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    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/135798

    Title: Forecasting Housing Markets from Number of Visits to Actual Price Registration System
    Authors: 林左裕
    Lin, Tsoyu Calvin
    Hsu, Shih-Hsun
    Contributors: 地政系
    Keywords: Actual price registration system;Hit rate;Search behavior;Big data;Vector auto-regression with exogenous variables
    Date: 2020-01
    Issue Date: 2021-06-16 14:51:52 (UTC+8)
    Abstract: Taiwan launched the actual price registration system for real estate transactions in 2012. Real estate–related information, for e.g., prices, area and location, can be obtained through a search on this platform. Most market participants, including potential buyers and sellers, obtain property information before making their transaction decision. If the search behavior can be transferred into supply or demand action, then the number of visits to a website can be used as a leading indicator of price changes or transaction volume. This study has collected the number of visits to the actual price registration system in New Taipei City in Taiwan and other macro-economic variables from 2014 to 2019 and applied a model with vector auto-regression with exogenous variables (VARX) for empirical analysis. We find two important results in our analysis: 1. the transaction volume significantly leads house prices and the number of visits to this system in most districts, and 2. the number of visits leads transaction volume only in the district with a very good transportation system and infrastructures, and leads the house prices only in districts that have affordable house prices or deemed to be a “good value”. This is the first empirical study done after Taiwan launched the actual price registration system. Governments in other countries can launch similar systems and market participants can apply the findings of this study to their future policy and investment decision making process.
    Relation: International Real Estate Review, 23:4, 1131-1162
    Data Type: article
    Appears in Collections:[地政學系] 期刊論文

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