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Predicting renewal rates to help their leasing decision

Abdul September 10, 2020

Leading US REIT

Problem Statement

  • Company had multiple nonintegrated data sources that were independently used for decision making by different teams; however, decisions were not based on an integrated data view
  • Vacancy costs resulted due to the time lag between a resident moving out and a new resident moving in
  • Potential gaps in the data that when filled could lead to better decision making

Analytics Led Approach

  • Unified view of multiple data sources
  • Correlation Analysis Information Value Analysis
  • Sub Market Clustering, Logistic Regression Model
  • Key Driver Impact Analysis, Amenity Impact Analysis
  • Integrated data view, Mathematical Model
  • Analysis Results

Business Impact

  • Accurate predictions: Accuracy level was 80 – 85%
  • Reduced vacancy cost: Clients had 3 months time to extend offers to profitable customers who were predicted to churn or find new customers
  • Better understanding of key drivers: The impact of each key driver on the renewal was quantified

Critical Success Factors

  • Increased model relevance: Separate models were developed for properties in each sub market group (similarly behaving sub market groups were identified through clustering)
  • Better understanding of how amenities affected rent and helped the client revamp the decisions making process for pricing decisions with predicted propensities

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