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Measuring the spatial effect of multiple sites: An application to housing rent and public transportation in Tokyo, Japan

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  • Sadayuki, Taisuke

Abstract

Geographical relationships between a housing unit and its major surrounding sites, such as public transportation stops and crime scenes, are fundamental factors that determine housing value. This paper proposes a new parametric approach to estimate the aggregate spatial effect of multiple heterogeneous sites while providing fruitful interpretations of the effect of each of these sites. While the proposed method is developed based on a traditional accessibility measure, the way in which it addresses the role of the proximity order of sites in the spatial analysis is novel. The method is applied empirically using rental housing data in Tokyo, Japan to examine how the clustering of train and subway stations influences the rental prices in their vicinity. The results reveal a discounting impact of the order of each station's proximity, even after controlling for the effect of distance. In addition, the results reveal that using a traditional accessibility measure without considering the proximity order leads to serious estimation biases. The proposed methodology is applicable to various spatial topics, such as transportation, neighborhood externalities and polycentric urban structures.

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  • Sadayuki, Taisuke, 2018. "Measuring the spatial effect of multiple sites: An application to housing rent and public transportation in Tokyo, Japan," Regional Science and Urban Economics, Elsevier, vol. 70(C), pages 155-173.
    • Handle: RePEc:eee:regeco:v:70:y:2018:i:c:p:155-173
    DOI: 10.1016/j.regsciurbeco.2018.03.002
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    4. Fukuda, Yukari, 2020. "Land prices and agglomeration: Theory and evidence from the Tokyo metropolitan area," Journal of the Japanese and International Economies, Elsevier, vol. 58(C).
    5. Zachary T. Keeler & Heather M. Stephens, 2023. "The capitalization of metro rail access in urban housing markets," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 51(3), pages 686-720, May.
    6. Asad Aziz & Muhammad Mushahid Anwar & Hazem Ghassan Abdo & Hussein Almohamad & Ahmed Abdullah Al Dughairi & Motrih Al-Mutiry, 2023. "Proximity to Neighborhood Services and Property Values in Urban Area: An Evaluation through the Hedonic Pricing Model," Land, MDPI, vol. 12(4), pages 1-12, April.
    7. Yingqun Zhang & Rui Song & Rob van Nes & Shiwei He & Weichuan Yin, 2019. "Identifying Urban Structure Based on Transit-Oriented Development," Sustainability, MDPI, vol. 11(24), pages 1-21, December.
    8. Panduro, Toke Emil & Jensen, Cathrine Ulla & Lundhede, Thomas Hedemark & von Graevenitz, Kathrine & Thorsen, Bo Jellesmark, 2018. "Eliciting preferences for urban parks," Regional Science and Urban Economics, Elsevier, vol. 73(C), pages 127-142.
    9. David Wolf & Kenji Takeuchi, 2022. "Who Gives a Dam? Capitalization of Flood Protection in Fukuoka, Japan," Discussion Papers 2203, Graduate School of Economics, Kobe University.
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    11. Sisman, S. & Aydinoglu, A.C., 2022. "A modelling approach with geographically weighted regression methods for determining geographic variation and influencing factors in housing price: A case in Istanbul," Land Use Policy, Elsevier, vol. 119(C).

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    More about this item

    Keywords

    Spatial analysis; Hedonic; Accessibility measure; Transportation; House price; Proximity ranking;
    All these keywords.

    JEL classification:

    • R30 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - General
    • R40 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - General

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