Data Science in Real Estate
Online short course
Learn how to utilize data to analyze patterns in property performance and make more informed real estate decisions.
6 weeks, excluding 1 week orientation.
7–10 hours of self-paced learning per week, entirely online.
Email: mitsap@getsmarter.com
Call: +1 617 997 4979
ABOUT THIS COURSE
Smart data analytics is giving real estate professionals and investors more insight into the factors impacting property value than they’ve ever had before. From assessing risks to analyzing evolving trends, we’re now able to anticipate the success of a property more accurately thanks to the abundance of information available to us.
The MIT School of Architecture and Planning (MIT SA+P) Data Science in Real Estate online short course is designed to give you the foundational tools required to predict and explain property performance. The program introduces you to the application of machine learning in the built environment. You’ll be taught to use data science methods to identify and interpret patterns in order to analyze property risks and opportunities. You’ll also learn about the growing number of data resources used by real estate practitioners, borrowed from a range of economic sectors.
Over six weeks, you’ll explore what it means to be a good data scientist, from both a technical and ethical perspective, as you gain the practical data science skills to make informed, data-driven investment decisions.
“Real estate is evolving rapidly as it responds to the human needs we are learning from data.”
Professor Dennis Frenchman
MIT Center for Real Estate
WHAT THIS PROGRAM COVERS
By making complex data science techniques more accessible to nontechnical professionals, this program provides you with the resources and knowledge to harness statistical insights in your real-life property endeavors. Over the duration of the program, you’ll examine a wide variety of data used by real estate practitioners, including nontraditional data on factors such as the availability of natural light, or access to highly rated amenities. You’ll also gain practical skills in the use of open-source data science tools to tidy, integrate, and evaluate data.
The course draws on real-life examples, while teaching you to generate a selection of basic data science and machine learning models that can be used to explain industry experiences and forecast asset values. Guided by industry experts, you’ll develop an understanding of the practical opportunities and challenges involved in using these data-driven methods to support investment decisions in real estate, and you’ll explore their ethical implications. You’ll also consider appropriate strategies to align data science analysis with performance analysis in real estate, and learn to communicate data-driven value propositions to relevant stakeholders.
The program is designed to cultivate the development of a community of forward-thinking professionals. With this objective in mind, it equips you with the knowledge to interrogate the quality and value-add of data science products and services. On completion of this course, you’ll understand how data science techniques can be practically applied to extract meaningful insights, and how these insights can support a holistic assessment of the potential of real estate investments or development ventures.
A POWERFUL COLLABORATION
The MIT School of Architecture and Planning (MIT SA+P) is collaborating with online education provider GetSmarter to create a new class of learning experience — one that is high-touch, intimate, and personalized for the working professional.
ABOUT MIT SA+P
The MIT School of Architecture and Planning is one of five schools at MIT, and comprises six main divisions. Alongside the MIT Center for Real Estate (MIT CRE), MIT SA+P includes the first Department of Architecture in the US, founded in 1868. It also contains the oldest continuous Department of Urban Studies and Planning, founded in 1933. The MIT Media Lab launched in 1980 at SA+P. The School is also home to the Norman B. Leventhal Center for Advanced Urbanism, which is dedicated to guiding the future of the built environment and cities, and the groundbreaking program in Art, Culture, and Technology.
ABOUT THE MIT CENTER FOR REAL ESTATE
Envisioning the real estate industry’s integrated stewardship of its land and products, the MIT CRE provides tomorrow’s practitioners with the means to transform an ever more vital, global, and complex market. Through research and education initiatives, MIT applies its tradition of excellence in technology, knowledge transfer, and global reach to the real estate industry, developing innovations to help practitioners build responsibly and profitably.
ABOUT THE MIT REAL ESTATE INNOVATION LAB
The MIT Real Estate Innovation Lab is a research and development laboratory for the built environment that measures the financial and economic performance of innovation in real estate, design, and planning. It’s a team of interdisciplinary researchers in planning, design, finance, and economics using data science and machine learning techniques to explain and predict the impact of innovation and technology in real estate.
About GetSmarter
GetSmarter, part of edX, partners with the world's leading universities and institutions to select, design and deliver premium online short courses with a data-driven focus on learning gain.
Technology meets academic rigor in GetSmarter’s people-mediated model, which enables lifelong learners across the globe to obtain industry-relevant skills that are certified by the world’s most reputable academic institutions.
As a participant of this program, you will also gain unlimited access to edX’s Career Engagement Network at no extra cost. This platform will provide you with valuable career resources and events to support your professional journey. You can look forward to benefits including rich content, career templates, webinars, workshops, career fairs, networking events, panel discussions, and exclusive recruitment opportunities to connect you with potential employers.
WHAT YOU'LL LEARN
You’ll be welcomed to the course and begin connecting with fellow participants, while exploring the navigation and tools of your Online Campus. Be alerted to key milestones in the learning path, and review how your results will be calculated and distributed.
You’ll be required to complete your participant profile and submit a digital copy of your passport/identity document.
Please note that module titles and their contents are subject to change during program development.
Understand data science and machine learning concepts used in the built environment.
- Recall the fundamentals of data science and machine learning
- Demonstrate a conceptual understanding of data science and machine learning in the built environment
- Discuss practical applications of data science and machine learning in the built environment
- Investigate new data science tools
- Assess the ethical issues related to data analysis and its application in the built environment
Learn how to prepare real-world data for analysis.
- Identify data management strategies and best practice
- Discuss the need for good data management
- Practice tidying data and detecting anomalies
- Implement the steps to join real estate data sets
- Reflect on the challenges of joining data sets and evaluating data
Understand the value of quantifying and detecting patterns in data.
- Discuss the use of frequency distributions and sample statistics to assess data
- Apply the frequency toolkit in R
- Identify the principles and attributes of correlation
- Practice doing a correlation analysis using R
- Execute a time series analysis using the toolkit in R
- Evaluate the outcomes from a time series analysis
- Execute a geospatial analysis using the toolkit in R
- Debate the outcomes of a geospatial analysis on real estate data
Discover the stories that live in real estate data that can be used to explain and predict outcomes.
- Identify features and outcomes in the context of real estate
- Articulate the use of outcomes to answer questions that support decision making
- Formulate a question that can be supported by an outcome
- Justify the features that could be used to answer your question
- Describe the importance of identifying the type of relationship between features before doing analyses
- Discuss regressive value proposition outcomes in real estate
- Outline how machine learning can be used to predict outcomes
- Discuss the challenges and opportunities related to econometrics and forecasting
Understand relationships between outcomes and features using regression analysis.
- Describe the drivers affecting value in real estate
- Practice regression analysis using real estate data
- Interpret the results from a regression analysis
- Analyze the accuracy of a regression analysis
- Investigate a strategy to communicate relationship information to non-technical stakeholders
- Assess how information can be presented in an ethical manner
Discover how to use machine learning to forecast outcomes and support decision making in real estate.
- Recognize the value of machine learning for forecasting
- Review machine learning methods
- Use machine learning methods to forecast the value of an asset
- Evaluate guided forecasts
- Evaluate the predictive performance of models
- Reflect on the ethical impact of using machine learning
- Demonstrate an understanding of the future of data science within the real estate industry
- Discuss a strategy to include relevant data science applications in your business
WHO SHOULD TAKE THIS COURSE
This program is designed for anyone with an interest in real estate development, finance and investment, or data science. The analytics skills taught over the duration of six weeks will benefit both real estate professionals and independent investors looking to grow their property portfolios. The content will also be useful for those with an existing knowledge of data science, who would like to explore how to use it in the real estate environment. Overall, the course will give professionals interested in the built environment a competitive edge, as it teaches skills that are relevant for analysis, valuation, and informed decision making.
While this course has no formal prerequisites, it’s recommended that you have a basic understanding of programming. You won't be expected to write code, but will need to understand and manipulate code written in the programming language R.
THIS PROGRAM IS FOR YOU IF YOU WANT TO:
IMPROVE DECISION MAKING
Harness data to make better investment decisions and discover new real estate opportunities.
APPLY NEW SKILLS
Update your skill set to perform statistical analyses and modeling using interactive software applications.
LEARN FROM EXPERTS
Gain insight into the real-world use of data in real estate, finance, and data science, guided by MIT faculty and industry experts.
ABOUT THE CERTIFICATE
This program offers you the opportunity to earn an MIT School of Architecture and Planning digital certificate as validation of your skills.
Assessment is continuous and based on a series of practical assignments completed online. In order to be issued with a digital certificate you’ll need to meet the requirements outlined in the course handbook. The handbook will be made available to you as soon as you begin the program.
Your certificate will be issued in your legal name and sent to you digitally upon successful completion of the program, as per the stipulated requirements.
WHO YOU’LL LEARN FROM
This subject matter expert from MIT SA+P guides the course design and appears in a number of program videos, along with a variety of industry professionals.
YOUR COURSE CONVENER
Dr Andrea Chegut
Director, MIT Real Estate Innovation Lab; Head of Research, MITdesignX; Research Scientist, MIT Center for Real Estate
Dr Andrea Chegut is the cofounder and director of the MIT Real Estate Innovation Lab, an interdisciplinary team that identifies innovative, built environments products, processes and technologies, and their financial and economic impact. Chegut is also a cofounder and the head of research for MITdesignX, an entrepreneurial accelerator for new student and faculty ventures from MIT SA+P that focuses on design, cities, and the built environment. In addition to her research, Chegut teaches classes on innovation, corporate finance, and entrepreneurship at MIT. Chegut did her PhD in financial economics and has worked at the intersection of innovation, urban economics, and real estate for over a decade. Prior to her work at MIT, she had a career in securities asset pricing, and worked in Europe on developing asset pricing models for commercial real estate, green buildings, and digital infrastructure.
INDUSTRY EXPERTS
Anne Kinsella Thompson
Visiting Lecturer and Real Estate Economist, MIT Center for Real Estate
Steve Weikal
Head of Industry Relations CRE Tech Lead, Real Estate Innovation Lab
Prof Dennis Frenchman
Class of 1922 Professor of Urban Design and Planning, Director, MIT Center for Real Estate
Prof Jacob Sagi
Wood Center in Real Estate Studies Distinguished Scholar, Kenan-Flagler Business School, University of North Carolina at Chapel Hill
Prof Christoph Reinhart
Professor of Architecture, Director of Building Technology Program, MIT School of Architecture and Planning; Founder and Director, Sustainable Design Lab
Wayne Yu
VP of Data Science, CompStak
Bob White
Founder and President, Real Capital Analytics
L.D. Salmanson
Cofounder, Cherre
Calandra Cruickshank
President and CEO, StateBook International
Ben Breslau
Chief Research Officer, Americas, JLL
Phoebe Holtzman
CEO, Live XYZ
Prof David Geltner
Professor of Real Estate Finance, MIT; Faculty Director of Commercial Real Estate Analysis and Investment online program
HOW YOU’LL LEARN
Every course is broken down into manageable, weekly modules, designed to accelerate your learning process through diverse learning activities:
- Work through your downloadable and online instructional material
- Interact with your peers and learning facilitators through weekly class-wide forums and reviewed small group discussions
- Enjoy a wide range of interactive content, including video lectures, infographics, live polls, and more
- Investigate rich, real-world case studies
- Apply what you learn each week to quizzes and ongoing project submissions, culminating in the ability to create guided property forecasts to support real-world decision making
YOUR SUCCESS TEAM
GetSmarter, with whom MIT SA+P is collaborating to deliver this online program, provides a personalized approach to online education that ensures you’re supported throughout your learning journey.
HEAD FACILITATOR
A subject expert who’ll guide you through content-related challenges.
SUCCESS ADVISER
Your one-on-one support, available during University hours (8a.m.–5p.m. EST) to resolve technical and administrative challenges.
GLOBAL SUCCESS TEAM
Available 24/7 to solve your tech-related and administrative queries and concerns.
“Data integration is the most important thing for real estate right now. The opportunity afforded to us as an industry, using big and wide data, and using the computing power that’s now available to us through programs like you have at MIT, I think it’s going to open up a huge opportunity for the industry as a whole.”
Dr Andrea Chegut
Director, MIT Real Estate Innovation Lab; Head of Research, MITdesignX; Research Scientist, MIT Center for Real Estate
TECHNICAL REQUIREMENTS
BASIC REQUIREMENTS
In order to complete this course, you’ll need a current email account and access to a computer and the internet, as well as a PDF Reader. You may need to view Microsoft PowerPoint presentations, and read and create documents in Microsoft Word or Excel.
BROWSER REQUIREMENTS
We recommend that you use Google Chrome as your internet browser when accessing the Online Campus. Although this is not a requirement, we have found that this browser performs best for ease of access to program material. This browser can be downloaded here.
ADDITIONAL REQUIREMENTS
Certain courses may require additional software and resources. These additional software and resource requirements will be communicated to you upon registration and/or at the beginning of the program. Please note that Google, Vimeo, and YouTube may be used in our course delivery, and if these services are blocked in your jurisdiction, you may have difficulty in accessing program content. Please check with an Enrollment Adviser before registering for this program if you have any concerns
