The Datapreneurs #2 | Pimp My App : How Gen AI is set to disrupt and transform the Real Estate industry
Pimp My App—a series where we explore how data and AI can transform everyday applications and take them to the next level.
A lot of people still say AI is just a trend 🤷♂️, but remember when people thought the same about calculators and computers? Look how essential they've become today. AI is no different—it’s here, and it’s here to stay. I’m certain of it. That’s why it’s so important not just to use AI, but to understand how it works. Think of it like a calculator: sure, it’s a powerful tool, but those who can calculate in their heads will always have an edge 🧠.
This is exactly why I created this newsletter—to help people like you use AI to build profitable apps, while also learning about AI through real business cases 📈.
But enough introspection, let’s get into today’s episode of Pimp My App—a series where we explore how data and AI can transform everyday applications and take them to the next level. 🚀
Real estate is one of the oldest industries in the world, but even this age-old sector is on the verge of a big change thanks to AI and generative models.
In this edition, we’ll explore how cutting-edge models like :
ControlNet
and Stable Diffusion are revolutionizing the real estate game.
Let’s dive in and discover how! 🏡💡
Pimp My App #2: How Gen AI is set to disrupt and transform the Real Estate industry
Ideal Customer Profile (ICP) 👤:
🏢 Real Estate Companies
🏡 Real Estate Buyers
📐 Architects
🛋️ Interior Designers
The Problem ControlNet and Stable Diffusion Solve:
Real estate is a visual industry—buyers want to envision their future home, sellers want their properties to shine, and real estate companies need efficient ways to showcase and improve listings. But there are three core challenges faced by all of them:
Easily visualize a new home 🏘️
Stage a home in just a few clicks 🖼️
Plan renovations before breaking ground 🛠️
Eureka💡: Use ControlNet to imagine the house of your dreams!
With ControlNet and Stable Diffusion XL, you can easily:
Remove unwanted objects from a room 🪑
Visualize renovations before committing to changes 🏗️
Design your dream home from scratch, based on your vision ✨
Before we dive into how, let me quickly explain what ControlNet is and how it works.
What is ControlNet?
ControlNet is a special type of neural network designed to "control" diffusion models like Stable Diffusion by adding extra conditions to the image generation process. Essentially, it’s like giving your AI very specific instructions and watching it generate images with precise control. Think of it as a blueprint generator 🏗️.
In this case, we can use ControlNet with Stable Diffusion to guide how we create images. For example, if we want to redesign a room, ControlNet can focus on specific aspects like depth, edges, or shapes, ensuring the AI understands the scene it's working with.
Here’s the key benefit: ControlNet is robust, even with smaller datasets (less than 50k images), making it accessible for people or businesses without huge resources. You can even train it on your personal device 💻, although for bigger projects, cloud computing services like GCP are better.
How It Works:
ControlNet enables a variety of image processing tasks:
Semantic segmentation: Identify objects in an image (great for redesigning interiors) 🛋️
Edge detection: Create outlines of objects for precise alterations 🖼️
Keypoints: Detect important points in a room layout 🔑
Here are some models you can use:
Models
lllyasviel/sd-controlnet-canny
Detects edges to outline objects in images (useful for defining spaces in a room).
lllyasviel/sd-controlnet-depth
Generates depth maps—useful for understanding the spatial layout of a home.
Soft edge detection for subtle design changes in a room.
Semantic segmentation for highlighting furniture, walls, and other objects.
How Can This Help in Real Estate?
Now, let’s get practical. Imagine this: You upload a picture of a room, and by using semantic segmentation (to detect furniture and layout) alongside line detection (to identify edges), ControlNet works with Stable Diffusion to generate an entirely new interior based on your prompt 🏡.
For example, you could prompt:
"Transform this room into an industrial-style living room with a fireplace." 🔥
And voilà—the AI transforms the room with just a few clicks. Here’s how this looks:
Enough Theory—Let’s Talk Implementation
Data Requirements:
Image Source: Photos of the rooms (you can use anything from property listings to personal photos).
Pre-trained Models: You can easily find these models on Hugging Face or through Google Cloud’s Vertex AI.
User Interface (UI): A clean and simple UI where users can upload their images and see the transformation in real time.
The Tech Stack:
To implement this, we’ll need the following:
Backend: Python to create the Api 🐍
Model: Hugging Face API or GCP (using Vertex AI) 🌩️
Frontend: React ⚛️ (or alternatives like Streamlit, Flask, or Django—whatever you’re comfortable with)
Data Architecture:
Let’s break down how to structure this app:
Train and fine-tune your ControlNet model 🎓
Create an API: Use Python with Django Rest Framework (DRF) or FastAPI to handle backend requests 🛠️.
Fetch the model: Use Hugging Face API to integrate your trained ControlNet model in the API calls 🤖.
Serve results to the UI: The frontend will display the before/after images based on user input 🖥️.
What we could do next ? 🤔
1.Real-time Augmented Reality (AR) Design Applications
What it could do: Allow users to interact with and modify spaces in real-time through AR headsets or smartphones (like Orion meta glasses).
Potential use: Users could "walk" through their homes while making design changes (moving walls, changing furniture, adjusting lighting, etc.) that are rendered instantly. For example, scanning a room with your phone or AR headset and using AI to modify it live.
2. Personalized AI Interior Designer
What it could do: An AI system that not only generates design concepts but adapts based on personal taste, budget, and space limitations.
Potential use: Homeowners could interact with an AI-driven interior designer that learns from their style preferences, suggesting decorations, furniture, or layouts. It would also take into account things like room dimensions, natural lighting, and color psychology.
3. 3D Reconstruction and Blueprint Generation
What it could do: Instead of just producing 2D images, these tools could generate 3D models of a home or an entire structure.
Potential use: This would allow architects, builders, and homeowners to explore fully realized 3D models that can be rotated, zoomed in on, and even "walked through" before building or renovating starts.
4. Automated Cost and Materials Estimation
What it could do: As part of home renovation or design generation, the AI could estimate the cost of materials, labor, and time for each project.
Potential use: After generating a dream home or renovation, the system could provide users with a detailed breakdown of what it would take to make the changes, right down to the materials needed and projected costs.
5. AI-driven Home Staging and Real Estate Visualization
What it could do: AI could assist real estate agents or homeowners in staging properties by virtually enhancing spaces with furniture, decor, or even improving lighting in photos.
Potential use: For homebuyers, these tools could generate various potential renovations or upgrades of properties before purchase. Real estate agents could provide clients with different versions of the same space (minimalist, modern, traditional).
6. Sustainable Design and Smart Energy Integration
What it could do: AI could suggest environmentally friendly renovation options or smart energy solutions for houses.
Potential use: Users could design homes that maximize natural energy sources (solar, geothermal, etc.), optimize insulation, or suggest how to integrate smart home technologies for energy efficiency.
These advancements would not only make AI-driven design tools more versatile and user-friendly but also significantly reduce the time, cost, and effort involved in home design, construction, and renovation.
Conclusion:
The integration of AI technologies like ControlNet and Stable Diffusion into everyday applications is opening up a whole new world of possibilities for how we interact with spaces 🏡. Imagine being able to delete unwanted objects from a room photo, stage a home in seconds, or even design your dream house—all with just the click of a button 🖱️.
These AI tools are already reshaping the real estate and design industries, but their potential goes far beyond that.
As we look toward the future, these tools are poised to evolve into fully immersive, collaborative platforms 🌐.
Picture a system where you can interact with spaces in real time, receiving personalized design suggestions that fit your taste and needs—whether it’s aesthetic, practical, or both. And it doesn't stop there.
AI could even offer automated cost estimates 💰 and sustainability insights 🌱, making design not just creative but also smart and eco-friendly.
The potential for AI in home design and renovation goes way beyond just making things look good. It’s about creating smarter, more efficient, and deeply personalized spaces that meet individual needs while taking into account practical constraints like budget, sustainability, and emotional well-being.
Imagine tools that offer real-time augmented reality (AR) design, automated compliance checks for building regulations, and even smart suggestions that help balance style with cost and environmental impact.
This is the future of home design—AI-driven solutions that revolutionize how we envision, plan, and build our living spaces, making them not just beautiful but also functional and forward-thinking.