Generative AI in Real Estate Market Size to Garner USD 1,047 Million by 2032

The global generative AI in real estate market size was estimated to be around US$ 351.9 billion in 2022. It is projected to reach US$ 1,047 billion by 2032, indicating a compound annual growth rate (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 market research report on the Generative AI in real estate market provides a comprehensive analysis of various key aspects. It includes the definition, classification, and application of Generative AI in real estate products. The report examines the development trends, competitive landscape, and industrial chain structure within the industry. Furthermore, it presents an overview of the industry, analyzes national policies and planning, and offers insights into the latest market dynamics and opportunities at a global level.

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Generative AI in Real Estate Market Report Scope

Read More: Generative Ai In Automotive Market Size to Garner USD 2,691.92 Million by 2032

The report presents the volume and value-based market size for the base year 2022 and forecasts the market’s growth between 2023 and 2032. It estimates market numbers based on product form and application, providing size and forecast for each application segment in both global and regional markets.

Focusing on the global Generative AI in real estate market, the report highlights its status, future forecasts, growth opportunities, key market players, and key market regions such as the United States, Europe, and China. The study aims to present the development of the Generative AI in real estate market by considering factors like Year-on-Year (Y-o-Y) growth, in addition to Compound Annual Growth Rate (CAGR). This approach enables a better understanding of market certainty and the identification of lucrative opportunities.

Regarding production, the report investigates the capacity, production, value, ex-factory price, growth rate, and market share of major manufacturers, regions, and product types. On the consumption side, the report focuses on the regional consumption of Generative AI in real estate products across different countries and applications.

Buyers of the report gain access to verified market figures, including global market size in terms of revenue and volume. The report provides reliable estimations and calculations for global revenue and volume by product type from 2023 to 2032. It also includes accurate figures for production capacity and production by region during the same period.

The research includes product parameters, production processes, cost structures, and data classified by region, technology, and application. Furthermore, it conducts SWOT analysis and investment feasibility studies for new projects.

This in-depth research report offers valuable insights into the Generative AI in real estate market. It employs an objective and fair approach to analyze industry trends, supporting customer competition analysis, development planning, and investment decision-making. The project received support and assistance from technicians and marketing personnel across various links in the industry chain.

The competitive landscape section of the report provides detailed information on Generative AI in real estate market competitors. It includes company overviews, financials, revenue generation, market potential, research and development investments, new market initiatives, global presence, production sites, production capacities, strengths and weaknesses, product launches, product range, and application dominance. However, the data points provided only focus on the companies’ activities related to the Generative AI in real estate market.

Prominent players in the market are expected to face tough competition from new entrants. Key players are targeting acquisitions of startup companies to maintain their dominance. The report

Reasons to Purchase this Report:

• Comprehensive market segmentation analysis incorporating qualitative and quantitative research, considering the impact of economic and policy factors.
• In-depth regional and country-level analysis, examining the demand and supply dynamics that influence market growth.
• Market size in USD million and volume in million units provided for each segment and sub-segment.
• Detailed competitive landscape, including market share of major players, recent projects, and strategies implemented over the past five years.
• Comprehensive company profiles encompassing product offerings, key financial information, recent developments, SWOT analysis, and employed strategies by major market players.

Key Players

• 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

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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