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Franz Fuerst and Gianluca Marcato

Testing and Improving Commercial Real Estate Market Segmentations with Cluster Analysis and Neural Network Techniques. Franz Fuerst and Gianluca Marcato. Real Estate Fund Management. Fund managers normally start from the sector vs. region dichotomy Asset allocation of a mixed fund

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Franz Fuerst and Gianluca Marcato

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  1. Testing and Improving Commercial Real Estate Market Segmentations with Cluster Analysis and Neural Network Techniques Franz Fuerst and Gianluca Marcato

  2. Real Estate Fund Management • Fund managers normally start from the sector vs. region dichotomy • Asset allocation of a mixed fund • Selling proposition of a specialised fund • … but fund managers also consider other characteristics • Indirectly • E.g. small funds; several small prop few big props • Directly • E.g. grade A vs. grade B buildings • … So can we ‘formalise’ this process ?

  3. Research Rationale • Review of cluster analysis technique in Romesberg 84 • Used to discover segmentations within specific sectors: residential (Kroll & Smith 89, Bourassa et al 99 and 03, Wilhelmsson 04), offices (Goetzmann & Watcher 95), hotels (Gallagher & Mansour 00) • Used to look at portfolio construction (Hoesli et al 97) or trading behaviour in housing markets (Piazzesi & Schneider 09) • Other previous research suggests that a sector and region classification insufficiently explains variations in return (Lee 01, Andrew 03, Devaney 03) • Objective of this paper: Explore possible segmentations that have higher predictive power • Methods applied: Cluster Analysis, Neural Networks, Discriminant Analysis

  4. Research Questions • Are “new” factors relevant to explain real estate returns? • Property size (i.e. small vs. big properties) • Yields (i.e. value vs. growth properties) • Tenant concentration (i.e. small vs. big number of tenants) • Lease length (i.e. short vs. long lease) • What are the implications for benchmarking and forecasting real estate returns? • Should we change our normal way of thinking?

  5. Implication: Expanding Asset Allocation Basic Asset Allocation Multi-Criteria Asset Allocation

  6. Results Overview • Benchmarking • We should be changing our way of thinking • “New” styles / risk factors explain portfolio returns • Property size is the main “new” risk factor • Part of alpha is paying for exposure to these factors • Forecasting and Segmentation • We should be changing our way of thinking • Individual real estate returns reveal new segmentations • Yield and tenants concentration are the main “new” risk factors • Ongoing process to be monitored JPM, Forthcoming

  7. Procedure • 2-step Cluster Analysis • Using either 10 (PAS) or 14 (PAS2) clusters • Done for all property and types of property (shopping centres, standard retail, office, standard industrial, distribution warehouses) • Discriminant Analysis to test consistency of clusters over time and to compare IPD PAS Segments with New Clusters (backward testing) • Neural Network technique to confirm results of cluster and discriminant analyses (backward testing) • To be done: Discriminant Analysis to confirm consistency between Cluster Analysis and Neural Network procedure

  8. Basics of Cluster Analysis • Minimize within cluster distances (homogeneity) • Maximize between cluster distances (heterogeneity) x2 max min x1

  9. Midlands and SW (1) Small CV, low # tenants, low ERV growth

  10. Wales & NW England (2) Slight exposure to # Tenants

  11. Scotland (5) Long leases, high ERV growth, low # tenants

  12. Central London (10) High ERV growth

  13. UK with Some Concentration

  14. Description of 10 Clusters (03-07)

  15. Time Consistency: 98-02 vs. 03-07 Clusters are fairly stable over time! 8 with 55%+, 7 with 70%+, 6 with 87%+ consistency

  16. No Small Clusters (14 clusters)

  17. Discriminant Analysis: Correctly Classified • On average the new clusters beat the IPD PAS segmentation • 6 clusters have 70%+ properties correctly classified • No IPD PAS segment is above 70% classification

  18. Neural Network (NN) Approach • Imitates human brain activity, learning • Adaptive system: changes as more information becomes available • Creates connections between observed cases and hidden layers • Has a training/learning phase and a testing phase • Frequently yields better results than linear parametric methods if • Large set of previous observations exists • Groups exist in the dataset

  19. Network structure Hidden Layers Output Inputs w1 w2 . . . w3500 x1 x2 . . . x3500

  20. NN Estimation of Total Returns

  21. NN: Sum of Squares Error (Model)

  22. NN: Sum of Squared Errors (Factor)

  23. NN Estimation of Total Returns (a) Regions and sub-sectors only – m8 (b) Full set of variables – m1

  24. Conclusions • New segments have higher predictive power • Returns are more predictable if we include variables on ERV growth, yield, property size, tenant diversification, lease terms and volatility measures • Seemingly unrelated regions, sectors, properties move together, -> cluster/discriminant analysis and neural networks detect these patterns • Both segmentations have their strenghts and weaknesses (IPD: easy to understand what each segment represents, Cluster Segments: higher predictive power)

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