This slideshow was generated from a series of three academic papers totaling over 150 pages in a graduate-level, group project at University of Wisconsin - Milwaukee. This mock exercise detailed a consulting engagement with a real estate development & management company planning to enter the Denver, CO multi-family market. The business analytics goals were to 1) find what apartment attributes, amenities and services are significant to renter satisfaction, 2) determine how apartment renter sentiments impact overall satisfaction, and 3) analyze Denver social media networks to advise how and where to market apartments in Denver, CO. The findings were that 1) good staff is most critical to renter satisfaction, 2) management has the highest trending impact on renter sentiment, and 3) Twitter and YouTube have specific examples of how social medial marketing can be effectively performed. To review the actual papers and results, please contact me via LinkedIn - https://www.linkedin.com/in/jamesyoung007

1. Bus Adm 741-Web Mining and Analytics
Group 2 Project Presentation
Ben Meyers, Ali Qureshi, James Young
Analysis of
Colorado’s Multi-
Family Housing
Market

2. • Multifamily Development, Inc. (MDI)
 A fictitious company, MDI plans to expand into the Denver,
CO market and therefore needs to gain insight into the real
estate vertical specific to multi-family residential apartment
buildings and complexes for the state of Colorado.
 MDI’s primary business goal is to effectively market, develop
and renovate multi-family housing that meets the expressed
needs of Colorado’s apartment renters.
 Group 2 will consult with MDI’s executive management to
answer specific business questions while utilizing data
analytics as the core of its consulting methodology.
Proposal

3. • Multifamily Development, Inc. (MDI)
 MDI (RE mgmt & development) is expanding into Denver CO
& needs find what properties represent the best investment.
MDI also needs to find out what the biggest renter issues are,
I.E., what are renter’s hot-buttons. MDI has three main goals
assigned to Group 2 constultants:
 MDI’s analytics goal is to find what apartment attributes,
amenities and services are most significant to renter satisfaction
and recommendation.
 MDI is interested in finding out how the sentiments of reviewers
impact their overall satisfaction and recommendation.
 MDI also wants to analyze Social Networks to find out where and
how they should market their properties
Business & Analytics Goals

4. • Dependent Variables
 Overall satisfaction (Ordinal)
 Recommended or not (Binary)
• Primary Independent
Variables
 Noise
 Maintenance
 Safety
 Neighborhood
 Grounds
 Office staff
• Source of data
 http://www.apartmentratings.com/co/denver/
Goal 1: Variables
• Other Independent
Variables
 Laundry facilities,
 Pets allowed or not

5. Data
Data Sample
SAT REC REC% NOISE NEIGH MAINT GROUN SAFE STAFF PET
2 NO 43% 2.5 2 2.5 2.5 2.5 2.5 YES
2.5 NO 41% 3 4.5 3 2.5 3 2.5 YES
2.5 NO 48% 3 4.5 3 3 3 2.5 YES
 Data Source, Collection, Cleansing

6. Data Distribution
Data from 110 properties in Denver area

7. Correlation

8. Regression

9. Regression Tree
Random Forest
Satisfaction Recommendation

10. Model Evaluation

11. Model Evaluation

12. Error Comparison
0
5
10
15
20
25
30
NN SVM Boosting Rtree RanF Lreg
Model Error Comparison
Error Rate MAE

13. • Findings
Pattern of dissatisfaction
Staff, Grounds, Noise – Satisfaction
Safety, Maintenance - Recommendation
• Recommendations
Resolution of dissatisfaction = opportunity
Provide good Staff, well maintained grounds
& buildings, Safety Procedures
Helps to overcome Noise & Neighborhood
Issues
Business Findings &
Recommendations

14. • Dependent Variables
 Overall satisfaction (Ordinal)
 Recommended or not (Binary)
• Data Preparation
 Picked 40 random properties from Denver with at least 3 reviews
 Captured 3 latest reviews
 Captured average 1 bed 1 bath rent for each property
 Binned 40 properties to High-Rent and Low-Rent apartments
 Total 10 files for Satisfaction DV and 4 files for Recommended DV
• Source of data
 http://www.apartmentratings.com/co/denver/
Goal 2: Variables and Data

15. Data Distribution
Low Rent High Rent
Stars Reviews Rec Reviews Stars Reviews Rec Reviews
1 26 0 37 1 12 0 27
2 6 1 38 2 12 1 18
3 10 3 3
4 14 4 9
5 19 5 9
Total 75Total 75 Total 45Total 45

16. Associations - Satisfaction
Low Rent Associations
manag problem issu mainten
complaint 0.99 care 0.99 first 1.00 first 1.00
often 0.99 per 0.99 hour 0.99 hour 1.00
run 0.99 resolv 0.99 mainten 0.99 issu 0.99
come 0.98 ride 0.99 alon 0.98 right 0.99
week 0.98 thought 0.99 got 0.98 anyth 0.98
door 0.97 unfortun 0.99 lot 0.98 everyth 0.98
away 0.98 right 0.98 got 0.98
cant 0.97 wasnt 0.98 your 0.98
Additional Low Rent Associations
area nois safe offic
addit 0.99 ever 0.98 alarm 1 better 0.99
larg 0.99 hour 0.98 fire 1 broke 0.99
singl 0.99 mainten 0.97 height 1 chang 0.99
summer 0.99 right 0.97 obvious 1 everywher 0.99
storag 1 furnitur 0.99
student 1 multipl 0.99
team 1 pretti 0.99
wish 1 respons 0.99
time 0.99

17. Associations - Satisfaction
High Rent Associations
problem like issu neighbor
everi 0.98 200 0.99 cherri 0.97 everi 1.00
neighbor 0.98 laundri 0.99 close 0.97 sign 1.00
sign 0.98 mgmt 0.99 creek 0.97 dont 0.99
dont 0.97 sometim 0.99 far 0.97 alarm 0.98
etc 0.96 request 0.97 attent 0.98
rent 0.96 bewar 0.98
bill 0.98
convers 0.98
Additional High Rent Associations
safe offic great love
alway 1.00 colleg 1.00 2nd 0.99 distanc 0.98
linda 1.00 correct 1.00 cut 0.99 neighborhood 0.98
locat 1.00 fan 1.00 deal 0.99 request 0.98
prompt 1.00 free 1.00 quiet 0.99 within 0.97
denver 0.99 guard 1.00 submit 0.99
distanc 0.99 heard 1.00
neighborhood 0.99 late 1.00

18. Recommended
Low Rent Term Frequency Count

19. Recommended
High Rent Term Frequency Count

20. Satisfaction
Non-Randomized Cloud
Low Rent Non-Randomized Cloud High Rent Non-Randomized Cloud
• Most relevent = Manag
• Other relevent terms = Offic,
Staff,Time, never, Lease
• Low rent terms are more -ive
• Most relevent = Manag
• Other relevent terms = peopl,
park, like, staff, nice, call, care
• High rent terms are more +ive

21. Satisfaction
Comparison Cloud
Low Rent Comparison Cloud High Rent Comparison cloud

22. Satisfaction
Commonality Cloud
Low Rent Commonality Cloud High Rent Commonality cloud
• Most relevent = Manag
• Other relevent terms = Offic,
place,time, area, issue, problem
• Most relevent = Manag
• Other relevent terms = peopl,
place, like, staff, nice, mainten

23. Recommended
Comparison Cloud
Low Rent Comparison Cloud High Rent Comparison cloud

24. Recommended
Commonality Cloud
Low Rent Commonality Cloud High Rent Commonality cloud
• Most relevent = Manag
• Other relevent terms = Offic,
mainten,time, never, Lease,issu,
problem
• Low rent terms are more -ive
• Most relevent = Manag
• Other relevent terms = peopl,
park, like, staff, nice
• High rent terms are more +ive

25. • Findings
 Low Rent
 Unhappy reviewers are more critical and extreme
 Renters complain more about maintenance and deposits
 Reviewers are less likely to rate 2 or 3 stars, instead they
tend to pick one star
 Management, staff and office are most significant
 High Rent
 More satisfied, happier, and less extreme in their reviews
 Management, staff and office are most significant
 Use more superlative adjectives like awesome, great,
love, nice
Business Findings

26. Management, staff and office are the most
significant
Fast maintenance is needed to get good
reviews
Good internal controls are necessary for
high quality, well trained staff
Recommendations

27. Social Network Analysis
• Twitter
 Used Twitter Search Network
 Search = ‘denver apartment’
 Just over 200 results
• YouTube
 Used YouTube Video Network
 Search = ‘denver apartment review’
 Just over 1000 results
Goal 3: Variables and Data

28. Twitter: Cleansing and Metrics
• Removed and merged edges
• Directed graph
• 196 Vertices
• 209 Unique Edges
• No Duplicate Edges
• 117 Self-loops
• 129 Connected Components
• 32 Max Vertices in CC
• 3 is the Max Geodesic
Distance

29. Metrics
• aldosvaldi – Aldo Svaldi is a reporter for Denver Post Newspaper
• denverpost – Denver’s main Newspaper
• denbizjournal – Denver’s biggest business journal
• camdenintrlckn – Camden Living– Multifamily RE owner/manager (REIT)

30. Metrics Analysis
• Minimum In-degree is zero
• Maximum In-degree is 30
• Minimum Out-degree is zero
• Maximum Out-degree is 4

31. Metrics Analysis

32. Twitter Terms

33. Betweeness with filtering

34. Group by Clusters
• Total 29 groups
• Light blue – Denver Post (denverpost and aldasvaldi)
• Dark Green – Denver Business Journal and Camden Living

35. YouTube: Cleansing & Metrics
• Removed and merged edges
• undirected graph
• 100 Vertices
• 981 Unique Edges
• No Duplicate Edges
• Zero Self-loops
• 13 Connected Components
• 32 Max Vertices in CC
• 1 is the Max Geodesic
Distance

36. Metrics Analysis
• Undirected – No in or out degree
• Maximum degree is 31
• Minimum degree is zero
• Average degree is 20
• Median degree is 25

37. Metrics Analysis

38. Group by Vertex Attribute

39. Vertices by number of views

40. Filtering by number of views

41. • Twitter
 Business Finding
 Properties can be advertised in major newspapers to get high visibility in
Denver area
 Having good contacts with newspaper reporters can provide huge benefit
 Twitter marketing can be modeled after similar companies
 Recommendation
 Denver Post and Denver Business Journal are the best resources for Public
Relations and Marketing
 Twitter should be used to market apartment buildings as Camden is doing
• YouTube
 Business Finding
 YouTube can be used to post walk-throughs and virtual tours of real estate
properties
 Recommendation
 Use ForRent.com’s marketing style and service to record and post video
walk-throughs of MDI properties
Business Findings &
Recommendations

42. Conclusion by Goals
• Goal 1 - MDI’s analytics goal is to find what apartment attributes, amenities
and services are most significant to renter satisfaction and recommendation.
 Resolution of dissatisfaction = opportunity
 Provide good Staff, well maintained grounds & buildings, Safety
Procedures
 Helps to overcome Noise & Neighborhood Issues.
• Goal 2 - MDI is interested in finding out how the sentiments of reviewers
impact their overall satisfaction and recommendation.
 Management, staff and office are the most significant
 Fast maintenance is needed to get good reviews
 Good internal controls are necessary for high quality, well trained staff

43. Conclusion by Goals
• Goal 3 - MDI also wants to analyze Social Networks to find out where and
how they should market their properties
 Denver Post and Denver Business Journal are the best resources for
Public Relations and Marketing
 Twitter should be used to market apartment buildings as Camden Living
is doing
 Use ForRent.com and Camden Living’s marketing style and service to
record and post video walk-throughs of MDI properties
“Use all the analytical methods explored in goals 1,2 and 3 to
properly develop the most effective marketing campaign”
- Group 2

44. Questions?