Dynamic RE10K Dataset - Split Archives

This directory contains the Dynamic RE10K dataset split into multiple archives for easier download and upload.

  📊 Dataset Overview

Dataset: Dynamic RE10K (Real Estate 10K with dynamic scenes) Total Scenes: ~8,000 clips Train/Test Split: Included Format: PNG images + JSON metadata + MP4 videos Archive Format: tar.gz (gzip compressed) Chunk Size: ~2 GB per archive

  📦 Files in This Directory

dynamic_re10k_part001.tar.gz… See the full description on the dataset page: https://huggingface.co/datasets/uva-cv-lab/dynamic-real-estate-10k.

yangtaointernship/RealEstate10K-subset dataset hosted on Hugging Face and contributed by the HF Datasets community

RealEstate10K is a large dataset of camera poses corresponding to 10 million frames derived from about 80,000 video clips, gathered from about 10,000 YouTube videos. For each clip, the poses form a trajectory where each pose specifies the camera position and orientation along the trajectory. These poses are derived by running SLAM and bundle adjustment algorithms on a large set of videos.

reach-vb/mls-eng-10k-repunct-test-v2 dataset hosted on Hugging Face and contributed by the HF Datasets community

10,000 real estate ads in the US with metadata.

Collected on 27/Jun/2021

Original Data

Acknowlegement

Foto von Dhruv Mehra auf Unsplash

The net cash of Anywhere Real Estate Inc. with headquarters in the United States amounted to *** million U.S. dollars in 2024. The reported fiscal year ends on December 31.Compared to the earliest depicted value from 2020 this is a total decrease by approximately *** million U.S. dollars. The trend from 2020 to 2024 shows, however, that this decrease did not happen continuously.

Main Dataset

I scrapped data from 99acres using their (kind of) hidden API. I scrapped almost 10,000+ data using my scrapper app see here.

DESCRIPTION OF THE DATA

• Contains details of properties of Gurgaon, Hyderabad, Mumbai, Kolkata cities of India.
• All datasets of different cities contains almost 10K properties.
• In some datasets, some columns are not available. Sorry!!
• Target column: PRICE

Data Usage

This dataset can be used for various real estate-related tasks, including:

• Property price prediction.
• Market analysis to identify trends and patterns.
• Identifying popular property types and locations.
• Evaluating the impact of property attributes on price.

EXPLANATION OF EACH COLUMNS

NOTE: Not all the columns are important for you so first try to understand your problem statement and then filter this dataset accordingly.

• AGE: The age of the property in years.
• ALT_TAG: An alternative tag or description.
• AMENITIES: Describes the amenities available with the property.
• AREA: The area of the property.
• BALCONY_NUM: The number of balconies in the property.
• BATHROOM_NUM: The number of bathrooms in the property.
• BEDROOM_NUM: The number of bedrooms in the property.
• BROKERAGE: Information about the brokerage or agency associated with the property listing.
• BUILDING_ID: An integer identifier for the building.
• BUILDING_NAME: The name of the building.
• BUILTUP_SQFT: The total built-up area of the property in square feet.
• CARPET_SQFT: The total carpet area of the property in square feet.
• CITY_ID: An identifier for the city in which the property is located.
• CITY: The city where the property is located.
• CLASS_HEADING: A heading for the property class.
• CLASS_LABEL: A label representing the property class.
• CLASS: A classification label for the property.
• COMMON_FURNISHING_ATTRIBUTES: Attributes related to the furnishings and amenities commonly found in the property.
• CONTACT_COMPANY_NAME: The name of the company or agency responsible for the property listing.
• CONTACT_NAME: The name of the contact person associated with the property listing.
• DEALER_PHOTO_URL: URL to a photo or image associated with the property dealer.
• DESCRIPTION: A description of the property listing.
• EXPIRY_DATE: The date when the listing expires.
• FACING: Indicates the direction the property is facing.
• FEATURES: Describes the features of the property.
• FLOOR_NUM: The floor number of the property.
• FORMATTED_LANDMARK_DETAILS: Details of nearby landmarks.
• FORMATTED: Formatted information related to the property.
• FSL_Data: Data related to the property, possibly specific to a particular real estate agency.
• FURNISH: Indicates whether the property is furnished.
• FURNISHING_ATTRIBUTES: Attributes describing the level of furnishing in the property.
• GROUP_NAME: The name of the group or organization to which the property may belong.
• LISTING: Information about the property listing, possibly including its status and other details.
• LOCALITY_WO_CITY: The locality name without the city information.
• LOCALITY: The specific locality or neighborhood where the property is situated.
• location: Additional location information.
• MAP_DETAILS: Contains latitude and longitude information.
• MAX_AREA_SQFT: The maximum area of the property in square feet.
• MAX_PRICE: The maximum price of the property.
• MEDIUM_PHOTO_URL: URL to a medium-sized photo or image of the property.
• metadata: Additional metadata or information about the dataset.
• MIN_AREA_SQFT: The minimum area of the property in square feet.
• MIN_PRICE: The minimum price of the property.
• OWNTYPE: An integer representing the ownership type.
• PD_URL: URL to additional property details.
• PHOTO_URL: URL to photos or images associated with the property.
• POSTING_DATE: The date when the property listing was posted.
• PREFERENCE: Indicates the preference type for the property listing (e.g., "S" for sale).
• PRICE_PER_UNIT_AREA: The price per unit area of the property.
• PRICE_SQFT: The price per square foot of the property.
• PRICE: The price of the property. This is target column for ML.
• PRIMARY_TAGS: Primary tags or labels.
• PRODUCT_TYPE: The type of product listing.
• profile: Profile information related to the property or listing.
• PROJ_ID: An integer identifier for the project.
• PROP_DETAILS_URL: URL to detailed property information.
• PROP_HEADING: A heading or title for the property.
• PROP_ID: A ...

Source

This dataset contains physical features for approximately ten-thousand residential parcels in Maricopa County that were collected via their API during September of 2021. Approximately half of the data contains Sales Prices and Sales Dates. You can find the MCA API Official Documentation here [PDF], and my Python API wrapper here. It can be installed in Python3 with pip3 install mcaapi.

Disclaimers

From MCA: The Assessor does not guarantee that any information provided on this website is accurate, complete, or current. In many instances, the Assessor has gathered information from independent sources and made it available on this site, and the original information may have contained errors and omissions. Errors and omissions may also have occurred in the process of gathering, interpreting, and reporting the information. Information on the website is not updated in "real time".

My Disclaimer: This data was collected using sources of public record, which may contain some sensitive identity information. Despite any sensitive information being publicly available, I have done my best to scrub those details from the set. If you notice any details that were missed, please email me at help@foxbatcs.com with a subject of "Sensitive Details Notice MCA" with a description of where the sensitive information is at in the set. I will do my best to remove it in a timely manner.

Additionally, I do not recommend using this dataset for any commercial purposes. The data was not randomly sampled, and may produce skewed models/results. This data is intended for educational purposes only, and I cannot guarantee the recency, accuracy, reliability, or liability of this information. If you would like legally certified data, please contact Maricopa County Assessor Data Sales. Thank you.

Acknowledgements

https://www.mcassessor.maricopa.gov/

The total assets of Anywhere Real Estate Inc. with headquarters in the United States amounted to **** billion U.S. dollars in 2024. The reported fiscal year ends on December 31.Compared to the earliest depicted value from 2020 this is a total decrease by approximately **** billion U.S. dollars. The trend from 2020 to 2024 shows, however, that this decrease did not happen continuously.

Explore the historical Whois records related to marfa-fortdavis-alpine-marathon-terlingua-presidio-bigbend.realestate (Domain). Get insights into ownership history and changes over time.

Annual Statistical Report on Land Administration Practitioner's Business Opening and Change (Shimen District) for the Year 105

Thailand Real Estate Activities: 7,501 - 10,000 Baht data was reported at 41.010 Person th in Mar 2018. This records an increase from the previous number of 37.750 Person th for Dec 2017. Thailand Real Estate Activities: 7,501 - 10,000 Baht data is updated quarterly, averaging 40.730 Person th from Mar 2014 (Median) to Mar 2018, with 17 observations. The data reached an all-time high of 56.610 Person th in Jun 2017 and a record low of 27.940 Person th in Jun 2014. Thailand Real Estate Activities: 7,501 - 10,000 Baht data remains active status in CEIC and is reported by National Statistical Office. The data is categorized under Global Database’s Thailand – Table TH.G014: Employee By Industry, Income Class, Whole Kingdom.

The revenue of Real Estate Investors with headquarters in the United Kingdom amounted to ************** British pounds in 2023. The reported fiscal year ends on December 31.Compared to 2019, this marks a decrease of approximately ************* British pounds. The trend from 2019 to 2023 shows, furthermore, that this decrease happened continuously.

The total assets of GREE REAL ESTATE with headquarters in China amounted to ***** billion yuan in 2023. The reported fiscal year ends on December 31.Compared to the earliest depicted value from 2020 this is a total decrease by approximately ***** billion yuan. The trend from 2020 to 2023 shows, furthermore, that this decrease happened continuously.

The net income of Alexandria Real Estate Equities, Inc. with headquarters in the United States amounted to ****** million U.S. dollars in 2024. The reported fiscal year ends on December 31.Compared to the earliest depicted value from 2020 this is a total decrease by approximately ****** million U.S. dollars. The trend from 2020 to 2024 shows, however, that this decrease did not happen continuously.

The revenue of Vesta Real Estate with headquarters in Mexico amounted to ****** million U.S. dollars in 2023. The reported fiscal year ends on December 31.Compared to the earliest depicted value from 2019 this is a total increase by approximately ***** million U.S. dollars. The trend from 2019 to 2023 shows, furthermore, that this increase happened continuously.

The net cash of Vesta Real Estate with headquarters in Mexico amounted to ***** million U.S. dollars in 2023. The reported fiscal year ends on December 31.Compared to the earliest depicted value from 2019 this is a total increase by approximately ***** million U.S. dollars. The trend from 2019 to 2023 shows, however, that this increase did not happen continuously.

The net cash of GREE REAL ESTATE with headquarters in China amounted to * billion yuan in 2023. The reported fiscal year ends on December 31.Compared to the earliest depicted value from 2020 this is a total increase by approximately **** billion yuan. The trend from 2020 to 2023 shows, however, that this increase did not happen continuously.

The total equity of Alexandria Real Estate Equities, Inc. with headquarters in the United States amounted to ***** billion U.S. dollars in 2024. The reported fiscal year ends on December 31.Compared to the earliest depicted value from 2020 this is a total increase by approximately **** billion U.S. dollars. The trend from 2020 to 2024 shows, however, that this increase did not happen continuously.

The net income of Anywhere Real Estate Inc. with headquarters in the United States amounted to **** million U.S. dollars in 2024. The reported fiscal year ends on December 31.Compared to the earliest depicted value from 2020 this is a total increase by approximately *** million U.S. dollars. The trend from 2020 to 2024 shows, however, that this increase did not happen continuously.