This document contains various data points and statistics from Adactus Housing group. Key details include: - Retained surplus increased from £9.9m in 2012 to £18.3m in 2015. - Annual increase in tenant arrears was 0.07% of rent roll (£43k) in March 2016. - Annual void loss was 0.7% of turnover (£404k) in March 2016. - Median non-emergency repair completion time was 7 days in 2015. - Net promoter scores from tenants was 54 and from staff was 47. - The development programme from 2015-2018 will create 1,526 new units.

1. May 2016
Paul Lees, group chief executive
paul.lees@adactushousing.co.uk
Brian Moran, group company secretary
brian.moran@adactushousing.co.uk
Our Greatest Data Hits

2. Retained Surplus
£9.9m £18.3m
2012 2015
Source: audited Financial Statements 2015

3. Annual increase in Tenant Arrears
0.07% of rent roll
£43k
Source: Mobysoft March 2016
with half the staff

4. Annual Void Loss
£404k
0.7% of turnover
Source: March 2016 Management Accounts

5. Non-Emergency Repair Completion Times
7 days
median
Source: audited Financial Statements 2015

6. Net Promoter Score - Tenants
54
Source: quarterly performance report, quart ended 31 March 2016

7. Net Promoter Score - Staff
47
Source: annual staff survey 2015

8. Development Programme 2015 - 2018
1,526 units
Source: audited Financial Statements 2015

9. Housing Management Department
(2008)

10. [THIS PAGE IS INTENTIONALLY LEFT BLANK]
Housing Management Department
(2010 onwards)

12. What’s Going On
Marvin Gaye
10
UP 6 1 YEAR IN CHART
“Oh, you know we've got to find a way
To bring some understanding here today”

13. What’s Going On
Marvin Gaye
10
UP 6 1 YEAR IN CHART
Rule association

14. RENT ARREARS RISK
Find the best predictor
OneR algorithm
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OnOOnOnOnOnOOneReeeeeReReeReReeeeeeeeReeeeeeeeee aaaaaaaalgggglggggggggglgggggggggglggglggggggggggggglgggggggggorrrrrororororrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrorrrrrrrritititititithmhhhmhhhhhhhhhhhhhhhhhhhhhmhhmhhmhhhhhhhmhhhhhhhhhhhhhhhhhh

15. RENT ARREARS RISK
Find the best predictor
OneR algorithm

16. VIDEO DEMO

18. There is a link between missed gas
appointments and high (>£250)
rent arrears cases

19. We can make sense of very
large data sets

20. Let’s Stay Together
Al Green
9
NEW 8 MONTHS IN CHART
“Oh let's, let's stay together
Lovin' you whether, whether
Times are good or bad, happy or sad”

21. Let’s Stay Together
Al Green
9
NEW 8 MONTHS IN CHART
K-means clustering

22. Assign to cluster
K-means clustering
CUSTOMISE SERVICES
& INFORMATION
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&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&& IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIINNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFOOOOORRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAATTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTIOOOOOOOOOOOOOOOOOOOOOOOOOOOONNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN

23. CUSTOMISE SERVICES
& INFORMATION
Assign to cluster
K-means clustering

24. VIDEO DEMO

26. We can automatically assign
tenants to clusters

27. We can use clusters to
customise services and
information

28. Robot Rock
Daft Punk
8
NEW 6 MONTHS IN CHART
“Rock, Robot Rock
Rock, Robot Rock”

29. Robot Rock
Daft Punk
8
NEW 6 MONTHS IN CHART
Machine learning classifiers

30. Check web form
Naive Bayes
Classification
This new case looks
like it’s of this type…
TRADE
Classification: Repairs
REPAIR
REQUEST
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31. TRADE
REPAIR
REQUEST
RRREEEPPPAAAIIIRRR
RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRREEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEESSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT
Check web form
Naive Bayes

32. “Our bathroom is
leaking through the
ceiling onto the stairs
and the ceiling is wet
through along with the
walls where the taps
are mounted. And
puddle on the stairs”
PLUMBER
Check web form
Naive Bayes

33. Check web form
Naive Bayes
bathroom
leaking
ceiling
stairsceiling
wet
walls taps
stairs
PLUMBER

34. VIDEO DEMO

36. Machine learning can
correctly identify trades for
70%+ of repair requests
from tenants

37. Machine learning can
support the creation of
'friction-free' self-service

38. I Predict A Riot
The Kaiser Chiefs
7
“I predict a riot, I predict a riot.
I predict a riot, I predict a riot.”
NON-MOVER 3 YEARS IN CHART

39. I Predict A Riot
The Kaiser Chiefs
7
Forecasting
NON-MOVER 3 YEARS IN CHART

40. Forecast time series data
Holt-Winters Triple
Exponential Smoothing
FFFFFFFFFFFFFFFFFFFoorrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrreeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeccccccccccccccccccccccccccccccccccccccccccaaaaaaaaaaaaaaaaaaaaasssstttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttt tttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttiiiiiiiiiiiiiiiimmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmme sssssssssssssssssssssssssssssssssssssssssssssssseeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeerrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrriiiesssssssssssssssssssssssssss dddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaatttaaaaaaaaaaaaaaaaaaaaaoooorrrreeeeccccaaaaaaasssstttt ttttiiiimmmmmmeeee sssseeeerrrriiiiiiieeeessss ddddaaaattttttt
Hoolt-Wintters Triplpp e
ExExpoponenentntiaiall SmSmoooooththiningg

41. Forecast time series data
Holt-Winters Triple
Exponential Smoothing

42. VIDEO DEMO

44. We can automatically
identify breaks in regular
payments

45. We can forecast other
seasonal data

46. What Difference Does It Make?
The Smiths
6
NEW 8 MONTHS IN CHART
“it makes none, but now you have gone,
and your prejudice won't keep you warm
tonight”

47. What Difference Does It Make?
The Smiths
6
NEW 8 MONTHS IN CHART
Randomised Controlled Trials

48. Test policy
RCT
No SOR provided
Detailed SOR provided

49. Test policy
RCT
No SOR provided
Detailed SOR provided

52. Schedule of Rates recording
makes little difference to
getting the repair done

53. We can put policy options to
the test

54. The Times They Are A Changing
Bob Dylan
DOWN 2 3 YEARS IN CHART
5
“Come gather 'round people
Wherever you roam”

55. The Times They Are A Changing
Bob Dylan
DOWN 2 3 YEARS IN CHART
5
Crowdsourcing data

56. "Man is the lowest-cost, 150-pound, nonlinear,
all-purpose computing system which can be
mass-produced by unskilled labor."
S. Fred Singer
The Case for Man in Space, 1965
http://quoteinvestigator.com/2016/02/01/computer/#note-12956-10

57. Crowd sourced data
Adactus500

58. Crowd sourced data
Adactus500

60. Apologies to XKCD: see https://xkcd.com/1235/

61. VIDEO DEMO

62. Send us a photo of your heating controls
How easy is to to use the controls?
VIDEO

63. We can get new forms of
data from our tenants

64. Tenants can act as sensors

65. Communication Breakdown
Led Zeppelin
4
“Communication Breakdown
It's always the same”
UP 5 3 YEARS IN CHART

66. Communication Breakdown
Led Zeppelin
4
Data visualisation
UP 5 3 YEARS IN CHART

67. Data viz
Tableau
Data vizData viz
Tableaaaaaaaaaaaaaaaaaaaaaaaaau

68. Data viz
Tableau

69. Data viz
Tableau

70. VIDEO DEMO

71. VIDEO DEMO

72. Risk now has a much higher
profile in the boardroom

73. How information is presented
affects the thoughts that
people have about it

74. Let’s Stick Together
Wilbert Harrison
3
“You ought'a make it stick together
Come on, come on, let's stick together”
DOWN 1 20 YEARS IN CHART

75. Let’s Stick Together
Wilbert Harrison
3
SQL Joins
DOWN 1 20 YEARS IN CHART

76. Join existing data
SQL

77. Join existing data
SQL
Join existing dataJ g
SQL

79. SQL JOINS
Original diagrams: C. L. Moffatt, http://www.codeproject.com/Articles/33052/Visual-Representation-of-SQL-Joins
SELECT <select_list>
FROM Table_A A
FULL OUTER JOIN Table_B B
ON A.Key = B.Key
Outer (Full) JOIN

80. SQL JOINS
Original diagrams: C. L. Moffatt, http://www.codeproject.com/Articles/33052/Visual-Representation-of-SQL-Joins
SELECT <select_list>
FROM Table_A A
INNER JOIN Table_B B
ON A.Key = B.Key
Inner JOIN
SELECT <select_list>
FROM Table_A A
FULL OUTER JOIN Table_B B
ON A.Key = B.Key
WHERE A.Key IS NULL OR
B.Key IS NULL
Outer Excluding JOIN

81. SQL JOINS
Original diagrams: C. L. Moffatt, http://www.codeproject.com/Articles/33052/Visual-Representation-of-SQL-Joins
SELECT <select_list>
FROM Table_A A
LEFT JOIN Table_B B
ON A.Key = B.Key
Left JOIN
SELECT <select_list>
FROM Table_A A
LEFT JOIN Table_B B
ON A.Key = B.Key
WHERE B.Key IS NULL
Left Excluding JOIN

82. SQL JOINS
Original diagrams: C. L. Moffatt, http://www.codeproject.com/Articles/33052/Visual-Representation-of-SQL-Joins
SELECT <select_list>
FROM Table_A A
RIGHT JOIN Table_B B
ON A.Key = B.Key
Right JOIN
SELECT <select_list>
FROM Table_A A
RIGHT JOIN Table_B B
ON A.Key = B.Key
WHERE A.Key IS NULL
Right Excluding JOIN

83. VIDEO DEMO

84. We can create balanced
measures of staff
performance

85. We can connect together
many sources of data

86. If 6 Was 9
The Jimi Hendrix Experience
2
“Now if a 6 turned out to be 9
Oh I don't mind, I don't mind”
UP 1 20 YEARS IN CHART

87. If 6 Was 9
The Jimi Hendrix Experience
2
Feature creation and engineering
UP 1 20 YEARS IN CHART

88. Create new feature
SQL

89. Create new feature
SQL

90. TENANCY BALANCE
REPAIRS SPEND
RECHARGEABLE REPAIRS
ASB CASES / LEGAL ACTION
GAS NO ACCESS
COMPLAINTS
CRM CONTACTS

91. PARETO SCORE

92. Distribution of Pareto Scores

93. Distribution of Pareto Scores
0.7% of tenancies are challenging

94. Less than 1% of tenancies
are challenging to manage

95. We can figure out ways to
measure proxies for data we
don't record

96. Survivor
Destiny's Child
1
NON-MOVER 2 YEARS IN CHART
“Even in my years to come
I'm still gon be here'”

97. Survivor
Destiny's Child
1
NON-MOVER 2 YEARS IN CHART
Survival analysis

98. Survival Analysis
Kaplan-Meier Estimator
S(t)=1−F(t)
= Prob(T≥t)
SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuurrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvviiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiivvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvaaaaaaaaaaaaaaallllllllllllllllllll AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAnnnnnnnnnnnnnnnnnnnnnnallysiissssssssssssssssssssssssssssssss
KaKKKaaaKaaaKaKaKKaaaaKaaKaKaKaaaaaaaKaKaaaKaaaaaaaaKaKaaaaaaKKKaaaKaKaKaaaaaaaaaaaaaplpplplpllppplplpplplplplpllppppplllllpplppllpllpppplllpllplllplplppppllpppppllpllpplplllllllpppppppllpppplp ananananaaananananaanannanananannanaaaaaanaaaaananannnnannnananaaaanaananannananaaaaaanaaanananannnanaaananaananaaaannaanaaaaaaannnnnaannaaaaannnnnnnannnaanaaaaaaaaanaaannnnnanaann-M---M-M-M-MMMMMMM-M-M-M-MMMMMMMMMM-MMMMM-M-M-MMMMMMMMMMMMMMMMMMMMMMMMM-MMMMM------MMMMM--MMMMMMMMMMMMMMMMMeieieieieieieiieieieieieieieiieieieieieiieieeeeiiiieieiieieiieeiiiiieieiieieeeeieieieieieieieieeeeeiiiiiiiiiiiiee ererereeeeereerereeerereeeeerereeeeeerereeeeeerrreeeerereeeeerererrerrereeeeeeeerrrrrrereeerrrererreeeeerer EEEstimattttttttttttttttttttttoroo
SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((ttttttttttttttttttttttttttttttttttttttttttttt)))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))===========================================111111111111111111111111111111111111111111111111111111111111111111111−−−−−−−−−−−−−−−−−−−−FFF(((ttt)))))))))))))))))))))))))))))))))))))))))))))))))))))))
============================================================================================ PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooobbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb((((((((((((((((((TTT≥≥≥TTTTT ttttttttt))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

99. Survival Analysis
Kaplan-Meier Estimator
S(t)=1−F(t)
= Prob(T≥t)

100. VIDEO DEMO

101. Histogram of Tenancy Length
(Current Tenants)

104. Survival Analysis can be
applied to many other
longitudinal data sets

105. Most tenancies churn quickly

106. Housing Association Costs

107. A Simple Model of Tenancy Lengths
Tenancy Length (years)

108. Tenancy Length (years)
A Simple Model of Tenancy Lengths

109. Tenancy Length (years)
Unlikely to need
component
replacements
Likely to need
component replacements
Implications for Planned Maintenance

110. Implications for Planned Maintenance

111. The 60:40 Rule is inefficient
Implications for Planned Maintenance

112. Major Works & Cyclical Maintenance Spend
£888pu £679pu
2012 2015
Excluding discretionary spend

114. Rule association
K-means clustering
Machine learning classifiers
Forecasting
Randomised Controlled Trials
Crowdsourcing data
Data visualisation
SQL Joins
Feature creation and engineering
Survival analysis

115. May 2016
Paul Lees, group chief executive
paul.lees@adactushousing.co.uk
Brian Moran, group company secretary
brian.moran@adactushousing.co.uk
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