Decoding AI: A primer for property management leaders

1 2023 © AppFolio, Inc. Confidential
Decoding AI
A discussion on the impact
of AI on rental real estate
August 31, 2023 | Virtual

Lead Product Marketing Manager
Sean Forster
Moderator
VP, Artificial Intelligence
Cat Allday
Presenter
Industry Principal
Stacy Holden
Presenter

Knowledge
Gain a better understanding of Artificial
Intelligence including its history, core
concepts, and terminology.
Today will be
successful if…
Preparation
Speculate on how it AI can impact the real
estate industry generally, and property
management specifically.
Readiness
Consider the steps our industry needs to take in
order to successfully embrace AI, including
ethical responsibilities.
Actionable next steps
Practical actions we can all take to start or
continue the journal of AI being an effective
and understood part of our businesses.

4
Yes. Jason M. Allen of Pueblo West, Colo.,
created “Théâtre D’opéra Spatial,” with
Midjourney, an artificial intelligence program that
turns lines of text into hyper-realistic graphics.
It won an annual competition at Colorado State
Fair in 2022.
Did AI create
this image?
Source: The New York Times. September 2022.

5
No. Mr. Mata’s legal team used ChatGPT to
create a 10-page brief in response to a motion to
dismiss that cited court cases with names like
“Martinez v. Delta Air Lines”, “Zicherman v.
Korean Air Lines” and “Varghese v. China
Southern Airlines”.
None of those cases were real.
Did AI help
win the case of
Mata v. Avianca?
Source: The New York Times. June 2023.

6
Probably. ChatGPT recently passed three levels
of the Master Sommelier Exam.
It has also scored highly in a number of other
challenging standardized tests including the SAT
(1410/1600), Uniform Bar Exam (90th percentile),
and many AP-level courses.
Does AI know
more about wine
than you?
Source: Vine Pair.March 2023.

Agenda
Knowledge

Everyday examples of AI…

…aren’t always recognized as being AI.
Source: Pew Research Center. February 2023.
Wearable fitness trackers that analyze
exercise & sleeping patterns
0% 20% 40%
60%
65%
64%
57%
51%
62%
68%
Chabot that immediately answers
customer questions
Product recommendations based on
previous purchases
Security camera that alerts when there is
an unrecognized person at the door
Music playlist recommendation
Email service categorizing an
email as spam
% of U.S. adults who identify that the following
use AI in multiple choice questions
% of U.S. adults who correctly identify
____ as using AI
0% 10% 20% 30%
High: All 6 of the
apps above
Medium: 3-5 of them
Low: 2 or fewer
30%
38%
31%

1943
McCulloch and
Pitts create a
computational
model for neural
networks
1950
Alan Turing
introduces the
Turing Test to
determine if a
computer has
real intelligence
1956
The term
“Artificial
Intelligence” is
coined at
Dartmouth
Conference
1969
The first AI winter
begins due to
criticism of AI’s
achievements and
the subsequent
reduction in funding
1957
The first AI boom,
also known as the
“Golden Years”,
where governments
provide ample
funding for AI
research
1972
The Prolog
programming
language, which
becomes popular
in AI research, is
created
1980-1987
The second AI boom,
driven by the rise of
expert systems, which
emulate the decision-
making ability of a
human expert.
1987-1993
The second AI
winter, caused
by the collapse
of the market for
AI and expert
systems.
EARLY YEARS
The history of AI
1997
IBM’s Deep Blue
defeats world
chess champion
Garry Kasparov,
marking a
significant
milestone in AI
development
2011
Siri is integrated
into the iPhone 4
IBM’s Watson
bests two human
rivals to win
Jeopardy!
2014
Virtual assistant
Alexa from
Amazon enters
countless homes
2017
The concept of
AutoML is
introduced,
which automates
the process of
applying
machine learning
to real-world
problems
2016
Google’s
DeepMind
develops
AlphaGo, an
AI program
that defeats a
world champion
Go player.
2018
OpenAI’s GPT-2
demonstrates the
ability of AI to
generate coherent
and contextually
relevant sentences,
a significant step in
NLP
2019
The rise of AI ethics
and explainability
becomes a focal point,
as the implication of AI
in society and the
need for transparent AI
models become
apparent
2021
OpenAI releases
the first Dall-E
generative AI
model for images
2023
Microsoft
commits to a
multibillion dollar
investment and
GPT-4 is
released
GOLDEN YEARS AND FIRST AI WINTER SECOND AI BOOM AND WINTER
2022
OpenAI releases
Dall-E 2 and then
releases
ChatGPT later in
the year
MODERN AI GENERATIVE AI

Distinguishing between the various flavors of AI
Artificial Intelligence
Any technique that enables machines to mimic human
intelligence, using logic, if-then rules, decision trees,
and machine learning (including deep learning).
Expert Systems
Query curated knowledge bases to return results
Rule-Based Systems
Predefined outputs for specific inputs (basic chat bots)
Search Algorithms
Chess bots search for and select best possible moves
Finite State Machines
Non-player characters in video games
Examples

Machine Learning
Machine Learning
A subset of AI that includes abstruse statistical
techniques that enables machines to improve at tasks
with experience.
Linear Regression
Using inputs to assess insurance risk, sales forecast, etc.
Logistical Regression
Email spam detection, credit scoring, etc.
K-means Clustering
Segmenting similar groups, detecting fraud, etc.
Decision Trees
Medical diagnoses, predicting equipment failure, etc.
Examples

Machine Learning
Deep Learning
A subset of machine learning composed of algorithms
that permit software to train itself to perform tasks by
exposing a multilayered neural network to vast amounts
of data.
Deep Learning
Image and Video Recognition
Photo categorization, autonomous driving, etc.
Recommendation Systems
Personalized content suggestions based on user inputs
Speech Recognition
Virtual assistants understand and process spoken inputs
Examples

Deep Learning
Machine Learning
Artificial General Intelligence
A type of artificial intelligence that possesses the ability
to understand, learn, and perform any intellectual task
that a human being can.
Unlike narrow or specialized AI, which is designed and
trained for a specific task, AGI can generalize learning
from one domain to another, adapting to new tasks with
little to no human intervention.
Whether and when this type of artificial intelligence
could be achieved is the topic of healthy debate
amongst experts.
AGI?

Why now?

“Large Language Model”
refers to a class of artificial intelligence
capable of generating human-like text
and understanding natural language

Increased computing power (from advancements
in cloud + GPUs) has fueled artificial neural
networks that train themselves with much less
human intervention
Speed & scale
01 Cerebras WSE-2
2.6 Trillion transistors
Largest GPU
54.2 Billion transistors

LLMs now have access to the knowledge
on the Internet, resulting in massive models
trained on billions of parameters — words,
phrases, and sentences
Depth & breadth
02

LLM creators have decided to offer these
models as a service, forever democratizing
the power of generative AI to Fortune 100,
start-ups, and hobbyists alike
Accessibility &
flexibility
03

Foundational Large Language Models

Agenda
Preparation

AI and natural language models
can benefit my company.

AI and natural language models (e.g. ChatGPT)
can benefit my company.
Strongly
disagree
50%
40%
30%
20%
10%
0%
Leadership
Staff
Disagree Neither agree
nor disagree
Agree Strongly
agree
Source: NAA Research sponsored by AppFolio. 2023.

Top Rental Housing Challenges YoY
(2021-2023)
Source: NAA Research sponsored by AppFolio. 2023.
Exterior % represents the total response of a “Top 3” challenge | Interior % shows responses listing a category as the #1 challenge
HR/Staffing/Training
80
60
40
20
0
74%
51% 49%
25%
42%
19%
63%
10%
61%
17%
76%
28%
Operational Efficiencies
48%
14%
54%
21%
61%
25%
Maximizing Revenue & Profits
2021
2022
2023

Why
Most anticipated ROI, in descending order:
1. Time Savings
2. Reduced Busywork
3. Streamlined Processes
4. Improved Efficiency
5. Cost Savings
How
Most common use cases cited, in descending order:
1. Communication
2. Writing Property Descriptions
3. Excel Tutorial / Calculations
4. Lead Generation
5. Review Leases
6. Search Engine
7. Planning Documents
8. Other Automation
How your peers are thinking about AI in their businesses
Source: AppFolio Market Research & Interviews

Property Management Process Map
There’s no shortage of starting points

We are more limited by our
imagination than by technology.

28
Create a world where
choosing, living in,
owning, and managing
communities feels
magical and effortless,
freeing people to thrive.

Agenda
Readiness

2020
2000 2010 2030
Value
Curve
Internet
Mobile
AI
We’ve been
here before.

What does our industry need to
effectively enable/adopt AI?

Top 3 challenges in starting AI projects, 2022
0% 10% 20% 30%
Proving business
value
Lack of executive
commitment
Choosing the right
AI technologies
34%
37%
33%
Source: Deloitte Survey, 2022 | Chart: 2023 AI Index Report
% of respondents
Getting started can be the hardest step

“Use your first AI pilot project to get the
flywheel turning as soon as possible.”
Andrew Ng, HBR
”How to Choose Your First AI Project”

Seeing is believing: AI pilot projects
Select a project that
can be quickly
successful
Appoint a leader Conduct business
value and technical
diligence
Build a small team Communicate
progress, learnings,
and results
01 02 03 04 05

Legal liability
Trusted partners have an opportunity to showcase a thoughtful
and ethical approach to AI
CONCERN INSIGHT
Accuracy
Workforce
changes
Losing human
relationships
Written communication can be enhanced via AI technology,
freeing up time for in-person relationships
Show individual users how using AI makes their jobs more
efficient, rather than eliminating them
AI should “show its work” - allow users to see what data was used
and validate that their intent was translated to the appropriate action
Source: AppFolio Research. Themes from interviews with property managers.
Build employee trust in AI

DALL-E image generator
vs
Simpler vertical-specific UX
User-friendly AI tools
Simplicity in design

Integrate AI into existing systems
Seamless technology

Lack of transparency
AI decisions are not always
intelligible to humans
AI’s challenges aren’t new,
but they are uniquely scalable
Bias & discrimination
Discriminatory outcomes,
embedded or inserted bias
Unintended consequences
Potential for AI to cause
harm despite being designed
for positive outcomes
Accountability
Needed to ensure AI systems
are used responsibly and in
an ethical manner
Privacy & security
Risks to privacy and data
protection, especially
personal data
Human judgment
AI can make faster and
more accurate decisions in
many cases but human
judgment is important

Responsible
AI values
Fairness
Reliability
Privacy & Security
Transparency
Accountability

Contextualization is necessary in
highly regulated industries like
real estate.

Ask us anything.

Agenda
Actionable
Next Steps

Find partners with context
As you apply AI in your business,
make sure the solutions you use
have the necessary guardrails.
Maintaining the momentum from today’s webinar
Experiment with LLMs
Try using ChatGPT, Bard, and
Claude to help you complete tasks
in your personal life.
Continue to learn
There are numerous high quality
and often free resources available
for you and your teams.
Ensure responsible use
Stay in the loop and monitor that both
the usage and outputs generated by AI
tools adhere to your policies.
Include your employees
Build trust and drive effective change
management by addressing concerns
and evangelizing the vision
Initiate pilot programs
Pick a manageable project for a
small team to learn from before
expanding to complex workflows.

Thank you

Decoding AI: A primer for property management leaders

Decoding AI: A primer for property management leaders

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Decoding AI: A primer for property management leaders