Retail and Place Attractiveness: The Effects of Big-Box Entry on Property Values

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Geographical Analysis (2020) 0, 1 – 3 2

1 doi: 10.1111/gean.12247

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Retail and Place Attractiveness: The Effects of Big-Box Entry on Property Values

Sven-Olov Daunfeldt1,2, Oana Mihaescu1, Özge Öner3,4,5, Niklas Rudholm1

1_{Institute of Retail Economics (HFI), Stockholm, Sweden,}2_{Dalarna University, Falun, Sweden,} 3_{Department of Land Economy,University of Cambridge, Cambridge, UK,}4_{Research Institute of Industrial}

Economics (IFN), Stockholm, Sweden, 5_{Centre for Entrepreneurship and Spatial Economics,Jönköping}

International Business School, Jönköping, Sweden

The opponents of big-box entry argue that large retail establishments generate a variety of negative externalities. The advocates, on the contrary, argue that access to a large spill-over effects, and therefore, can be considered a consumer amenity that increases the attractiveness of the entry location. To test the validity of these competing arguments, we use the entry of IKEA in Sweden as a quasi-experiment and investigate if increased access to retail is associated with place attractiveness, where attractiveness is proxied by residential entry cities by, on average, 4.2% or 62,980 SEK (approximately 6,600 USD), but such an IKEA retail trade area. We also observe an attenuation of the effect with distance from the new IKEA store, where the properties located 10 km away experience a 2% price increase. Our results indicate that large retailers have the potential to increase place attractiveness, but perhaps not in the immediate vicinity of the new establishment.

Introduction

Page 1 of 33 Retail and Place Attractiveness: The Effects of Big‐Box Entry on Property Values

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Geographical Analysis

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Sven-Olov Daunfeldt et al. Retail and Place Attractiveness

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Theoretical framework

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Empirical studies

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Empirical analysis

Data and descriptive statistics

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12 Figure 1.

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13 Figure 2.

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15 Identification strategy and empirical model

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Geographical Analysis 16 imt m TR  t  m t × m n Price l _imt= 0+ 1TR+ 2TR2+ t+ m+ ( t× m)+imt, Table 3. Year Year 294 26.97 6 276 21.82 333 26.32 78 6.17 254 20.99 375 30.99 40 3.31 331 27.65 331 27.65 42 3.51 257 20.46 406 32.32 42 3.34 257 21.22 478 39.47 43 3.55 270 16.57 798 48.99 45 2.76 298 25.40 225 19.18 308 26.26 290 24.72 52 4.43 181 22.85 158 19.95 220 27.78 208 26.26 25 3.16 Note: Years after IKEA entry in bold.

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Sven-Olov Daunfeldt et al. Retail and Place Attractiveness 17  imt TR P dist dist TR TR X_i X i Results imt  TR Table 4. P

***Significant at the 1% level. **Significant at the 5% level. *Significant at the 10% level.

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i

Figure 3.

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Concluding remarks

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Acknowledgements

Notes

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References

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Housing Policy D

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Analysis

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Appendix 1. Entry locations

Figure A1.

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27 Figure A2.

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28 Figure A3.

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Appendix 2. Robustness estimations using proximity bands

ln Price_imt= 0+ 1TR02+ 2TR25+3TR510+ t+ m+ ( t× m)+imt,

TR Figure A4.

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30 Table A1.

P P

***Significant at the 1% level. **Significant at the 5% level. *Significant at the 10% level. Table A2.

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