Journal of Information Systems Education Teaching Case California

Journal of

Information

Systems

Education

Volume 35

Issue 2

Spring 2024

Teaching Case

California Dreamin’ Housing Market Visualizations in

Tableau

Aimee Jacobs, Jacquelin J. Curry, Concetta A. DePaolo, and Fernando

Parra

Recommended Citation: Jacobs, A., Curry, J. J., DePaolo, C. A., & Parra, F. (2024).

Teaching Case: California Dreamin’ Housing Market Visualizations in Tableau.

Journal of Information Systems Education, 35(2), 144-147.

https://doi.org/10.62273/YZHB9002

Article Link: https://jise.org/Volume35/n2/JISE2024v35n2pp144-147.html

Received: June 5, 2023

First Decision: July 6, 2023

Accepted: October 25, 2023

Published: June 15, 2024

Find archived papers, submission instructions, terms of use, and much more at the JISE website:

https://jise.org

ISSN: 2574-3872 (Online) 1055-3096 (Print)

Journal of Information Systems Education, 35(2), 144-147, Spring 2024

https://doi.org/10.62273/YZHB9002

144

Teaching Case

California Dreamin’ Housing Market Visualizations in

Tableau

Aimee Jacobs

Jacquelin J. Curry

Craig School of Business

California State University Fresno

Fresno, CA 93740, USA

ajacobs@csufresno.edu, jacquelinc@csufresno.edu

Concetta A. DePaolo

Scott College of Business

Indiana State University

Terre Haute, IN 47809, USA

[email protected]

Fernando Parra

Craig School of Business

California State University Fresno

Fresno, CA 93740, USA

[email protected]

ABSTRACT

This manuscript describes the use of real data applied to a fictional real-estate firm for teaching data visualization to university

students. In the case study, students employ data analytic techniques in Tableau to clean, organize, and analyze real estate data. By

creating visualizations, students address several questions about how selling prices of homes are affected by various factors such

as location, industry trends, and property characteristics. The case has been used in business analytics courses; students reported

finding the case study relevant and useful, and they were found to largely meet the learning goals of the case, including proficiency

with cleaning and filtering data, and creating clear and useful visualizations to convey meaning in Tableau.

Keywords: Data analytics, Data visualization, Tableau, Data mining, Data exploration

1. CASE SUMMARY

Orchard Grove Realty, a real estate firm in the San Joaquin

Valley, California, is committed to providing exceptional

customer service and assisting clients in finding their ideal

properties. Orchard Grove aims to analyze real estate market

trends and key indicators to better serve its clients. In pursuit of

this goal, Orchard Grove has access to a large, updated, and

complex real estate market data set spanning thirteen years,

covering various property attributes and transaction details.

Orchard Grove is particularly interested in leveraging data

analysis and visualization techniques using Tableau (2023) to

gain insights into market trends and assist clients in making

informed decisions. Students, acting as data analysts, will need

to apply all stages of the process from data cleansing to

reporting, including exploration, mining, visualization, and

model building. After becoming familiar with the case

background and data set, students are asked to cleanse the data

(see Section 3.2) and to create visualizations that address

questions about the status and trends in the real estate market

(see Section 4).

2. CASE BACKGROUND

Orchard Grove Realty is a full-service real estate firm located

in the San Joaquin Valley in California. Orchard Grove prides

itself on providing excellent customer service and a desire to

assist customers in finding the ideal property. Orchard Grove

Realty offers a range of comprehensive services to meet the

diverse needs of its clients in its service area. The firm is

Journal of Information Systems Education, 35(2), 144-147, Spring 2024

https://doi.org/10.62273/YZHB9002

145

interested in analyzing trends regarding real estate market

valuations and other key relevant indicators to better serve its

clients more effectively. For example, Orchard Grove can assist

sellers in determining the optimal selling price for their

property. This ensures that sellers receive the best value for

their investments while attracting numerous potential buyers.

Orchard Grove Realty’s market value estimations also provide

buyers with a clear understanding of the current market

conditions, helping them identify properties that align with their

budget and expectations. Data analysts and scientists use

visualizations to tell the story of the data. Orchard Grove Realty

also understands the importance of technology in the real estate

industry. The firm leverages advanced tools and technologies

to enhance their market analysis capabilities. By utilizing data-

driven approaches and innovative software, Orchard Grove can

provide even more accurate estimations and insights into

market trends, ensuring that clients receive the most up-to-date

and reliable information.

Orchard Grove maintains a large data set covering 13 years

of real-world measures that relate to real estate market value

including days on market, list price, and selling price among

others. Orchard Grove has asked you, as one of their

experienced data analysts, to help generate real estate metrics

and visualizations in Tableau to provide more market insight.

As such, the overall purpose of your analysis will be to describe

the valuation of real estate property trends by developing a

Tableau Storyboard. You will report your findings to your

supervisor, the real estate broker, in the form of a Tableau

storyboard with visuals outlining your analysis and

recommendations. You may be asked to write a report or

present your findings to your boss.

3. CASE DATA

3.1 Data Description

The data set consists of 96,625 residential properties sold

during 2008-2021. The variables and definitions that will be

used in the case can be found in Table 1. Note: Your data file

may not have all of the variables so you may have to create

calculated fields for any missing variables.

3.2 Data Cleansing

Before beginning the data analysis process, it is important to

undertake data cleansing techniques to improve data quality and

remove errors or inconsistencies. The data should be normalized

by converting different units of measurement into a standardized

unit. For example, some homeowner association dues are

expressed as monthly costs while others are yearly.

All data errors should be corrected through a process of

identifying and rectifying erroneous data, in particular,

addressing extreme or invalid values. Any inconsistencies or

conflicts within text variables should be resolved. For example,

the homeowner association frequency variable (HOA Freq)

might say dues are collected “Monthly” or “Per Month” and so

those text entries should be edited for uniformity. Furthermore,

it is recommended to review the variable names to ensure

consistent naming conventions.

4. ASSIGNMENT

As one of Orchard Grove’s experienced data analysts, you are

tasked with creating visualizations to address the following real

estate questions in order to provide a market analysis of your

data set. Students could also be directed to various real estate

YouTube instructional videos or specific websites in order to

better understand real estate terminology, current issues and

concerns. Some website examples include but are certainly not

limited to the following:

https://www.opendoor.com/articles/real-estate-terms-you-

should-know (Opendoor Team, 2019) or

https://www.investopedia.com/mortgage/real-estate-investing-

guide (The Investopedia Team, 2022).

Database

Variable

Description

Selling Price

Price of the property when sold

APN

Assessor’s Parcel Number -

identification number assigned to parcels

of property by the tax assessor of a

particular jurisdiction

ML_Number

Multiple Listing Service number

# of

Fireplaces

Number of fireplaces in the property

Address - ZIP

Postal code used to provide a location of

a property to the United States Postal

Service (USPS)

Total

Bathrooms

Number of bathrooms on the property

Bedrooms

Number of bedrooms on the property

Financing

Desc

Type of financing used by the buyer to

purchase the property

Garage Spaces

Number of spaces for cars to fit in the

garage

HOA Dues

Homeowner Association fee charged at a

given frequency

HOA Freq

Frequency of payments to the

Homeowner Association

Listing Price

The initial suggested selling price of the

property

Listing Date

Date property is listed on the market for

sale

Selling Date

Date property is sold

Lot Size

Size of the land according to boundary

lines

Lot Type

The type of lot size, i.e., acres or square

feet

Pool

Whether the property has a pool or not,

Y=Yes/N=No

Roofing Desc

Type of roof on the property

School

District

School district where the property is

located

Solar Display

Whether the property has a solar or not,

Y=Yes/N=No

Square

Footage

Size of the property in square feet

Year Built

Year the property was first built

DOM

Number of days the property was on the

market before being sold

SP%LP

Ratio of selling price to listing price

Table 1. Variables and Definitions

Journal of Information Systems Education, 35(2), 144-147, Spring 2024

https://doi.org/10.62273/YZHB9002

146

4.1 Case Questions

1. What is the median selling price for your data set?

Why do we use median and not mean selling price?

2. What characteristics do homes with higher selling

prices have in common?

3. What proportion of homes were sold based on number

of bathrooms?

4. How does a pool affect a home’s days on market and

selling price?

5. What is the average percent of selling price over listing

price?

6. What is the average price per square foot?

7. How did market growth change across the years?

Explain any drastic changes.

8. Is there a difference in selling price based on

financing, e.g., cash, mortgage, etc.?

9. How do selling prices vary by the school district?

Would you recommend clients move into a particular

school district?

10. What periods of the year do attributes have an impact

on home prices? (for example, pool, # of fireplaces,

solar, etc.)?

11. How many days are comparable houses (possibly sqft.,

bathrooms, bedrooms, etc.) staying on the market?

12. What are the median selling price trends? Add a

forecast.

13. When is the best time of the year to sell your house?

When is the best time to buy?

14. How has COVID-19 impacted the local market?

15. Did you find anything interesting or unusual in your

data set?

4.2 Visualization Requirements

Your dashboards, charts, and storyboard should:

• Utilize the data from your data source appropriately;

•

Show evidence of thoughtful and effective design

within worksheets and between worksheet elements of

dashboards and stories;

•

Convey a meaningful message to the viewer;

•

Provide the user with an opportunity to explore the

visuals and make their own discoveries; and

•

Organize your workbook so it follows the appropriate

hierarchy considering the story sequence.

4.3 Deliverables

Submit your visuals in a single packaged Tableau workbook

(.twbx) file, so the file contains both the workbook properties

and an extract of the data used in the workbook.

Within your workbook, sequence the tabs so the story you

want the viewer to explore comes first. Place any supporting

worksheets or dashboards after these presentation tabs. Do not

hide the supporting worksheets, just place their tabs to the right

of your dashboards and storyboard tabs. Ensure that each of the

tabs has descriptive names.

If you are asked to prepare a report, then provide the

following information:

1. Create a professionally written report that discusses

each of the tabbed items in your workbook by name.

2. Describe the main message(s) of your analysis and final

recommendation that you intend the viewer to receive

from your storyboard.

3. Report how you incorporated the elements of data

visualization in your work.

4. Detail any opportunities for the viewer to use selection

and filtering to explore the visuals.

5. Add any additional aspects of your workbook that you’d

like to mention.

If you are asked to prepare a Tableau presentation, then

follow these guidelines: All data analyst team members should

understand how the Tableau workbook views were created and

how the workbook functions. You may be asked to recreate an

element in the submitted workbook based on your client’s

feedback.

5. REFERENCES

Opendoor Team. (2019, October 17). 52 Essential Real Estate

Terms You Should Know. Opendoor.

https://www.opendoor.com/articles/real-estate-terms-you-

should-know

Tableau Software. (2023). https://www.tableau.com/

The Investopedia Team. (2022, June 28). A Beginner’s Guide

to Real Estate Investing. Investopedia.

https://www.investopedia.com/mortgage/real-estate-

investing-guide/

Journal of Information Systems Education, 35(2), 144-147, Spring 2024

https://doi.org/10.62273/YZHB9002

147

AUTHOR BIOGRAPHIES

Aimee Jacobs is an associate professor in data analytics. Dr.

Jacobs holds a BSc in Management

Information Systems from Indiana

State University, MBA from Indiana

State University, and PhD in

Informatics from the University of

Reading – UK. Since joining

California State University, Dr.

Jacobs has been involved with

studies related to digital and social

technologies, statistics education, and teaching with

technology, and data analytics.

Jacquelin J. Curry earned a Bachelor of Science degree in

business administration with an

option in real estate and land

economics from Fresno State. She

subsequently earned a Juris Doctor

from San Joaquin College of Law in

Clovis, California. Dr. Curry’s

scholarly pursuits encompass a

spectrum of research interests

including areas of real estate

valuation; environmental and sustainability issues in real estate;

ethics, inclusion, and equity, as well as business pedagogy and

education including data analytics.

Concetta A. DePaolo is a professor of operations & supply

chain management in the Scott

College of Business at Indiana State

University. She earned her master’s

and Ph.D. in Operations Research

from Rutgers University, and an

undergraduate degree in

Mathematical Sciences from

Worcester Polytechnic Institute. She

became a Certified Analytics

Professional (CAP

) in 2015. Her research interests include

optimization, statistical methods, statistics education, and

teaching with technology. She has taught statistics, business

analytics and management science at the Scott College since

2000.

Fernando Parra, an associate professor of accountancy and

Director of the Institute for Family

Business at Fresno State, prioritizes

experiential learning and aligns his

teaching with current professional

standards. His research centers on

pedagogy, digital transformation’s

impact on businesses, and socio-

economic factors.

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initial editor screening and double-blind refereeing by three or more expert referees.

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ISSN: 2574-3872 (Online) 1055-3096 (Print)