a cross-sectional model of german high-street retail rents
DESCRIPTION
A Cross-Sectional Model of German High-Street Retail Rents. Matthias Segerer /Kurt Klein Internation real Estate Business School(IRE BS), University of Regensburg. Motivation. Scope of the study. German retail property market in the focus of international real estate investors - PowerPoint PPT PresentationTRANSCRIPT
A Cross-Sectional Model of German High-Street Retail Rents
Matthias Segerer/Kurt Klein
Internation real Estate Business School(IREBS), University of Regensburg
2ERES 6-14-2012, Edinburgh© Matthias Segerer
German retail property market in the focus of international real estate investors
Focus on core-objects – especially shopping centers and high-street properties
Þ so far: Scientific studies of the German retail market are hardly available
time series model
LINSIN 2004, JUST 2008, LADEMANN 2011 (retail turnover, population growth)
cross-sectional model
LIPP/GORTAN 2001 (univariate, non-scientific, passersby frequency)
Scope: To identify and to structure the main determinants of high-street retail rents (cross-sectional)
Motivation
Scope of the study
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Agenda
1 Theoretical Framework
2 Data
4 Discussion
3 Model
5 Conclusion
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1 Theoretical Framework
Author Year Journal Property Type
SIRMANS, S. / GUIDRY, K. 1993 JRER Shopping Center
ROBERTSON, M / JONES, C. 1999 JPR high-street
CARTER, C. / VANDELL, K. 2005 JRER Shopping Center
DES ROSIERS, F. ET AL. 2006 JRER Shopping Center
HUI, E. / YIU, C. / YAU, Y. 2007 JPIF Shopping Center
DES ROSIERS ET AL. 2009 JRER Shopping Center
KIM, J. / JEONG, S.-Y. 2011 ERES conference high-street
Literature Review: Cross sectional rent models
ROBERTSON, M / JONES, C. 1999 JPR high-street
KIM, J. / JEONG, S.-Y. 2011 ERES conference high-street
Retail Sales
SupplyDemand
Frontage
Dominant rent determinants
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Bid rent theory (ALONSO 1964)
1 Theoretical Framework
Theories
Demand-Supply theory (FRASER 1993)
Source: JONES/SIMMONS 1990 Source: FRASER 1993
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1 Theoretical Framework
Rent Determinants
High-Street Retail Rents
macro-level micro-level
population
retail purchasingpower
retail turnover
Demand (D)
passersby frequency
micro-level
frontage
number of shops
department store
share of chain stores
share of fashion shops
Supply (S)
micro-level
‚Raumtyp‘
commuter surplus
retail centrality
Spatial Structure (Sp)
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2 Data
High street locations: Definition
According to Jones Lang LaSalle high-street locations are defined by the following criteria:
Geographic: inner-city location, usually pedestrial zone
Chain stores: tenant-stock of national, international and local retailers
Passersby frequency
Branch of trade
ÞSome Cities have with more than one high-street location
Source: JLL 2012
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2 Data
High street locations: Spatial Distribution
141 high street locations in 98 German towns
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2 Data
Variables
Symbol Description Aggregation Level Source
R rent recoverable market rent/m² (shop size: appr. 100 m² ) High-street location Jones Lang LaSalle 2010
ln_pop ln (population) Town offi ce for statistics 2010
purchasing index (Germany = 100) Town Brockhoff 2010
sales index ( Germany = 100) Town Brockhoff 2010
D micro pass maximum frequency persons/hour High-street location BBE and Jones LangLasalle 2010
frontage length of highstreet location High-street location Own calculation according to Jones Lang LaSalle 2010nr_shops number of shops High-street location Own calculation according to Jones Lang LaSalle 2011department presence of Galeria Kaufhof (0/1) High-street location Own calculation according to Jones Lang LaSalle 2012chain number chain shops/number of all shops High-street location Jones Lang LaSalle 2010
fashion number fashion shops/number of all shops High-street location Brockkhoff 2010
sp_cat space category (1 = rural to 3 = agglomeration) Town BBSR 2008
commuter in-commuter /out-commuter Town offi ce for statistics 2010
centrality retail potential within a town/whole retail turnover Town Jones LangLaSalle 2010
Descriptive Statistics
D macro
S
Sp
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2 Data
Correlations
rent ln_pop purchasing sales pass frontage nr_shops chain fashion sp_cat commuter centrality
rent 1
ln_pop ,519** 1
purchasing ,348** -,070 1
sales ,145 -,332** ,348** 1
pass ,688** ,286** ,206* ,110 1
frontage ,223** ,359** -,209* -,240** ,296** 1
nr_shops ,052 ,195* -,151 -,219* ,143 ,799** 1
chain ,542** ,106 ,184* ,185* ,583** ,051 -,039 1
fashion ,506** ,062 ,247** ,246** ,336** -,084 -,099 ,446** 1
sp_cat ,290** ,524** ,182* -,509** ,171* ,100 ,034 ,098 ,005 1
commuter ,417** ,086 ,303** ,426** ,200* -,151 -,243** ,234** ,293** -,134 1
centrality ,007 -,343** -,020 ,928** ,028 -,177* -,175* ,119 ,162 -,619** ,330** 1
Correlations
**. Correlation is s ignifi cant at the 0.01 level (2-ta i led).*. Correlation i s s ignifi cant at the 0.05 level (2-tai led).
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3 Model
Three step approach
Step 1: linear cross-sectional step-wise regression (Variables)
Step 2: Factor analysis
Step 3: Linear cross-sectional regression (Factors)
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3 Model
Step 1: Step-wise regression
Standardized
B Std. Error Beta t Sig. Adj. R²
1 pass ,015 ,001 ,673 10,464 ,000 ,673 ,453
2 ln_pop 17,380 2,885 ,358 6,024 ,000 ,756 ,572
3 fashion 1,989 ,318 ,334 6,246 ,000 ,819 ,671
4 commuter 10,848 2,269 ,235 4,781 ,000 ,849 ,720
5 purchasing 1,346 ,384 ,171 3,504 ,001 ,863 ,745
6 chain ,578 ,279 ,121 2,075 ,040 ,868 ,753
R²
SummaryCoefficients
Model
Unstandardized
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3 Model
Step 2: Factor analysis
Model Total % of Variance Cumulative % 1 2 37 1 3,201 29,103 29,103 ln_pop -,574 ,261 ,356
2 2,410 21,911 51,015 purchasing -,045 ,533 -,329
3 1,707 15,519 66,533 sales ,836 ,399 -,151
4 ,899 8,171 74,704 pass -,088 ,727 ,385
5 ,861 7,824 82,527 frontage -,137 ,006 ,927
6 ,630 5,731 88,258 nr_shops -,069 -,114 ,877
7 ,502 4,561 92,819 chain -,020 ,773 ,092
8 ,361 3,280 96,099 fashion ,054 ,678 -,071
9 ,262 2,383 98,482 sp_cat -,859 ,177 -,035
10 ,165 1,502 99,984 commuter ,248 ,586 -,203
11 ,002 ,016 100,000 centrality ,912 ,212 -,041
Total Variance Explained Rotated Component Matrix
Component
Initial Eigenvalues
Component
Town (solitaire)
Location(demand)
Location(supply)
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3 Model
Step 3: Regression (factors)
Standardized
B Std. Error Beta t Sig. Adj. R²
(Constant) 91,731 2,700 33,977 ,000
Town -10,871 2,710 -,187 -4,012 ,000
Location (demand) 46,336 2,710 ,797 17,099 ,000
Location (supply) 12,794 2,710 ,220 4,721 ,000
Coefficients
Model Unstandardized
8
Summary
R²
,718 ,711
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4 Discussion
Results
Step 1: Step-wise Regression
Macro and micro demand variables primarily determine rents
The variable passersby frequency is the dominating retail rent determinant
Step 2 and 3: Factors
number of shops
frontage
population
rent (rent)
Factor 2LOCATION(demand)
Factor 1Town(Rural
Centre)
sales
centrality
space category
purchasing power
passersby frequency
comuter surplus
share of chain stores
share of fashion stores
Factor 3Location(supply)
(+)
(-)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(-)
(+)
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5 Discussion
number of shops
frontage
population
rent (rent)
Factor 2LOCATION(demand)
Factor 1TOWN
sales
centrality
‘Raumtyp’ (space category)
purchasing power
passersby frequency
commuter surplus
share of chain stores
share of fashion stores
Factor 3Location(supply)
(+)
(-)
(+)
(+)
(+)
(+)(+)
(+)
(+)
(+)
(+)
(+)
(-)
(+)
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4 Discussion
Limitations
Heteroscedasticity
A few towns with more than one high-street location per towns in the sample
Recoverable rent
Spatial distribution of demand (population)
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20ERES 6-14-2012, Edinburgh© Matthias Segerer
5 Conclusion
Results
First cross-sectional model for German retail rents
Base for
a better market transparency within the German retail market
a better evaluation of retail property investments (market selection)
Results have to be confirmed using other rental data (IVD, Brockhoff)
Next step: Integrating object data for a local rent model
Outlook
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Contact
Contact:
Prof. Dr. Kurt Klein Matthias Segerer
email: [email protected] Email: [email protected]
tel.: 0941 943-3618 tel. 0941 943-3616
fax: 0941 943-4951 fax: 0941 943-4951