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Brooklyn Property Sales - DATA WAREHOUSE

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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).

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Brooklyn Property Sales - DATA WAREHOUSE

  1. 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. 2. Who we are Sotiris Baratsas sotbaratsas@gmail.com Nikos Spanos nickosspan@gmail.com
  3. 3. BROOKLYN ESTATE INCBK 20 years BK based $ recent struggles
  4. 4. The Business Challenge Mr. Taki I only believe God. Everyone else, bring data! “
  5. 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. 6. Presentation Outline Building the Data Warehouse1 Extracted Insights2 Real Client Examples3
  7. 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. 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. 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. 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. 11. Cleaning the data 20 highly valuable columns 11 dimensions 9 measures We removed irrelevant and duplicate columns, and ended up with
  12. 12. Let’s take a lookLet’s take a look
  13. 13. Let’s take a lookLet’s take a lookLet’s take a lookLet’s take a look
  14. 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. 15. Building the Data Warehouse 1. Defined our schema
  16. 16. Building the Data Warehouse 2. Creating the Data Source connection
  17. 17. Building the Data Warehouse 3. Connecting R with SQL Server
  18. 18. Building the Data Warehouse 4. Importing and populating the tables
  19. 19. Building the Data Warehouse 5. Checking if the tables and their relationships have been imported correctly
  20. 20. Building the Data Warehouse 6. Constructing a Multidimensional Cube
  21. 21. Building the Data Warehouse 7. Calculated measures
  22. 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. 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. 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. 25. OLAP Reports Checking if there is a seasonality effect on the number of property sales
  26. 26. OLAP Reports Checking if there is a seasonality effect on the number of property sales
  27. 27. OLAP Reports Drilling down to check for seasonality per district
  28. 28. OLAP Reports In which areas should we focus our budget and manpower?
  29. 29. OLAP Reports In which areas should we focus our budget and manpower?
  30. 30. OLAP Reports In which areas should we focus our budget and manpower?
  31. 31. OLAP Reports In which areas should we focus our budget and manpower?
  32. 32. OLAP Reports In what type of property should we focus on?
  33. 33. OLAP Reports In what type of property should we focus on?
  34. 34. OLAP Reports In what type of property should we focus on?
  35. 35. OLAP Reports How can we make data-driven and informed decisions, based on market conditions?
  36. 36. OLAP Reports How can we make data-driven and informed decisions, based on market conditions?
  37. 37. OLAP Reports How can we make data-driven and informed decisions, based on market conditions?
  38. 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. 39. CLIENT EXAMPLES
  40. 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. 41. Client Examples On average, all lot types on Brooklyn are sold above the Assessed Value
  42. 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. 43. Client Examples Our agent can access valuable information in seconds, appear as an expert and make smarter pricing decisions.
  44. 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

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