Identification of Geographical Segmentation of the Rental Apartment Market in the Tokyo Metropolitan Area (Short Paper)

Authors Ryo Inoue , Rihoko Ishiyama, Ayako Sugiura



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Author Details

Ryo Inoue
  • Graduate School of Information Sciences, Tohoku University, 6-6-06 Aramaki-Aoba, Aoba, Sendai, Miyagi 980-8579, Japan
Rihoko Ishiyama
  • Graduate School of Information Sciences, Tohoku University, 6-6-06 Aramaki-Aoba, Aoba, Sendai, Miyagi 980-8579, Japan
Ayako Sugiura
  • Phronesis Inc., 1-14-9, Nishi-Shimbashi, Minato, Tokyo 105-0003, Japan

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Ryo Inoue, Rihoko Ishiyama, and Ayako Sugiura. Identification of Geographical Segmentation of the Rental Apartment Market in the Tokyo Metropolitan Area (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 32:1-32:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018) https://doi.org/10.4230/LIPIcs.GISCIENCE.2018.32

Abstract

It is often said that the real estate market is divided geographically in such a manner that the value of attributes of real estate properties is different for each area. This study proposes a new approach to the investigation of the geographical segmentation of the real estate market. We develop a price model with many regional explanatory variables, and implement the generalized fused lasso - a regression method for promoting sparsity - to extract the areas where the valuation standard is the same. The proposed method is applied to rental data of apartments in the Tokyo metropolitan area, and we find that the geographical segmentation displays hierarchal patterns. Specifically, we observe that the market is divided by wards, railway lines and stations, and neighbourhoods.

Subject Classification

ACM Subject Classification
  • Applied computing → Economics
Keywords
  • geographical market segmentations
  • rental housing market
  • sparse modelling
  • generalised fused lasso
  • Tokyo metropolitan area

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References

  1. Allen C. Goodman and Thomas G.Thibodeau. Housing market segmentation and hedonic prediction accuracy. Journal of Housing Economics, 12(3):181-201, 2003. URL: http://dx.doi.org/10.1016/S1051-1377(03)00031-7.
  2. Robert Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B (Methodological), 58(1):267-288, 1996. URL: http://dx.doi.org/10.1111/j.1467-9868.2011.00771.x.
  3. Robert Tibshirani, Michael Saunders, Saharon Rosset, Ji Zhu, and Keith Knight. Sparsity and smoothness via the fusedlasso. Journal of the Royal Statistical Society, Series B (Methodological), 67(1):91-108, 2005. URL: http://dx.doi.org/10.1111/j.1467-9868.2005.00490.x.
  4. Hao Wang and Abel Rodríguez. Identifying pediatric cancer clusters in florida using loglinear models and generalized lasso penalties. Statistics and Public Policy, 1(1):86-96, 2014. URL: http://dx.doi.org/10.1080/2330443X.2014.960120.
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