1. Three Ways AI Can Improve Efficiencies
Daniel Faggella, CEO at TechEmergence
AI in Real Estate
@danfaggella

2. Background Brief
I’m Dan Faggella, CEO/Founder atTechEmergence.com
We’re a market research and media firm with one goal:To cut
through hype and show business leaders the implications,
applications, and important companies in artificial intelligence.
We have business readers all over the world (biggest following
in SF, NYC, Bangalore, London).
@danfaggella

3. Outline of the Talk
1. The State of AI in the EnterpriseToday
2. Burgeoning AI Use-Cases in Real Estate
3. Becoming “Hype-Proof” - What to Pay
Attention to in the Future
@danfaggella

4. @danfaggella
Presentation Part 1:
A Primer on AI in the Enterprise

5. Why it Matters
• Machine learning will likely overhaul entire industries in the next 15
years (will be essential in security, customer service, marketing, BI/
analytics)
• We interview hundreds and hundreds of execs and researchers, and
while these all have different opinions on timelines, they agree on the
inevitability of AI transforming industry, much the same way the
internet did
• AI will impact your business, but you need to know what to pay
attention to.You will be bombarded with hype and news about AI and
you should know how to “prime your antennae”, paying attention only
to what matters, and that’s what I’ll help you with in this presentation
@danfaggella

6. The State of AI in Business
• Make no mistake about it: It’s mostly pilots, testing (not
concrete ROI)
• For every 100 “AI companies”, we’ve found that only 1/3 is
actually leveraging AI in any serious way, and only 1/3 of those
companies are past the stage of “piloting” their product or
service (Maybe 1 in 10 “AI” companies is actually selling
something that has had a positive impact on a business)
@danfaggella

7. What Smart Executives Do
• Stay on top of what the real estate GIANTS are investing in
for AI, and pay attention to what actually generates ROI
• Assume that 90% is PR garbage, 10% may have promise
• AtTechEmergence.com this is the research we do every day,
this is our sole focus (Case studies, industry comparisons, etc)
@danfaggella

8. Everyone can load their data into Hadoop… but doing something with
that data (in terms of ML applications) often reveals structural problems:
• No ML talent in place (and consultants can’t do it all for you - see
Machiavelli’s quotes on auxiliary troops)
• More importantly, no management structures in place to (a) deal with
uncertainty of AI applications (there is no guarantee on if they will
work, or when), (b) deal with the lengthy R/D process of AI, (c) getting
buy-in or understanding from the top
• Some vendors / consultants I’ve spoken with think that acquisitions will
be the way that enterprise innovates, not R/D
Biggest Challenges
@danfaggella

9. =
ROI
Criterion
@danfaggella

10. @danfaggella
Presentation Part 2:
Examples

11. Real estate businesspeople will likely see proxies for AI
applications in other industries before they see them in their
own, including:
• Consumer tech and eCommerce (for anything customer
facing)
• Manufacturing / equipment maintenance (for driving
efficiencies)
Real Estate is Not First to the
AI Party
@danfaggella

12. Analytics in Building
Automation Systems
• Savings: The biggest opportunities for savings in building automation
come from energy efficiency upgrades — savings will make the ROI case
for widespread adoption of “sensorized” building system devices (e.g. the
Internet ofThings)
• Market Opportunity: Data from Internet ofThings (IoT) devices in
building systems will support an emergent market for predictive analytics
• Threats: Security software automation will drive savings, but will place
additional emphasis on the need for robust cybersecurity in commercial
buildings
@danfaggella
Application Area 1:

13. Analytics in Building
Automation Systems
• Challenges: Machine learning models have trouble with time series
data that varies. Real estate data varies by day, by week, and usually
heavily by season, and makes predictive analytics hard to do.
• AI applications are likely to be rather niche, and buildings that “think for
themselves” in managing energy or heat are rather far off.
@danfaggella
Application Area 1:

14. Analytics in Building
Automation Systems
• Security Issues: Target Corporation was breached through their
smart HVAC systems. Connected and smart devices potentially open
up another channel of access for attackers. Large and prominent real
estate firms are best to consult data security experts before deeply
integrating “smart” IoT devices across their properties.
@danfaggella
Application Area 1:

15. PointGrab
Description:
• Provides information about the location and behavior of building
occupants.Where they are and where they are moving.
• Uses smart sensors within rooms that detect human presence via
visual sensors.
@danfaggella
Companies

16. PointGrab
@danfaggella
Companies

17. TellMePlus
Description:
• Works in both the heavy industry and real estate space, connects
sensor data to relevant events related to clients’ assets.
@danfaggella
Companies

18. BuildingIQ
Description:
• Claims, among other things:“enables tracking, analysis, aggregation,
modeling and reporting of energy data at the enterprise, facility,
meter, and submeter levels. Performance is calculated hourly and
incorporates weather data and time of the week use to calculate
expected consumption for a building.“
@danfaggella
Companies

19. A Note on These Companies
All of these companies are relatively young and small (i.e. under 50
employees) Some of the do have case studies, however, which is
promising.
@danfaggella

20. Automation in Property
Management Job Functions
• Savings: Managers want visibility into sales and agent
performance data to drive prospect conversion and improve
outcomes for business development team
• Market Opportunity: AI chatbots will interact with building
occupants through Amazon Echo, which boutique property
owners will use to delight and reward commercial tenants with
distributed concierge services
@danfaggella
Application Area 2:

21. Automation in Property
Management Job Functions
• Companies:
• VTS and Appfolio focus on…
• In contrast, solutions like Buildium, Rentalutions and Zenplace
– all predominantly focused on residential rather than
commercial property – apply process automation technology
to improve the tenant/landlord service relationship.
@danfaggella
Application Area 2:

22. Zenplace
Zenplace claims to have developed a chatbot to answer basic
questions from current tenets.Tenants can pay their rent easily,
manage their energy usage and billing, and even report issues with
the property 24/7 (Amazon Alexa is used for this bot).
@danfaggella
Companies

23. Machine Learning in Real
Estate Marketplaces
• Savings: Property search analytics will improve matching
between prospective buyers and desirable properties,
augmenting broker’s labor
• Market Opportunity: Virtual reality marketplaces will reduce
friction and accelerate dealflow
@danfaggella
Application Area 3:

24. Machine Learning in Real
Estate Marketplaces
Companies like ApartmentOcean and Automabots are already
well after this market, but selling to realtors with websites rather
than using chatbots to go after buyers directly.
However, some brokers have argued that the technology is still
too new and risks turning off prospects who might find the bot
rude or unhelpful.
@danfaggella
Application Area 3:

25. Machine Learning in Real
Estate Marketplaces
Other examples:
• Sites like Zillow and RedFin andTrulia use AI for home or rental
recommendations for users.
• Goldman Sachs Research estimates that the market for virtual
reality (VR) in real estate alone could generate as much as $2.6
billion by 2025. Given that much AI development today is in
computer vision it’s likely to see 3D property visualization
creating new tools for brokers and property managers.
@danfaggella
Application Area 3:

26. @danfaggella
Our Interview with the Chief Analytics Officers at Zillow
Sheds Light on AI Use-Cases for Real Estate Marketplaces

27. @danfaggella
Presentation Part 3:
Avoiding Hype, Moving Forward

28. • Think about AI adoption the same way you would think of
adoption of any other emerging technology that you consider
to be essential to the future of your industry.You might test
and try with a little most gusto because AI is indeed
inevitable, but don’t be rash for “FoMo” sake!
• AI adoption should involve an informed, forward-looking
industry / competitive analysis (like the kind that you’d do at a
quarterly off-site), nothing less.
Where AI “Should” Be Used
@danfaggella

29. • Common cardinal sin:“Toy” applications
• “Toy” applications are technologies or projects taken on because
they use AI, not because they solve a business problem.Vendors
play into this because they need guinea-pigs to “pilot” products,
and they’ll sometimes encourage closing deals even if they aren’t
well organized
• They almost all end the same way: Lacking resources to back
them, lacking gusto to carry them through, and negatively
impacting the funds and human resources of the company (and
making the “toy” initiator into a fool).
Cardinal Sin of AI Applications
@danfaggella

30. Adopt or Wait?
• For any given AI application area (marketing, business
intelligence, procurement, etc), determine where you want to
be on the adoption curve (FEW established firms must be or
should be “innovators” or even “early adopters”)
@danfaggella

31. Concluding Thoughts
Priorities for executives:
1. Understand what kinds of problems are solve-able with AI
2. Consider the domains within your business that can most
benefit from the use of AI
3. Talk to companies who have developed and implemented
similar applications and get a realistic understanding of what it
would take to implement them yourself
4. Then you can make your decision on whether or not to invest
in AI, to develop AI, to acquire AI, etc…
@danfaggella

32. That’s All, Folks
Email
info@techemergence.com
- and I’ll be happy to reply. Feel free to include any relevant
questions from this presentation.
@danfaggella

33. Resources (1)
• TechEmergence articles about real estate:
• https://www.techemergence.com/artificial-intelligence-in-commercial-real-estate/
• A good overview of AI trends in real estate, researched by Ed Zagorin - one of our
contracted writers with experience in procurement and real estate
• https://www.techemergence.com/zillow-data-driven-real-estate-appraisals-at-your-
fingertips/
• Machine learning in marketing:
• https://www.techemergence.com/machine-learning-marketing/
• Applying AI to Business Problems (General Understanding):
• https://www.techemergence.com/how-to-apply-machine-learning-to-business-problems/

34. Resources (2)
• https://www.techemergence.com/enterprise-adoption-of-artificial-intelligence/
^ My best summary of the current challenges of integrating AI into companies that aren’t used to
data science.A good primer for applying AI in any industry.
• https://hbr.org/2017/04/the-first-wave-of-corporate-ai-is-doomed-to-fail
^ Quote from this article:“We believe AI will indeed transform industries. But the companies that
will succeed with AI are the ones that focus on creating organizational learning and changing
organizational DNA”
• http://www.gartner.com/smarterwithgartner/artificial-intelligence-and-the-enterprise/
^ Most relevant part of this article is the third question “How will AI impact the talent needs of an
organization?”
• https://hbr.org/2017/06/if-your-company-isnt-good-at-analytics-its-not-ready-for-ai
^ Extremely useful perspective on the “baby steps” needed to begin working with AI seriously.