Global Generative AI In Real Estate Market By Component (Software Tools, Services, and Platforms), By Deployment Mode (Cloud-based and On-premises), By Application (Property Valuation, Building Design, and Others), By End-User (Real estate agents,Architects, Engineers, and Others), By Region and Companies - Industry Segment Outlook, Market Assessment, Competition Scenario, Trends, and Forecast 2023-2032

Report Overview

Generative AI In Real Estate Market size is expected to be worth around USD 924.2 Mn by 2032 from USD 328.3 Mn in 2022, growing at a CAGR of 11.2% during the forecast period from 2023 to 2032.

The real estate industry is diverse and vast, and the adoption of generative AI can vary between regions and market segments. It is recommended that you consult market research reports and news articles focusing on real estate technology and AI to get the most current and accurate information about the global generative AI market. Generative AI for real estate is the use of artificial intelligence, notably generative models like generative adversarial networks (GANs) in the industry. AI algorithms are used to enhance and generate various aspects of real estates, such as property design, virtual staging, and property valuation. As AI technology advances and the demand for innovative solutions increases, the adoption of generative AI in real estate has increased. This market is expected to grow substantially in the coming years.

Driving factors

Investors and buyers of real estate are seeking more personalized solutions and experiences. Generative AI allows for the creation of personalized property designs, virtual staging, and tailored recommendations. This enhances the user experience while meeting evolving customer demands.  The use of AI in real estate can automate many processes including virtual staging and valuation, as well as property design. It can reduce the time and cost associated with traditional methods, such as manual property valuation or physical staging, by automating these tasks. The use of Generative AI allows potential investors and buyers to visualize property through 3D models or interactive tours. It allows individuals to assess and explore properties remotely. This could increase sales and investment opportunities.

The algorithms of generative AI can analyse vast amounts of data on real estate, market trends, and historical records. Generative AI can extract meaningful insights from these data to provide more accurate market analyses, property valuations, and price predictions. Real estate professionals can use this information to make informed decisions about their investments. The use of Generative AI allows for the creation of novel and innovative designs, based on parameters or criteria. This technology helps architects and developers explore a variety of design options and optimize space usage, as well as create unique and visually pleasing properties.

Restraining Factors

To generate accurate and reliable outcomes, generative AI models need a large amount of high-quality and available data. It can be difficult to obtain comprehensive and current data in the real estate industry. Property data can be fragmented and inconsistent. It is not always available in a standard format. This limitation can hamper the training and performance of generative AI models.

Generative AI, especially deep learning-based models, are often considered automotive black boxes. Although they can produce impressive results, the inner workings and reasoning behind the model's decisions may not be transparent. Interpretability and explainability are crucial in the real estate industry, where decisions can have significant financial consequences. Lack of transparency can undermine the trust between stakeholders and make it hard for them to understand and validate generated outputs.

The real estate market is subject to a variety of regulatory frameworks and requirements. Legal challenges may be faced by models of generative AI with privacy issues, intellectual property, fair housing laws, and consumer protection regulations. Complying with these regulations is complex and adapting generative AI to adhere to ethical and legal standards can be a challenge.

Generative AI is trained using historical data that can reflect inequalities and biases. AI models can magnify and perpetuate these biases if they are not addressed properly. This could lead to unfair or discriminatory results. The potential for bias within generative AI systems is critical in the real estate industry, where equality of opportunity and nondiscrimination is important. Integrating generative AI into existing real-estate workflows and systems is a time-consuming and complex process. Agents, appraisers, and developers may need training and support to use generative AI.

Covid-19 Impact on Generative AI In Real Estate Market

Due to a lockdown and social distance measures, property viewings in person, open houses, and interactions between agents, buyers, and sellers were restricted. Virtual staging, 3D tours, and AR applications that use generative AI to showcase properties remotely have become valuable tools. It allowed buyers to view properties virtually, and make informed decisions without having to visit the property.

The pandemic has accelerated the adoption and use of digital technologies and tools in the real estate sector, including generative artificial intelligence. Agents, developers, and marketers in the real estate industry have increasingly used generative AI to perform tasks such as creating realistic images of properties, designing virtual renovations, or creating personalized recommendations for property. These tools helped streamline the processes, improve marketing efforts, and bridge the gap caused by limited in-person interaction.

There was a growing demand for enhanced visualization techniques, which generative AI can provide. This is due to the limited availability of properties. Virtual staging allows vacant properties to virtually be furnished and presented to potential buyers. This creates a more immersive and appealing experience. AI-powered tools enabled architects and designers, for example, to create virtual walkthroughs and renderings. This allowed stakeholders to assess and visualize properties or constructions remotely. Generative AI is based on data to train and generate outputs.

The pandemic brought to light the importance of accurate, up-to-date data when making real estate decisions. COVID-19, however, introduced uncertainty and fluctuation in the market making it difficult to accurately predict trends. Real estate professionals needed to adjust their AI models and their analyses to take into account the new market dynamics.

Component Analysis

By the component analysis, the services sector dominates the market with a 43% market share.

By component, the analysis market is divided into software tools, services, and platforms. The services segment dominates the market because Service providers offer AI-powered chatbots or virtual assistants that respond to customer inquiries, give details about the property, and aid with the purchasing or renting process. service providers use the power of generative AI to analyze market data on real estate and produce complete reports and data that help businesses make educated choices.

Also, software tools that use the power of generative AI to offer precise property valuations that are based on different variables like location, amenities, as well as market information. Property Listing Generation Tools that utilize the power of generative AI to generate an automatic listing of properties, which include images, descriptions along with virtual tours. AI-driven platforms that automatize the management of property tasks such as rent collection and maintenance requests and lease management by leveraging generative AI capabilities.

Deployment Mode Analysis

By the deployment mode analysis, Cloud-based Deployment mode dominates the market with a 59% market share.

By deployment mode analysis market is divided into two segments, such cloud-based solutions and on-premise based. Cloud-based Deployment mode dominates the market because Cloud-based solutions can be accessed in any place that has an internet connection which allows realtors to use the generative AI applications and tools from multiple devices. Cloud-based services can be scalable that allows for more processing power, storage, and computational capacity when needed, making them suited for large-scale datasets as well as complex AI algorithms.

Cloud-based solutions typically use a pay-as-you-go model where customers pay for the resources and services they require. This reduces the necessity for upfront infrastructure investment and also reduces the cost of maintenance.

On-premises deployments allow greater flexibility to customize the dynamic AI software and other services to meet specific business needs. Real estate firms take full control of the technology, infrastructure, and algorithms that are used. On-premise deployments may offer faster response times and less delay because the algorithmic AI computations are done in-house, not dependent on any external networks.

Certain real estate companies might have legal or regulatory demands that require on-premises deployments to ensure compliance with data protection laws and internal policy. On-premise deployments require an upfront investment in software, hardware licenses along with IT infrastructure. The organizations also have the burden of maintaining as well as updates and security measures.

Application Analysis

By the application analysis, the Property valuation segment dominates the market with a 32% market share.

Based on application, the market is divided into Property Valuation, Building Design, Predictive Maintenance, and Energy Management.  The property valuation segment dominates the market because generative AI can analyze large amounts of property information, including historical sales data, market trends, and property traits, and provide precise property valuations. It can consider many aspects such as the location and amenities, the size, and the condition of the property to produce estimates that reflect current market conditions and the trends in property values.

Generative AI is also able to help designers and architects explore new design options and create innovative solutions for specific design problems. It can generate predictive models for maintenance which take into consideration previous data, environmental factors, and specifications for equipment to determine maintenance priorities.

Generative AI can analyze energy consumption data and provide information to improve the management of energy in buildings. It can identify opportunities for energy savings and recommend energy-efficient technology and create models that can be used to predict energy consumption. Generative AI is a great tool for developing smart building systems which automatically adjust the amount of energy consumed according to the occupancy of buildings, weather conditions, and many other variables.

Generative AI is a great tool to create virtual staging, decorating, and furnishing homes to increase their appeal to promote their services. It can assist in the creation of a listing of properties by automatically creating captivating descriptions of the property, selecting suitable images, and even designing virtual tours. Generative AI can analyze market data and offer insights on trends in real estate to help professionals make informed decisions when buying, selling, or investing in properties.

End-User Analysis

By the end-user analysis, the real estate agent sector dominates the market with a 31% market share.

By the end user analysis market is divided into four segments Real Estate Agents, Property Managers, Architects & Engineers. The real estate agent sector dominates the market because agents in real estate can benefit from the power of generative AI tools to appraise a property that provides accurate and timely valuations using information about the property's market value and its particulars.

Generative AI can aid in creating property listings, by automatically generating appealing descriptions, identifying suitable pictures, as well as creating 3D models or virtual tours. AI-powered chatbots, also known as virtual assistants can assist agents answer customer queries and provide information about properties or schedule viewings. They also aid in the renting or buying process.Generative AI is a tool that can assist property managers with various aspects of their work.

Artificial Intelligence-powered property management software can automatize tasks like rent collection, maintenance coordination lease management, as well as communication with tenants. Predictive maintenance employing the power of generative AI will assist building managers to identify needs for maintenance in maximizing schedules and ensuring efficient operations for buildings. Tools for managing energy powered by intelligent AI can assist property managers optimize their energy use, reduce expenses, and enhance sustainability. Generative AI can assist architects in designing buildings by generating design modifications following predefined criteria as well as the preference of the client.

Key Market Segments

Based on Component

• Software tools
• Services
• Platforms

Based on Deployment mode

• Cloud-based
• On-premises

Based on Applications

• Property Valuation
• Building Design
• Predictive Maintenance
• Energy Management
• Other Applications

Based on End-User

• Real Estate Agents
• Property Managers
• Architects
• Engineers
• Other End-Users

Growth Opportunity

Generative artificial intelligence enables realistic, immersive, and interactive property visualization. This allows potential buyers to experience and explore properties virtually. Virtual staging, 3D tours, and augmented-reality applications can improve property marketing and attract more buyers. The demand for online viewing of properties and remote interaction will continue to increase.

Generative AI tools with interactive and captivating visualizations are in high demand. Generative AI algorithms analyze vast quantities of data including buyer preferences, property features, and market trends to produce personalized property recommendations.

These recommendations help buyers find properties that match their needs and preferences. Real estate agents and platforms that use generative AI can provide tailored property suggestions to improve the customer experience. AI models that generate data can be used to analyze market trends, historical property information, and different property attributes to produce more accurate valuations.

Both buyers and sellers can benefit from this by ensuring fair pricing and making informed decisions. Real estate professionals can use generative AI to automate the property assessment process, reduce manual effort and improve accuracy. Industry participants gain a competitive advantage by being able to identify and predict market trends.

Latest Trends

Generative Adversarial Networks are a popular technique for generative AI. It involves training two neural networks a generator and a discriminator to produce realistic and high-quality outputs. In the real estate market, GANs can be used to perform tasks like virtual staging, creating realistic images of the property, and creating virtual experiences that are immersive for potential buyers. Virtual Reality, Augmented Reality, and Generative AI are all being combined to create immersive and interactive real estate experiences. Virtual reality lets users explore and interact in a virtual environment.

Augmented reality overlays virtual elements in the real world to allow users to visualize renovations, interior design, and other options. AI algorithms that generate valuation estimates based on comprehensive data analysis are being increasingly used to analyze large datasets including historical sales of properties, market trends, and property attributes. This allows for more accurate property values.

These algorithms can consider many factors and produce valuation estimates that are based on a comprehensive analysis of data. Generative AI has been applied to the design and development of architectural structures. By generating multiple design options using specified criteria, generative artificial intelligence can help architects and designers explore many possibilities and optimize designs based on factors such as aesthetic appeal, energy efficiency, and space utilization.

Generative AI can be used to give personalized recommendations to buyers, based on factors such as their budget, preferences, and other relevant information. Generative AI helps buyers find properties that meet their requirements by analyzing large datasets, using recommendation algorithms, and utilizing generative AI.

Regional Analysis

North America Accounted for the Largest Revenue Share in Generative AI in the real estate Market in 2022.

North America and the United States have been leading the adoption of generative AI for the real estate industry. In major cities such as New York, San Francisco, and Los Angeles, generative AI has been used for property valuations, virtual staging, and personalized recommendations. A strong tech ecosystem in the region, which includes AI startups and technology firms, is driving innovation in real estate.

Europe is expected as Fastest Growing Region in Projected Period in Generative AI in real estate Market.

Europe has seen significant adoption of generative AI for real estate. Countries like France, Germany, and the United Kingdom have led the way. Virtual property tours, AI-driven valuation, and generative design of architectural projects are becoming more popular. Europe is also focusing on sustainable building practices. Generative AI is used to optimize designs to meet energy efficiency standards and green building requirements.

In the Asia Pacific region including China, Japan, and Singapore, generative AI has gained popularity in the real estate industry. In China, AI-powered platforms for property recommendations and virtual property tours have become popular. Singapore is exploring the use of generative AI in urban planning and development. The rapidly expanding real estate market in the region and its technological advances present a significant growth opportunity for generative AI.

Key Regions and Countries

North America

• US
• Canada
• Mexico

Western Europe

• Germany
• France
• The UK
• Spain
• Italy
• Portugal
• Ireland
• Austria
• Switzerland
• Benelux
• Nordic
• Rest of Western Europe

Eastern Europe

• Russia
• Poland
• The Czech Republic
• Greece
• Rest of Eastern Europe

APAC

• China
• Japan
• South Korea
• India
• Australia & New Zealand
• Indonesia
• Malaysia
• Philippines
• Singapore
• Thailand
• Vietnam
• Rest of APAC

Latin America

• Brazil
• Colombia
• Chile
• Argentina
• Costa Rica
• Rest of Latin America

Middle East & Africa

• Algeria
• Egypt
• Israel
• Kuwait
• Nigeria
• Saudi Arabia
• South Africa
• Turkey
• United Arab Emirates
• Rest of MEA

Market Share & Key Players Analysis

The generative AI market in the real estate industry comprises several key players. While it's challenging to provide an up-to-date market share analysis, here are some prominent companies and organizations that have made significant contributions to the field.

Top Key Players in Generative AI In Real Estate Market

• Autodesk
• OpenAI
• Matterport
• Archistar
• Gridics
• Cherry
• HqO
• ai
• io
• Other Key Players

Recent Development

• In 2020: The year 2020 will be the year that virtual staging using artificial intelligence generative AI became popular. Realtors and sellers have begun using AI-powered tools to virtual stage homes which allow prospective buyers to envision various design and furnishing options without having to physically stage the property.
• In 2020-2021: Researchers are focused on the development of AI systems that can automatically create 3D models of neighborhoods and buildings. Utilizing deep learning techniques and large data sets, these models aim to create realistic and precise 3D models of real estate property and their surroundings.
• In 2021: Researchers studied the application of generative AI in property-related recommendation systems. Through analyzing the preferences of users and previous information, AI algorithms were used to produce personalized recommendations about properties that meet the individual needs and preferences of each buyer or renter.

Report Scope