| Budget Amount *help |
¥15,800,000 (Direct Cost: ¥15,800,000)
Fiscal Year 2003: ¥2,900,000 (Direct Cost: ¥2,900,000)
Fiscal Year 2002: ¥3,900,000 (Direct Cost: ¥3,900,000)
Fiscal Year 2001: ¥2,900,000 (Direct Cost: ¥2,900,000)
Fiscal Year 2000: ¥6,100,000 (Direct Cost: ¥6,100,000)
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| Research Abstract |
A statistical method for estimating the boundary lines of lots are developed for estimating the lot shape. More specifically, programming codes are devised for estimating the most likely boundary lines, which will adjust tentatively given boundary lines so that the resulting lot shape conforms to lot information such as length of frontage and lot area. The prediction of boundary lines can be formulated as double purpose optimization functions consisting of the maximization of likelihood and the minimization of change in lot shape. This problem is solved introducing a concept of fuzzy decision support theory. The method is applied to 330 lots in Tokyo as sample lots, and about 85% of the lots are properly estimated, showing the validity of the developed method. A method for deriving typical lots in a block is also developed through the shape similarity matrix among lots. The method is applied to 20 blocks in Setagaya Ward in Tokyo. Rectangular like lots tend to be chosen as typical lots irrespective of the shape of blocks. This is influenced by the fact that rectangular lots are often preferred in the choice of lots in reality. In rectangular shaped blocks, a lot having an edge, whose length is approximately equal to the length of shorter edge of the block, as a typical lot. Moreover, two lots, whose frontages differ in the factor of 2 and whose depths are equal, tend to be chosen as typical lots. Those results expresses well the history of subdivision of the block. As described above, I could develop fundamental technique to partition blocks optimally, and I could also develop a method to find typical lots for the block. Furthermore, demand structure in housing market is analyzed based on the database of households who bought or rented houses, in order to analyze how residential environment are valued by consumers. The results suggests that there are hour types of households, who differ in the priority of valuing in-door quality and out-door quality.
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