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.

This dataset contains expert-labeled telemetry anomaly data from the Mars Science Laboratory (MSL) rover, Curiosity.

Real spacecraft and curiosity rover anomalies for anomaly detection Indications of telemetry anomalies can be found within previously mentioned ISA reports. All telemetry channels discussed in an individual ISA were reviewed to ensure that the anomaly was evident in the associated telemetry data, and specific anomalous time ranges were manually labeled for each channel. If multiple anomalous sequences and channels closely resembled each other, only one was kept for the experiment in order to create a diverse and balanced set. Anomalies were classified into two categories, point and contextual, to distinguish between anomalies that would likely be identified by properly set alarms or distance-based methods that ignore temporal information (point anomalies) and those that require more complex methodologies such as LSTMs or Hierarchical Temporal Memory (HTM) approaches to detect (contextual anomalies)

MSL: TM Channels (27) Total TM values (66,709) Total anomalies (36)

Unlock the Potential of U.S. National MLS Real Estate Data

Discover the wealth of information encapsulated in licensing bulk MLS (Multiple Listing Service) data, a cornerstone of the real estate realm. From property particulars to market trends, delve into the significance and multifaceted utility of MLS data across diverse industries.

MLS Real Estate Data includes:

• Property Information: Address, size, layout, condition, amenities, and more.
• Price History: Historical price changes, listing dates, and sales dates.
• Geographic Insights: Location, neighborhood information, school districts, and proximity to amenities.
• Property Photos: MLS images of properties (see the condition of a property inside and out.)
• Agent/Broker Information: Certain details about the listing agent or broker as well as their notes on properties.
• Market Dynamics: Data on local real estate market conditions, including inventory levels, price trends, and days on the market.

This data set contains the EDR data from the MSL Radiation Assessment Detector (RAD) instrument. The EDR data are raw data reconstructed from telemetry data products.

This data set contains the RDR data from the MSL Radiation Assessment Detector (RAD) instrument. The RDR data are calibrated data reconstructed from EDR data.

Please note that the file msl-labeled-data-set-v2.1.zip below contains the latest images and labels associated with this data set.

Data Set Description

The data set consists of 6,820 images that were collected by the Mars Science Laboratory (MSL) Curiosity Rover by three instruments: (1) the Mast Camera (Mastcam) Left Eye; (2) the Mast Camera Right Eye; (3) the Mars Hand Lens Imager (MAHLI). With the help from Dr. Raymond Francis, a member of the MSL operations team, we identified 19 classes with science and engineering interests (see the "Classes" section for more information), and each image is assigned with 1 class label. We split the data set into training, validation, and test sets in order to train and evaluate machine learning algorithms. The training set contains 5,920 images (including augmented images; see the "Image Augmentation" section for more information); the validation set contains 300 images; the test set contains 600 images. The training set images were randomly sampled from sol (Martian day) range 1 - 948; validation set images were randomly sampled from sol range 949 - 1920; test set images were randomly sampled from sol range 1921 - 2224. All images are resized to 227 x 227 pixels without preserving the original height/width aspect ratio.

Directory Contents

• images - contains all 6,820 images
• class_map.csv - string-integer class mappings
• train-set-v2.1.txt - label file for the training set
• val-set-v2.1.txt - label file for the validation set
• test-set-v2.1.txt - label file for the test set

The label files are formatted as below:

"Image-file-name class_in_integer_representation"

Labeling Process

Each image was labeled with help from three different volunteers (see Contributor list). The final labels are determined using the following processes:

• If all three labels agree with each other, then use the label as the final label.
• If the three labels do not agree with each other, then we manually review the labels and decide the final label.
• We also performed error analysis to correct labels as a post-processing step in order to remove noisy/incorrect labels in the data set.

Classes

There are 19 classes identified in this data set. In order to simplify our training and evaluation algorithms, we mapped the class names from string to integer representations. The names of classes, string-integer mappings, distributions are shown below:

Class name, counts (training set), counts (validation set), counts (test set), integer representation

Arm cover, 10, 1, 4, 0

Other rover part, 190, 11, 10, 1

Artifact, 680, 62, 132, 2

Nearby surface, 1554, 74, 187, 3

Close-up rock, 1422, 50, 84, 4

DRT, 8, 4, 6, 5

DRT spot, 214, 1, 7, 6

Distant landscape, 342, 14, 34, 7

Drill hole, 252, 5, 12, 8

Night sky, 40, 3, 4, 9

Float, 190, 5, 1, 10

Layers, 182, 21, 17, 11

Light-toned veins, 42, 4, 27, 12

Mastcam cal target, 122, 12, 29, 13

Sand, 228, 19, 16, 14

Sun, 182, 5, 19, 15

Wheel, 212, 5, 5, 16

Wheel joint, 62, 1, 5, 17

Wheel tracks, 26, 3, 1, 18

Image Augmentation

Only the training set contains augmented images. 3,920 of the 5,920 images in the training set are augmented versions of the remaining 2000 original training images. Images taken by different instruments were augmented differently. As shown below, we employed 5 different methods to augment images. Images taken by the Mastcam left and right eye cameras were augmented using a horizontal flipping method, and images taken by the MAHLI camera were augmented using all 5 methods. Note that one can filter based on the file names listed in the train-set.txt file to obtain a set of non-augmented images.

• 90 degrees clockwise rotation (file name ends with -r90.jpg)
• 180 degrees clockwise rotation (file name ends with -r180.jpg)
• 270 degrees clockwise rotation (file name ends with -r270.jpg)
• Horizontal flip (file name ends with -fh.jpg)
• Vertical flip (file name ends with -fv.jpg)

Acknowledgment

The authors would like to thank the volunteers (as in the Contributor list) who provided annotations for this data set. We would also like to thank the PDS Imaging Note for the continuous support of this work.

Unlock access to all available property types, from multi-family and rental to land, commercial listings and more - powering your business with richer data and the ability to uncover new insights.

Key features: • New Property Types: Access all available property types within an MLS, covering residential, multi-family, land, commercial, rental, farm and more. • MLS Coverage: Available for select MLSs now, with additional markets becoming available throughout 2025 • Expanded Schema: New fields to support non-residential data, so you can dive deeper into insights • Fast & Fresh: Updated daily with data sourced directly from MLSs

The sample data covers one listing in JSON format. For access to a broader set of sample listings (10,000+), reach out to the REdistribute sales contact.

ABOUT REDISTRIBUTE

REdistribute aims to modernize real estate data accessibility, fostering innovation and transparency through direct access to the most reliable MLS data. Our commitment to data integrity and direct MLS involvement guarantees the freshest, most accurate insights, empowering businesses across industries to drive innovation and make informed decisions.

The MSL ChemCam SOH EDR data set consists of all raw state of health data collected by the ChemCam instrument on the Mars Science Laboratory rover.

Multiple Listing Service (MLS) Listing Software Market Outlook

The global Multiple Listing Service (MLS) Listing Software Market is projected to witness a robust growth rate, with a forecasted CAGR of 8.5% from 2024 to 2032. This growth is driven by the increasing digitization of the real estate industry and the rising demand for efficient property management solutions.

A significant growth factor for the MLS Listing Software market is the burgeoning adoption of cloud-based solutions. Cloud computing offers unparalleled benefits such as scalability, cost-effectiveness, and accessibility, making it an attractive option for real estate professionals. Furthermore, cloud technology allows for seamless updates and integration, which is crucial for maintaining an up-to-date MLS database. The shift towards cloud-based MLS solutions is also driven by the need for remote accessibility, enabling agents and brokers to access and manage listings from anywhere, thus enhancing operational efficiency.

Another catalyst for growth in this market is the increasing emphasis on data analytics and customer relationship management (CRM). Modern MLS listing software often includes advanced analytics tools that can provide insights into market trends, customer preferences, and property performance. This data-driven approach allows real estate professionals to make informed decisions, optimize marketing strategies, and enhance customer engagement. The integration of CRM functionalities within MLS software also enables more effective client management, fostering stronger relationships and improving service delivery.

The rising demand for comprehensive marketing tools within MLS listing software further propels market growth. Features such as automated listing syndication, virtual tours, and social media integration are becoming standard in MLS platforms. These tools help real estate professionals to effectively market properties, reach a broader audience, and generate leads. As the competition in the real estate market intensifies, the need for sophisticated marketing capabilities within MLS software becomes more pronounced, driving the adoption of such solutions.

Real Estate CMA Software plays a pivotal role in the real estate industry by providing comparative market analysis tools that help agents and brokers evaluate property values accurately. This software enables professionals to analyze market trends, assess property conditions, and compare similar properties within a specific area, thereby facilitating informed pricing strategies. By leveraging Real Estate CMA Software, agents can offer clients detailed insights into market dynamics, ensuring competitive pricing and enhancing their decision-making process. As the real estate market becomes increasingly data-driven, the integration of CMA software into MLS platforms is becoming essential for professionals seeking to maintain a competitive edge.

Regionally, North America remains a dominant player in the MLS listing software market, attributed to the high adoption rate of advanced technology solutions and the presence of major market players. The region's robust real estate sector and the growing trend of digital transformation contribute significantly to market growth. Additionally, the Asia Pacific region is expected to witness substantial growth, driven by the rapid urbanization, increasing internet penetration, and rising awareness about the benefits of MLS software. Emerging economies in Latin America and the Middle East & Africa are also gradually adopting MLS software, albeit at a slower pace, due to infrastructural and economic challenges.

Deployment Type Analysis

The deployment type segment of the MLS Listing Software market is categorized into Cloud-Based and On-Premises solutions. Cloud-based deployment has gained significant traction in recent years due to its numerous advantages over traditional on-premises systems. Cloud-based MLS software offers greater flexibility, allowing real estate professionals to access data and manage listings from any location with internet connectivity. This accessibility is particularly beneficial in the current landscape where remote work is becoming more commonplace. Moreover, cloud solutions often come with lower upfront costs and reduced IT infrastructure requirements, making them an appealing option for small and medium-sized enterprises (SMEs).

On the other hand, on-premises MLS software remains relevant, es

Unlock access to premium residential listing data, with a comprehensive set of fields designed to empower your business with deeper insights and the most up-to-date information.

Key features: • Residential Data Type: Access to high-quality residential data to enhance your business analysis • Comprehensive Fields: A wide array of fields (800+) that offer a thorough view of residential property data • Fast & Fresh: Updated daily with data sourced directly from MLSs

The sample data covers one listing in JSON format. For access to a broader set of sample listings (10,000+), reach out to the REdistribute sales contact.

ABOUT REDISTRIBUTE

REdistribute aims to modernize real estate data accessibility, fostering innovation and transparency through direct access to the most reliable MLS data. Our commitment to data integrity and direct MLS involvement guarantees the freshest, most accurate insights, empowering businesses across industries to drive innovation and make informed decisions.

The CheMin instrument determines the mineralogy and elemental composition of powdered samples through the combined application of X-ray diffraction (XRD, producing mineral identification and quantification) and X-ray fluorescence (chemical analysis based on Energy Dispersive Histograms, EDH). This CheMin RDR data set contains mineral identification, abundance, and estimated errors derived from CheMin observations.

MSL

## Overview

MSL is a dataset for object detection tasks - it contains Tissue annotations for 243 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

U.S. National MLS (Multiple Listing Service) Property Listings Data provides comprehensive and up-to-date information about properties available for sale or rent. MLS Property Data is rich with pricing and property details and encompasses a wide range of information.

As of June 2024, the most popular open-source database management system (DBMS) in the world was MySQL, with a ranking score of 1061. Oracle was the most popular commercial DBMS at that time, with a ranking score of 1244.

Abstract

A multiple listing service (MLS) is an exchange where real estate brokers share information about properties they are selling. Other real estate brokers review the listings, and are compensated if they can identify a buyer for a property. Multiple listing services promote cooperation and mutual benefit for real estate brokers representing buyers and sellers. The CoreLogic Multiple Listing Service data contains listings from 135 real estate boards utilizing CoreLogic’s multiple listing service software. The data was produced in August 2024.

Methodology

The data consists of listings from 135 real estate boards that use CoreLogic listing software. The data DOES NOT cover listings from all real estate boards in the United States. The National Association of Realtors maintains the most complete and up-to-date list of real estate boards; however, this information is only available to members of the National Association of Realtors.

For more information about how the data was prepared for Redivis, please see CoreLogic 2024 GitLab.

Usage

Quick Search (QS) contains the most recent listing data (as of August 2024). In order to see the entire listing history of a property/record, you will need to search the Quick History (QH) table on the SysPropertyID, which is a unique key for a listing across multiple listing boards. You can use the variable FA_PostDate to see when updates occurred. Updates include name changes, price changes, etc.

During upload to Data Farm, a small number of invalid records were dropped from the Quick History (QH) table. For more information, see CoreLogic 2024 GitLab. To access the complete data (including invalid records), please see Bulk Data Access instructions, below.

Bulk Data Access

Data access is required to view this section.

Unprocessed experiment data from the CheMin instrument aboard the Mars Science Laboratory rover.

Customs records of are available for MSL DO BRASIL AGENC E. TRANSPLTDA. Learn about its Importer, supply capabilities and the countries to which it supplies goods

南黄海 MSS 值 (118°42' – 120°47', 34°32' – 36°28')

🇺🇸 미국

The CheMin instrument determines the mineralogy and elemental composition of powdered samples through the combined application of X-ray diffraction (XRD, producing mineral identification and quantification) and X-ray fluorescence (chemical analysis based on Energy Dispersive Histograms, EDH). This CheMin RDR data set contains tables of energy and X-ray diffraction data derived from raw CheMin observations.