The Impacts of Light Rail Line Construction and Relevant Public Facilities on the House Prices in Tamsui New Town District

• Main Authors: Chen,Chi-Chu, 陳麒竹
• Other Authors: Hsieh, Chia-Lin
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/68299234165969235817

碩士 === 真理大學 === 統計與精算學系碩士班 === 103 === Real estate transactions in Taiwan are an issue of concern for most people. However, the prices of Taiwan’s real estate fluctuate greatly and are subject to the influence of various external factors. It is necessary to understand and analyze the factors that influence the variations in housing prices.The study explores the influence factors of the dwelling house price and establishes the relevant house price index, which is indeed a very important task among the relevant measures to make the internal real estate market perfect. First of all, as we know, MRT stations raise surrounding real estate prices.This study would like to figure out the factors to influence the house pricing in the surrounding Dan-Hai New Town area after the Green Mountain Lineof the MRT systemoperates. The Green Mountain Line starts from the Metro Hongshulin Station to the northern boundary of the Phase 1 First Development Area at Dan-hai New Town. There are 7 stations (G1-G7). The data includesthe surroundings of the stations G3, G4, G5 and G6.The study adopts the “real estate transaction price message” information provided by the Department of Land Administration and the database of the Sin-yi Realty to construct our model. We implement on some empirical analysis to verify the forecasted house prices in Dan-hai New Town District. The data time period starts from August, 2012 to September,2014.The parameters used in the models include the floor number, building age, pattern, house types, whole economy index, environment issueetc. We developed the house price models for the districts of Phase 1 First Development Area at Dan-hai New Town. House price models were constructed to know the difference of the house pricing after the MRT operatesby using SPSS multi-linear regression based on the transactions records from the Real Price Registration System.The results show that the transfer square feet of house and land, age, square of the age, outside space, the structure of the building, the relationship to the street, school, business area, distances to MRT and the accompanying environmental and economic factors will significantly influence the house prices.