The real estate industry has not developed or implemented technology as quickly as other industries. One reason for this slow adoption is the lack of technology experience within the industry. This course contains 4 modules designed to provide a strong introduction to the foundations of data, strategy, and analytical tools such as Artificial Intelligence (AI) and Machine Learning (ML) as they apply to real estate specific concepts.
The course has been completely redesigned with:
- Additional lessons on machine learning
- Additional Columbia University professors for guest lectures covering technical concepts
- Interactive visualizations and a set of code files to provide learners with hands-on experience in developing artificial intelligence and machine learning applications.
- A group project with other students to work through the design of a real-world AI/machine learning application
Real estate professionals are experts in real estate, but typically don’t have backgrounds in technology. The lack of technical experience makes it extremely difficult to identify viable use cases for AI and ML internally, almost impossible to vet the feasibility and value-add of opaque, third-party technology pitches that real estate companies are inundated with, and challenging to understand how to use AI and ML for use cases that will bring strong competitive advantages to a firm.
These modules will focus on the intersection of AI and real estate rather than on either AI or real estate alone. We believe this approach will allow participants to better gain an intuitive understanding of the interaction between valuable real estate outcomes, the data relevant to the outcome, and the models that help make processes more efficient and develop better insights for decision-making purposes. Those with a real estate background should find value in the technical fundamentals and those with a technical background should find value in the discussions around real estate fundamentals. But the real value is in learning how to bring real estate and technology together more successfully.
There will be no programming in this course. In addition, while we will cover technical concepts, the course is designed for those with a non-technical background. The idea is to slowly build a conceptual intuition for AI and ML based on an understanding of underlying fundamentals without using heavy mathematics.
The potential of AI is enormous, but only if understood and developed properly. Only by understanding the fundamentals of these tools can you consistently identify use cases, vet potential applications, and implement valuable tools into a firm. The use of AI is growing rapidly and those individuals and firms with knowledge of these tools can develop significant competitive advantages.
The Center for Artificial Intelligence in Business Analytics and Financial Technology has spent years working with top financial and real estate firms to ideate and develop artificial intelligence, machine learning, and deep learning technologies. This work has provided invaluable lessons on what works, what doesn’t, why it works, and what processes and knowledge are required to build effective AI systems. Those lessons make up the content of this course and are designed to allow individuals and firms to rapidly upskill their AI capabilities.
Each of the 16 sessions will include 120-minute live sessions delivered via Zoom. All times for live sessions are listed in EST. Some of the guest lectures may be pre-recorded to ensure proper flow of the course material, but the session will still be held live for questions and discussion with Josh Panknin.
*Use the discount code AIRE500 until 2/21/24 for $500 off the enrollment fee.
What You Will Learn
- Module 1: How AI Works in Real Estate – An Introduction to AI, Machine Learning, Data, and Models
Much of the popular narrative around Artificial Intelligence makes AI sound like magic. In Module 1, we’ll dive into what AI is and what it isn’t, the different types of AI, how AI uses data in a real estate context, and briefly go into some potential use cases as well as challenges to implementing AI successfully. By the end of the module you should have a good foundation to separate the reality from the myths and hype around AI.
Session 1: Introduction and overview of Artificial Intelligence
🗓️ April 1, 2024
- In this first session, we’ll introduce the course and provide motivation for what will be covered in the four modules.
- We’ll also provide a basic description of the different types of Artificial Intelligence so we have a common ground to work from.
Session 2: Introduction to models and data in a real estate context
🗓️ April 3, 2024
- This session will explain models and how they represent the real world to gain an intuition about what a computer does well and what it doesn't do well.
- To supplement model development, we’ll explore the types and limitations of data in a real estate context.
- A discussion about models and data will develop a better foundation for coverage of AI and Machine models for real estate applications.
- Module 2: Machine Learning in Real Estate
When you hear about Artificial Intelligence today, chances are you’re hearing about a subset of AI called machine learning. Whereas classic AI includes the use of data and algorithms to automate tasks, machine learning uses data and algorithms to “learn” the relationships that exist between data and the outcome. For example, the features of a single-family home (such as number of bedrooms and bathrooms, square footage, year built, lot size, etc.) determine the value of the home. Machine learning allows us to use the features of a home to develop predictive models of the value of any home.
Session 1: A gentle introduction to ML and how it works
🗓️ April 8, 2024
- An introduction to machine learning algorithms
- The difference between a model and a learning algorithm
- Linear regression walkthrough
- Classification walkthrough
Session 2: Housing example walkthrough
🗓️ April 10, 2024
- We’ll walk through a sample analysis of the housing market for a simple introduction to the machine learning pipeline
Session 3: Different types of machine learning algorithms
🗓️ April 15, 2024
- Just as a carpenter has many tools for different uses, machine learning offers many types of learning algorithms that allow us to address very specific types of problems
- Exploration of different types of machine learning algorithms and concepts, including: Linear regression; logistic regression; polynomial regression; K-nearest neighbors; decision trees; random forests; clustering; hierarchical clustering; multistate models
Session 4: Real-world examples and walkthroughs
🗓️ April 17, 2024
- Revisiting the housing analysis to highlight processes and nuances to achieving high-quality AI/ML applications
- Walkthrough of Center research – asset allocation and neighborhood selection tools
Session 5: A look at more complex ML applications
🗓️ April 22, 2024
- A look at some advanced ML applications that highlight the technical complexity required to address the more complex real estate and economic concepts relevant to the industry
Session 6: Where does ML fit into the software development process
🗓️ April 24, 2024
- Software development process – where does AI and ML fit in with developing production-ready technology applications
- Potentials and limitations of ML
- Ideas for further exploration of ML and its use cases
- Open discussion and module wrap-up
- Module 3: System Design for Real Estate AI – How to Identify and Structure Projects Effectively
A key to understanding how to effectively choose and develop AI projects is gaining an in-depth understanding of the problem you’re trying to solve or the capability you want to achieve. This module will provide an introduction to our version of a combination of design thinking and systems thinking.
Session 1: Introduction and overview of systems
🗓️ April 29, 2024
- We’ll start by discussing what systems are and why they’re important
- The components of a system – elements and interactions – will be defined and illustrated
- What are stocks and flows and why are these important to understanding real estate markets
- Then we’ll look at how systems help us understand real estate and AI
Session 2: Creating systems for our class project
🗓️ May 1, 2024
- Creating systems
- Tree-based systems
- Process-based systems
Session 3: Integrating automation, AI, and ML into a system
🗓️ May 6, 2024
- With the system well-defined, we’ll start identifying parts of the system that can be automated or analyzed
- We’ll look at what methods to use to make systems more efficient and effective
- Identification of appropriate data, the source of that data, and the method of collecting the data
Session 4: Translating systems and process flows into executable projects and module conclusion
🗓️ May 8, 2024
- In this session, we’ll translate the system into an executable plan for using data collection, processing, storage, and modeling to create an effective application
- We’ll develop an “instruction set” that allows teams across the organization to understand project implementation
- Module 4: Vetting AI and Designing an AI Strategy for Real Estate
Real estate companies are constantly trying to find technology to integrate into their organization. Companies are either inundated with pitches from third-parties hoping to sell their product or service, unsure which internal AI projects to pursue, or have trouble deciding which venture capital or startup firm to invest in. In addition, AI is too often viewed as a way to improve current processes and capabilities. Instead, the vision for AI should include the ability to recognize and develop new capabilities that lead to transformational progress within the firm and the industry as a whole. This module will define the difference between an “operational” focus and a “strategic” focus for the development of artificial intelligence and related tools within the real estate industry, in turn significantly improving the ability to vet products and services that will add the most value to the firm.
Session 1: Converting business problems to analytics problems
🗓️ May 13, 2024
- Understanding the difference between incrementation and innovation
- Definition of operational vs. strategic applications
- Operational applications are defined as automation and more general day-to-day functions
- Strategic applications are defined as those that produce a strong competitive advantage for a firms’ long-term goals
- Using the system breakdown to choose higher value operational and strategic projects
Session 2: Identify operational and strategic functions within the firm
🗓️ May 15, 2024
- Understanding which functions are operational and which are strategic allows firms to choose higher-value projects for resource allocation
Session 3: Blending proper vetting with desired use cases
🗓️ May 20, 2024
- Often there are capabilities a firm wants, but choosing projects that are not viable from the beginning will result in lost money, time, resources, and potential
- Evaluating and prioritizing desired outcomes with feasible outcomes can lead to maximizing value from AI/ML applications
Session 4: Evaluating ROI and providers and course wrap-up
🗓️ May 22, 2024
- ROI – when it’s useful and when it’s harmful to project decisions
- Provider and people evaluation – While there are many stories about big outocmes in real estate technology, there are questions we can ask that allow us to make better upfront decisions about who to engage with and who to pass on
Josh is currently a member of the Center for Artificial Intelligence in Business Analytics and Financial Technology where he serves as the Director of Real Estate Artificial Intelligence Research & Innovation in the School of Engineering and Applied Science (“SEAS”) at Columbia University. He joined Columbia in 2016 and is the former Director of Real Estate Technology Initiatives at SEAS. His focus is on using practical applications of artificial intelligence and machine learning to address inefficiencies and create new capabilities within real estate.
Prior to academia, Josh spent 12 years in various roles in real estate. He was Head of Credit Modeling and Analytics at Deutsche Bank’s secondary CMBS trading desk, where he helped develop and implement automated models for CMBS loan and bond valuation. He also worked at the Ackman-Ziff Real Estate Group and in various other roles in research, acquisitions, and redevelopment. Josh has a master’s degree in finance from San Diego State University, a master’s degree in real estate finance from New York University’s Schack Institute of Real Estate, and is currently pursuing degrees in applied mathematics and data science at Columbia University.
Ali Hirsa has been a member of the Department of Industrial Engineering and Operations Research since July 2017, and has been an Adjunct Professor at Columbia University since 2000. He is also the Managing Partner at Sauma Capital, LLC. Previously, he served as the Managing Director at DV Trading, LLC, and was the Head of Analytical Trading Strategy at Caspian Capital Management, LLC. With a background at Morgan Stanley, Banc of America Securities, and Prudential Securities, he served as a Fellow at NYU's Courant Institute from 2004 to 2014.
Ali's research delves into algorithmic trading, machine learning, and quantitative finance, particularly in asset management. Author and co-author of influential works, he's also the Editor-in-Chief of the Journal of Investment Strategies. A co-inventor (US Patent 8,799,146), he addresses biases in post-trade allocation methods.
Currently on the Board of Visitors at the University of Maryland College Park, Ali earned his PhD in Applied Mathematics from the University of Maryland under Professors Howard C. Elman and Dilip B. Madan.
Hardeep received an M.A. in Economics from the Birla Institute of Technology and Science and is a Fellow of the Indian Institute of Management Calcutta. He received a Ph.D. in Information Systems from the Stern School of Business, New York University in 1994. Prior to joining Columbia, Johar has worked as a quantitative trader at Morgan Stanley, Credit Suisse and Deutsche Bank, at a tech startup (MSpoke), and has taught at NYU Stern School of Business and the Gabelli School of Business Fordham University.