See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/326838388 LSTM network time series predicts high-risk tenants Presentation · July 2018 DOI: 10.13140/RG.2.2.19849.95849 CITATIONS 0 READS 5 6 authors, including: Some of the authors of this publication are also working on these related projects: One Small Step for Machine, One Giant Leap for the Market: Lessons Learnt from a Real World Machine Learning Project View project Bus Service Optimisations View project Wolfgang Garn University of Surrey 17 PUBLICATIONS 22 CITATIONS SEE PROFILE All content following this page was uploaded by Wolfgang Garn on 06 August 2018. The user has requested enhancement of the downloaded file.
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See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/326838388
LSTM network time series predicts high-risk tenants
Presentation · July 2018
DOI: 10.13140/RG.2.2.19849.95849
CITATIONS
0READS
5
6 authors, including:
Some of the authors of this publication are also working on these related projects:
One Small Step for Machine, One Giant Leap for the Market: Lessons Learnt from a Real World Machine Learning Project View project
Bus Service Optimisations View project
Wolfgang Garn
University of Surrey
17 PUBLICATIONS 22 CITATIONS
SEE PROFILE
All content following this page was uploaded by Wolfgang Garn on 06 August 2018.
The user has requested enhancement of the downloaded file.
WOLFGANG GARN, Y IN HU, PAUL NICHOLSON, BEVAN JONES,
HONGYING TANG, WILLIAM WRIGHT
LSTM network time series predicts high-risk tenantsWolfgang Garn, Yin Hu, Paul Nicholson, Bevan Jones, Hongying Tang
OverviewBenefits to the real estate sector decreasing lost revenue, increasing efficiency providing more help to tenants before their debt becomes
unmanageable
How? Measure arrears risk for each individual tenant differentiate between short-term and long-term arrears risk predict trajectory of arrears Identify factors that can be operationally used to assist tenants
AbstractIn the United Kingdom, local councils and housingassociations provide social housing at secure, low-renthousing options to those most in need. Occasionally manytenants have difficulties in paying their rent on time and fallinto arrears. The lost revenue can cause substantial financialburden for these agents, while falling into arrears can causestress to tenants. An efficient arrears management scheme isto target those who are more at risk of falling into long-termarrears so that interventions can be used for persistent loss ofrevenue. In our research, a Long Short-Term Memory Network(LSTM) based time series prediction model is implemented todifferentiate the high-risk tenants from relatively temporaryones. This model measures the arrears risk to differentiatebetween short-term and long-term arrears risk and predictsthe trajectory of arrears for each individual tenant. Moreover,further arrears analysis is conducted to investigate whichfactors could provide more assistance for tenants before theirdebt becomes unmanageable. A five-year rent arrears datasetis used to train and evaluate the proposed model. The rootmean squared error (RMSE) is used to punish large errors bymeasuring of the differences between the arrears actuallyobserved and arrears predicted by a model. Hence, this modelbrings benefits to the real estate sector by decreasing lostrevenue, increasing efficiency and providing more help totenants before their debt becomes unmanageable.
LSTM network time series predicts high-risk tenantsWolfgang Garn, Yin Hu, Paul Nicholson, Bevan Jones, Hongying Tang
BackgroundHastoe Housing Association Creating opportunities to innovate
Sustainable Homes Ltd Influencing policy and practice
University of Surrey - Computing and Business School
The associate
Innovate UK funding for 3 years
Knowledge Transfer Partnership
10/07/2018
2
LSTM network time series predicts high-risk tenantsWolfgang Garn, Yin Hu, Paul Nicholson, Bevan Jones, Hongying Tang
LSTM network time series predicts high-risk tenantsWolfgang Garn, Yin Hu, Paul Nicholson, Bevan Jones, Hongying Tang
Arrears prediction
Time-series forecasting
85.4% accuracy over 18mths
Tenant Clusters – for policies
Towards automated processes
Human intervention where most needed
Time [month]
Arr
ears
[m
on
th] f
or
ten
ant
x
LSTM network time series predicts high-risk tenantsWolfgang Garn, Yin Hu, Paul Nicholson, Bevan Jones, Hongying Tang
Quality - Confusion matrixMajority of arrears and non-arrears are identified correctly.
Assuming difference between actual arrears and predicted arrears <£50 is acceptable for certain month
LSTM network time series predicts high-risk tenantsWolfgang Garn, Yin Hu, Paul Nicholson, Bevan Jones, Hongying Tang
Arrears per clusterSix types of tenants were identified
All levels are “pretty” stable Four distinctive levels of avg. rent arrears
Three types of tenants with similar levels oscillate around the zero level
One type has “negative” rent arrears i.e. pay rent in advance
~£400/month in arrears tenants Tenant’s benefit: comparable to a £400 free credit
Association’s cost: £146.8k x 30% = £44.0k per year (for cluster) 30% = administrative cost related to reminders & help strategies
~£1,000/month in arrears tenants Tenant’s benefit: comparable to a £1,000 free credit
Association’s cost: £159k x 30% = £47.4k per year (for cluster)Time [month]av
g. r
ent
arre
ars
per
clu
ster
10/07/2018
7
LSTM network time series predicts high-risk tenantsWolfgang Garn, Yin Hu, Paul Nicholson, Bevan Jones, Hongying Tang
Arrears – regional distributionGood predictions for east & west
Prediction for south “interesting”
Geography matters ->
model can be improved using this
East (201x) arrears: 34.8%• Actual - Predicted: 4.93%
South (201x) arrears: 35.7%• Actual - Predicted: -23.8%
West (201x) arrears: 29.5%• Actual – Predicted: 4.37%
LSTM network time series predicts high-risk tenantsWolfgang Garn, Yin Hu, Paul Nicholson, Bevan Jones, Hongying Tang
Results & Insights - summaryBenefits to the real estate sector decreasing lost revenue,
“initial customer selection”, Tackle lowest SAP properties
increasing efficiency “credit” acceptance rather than “chasing” (or automatization)
providing more help to tenants before their debt becomes unmanageable
How? Measure arrears risk for each individual tenant
85.4% accuracy
differentiate between short-term and long-term arrears risk
predict trajectory of arrears
Identify factors that can be operationally used to assist tenants arrears management £rent arrears, rel. rent arrears, region, SAP rating, benefits
Time [month]Arr
ears
[m
on
th]
for
ten
ant
x
LSTM network time series predicts high-risk tenantsWolfgang Garn, Yin Hu, Paul Nicholson, Bevan Jones, Hongying Tang
AppendixoBlogs: Sustainable Homes (Hastoe) - KTP project - machine learning techniques to gain insights into the sustainability of homes o The rise of the machines - learning for environmental good and cost savings (Blog, May 24, 2017)
o Are we “boiled” for choice? (Blog, August 3, 2017)
o AI detects roof status using Drones (BA MSc dissertation summary, October 10, 2017)
oHastoe Analytics, OR & Data Science applications
oNeural Networks
oReal estate valuation using regression models and artificial neural networks: An applied study in Thessaloniki by Alexios Georgiadis