Receive up to € 20k to create your proof of concept

WORK WITH REAL DATA FROM LEADING CORPORATIONS

WHAT WE CAN DO FOR START-UPS
Access to leading corporate partners and opportunities for long-term collaboration
Receive up to EUR 20,000 in funding to draft a proof of concept – without giving up equity
Real-world use cases and unique datasets from established corporations

Data Challenges

DataHub Ruhr is a business building program that connects start-ups with established corporations in the Ruhr region. The program tasks start-ups with developing innovative, data-driven ideas to tackle use cases provided by our corporate partners. As part of a three-month collaboration, start-ups will draft a proof of concept, with the opportunity to receive up to EUR 20,000 in funding.
Tenant Happiness :)
Result
Keeping reliable tenants happy is of high importance to landlords. There are many early indicators before a tenant cancels their contract. Their decision might be explained by increasing rent or by complaining about the same issue several times. Can we develop an early warning system for tenants that are about to leave?

Use Case

VIVAWEST owns more than 120,000 apartments in the province of North Rhine-Westphalia. Roughly 8% of tenants cancel their contracts every year. Reducing that number just a little would have a tremendous monetary impact, but could also substantially improve the satisfaction of thousands of tenants.


Our goal is to spot tenants who are about to cancel their contracts. In a next step, these tenants could then be contacted to see if a resolution to their problem can be found. A variety of sources influences tenants’ decision, e.g. changes in the neighbourhood, rent increases, damages in the house/apartment or mistakes in the statement of utility costs. Additionally, tenants might be sending signals before they give up and cancel, e.g. they might inquire about the cancelation policy or complain about problems.


This data has not been analysed yet since different data sources need to be combined and the influence of each factor is uncertain. As of now this is done by experts and their individual experience. We would like to develop a system that gives a prognosis of how likely a tenant is to leave within the next six months.



A project duration of approximately 2 months is expected.

What you will need

  • Experience in the field of tenant management or real estate
  • In-depth knowledge in (churn) prediction, survival modeling and text mining
  • Proficiency with survival models (proportional hazard etc.), logistic regression or other classification models
  • Ability to handle large amounts of (semi-) structured data from different sources

Expected result

  • Reliable forecasts: cancellation probability within six months
  • Very high true positive rate/sensitivity: we do not want to contact people who didn’t plan on cancelling
  • Any noteworthy relation between data points and/or external influence factors shall be documented

MILESTONES

The project can be divided into three milestones:

  1. The first milestone is reached when the available data has been cleaned and brought into the right format for further analysis. Visualizations and descriptive statistics have been used to understand the problem of identifying relevant tenants.
  2. The second milestone is reached when a model/machine has been developed that accurately predicts which tenants are about to cancel their contracts.
  3. The third milestone is reached when the algorithm makes suggestions about which customers to contact right now and when contacting these tenants confirms the prediction. The result is presented to and accepted by VIVAWEST.

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Felix Schröder
Program Manager DataHub Ruhr

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Let's talk

Felix Schröder
Program Manager DataHub Ruhr