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
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Business Strategy Analytics - Colorado's Multi-Family Housing Market
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
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
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
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
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
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