Report includes a snapshot of active projects where DASNY delivers some level of project management oversight.

Dataset Overview

This dataset is a simulated dataset containing 1,000 entries of construction cost estimates. It is designed for use in predictive modeling, machine learning, and business analytics, particularly in the construction and project management domains. The dataset includes both numerical and textual data, providing opportunities for hybrid modeling approaches that combine structured data and natural language processing.

The primary objective of this dataset is to facilitate modeling of construction cost estimation while considering policy-driven adjustments (discounts or markups). It can be used to analyze and predict how various factors, such as material costs, labor costs, and policy reasons, affect final project estimates.

Feature Descriptions

1) Material_Cost (numeric):

• Represents the cost of materials required for a construction project, measured in USD.
• Range: $10,000–$50,000.
• This is one of the primary cost components in project estimation.

2) Labor_Cost (numeric):

• Represents the labor cost involved in completing the project, measured in USD.
• Range: $5,000–$30,000.
• Indicates the cost of human resources required for the construction.

3) Profit_Rate (numeric):

• Represents the percentage of profit added to the combined material and labor costs.
• Range: 10%–30% (5 distinct values: 10%, 15%, 20%, 25%, 30%).
• Reflects the margin a company expects from the project.

4) Discount_or_Markup (numeric):

• Represents the adjustment amount applied to the project cost, measured in USD.
• Range: -$10,000 to $10,000.
• A negative value indicates a discount, and a positive value indicates a markup, based on the

5) Policy_Reason (text):

• A textual explanation (up to 50 words) justifying the discount or markup applied to the cost. Examples include: "This client is new but has potential for growth." "The client is a repeat customer, deserving a loyalty discount." "Strategic importance of this client justifies special pricing." This feature is ideal for natural language processing (NLP) applications.

6) Total_Estimate (numeric): - The final estimated project cost, calculated as:

    (Material_Cost + Labor_Cost) × (1 + Profit_Rate/100) + Discount_or_Markup

• Represents the overall cost of the project after including the profit margin and any adjustments

Success.ai’s Construction Data for Building Materials & Construction Industry Leaders in Europe provides a reliable dataset tailored for businesses seeking to connect with leaders in the European construction and building materials sectors. Covering contractors, suppliers, architects, and project managers, this dataset offers verified profiles, firmographic insights, and decision-maker contacts.

With access to over 700 million verified global profiles and data from 70 million businesses, Success.ai ensures that your outreach, market analysis, and strategic partnerships are powered by accurate, continuously updated, and AI-validated information. Backed by our Best Price Guarantee, this solution empowers you to engage effectively with the construction industry across Europe.

Why Choose Success.ai’s Construction Data?

1. Verified Contact Data for Industry Leaders

   • Access verified work emails, phone numbers, and LinkedIn profiles of construction executives, project managers, and building material suppliers.
   • AI-driven validation ensures 99% accuracy, optimizing outreach and minimizing inefficiencies in communication.

2. Comprehensive Coverage Across Europe’s Construction Sector

   • Includes profiles from major construction hubs such as Germany, France, the UK, Italy, and Spain, covering a diverse range of projects and organizations.
   • Gain insights into regional construction trends, material sourcing strategies, and large-scale project developments.

3. Continuously Updated Datasets

   • Real-time updates reflect changes in leadership, market expansions, material innovations, and project announcements.
   • Stay ahead of market trends to align your strategies with evolving industry needs.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible use of data and compliance with legal standards.

Data Highlights:

• 700M+ Verified Global Profiles: Engage with decision-makers, contractors, architects, and engineers in Europe’s construction sector.
• 70M Business Profiles: Access firmographic data, including company sizes, revenue ranges, and geographic locations.
• Decision-Maker Contacts: Connect directly with CEOs, procurement officers, and project leads driving construction projects and material procurement.
• Industry Insights: Gain visibility into supply chain networks, sustainable building initiatives, and innovative construction techniques.

Key Features of the Dataset:

1. Leadership Profiles in Construction

   • Identify and connect with leaders responsible for major construction projects, material sourcing, and architectural planning.
   • Target professionals making decisions on vendor selection, project timelines, and compliance.

2. Advanced Filters for Precision Campaigns

   • Filter companies by industry segment (commercial construction, residential, infrastructure), geographic location, or revenue size.
   • Tailor campaigns to align with regional construction challenges, such as sustainability, cost management, or urbanization.

3. Firmographic Insights and Project Data

   • Access data on company structures, project scopes, and market positioning to refine your targeting strategy.
   • Use these insights to identify high-value prospects and uncover new business opportunities.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data enable personalized messaging, highlight unique value propositions, and improve engagement outcomes with construction stakeholders.

Strategic Use Cases:

1. Sales and Vendor Development

   • Offer construction materials, tools, or technology solutions to procurement teams and project managers in the construction industry.
   • Build relationships with vendors and contractors seeking innovative solutions to streamline operations and reduce costs.

2. Market Research and Competitive Analysis

   • Analyze trends in material usage, construction technologies, and sustainable practices to guide product development and marketing strategies.
   • Benchmark against competitors to identify market gaps, emerging needs, and high-growth opportunities.

3. Partnership Development and Supply Chain Optimization

   • Engage with companies seeking partnerships for large-scale projects, material sourcing, or technology integration.
   • Foster alliances that drive efficiency, quality, and innovation in construction projects.

4. Recruitment and Workforce Solutions

   • Target HR professionals and hiring managers recruiting skilled workers, architects, or engineers for ongoing and upcoming projects.
   • Provide staffing services, training platforms, or workforce optimization tools tailored to the construction sector.

Why Choose Success.ai?

1. Best Price Guarantee
   • Access premium-quality construction data at competitive prices, ensuring strong ROI for your marketing, sales, and bu...

This is the dataset included in the paper "Causes of time and cost overruns in construction projects: a scoping review"

China Construction: Project Cost data was reported at 24,632,363.170 RMB mn in 2022. This records an increase from the previous number of 24,139,114.125 RMB mn for 2021. China Construction: Project Cost data is updated yearly, averaging 4,797,161.861 RMB mn from Dec 1993 (Median) to 2022, with 30 observations. The data reached an all-time high of 24,632,363.170 RMB mn in 2022 and a record low of 265,141.530 RMB mn in 1993. China Construction: Project Cost data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Construction Sector – Table CN.EE: Construction Enterprise: All.

The F10 Database contains details of planned construction projects, provided to HSE by Construction Design Management co-ordinators on those projects. The types of projects that must notify HSE (those that last longer than 30 days or involve more than 500 person days of construction work) and the information that must be provided, are specified in Schedule 1 of The Construction Design and Management Regulations (CDM) 2007, which creates a legal duty to notify in these circumstances. The F10 database currently holds 292,948 records (as at 27/08/2013). F10 notifications remain in the database for 2 years from the project end date. Information held in the database is used to target HSE’s inspection work and for research to understand the shape and nature of the industry. The information held includes: Address of the construction site; Description of project type; Name and contact details for the CDM Co-ordinator, Principal Contractor and Client on the project; Name and contact details for any designers or other contractors on the project; Planned start date and duration of the project; Number of people and contractors at work on the site. Records cover Great Britain.

The database is mainly concerned by my researches in construction project management. The majority of the research relied on decision support system approaches and techniques. They include project delivery, planning for repetitive project's nature, value engineering study, evaluation, and selection process, and other issues as indicated in my database.

Comprehensive database showcasing planned and in-construction infrastructure projects worldwide, uncovering technology, capital flows, people in upcoming mega investments and business opportunities for market research and business intelligence

About this Dataset

A database of current and future construction projects in the South and Central America region, with contact types and business information. Includes geolocation.

Category

Business Information & Financials

Keywords

Construction,engineering,civil engineer,dataset,Data

Row Count

6625

Price

$1499.00

About this Dataset

A database of current and future construction projects in the Asia Pacific region, with contact types and business information. Includes geolocation.

Category

Business Information & Financials

Keywords

Construction,engineering,civil engineer,asia,pacific

Row Count

31680

Price

$1499.00

China Construction: Project Revenue data was reported at 26,800,738.923 RMB mn in 2022. This records an increase from the previous number of 26,245,381.074 RMB mn for 2021. China Construction: Project Revenue data is updated yearly, averaging 3,940,958.660 RMB mn from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 26,800,738.923 RMB mn in 2022 and a record low of 116,953.690 RMB mn in 1990. China Construction: Project Revenue data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Construction Sector – Table CN.EE: Construction Enterprise: All.

Construction Bidding Cost Database Market Outlook

According to our latest research, the global Construction Bidding Cost Database market size reached USD 1.98 billion in 2024. The market is exhibiting robust momentum, propelled by the increasing digitization of construction processes and a heightened demand for cost transparency and efficiency. With a projected compound annual growth rate (CAGR) of 11.7% from 2025 to 2033, the market is forecasted to attain a value of USD 5.51 billion by 2033. This impressive growth trajectory is primarily driven by the widespread adoption of advanced software solutions in construction project management, a growing emphasis on minimizing bid errors, and the rising need for real-time data analytics to support accurate project estimation and cost control.

One of the primary growth factors for the Construction Bidding Cost Database market is the increasing complexity of construction projects globally. As projects become larger and more intricate, stakeholders demand precise cost estimation and bidding processes to ensure profitability and competitiveness. The integration of artificial intelligence and machine learning within construction bidding platforms has further enhanced accuracy and streamlined workflows. These technologies enable predictive analytics, automate repetitive tasks, and reduce human errors, thereby improving overall project outcomes. The need for robust cost databases is underscored by the rising frequency of design changes and scope modifications, which necessitate agile and responsive cost management solutions throughout the project lifecycle.

Another significant driver is the regulatory environment and the growing focus on compliance and risk management in construction. Governments and industry bodies are increasingly mandating transparent bidding processes and accurate cost reporting to mitigate risks of fraud, misallocation of funds, and project delays. The adoption of Construction Bidding Cost Database solutions enables stakeholders to maintain comprehensive audit trails, adhere to regulatory standards, and facilitate more effective communication among project participants. This is particularly evident in public infrastructure projects, where transparency and accountability are paramount. As a result, the market is witnessing accelerated adoption across both public and private sectors, further fueling its expansion.

The proliferation of cloud-based solutions is another key factor contributing to market growth. Cloud deployment offers unparalleled accessibility, scalability, and integration capabilities, allowing construction firms to manage bids and cost data across multiple locations and projects seamlessly. This has proven invaluable amidst the ongoing digital transformation of the construction industry, as organizations seek to leverage real-time data sharing and collaboration tools. Additionally, the shift towards integrated project delivery models and the use of Building Information Modeling (BIM) are reinforcing the need for comprehensive cost databases, enabling more accurate forecasting, budgeting, and resource allocation.

In the realm of construction project management, Construction Takeoff Software plays a pivotal role by automating the quantification process of materials and labor needed for a project. This software significantly reduces the time and effort required for manual takeoffs, thereby enhancing accuracy and efficiency. By integrating with construction bidding cost databases, takeoff software ensures that estimators have access to real-time data, facilitating more precise cost estimations and competitive bidding. The ability to quickly adjust to design changes and scope modifications is another advantage, as it allows construction firms to remain agile and responsive to client needs. As the industry continues to embrace digital transformation, the adoption of construction takeoff software is expected to rise, further driving the demand for comprehensive cost database solutions.

From a regional perspective, North America currently dominates the Construction Bidding Cost Database market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific. The United States leads in terms of technology adoption and investment in digital construction solutions, while the Asia Pacific region is experiencing the fastest growth, driven by rapid urbaniza

As of November 2024, several high-value construction projects to develop wind farms and clean energy infrastructure among other projects had been added to the Australian Construction Infrastructure Forum (ACIF) Major Projects Database in Australia, with commencement dates between 2025 and 2030. The Elanora offshore wind farm stages 1 & 2 project had the highest value across the thirty major construction projects at around ** billion Australian dollars, with an expected start date of June 2029.

As of November 2024, several high-value construction projects to develop residential buildings had been added to the Australian Construction Industry Forum (ACIF) Major Projects Database in Australia, with commencement dates between 2018 and 2028. The Former West End Brewery Site Mixed Used Development and Rosehill Race Course Residential & School Redevelopment projects had the highest values across the thirty major construction projects at around *********** Australian dollars, respectively.

The Capital Projects Database reports information at the project level on discrete capital investments from the Capital Commitment Plan.Each row is uniquely identified by its Financial Management Service (FMS) ID, and contains data pertaining to the sponsoring and managing agency.

To explore the data, please visit Capital Planning Explorer

For additional information, please visit A Guide to The Capital Budget. Current version: 25exec

About this Dataset

A database of current and future construction projects in the Middle East and Africa region, with contact types and business information. Includes geolocation.

Category

Business Information & Financials

Keywords

Construction,engineering,civil engineer,dataset

Row Count

14843

Price

$1499.00

Russia Construction Projects Completed: Production: Lumber data was reported at 669.500 Cub m th in 2016. This records an increase from the previous number of 451.800 Cub m th for 2015. Russia Construction Projects Completed: Production: Lumber data is updated yearly, averaging 415.400 Cub m th from Dec 1992 (Median) to 2016, with 23 observations. The data reached an all-time high of 1,596.000 Cub m th in 2004 and a record low of 12.000 Cub m th in 1992. Russia Construction Projects Completed: Production: Lumber data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Construction and Properties Sector – Table RU.EC002: Construction Projects Completed: by Industry.

The Project Management Data Retrieval and Integration Database (PMDRI) is a system that presents data from the VA Financial Management System(FMS) in a structured format so that CFM engineering personnel have an up-to-date accounting of construction contract spending. Due to the nature of the information, access is restricted to CFM personnel and select VA personnel working with construction projects.

Russia Construction Projects Completed: Grain Elevators: Capacity data was reported at 224.500 Ton th in 2016. This records a decrease from the previous number of 301.000 Ton th for 2015. Russia Construction Projects Completed: Grain Elevators: Capacity data is updated yearly, averaging 65.000 Ton th from Dec 1992 (Median) to 2016, with 19 observations. The data reached an all-time high of 393.500 Ton th in 2012 and a record low of 2.400 Ton th in 2005. Russia Construction Projects Completed: Grain Elevators: Capacity data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Construction and Properties Sector – Table RU.EC002: Construction Projects Completed: by Industry.

The NYC Department of City Planning's (DCP) Housing Database contains all NYC Department of Buildings (DOB) approved housing construction and demolition jobs filed or completed in NYC since January 1, 2010. It includes the three primary construction job types that add or remove residential units: new buildings, major alterations, and demolitions, and can be used to determine the change in legal housing units across time and space. Records in the Housing Database Project-Level Files are geocoded to the greatest level of precision possible, subject to numerous quality assurance and control checks, recoded for usability, and joined to other housing data sources relevant to city planners and analysts. Data are updated semiannually, at the end of the second and fourth quarters of each year. Please see DCP's annual Housing Production Snapshot summarizing findings from the 21Q4 data release here. Additional Housing and Economic analyses are also available. All previously released versions of this data are available on the DCP Website: BYTES of the BIG APPLE. Current version: 25q2