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LSTM network time series predicts high-risk tenants

Garn, Wolfgang, Hu, Yin, Nicholson, Paul, Jones, Bevan and Tang, Hongying (2018) LSTM network time series predicts high-risk tenants In: Euro 2018 - 29th European Conference on Operational Research, 08-11 Jul 2018, Valencia, Spain.

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Abstract

In the United Kingdom, local councils and housing associations provide social housing at secure, low-rent housing options to those most in need. Occasionally tenants have difficulties in paying their rent on time and fall into arrears. The lost revenue can cause financial burden and stress to tenants. An efficient arrear management scheme is to target those who are more at risk of falling into long-term arrears so that interventions can avoid lost revenue. In our research, a Long Short-Term Memory Network (LSTM) based time series prediction model is implemented to differentiate the high-risk tenants from temporary ones. The model measures the arrear risk for each individual tenant and differentiates between short-term and long-term arrears risk. Furthermore it predicts the trajectory of arrears for each individual tenant. The arrears analysis investigates factors that provide assistance to tenants to trigger preventions before their debt becomes unmanageable. A five-years rent arrears dataset is used to train and evaluate the proposed model. The root mean squared error (RMSE) punishes large errors by measuring differences between actually observed and predicted arrears. The novel model benefits the sector by allowing a decrease in lost revenue; an increase in efficiency; and protects tenants from unmanageable debt.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Arts and Social Sciences > Surrey Business School
Authors :
NameEmailORCID
Garn, WolfgangW.Garn@surrey.ac.uk
Hu, Yinyin.hu@surrey.ac.uk
Nicholson, Paul
Jones, Bevan
Tang, Hongying
Date : 10 June 2018
Uncontrolled Keywords : Machine Learning; Forecasting; Decision Support Systems
Related URLs :
Additional Information : Invited abstract in session TD-58: Machine Learning and Data Analysis I, stream Emerging Applications of Data Analysis.
Depositing User : Clive Harris
Date Deposited : 14 Sep 2018 09:33
Last Modified : 14 Sep 2018 09:33
URI: http://epubs.surrey.ac.uk/id/eprint/849291

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