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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(IRE  BS), University of Regensburg. Motivation. Scope of the study. German retail property market in the focus of international real estate investors

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

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  1. A Cross-Sectional Model of German High-Street Retail Rents Matthias Segerer/Kurt Klein Internation real Estate Business School(IREBS), University of Regensburg

  2. Motivation Scope of the study • German retail property market in the focus of international real estate investors • Focus on core-objects – especially shopping centers and high-street properties • 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/Gortan2001 (univariate, non-scientific, passersby frequency) • Scope: To identify and to structure the main determinants of high-street retail rents (cross-sectional)

  3. Agenda 1 Theoretical Framework 2 Data 3Model 4 Discussion 5 Conclusion

  4. 1 Theoretical Framework Literature Review: Cross sectional rent models 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 Robertson, M / Jones, C. 1999 JPR high-street Kim, J. / Jeong, S.-Y.2011 ERES conference high-street Dominant rentdeterminants Demand Supply Frontage Retail Sales

  5. 1 Theoretical Framework Theories • Demand-Supply theory (Fraser 1993) • Bid rent theory (Alonso 1964) Source: Jones/Simmons 1990 Source: Fraser 1993

  6. 1 Theoretical Framework Rent Determinants

  7. 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 • Some Cities have with more than one high-street location • Source: JLL 2012

  8. 2 Data High street locations: Spatial Distribution 141 highstreetlocations in 98 German towns

  9. 2 Data Variables

  10. 2 Data Correlations

  11. 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)

  12. 3 Model Step 1: Step-wise regression

  13. 3 Model Step 2: Factor analysis Location (supply) Location (demand) Town (solitaire)

  14. 3 Model Step 3: Regression (factors)

  15. 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

  16. 5 Discussion sales population (+) Factor 1 TOWN ‘Raumtyp’ (space category) (+) centrality (-) (-) (+) purchasing power passersby frequency (+) share of chain stores Factor 2 LOCATION (demand) (+) share of fashion stores (+) (+) rent (rent) (+) commuter surplus (+) (+) number of shops Factor 3 Location (supply) frontage (+) (+)

  17. 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)

  18. 5Conclusion 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

  19. Contact Contact: Prof. Dr. Kurt Klein Matthias Segerer email: kurt.klein@irebs.de Email: matthias.segerer@irebs.de tel.: 0941 943-3618 tel. 0941 943-3616 fax: 0941 943-4951 fax: 0941 943-4951

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