by The generative AI in real estate market size is poised to grow by USD 1,047 million by 2032 from USD 351.9 million in 2022, exhibiting a CAGR of 11.52% during the forecast period 2023-2032.
Key Takeaways:
- North America contributed more than 41% of revenue share in 2022.
- By component, the services segment shows a leading growth in the generative AI in real estate market.
- By deployment mode, the cloud-based segment generated more than 60% of the revenue share in 2022.
- By application, property valuation is the dominating segment in the generative AI in real estate market during the forecast period.
- By end-user, the real estate agents segment shares the maximum CAGR during the projection period.
Precedence Research has conducted a comprehensive market study that provides valuable insights into the performance of the market during the forecast period. The study identifies significant trends that are shaping the growth of the Generative AI in real estate market. In this recently published report, essential dynamics such as drivers, restraints, and opportunities are highlighted for both established market players and emerging participants involved in production and supply.
To begin with, the Generative AI in real estate Market report features an executive summary that offers a concise overview of the marketplace. It outlines the key players and industry categories expected to have an impact on the market in the coming years. The executive summary provides an unbiased summary of the market.
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Generative AI in Real Estate Market Report Scope
| Report Coverage | Details |
| Market Size in 2023 | USD 392.44 Million |
| Market Size by 2032 | USD 1,047 Million |
| Growth Rate from 2023 to 2032 | CAGR of 11.52% |
| Largest Market | North America |
| Base Year | 2022 |
| Forecast Period | 2023 To 2032 |
| Segments Covered | By Component, By Deployment Mode, By Applications, and By End-User |
| Regions Covered | North America, Europe, Asia-Pacific, Latin America, and Middle East & Africa |
Read More: Generative Ai In Automotive Market Size To Gain USD 2,691.92 Mn by 2032
The empirical study on the global Generative AI in real estate market primarily focuses on the drivers in subsequent sections. It demonstrates how changing demographics are projected to influence the supply and demand dynamics in the Generative AI in real estate Market. Our market report for the Generative AI in real estate market also delves into the significant rules and regulations that are likely to impact the future growth of this sector. Moreover, in order to comprehend the underlying demand factors, industry experts have provided insights into its fundamental origins.
Top Key Players:
- Autodesk
- OpenAI
- Gridics
- Cherry
- HqO
- ai
- Io
- Matterport
- Archistar
Data Sources and Methodology
To gather comprehensive insights on the Global Generative AI in real estate Market, we relied on a range of data sources and followed a well-defined methodology. Our approach involved interactions with industry experts and key stakeholders across the market’s value chain, including management organizations, processing organizations, and analytics service providers.
We followed a rigorous data analysis process to ensure the quality and credibility of our research. The gathered information was carefully evaluated, and relevant quantitative data was subjected to statistical analysis. By employing robust analytical techniques, we were able to derive meaningful insights and present a comprehensive overview of the Global Generative AI in real estate Market.
The most resonating, simple, genuine, and important causes because of which you must decide to buy the Generative AI in real estate market report exclusively from precedence research
- The research report has been meticulously crafted to provide comprehensive knowledge on essential marketing strategies and a holistic understanding of crucial marketing plans spanning the forecasted period from 2023 to 2032.
Key Features of the Report:
- Comprehensive Coverage: The report extensively encompasses a detailed explanation of highly effective analytical marketing methods applicable to companies across all industry sectors.
- Decision-Making Enhancement: It outlines a concise overview of the decision-making process while highlighting key techniques to enhance it, ensuring favorable business outcomes in the future.
- Articulated R&D Approach: The report presents a well-defined approach to conducting research and development (R&D) activities, enabling accurate data acquisition on current and future marketing conditions.
Generative AI in Real Estate Market Segmentation:
By Component
- Software Tools
- Services
- Platforms
By Deployment Mode
- Cloud-based
- On-premise
By Applications
- Property Valuation
- Building Design
- Predictive Maintenance
- Energy Management
By End-User
- Real Estate Agents
- Property Managers
- Architects
- Engineers
By Geography
- North America
- Europe
- Asia-Pacific
- Latin America
- Middle East and Africa
Reasons to Consider Purchasing the Report:
- Enhance your market research capabilities by accessing this comprehensive and precise report on the global Generative AI in real estate market.
- Gain a thorough understanding of the overall market landscape and be prepared to overcome challenges while ensuring robust growth.
- Benefit from in-depth research and analysis of the latest trends shaping the global Generative AI in real estate market.
- Obtain detailed insights into evolving market trends, current and future technologies, and strategic approaches employed by key players in the global Generative AI in real estate market.
- Receive valuable recommendations and guidance for both new entrants and established players seeking further market expansion.
- Discover not only the cutting-edge technological advancements in the global Generative AI in real estate market but also the strategic plans of industry leaders.
Table of Content
Chapter 1. Introduction
1.1. Research Objective
1.2. Scope of the Study
1.3. Definition
Chapter 2. Research Methodology (Premium Insights)
2.1. Research Approach
2.2. Data Sources
2.3. Assumptions & Limitations
Chapter 3. Executive Summary
3.1. Market Snapshot
Chapter 4. Market Variables and Scope
4.1. Introduction
4.2. Market Classification and Scope
4.3. Industry Value Chain Analysis
4.3.1. Raw Material Procurement Analysis
4.3.2. Sales and Distribution Channel Analysis
4.3.3. Downstream Buyer Analysis
Chapter 5. COVID 19 Impact on Generative AI in Real Estate Market
5.1. COVID-19 Landscape: Generative AI in Real Estate Industry Impact
5.2. COVID 19 – Impact Assessment for the Industry
5.3. COVID 19 Impact: Global Major Government Policy
5.4. Market Trends and Opportunities in the COVID-19 Landscape
Chapter 6. Market Dynamics Analysis and Trends
6.1. Market Dynamics
6.1.1. Market Drivers
6.1.2. Market Restraints
6.1.3. Market Opportunities
6.2. Porter’s Five Forces Analysis
6.2.1. Bargaining power of suppliers
6.2.2. Bargaining power of buyers
6.2.3. Threat of substitute
6.2.4. Threat of new entrants
6.2.5. Degree of competition
Chapter 7. Competitive Landscape
7.1.1. Company Market Share/Positioning Analysis
7.1.2. Key Strategies Adopted by Players
7.1.3. Vendor Landscape
7.1.3.1. List of Suppliers
7.1.3.2. List of Buyers
Chapter 8. Global Generative AI in Real Estate Market, By Component
8.1. Generative AI in Real Estate Market, by Component, 2023-2032
8.1.1. Software Tools
8.1.1.1. Market Revenue and Forecast (2020-2032)
8.1.2. Services
8.1.2.1. Market Revenue and Forecast (2020-2032)
8.1.3. Platforms
8.1.3.1. Market Revenue and Forecast (2020-2032)
Chapter 9. Global Generative AI in Real Estate Market, By Deployment Mode
9.1. Generative AI in Real Estate Market, by Deployment Mode, 2023-2032
9.1.1. Cloud-based
9.1.1.1. Market Revenue and Forecast (2020-2032)
9.1.2. On-premise
9.1.2.1. Market Revenue and Forecast (2020-2032)
Chapter 10. Global Generative AI in Real Estate Market, By Applications
10.1. Generative AI in Real Estate Market, by Applications, 2023-2032
10.1.1. Property Valuation
10.1.1.1. Market Revenue and Forecast (2020-2032)
10.1.2. Building Design
10.1.2.1. Market Revenue and Forecast (2020-2032)
10.1.3. Predictive Maintenance
10.1.3.1. Market Revenue and Forecast (2020-2032)
10.1.4. Energy Management
10.1.4.1. Market Revenue and Forecast (2020-2032)
Chapter 11. Global Generative AI in Real Estate Market, By End-User
11.1. Generative AI in Real Estate Market, by End-User, 2023-2032
11.1.1. Real Estate Agents
11.1.1.1. Market Revenue and Forecast (2020-2032)
11.1.2. Property Managers
11.1.2.1. Market Revenue and Forecast (2020-2032)
11.1.3. Architects
11.1.3.1. Market Revenue and Forecast (2020-2032)
11.1.4. Engineers
11.1.4.1. Market Revenue and Forecast (2020-2032)
Chapter 12. Global Generative AI in Real Estate Market, Regional Estimates and Trend Forecast
12.1. North America
12.1.1. Market Revenue and Forecast, by Component (2020-2032)
12.1.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
12.1.3. Market Revenue and Forecast, by Applications (2020-2032)
12.1.4. Market Revenue and Forecast, by End-User (2020-2032)
12.1.5. U.S.
12.1.5.1. Market Revenue and Forecast, by Component (2020-2032)
12.1.5.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
12.1.5.3. Market Revenue and Forecast, by Applications (2020-2032)
12.1.5.4. Market Revenue and Forecast, by End-User (2020-2032)
12.1.6. Rest of North America
12.1.6.1. Market Revenue and Forecast, by Component (2020-2032)
12.1.6.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
12.1.6.3. Market Revenue and Forecast, by Applications (2020-2032)
12.1.6.4. Market Revenue and Forecast, by End-User (2020-2032)
12.2. Europe
12.2.1. Market Revenue and Forecast, by Component (2020-2032)
12.2.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
12.2.3. Market Revenue and Forecast, by Applications (2020-2032)
12.2.4. Market Revenue and Forecast, by End-User (2020-2032)
12.2.5. UK
12.2.5.1. Market Revenue and Forecast, by Component (2020-2032)
12.2.5.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
12.2.5.3. Market Revenue and Forecast, by Applications (2020-2032)
12.2.5.4. Market Revenue and Forecast, by End-User (2020-2032)
12.2.6. Germany
12.2.6.1. Market Revenue and Forecast, by Component (2020-2032)
12.2.6.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
12.2.6.3. Market Revenue and Forecast, by Applications (2020-2032)
12.2.6.4. Market Revenue and Forecast, by End-User (2020-2032)
12.2.7. France
12.2.7.1. Market Revenue and Forecast, by Component (2020-2032)
12.2.7.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
12.2.7.3. Market Revenue and Forecast, by Applications (2020-2032)
12.2.7.4. Market Revenue and Forecast, by End-User (2020-2032)
12.2.8. Rest of Europe
12.2.8.1. Market Revenue and Forecast, by Component (2020-2032)
12.2.8.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
12.2.8.3. Market Revenue and Forecast, by Applications (2020-2032)
12.2.8.4. Market Revenue and Forecast, by End-User (2020-2032)
12.3. APAC
12.3.1. Market Revenue and Forecast, by Component (2020-2032)
12.3.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
12.3.3. Market Revenue and Forecast, by Applications (2020-2032)
12.3.4. Market Revenue and Forecast, by End-User (2020-2032)
12.3.5. India
12.3.5.1. Market Revenue and Forecast, by Component (2020-2032)
12.3.5.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
12.3.5.3. Market Revenue and Forecast, by Applications (2020-2032)
12.3.5.4. Market Revenue and Forecast, by End-User (2020-2032)
12.3.6. China
12.3.6.1. Market Revenue and Forecast, by Component (2020-2032)
12.3.6.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
12.3.6.3. Market Revenue and Forecast, by Applications (2020-2032)
12.3.6.4. Market Revenue and Forecast, by End-User (2020-2032)
12.3.7. Japan
12.3.7.1. Market Revenue and Forecast, by Component (2020-2032)
12.3.7.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
12.3.7.3. Market Revenue and Forecast, by Applications (2020-2032)
12.3.7.4. Market Revenue and Forecast, by End-User (2020-2032)
12.3.8. Rest of APAC
12.3.8.1. Market Revenue and Forecast, by Component (2020-2032)
12.3.8.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
12.3.8.3. Market Revenue and Forecast, by Applications (2020-2032)
12.3.8.4. Market Revenue and Forecast, by End-User (2020-2032)
12.4. MEA
12.4.1. Market Revenue and Forecast, by Component (2020-2032)
12.4.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
12.4.3. Market Revenue and Forecast, by Applications (2020-2032)
12.4.4. Market Revenue and Forecast, by End-User (2020-2032)
12.4.5. GCC
12.4.5.1. Market Revenue and Forecast, by Component (2020-2032)
12.4.5.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
12.4.5.3. Market Revenue and Forecast, by Applications (2020-2032)
12.4.5.4. Market Revenue and Forecast, by End-User (2020-2032)
12.4.6. North Africa
12.4.6.1. Market Revenue and Forecast, by Component (2020-2032)
12.4.6.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
12.4.6.3. Market Revenue and Forecast, by Applications (2020-2032)
12.4.6.4. Market Revenue and Forecast, by End-User (2020-2032)
12.4.7. South Africa
12.4.7.1. Market Revenue and Forecast, by Component (2020-2032)
12.4.7.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
12.4.7.3. Market Revenue and Forecast, by Applications (2020-2032)
12.4.7.4. Market Revenue and Forecast, by End-User (2020-2032)
12.4.8. Rest of MEA
12.4.8.1. Market Revenue and Forecast, by Component (2020-2032)
12.4.8.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
12.4.8.3. Market Revenue and Forecast, by Applications (2020-2032)
12.4.8.4. Market Revenue and Forecast, by End-User (2020-2032)
12.5. Latin America
12.5.1. Market Revenue and Forecast, by Component (2020-2032)
12.5.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
12.5.3. Market Revenue and Forecast, by Applications (2020-2032)
12.5.4. Market Revenue and Forecast, by End-User (2020-2032)
12.5.5. Brazil
12.5.5.1. Market Revenue and Forecast, by Component (2020-2032)
12.5.5.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
12.5.5.3. Market Revenue and Forecast, by Applications (2020-2032)
12.5.5.4. Market Revenue and Forecast, by End-User (2020-2032)
12.5.6. Rest of LATAM
12.5.6.1. Market Revenue and Forecast, by Component (2020-2032)
12.5.6.2. Market Revenue and Forecast, by Deployment Mode (2020-2032)
12.5.6.3. Market Revenue and Forecast, by Applications (2020-2032)
12.5.6.4. Market Revenue and Forecast, by End-User (2020-2032)
Chapter 13. Company Profiles
13.1. Autodesk
13.1.1. Company Overview
13.1.2. Product Offerings
13.1.3. Financial Performance
13.1.4. Recent Initiatives
13.2. OpenAI
13.2.1. Company Overview
13.2.2. Product Offerings
13.2.3. Financial Performance
13.2.4. Recent Initiatives
13.3. Gridics
13.3.1. Company Overview
13.3.2. Product Offerings
13.3.3. Financial Performance
13.3.4. Recent Initiatives
13.4. Cherry
13.4.1. Company Overview
13.4.2. Product Offerings
13.4.3. Financial Performance
13.4.4. Recent Initiatives
13.5. HqO
13.5.1. Company Overview
13.5.2. Product Offerings
13.5.3. Financial Performance
13.5.4. Recent Initiatives
13.6. ai
13.6.1. Company Overview
13.6.2. Product Offerings
13.6.3. Financial Performance
13.6.4. Recent Initiatives
13.7. Io
13.7.1. Company Overview
13.7.2. Product Offerings
13.7.3. Financial Performance
13.7.4. Recent Initiatives
13.8. Matterport
13.8.1. Company Overview
13.8.2. Product Offerings
13.8.3. Financial Performance
13.8.4. Recent Initiatives
13.9. Archistar
13.9.1. Company Overview
13.9.2. Product Offerings
13.9.3. Financial Performance
13.9.4. Recent Initiatives
Chapter 14. Research Methodology
14.1. Primary Research
14.2. Secondary Research
14.3. Assumptions
Chapter 15. Appendix
15.1. About Us
15.2. Glossary of Terms
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