AI in real estate: 2022 VS 2025

Neural networks are now universal assistants across various business sectors, including real estate. They've started automating many tasks and became psychologists, analysts, designers, and much more for us.

To understand how artificial intelligence evolved, how we reached today's point, and what development prospects emerged in real estate, we've prepared this analysis.

Why it's important to track AI technology market development in real estate

1. See the industry's strategic direction

Understanding AI adoption helps predict market direction: from simple text generation to smart platforms personalizing every client contact. This allows strategy adjustment and implementing proven tools.

2. Identify growth points

Evolution shows when technologies become commercially mature. If AI was experimental in 2022, by 2025 it became full infrastructure.

3. Track client demand and market trust

As technologies develop, clients realize AI improves property search, analysis, verification, responds faster to inquiries — boosting loyalty and transparency toward digital services. Gradually comes understanding that recommendations are backed not by "airy" numbers, but algorithms based on real data.

Studying AI market evolution is not about the past, but forecasting the future. It's crucial to understand that AI technologies will be implemented in every company going forward, and the later you start diving into this direction, the faster competitors will overtake you.

AI in 2022: Toy for "geeks"

ChatGPT (GPT-3.5) emergence and first public development access
First attempts solving coding problems in chat
Text generation attempts: dissertations, essays
Explaining complex math topics "on fingers"

AI in 2023: Mass personal usage

GPT-4, ChatGPT Plus emergence
Business decision assistance
Active reflection and personal application
First AI integrations into Telegram, Instagram interfaces
API development and fierce competition for user attention (Claude, Llama, Gemini, Mistral)

AI in 2024–2025: Business ecosystem integration

Selling ready solutions, custom-trained agents integrated into business via API.

AI as B2B solution

Creating dedicated AI applications
Interaction with multiple interfaces (CRM, project databases, etc.)
Training for specific tasks
Impact on metrics and performance indicators

Follow our #ai_realestate series in Telegram channel RED LAB

We regularly publish materials there about AI development in real estate:

analyzing AI implementation challenges in the industry;
showing what functionality can already be trusted to AI vs. what it still handles worse than humans;
sharing examples of AI implementation in companies and where to start.