© Henley Business School 2008 www.henley.reading.ac.uk
School of Real Estate & Planning
21 April 2023
Testing and Improving Commercial Real Estate Market Segmentations with Cluster Analysis and Neural Network Techniques
Franz Fuerst and Gianluca Marcato
School of Real Estate & Planning
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 ?
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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
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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?
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Implication: Expanding Asset Allocation
BasicAsset
Allocation
Multi-CriteriaAsset
Allocation
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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
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JPM,Forthcoming
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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
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Basics of Cluster Analysis• Minimize within cluster distances (homogeneity)
• Maximize between cluster distances (heterogeneity)
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x1
x2
max
min
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Midlands and SW (1)
0
50
100
150TRR
ERV Growth
E. Yield
Cap.ValueLease Length
# Tenants
Large tenants
Small CV, low # tenants, low ERV growth
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School of Real Estate & Planning
Wales & NW England (2)
10
0
50
100
150TRR
ERV Growth
E. Yield
Cap.ValueLease Length
# Tenants
Large tenants
Slight exposure to # Tenants
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Scotland (5)
0
50
100
150
200TRR
ERV Growth
E. Yield
Cap.ValueLease Length
# Tenants
Large tenants
Long leases, high ERV growth, low # tenants
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School of Real Estate & Planning
Central London (10)
0
50
100
150
200TRR
ERV Growth
E. Yield
Cap.ValueLease Length
# Tenants
Large tenants
High ERV growth
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UK with Some Concentration
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Description of 10 Clusters (03-07)No. DESCRIPTION TRR ERVg EY CV
LEASE TERMS
TENANTS
1 Midlands & SW 12.59 1.54 7.56 7,173,093 8.25 3.86
2 Wales & NW 13.35 2.87 5.90 10,273,485 11.73 3.29
3Retail
Small with high EY13.32 1.50 7.73 5,765,593 8.21 3.38
4 Long lease 13.16 3.73 6.29 10,705,206 18.12 3.73
5Retail
Scotland14.47 3.57 5.89 8,907,471 12.81 2.98
6Retail
Low EY14.58 3.54 5.84 8,669,114 12.69 2.62
7Short leases and
low ERVg11.28 0.00 7.25 8,815,859 7.58 2.94
8Retail
Low ERVg13.18 1.19 7.18 9,669,371 8.58 3.87
9Big with high # ten and short leases
13.59 2.27 7.45 18,550,549 5.65 23.80
10 London 15.52 4.39 6.06 13,664,432 9.59 5.67
OL Outlier 12.08 7.10 7.77 171,064,487 22.57 101.39
All All 13.56 2.49 6.68 11,789,582 10.05 6.84
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Time Consistency: 98-02 vs. 03-07
Cluster 1998-2002
Total
Outlier Cluster 1 2 3 4 5 6 7 8 9 10
Cluster 2003-2007
Outlier Cluster 66.67% 0.00% 33.33% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 3
1 0.00% 0.00% 2.50% 0.00% 0.00% 0.00% 20.00% 0.00% 0.00% 70.00% 7.50% 40
2 0.00% 0.00% 7.00% 0.00% 0.00% 0.00% 33.00% 17.00% 20.00% 17.00% 6.00% 100
3 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 9.68% 0.00% 87.10% 3.23% 31
4 3.23% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 87.10% 0.00% 0.00% 9.68% 31
5 0.00% 0.00% 6.25% 0.00% 0.00% 0.00% 26.25% 0.00% 55.00% 10.00% 2.50% 80
6 0.00% 1.18% 27.65% 32.35% 0.00% 35.88% 0.59% 0.00% 0.00% 0.00% 2.35% 170
7 0.00% 0.00% 3.70% 0.00% 93.83% 1.23% 0.00% 0.00% 0.00% 0.00% 1.23% 81
8 0.00% 1.52% 96.97% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 1.52% 66
9 1.05% 1.05% 1.05% 0.00% 1.05% 0.00% 0.00% 0.00% 0.00% 3.16% 92.63% 95
10 0.00% 88.35% 0.00% 0.00% 0.00% 0.97% 0.97% 1.94% 0.00% 0.00% 7.77% 103
Total 4 95 129 55 77 63 64 49 64 83 117 800
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Clusters are fairly stable over time!8 with 55%+, 7 with 70%+, 6 with 87%+ consistency
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No Small Clusters (14 clusters)
2003-2007 Frequency %
Cluster 1 259 6.7%2 256 6.6%3 197 5.1%4 241 6.2%5 304 7.8%6 194 5.0%7 314 8.1%8 293 7.5%9 306 7.9%10 315 8.1%11 202 5.2%12 323 8.3%13 172 4.4%14 490 12.6%
Total 3890 100.0%
1998-2002 Frequency %
Cluster 1 211 9.7%2 106 4.9%3 143 6.6%4 213 9.8%5 141 6.5%6 159 7.3%7 129 6.0%8 182 8.4%9 154 7.1%10 126 5.8%11 108 5.0%12 126 5.8%13 205 9.5%14 150 6.9%
Total 2165 100.0%
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Clusters IPD10 NEW 10
1 39.6% 75.2%
2 49.2% 27.9%
3 56.9% 65.5%
4 45.6% 72.8%
5 54.4% 79.5%
6 64.4% 44.2%
7 66.2% 53.7%
8 37.2% 71.7%
9 35.4% 78.9%
10 38.4% 93.4%
Outlier 41.5% 84.8%
Average 48.1% 68.0%
St.Dev. 11.0% 19.1%
Min 35.4% 27.9%
Max 66.2% 93.4%
Discriminant Analysis: Correctly Classified
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• 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
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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
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x1
x2
.
.
.
x3500
Hidden Layers OutputInputs
w1
w2
.
.
.
w3500
Network structure
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NN Estimation of Total Returns
1 2 3 4 5 6 7 8 9
Covariates
ERVg
TR(-1) TR(-1)
EY EY EY
CV CV CV CV
lease term
lease term
lease term
lease term
lease term
#tenants #tenants #tenants #tenants #tenants #tenants
ten. conc.
ten. conc.
ten. conc.
ten. conc.
ten. conc.
ten. conc.
ten. conc.
st.dev. st.dev st.dev st.dev st.dev st.dev st.dev st,dev
Factors region region region region region region region region region
PAS segm.
PAS segm.
PAS segm.
PAS segm.
PAS segm.
PAS segm.
PAS segm.
PAS segm.
PAS segm.
local auth.
local auth.
local auth.
local auth.
local auth.
local auth.
local auth.
local auth.
local auth.
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NN: Sum of Squares Error (Model)
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NN: Sum of Squared Errors (Factor)
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NN Estimation of Total Returns
(b) Full set of variables – m1(a) Regions and sub-sectors only – m8
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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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