| Project/Area Number |
09650578
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
交通工学・国土計画
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| Research Institution | TOHOKU UNIVERSITY |
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Principal Investigator |
ANDO Asao Graduate School of Information Sciences, Tohoku University, Associate Professor, 大学院・情報化学研究科, 助教授 (80159524)
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| Co-Investigator(Kenkyū-buntansha) |
YAZAWA Norihiko Faculty of Commerce, Tokyo International University, Lecturer, 商学部, 講師 (60250859)
MUN Se-il Graduate School of Economics, Kyoto Univ., Associate Professor, 大学院・経済学研究科, 助教授 (40192736)
SASAKI Komei Graduate School of Information Sciences, Tohoku University, Professor, 大学院・情報化学研究科, 教授 (10007148)
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| Project Period (FY) |
1997 – 1999
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| Project Status |
Completed (Fiscal Year 1999)
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| Budget Amount *help |
¥3,200,000 (Direct Cost: ¥3,200,000)
Fiscal Year 1999: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 1998: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 1997: ¥1,300,000 (Direct Cost: ¥1,300,000)
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| Keywords | land prices / public notification / space-time database / diffusion model / space-time autocorrelation / overlapping generation model / spatial weight matrix / ヘドニック関数 |
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| Research Abstract |
This study is aimed at clarifying the space-time characteristics of land asset bubbles from three different viewpoints.
The first is to check whether the diffusion process succeeds in reproducing the space-time changes in land prices when the data from the downward phase being included. Thus we first extend our data period to 1996 from the original duration of 1976 to 91. The compilation procedure includes estimations of 21 cross-sectional land price functions, whose parameters are to summarize the changes in land price formations in Tokyo Metropolitan Area. Accordingly, chapter 1 is devoted to the discussions on the technical alterations needed to extend our data base along with the chronological changes in parameters in land price functions. Chapter 2 utilizes the data base obtained above to examine the applicability of the diffusion model to the downward phase. The parameters obtained from both one and two-dimensional models are compared with those based on the data limited to the uprising phase.
The second direction is to analyze the data as the spatial or space-time autocorrelation processes. In preparation for the direction, chapter 3 reviews the methods of hypothesis testing and parameter estimation under spatial autocorrelation. As an application, chapter 4 discusses a spatial error autocorrelation model with the hierarchical spatial weights, and applies the model to the land price data in Kawasaki City.
Unlike the previous two directions, the third one is an effort to theoretically explain land price formations based on the dynamic behaviors of economic agents. An overlapping generation model considering land and financial assets may serve for the purpose. Along this line, chapter 5 formulates a prototype model that considers the saving behavior of households.
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