According to the research report, the global generative AI in real estate market size is expected to touch USD 1,047 million by 2032, from USD 351.9 million in 2022, growing with a significant CAGR of 11.52% from 2023 to 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.
The generative AI in real estate report offers a comprehensive study of the current state expected at the major drivers, market strategies, and key vendors’ growth. The report presents energetic visions to conclude and study the market size, market hopes, and competitive surroundings. The research also focuses on the important achievements of the market, research & development, and regional growth of the leading competitors operating in the market. The current trends of the global generative AI in real estate in conjunction with the geographical landscape of this vertical have also been included in this report.
The report offers intricate dynamics about different aspects of the global generative AI in real estate market, which aids companies operating in the market in making strategic development decisions. The study also elaborates on significant changes that are highly anticipated to configure growth of the global generative AI in real estate during the forecast period. It also includes a key indicator assessment that highlights growth prospects of this market and estimates statistics related to growth of the market in terms of value (US$ Mn) and volume (tons).
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This study covers a detailed segmentation of the global generative AI in real estate market, along with key information and a competition outlook. The report mentions company profiles of players that are currently dominating the global generative AI in real estate market, wherein various developments, expansions, and winning strategies practiced and implemented by leading players have been presented in detail.
Key Players
- Autodesk
- OpenAI
- Gridics
- Cherry
- HqO
- ai
- Io
- Matterport
- Archistar
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
Research Methodology
The research methodology adopted by analysts for compiling the global generative AI in real estate report is based on detailed primary as well as secondary research. With the help of in-depth insights of the market-affiliated information that is obtained and legitimated by market-admissible resources, analysts have offered riveting observations and authentic forecasts for the global market.
During the primary research phase, analysts interviewed market stakeholders, investors, brand managers, vice presidents, and sales and marketing managers. Based on data obtained through interviews of genuine resources, analysts have emphasized the changing scenario of the global market.
For secondary research, analysts scrutinized numerous annual report publications, white papers, market association publications, and company websites to obtain the necessary understanding of the global generative AI in real estate market.
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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