Success.ai’s Commercial Real Estate Data and B2B Contact Data for Global Real Estate Professionals is a comprehensive dataset designed to connect businesses with industry leaders in real estate worldwide. With over 170M verified profiles, including work emails and direct phone numbers, this solution ensures precise outreach to agents, brokers, property developers, and key decision-makers in the real estate sector.

Utilizing advanced AI-driven validation, our data is continuously updated to maintain 99% accuracy, offering actionable insights that empower targeted marketing, streamlined sales strategies, and efficient recruitment efforts. Whether you’re engaging with top real estate executives or sourcing local property experts, Success.ai provides reliable and compliant data tailored to your needs.

Key Features of Success.ai’s Real Estate Professional Contact Data

• Comprehensive Industry Coverage Gain direct access to verified profiles of real estate professionals across the globe, including:

1. Real Estate Agents: Professionals facilitating property sales and purchases.
2. Brokers: Key intermediaries managing transactions between buyers and sellers.
3. Property Developers: Decision-makers shaping residential, commercial, and industrial projects.
4. Real Estate Executives: Leaders overseeing multi-regional operations and business strategies.
5. Architects & Consultants: Experts driving design and project feasibility.

• Verified and Continuously Updated Data

AI-Powered Validation: All profiles are verified using cutting-edge AI to ensure up-to-date accuracy. Real-Time Updates: Our database is refreshed continuously to reflect the most current information. Global Compliance: Fully aligned with GDPR, CCPA, and other regional regulations for ethical data use.

• Customizable Data Delivery Tailor your data access to align with your operational goals:

API Integration: Directly integrate data into your CRM or project management systems for seamless workflows. Custom Flat Files: Receive detailed datasets customized to your specifications, ready for immediate application.

Why Choose Success.ai for Real Estate Contact Data?

• Best Price Guarantee Enjoy competitive pricing that delivers exceptional value for verified, comprehensive contact data.

• Precision Targeting for Real Estate Professionals Our dataset equips you to connect directly with real estate decision-makers, minimizing misdirected efforts and improving ROI.

• Strategic Use Cases

  Lead Generation: Target qualified real estate agents and brokers to expand your network. Sales Outreach: Engage with property developers and executives to close high-value deals. Marketing Campaigns: Drive targeted campaigns tailored to real estate markets and demographics. Recruitment: Identify and attract top talent in real estate for your growing team. Market Research: Access firmographic and demographic data for in-depth industry analysis.

• Data Highlights 170M+ Verified Professional Profiles 50M Work Emails 30M Company Profiles 700M Global Professional Profiles

• Powerful APIs for Enhanced Functionality

  Enrichment API Ensure your contact database remains relevant and up-to-date with real-time enrichment. Ideal for businesses seeking to maintain competitive agility in dynamic markets.

Lead Generation API Boost your lead generation with verified contact details for real estate professionals, supporting up to 860,000 API calls per day for robust scalability.

• Use Cases for Real Estate Contact Data

1. Targeted Outreach for New Projects Connect with property developers and brokers to pitch your services or collaborate on upcoming projects.

2. Real Estate Marketing Campaigns Execute personalized marketing campaigns targeting agents and clients in residential, commercial, or industrial sectors.

3. Enhanced Sales Strategies Shorten sales cycles by directly engaging with decision-makers and key stakeholders.

4. Recruitment and Talent Acquisition Access profiles of highly skilled professionals to strengthen your real estate team.

5. Market Analysis and Intelligence Leverage firmographic and demographic insights to identify trends and optimize business strategies.

• What Makes Us Stand Out? >> Unmatched Data Accuracy: Our AI-driven validation ensures 99% accuracy for all contact details. >> Comprehensive Global Reach: Covering professionals across diverse real estate markets worldwide. >> Flexible Delivery Options: Access data in formats that seamlessly fit your existing systems. >> Ethical and Compliant Data Practices: Adherence to global standards for secure and responsible data use.

Success.ai’s B2B Contact Data for Global Real Estate Professionals delivers the tools you need to connect with the right people at the right time, driving efficiency and success in your business operations. From agents and brokers to property developers and executiv...

Introduction

This US English Call Center Speech Dataset for the Real Estate industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for English -speaking Real Estate customers. With over 30 hours of unscripted, real-world audio, this dataset captures authentic conversations between customers and real estate agents ideal for building robust ASR models.

Curated by FutureBeeAI, this dataset equips voice AI developers, real estate tech platforms, and NLP researchers with the data needed to create high-accuracy, production-ready models for property-focused use cases.

Speech Data

The dataset features 30 hours of dual-channel call center recordings between native US English speakers. Captured in realistic real estate consultation and support contexts, these conversations span a wide array of property-related topics from inquiries to investment advice offering deep domain coverage for AI model development.

Topic Diversity

This speech corpus includes both inbound and outbound calls, featuring positive, neutral, and negative outcomes across a wide range of real estate scenarios.

Such domain-rich variety ensures model generalization across common real estate support conversations.

Transcription

All recordings are accompanied by precise, manually verified transcriptions in JSON format.

These transcriptions streamline ASR and NLP development for English real estate voice applications.

Metadata

Detailed metadata accompanies each participant and conversation:

This enables smart filtering, dialect-focused model training, and structured dataset exploration.

Usage and Applications

This dataset is ideal for voice AI and NLP systems built for the real estate sector:

Introduction

This Egyptian Arabic Call Center Speech Dataset for the Real Estate industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Arabic -speaking Real Estate customers. With over 40 hours of unscripted, real-world audio, this dataset captures authentic conversations between customers and real estate agents ideal for building robust ASR models.

Curated by FutureBeeAI, this dataset equips voice AI developers, real estate tech platforms, and NLP researchers with the data needed to create high-accuracy, production-ready models for property-focused use cases.

Speech Data

The dataset features 40 hours of dual-channel call center recordings between native Egyptian Arabic speakers. Captured in realistic real estate consultation and support contexts, these conversations span a wide array of property-related topics from inquiries to investment advice offering deep domain coverage for AI model development.

Topic Diversity

This speech corpus includes both inbound and outbound calls, featuring positive, neutral, and negative outcomes across a wide range of real estate scenarios.

Such domain-rich variety ensures model generalization across common real estate support conversations.

Transcription

All recordings are accompanied by precise, manually verified transcriptions in JSON format.

These transcriptions streamline ASR and NLP development for Arabic real estate voice applications.

Metadata

Detailed metadata accompanies each participant and conversation:

This enables smart filtering, dialect-focused model training, and structured dataset exploration.

Usage and Applications

This dataset is ideal for voice AI and NLP systems built for the real estate sector:

Introduction

This Japanese Call Center Speech Dataset for the Real Estate industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Japanese -speaking Real Estate customers. With over 40 hours of unscripted, real-world audio, this dataset captures authentic conversations between customers and real estate agents ideal for building robust ASR models.

Curated by FutureBeeAI, this dataset equips voice AI developers, real estate tech platforms, and NLP researchers with the data needed to create high-accuracy, production-ready models for property-focused use cases.

Speech Data

The dataset features 40 hours of dual-channel call center recordings between native Japanese speakers. Captured in realistic real estate consultation and support contexts, these conversations span a wide array of property-related topics from inquiries to investment advice offering deep domain coverage for AI model development.

Topic Diversity

This speech corpus includes both inbound and outbound calls, featuring positive, neutral, and negative outcomes across a wide range of real estate scenarios.

Such domain-rich variety ensures model generalization across common real estate support conversations.

Transcription

All recordings are accompanied by precise, manually verified transcriptions in JSON format.

These transcriptions streamline ASR and NLP development for Japanese real estate voice applications.

Metadata

Detailed metadata accompanies each participant and conversation:

This enables smart filtering, dialect-focused model training, and structured dataset exploration.

Usage and Applications

This dataset is ideal for voice AI and NLP systems built for the real estate sector:

ListBuilder combines 600+ property data, MLS, home ownership data, mortgage data, demographic data, geographic data, and contact data points within the self-service ListBuilding tool.

Easily search filters and narrow your list results to identify the U.S. homeowners, distressed property owners, potential borrowers, commercial property owners, investors, or home service consumers that best fit your target profile. All your property data and home ownership data in one place!

ListBuilder is used by marketing agencies, real estate professionals, home service providers, and operations teams to improve operations and optimize sales effectiveness.

Backed by the industries most accurate and comprehensive property and skip tracing sources (BatchData APIs), ListBuilder offers more granular targeting capabilities, with top-tier contact data accuracy.

Introduction

This Mexican Spanish Call Center Speech Dataset for the Real Estate industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Spanish -speaking Real Estate customers. With over 30 hours of unscripted, real-world audio, this dataset captures authentic conversations between customers and real estate agents ideal for building robust ASR models.

Curated by FutureBeeAI, this dataset equips voice AI developers, real estate tech platforms, and NLP researchers with the data needed to create high-accuracy, production-ready models for property-focused use cases.

Speech Data

The dataset features 30 hours of dual-channel call center recordings between native Mexican Spanish speakers. Captured in realistic real estate consultation and support contexts, these conversations span a wide array of property-related topics from inquiries to investment advice offering deep domain coverage for AI model development.

Topic Diversity

This speech corpus includes both inbound and outbound calls, featuring positive, neutral, and negative outcomes across a wide range of real estate scenarios.

Such domain-rich variety ensures model generalization across common real estate support conversations.

Transcription

All recordings are accompanied by precise, manually verified transcriptions in JSON format.

These transcriptions streamline ASR and NLP development for Spanish real estate voice applications.

Metadata

Detailed metadata accompanies each participant and conversation:

This enables smart filtering, dialect-focused model training, and structured dataset exploration.

Usage and Applications

This dataset is ideal for voice AI and NLP systems built for the real estate sector:

Introduction

This Bengali Call Center Speech Dataset for the Real Estate industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Bengali -speaking Real Estate customers. With over 30 hours of unscripted, real-world audio, this dataset captures authentic conversations between customers and real estate agents ideal for building robust ASR models.

Curated by FutureBeeAI, this dataset equips voice AI developers, real estate tech platforms, and NLP researchers with the data needed to create high-accuracy, production-ready models for property-focused use cases.

Speech Data

The dataset features 30 hours of dual-channel call center recordings between native Bengali speakers. Captured in realistic real estate consultation and support contexts, these conversations span a wide array of property-related topics from inquiries to investment advice offering deep domain coverage for AI model development.

Topic Diversity

This speech corpus includes both inbound and outbound calls, featuring positive, neutral, and negative outcomes across a wide range of real estate scenarios.

Such domain-rich variety ensures model generalization across common real estate support conversations.

Transcription

All recordings are accompanied by precise, manually verified transcriptions in JSON format.

These transcriptions streamline ASR and NLP development for Bengali real estate voice applications.

Metadata

Detailed metadata accompanies each participant and conversation:

This enables smart filtering, dialect-focused model training, and structured dataset exploration.

Usage and Applications

This dataset is ideal for voice AI and NLP systems built for the real estate sector:

Introduction

This Swedish Call Center Speech Dataset for the Real Estate industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Swedish -speaking Real Estate customers. With over 30 hours of unscripted, real-world audio, this dataset captures authentic conversations between customers and real estate agents ideal for building robust ASR models.

Curated by FutureBeeAI, this dataset equips voice AI developers, real estate tech platforms, and NLP researchers with the data needed to create high-accuracy, production-ready models for property-focused use cases.

Speech Data

The dataset features 30 hours of dual-channel call center recordings between native Swedish speakers. Captured in realistic real estate consultation and support contexts, these conversations span a wide array of property-related topics from inquiries to investment advice offering deep domain coverage for AI model development.

Topic Diversity

This speech corpus includes both inbound and outbound calls, featuring positive, neutral, and negative outcomes across a wide range of real estate scenarios.

Such domain-rich variety ensures model generalization across common real estate support conversations.

Transcription

All recordings are accompanied by precise, manually verified transcriptions in JSON format.

These transcriptions streamline ASR and NLP development for Swedish real estate voice applications.

Metadata

Detailed metadata accompanies each participant and conversation:

This enables smart filtering, dialect-focused model training, and structured dataset exploration.

Usage and Applications

This dataset is ideal for voice AI and NLP systems built for the real estate sector:

Introduction

This German Call Center Speech Dataset for the Real Estate industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for German -speaking Real Estate customers. With over 30 hours of unscripted, real-world audio, this dataset captures authentic conversations between customers and real estate agents ideal for building robust ASR models.

Curated by FutureBeeAI, this dataset equips voice AI developers, real estate tech platforms, and NLP researchers with the data needed to create high-accuracy, production-ready models for property-focused use cases.

Speech Data

The dataset features 30 hours of dual-channel call center recordings between native German speakers. Captured in realistic real estate consultation and support contexts, these conversations span a wide array of property-related topics from inquiries to investment advice offering deep domain coverage for AI model development.

Topic Diversity

This speech corpus includes both inbound and outbound calls, featuring positive, neutral, and negative outcomes across a wide range of real estate scenarios.

Such domain-rich variety ensures model generalization across common real estate support conversations.

Transcription

All recordings are accompanied by precise, manually verified transcriptions in JSON format.

These transcriptions streamline ASR and NLP development for German real estate voice applications.

Metadata

Detailed metadata accompanies each participant and conversation:

This enables smart filtering, dialect-focused model training, and structured dataset exploration.

Usage and Applications

This dataset is ideal for voice AI and NLP systems built for the real estate sector:

Amazon AWS - Cloud Platforms & Services

Companies using Amazon AWS

We have data on 1,070,574 companies that use Amazon AWS. The companies using Amazon AWS are most often found in United States and in the Computer Software industry. Amazon AWS is most often used by companies with 10-50 employees and 1M-10M dollars in revenue. Our data for Amazon AWS usage goes back as far as 2 years and 1 months.

What is Amazon AWS?

Amazon Web Services (AWS) is a collection of remote computing services, also called web services that make up a cloud computing platform offered by Amazon.com.

Top Industries that use Amazon AWS

Looking at Amazon AWS customers by industry, we find that Computer Software (6%) is the largest segment.

Distribution of companies using Amazon AWS by Industry

 Computer software - 67, 537 companies  Hospitals & Healthcare - 54, 293 companies  Retail - 39, 543 companies  Information Technology and Services - 35, 382 companies  Real Estate - 31, 676 companies  Restaurants - 30, 302 companies  Construction - 29, 207 companies  Automotive - 28, 469 companies  Financial Services - 23, 680 companies  Education Management - 21, 548 companies

Top Countries that use Amazon AWS

49% of Amazon AWS customers are in United States and 7% are in United Kingdom.

Distribution of companies using Amazon AWS by country

 United Sates – 616 2275 companies  United Kingdom – 68 219 companies  Australia – 44 601 companies  Canada – 42 770 companies  Germany – 31 541 companies  India – 30 949 companies  Netherlands – 19 543 companies  Brazil – 17 165 companies  Italy – 14 876 companies  Spain – 14 675 companies

Contact Information of Fields Include:-

• Company Name • Business contact number • Title
• Name • Email Address • Country, State, City, Zip Code • Phone, Mobile and Fax • Website • Industry • SIC & NAICS Code • Employees Size
• Revenue Size
• And more…

Why Buy AWS Users List from DataCaptive?

• More than 1,070,574 companies
• Responsive database • Customizable as per your requirements • Email and Tele-verified list • Team of 100+ market researchers • Authentic data sources

What’s in for you?

Over choosing us, here are a few advantages we authenticate-

• Locate, target, and prospect leads from 170+ countries • Design and execute ABM and multi-channel campaigns • Seamless and smooth pre-and post-sale customer service • Connect with old leads and build a fruitful customer relationship • Analyze the market for product development and sales campaigns • Boost sales and ROI with increased customer acquisition and retention

Our security compliance

We use of globally recognized data laws like –

GDPR, CCPA, ACMA, EDPS, CAN-SPAM and ANTI CAN-SPAM to ensure the privacy and security of our database. We engage certified auditors to validate our security and privacy by providing us with certificates to represent our security compliance.

Our USPs- what makes us your ideal choice?

At DataCaptive™, we strive consistently to improve our services and cater to the needs of businesses around the world while keeping up with industry trends.

• Elaborate data mining from credible sources • 7-tier verification, including manual quality check • Strict adherence to global and local data policies • Guaranteed 95% accuracy or cash-back • Free sample database available on request

Guaranteed benefits of our Amazon AWS users email database!

85% email deliverability and 95% accuracy on other data fields

We understand the importance of data accuracy and employ every avenue to keep our database fresh and updated. We execute a multi-step QC process backed by our Patented AI and Machine learning tools to prevent anomalies in consistency and data precision. This cycle repeats every 45 days. Although maintaining 100% accuracy is quite impractical, since data such as email, physical addresses, and phone numbers are subjected to change, we guarantee 85% email deliverability and 95% accuracy on other data points.

100% replacement in case of hard bounces

Every data point is meticulously verified and then re-verified to ensure you get the best. Data Accuracy is paramount in successfully penetrating a new market or working within a familiar one. We are committed to precision. However, in an unlikely event where hard bounces or inaccuracies exceed the guaranteed percentage, we offer replacement with immediate effect. If need be, we even offer credits and/or refunds for inaccurate contacts.

Other promised benefits

• Contacts are for the perpetual usage • The database comprises consent-based opt-in contacts only • The list is free of duplicate contacts and generic emails • Round-the-clock customer service assistance • 360-degree database solutions

As of March 2025, there were a reported 5,426 data centers in the United States, the most of any country worldwide. A further 529 were located in Germany, while 523 were located in the United Kingdom. What is a data center? A data center is a network of computing and storage resources that enables the delivery of shared software applications and data. These facilities can house large amounts of critical and important data, and therefore are vital to the daily functions of companies and consumers alike. As a result, whether it is a cloud, colocation, or managed service, data center real estate will have increasing importance worldwide. Hyperscale data centers In the past, data centers were highly controlled physical infrastructures, but the cloud has since changed that model. A cloud data service is a remote version of a data center – located somewhere away from a company's physical premises. Cloud IT infrastructure spending has grown and is forecast to rise further in the coming years. The evolution of technology, along with the rapid growth in demand for data across the globe, is largely driven by the leading hyperscale data center providers.

The rental housing developments listed below are among the thousands of affordable units that are supported by City of Chicago programs to maintain affordability in local neighborhoods. The list is updated periodically when construction is completed for new projects or when the compliance period for older projects expire, typically after 30 years. The list is provided as a courtesy to the public. It does not include every City-assisted affordable housing unit that may be available for rent, nor does it include the hundreds of thousands of naturally occurring affordable housing units located throughout Chicago without City subsidies. For information on rents, income requirements and availability for the projects listed, contact each property directly. For information on other affordable rental properties in Chicago and Illinois, call (877) 428-8844, or visit www.ILHousingSearch.org.

The Geospatial Imagery Analytics Marketsize was valued at USD 11.88 USD Billion in 2023 and is projected to reach USD 83.39 USD Billion by 2032, exhibiting a CAGR of 32.1 % during the forecast period.Geospatial analytics gathers, manipulates, and displays geographic information system (GIS) data and imagery including GPS and satellite photographs. Geospatial data analytics rely on geographic coordinates and specific identifiers such as street address and zip code. geospatial visualization enables businesses to better understand complex information and make informed decisions. They can quickly see patterns and trends and assess the impact of different variables by visualizing data in a spatial context. The field encompasses several techniques and algorithms, such as spatial interpolation, spatial regression, spatial clustering, and spatial autocorrelation analysis, which help extract insights from various geospatial data sources. The growing adoption of location-based services in various industries, including agriculture, defense, and urban planning, is driving the demand for geospatial imagery analytics. Recent developments include: August 2023: onX, a digital navigation company, partnered with Planet Labs PBC, a satellite imagery provider, to introduce a new feature called ‘Recent Imagery’. This feature offers onX app users updated satellite imagery maps every two weeks, enhancing the user experience across onX Hunt, onX Offroad, and onX Backcountry apps. This frequent data update helps outdoor enthusiasts access real-time information for safer and more informed outdoor activities., August 2023: Quant Data & Analytics, a provider of data products and enterprise solutions for real estate and retail, partnered with Satellogic Inc. to utilize Satellogic’s high-resolution satellite imagery to enhance property technology in Saudi Arabia and the Gulf region., April 2023: Astraea, a spatiotemporal data and analytics platform, introduced a new ordering service that grants customers scalable access to top-tier commercial satellite imagery from providers such as Planet Labs PBC and others., May 2022: Satellogic Inc. established a partnership with UP42. This geospatial developer platform enables direct access to Satellogic’s satellite tasking capabilities, including high-resolution multispectral and wide-area hyperspectral imagery, through the UP42 API-based platform., April 2022: TomTom International BV, a geolocation tech company, broadened its partnership with Maxar Technologies, a space solution provider. This expansion involves integrating high-resolution global satellite imagery from Maxar’s Vivid imagery base maps into TomTom’s product lineup, enhancing their visualization solutions for customers.. Key drivers for this market are: Growing Demand for Location-based Insights across Diverse Industries to Fuel Market Growth. Potential restraints include: Complexity and Cost Associated with Data Acquisition and Processing May Hamper Market Growth. Notable trends are: Growing Implementation of Touch-based and Voice-based Infotainment Systems to Increase Adoption of Intelligent Cars.

Since 2019, the Directorate-General for Public Finance (DGFIP) has made available to the general public the dataset “Requests for Land Values” (DVF). These data allow us to know the real estate transactions that took place over the last five years in metropolitan territory and the DOM-TOM, with the exception of Alsace, Moselle and Mayotte. The data are derived from notarial deeds and cadastral information. CEREMA offers an open data, cleaned and structured version of the file, called DVF+. This data is organised at the transaction, making it easier to use. However, their operation remains complex. Here we propose a version of the DVF+ data that aggregates information at the municipal level and manages upstream the complexity of the information by applying a number of filters. The objective is to propose stabilised and homogeneous data on clearly identified perimeters. The organisation of this information at the level of the municipality (commune 2020 and communal districts) aims to make access to this information easier without losing its quality. The dataset is accompanied by a PJ document describing the method developed in a collegial way within a workshop of the LIFTI (Laboratoire d’Initiatives Foncières et Territoriales Innovantes). This workshop, led by the CDC, allowed the creation of the dataset, with the help of the company Modaal which supported the script. This document is supplemented by a dictionary that explains the methods of setting up indicators and the set of filters applied to the data. In the dataset made available, the indicators are calculated year by year, between 2014 and 2020, with a last year 2020 incomplete in terms of number of transfers. ******** ** ** * The DVF+ vintage mobilised is that of April 2022; https://cerema.app.box.com/v/dvfplus-opendata * Communes with less than 5 mutations (over the year or over the period) are absent from the dataset; * For more information on the DVF source and best practices for this source, please refer to the Vademecum available at this link: (https://opendata.caissedesdepots.fr/assets/theme_image/LIFTI_Logo%202017.jpg)!(/assets/theme_image/MODAAL_V4-03.png)

Introduction

This Russian Call Center Speech Dataset for the Real Estate industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Russian -speaking Real Estate customers. With over 30 hours of unscripted, real-world audio, this dataset captures authentic conversations between customers and real estate agents ideal for building robust ASR models.

Curated by FutureBeeAI, this dataset equips voice AI developers, real estate tech platforms, and NLP researchers with the data needed to create high-accuracy, production-ready models for property-focused use cases.

Speech Data

The dataset features 30 hours of dual-channel call center recordings between native Russian speakers. Captured in realistic real estate consultation and support contexts, these conversations span a wide array of property-related topics from inquiries to investment advice offering deep domain coverage for AI model development.

Topic Diversity

This speech corpus includes both inbound and outbound calls, featuring positive, neutral, and negative outcomes across a wide range of real estate scenarios.

Such domain-rich variety ensures model generalization across common real estate support conversations.

Transcription

All recordings are accompanied by precise, manually verified transcriptions in JSON format.

These transcriptions streamline ASR and NLP development for Russian real estate voice applications.

Metadata

Detailed metadata accompanies each participant and conversation:

This enables smart filtering, dialect-focused model training, and structured dataset exploration.

Usage and Applications

This dataset is ideal for voice AI and NLP systems built for the real estate sector:

This special topic poll, fielded May 31-June 3, is part of a continuing series of monthly surveys that solicit public opinion on the presidency and on a range of other political and social issues. Respondents were asked to give their opinion of President Bill Clinton and his handling of the presidency, and of various issues such as foreign policy and the economy. Opinions were solicited on the condition of the national economy, what was the most important problem facing respondents and their families, their communities, and the country, how much the president could help with those problems, and whether they approved of the way Congress was handling its job. Respondents were asked whether they had been paying attention to the 1996 Presidential campaign, which candidate they would vote for if the presidential and United States House of Representatives elections were being held that day, and to give their opinions of Senator Bob Dole, First Lady Hillary Clinton, and Speaker of the House of Representatives Newt Gingrich. Several questions asked whether respondents leaned more toward Bill Clinton or Bob Dole based on specific issues, such as unemployment, family values and illegal drugs, whether it is better to have a president from the same political party that controls Congress, and whether the campaigns of Bill Clinton and Bob Dole have been more positive than past presidential campaigns. Respondents were asked whether they knew about the Clinton's past involvement in the Arkansas real estate development called Whitewater, whether the Clintons were trustworthy, whether the Whitewater issue was of great importance to the nation, and whether the verdicts in the Whitewater trial of Bill Clinton's former business partners affected their opinion of Bill Clinton. A series of questions asked about issues dealing with crime, including whether crime increased in the country and in respondents' communities within the last year, teenage crime, whether respondents or their family members had been the victim of a crime within the last year, whether the respondent's community was safe for women and children, what was the most important cause of crime, whether parents should be held legally accountable for their school-aged children's crimes, and whether respondents would approve of a curfew for children under the age of 18 within their community. Information was also collected on whether respondents considered themselves part of the religious right movement, and whether they listened to political call-in radio shows. Additional topics included abortion, the environment, the government, taxes and the budget deficit, job and financial security, and union involvement in political campaigns. Demographic variables include sex, age, race, education level, voter registration status and participation history, household income, religious preference, household union membership, political ideology, political party affiliation, political philosophy, whether respondents had any children under the age of 18, and whether respondents had any children entering the ninth grade in the fall.

Introduction

This Urdu Call Center Speech Dataset for the Real Estate industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Urdu -speaking Real Estate customers. With over 30 hours of unscripted, real-world audio, this dataset captures authentic conversations between customers and real estate agents ideal for building robust ASR models.

Curated by FutureBeeAI, this dataset equips voice AI developers, real estate tech platforms, and NLP researchers with the data needed to create high-accuracy, production-ready models for property-focused use cases.

Speech Data

The dataset features 30 hours of dual-channel call center recordings between native Urdu speakers. Captured in realistic real estate consultation and support contexts, these conversations span a wide array of property-related topics from inquiries to investment advice offering deep domain coverage for AI model development.

Topic Diversity

This speech corpus includes both inbound and outbound calls, featuring positive, neutral, and negative outcomes across a wide range of real estate scenarios.

Such domain-rich variety ensures model generalization across common real estate support conversations.

Transcription

All recordings are accompanied by precise, manually verified transcriptions in JSON format.

These transcriptions streamline ASR and NLP development for Urdu real estate voice applications.

Metadata

Detailed metadata accompanies each participant and conversation:

This enables smart filtering, dialect-focused model training, and structured dataset exploration.

Usage and Applications

This dataset is ideal for voice AI and NLP systems built for the real estate sector:

HOLC, in consultation with local real estate professionals and local policymakers, categorized neighborhoods in hundreds of cities in the United States into four types: Best (A), Still Desirable (B), Definitely Declining (C), and Hazardous (D). So-called “hazardous” zones were colored red on these maps. These zones were then used to approve or deny credit-lending and mortgage-backing by banks and the Federal Housing Administration. The descriptions provided by HOLC in their reports rely heavily on race and ethnicity as critical elements in assigning these grades. According to the University of Richmond's Mapping Inequality project, “Arguably the HOLC agents in the other two hundred-plus cities graded through this program adopted a consistently white, elite standpoint or perspective. HOLC assumed and insisted that the residency of African-Americans and immigrants, as well as working-class whites, compromised the values of homes and the security of mortgages” (Mapping Inequality). HOLC’s classifications were one contributory factor in underinvestment in a neighborhood, and generally, although not always, closed off many, especially people of color, from the credit necessary to purchase their own homes.The 15 Worcester neighborhood zones included on the map are ordered from Zone 1 (categorized as "Best") to Zone 15, with the highest numbered zones included in the least desirable "Hazardous" category. The exact descriptions used by HOLC to classify the neighborhoods in 1936 are included, and therefore may contain some disturbing language. Many scholars and institutions have focused their efforts on tracking the effects the 1930s redlining maps still have today. The Mapping Inequality project by the University of Richmond has collected and analyzed a comprehensive set of redlining maps for more than 200 cities in the U.S. One of their conclusions is that, for most cities, there are striking and persistent geographic similarities between redlined zones and currently vulnerable areas even after eighty years. See the Mapping Inequality website for more information (https://dsl.richmond.edu/panorama/redlining).This digitized version prepared by the Worcester Regional Research Bureau was based on a scanned copy from the National Archives, obtained thanks to Dr. Robert Nelson, the Digital Scholarship Lab, and the rest of his team at Mapping Inequality at the University of Richmond. Dr. Nelson worked with The Research Bureau directly to track it down in the Archives.Informing Worcester is the City of Worcester's open data portal where interested parties can obtain public information at no cost.

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Private Equity Market Size 2025-2029

The private equity market size is forecast to increase by USD 885.7 billion at a CAGR of 9.5% between 2024 and 2029.

The private equity and venture capital investment landscape is experiencing significant growth, driven by an increase in deal volumes and the rising number of high-net-worth individuals (HNWIs) worldwide. This trend is fueled by the attractive returns offered by private equity and venture capital investments, which have become a popular asset class for wealth management portfolios. However, this market is not without challenges. Transaction risks, such as regulatory changes and foreign exchange fluctuations, can pose significant hurdles for investors. Additionally, there is a growing demand for impact investing, particularly in sectors like renewable energy, as investors seek to align their financial goals with social and environmental objectives.
Navigating these trends and challenges requires a deep understanding of market dynamics and a strategic approach to investment opportunities. This market trends and analysis report delves deeper into these topics, providing valuable insights for professionals seeking to maximize their private equity investments.

What will be the Size of the Private Equity Market during the forecast period?

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The markets continue to evolve, with investment strategies becoming increasingly data-driven and sophisticated. Investor returns remain a key focus, with growth stage investing and innovation hubs driving value creation. Risk management is crucial in this industry, with deal origination and fundraising strategies carefully considered. Management fees and capital calls are essential components of the fund lifecycle, while deal closing and post-investment management ensure optimal portfolio performance. Cryptocurrency investments represent an emerging trend, with digital assets joining traditional assets in investment portfolios. Impact measurement and regulatory compliance are also critical, as private equity firms strive for transparency and customer experience.
ESG integration and industry consolidation are shaping the venture capital ecosystem, with secondary market sales providing liquidity for investors. Fund size and investment strategies vary, with some focusing on start-ups and emerging technologies. Technology adoption is a significant factor in fund performance, with customer acquisition and retention key to long-term success. Fund returns are closely monitored, with performance fees incentivizing top-performing funds. In the global private equity landscape, fundraising strategies and industry trends continue to evolve. Regulatory compliance and customer experience are paramount, with digital assets investment and ESG integration shaping the future of the industry.
Private equity sales and industry consolidation are ongoing, with post-investment management and portfolio optimization crucial to maximizing returns.

How is this Private Equity Industry segmented?

The private equity industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

End-user

  Privately held companies
  Start-up companies


Application

  Leveraged buyouts
  Venture capital
  Equity investment
  Enterpreneurship


Investments

  Large Cap
  Upper Middle Market
  Lower Middle Market
  Real Estate
  Large Cap
  Upper Middle Market
  Lower Middle Market
  Real Estate


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa



  APAC

    Australia
    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By End-user Insights

The privately held companies segment is estimated to witness significant growth during the forecast period.

In the realm of investment, private equity portfolios play a significant role in the additive manufacturing market. These portfolios encompass various investment vehicles, such as buyout funds, growth equity funds, strategic investments, and late-stage funding. Each type caters to different growth stages of companies in the sector. Buyout funds focus on acquiring controlling stakes in mature companies, often facilitating digital transformation and operational improvements. Growth equity funds, on the other hand, invest in companies with proven business models, aiming to fuel their expansion through capital infusion and industry expertise. Strategic investments are made by firms seeking to gain a foothold in a new market or expand their existing presence.

Legal frameworks and regulatory landscapes play a crucial role in shaping the market dynamics. Alternative investments, such as distressed debt funds and private debt, provide opportunities

A Global Database on Whole Life Carbon, Energy and Material Intensity of Buildings (CarbEnMats-Buildings)

• Data descriptor article (Preprint): https://doi.org/10.5281/zenodo.8378939
• Data records / database (this repo): https://doi.org/10.5281/zenodo.8363895
• Latest version: Available via https://github.com/mroeck/carbenmats-buildings (main)

Abstract

Globally, interest in understanding the life cycle related greenhouse gas (GHG) emissions of buildings is increasing. Robust data is required for benchmarking and analysis of parameters driving resource use and whole life carbon (WLC) emissions. However, open datasets combining information on energy and material use as well as whole life carbon emissions remain largely unavailable – until now.

We present a global database on whole life carbon, energy use, and material intensity of buildings. It contains data on more than 1,200 building case studies and includes over 300 attributes addressing context and site, building design, assessment methods, energy and material use, as well as WLC emissions across different life cycle stages. The data was collected through various meta-studies, using a dedicated data collection template (DCT) and processing scripts (Python Jupyter Notebooks), all of which are shared alongside this data descriptor.

This dataset is valuable for industrial ecology and sustainable construction research and will help inform decision-making in the building industry as well as the climate policy context.

Background & Summary

The need for reducing greenhouse gas (GHG) emissions across Europe require defining and implementing a performance system for both operational and embodied carbon at the building level that provides relevant guidance for policymakers and the building industry. So-called whole life carbon (WLC) of buildings is gaining increasing attention among decision-makers concerned with climate and industrial policy, as well as building procurement, design, and operation. However, most open buildings datasets published thus far have been focusing on building’s operational energy consumption and related parameters 1,2,2–4. Recent years furthermore brought large-scale datasets on building geometry (footprint, height) 5,6 as well as the publication of some datasets on building construction systems and material intensity 7,8. Heeren and Fishman’s database seed on material intensity (MI) of buildings 7, an essential reference to this work, was a first step towards an open data repository on material-related environmental impacts of buildings. In their 2019 descriptor, the authors present data on the material coefficients of more than 300 building cases intended for use in studies applying material flow analysis (MFA), input-output (IO) or life cycle assessment (LCA) methods. Guven et al. 8 elaborated on this effort by publishing a construction classification system database for understanding resource use in building construction. However, thus far, there is a lack of publicly available data that combines material composition, energy use and also considers life cycle-related environmental impacts, such as life cycle-related GHG emissions, also referred to as building’s whole life carbon.

The Global Database on Whole Life Carbon, Energy Use, and Material Intensity of Buildings (CarbEnMats-Buildings) published alongside this descriptor provides information on more than 1,200 buildings worldwide. The dataset includes attributes on geographical context and site, main building design characteristics, LCA-based assessment methods, as well as information on energy and material use, and related life cycle greenhouse gas (GHG) emissions, commonly referred to as whole life carbon (WLC), with a focus on embodied carbon (EC) emissions. The dataset compiles data obtained through a systematic review of the scientific literature as well as systematic data collection from both literature sources and industry partners. By applying a uniform data collection template (DCT) and related automated procedures for systematic data collection and compilation, we facilitate the processing, analysis and visualization along predefined categories and attributes, and support the consistency of data types and units. The descriptor includes specifications related to the DCT spreadsheet form used for obtaining these data as well as explanations of the data processing and feature engineering steps undertaken to clean and harmonise the data records. The validation focuses on describing the composition of the dataset and values observed for attributes related to whole life carbon, energy and material intensity.

The data published with this descriptor offers the largest open compilation of data on whole life carbon emissions, energy use and material intensity of buildings published to date. This open dataset is expected to be valuable for research applications in the context of MFA, I/O and LCA modelling. It also offers a unique data source for benchmarking whole life carbon, energy use and material intensity of buildings to inform policy and decision-making in the context of the decarbonization of building construction and operation as well as commercial real estate in Europe and beyond.

Files

All files related to this descriptor are available on a public GitHub repository and related release via Zenodo (https://doi.org/10.5281/zenodo.8363895). The repository contains the following files:

• README.md is a text file with instructions on how to use the files and documents.
• CarbEnMats_attributes.XLSX is a table with the complete attribute description.
• CarbEnMats_materials.XLSX is the table of material options and mappings.
• CarbEnMats_dataset.XLSX is the building dataset in MS Excel format.
• CarbEnMats_dataset.txt is the building dataset in tab-delimited TXT format.

Further information

Please consult the related data descriptor article (linked at the top) for further information, e.g.:

• Methods (Data collection; data processing)
• Data records (Files; Sources; Attributes)
• Technical validation (Data overview; Data consistency)
• Usage Notes (Attribute priority; Scope summary, Missing information)

Code availability (LICENSE)

The dataset, the data collection template as well as the code used for processing, harmonization and visualization are published under a GNU General Public License v3.0. The GNU General Public License is a free, copyleft license for software and other kinds of works. We encourage you to review, reuse, and refine the data and scripts and eventually share-alike.

Contributing

The CarbEnMats-Buildings database is the results of a highly collaborative effort and needs your active contributions to further improve and grow the open building data landscape. Reach out to the lead author (email, linkedin) if you are interested to contribute your data or time.

Cite as

When referring to this work, please cite both the descriptor and the dataset:

• Descriptor: RÖCK, Martin, SORENSEN, Andreas, BALOUKTSI, Maria, RUSCHI MENDES SAADE, Marcella, RASMUSSEN, Freja Nygaard, BIRGISDOTTIR, Harpa, FRISCHKNECHT, Rolf, LÜTZKENDORF, Thomas, HOXHA, Endrit, HABERT, Guillaume, SATOLA, Daniel, TRUGER, Barbara, TOZAN, Buket, KUITTINEN, Matti, ALAUX, Nicolas, ALLACKER, Karen, & PASSER, Alexader. (2023). A Global Database on Whole Life Carbon, Energy and Material Intensity of Buildings (CarbEnMats-Buildings) [Preprint]. Zenodo. https://doi.org/10.5281/zenodo.8378939
• Dataset: Martin Röck. (2023). mroeck/carbenmats-buildings: Pre-release (0.1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8363895