Properati presentation about Real Estate + AI

1. Gabriel Gruber
AI in RE
07-June-19
The best way to your next home

2. AGENDA
1. OLX Group+Properati
2. AI 101
3. AI at OLX Group
4. AI at Properati
5. AI takeaways

3. 01.
OLX Group
and
Properati

4. Almost 1 year ago ...
01 PROPERATI

5. Properati became the “New kids on the block” at OLX Group
OLX GROUP TODAY: THE WORLD'S #1 CLASSIFIEDS BUSINESS
HORIZONTALS REAL ESTATE
VERTICALS
OTHER
VERTICALS
CARS
VERTICALS
Global
USA
Russia
UAE
Africa and
Philippines
Russia
Portugal
Poland
Romania,
Egypt
Heavy
machinery,
Global
Services
Poland
Poland
South Africa
Romania
Portugal
CONVENIENT
TRANSACTIONS
Cars
Global
Cars
UAE
Cars
UAE
Latin
America
02 PROPERATI
Furniture
France
Jobs
India

6. Argentina
Uruguay
Colombia
Ecuador
Perú
We launched 3 new countries, so far :)
03 PROPERATI

7. 01 PROPERATI
OUR MISSION
To empower buyers so they can have the
best way to their next home through tech
tools and relevant data. In parallel, we seek
to help sellers achieve a more efficient sales
process by enabling the best service to their
potential buyers.
04 PROPERATI

8. As Seen On TV
05 PROPERATI

9. 02.
AI 101

10. AI vs ML
What's the difference between Machine Learning and Artificial
Intelligence?
● If it is written in Python, it's probably Machine Learning.
● If it is written in PowerPoint, it's probably AI :)
06 PROPERATI

11. 07
AI challenges for all tech companies
PROPERATI
● Data scale.
● Scale in infrastructure and engineering capacity to process
all that data.
● The ability to apply AI to solve specific customer problems.

12. 08
AI advantage for the tech giants
PROPERATI
● AI looks tailor-made for the incumbent tech giants.
● They have quickly moved to put AI at the center of their
strategies.
● Aware of its massive potential, they have started to invest
appreciably in AI talent, data infrastructure and even their
own dedicated chips.

13. 09
Warren Buffett´s castle moat in the age of AI
PROPERATI
🐊AI🐊AI
🐊AI

14. 10
But there are some AI opportunities for the rest of us
PROPERATI
● While the tech powerhouses certainly have most of the data
now, their Achilles heel may be a lack of deep domain
expertise.
● Many new winners can be created by applying AI to distinct
problems in the titans’ blind spots.
○ Integrating AI very tightly into your business
processes should also allow companies to compete
with the giants previously thought to be invulnerable.

15. 04.
AI at OLX Group

16. 11
AI at OLX Group
PROPERATI

17. 12
AI at OLX Group
PROPERATI

18. 13
AI at OLX Group
PROPERATI

19. 01 PROPERATIPROPERATI11
05.
AI at Properati

20. 14
Con AI help us answer this question in LATAM ?
PROPERATI

21. 15
Let's have a look at Properati today
PROPERATI
Listings =
Datasets
Statistics
Price valuator tool

22. 16
We have years of listings history that we can use as datasets
PROPERATI
latitude longitude total_surface covered_surface Price (cop $)
4.731 -74.036 112 112 5.500.000
4.694 -74.078 36 36 2.500.000
4.707 -74.069 124 124 3.000.000
4.696 -74.036 100 100 2.586.000
4.609 -74.067 80 80 2.100.000
Attributes
Response
(target)

23. 17
Let's say that the price is a function of the total surface (chart)
PROPERATI
residual
a is the slope,
price per unit of
surface
b is the intercept,
the price at zero surface

24. 18
This is called “linear regression” model
PROPERATI
There is a plethora of options for defining the function f — the
relationship between the price and the attributes of a property.
There is one that:
● was invented 100 years ago,
● is still used
● is simple
● and awesome
It is the linear regression.
We are going to pick just one attribute for the
moment, the total surface.
a and b are parameters of the model.
A linear regression with only 1 variable is not optimal for price valuation

25. 19
So let´s do a multiple linear regression
PROPERATI
We use multiple attributes to estimate the price.
total surface # rooms etc.
Each variable is accompanied by
a coefficient.

26. 20
Now let´s go from multiple regression to a “neuron”
PROPERATI
total surface
# rooms
price
sum
Ps: for simplicity, we have left aside activation functions and non-linearities.
product
Coefficients are also called
weights in this context.

27. And finally from one neuron to a “neural network”
total surface
# rooms
price
This is what it looks like when some neurons, from now on units, are
connected in a feed-forward manner:
Inputs
Outputs
Information propagates from inputs to
outputs.
Layer
● A column of units is called a layer.
● Units within a layer do not connect.
● A unit is connected to all units of
adjacent layers.
21 PROPERATI

28. And finally from one neuron to a “neural network”
total surface
# rooms
price
This is just like a linear regression.
Each connection is weighted, so this model has more parameters.
22 PROPERATI

29. And finally from one neuron to a “neural network”
total surface
# rooms
price
This is another linear regression.
These regressions do not predict the price but a value that is related to it.
23 PROPERATI

30. And finally from one neuron to a “neural network”
total surface
# rooms
price
And one more linear regression.
This regression does predict the price but from some new attributes that are
constructed from the originals.
24 PROPERATI

31. Neural Networks 101
● The family of functions that represent feed-forward multilayer networks
is somewhat more complex than the family of linear regression; mainly
because those functions are compositions of linear regressions.
● Neural networks usually have much more parameters than simpler
models like the regression example
● Still both type of models can be trained using gradient descent; neural
networks additionally require the backpropagation algorithm.
● More parameters mean more power to capture the underlying patterns
in the data.
25 PROPERATI

32. 26
AI + UI/UX and we have our price valuator tool !
PROPERATI

33. 06.
AI takeaways

34. 27
AI key takeaways
PROPERATI
● AI is just simple math repeated trillions of times.
● AI is not programmed, it is taught with labeled data.
● Biases in the data are transferred to AI – “what you get out is only as good as the
labeled data you put in”.
● Data quantity becomes an advantage when applying deep neural networks.
● Transfer learning can save a lot of time – the intelligence in a neural network is
just a long list of numbers, which can be transferred to another “empty” neural
network.
● In practice there’s often a trade-off between feature engineering and model fine-
tuning, computation resources.
● Machines are not really intelligent, they don’t have real understanding (yet).

35. The best way to your next home
Thanks!