Data Analytics and Visualization
RE 519, Runstad Department of Real Estate, University of Washington, Autumn 2025
Monday & Wednesday, 2:30 - 3:50pm, Mechanical Engineering Building 245
Haoyu Yue (Pre-doctoral Instructor), [email protected], Schedule Office Hours
Real estate decision-making requires the assessment of interdisciplinary datasets, which include socioeconomic, financial, and environmental data. Determining evolving patterns, analyzing and visualizing them, is critical in holistically assessing an area and a real estate decision to be made. This course aims to provide you with an opportunity to improve your coding ability and demonstrate that using R is more replicable and efficient than Excel.
We will work in groups to solve tricky data analytics & visualization problems. Of course, we will encounter a lot of new commands and new ways of thinking about how data is organized. However, we will most importantly learn how to understand the process of data analysis and how to best inform our audience and honestly describe the underlying data. The course is developed based on materials from Dr. Feiyang Sun at UC San Diego, Siman Ning, and Christian Phillips.
October 21, 2025
Dear all, Canvas is now back online, and Lab 3 is due tonight (October 21).
We will not have class on Tuesday, October 22, as I’ll be attending the ACSP Annual Conference 2025 in Minneapolis. Please use this time to continue working on Lab 4 and try to explore more possibilities of ggplot2! Lab 4 will be due next Wednesday (October 29).
Next Monday (October 27), we’ll welcome Drew Dolan (Principal, DXD Capital) for a guest lecture on data-driven real estate investment. Please participate in person if you are available.
Schedules
Go to the recent section. This schedule is subject to change, and please check back regularly for updates. All readings and materials can be directly accessed via the links below, although some may require a UW NetID login. Some readings and links about R/coding are on each lab session page. Please give us any anonymous suggestions about the lectures, labs, or anything using the anonymous suggestions box.
I - Introduction to Data Science and R
- Sep 24
- Overview and the Values of Data Science
- Slides / Pre-class Survey
- OptionalData Science and its Relationship to Big Data and Data-driven Decision Making. Foster Provost and Tom Fawcett. 2013.
OptionalGetting ahead of the Market: How Big Data is Transforming Real Estate. McKinsey & Company. 2018.- Sep 29
- Data in Real Estate
- Slides
- LAB 1-B Basic of R/RStudio and Markdown
- REQUIREDChapter 1-2, Real Estate Analysis in the Information Age. Kimberly Winson-Geideman, et al. 2018.
OptionalChapter 1-3, Seeing Theory - A Visual Introduction to Probability and Statistics. Daniel Kunin.
OptionalChapter 5: Market, Place, Interface, All Data Are Local: Thinking Critically in a Data-Driven Society. Yanni Alexander Loukissas. 2019.- Oct 01
- Diving into R
- LAB 1-C Dataframes and Accessing Census Data
- OptionalTidyverse Style Guide. The Tidyverse Team.
OptionalUnderstanding and Using American Community Survey Data. United States Census Bureau. 2020. - LAB 1-C Dataframes and Accessing Census Data
- Oct 06
- Data Science Workflows and Components
- Slides
- LAB 1 DUE 11:59 PM
- REQUIREDChapter 3-4, Real Estate Analysis in the Information Age. Kimberly Winson-Geideman, et al. 2018.
OptionalWhat Is Open Reproducible Science. Jenny Palomino, et al. 2020,
OptionalHow do Data Science Workers Collaborate? Roles, Workflows, and Tools. Amy X. Zhang, et al. 2020.- Oct 08
- Use Cases of Data Science in Real Estate
- LAB 2-B Summarizing and Joining Dataframes, GitHub
- REQUIREDChapter 7 and 9, Real Estate Analysis in the Information Age. Kimberly Winson-Geideman, et al. 2018.
OptionalFinding Common Ground: Best Practices for State Policies Supporting Transit-Oriented Development. Mason A. Virant, et al. 2024.
OptionalUrban Big Data: City Management and Real Estate Markets. Richard Barkham, et al. 2022.
OptionalLearn Git Branching. Learn to use Git through Terminal Rather than using GitHub Desktop.- Team Formation Due
- Project Team Formation Instruction
- LAB 2-B Summarizing and Joining Dataframes, GitHub
II - Data Visualization
- Oct 13
- Data Visual Design
- Slides
- LAB 2 DUE 11:59 PM
OptionalData Viz Project by ferdio.
OptionalFrom Data to Viz.
OptionalDesign and Redesign in Data Visualization. Martin Wattenberg and Fernanda Viégas. 2015.
OptionalVisual and Statistical Thinking: Displays of Evidence for Making Decisions. Edward Tufte. 1997.- Oct 15
- Exploratory Data Analysis (EDA)
- LAB 3-B Basic Exploratory Data Analysis in R
- REQUIREDChapter 8, Real Estate Analysis in the Information Age. Kimberly Winson-Geideman, et al. 2018.
OptionalExploratory Data Analysis. United States Environmental Protection Agency. - LAB 3-B Basic Exploratory Data Analysis in R
- Oct 20
- Visualization Perception
- Slides
- LAB 3 DUE 11:59 PM
- REQUIREDDefense Against Dishonest Charts. Nathan Yau.
OptionalAutomating the Design of Graphical Presentations of Relational Information. Jock Mackinlay. 1986.
OptionalChapter 9, Visualize This. Nathan Yau. 2024.
OptionalHow to Lie with Charts. Gerald Everett Jones. 2018.- Oct 22
- No Class
- Instructor leave due to ACSP Annual Conference 2025
- Oct 27
- Guest Speaker: Drew Dolan, Principal, Fund Manager, DXD Capital
- Oct 29
- Introduction to Tableau
- LAB 5-A Tableau
- Page / Packaged Tableau / Airbnb Data / Affordability Data
- LAB 4 DUE 11:59 PM
- OptionalLearning Tableau Desktop by Tableau
OptionalTableau Tutorial by GeeksforGeeks
OptionalTableau: An Introduction. Princeton University.
OptionalTableau Viz Gallery.
OptionalTableau Dashboard Showcase. - Nov 03
- Analyze and Visualize Space
- LAB 5-B Sptatial Data and Mapping using R
- Extra credits - data sharing DUE 11:59 PM
- LAB 5-B Sptatial Data and Mapping using R
- Nov 05
- Interactive Visualization
- LAB 6-A Interative Visualization using Tableau
- LAB 5 DUE 11:59 PM
- LAB 6-A Interative Visualization using Tableau
- Nov 10
- Dashboard
- LAB 6-B Dashboard using Tableau
III - Data Modeling (Subject to Change)
- Nov 12
- Linear Regression
- LAB 7-A Linear Regression
- LAB 6 DUE 11:59 PM
- Project Proposal Due 11:59 PM
- Project Proposal Instruction
REQUIREDChapter 10 and 12, Real Estate Analysis in the Information Age. Kimberly Winson-Geideman, et al. 2018.
OptionalChapter 4-6, Seeing Theory - A Visual Introduction to Probability and Statistics. Daniel Kunin.
OptionalStatistical Modeling: The Two Cultures. Leo Breiman. 2001. - LAB 7-A Linear Regression
- Nov 17
- Time Series Analysis
- LAB 7-B Time Series Analysis
- Nov 19
- Supervised Learning
- LAB 8-A Random Forests and Support Vector Machines
- LAB 7 DUE 11:59 PM
- LAB 8-A Random Forests and Support Vector Machines
- Nov 24
- Unsupervised Learning
- LAB 8-B K-Means Clustering and Principal Component Analysis
- Nov 26
- Project + Coding Clinic (Optional Online Session)
- Zoom
- Dec 01
- Neural Networks and AI Ethnics
REQUIREDChapter 13-14, Real Estate Analysis in the Information Age. Kimberly Winson-Geideman, et al. 2018.
OptionalA Brief Overview of AI governance for Responsible Machine Learning Systems. Navdeep Gill et al. 2022.
OptionalAI for Social Good. Nature Communications. Nenad Tomašev et al. 2020.
OptionalAtlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Kate Crawford. 2022.
OptionalA Golden Decade of Deep Learning: Computing Systems & Applications. Jeffrey Dean (UW Alumni, Google). 2022.
OptionalArtificial Intelligence: Real Estate Revolution or Evolution? JLL Inc.
OptionalWhat is a Neural Network. 3Blue1Brown. 2017.- LAB 8 DUE 11:59 PM
IV - Data Analytics and Visualization Projects
- Dec 03
- Project Presentation
- Project Presentation Instruction
- Dec 12
- Final Project Submission
- Project Submission Instruction
- Final Project DUE 11:59 PM
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Built on Just the Class developed by Kevin Lin at Allen School