Equinox

    dimension
    description




    sun_201803210800
    Daylight at 08:00 on 21st of March


    sun_201803211000
    Daylight at 10:00 on 21st of March


    sun_201803211200
    Daylight at 12:00 on 21st of March


    sun_201803211400
    Daylight at 14:00 on 21st of March


    sun_201803211600
    Daylight at 16:00 on 21st of March


    sun_201803211800
    Daylight at 18:00 on 21st of March

Summer Solstice

    dimension
    description




    sun_201806210600
    Daylight at 06:00 on 21st of June


    sun_201806210800
    Daylight at 08:00 on 21st of June


    sun_201806211000
    Daylight at 10:00 on 21st of June


    sun_201806211200
    Daylight at 12:00 on 21st of June


    sun_201806211400
    Daylight at 14:00 on 21st of June


    sun_201806211600
    Daylight at 16:00 on 21st of June


    sun_201806211800
    Daylight at 18:00 on 21st of June


    sun_201806212000
    Daylight at 20:00 on 21st of June

Winter Solstice

    dimension
    description




    sun_201812211000
    Daylight at 10:00 on 21st of December


    sun_201812211200
    Daylight at 12:00 on 21st of December


    sun_201812211400
    Daylight at 14:00 on 21st of December


    sun_201812211600
    Daylight at 16:00 on 21st of December

Noise / Window Noise

Noise level and the distribution of elements from an area helps to understand how an object is exposed to the acoustics of this area. The acoustic simulation calculates the noise intensity on each individual hexagon from the analyzed object considering traffic and train noise datasets. Adjacent buildings are considered as noise blocking elements. The values are expressed in dBA (decibels).

Window Noise

The noise per window of a given area is aggregated via min and max. For instance, window_noise_train_day_max represents the maximum amount of noise received on any window of the area.

    dimension
    description




    window_noise_traffic_day
    The amount of noise received on the area’s windows

This pie chart displays books per BNB id using the aggregation count. The data is filtered where the book publisher is Real Estate Education Co.. The data is about books.

Table of INEBase Buildings mainly or exclusively for housing and no. of real estate properties by municipality (with more than 2,000 inhabitants), state of the building and year of construction (aggregate). Population and Housing Censuses

Our comprehensive dataset serves as a cornerstone for real estate and financial professionals. Built on a foundation of rigorous research and wide-scale data aggregation.

Real Estate Data: Dive into an extensive database encompassing property valuations, historical sales records, regional market trends, and more. Whether you're a realtor scouting for prime properties, an investor gauging market potential, or a homeowner seeking comparable listings, our data empowers you with the insights you need to navigate the real estate landscape confidently.

Financial Data: Financial data is the lifeblood of any economy. Our dataset is designed to be a compass for those navigating these waters. With detailed records on investment behaviors, professionals can derive actionable strategies. Whether you're a bank refining your loan approval process, an investor spotting market trends, or a financial consultant advising clients, our data equips you with a nuanced understanding of financial behaviors.

Flexible Access Models: Catering to a diverse clientele, we offer multiple access models ranging from one-time data pulls to continuous subscriptions, ensuring that businesses of all sizes and needs can harness the power of our dataset.

This bar chart displays books by BNB id using the aggregation count. The data is filtered where the book publisher is Real Estate Education Co.. The data is about books.

United Arab Emirates Insurance Company: Agg: DO: Assets: Investments: LR: Real Estate: Constructed and Under Construction data was reported at 3,544,895.145 AED th in 2015. This records an increase from the previous number of 3,445,323.000 AED th for 2014. United Arab Emirates Insurance Company: Agg: DO: Assets: Investments: LR: Real Estate: Constructed and Under Construction data is updated yearly, averaging 2,388,238.000 AED th from Dec 2003 (Median) to 2015, with 13 observations. The data reached an all-time high of 4,007,344.000 AED th in 2013 and a record low of 583,470.000 AED th in 2003. United Arab Emirates Insurance Company: Agg: DO: Assets: Investments: LR: Real Estate: Constructed and Under Construction data remains active status in CEIC and is reported by Insurance Authority. The data is categorized under Global Database’s United Arab Emirates – Table AE.Z023: Insurance Company: Aggregate: Assets.

To investigate the issue of inflation-hedging to find appropriate hedging assets against inflation by using the VAR or VECM model. We have collected data encompassing housing price indices, stock indices, price indexes, and money supply from five countries: the United States, Hong Kong, South Korea, Singapore, and Taiwan. The housing price index focuses on the transaction prices of listed residential houses in the metropolitan area as the benchmark, the stock price index is the ordinary stock market index of various countries, the price index is the consumer price index (CPI), and the money supply is M2 aggregate. The time period for obtaining data on the housing price index and stock price index is not the same.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The 2017-2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Table of INEBase Buildings mainly or exclusively for housing and no. of real estate properties by municipality (with more than 2,000 inhabitants), state of the building and year of construction (aggregate). Population and Housing Censuses

Introduction This dataset contains detailed data on over 42,500 apartments (250,000 rooms) in ~3,100 buildings including their geometries, room typology as well as their visual, acoustical, topological, and daylight characteristics. Additionally, we have included location-specific characteristics for the buildings, including climatic data and points of interest within walking distance. Changelog v2.2.0 (2023-03-10): A file, location_ratings.csv, has been included to provide ratings of the locations in which the buildings are situated. The ratings, provided by Fahrländer Partner AG, give insights into the living situation at the buildings' addresses. Details for the different dimensions are provided below. v2.1.0 (2022-12-23): A file, locations.csv, has been included to provide information on the climatic and infrastructural characteristics of the locations in which each building is situated v2.0.0 (2022-10-17): Additional to the residential units, we also include the commercial and public parts (such as staircases) of the models. The field unit_usage describes whether an area belongs to a commercial, residential, janitor or public part of the building Added the fields elevation and height to geometries.csv to describe the elevation above the terrain surface and the height of objects. Added the field plan_id which allows identifying which floors are based on the same floor plan (in some cases multiple floors of a building share the same floor plan Improved the ordering of fields in the CSV files (instead of alphabetic order) Minor changes to individual sites Procurement The data is sourced from commercial clients of Archilyse AG specializing on the digitization and analysis of buildings. The existing building plans of clients are converted into a geo-referenced, semantically annotated representation and undergo a manual Q/A process to ensure the accuracy of the data and to ensure a maximum 5%-deviation in the apartments' areas (validated with a median deviation of 1.2%). Geometries The dataset contains a file geometries.csv which contains the geometries of all areas, walls, railings, columns, windows, doors and features (sinks, bathtubs, etc.) of an apartment. In total, the datasets contain the 2D geometry of ~1.5 million separators (walls, railings), ~670,000 openings (windows, doors), ca. 400,000 areas (rooms, bathrooms, kitchens, etc.), and ~290,000 features (sinks, toilets, bathtubs, etc.). Each row contains: apartment_id: The ID of the apartment (for features, areas), note: an apartment id is only unique per site site_id: The ID of the site building_id: The ID of the building floor_id: The ID of the floor plan_id: The ID of the plan on which the floor is based, multiple floors of a building might be based on the same plan unit_id: The ID of the unit in which the element is spatially contained (for features, areas) area_id: The ID of the area in which the element is spatially contained (for features) unit_usage: The usage of the unit, possible values are: RESIDENTIAL, COMMERCIAL, PUBLIC, JANITOR entity_type: The entity type (area, separator, opening, feature) entity_subtype: The entity’s sub-type (e.g. WALL) geometry: The element’s geometry as a WKT geometry in meters. The geometry is given in the site’s local coordinate system. I.e. the position between elements of the same site are correct in respect to each other. The +y direction points northwards, the +x direction points eastwards. elevation: The object's elevation above the terrain surface in meters. We assume one terrain baseline per building, thus all walls in a given floor share the same elevation value. However, windows in particular might start at different elevations and have differing heights. height: The height of the entity in meters, note: In many cases, a default height is assumed An example: column apartment_id d4438f2129b30290845ce7eef98a5ba7 site_id 127 building_id 164 plan_id 492 floor_id 861 unit_id 63777 area_id 767676 unit_usage RESIDENTIAL entity_type area entity_subtype LIVING_ROOM geometry POLYGON ((-6.1501158933490139 -4.8490786654693... elevation 0 height 2.6 Simulations Besides the geometrical model, we also provide simulation data on the visual, acoustic, solar, layout, and connectivity-related characteristics of the apartments. The file simulations.csv contains the simulation data aggregated on a per-area basis. Each row contains the identifier columns area_id, unit_id, apartment_id, floor_id, building_id, site_id as defined above as well as 367 simulation columns. Each simulation column is formatted as:

United Arab Emirates Insurance Company: Agg: Assets: Investments: Land and Real Estate (LR) data was reported at 5,233,257.511 AED th in 2015. This records an increase from the previous number of 4,942,620.000 AED th for 2014. United Arab Emirates Insurance Company: Agg: Assets: Investments: Land and Real Estate (LR) data is updated yearly, averaging 4,531,394.000 AED th from Dec 2003 (Median) to 2015, with 13 observations. The data reached an all-time high of 5,914,566.000 AED th in 2013 and a record low of 1,005,788.000 AED th in 2003. United Arab Emirates Insurance Company: Agg: Assets: Investments: Land and Real Estate (LR) data remains active status in CEIC and is reported by Insurance Authority. The data is categorized under Global Database’s United Arab Emirates – Table AE.Z023: Insurance Company: Aggregate: Assets.