Interest in the global unaffordable housing dilemma is manifest in its growing publications. However, there is a limited systematic review of the literature concerning data science approaches to address the social issues of owning affordable homes through Housing and Urban Development (HUD) programs. The systematic literature review was performed using Google Scholar and followed the phases prescribed in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). This study synthesizes data sources, tools, analytical approaches, and theoretical frameworks from the literature on affordable housing issues using data science methods. Our findings indicate that researchers have approached the issue completely differently from each other, with census data and usage of mapping visualizations as being a common trend.

1. Bharani
Kothareddy
M.S Data Science
Karthikeyan
Umapathy
(Presenter)
Co-Director of Florida Data
Science for Social Good
Associate Professor
School of Computing,
University of North Florida
Jacksonville, FL
A Systematic Review of
Affordable
Homeownership using
Data Science Methods
This research is conducted as a part of

2. Affordable Housing
▹ Housing affordability refers to getting a particular standard of housing at
a price or rent that does not impose an undue strain on household
incomes in the eyes of a third party (typically the government)
▸ Both rental and home ownership
▹ A number of terms have been used to explain housing forms that are
affordable to middle- and low-income earners or the poor in society
▸ Affordable housing
▸ Public and social housing
▸ Cooperative housing
▹ Despite the benefits of maintaining housing affordability and accessibility
for socioeconomic growth, the global housing affordability crisis
continues to be a major challenge for developed and developing
countries
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3. Research Purpose
▹ There is a limited systematic review of the literature
concerning data science approaches to address the
social issues of affordable housing
▹ Synthesize data sources, tools, analytical approaches,
and theoretical frameworks from the literature on
affordable housing issues using data science methods
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4. Systematic Literature Review: Data Collection
▹ Google Scholar was chosen as key scientific database for data collection
▹ Keywords including “affordable housing” and “low income housing” were
used in combination with “data analytics”, “machine learning”, and “data
science”
▹ Perish or publish software was used to get the details such as title,
score, Article URL, abstract, cites per year, cites per author, author
count, volume, issue, ECC, DOI, ISSN, Citation URL, Volume, Issue,
Start page, End page, ECC, Cites per year, Cites per author, Author
count, age, abstract, Related URL
▹ The search results were also refined to include articles published in
English language in the past seven years (2015–2022, inclusive) from
January 2022
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5. Search
Result
Screenin
g
5

6. Data
Source
s
6
Census Data 13
HUD data 5
Energy Information
Administration Data
1
Tax records 5
Residential zones 2
Housing assests 1
Police
Department data
1
Court Clerk data 1
Energy disclosure
data
1
Crime data 1

7. Tools
7

8. Analytical
Techniqu
es
8

9. Researc
h Focus
9

10. Researc
h Focus
10

11. Why we got interested in
Affordable Housing
issue?
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12. HabiJax is one of the largest
nonprofit affordable housing builders
in Duval County, Florida and advocates
for affordable housing and fair housing
policies.
Apart from building homes, HabiJax
provides mortgage lending services as
well as organizes workshops and other
training to help families improve their
housing conditions.
12 Overview
HabiJax is one of the most successful
Habitat for Humanity affiliates
in the United States.
Providing
Homeownership
Opportunities
and
Other Housing Services
to over
2300+
Families

13. HabiJax reached out to
Florida Data Science for
Social Good (FL-DSSG)
for assistance with assessing
impact of the services
provided.
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Data 4 Good
Project
Identify a Nonprofit or
Public sector organization
with a “Wicked Problem”
Gather Data and Formulate
a Plan
Analyze the Data
Improve Decision Making
Process for the Community
Partner
Data Science for Social
Good (DSSG) Process

14. Public Data Sources vs. Collecting Primary Data
Initial Goals
Our initial goals was use publicly available
data sources to objectively measure impact
of homeownership of HabiJax homes.
HabiJax conducted a quality of life survey
but received only 60 responses.
We looked into Property Appraiser, and
Tax records; but datasets were not
relevant to formulate impact of affordable
homeownership.
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Longitudinal Goal
The HabiJax executive team is tasking
FL-DSSG to conduct a retrospective
impact evaluation of affordable housing
provided to income-constrained families
over the past 30 years.

15. Research Questions
1. What are the generational
impacts HabiJax has on low-
income partner families?
2. Do the future generations of
the HabiJax partner families
benefit from their program?
3. Are they better or worse off,
and in what areas?
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Research Objectives
1. Select a sample of HabiJax
homeowners who live in the
Jacksonville, FL area willing to
participate in our study.
2. Collect qualitative open-ended
interview responses from them
regarding their experiences with
the program; as well as to collect
quantitative data from them in the
form demographic information.
3. Synthesize and analyze the data.
4. Repeat objectives 1-3 in 5-year
intervals of the next 30 years.

16. Thank
You!
Karthikeyan
Umapathy
k.umapathy@unf.edu
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