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    题名: 淡海新市鎮房價資料的模型配適與分析
    其它题名: Modeling and analysis for house prices in Danhai new town
    作者: 陳柏頤;Chen, Po-Yi
    贡献者: 淡江大學統計學系碩士班
    王藝華
    关键词: 實價登錄;房價資料;線性迴歸模型;空間模型;Actual price registration system;House Price;Linear regression model;Spatial model
    日期: 2017
    上传时间: 2018-08-03 14:52:54 (UTC+8)
    摘要: 近年來由於房價高漲,一般民眾無法負擔市區的房價,而轉往鄰近新開發的地區購屋,因此,本研究利用內政部不動產交易實價登錄系統的資料,來配適與分析新北市淡海新市鎮的房價資料,希望找出一個淡海新市鎮交易房價與其重要的解釋變數之間的函數關係,以提供欲購買此地房屋者在衡量合理房價的參考。我們除了使用實價登錄裡原有的資料做為解釋變數外,另外計算了交易房屋與淡海新市鎮家樂福的距離作為新變數放入模型中,以增加模型的解釋力。在此研究中,我們除了使用了最小平方法的線性迴歸模型、加權最小平方法的線性迴歸模型外,也考慮交易房屋之間具有地理相關性的空間自我相關模型與誤差項含空間自相關的迴歸模型等四種方法來配飾淡海新市鎮房價資料,最後使用決定係數與AIC值來比較不同模型間的解釋力。
    In recent years, because of extravagant housing prices, many people can not afford buying houses in downtown. Hence, they change their targets to the houses in nearby developing areas. Therefore, we study and analyze the house prices of Danhai new town in New Taipei city from the actual price registration system of real estate of the Ministry of the Interior. The relationship between the house prices and a set of representative explanatory variables is found to provide the purchasers some useful information for measuring reasonable housing prices. Besides the explanatory variables selected from the data of the actual price registration system of real estate, the distance between the house and the Carrefour in Danhai new town is considered to be a new explanatory variable to improve our model. In this study, the least-squares linear regression model, weighted least-squares linear regression model, spatial autoregressive model, and linear regression model with spatially autocorrelated error terms for considering the dependence of housing prices due to the spatial correlation between neighbouring areas are used to describe the relationship between the house prices and explanatory variables. Finally, coefficient of determination and Akaike information criterion are used to compare these models.
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