According to the new research report published by Precedence Research, titled “Generative AI in Real Estate Market (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) – Global Industry Analysis, Size, Share, Growth, Trends, Regional Outlook, and Forecast 2023-2032 (By Product: Traditional, Advanced; By Application: Pottery, Tiles, Abrasives, Sanitary wave, Bricks & pipes, Others; By End User: Medical, Industrial, Building & Construction, Others) – Global Industry Analysis, Size, Share, Growth, Trends, Regional Outlook, and Forecast 2023-2032”, the global generative AI in real estate market size is expected to be worth around US$ 1,047 million by 2032 and is poised to record a yearly growth rate of 11.52% from 2023 to 2032. The study investigates several elements and their consequences on the growth of the all-wheel drive market.
This report focuses on generative AI in real estate market volume and value at the global level, regional level and company level. From a global perspective, this report represents the overall generative AI in real estate market size by analysing historical data and future prospects. Regionally, this report focuses on several key regions: North America, Europe, the Middle East & Africa, Latin America, etc.
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 research report includes specific segments by region (country), by company, by all segments. This study provides information about the growth and revenue during the historic and forecasted period of 2017 to 2032. Understanding the segments helps in identifying the importance of different factors that aid the market growth.
Download a Free Copy of Our Latest Sample Report@ https://www.precedenceresearch.com/sample/3117
The study also provides important advancements in organic and inorganic growth strategies in the worldwide generative AI in real estate market. A lot of corporations are prioritizing new launches, product approvals, and other business expansion techniques. In addition, the report offers profiles of generative AI in real estate market firms and market strategies. Furthermore, the research focuses on top industry participants, providing information such as company profiles, components and services offered, recent financial data, and key developments.
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 |
Also read: Generative Ai In Automotive Market Size to Worth US$ 2,691.92 Million By 2032
Major Key Points Covered in Report:
Executive Summary: It includes key trends of the electric vehicle fuel cell market related to products, applications, and other crucial factors. It also provides analysis of the competitive landscape and CAGR and market size of the electric vehicle fuel cell market based on production and revenue.
Production and Consumption by Region: It covers all regional markets to which the research study relates. Prices and key players in addition to production and consumption in each regional market are discussed.
Key Players: Here, the report throws light on financial ratios, pricing structure, production cost, gross profit, sales volume, revenue, and gross margin of leading and prominent companies competing in the Electric vehicle fuel cell market.
Market Segments: This part of the report discusses product, application and other segments of the electric vehicle fuel cell market based on market share, CAGR, market size, and various other factors.
Research Methodology: This section discusses the research methodology and approach used to prepare the report. It covers data triangulation, market breakdown, market size estimation, and research design and/or programs.
Market Key Players
Company profiles have been included in the report, which include essentials such as product portfolio, key strategies, along with all-inclusive SWOT analysis on each player. Company presence is mapped and presented through a matrix for all the prominent players, thus providing readers with actionable insights, which helps in thoughtfully presenting market status and predicting the competition level in the generative AI in real estate market.
Some of the prominent players in the generative AI in real estate market include
- Autodesk
- OpenAI
- Gridics
- Cherry
- HqO
- ai
- Io
- Matterport
- Archistar
Generative AI in Real Estate Market Segmentations
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
Report Objectives
- To define, segment, and project the global market size for generative AI in real estate market
- To understand the structure of the market by identifying its various sub-segments
- To provide detailed information about the key factors influencing the growth of the market (drivers, restraints, opportunities, and industry-specific challenges)
- To analyse the micro-markets, with respect to individual growth trends, future prospects, and their contributions to the total market
- To project the size of the market and its submarkets, in terms of value, with respect to global. (along with their respective key countries)
- To profile key players and comprehensively analyse their core competencies
- To understand the competitive landscape and identify major growth strategies adopted by players across the globe.
- To analyse the competitive developments such as expansions & investments, new product launches, mergers & acquisitions, joint ventures, and agreements
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
Why should you invest in this report?
If you are aiming to enter the global generative AI in real estate market, this report is a comprehensive guide that provides crystal clear insights into this niche market. All the major application areas for generative AI in real estate are covered in this report and information is given on the important regions of the world where this market is likely to boom during the forecast period of 2023-2030 so that you can plan your strategies to enter this market accordingly.
Besides, through this report, you can have a complete grasp of the level of competition you will be facing in this hugely competitive market and if you are an established player in this market already, this report will help you gauge the strategies that your competitors have adopted to stay as market leaders in this market. For new entrants to this market, the voluminous data provided in this report is invaluable.
Contact Us:
Mr. Alex
Sales Manager
Call: +1 9197 992 333
Email: sales@precedenceresearch.com
Web: https://www.precedenceresearch.com
Blog: https://www.pharma-geek.com
