Unleash the power of data science to resolve complex challenges and enhance decision-making in the real estate development process. In this new two-day course led by experts from the MIT Center for Real Estate, you’ll develop a foundational understanding of data science, explore the machine learning tools currently being applied to real estate development problems, and create a toolkit for explaining your data-driven forecasts to relevant stakeholders. 

Course Overview

THIS COURSE MAY BE TAKEN INDIVIDUALLY OR AS PART OF THE PROFESSIONAL CERTIFICATE PROGRAM IN REAL ESTATE FINANCE & DEVELOPMENT

Across nearly every industry, data science is helping drive better decision-making. Within real estate, the use of data guides financial decision-making through the use of econometrics—the quantitative analysis of economic data.  

Applying econometrics in real estate development is not a simple task, as the industry involves a wide variety of complex tasks, critical goals, and data sources. But when used effectively, data science can help expand the margin of a professional’s experience by learning from data precedents across a market or sector. In this way, real estate developers can leverage historical data to develop better strategies and forecasts.     In this Professional Education course, Data Science Strategies for Real Estate Development, presented by the MIT Center for Real Estate, you will gain valuable insights into how to effectively implement data science approaches to solve pressing real estate development challenges. Led by real estate experts who regularly utilize econometric techniques, you will acquire tested techniques, and expand your econometric knowledge by exploring applicable developments in data science, machine learning, and proptech ecosystem.    Alongside a cohort of accomplished peers, you will delve into the big and wide data sets—ranging from new data types like social media and rating platforms, to core financial and economic information—used by real estate developers to guide decision-making. You’ll also develop practical approaches for cleaning and evaluating data, aligning data science analysis with performance analysis in real estate, and communicating data-driven outcomes to relevant stakeholders.   While this course is built on data science real estate experiences at MIT, you will also hear from industry experts who regularly use data to answer critical questions for real estate developers. 

Participant Takeaways

In this course, participants will:

• Ethically employ data science to answer relevant questions and develop meaningful strategies for real estate development
• Explore different data types, evaluation criteria, and data sources for real estate development
• Learn from experts in real estate data science who are using cutting-edge data tools to address important real estate development issues
• Develop a strategy tool kit for communicating data analytics and machine learning forecasts to relevant stakeholders

Who Should Attend

This course is well-suited for executives, junior associates, and data scientists who work in real estate, as well as similar-level professionals from other fields who want to develop data science skills catered to real estate development. 

Requirements

This is an introductory course and does not require prior experience in data science.

Program Outline

This course runs 10:00 am – 4:30 pm on the first day and 8:00 am – 4:30 pm on the second day. Please note all times are US Eastern Daylight Time. The schedule is subject to change.

Day One Topics:

Lecture: Why Data Science for Real Estate Development?

Group Class Discussion

In-Class Data Science Application

Lecture: Data Science, Machine Learning and an Autonomous Future in Real Estate Development

Coding Environment Lab Work

Guest Panel

Introduce Project Presentation and Coding Exercise

Day Two Topics:

Coding Environment Lab Work

Reviewing Stakeholder Strategies for Real Estate Development Toolkit

Employ RED Data Science Toolkit in Class Assignment

Group Presentations

Strategy Discussion – “How to talk to stakeholders about data science outcomes for real estate decision making”