Bus Adm 741-Web Mining and AnalyticsGroup 2 Project Presentation Ben Meyers, Ali Qureshi, James Young

Analysis of Col-orado’s Multi-Family Housing Market

• 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 an-swer specific business questions while utilizing data analytics as the core of its consulting methodology.

Proposal

• 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 as-signed 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

• Dependent Variables Overall satisfaction (Ordinal) Recommended or not (Binary)

• Primary Independent Vari-ables

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

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

Data Distribution

Data from 110 properties in Denver area

Correlation

Regression

Regression TreeRandom Forest

Satisfaction Recommendation

Model Evaluation

Model Evaluation

Error Comparison

NN SVM Boosting Rtree RanF Lreg0

5

10

15

20

25

30

Model Error Comparison

Error Rate MAE

• FindingsPattern of dissatisfaction

Staff, Grounds, Noise – SatisfactionSafety, Maintenance - Recommendation

• RecommendationsResolution of dissatisfaction = opportunityProvide good Staff, well maintained grounds &

buildings, Safety ProceduresHelps to overcome Noise & Neighborhood Issues

Business Findings &Recommendations

• 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

Data Distribution

Low Rent High Rent

Stars Reviews Rec Reviews Stars Reviews Rec Reviews1 26 0 37 1 12 0 272 6 1 38 2 12 1 183 10    3 3   4 14    4 9   5 19    5 9   

Total 75Total 75 Total 45Total 45

Associations - SatisfactionLow 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 

Associations - SatisfactionHigh 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             

RecommendedLow Rent Term Frequency Count

RecommendedHigh Rent Term Frequency Count

SatisfactionNon-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

SatisfactionComparison Cloud

Low Rent Comparison Cloud  High Rent Comparison cloud 

 

SatisfactionCommonality Cloud

Low Rent Commonality Cloud  High Rent Commonality cloud 

 

• Most relevent = Manag• Other relevent terms =

Offic, place,time, area, is-sue, problem

• Most relevent = Manag• Other relevent terms =

peopl, place, like, staff, nice, mainten

RecommendedComparison Cloud

Low Rent Comparison Cloud  High Rent Comparison cloud 

 

RecommendedCommonality 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

• Findings Low Rent

Unhappy reviewers are more critical and extremeRenters complain more about maintenance and depositsReviewers are less likely to rate 2 or 3 stars, instead they tend

to pick one starManagement, staff and office are most significant

High RentMore satisfied, happier, and less extreme in their reviewsManagement, staff and office are most significantUse more superlative adjectives like awesome, great, love, nice

Business Findings

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

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

Twitter: Cleansing and Metrics

• Removed and merged edges

• Directed graph• 196 Vertices• 209 Unique Edges• No Duplicate Edges• 117 Self-loops• 129 Connected Compo-

nents• 32 Max Vertices in CC• 3 is the Max Geodesic

Distance

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)

Metrics Analysis

• Minimum In-degree is zero• Maximum In-degree is 30• Minimum Out-degree is zero• Maximum Out-degree is 4

Metrics Analysis

Twitter Terms

Betweeness with filtering

Group by Clusters

• Total 29 groups• Light blue – Denver Post (denverpost and aldasvaldi)• Dark Green – Denver Business Journal and Camden

Living

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

Metrics Analysis

• Undirected – No in or out de-gree

• Maximum degree is 31• Minimum degree is zero• Average degree is 20• Median degree is 25

Metrics Analysis

Group by Vertex Attribute

Vertices by number of views

Filtering by number of views

• 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 & Recommenda-tions

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 Proce-

dures Helps to overcome Noise & Neighborhood Issues.

• Goal 2 - MDI is interested in finding out how the sentiments of reviewers im-pact 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

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 prop-erly develop the most effective marketing campaign”

- Group 2

Questions?