“The real estate investment industry is undergoing a rapid transformation due to advancements in technology. From customer engagement to asset management, digital tools and platforms are changing the way real estate investment companies operate. Here are five key trends shaping the future of the industry.
- Automation of back-office operations: Real estate investment companies are adopting artificial intelligence and machine learning to automate manual tasks, such as contract generation, property valuations, and market analysis. This helps reduce the risk of errors and save time, allowing investment companies to focus on high-value tasks and better serve their clients.
- Customer engagement: Investment companies are leveraging chatbots and virtual assistants to provide 24/7 customer support, answer frequently asked questions, and guide users through the investment process. By automating these tasks, investment companies can enhance the customer experience and increase customer satisfaction.
- Use of data and analytics: Real estate investment companies are using big data and analytics to gain insights into market trends, property valuations, and investment opportunities. This allows them to make informed investment decisions, identify and target potential leads, and enhance their overall performance.
- Mobile and cloud technologies: Mobile and cloud technologies are becoming increasingly important in the real estate investment industry. Investment companies are using mobile apps to manage their assets, monitor market trends, and communicate with clients. Cloud technologies are allowing investment companies to store and access data from anywhere, improving efficiency and collaboration.
- Integration with proptech: Real estate investment companies are partnering with proptech firms to access innovative technologies, such as virtual reality, augmented reality, and blockchain. These technologies allow investment companies to provide a more immersive and secure investment experience for their clients.
In conclusion, the digital transformation of the real estate investment industry is ongoing, and investment companies must adapt to stay ahead of the curve. By embracing these trends, investment companies can enhance their operations, improve the investment experience for their clients, and remain competitive in a rapidly changing market.”
Source: ChatGPT
It’s hard to imagine the passage above is a direct output of innovative technology, ChatGPT. We were intrigued by what the chatbot driven by generative artificial intelligence could tell us about digital transformation in real estate, and it took only seconds for it to come back with the response.
While several of these AI-powered text generators exist, ChatGPT is perhaps the best known. The technology was launched in November 2022 by San Francisco-based OpenAI. ChatGPT builds on Generative Pre-trained Transformer (GPT) architecture that uses unsupervised machine learning to find patterns in a dataset without being given labeled examples or explicit instructions. The training process of generative AI involves powerful algorithms and advanced computer hardware, allowing the chatbot model to learn from vast amounts of data ingested from the internet, with the objective of generating informative communication.
Generative AI has a wide range of applications that will revolutionize the real estate industry. Already, AI-powered solutions are helping transform real estate organizations by:
- Streamlining workflows: Real estate organizations analyze large amounts of data, such as property listings, contracts, and client information. Generative AI can help automate many of these manual processes and eliminate repetitive responsibilities, freeing up time for real estate professionals to focus on more important tasks. For example, AI can be used to quickly analyze property listings to identify potential matches for investment or tenancy, reducing the time necessary to execute the transaction.
- Improving decision-making: Real estate organizations can leverage AI to make data-driven decisions. AI algorithms can be trained on large amounts of real estate data to identify patterns and trends that can be used to inform investment decisions. For example, AI can be used to identify geographies, asset classes, and capital market trends, to project real estate demand, allowing organizations to invest with strategic precision.
- Enhancing customer experience: Generative AI can help real estate organizations provide a better experience for their clients. For example, AI-powered chatbots can be used to provide quick and personalized responses to client inquiries. Additionally, AI can be used to provide virtual tours, allowing clients to experience properties without the time, cost or inconvenience associated with travel.
- Reducing risks: Real estate transactions often involve large sums of money, making it important to minimize risks. Generative AI can help real estate organizations identify potential risks early on, such as fraud or technology gaps. By using AI to analyze data and identify potential risks, real estate organizations can make informed decisions and reduce the likelihood of losses or breaches.
Limitations with demand
As usage of generative AI spreads globally, so does widespread criticism of its potentially serious flaws. The technology is trained to absorb and learn from multiple sources including unverified, unsourced and second-hand data, which creates a material risk of disinformation that may be incomplete, biased or wrong. Additionally, Generative AI systems might not pick up on controversial or unethical nuances, and therefore could multiply the spread of misinformation and, in extreme cases, serve as potential weapons for deceit.
Regardless, the popularity of Generative AI continues to grow. ChatGPT is the fastest-growing consumer application to date, having reached over 100 million users in January 2023, according to Wikipedia. Intense demand for the technology has resulted in server bottlenecks that have affected user access during high-volume periods, which will hopefully be alleviated with the launch of subscription models.
Illustrating the speed of this trend, the below visual represents Google searches for “Generative AI” over the past 12 months, with numbers representing search interest relative to the highest point on the chart for the given time. A value of 100 is the peak popularity for the term. A value of 50 means that the term is half as popular. A score of 0 means there was not enough data for this term.
Source: Google Trends; RSM US
Competitive market
Despite the present risks, technology giants are betting heavily on Generative AI. Microsoft deepened its relationship with OpenAI in January, with a multi-year investment valued at $10 billion that gave it a part-claim on OpenAI’s future profits in exchange for the computing power of Microsoft’s Azure cloud network. In addition, Microsoft is integrating the technology into its Bing search engine.
According to media reports, Google employees are intensively testing a chatbot called “Apprentice Bard”, and Google is preparing to use this “apprentice” to compete with ChatGPT. The Chinese search engine firm Baidu announced it would be launching a ChatGPT-style service called “Wenxin Yiyan” in Chinese or “ERNIE Bot” in English sometime in March.
Generative AI is rapidly becoming a reality. According to Bloomberg Business, the AI market is projected to reach $422.37 billion by 2028. Illustrated below, most large technology companies have already boosted capital spending on generative AI through the incorporation of large language models (LLM) into their cloud infrastructure. LLMs are used in systems such as generative AI and machine learning.
Source: Bloomberg Intelligence
The takeaway
Generative AI further expands the AI landscape that includes predictive analytics, computer vision and machine learning. Swiftly moving toward the point of singularity, technology is transforming our reality as we know it, and while there is room for improvement and a need for oversight, we expect to see increased adoption across every sector of real estate as a key driver of the industry’s digital transformation.
