Practical AI for Real Estate
7h - ONLINE COURSE
CHOOSE YOUR COURSE (BST times)
One full day, 9am-5pm GMT
or 3x2h 3pm-5:20pm GMT
Fully practical - leave ready to use AI
Delivered by expert academics from University College London
Tailored to real estate professionals
Practical AI for real estate - course description
The course is an essential primer on generative AI for real estate professionals. It covers the basics of what AI is and how to use it in an accessible way tailored to the real estate industry. It requires no technical knowledge or AI skills.
We use experiential learning techniques that are both engaging and effective in delivering applicable knowledge fast.
The course is extremely practical and features multiple cases studies designed by a team of world-leading academics from University College London (ranked #1 in the world for Built Environment) specifically for this course.
Each case study demonstrates an application of state-of-art, research-based use of AI techniques to solve a very relatable problem.
All uses cases we present have been developed in-house by academics collaborating with industry experts. They have been extensively tested on real life problems by course instructors.
ON COMPLETION OF THIS COURSE YOU WILL:
Understand the fundamentals of AI and Machine Learning and their application in real estate.
Have the skills to apply AI for real estate market research, strategy and risk analysis and data visualisation.
Understand ethical considerations of using AI in real estate.
Know the advantages and potential pitfalls of AI implementation in real estate.
COURSE CURRICULUM
Session 1: Practical introduction to AI models
This session offers the essential knowledge of what AI is and how it operates, tailored for real estate professionals eager to incorporate AI into their business practices. It is designed to be accessible to everyone, irrespective of technical background, providing a necessary foundation for all subsequent sessions and future independent work with AI. More specifically, the session covers:
An accessible overview of artificial intelligence, with a particular focus on conversational AI and advanced language models like GPT-4.
The distinction between AI and traditional software, highlighting AI's ability to derive rules from objectives, in contrast to the predefined rules in conventional software.
The inner workings of neural networks, the AI learning process, and text understanding through techniques such as tokenization, embedding, and self-attention.
Strategies for minimizing AI output errors, effective prompting techniques, and an overview of the top AI products.
Session 2: Market research with generative AI
The session follows an office market research project to explain and demonstrate key skills for leveraging AI in handling documents and reports such as:
prompt engineering
AI-powered searching
critical thinking with AI
insights and recommendations with AI
limitations of AI in working with text
It delves into AI's capability in enhancing document analysis, underscoring the significance of context in communication and the development of effective AI prompting strategies.
The session highlights the crucial role of AI in synthesizing vast amounts of market data, drawing objective, context-aware conclusions. It stresses the importance for real estate professionals to not only understand but also effectively utilize these advanced tools for gaining competitive insights.
Session 3: Analysis, strategy and forecasting
The session introduces using generative AI in business analysis, in the real estate sector.
It starts by covering the integration of qualitative information in analysis, outlining the benefits and challenges of using qualitative data and how generative AI can enhance precision and speed in this context.
Then, it delves into the creation of strategies with AI assistance, highlighting steps like setting context, defining problems, specifying tasks, and prompt generation for effective strategy formulation.
It considers two key case studies: using AI as a tool to execute strategy generation and using AI as a consultant to develop a complete strategy for a specific problem.
Next, the session explores the application of AI in mathematical operations, contrasting the capabilities of Excel and large language models (LLMs) in handling mathematical tasks.
Finally, it progresses to practical aspects of strategic real estate analysis, like simple valuation, sensitivity analysis, and Monte Carlo Analysis, demonstrating how these can be approached using gen AI. It concludes with a case study on forecasting and building custom models using AI.
Session 4: Data visualization
This session presents AI capabilities to write and execute code from natural language prompts using the example of creating data visualizations relevant to real estate professionals. In particular, it covers:
Basic data processing and analysis tasks using real data and how to turn it into an advanced analysis by entering the right prompts.
Creation, customization, and analysis of diverse chart types, including bar charts, pie charts, and time series plots, all made accessible through natural language commands.
How to effortlessly generate maps with your own data, regardless of technical skills or background.
Animated visualizations of graphs and maps, adding an engaging dimension to data presentation.
Development of interactive visualizations such as dynamic dashboards or even interactive maps.
Key takeaways include the ability to handle diverse data types, the use of AI to interpret code and generate visuals, and techniques for creating compelling, interactive data presentations.
Session 5: Navigating AI in real estate
The session consists of three diverse presentations that delve into advanced aspects of AI and its practical applications in real estate.
First, Monika offers a deeper insight into the philosophical and technical intricacies of AI, discussing future trends, ethical considerations, and the impact of AI on the workforce.
Second, Thomas provides a more hands-on approach, detailing how AI can be pragmatically implemented in business operations, with a focus on efficiency and innovation.
Third, Niko concentrates on the practical aspects of integrating AI into businesses, particularly emphasizing the skills required for successful adoption and the transformation of job roles in an AI-driven corporate landscape.
Together, these lectures provide a comprehensive understanding of AI's potential, challenges, and the evolving dynamics of its integration into various industries.
As presented at:
