SCOPE OF THE PROJECT: The project is focused on the creation of a Data Warehouse application, for the analysis of property sales in Brooklyn, one of the five boroughs of New York CIty. The project is split into 5 main phases: Phase 1: ​Finding the dataset, understanding its structure and what are the meaningful business questions, this dataset could answer. Phase 2: ​Extract-Transform-Load processes for the data warehouse, using R Studio. Phase 3: ​Building of the Data Warehouse using Microsoft SQL Server. Phase 4: ​Building the Multidimensional Cube using Microsoft Analysis Services and Visual Studio. Phase 5: ​OLAP Report and Data Visualization (using Tableau).

1. Front page
f o r B r o o k l y n P r o p e r t y S a l e s D a t a
DATA WAREHOUSE
BARATSAS
SOTIRIS
SPANOS
NIKOS
A T H E N S U N I V E R S I T Y O F E C O N O M I C S A N D B U S I N E S S
M S c i n B u s i n e s s A n a l y t i c s
P R O J E C T
D A T A M A N A G E M E N T & B U S I N E S S I N T E L L I G E N C E

2. Who we are
Sotiris Baratsas
sotbaratsas@gmail.com
Nikos Spanos
nickosspan@gmail.com

3. BROOKLYN
ESTATE INCBK 20
years
BK
based
$
recent
struggles

4. The Business Challenge
Mr. Taki
I only believe God.
Everyone else, bring data!
“

5. The Business Challenge
Make better decisions
Which areas to focus
our marketing efforts
and manpower on?
Which kind of properties
would be the best use of
our time & budget?
How can we take
advantage of market
changes quickly?
Data-driven pricing for increased commissions
and shorter order fulfillment time

6. Presentation Outline
Building the
Data Warehouse1 Extracted
Insights2 Real Client
Examples3

7. Dataset & Challenges
SOURCE
NEW YORK CITY
DEPARTMENT OF FINANCE
ANNUAL ROLLING SALES DATA
AGGREGATED FOR 2003-2017
*Dataset Link: https://www.kaggle.com/tianhwu/brooklynhomes2003to2017

8. Dataset & Challenges
SOURCE
NEW YORK CITY
DEPARTMENT OF FINANCE
ANNUAL ROLLING SALES DATA
AGGREGATED FOR 2003-2017
VERY LARGE DATASET
390.883 observations
*Dataset Link: https://www.kaggle.com/tianhwu/brooklynhomes2003to2017

9. Dataset & Challenges
SOURCE
NEW YORK CITY
DEPARTMENT OF FINANCE
ANNUAL ROLLING SALES DATA
AGGREGATED FOR 2003-2017
VERY LARGE DATASET
390.883 observations
*Dataset Link: https://www.kaggle.com/tianhwu/brooklynhomes2003to2017
TAX-FOCUSED DATASET
111 columns with mostly tax-related,
bureaucratic or duplicate variables

10. Dataset & Challenges
SOURCE
NEW YORK CITY
DEPARTMENT OF FINANCE
ANNUAL ROLLING SALES DATA
AGGREGATED FOR 2003-2017
VERY LARGE DATASET
390.883 observations
*Dataset Link: https://www.kaggle.com/tianhwu/brooklynhomes2003to2017
TAX-FOCUSED DATASET
111 columns with mostly tax-related,
bureaucratic or duplicate variables
COMPLICATED TAX SYSTEM
We needed to read a lot about legal terms of the NYC
tax system and extract data from additional sources

11. Cleaning the data
20
highly valuable
columns
11
dimensions
9
measures
We removed irrelevant and duplicate columns,
and ended up with

12. Let’s take a lookLet’s take a look

13. Let’s take a lookLet’s take a lookLet’s take a lookLet’s take a look

14. Cleaning the data
Replaced missing
measure values with NULL
Replaced missing
dimension values with NA
Removed false values
(years, ZIP Codes, districts)
Extracted data from
additional data sources
(bldg classes, lot type, land use)
Extracted ”Month Sold”
and ”Year Sold” columns
Identified the correct data
type for each column

15. Building the Data Warehouse
1. Defined
our schema

16. Building the Data Warehouse
2. Creating the Data
Source connection

17. Building the Data Warehouse
3. Connecting R
with SQL Server

18. Building the Data Warehouse
4. Importing and
populating the tables

19. Building the Data Warehouse
5. Checking if the tables
and their relationships have
been imported correctly

20. Building the Data Warehouse
6. Constructing a
Multidimensional
Cube

21. Building the Data Warehouse
7. Calculated
measures

22. OLAP Reports
How have property sales in Brooklyn, moved
over the last 15 years (in terms of value)?
Can we identify any trends?

23. OLAP Reports
How have property sales in
Brooklyn, moved over the last
15 years (in terms of value)?
Can we identify any trends?

24. OLAP Reports
How have property sales in
Brooklyn, moved over the last
15 years (in terms of value)?
Can we identify any trends?

25. OLAP Reports
Checking if there is a
seasonality effect on the
number of property sales

26. OLAP Reports
Checking if there is a
seasonality effect on the
number of property sales

27. OLAP Reports
Drilling down to check for
seasonality per district

28. OLAP Reports
In which areas should
we focus our budget
and manpower?

29. OLAP Reports
In which areas should
we focus our budget
and manpower?

30. OLAP Reports
In which areas should
we focus our budget
and manpower?

31. OLAP Reports
In which areas should
we focus our budget
and manpower?

32. OLAP Reports
In what type of property
should we focus on?

33. OLAP Reports
In what type of property
should we focus on?

34. OLAP Reports
In what type of property
should we focus on?

35. OLAP Reports
How can we make data-driven
and informed decisions, based
on market conditions?

36. OLAP Reports
How can we make data-driven
and informed decisions, based
on market conditions?

37. OLAP Reports
How can we make data-driven
and informed decisions, based
on market conditions?

38. OLAP Reports
How can we make data-driven
and informed decisions, based
on market conditions?
Avg Price per Sq Ft
# of Sales per year

39. CLIENT
EXAMPLES

40. Client Examples
A client wants to sell her property.
Aware of the Assessed Value that the city’s finance
department has placed on her lot, she wants to set
the starting price equal to the Assessed Value.

41. Client Examples
On average, all lot types
on Brooklyn are sold
above the Assessed Value

42. Client Examples
A client comes to us, to sell his property.
He tells us he has a 2-floor, inside lot in East New York.
To get the job, our agent has to show he has a good
understanding of the prices of this type of property.

43. Client Examples
Our agent can access
valuable information in
seconds, appear as an
expert and make smarter
pricing decisions.

44. Front page
f o r y o u r a t t e n t i o n
THANK YOU
BARATSAS
SOTIRIS
SPANOS
NIKOS