The Elephant in the Room: AI in Real Estate

This article was written by Anton Tonev, co-founder of Rexera, who talks about everything real estate-related and the impact of technology on the future of the industry.

In my first post, I discussed how the real estate industry is facing its “Model T moment” – a pivotal point where innovation and technology can transform the way we do business. Just as the Model T revolutionized the automotive industry, AI has the potential to reshape real estate transactions, making them more efficient, accurate, and streamlined.

However, it’s crucial to address the elephant in the room: the concern that AI might replace the human touch in real estate. Let me be clear – real estate is, and always will be, about personal connections, trust, and helping people through one of the most significant moments in their lives. AI is not here to replace that; it’s here to enhance it.

What Is AI, Exactly?

Artificial Intelligence (AI) is a broad term that encompasses various technologies and approaches aimed at enabling machines to perform tasks that typically require human intelligence. This includes problem-solving, learning, perception, and decision-making.

A Brief History of AI

Artificial Intelligence traces its roots to the mid-20th century when pioneers like Alan Turing explored the potential of machines to mimic human intelligence. The term “AI” was coined at the 1956 Dartmouth Conference, marking the official start of AI research. Early advancements were slow, marked by periods of high expectations followed by disappointment and reduced funding, known as “AI winters.” 

However, In recent years, the exponential growth in data and computing power (particularly GPUs (graphics processing units)) has fueled the rapid development of AI. The advent of big data and cloud computing has made it possible to train AI models on vast amounts of information, enabling them to learn and adapt at an unprecedented scale. This has led to breakthroughs in various subfields of AI, such as machine learning and natural language processing.

Types of AI

So, how did we arrive at Large Language Models (LLMS)?

Statistics and Early AI

Early AI approaches were rooted in statistics and mathematical modeling. While groundbreaking, these methods were limited by the lack of sufficient data and computing power to fully realize their potential. Despite these limitations, statistical approaches laid the groundwork for more advanced AI techniques that would emerge in the following decades.

Machine Learning

As technology progressed, Machine Learning emerged as a significant milestone in AI. Machine Learning algorithms, such as decision trees, neural networks, and support vector machines, enabled computers to learn from data without being explicitly programmed. These techniques have been successfully applied to a wide range of problems, from image classification to predictive analytics. The increasing availability of data and computing power has allowed Machine Learning models to become more sophisticated and accurate over time.

Large Language Models (LLMs)

The most recent and perhaps most exciting development in AI is the rise of Large Language Models (LLMs). LLMs, such as GPT-4 (ChatGPT), BERT (Google), and Llama (Meta), represent a paradigm shift in AI. Unlike traditional Machine Learning models, LLMs are trained on vast amounts of unlabeled text data, allowing them to develop a deep understanding of language and perform a wide range of tasks without being explicitly trained for each one. The scale and generalization ability of LLMs set them apart from previous AI approaches. How LLMs work is beyond the scope of this post; however, these sources are great if you want to dig deeper:

1. A jargon-free explanation of how AI large language models work | Ars Technica
2. Generative AI exists because of the transformer
3. LLMs for Dummies

AI in Other Industries

AI has already proven its worth in various industries, driving significant improvements in efficiency, accuracy, and customer experience. For example:

• In healthcare, AI is being used to analyze medical images, predict disease outcomes, and personalize treatment plans. AI-powered tools have helped doctors make more accurate diagnoses and develop targeted therapies for patients.
• In finance, AI is employed for fraud detection, risk assessment, and algorithmic trading. AI algorithms can analyze vast amounts of financial data in real time, identifying potential risks and opportunities that human analysts might miss.
• In manufacturing, AI powers predictive maintenance, quality control, and supply chain optimization. By analyzing sensor data from equipment, AI can predict when maintenance is needed, reducing downtime and extending the lifespan of machinery.

These success stories demonstrate that AI is not just a theoretical concept; it has real-world applications that can drive tangible business outcomes. As AI continues to evolve, we can expect to see even more innovative use cases emerge across industries.

Human vs. & Machine: Match Made in Heaven

When it comes to AI in real estate, it’s understandable why some worry about job security, cyber fraud, and the potential loss of the personal touch that clients value. These concerns are valid and based on the perception that technology might automate too much, expose us to risks we don’t understand, and reduce the need for human interaction. But it’s important to remember that AI can enhance the tasks that don’t touch confidential information and don’t need to put a personal touch on. All those manual data entry tasks, follow-ups, document analyses, and error checks no longer need to steal time from your day.  

I am convinced that advancements in AI will allow us to create real estate’s own “Model T.” Here are a few examples of why I feel so strongly and positively about AI and Real Estate.

What AI Can Help You With

Integrating AI can be as simple as using it to generate FAQs (Frequently Asked Questions), ask the AI model questions (always being mindful of hallucinations), or become an expert overnight in Word and Excel using Microsoft’s Copilot. This approach allows everyone on the team to get comfortable with the technology on a smaller scale. 

As confidence in using AI grows, you can gradually expand its role in more complex areas of your business. At Rexera, for example, we use AI to help us predict any issues with document requests to HOAs. Similarly, you can integrate AI in tasks that traditionally take an unnecessarily long time— like extracting and interpreting key data points from complicated closing documents. 

And this is just the tip of the iceberg. Achieving full automation will take time and might never happen in certain corner cases. However, imagine a real state professional who doesn’t spend much time ordering Title Searches, HOA Documents, Payoffs, Credit Reports (or your favorite closing document). I am willing to bet dollars to donuts that this real estate professional will provide their clients with exceptional customer service and build long-lasting relationships ultimately leading to more business.

Guiding AI in the Right Direction 

We should also remember that AI is guided by people, and its direction depends on us. To maintain trust and integrity in our client relationships, we need to ensure that we use AI ethically. 

This means being transparent and fair in how we use and interpret data, ensuring that all actions are openly communicated, and ensuring that the algorithms employed do not inadvertently discriminate or bias against any group of people.

In the same manner, confidentiality in our industry is crucial. When you start with AI, never share confidential information (especially client data). Even though the privacy problem is solved, you must first master the basics.

The Impact of AI on Relationship Building

AI’s true value lies in its ability to handle repetitive tasks, freeing up real estate professionals to invest more time in what they do best—building relationships and helping clients with one of the most important decisions in their lives.

When we talk about AI in real estate, it all comes down to using AI as a tool to enhance, not replace, the personal connections that are crucial. 

Now that AI handles the data-heavy lifting, we can choose whether we’ll take advantage of its capabilities. Whether we should use it to help us get back the time to focus on understanding and meeting clients’ needs— a task that technology can’t replicate. 

This collaboration is about harnessing the best of both worlds to deliver exceptional, unreasonable hospitality in customer service.

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