The document describes a project that uses unsupervised machine learning to analyze property data and recommend properties for investment. Key steps include: 1. Preprocessing data and selecting key performance indicators of rent yield, income-to-rent ratio, and population. 2. Using k-means clustering to group properties into 7 clusters based on the selected indicators. 3. Analyzing the clusters to profile them and rank their investment potential from 1 to 5, with 1 being the most attractive, based on characteristics like population size and potential for rent increases.

1. JIGSAW ANALYTICS CONTEST
(PROPERTY RECOMMENDATION FOR INVESTMENT)
Parindsheel S. Dhillon

2. RECOMMEND PROPERTY FOR INVESTMENT
2
Project Approach
 Data preprocessing - data transformation & outlier handling
based on industry historical data
 Key Performance Indicator selection - Rent yield, Income to
Rent Ratio & Population of housing units based on multi-co-
linearity & noise reduction
 Data modeling through unsupervised learning by Classification
with use of k means clustering technique
 Cluster profiling led to investment ranking recommendation
with scale from 1 to 5 for strategic investment selection

3. SWOT ANALYSIS OF ADOPTED CLUSTERING MODEL
3
STRENGTH
• Optimized number of clusters (7) by plotting of no. of clusters v/s
within sum of squared distances
• High ratio of Between SS / Total SS = 87% & 80%
• Good cluster profiling with varied investor choices
a) High population size places with high yield and good scope of
increasing rents
b) Medium population size places with high yield & no scope of
increasing rents
c) Medium population size places with relatively ok yield & high
scope of increasing rents
d) Small size population places with high yield & some scope of
increasing rents
WEAKNESS
• Clusters may have few property areas with the different
characteristics, further classification may be required for the final
investment decision
• There is a possibility of increase in the yield because of reduction in the
property prices resulting in the probable wrong conclusions
• Yield could be high due to the location with the heavy concentration of
the housing commission homes for higher rents in comparison with the
property prices
OPPORTUNITY
• Scope of adding more variables e.g. distress areas factor, climate risk
factors etc
• Addition of another variable for comparison to history rates could
counter the reduction in property prices problem leading to the rise in
yield
• Geographical Heat map can be used for segmenting & locating presence
of many markets in suburbs. Longitude & Latitude data is required for this
activity.
• Resource optimization can be further implemented to finalize
investment e.g. capital investment budget optimization
THREAT
• Presence of any historical housing data can provide a measure of
imminent property bubble. But in our dataset, there is no scope of
identifying such catastrophe, which is certainly what investors will be
interested in to check before investing.
• Strong dependence on the median prices & rent yield could lead to
anomalies especially in areas where many housing markets in single
suburb. It could lead to a wrong investment criteria.

4. CLUSTER VISUALIZATION
4
3 D cluster snapshot
visualization using
RGL package
Principal Component Analysis
Adjusted Box plots comparisons of clusters for KPIs

5. RECOMMENDATIONS
5
 Investor strategic alignment with property is the most important
aspect to consider rather building property portfolio based on scale
suggested by clustering model
 Clustering can not absorb uniqueness of each property. It classify
properties into clusters based on property characteristics. Further
cluster refinement will add value to investment decisions
 Additional variables such as risks, depressed area, historical housing
price, longitude & latitude need to be added to data set to fine tune
the clusters & bring more insights

6. DETAILED SUMMARY OF ANALYSIS

7. SYNOPSIS
Objective
– Recommend properties/places to investors
Property valuation approach
– Data Pre-processing
– Analytics KPI selection for Data modeling
• Multi-co-linearity & Noise reduction by skipping highly co-
related independent variables for analysis
– Data modeling through unsupervised learning by Classification
with use of k means clustering technique
– Recommendation based on clusters characteristics

8. METHODOLOGY
Data Pre-processing
– Excel data pre-processing
• Zip code observation alteration in excel
• Separate data set saving for individual analysis
• $ and , sign removal in excel from variable values
– R data pre-processing
• Scaling/ Normalizing/ Standardizing Data
– Various methods tried such as scaling with mean=0 and std dev=1
– Reducing prices and population values by dividing with standard value (e.g. 10000)
– Based on between sum of sq/total sum of sq & characteristics of clusters, data processing has
been finalized. The above ratio varies from 80 to 87%.
– Achieved best ratio with data where reduction of values by dividing is done. Moreover, by this
method, Interpretation is easy as compare to scaling.
• Inversion of Rent/Income to Income/Rent
– Data scaling & better data comparison, understanding
• Data type conversion for state and place Type to numeric
• Handling data anomalies
– Based on historical data of USA rent yield and rent to income variables, imputation done on
impossible values appendix (i)
– Multivariate imputation by chained equations (MICE) for values
» Rent yield >20%
» Rent/Income >30%

9. METHODOLOGY (CONT.)
• Key performance indicator selection for modeling
– Zip Dataset
• Rent Yield
• Median Income to Median Rent
• Population in occupied housing units
– Place Dataset
• In addition to above variables in zip Dataset
– Place Type as numeric
– State as numeric
• Highly Co-related variables skipped to reduce multi-co-linearity and noise
addition
– Median Rent, median income and median value of property
(variables effect already covered in yield & income to rent)
– Total Population( variable effect marginally covered in
population in occupied housing)

10. METHODOLOGY (CONT.)
• Data analysis
– Classification of given unsupervised dataset done by using
k means clustering
• K value optimization by use of graphical analysis
against within sum of square value
• Cluster selection based on visualization and
characteristics
– Adjusted Box plot of yield, income/rent and
population variable for all clusters comparison
Appendix(ii)
– Range, mean centers and other statistical
characteristics of cluster comparison

11. PROJECT SUMMARY
Based on rent yield, income/rent ratio & population, various properties
have been grouped in clusters
 Between sum of squares to total sum of square has been
maximized while focusing on cluster characteristics
 Every property is unique, clusters can provide foundation to
property selection for investment purposes based on KPI for
property valuation
 Based on investor requirements, property from strategic aligned
clusters can be chosen
 Additionally Clusters refining using sub-setting will help to get
desired characteristics property

12. RECOMMENDATION
 Tables in next slides will show various types of options available for
investment purposes.
 E.g. Clusters with high yield and medium size population having less
scope of increase in rents(low income/rent) can attract those
investors who are willing to invest in property which is already giving
good return of investment, although there is no additional scope of
increase in rents(however property price can be used)
 In second example, we can talk about cluster having high populated
areas & high scope of increasing rents with current yield as
marginally good (if not best), investor looking with future high return
can invest in such property
 As mentioned above, various categories for various clusters have been
recommended in next slides for investment purposes
 Ranking for investment has been done. Scales of 1 to 5 has been given.
1 as best attractive property to invest and 5 as least attractive
property.
 However there is no strict rule, as it depends purely on investor
strategic decisions to invest.

13. ZIP DATASET CLUSTER ANALYSIS
Type of property Zip Dataset –cluster
number
(no of properties)
Investing Rank
Preference(1 to 5)
1- highly recommended
5- least recommended
High yield, but less scope of increasing
rent, medium size of population
Cluster no 3
(No of properties -
3218)
Scale 1
High yield, no scope of increasing rent,
smallest population
Cluster no -2
(No of properties -
1100)
Scale 5
Relatively good yield with some scope
of increase in rents, smaller size
Population
Cluster no -6
(No of properties -
5982)
Scale 4
Relatively ok yield with little scope of
increasing rents, high population
Cluster no -1
(No of properties -
2107)
Scale 2
Yield little lower side, but very high
scope of increase in rent, medium size
of population
Cluster no -5
(No of properties -
7831)
Scale 3

14. PLACE DATASET CLUSTERS ANALYSIS
Type of property Place Dataset –cluster
number
(no of places)
Investing Rank Preference(1 to 5)
1- highly recommended
5- least recommended
high yield, high scope of increasing rent,
medium size population
Cluster 5
(No of places - 305)
Scale 1
Good yield, good scope of increasing rent,
smaller size population
Cluster 4
(No of places - 219)
Scale 4
High yield, with high scope of increasing rent,
smallest size of population
Cluster 7
(No of places - 200)
Scale 5
High yield, no scope of increasing rent,
medium size population
Cluster 2
(No of places - 156)
Scale 2
Relative ok yield, some scope of increasing
rents, large size population
Cluster 3
(No of places 272)
Scale 3
No strict scale rule, as it depends purely on investor strategic decisions to invest These clusters can be
refined to get better feel of properties by various means such as sub-setting. Moreover depressed areas like
flint and Detroit are in cluster 4, such factors need to be re-checked as there was no variable assigned to
them. Further analysis can be done on chosen cluster to get property as per investor requirements.

15. MORE RECOMMENDATION
 Integer optimization in conjunction with clustering to utilize resources efficiently
 Based on constraints such as investment budget, we can optimize
various property investments along with investor personalization.
 Geographical heat maps based on zip code for property investment
recommendation could be good option
 Addition of another variables to dataset
 Variable for distressing areas can be added into the dataset rather
looking individually after clustering
 Risk variable to be added in future (e.g. Typhoon prone area)
 There is possibility of increase in yield may be due to reduction in
property prices due to some reason. Comparison to history rates is
necessary in that case by adding another variable in dataset
 Things to check before finalizing investment
 Yield is calculated using median rent and median prices. Both variables
are highly susceptible to statistical anomaly especially where many
housing markets in single suburb
 Yield could be high due to location with heavy concentration of housing
commission homes for higher rent in comparison with property price

16. BOTTOM LINE
 Each property is unique with unique characteristics
 Clustering can help to figure out the various groups for
investment, Refinement will be advantageous before finalizing
property for investment
 Additional factors such as risk, depressed area etc need to be
considered in addition to some risks mentioned in last slide
 Investor strategic alignment with property is the most
important aspect to consider rather scale of property
provided.

17. APPENDIX(I)
• Historical values of KPI for outlier removal
– http://www.realestateanalysisfree.com/blog/real-estate-
analysis/price-to-rent-ratio-rental-yield-of-all-us-states
– http://seattlebubble.com/blog/2013/03/29/top-30-cities-
price-to-rent-price-to-income-ratios-2011/

18. CLUSTERS COMPARISON BASED ON KPI BY USING ADJUSTED
BOX PLOT FOR ZIP DATA (APPENDIX II)

19. APPENDIX (III)
GRAPHICAL ESTIMATION OF CLUSTERS
& ANALYSIS FOR CLUSTERING

20. APPENDIX(IV)
CLUSTERS VISUALIZATION CO-RELATION OF
VARIABLES