The Easiest Way to Collect Data from the Internet Download anything you see on the internet into spreadsheets within a few clicks using our ready-made web crawlers or a few lines of code using our APIs

We have made it as simple as possible to collect data from websites

Easy to Use Crawlers Amazon Product Details and Pricing Scraper Amazon Product Details and Pricing Scraper Get product information, pricing, FBA, best seller rank, and much more from Amazon.

Google Maps Search Results Google Maps Search Results Get details like place name, phone number, address, website, ratings, and open hours from Google Maps or Google Places search results.

Twitter Scraper Twitter Scraper Get tweets, Twitter handle, content, number of replies, number of retweets, and more. All you need to provide is a URL to a profile, hashtag, or an advance search URL from Twitter.

Amazon Product Reviews and Ratings Amazon Product Reviews and Ratings Get customer reviews for any product on Amazon and get details like product name, brand, reviews and ratings, and more from Amazon.

Google Reviews Scraper Google Reviews Scraper Scrape Google reviews and get details like business or location name, address, review, ratings, and more for business and places.

Walmart Product Details & Pricing Walmart Product Details & Pricing Get the product name, pricing, number of ratings, reviews, product images, URL other product-related data from Walmart.

Amazon Search Results Scraper Amazon Search Results Scraper Get product search rank, pricing, availability, best seller rank, and much more from Amazon.

Amazon Best Sellers Amazon Best Sellers Get the bestseller rank, product name, pricing, number of ratings, rating, product images, and more from any Amazon Bestseller List.

Google Search Scraper Google Search Scraper Scrape Google search results and get details like search rank, paid and organic results, knowledge graph, related search results, and more.

Walmart Product Reviews & Ratings Walmart Product Reviews & Ratings Get customer reviews for any product on Walmart.com and get details like product name, brand, reviews, and ratings.

Scrape Emails and Contact Details Scrape Emails and Contact Details Get emails, addresses, contact numbers, social media links from any website.

Walmart Search Results Scraper Walmart Search Results Scraper Get Product details such as pricing, availability, reviews, ratings, and more from Walmart search results and categories.

Glassdoor Job Listings Glassdoor Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Glassdoor.

Indeed Job Listings Indeed Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Indeed.

LinkedIn Jobs Scraper Premium LinkedIn Jobs Scraper Scrape job listings on LinkedIn and extract job details such as job title, job description, location, company name, number of reviews, and more.

Redfin Scraper Premium Redfin Scraper Scrape real estate listings from Redfin. Extract property details such as address, price, mortgage, redfin estimate, broker name and more.

Yelp Business Details Scraper Yelp Business Details Scraper Scrape business details from Yelp such as phone number, address, website, and more from Yelp search and business details page.

Zillow Scraper Premium Zillow Scraper Scrape real estate listings from Zillow. Extract property details such as address, price, Broker, broker name and more.

Amazon product offers and third party sellers Amazon product offers and third party sellers Get product pricing, delivery details, FBA, seller details, and much more from the Amazon offer listing page.

Realtor Scraper Premium Realtor Scraper Scrape real estate listings from Realtor.com. Extract property details such as Address, Price, Area, Broker and more.

Target Product Details & Pricing Target Product Details & Pricing Get product details from search results and category pages such as pricing, availability, rating, reviews, and 20+ data points from Target.

Trulia Scraper Premium Trulia Scraper Scrape real estate listings from Trulia. Extract property details such as Address, Price, Area, Mortgage and more.

Amazon Customer FAQs Amazon Customer FAQs Get FAQs for any product on Amazon and get details like the question, answer, answered user name, and more.

Yellow Pages Scraper Yellow Pages Scraper Get details like business name, phone number, address, website, ratings, and more from Yellow Pages search results.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This text file contains the Central Index Key (CIK) numbers, company names, SEC reporting file numbers, and addresses (business addresses are provided when mailing addresses are not available) of active broker-dealers who are registered with the SEC.

Real Estate Agents And Brokers Directory Utah

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This industry involves the publishing of lists of facts and information like databases that are protected in their form, but not in their content. These lists can be published in printed or electronic form.

This data contains active Real Estate Salesperson and Broker Licenses from New York State Department of State (DOS). Each line will be either an individual or business licensee which holds business address and license number information. If the license type is an individual, the business name that the individual works for will be listed.

The Data Monetization Market size was valued at USD 2.99 USD Billion in 2023 and is projected to reach USD 8.76 USD Billion by 2032, exhibiting a CAGR of 16.6 % during the forecast period. Data Monetization refers to the process of using data to obtain quantifiable economic benefit. Internal or indirect methods include using data to make measurable business performance improvements and inform decisions. External or direct methods include data sharing to gain beneficial terms or conditions from business partners, information bartering, selling data outright (via a data broker or independently), or offering information products and services (for example, including information as a value-added component of an existing offering). Data can be sold in its raw form or in a form that already incorporates insights and analysis. A simple example of direct data monetization is contact lists of potential business prospects that influence buyers’ businesses or trades. Indirect or internal data monetization involves making measurable business performance improvements and informed decisions using data. Recent developments include: October 2023 – Narrative I/O collaborated with Snowflake to display data within the Trade Desk through the Snowflake Marketplace. It helps create effective, data-driven digital media campaigns. This initiative by Narrative and Snowflake will provide joint customers with a cost-effective solution for activating and distributing audiences from the Snowflake Marketplace., January 2023 – Inbounds.com completed the acquisition of Data Prosper, which provides data list management services, data prosper, data brokerage, and data monetization, among others. This acquisition resulted in enhancements to inbound data analytics and improvement in the client’s return on investments to boost business growth., December 2022 – Wipro launched Wipro Data Intelligence Suite, which is a solution that provides advanced functionalities concerning monetization and cloud modernization. With the additional cloud support, the company aimed to boost digital transformation and enable business growth., March 2022 – Silverback United Inc. completed the acquisition of Darwin Data Capital Inc., which was a provider of data-backed asset solutions and products with major end-users of large industries. This acquisition resulted in a geographical expansion in the U.S., July 2021 – Axia collaborated with UDBI to monetize data and enhance business growth. This collaboration resulted in internal and external monetization of data to improve the consumer experience by implementing personalized marketing and enhancing the consumer experience.. Key drivers for this market are: Increasing Practice of Data Generation and Data Collection within Businesses to Drive the Market Growth. Potential restraints include: Fewer Skilled Workforce and Talent Management Issues to Limit the Market Growth. Notable trends are: Increasing Demand for Private Data and Analytics by Emerging Industries to Fuel Market Growth.

This industry involves the publishing of lists of facts and information like databases that are protected in their form, but not in their content. These lists can be published in printed or electronic form.

Provide a list of legitimate real estate brokers in Taoyuan City

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.

Canada remains deeply committed to supporting Ukraine’s sovereignty and territorial integrity. Canada remains concerned by the Government of Russia’s ongoing aggression, destabilizing activities and escalation. Accordingly, effective immediately, Canada will stop the issuance of new permits for the export and brokering of controlled goods and technology to Russia.

We supply below fields for each record:

Direct Email Address First Name Last Name Job Level Job Title Job Function Job Department Company Name Major Division Description SIC 2 Code SIC 2 Description SIC 4 Code SIC 4 Description BYD Industry Postal Address City State Zip Code Employee Revenue Area Code County Country Founded Type Phone Number Fax Number Website

All emails are direct, no generic emails. All emails are from corporate domains, no free email providers.

This industry involves the publishing of lists of facts and information like databases that are protected in their form, but not in their content. These lists can be published in printed or electronic form.

NMLS Loan Originator Mortgage Data provides strong intelligence to inform your next move. Access Loan Originator NMLS IDs, Loan Originator Names, Loan Origination Company NMLS IDs, Loan Origination Company Names, Mortgage Brokers NMLS IDs, and Mortgage Broker Names.

Browse LSEG's Aftermarket Research, discover our range of data, indices & benchmarks. Our Data Catalogue offers unrivalled data and delivery mechanisms.

BatchData's property listings data provides comprehensive insights with over 140 data points and nationwide listing data inclusive of For Sale By Owner (FSBO) listings across the United States. Updated daily in most markets, the data includes:

• Listing Details: property listings descriptions, property characteristics, pricing, days on market, and more.
• Agent Information: agent names, license numbers, contact details, listing counts, and listing histories.
• Broker Information: Broker names, locations, URLs, emails, phone numbers, and licensing information.
• Additional Details: Information about schools, neighborhoods, subdivisions, and tax data.

Common Use Cases: - Recruiting Teams: Enhance talent acquisition by analyzing agents' listing counts, close rates, property types, and client profiles. - Proptech Software & Marketplaces: Integrate current and historical listings to create detailed property profiles, advanced search features, and robust analytics. - Home Service Providers: Target marketing and outreach efforts to homeowners, whether they are preparing to move or have recently relocated. - Real Estate Agents & Investors: Identify undervalued properties, connect with buyers/sellers based on activity, analyze market trends, and develop effective marketing strategies.

Our property listings data can be delivered in a variety of formats to suit your needs. Choose from API integration for seamless, real-time data access, bulk data delivery for extensive datasets, S3 bucket storage for scalable cloud solutions, and more. This flexibility ensures that you can incorporate our comprehensive property information into your systems efficiently and effectively, whether you're building a new platform, enhancing existing tools, or conducting in-depth analyses.

Real Estate Brokerage Software Market size was valued at USD 21.3 Billion in 2024 and is projected to reach USD 44.5 Billion by 2031, growing at a CAGR of 9.55% during the forecasted period 2024 to 2031

Global Real Estate Brokerage Software Market Driver

Growing Adoption of Digital Solutions: To improve customer satisfaction, increase efficiency, and streamline operations, real estate brokerages are embracing digital technologies more and more. With the use of technologies like customer relationship management (CRM), transaction management, marketing automation, and property listing management, real estate brokerages can digitize their workflows and operations.

Growing Complexity of Real Estate Transactions: Real estate transactions are becoming more and more complicated since they include a number of parties, intricate paperwork, and regulatory compliance. With the use of real estate brokerage software, brokers and agents may manage listings, agreements, contracts, and financial transactions more easily and with less administrative work.

Growing Need for Client Relationship Management: To draw in and keep clients, real estate brokerages must establish and nurture excellent client relationships. CRM features for keeping client contacts, tracking interactions, setting up appointments, and sending tailored emails are all included in real estate brokerage software. These features assist brokers in nurturing leads and offering clients individualized service.

Focus on Lead Generation and Marketing: Real estate brokerages rely heavily on marketing to draw in buyers, sellers, and investors. With the use of capabilities for marketing properties, listing advertisements, virtual tours, and lead generation via digital channels including websites, social media, and email campaigns, real estate brokerage software enables brokers to reach a larger audience and create more business opportunities.

Transaction Management Solutions Are Needed: In order to manage a real estate transaction, several parties must coordinate their efforts, including buyers, sellers, agents, lenders, and lawyers. With the help of transaction management features found in real estate brokerage software, brokers may effectively manage deals and reduce risks by keeping track of deadlines, organizing papers, promoting communication, and guaranteeing compliance with legal and regulatory standards.

Demand for Business Intelligence and Data Analytics: In the real estate sector, data-driven decision-making is becoming more and more crucial. With the analytics and reporting features that real estate brokerage software provides, brokers may make well-informed business decisions and obtain a competitive edge by tracking key performance indicators (KPIs), keeping an eye on market trends, analyzing client preferences, and optimizing marketing campaigns.

Trend toward Remote Work and Collaboration: Cloud-based real estate brokerage software is becoming more widely used as a result of the growth of remote work and online collaboration. Cloud-based solutions facilitate team collaboration and offer flexibility in handling transactions remotely by enabling brokers and agents to view property listings, documents, and client information from any place with internet connectivity.

Integration of modern Technologies: To improve the functionality and performance of their platforms, real estate brokerage software providers are integrating modern technologies including augmented reality (AR), virtual reality (VR), machine learning (ML), and artificial intelligence (AI). Features like virtual staging, 3D property tours, predictive analytics, and property appraisal are made possible by these technologies, which improve user experience and increase demand.

Regulatory Compliance and Risk Management: Legal commitments, compliance standards, and regulatory regulations all apply to real estate transactions. With the provision of audit trails, electronic signatures, and secure document storage, real estate brokerage software assists brokerages in managing paperwork, ensuring regulatory compliance, and reducing risks related to real estate transactions.

Market development and Globalization: The need for scalable and adaptable real estate brokerage software solutions is driven by the development of real estate brokerages into new geographic areas and the globalization of real estate markets. Software vendors respond to the varied needs of global customers and marketplaces by providing multi-language support, multi-currency capabilities, and localization tools.

Studies of electoral clientelism—the contingent exchange of material benefits for electoral support—frequently presume the presence of strong parties. Parties facilitate monitoring and enforcement of vote-buying and allow brokers to identify core voters for turnout-buying. Where money fuels campaigns but elections center around candidates, not parties, how do candidates pitch electoral handouts? We analyze candidates’ distribution of cash during an Indonesian election. Drawing upon varied data, including surveys of voters and brokers, candidates’ cash-distribution lists, and focus-group discussions, we find heavy spending, but little evidence of vote-buying or turnout-buying. Instead, candidates buy brokers more than voters: with little loyalty or party brand to draw on, candidates seek to establish credibility with well-networked brokers, who then protect their turf with token payments for their own presumed bloc of voters. Consistent with our argument that these are non-contingent payments, we find little evidence of monitoring of either voter or broker behavior.

A data set shows a list of basis and demands to suspend the broker’s license at the company’s request, and its issuance date.