A Cross-Sectional Model of German High-Street Retail Rents

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


Agenda

1 Theoretical Framework

2 Data

4 Discussion

3 Model

5 Conclusion



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


Bid rent theory (ALONSO 1964)


Theories

Demand-Supply theory (FRASER 1993)

Source: JONES/SIMMONS 1990 Source: FRASER 1993



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)


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


2 Data

High street locations: Spatial Distribution

141 high street locations in 98 German towns



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


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).


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)


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


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)


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


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 fashion stores

Factor 3Location(supply)

(+)

(-)

(+)

(+)

(+)

(+)

(+)

(+)

(+)

(+)

(+)

(+)

(-)

(+)


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 fashion stores

Factor 3Location(supply)

(+)

(-)

(+)

(+)

(+)

(+)(+)

(+)

(+)

(+)

(+)

(+)

(-)

(+)


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)



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


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

mailto:[email protected]

mailto:[email protected]

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A Cross-Sectional Model of German High-Street Retail Rents