1. Using Elastic
to discover
and analyse
undervalued
distressed
real estate
1
venduetech.com
Ivan Livić, CEO
Jelena Joksimović, (PhD) Data Science

2. Distressed property refers to real estate either
under foreclosure, pre-foreclosure or control of
the lender/bank. A property becomes "distressed"
when the owner (business or individual) falls
behind on their mortgage payments and/or
property tax bills.*
* forbes.com
Definition
”
venduetech.com
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3. Founding Team
Jurica Šonje, CTO
Singapore
Telecommunications >
System Integrations
Cyber Intelligence >
Platforms B2G Pre-Sales
Marin Mastilica, CDO
Ireland
Energy management in real-
estate > Energy efficiency data
platforms
Ivan Livić, CEO
Croatia
5x Founder
Ex Amazon Web Services,
CloudCover, Axiata Digital
dr Iwona Skowronek, COO
Poland
R&D Project Manager
Technological innovations >
Operations >
Growth strategies
venduetech.com

4. A team with experience in R&D, technology,
law, real estate, UX, product and data
Filip
Netherlands
Software
architecture,
Development
Ivana
Singapore
Product
design lead
Barbara
Croatia
Legal and GDPR
Croatia
Brand and
product design
Krešimir
Nikola
Croatia
Senior researcher,
Development,
Machine Learning
Matija
Croatia
Web
development
Ante
Croatia
Legal and
Policies research
venduetech.com
Valentina
Ireland
Data analyst
Linda
Croatia
Marketing
Serbia
Data Science
Jelena, PhD
CTO wanted
Worldwide
Equity based
part time CTO

5. Turning auctioning chaos into
actionable data with Elastic!

6. Cloud architecture
⬤ Web scrapers written in language Go for 7 data sources in 5 countries
⬤ Using Dockers and microservices
⬤ Built 4 prototypes
1 / Automated auction system for e-auctions: reading and reacting on HTML/JS changes
2 / New auctions alerts are sent to Slack channel
3 / Dashboards with real data done in Elastic Kibana
4 / Data scrapers from 2 legal data sources and 1 cadastre dataset
venduetech.com
venduetech.com

7. Using Kibana to visualise real time auctions
venduetech.com

8. Preview of Elastic Kibana dashboards
venduetech.com

9. Detecting and notifying discounts
greater than 60%
venduetech.com

10. Discover,
Analyze and
Co-invest in
undervalued
distressed
real estate
venduetech.com

11. Data flow
venduetech.com

12. Naslov…
venduetech.com

13. Data volume in Vendue Tech database
7,306
17,130
13,769
venduetech.com
Coming soon:
Normalized datasets and translated to English:
36,094

14. venduetech.com
Our objective
P ― Property data A ― Auction data L ― Legal data

15. Scraping Data from Multiple Sources
bring few challenges
⬤ Important data is locked in PDFs
⬤ Database schema isn’t universal
⬤ Multiple languages make it hard to extract regex
venduetech.com

16. What are our moonshots
venduetech.com

17. Naslov…
* Based on our analysis of 36,000 auctions
venduetech.com

18. Use OCR to extract value out of thousands of
official PDFs
Why?
1. Every corporate bankruptcy is
required by law to have an
independent official elaborate
2. They are mainly in PDF form
3. Contain lots of valuable intelligence
which isn’t connected to a central
database
4. If connected to Vendue Tech’s data
lake, and applications, it generates
big value to customers
venduetech.com

19. Extracting value from thousands of
Public Official Documents
Location: Milan, Italy
Address:
Via Generale Gustavo Fara 29/4
Type: apartment
Starting price: €282,000
Minimum offer: €211,500
Bidding date: June 16, 2023,
Surface area: 47 square meters
Description: Living room with a
kitchenette, a bedroom, a bathroom,
and two small balconies.
venduetech.com

20. Results of data cleaning techniques
⬤ 5,200 (20%) types of real estate found in Description field and populated
instead of ‘other’ or ‘unclassified’
⬤ Surface area found for all Italian real estates (>7,000) in text fields
⬤ Approximate starting price populated where ever missing
(2,000 real estate) - calculated as a dependant variable on minimum
price, location and estimated value
⬤ Cities extracted based on location variable (latitude, longitude mapping)
venduetech.com

21. Visual results in Kibana
venduetech.com

22. ⬤ To generate precise ROI calculators (short-term/long-term rental opportunities)
⬤ To create VT indexes, in order to rank best properties and auctions automatically
⬤ To answer precisely on users’ questions like:
Mark the properties with minimal renovation work that can be best rented for
families on a coast?
Give me the best office space properties for 200 people for an Law firm.
Give me cheapest yards in Southern Italy that are bigger than 10 square kilometers.
What can I buy in Zagreb now for less than 80,000 euros?
Vision with data
venduetech.com

23. Use OCR to extract value out of thousands of
official PDFs 2/2
Why?
⬤ Every corporate bankruptcy
is required by law to have an
independent official elaborate
⬤ They are mainly in PDF form
⬤ Contain lots of valuable intelligence
which isn’t connected to a central database
⬤ If connected to Vendue Tech’s data
lake, and applications, it generates big
value to customers
venduetech.com

24. Data volume and quality
⬤ Estimated Value: Exists in 90% of records. Very good.
⬤ Starting Price: Exists in 57% of records. TBD.
⬤ Size in Sqm: Exists in 52% of records. TBD.
⬤ Discount: Depends on 2) and 1). TBD.
⬤ Auction Start Time: Appears in 100%. Excellent.
⬤ Real Estate Type: 98%. Very good.
⬤ City: 89%. Very good.
venduetech.com
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25. Product design
● Designed the prototypes of various dashboards, product screens, marketplace
● Brand design elements, logo, color scheme
venduetech.com
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26. venduetech.com
Naslov…

27. venduetech.com

28. Vision is to allow multiple investors and
analysts collaborate on auction
and property data
venduetech.com

29. Bring multiple data sources
into a single view
venduetech.com

30. Statistical analysis done in Excel,
Tableau and Kibana

31. Preview of Elastic Kibana dashboards
venduetech.com

32. Normalizing the data from public
data sets (N=48k)
Data source: https://narodne-
novine.nn.hr/clanci/oglasi/188605.html
venduetech.com

33. Unique identifier (ST number, Court) used
to create social graph
venduetech.com

34. Automatically recognize
and classify the entities
Why?
⬤ To gather intelligence
from historical auctions,
participants, buyers, bidders,
bankrupt firms and individuals
⬤ To get an investment edge
over competitors
⬤ Offer more precise reports and
actionable advice to
customers
Which can result in:
Higher ROI
Better purchasing signals
Ahead of others
More quality data for
decision making
More details on auction
process and properties
venduetech.com

35. Use OCR to extract value out of thousands of
official PDFs 1/2
Why?
1. Every corporate bankruptcy is required
by law to have an independent official
elaborate
2. They are mainly in PDF form
3. Contain lots of valuable intelligence
which isn’t connected to a central
database
4. If connected to Vendue Tech’s data lake,
and applications, it generates big value
to customers
venduetech.com

36. Use OCR to extract value out of thousands of
official PDFs 2/2
Why?
Every corporate bankruptcy is
required by law to have an
independent official elaborate
They are mainly in PDF form
Contain lots of valuable intelligence which
isn’t connected to a central database
If connected to Vendue Tech’s data
lake, and applications, it generates
big value to customers
venduetech.com

37. Objective: Use modern tools to tell stories for
various audiences
venduetech.com

38. Grafana, Kibana, Google Data Studio,
D3.js, Plotly
venduetech.com

39. Creating social graphs, heat maps, tree maps
venduetech.com

40. Objective: Connect whole EU into
a single data lake
venduetech.com

41. Connected to Google
Maps API
Why?
1. Points of interest
2. Show nearest hospital, shops, schools,
metro stations, public transport
3. Micro climate conditions
4. Zones Walking, Jogging
5. Traffic conditions
An API service which takes geolocational
information (lat,lon coordinates or address)
as an input, and returns the proximity of a
nearest hospital, grocery store, school, as well
as current prices from AirBnb for that area.
venduetech.com

42. Use public datasets for higher customer value
Why?
1. To generate precise ROI calculators
2. Offer remote bidders objective
calculations and predictive models
for profit on rental (Short-term vs Long-
term vs hold vs Flip vs Live)
1. To create VT indexes, in order
to rank best properties and
auctions automatically
1. To create custom filters, and
recommendations for various
types of customers
t
venduetech.com

43. venduetech.com

44. Thanks for the attention!
venduetech.com