1.
RETAIL TO REAL ESTATE:
Why has the real estate industry been slow to adopt AI-
assisted information processing in its organizational
decision making?
Final Paper
Behavioral Approaches to Strategy
MAN7778
at the
University of Florida
Bryan Dentici
December 12th, 2019

2.
Abstract
Artificial Intelligence has been around for almost 70 years, but only in recent years has it become
a major disrupter for many industries due to the convergence of big data, processing power and
cloud computing. This has led to the development of “deep learning”, which allows a type of
computer intelligence that closely mimics human decision-making. In this paper, I take look at
the evolution of Artificial Intelligence, along with two disparate industries: Retail and Real
Estate. These industries have adopted AI at different speeds. Also, each industry has its own
form of resistance and uses for the technology. My theory is that there are forms of technology
resistance by major players in the real estate industry in combination with the long industry
cycles that are causing slow adoption. The research question I will focus on is, “Why has the
real estate industry been slow to adopt AI-assisted information processing in its organizational
decision making?”
Introduction
Artificial Intelligence (AI) is being utilized by organizations in a wide range of industries to
supplement human decision-making. AI has been explored as far back as the 1950’s when
mathematicians such as Alan Turing asked the question, “Why can’t machines solve problems
and make decisions like humans? (Harvard 2018)” In 1956, Herbert Simon, Allen Newell and
John Shaw created the first Artificial Intelligence program that was designed to mimic human
problem-solving skills (Computer History 2019). Additional research over the years has helped
AI to grow and as computers became more accessible, AI technology has been able to help more
and more businesses. Now, it permeates our daily life in the form of algorithms in social
networks, mapping applications, voice recognition and data organizing software. Within
organizations, the application of AI and more advanced machine learning capabilities has been

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able to add tremendous value. Humans have a capacity limit on the volume of data they can
store and process due to “bounded rationality” (Simon 1956). Because of this, AI has been able
to far surpass human abilities to process information in both accuracy and speed. While humans
have used this AI technology to assist them in the past, AI is now equipped with advanced
algorithms and machine learning that can replace humans in some industries. Unfortunately,
humans commit errors and can be inefficient. Artificial Intelligence can replicate human
operation functions with much more efficiency, which leads to more profits for organizations. In
a study done by Forrester in 2016, they concluded that AI, machine learning, and automation will
replace 7 percent of US jobs by 2025 (Forrester 2016).
Those who work in the retail and real estate industries have very different responsibilities and
tasks. The real estate industry has been much slower adopting AI, whereas the retail industry has
been dynamically transformed by it since e-commerce started growing in the 2000’s.
AI in Retail
The retail industry has completely changed since the year 2000 and the pace of AI innovations
has increased exponentially. One of the casualties of the lightning speed of AI innovation is the
retail salesperson. The retail “salesperson” used to be vital to retailers as they would help
customers find the right product that fitted their needs or preferences. It was about giving great
customer service. Now, algorithms built into e-commerce platforms can offer many more
choices and they can do it much quicker than humans ever could. According to CNN, the retail
sector has lost nearly 200,000 jobs since the beginning of 2017 (CNN 2019). The retail
organizations who adapt to the growth of technology will be able to survive as the industry
continues to evolve. In a 2017 article from the Work in Progress journal, it was stated that
Target now views selling as “routinized” and transactions are judged on the speed of processing

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rather than the quality of service. Because of the retail industry losing so many jobs due to e-
commerce, CBRE estimated that the demand for warehouse and distribution jobs from 2018 to
2019 was 452,000 (CBRE 2018). Although, what happens when warehouses become
increasingly automated with AI robots? A company in Tokyo called Uniqlo cut 90 percent of its
warehouse staff and replaced them with robots who can inspect clothes 24 hours a day (Quartz
2018). Walmart has also invested in bots to help with distribution. A grocery distribution center
will open in 2020 in Shafter, California that will have bots shuttle perishable goods around the
warehouse without damaging them (Fast Company 2018).
Retailers worldwide are integrating AI into logistics and supply chains. According to a survey
by Statista, 49 percent of respondents expect supply chain AI to reduce costs (Statista 2019). AI
inventory management is also expected to reduce costs for retailers.
In The Future of Retailing, the authors had five categories where technology would be used.
They include the following: To Facilitate Decision Making, Visual Display & Merchandise
Offer Decisions, Big Data Collection & Usage, Analytics & Probability, and Consumption &
Engagement (Grewal, Roggeveen, Nordfalt 2017). Organizations in the retail industry are now
at a point where they must be on the cutting edge of technology because consumer expectations
have changed. Technologies such as Augmented Reality on mobile phones and in-store “virtual”
fitting rooms are currently being developed to show consumers how a product would look within
the context of their home or on themselves. Companies such as PayPal have developed fraud
detection algorithms for digital transactions and since time cost is now at an all-time premium,
new “frictionless” payment systems are being developed by retailers to make the in-store
experience more efficient. Amazon already has their Amazon Go stores with cashier-less
checkout lanes where the items are automatically detected in a consumer’s virtual cart (Amazon

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2016). This technology has the potential to increase brick-and-mortar store visits. In 2017,
Mastercard tested an Augmented Reality iris authentication payment system (Mastercard 2017).
How long until we no longer need cashiers in brick-and-mortar retailers?
In a small survey I conducted on retail technologies, I asked the question of how new
“frictionless” payment technologies will affect overall store visits to all retailers. 51 percent of
survey respondents said they “Maybe would go shopping once or twice more per week.” In the
same survey, I asked about Augmented Reality’s effect on consumers for total clothing store
purchases. 24 percent said they would spend $20 to $50 more per month on clothing, and 18
percent said they would spend $51-$100 more per month on clothing. Again, this was a small
survey with a small sample size (n = 50), but it gave me an idea on how consumers perceived
this new technology.
AI has been used for years by companies like Amazon through algorithms that utilize predictive
analysis to suggest what consumers want or need, even before they know it themselves (Forbes
2018). Pricing strategies have advanced through machine learning algorithms from consumer
behavior. This has led to dynamic optimized pricing, which is a vital component in a company’s
profitability (Tryolabs 2019). Smart assistants use data from consumer conversations and
product use cases that mimic a human salesperson. This specific capability has deskilled
hundreds of thousands of retail employees since 2010. In this era of e-commerce, it seems like
delivery positions may be the one area that gets a boost, but not so fast. In 2016, Amazon
announced a partnership with the UK government to test parcel delivery with drones that
delivered up to 5-pound packages in under 30 minutes (EmerJ 2019).

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Figure 1. Image from “Dice Insights” (2018).
The consumers in the retail industry are the main trigger for how fast it is evolving, There has
been a major industry disrupter in Amazon and many companies have followed them in the e-
commerce model. As consumers continue to demand more convenience and on-demand service,
businesses will continue to adopt new technologies to add value to the consumer experience.
AI in Real Estate
The real estate industry has historically been based on relationships and gut instincts. Most tasks
are handled manually by industry professionals who are resistant to change. There are
applications in the real estate industry that are beginning to adopt AI as it is becoming
increasingly evident that this technology will eventually be adopted by competitors who will
subsequently gain an advantage. Overall, the real estate industry is moving much slower than
other industries when it comes to technology adoption. The industry is still discovering the
usefulness of new AI technology. Some real estate companies are starting to implement new
technologies, while other more progressive firms are investing in the technology themselves (St.
Louis Business Journal 2019) because of the massive potential. The real estate industry has gone

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through structural inertia (Hannon 1984) when it came to adoption of the newest technology, but
the emergence of industry disrupters has forced firms to take notice.
Artificial intelligence has been able to help tenants in the search process as AI chatbots filter
properties to fit preferences. Zillow has become a major disrupter in residential real estate since
2006 by using machine learning to give consumers the most up to date analytics. They even
have their own proprietary data-driven formula called “Zestimate” (Information Week 2016).
Zillow has continued to implement AI into its platform which helped transform it from a search
box to a virtual assistant. At Zillow’s annual AI forum this year, one of the technologies that
was presented was the use of innovative augmented reality that used “time of flight cameras” to
capture data from real-world locations through neural network 3D point clouds. This dynamic
data was then reconstructed to synthesize an incredibly accurate 3D video of the inside of a home
(Zillow 2019).
For many real estate businesses, these AI-driven technologies will take away a large source of
leverage. In fact, this may be one of the main reasons the industry has been slow to adapt. In the
U.S., transparency is at a relatively high level, but even more transparency will improve
transactions for both investors and the public. Proptech is a name for technology developed for
the real estate industry. According to JLL, $6 billion has been raised between 2018 to 2016 for
proptech startups. In countries like China and Mexico where transparency isn’t as embraced,
innovations in proptech will help standardize operations and create more globalized transactions
(JLL 2018).
Some areas of the real estate industry have adopted proptech tools more than others. Below is a
chart from JLL.

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Figure 2. Image from JLL’s “Global Real Estate Transparency Index” (2018).
The transaction process in the real estate industry is notoriously stressful for many consumers,
but a startup called Jet Closing has digitalized the entire process with the use of machine
learning. The application will help real estate agents deliver a more “frictionless” experience for
clients (JetClosing 2019).
Even though real estate is the largest asset class in the world, it has only been recently that
industry professionals have begun to utilize the new technology available. The commercial real
estate industry is starting to use new business models, according to a new report from Altus
Group. The following are processes in which AI is being applied as of February of 2019
(Building Design + Construction Network 2019):
· 41% of firms are using automation for benchmarking and performance analysis
· 39% for scenario and sensitivity analysis
· 36% for budgeting and forecasting
· 19% are using AI and machine learning for scenario and sensitivity analysis
· 16% are using AI and machine learning for accounting and property management

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The Development of Deep Learning
There are three main terms that are often confused but represent subsets of computer
intelligence. They are Artificial Intelligence, Machine Learning and Deep Learning. Machine
learning is a subset of AI that requires structured data to produce output. In the past, humans
were required to manually analyze data to come up with insights. This process was cumbersome
as engineers would copy data over and over, eventually putting together datasets that took a long
time. In the 21st
century, machine learning has helped company productivity by coming up with
real-time analytics, predictive analysis, competitive intelligence and cognitive insights. The
earliest machine learning techniques used for AI were hard-coded algorithms or fixed rule-based
systems, but they were not enough for high-level intelligence such as facial recognition. This is
where deep learning came in.
Deep learning is a subset of machine learning that feeds on many levels of “Artificial Neural
Networks” (ANN).
Figure 3. Image from “The Scientist” (2019).

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The process of deep learning is modeled after the human brain, which receives messages from
dopaminergic electro-chemical signals from billions of “neurons”. Artificial neural networks are
interconnected nodes exposed to many data points. Below is a comparison between a human
brain and the concept of an AI brain.
Figure 4. Image from Stanford CS231n (2017).
Whereas machine learning needs human intervention with pre-defined criteria, deep learning
networks learn through their own errors (reinforcement learning) as data travels through complex
non-linear layers of neural networks (Hacker Noon 2019). Also, these layers are not developed
by human engineers. In 2006, popular face recognition algorithms were analyzed. The findings
concluded that the current algorithms were ten times more accurate than the facial recognition
algorithms in 2002 and 100 times more accurate than those in 1995 (Dataversity 2019). Deep
learning is being developed to not only mimic the interpretations of a human, but to mimic the
way a human brain learns.

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Figure 5. Image from “Xenon Stack” (2018).
There is a reason why AI has been around for so long but has only made exponential
advancement the past ten years. The director of AI at Tesla, Andrej Karpathy, stated that there
are four factors that have held back Artificial Intelligence. These were computing power, data,
algorithms, and infrastructure. The advancement of GPUs and data has led to better algorithms
and infrastructure (Medium 2017). Deep learning has had a 175 percent average growth between
2013 and 2016 (Towards Data Science 2019). Below is a graphic of the history of AI according
to the 2019 World Intellectual Property Organization’s Technology Trends report on Artificial
Intelligence.

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Figure 6. WIPO – 2019 Technology Trends report on Artificial Intelligence.
Deep learning has had a relatively brief history. According to WIPO’s report on Artificial
Intelligence, 50 percent of all AI patents have been published in the past 5 years (Towards Data
Science 2019). The first AI patent was issued in the early 1980s, so our most recent
technological advancements are finally allowing us to see what AI can do when deep learning is
developed.
Stages of Artificial Intelligence Adoption
The retail and real estate industries are at different stages in the technology acceptance model
(Davis 1986), which was adopted from the theory of reasoned action (TRA) model (Fishbein &
Ajzen 1975) that was expanded on to explain user acceptance of information systems (Davis,
Bagozzi, and Warshaw 2989). In Fred D. Davis’s 1989 paper, Perceived Usefulness, Perceived
Ease of Use, and User Acceptance of Information Technology, he states that the most important

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quality in perceived usefulness to survey respondents was effectiveness. For perceived ease of
use, the most important quality was for the technology to be controllable (Davis 1989). The
retail industry has seen first-hand the effectiveness of technology through the “Retail
Apocalypse” that began in 2010 (The Atlantic 2017). They saw the repercussions of falling
behind in technological adaptation and many companies are now at the Intention to Use or
Actual Usage stages in the TAM as shown in figure7.
Figure 7. Technology Acceptance model (TAM) (1989).
For professionals in the real estate industry, it appears that most of them are at the Attitudes
Towards Use stage and this is what prevents them from moving forward. Why do they not have
the motivation to adopt potentially profitable technology?
Reasons for Slow AI Adoption in Real Estate
In the real estate industry, cycles are relatively long compared to retail. According to Berkshire
Hathaway, real estate cycles have been 18 years since the early 1800’s. The most recent cycle
had begun to rise in 2008. This includes a 7 to 8-year gradual rise, followed by a 7 to 8-year
rapid rise. According to their projections, 2020 will put us in the middle of a boom. Why is this
important for industry adoption of technology? It means that the results from new technology
use are not yet observable.

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Figure 8. National Association of Realtors (NAR) (2018).
Another reason for slow adoption is the gap between software developers and “domain
expertise”, which is had by those with a deep business understanding, along with many years in
the real estate industry (Urban Land Institute 2016). New technology needs actual “battlefield”
feedback from those in the industry. Because of these long real estate cycles, the professionals
who have a track record of success are not digital natives.
The concept of controlling information in the real estate industry is important to note. Many
business leaders consider this a source of competitive advantage, even if the proliferation of data
would be better for the industry overall. Ultimately, the business leaders in the real estate
industry must weigh the costs and benefits of sharing information. According to the Urban Land
Institute, the “big data” that is currently benefiting other industries, like retail, is not even
available to real estate professionals.
Real estate investors are also different than the venture capital investors that have financed many
tech startups over the years. Venture capital investors have many “losers” in their investments,

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but because of this, they will also have some tech investments that succeed. Real estate investors
do not invest this way. Real estate investors believe in only putting capital into the intended
purpose: real estate.
Conclusion
The real estate industry and its leaders are very path dependent and “experiential learning”
based. The real estate industries “routines” have persisted over the years in a notoriously slow-
moving industry that has historically rewarded patience. The long business cycles in real estate
means that successful strategies that have been implemented with new AI technologies are not
yet observable. Because of this long process and the industries nature of being dependent on
expertise, exploration strategies have not been used to venture into AI technology to the extent of
the retail industry. In addition, due to the slow-moving nature of the real estate industry, the
conventional real estate firm was not designed for environmental turbulence (Siggelkow and
Rivkin 2005). The increasing complexity of our digitalized world has caused the environment to
move faster than the traditional real estate firm, creating organizational inertia.
The balance in alliance formation search strategies has mostly been across the structure
(partners) and attribute domains, but not in functional domains that may have been useful in
integrating new technology (Lavie & Rosenkopf 2006). Until only recently, real estate firms
limited themselves to investing in real estate, not technology. In the past few years, there have
been more investments in real estate technology, which shows promise of more functional
exploration. As more inter-industry alliances get formed between real estate and technology, the
more innovative the real estate industry will become.

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The structure of modern real estate organizations will need “Gatekeepers” that have both domain
expertise and digital literacy. This could be in the form of a chief technology officer (CTO) who
is embedded in both interfirm and intrafirm networks. This CTO or “Gatekeeper” for the firm
could be instrumental for the firm developing, adopting, and integrating new technology. This
firm agent will also be the key in cultivating strategic interfirm linkages (Ahuja 2000) between
the tech and real estate industry.
Real estate professionals have needed to make forward-looking decisions based on past
indicators. The industry has long favored stability and not adaptation (Levinthal and Posen
2007). Historically, the limited amount of data available also contributed toward less adaptation.
It has only been in recent years that aggregate data was so available to everyone, but some of
those in the real estate industry still resist the idea of open and free data.
Future research could focus on differences in the generation gaps within the real estate industry
and the tendency for the younger generations to balance more exploration with exploiting
existing organizational abilities. As younger professionals enter the industry, it could contribute
towards more exploration of innovative solutions. There will be also more digital natives who
have real estate expertise.
It remains to be seen if the real estate industry will ever catch up to the retail industry in
technological adaptation. The nature of real estate favors patience for good returns, so this also
applies to the future of the industry. We will need to be patient in order to find out if artificial
intelligence-based technologies will indeed give the real estate industry a rewarding rate of
return or not.

17.
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