政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/118696

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/118696

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政大典藏 > College of Commerce > Department of Finance > Theses > Item 140.119/118696

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/118696

Title:	百度指數對中國大陸房地產價格之解釋能力分析 An empirical study of explanatory power for house price by Baidu Index in China
Authors:	劉激揚 Liu, Ji-Yang
Contributors:	陳明吉 Chen, Ming-Chi 劉激揚 Liu, Ji-Yang
Keywords:	有限關注房價百度指數 Limited attention House price Baidu index
Date:	2018
Issue Date:	2018-07-17 11:25:24 (UTC+8)
Abstract:	本文從有限關注理論出發，探索以百度指數為注意力的代理變量對中國大陸房地產價格走勢的解釋能力。本文先選取了2011年—2017年全國月度房價指數數據與百度指數數據，並採用前一期房地產價格、土地價格、股票指數、廣義貨幣供應量增長率等因子作為控制變量，利用多元回歸模型進行分析，探索不同百度指數關鍵字對房價的解釋能力和方向。本研究發現全國範圍下百度指數中，房產仲介類關鍵字對未來一期房價指數有正向解釋效果。接著，本文提出不同規模、不同地域的城市由於其居民構成和房價差異化等原因，可能使得不同關鍵字之百度指數的解釋能力出現差別。本文選取42個不同規模城市之月度數據，按照“一二三線”和“東中西部”城市進行分組面板回歸，實證發現，“房市”關鍵字之百度指數僅對一線城市及東部城市有解釋能力，房地產調控類關鍵字在一二線及東部城市具有解釋能力，“房貸計算器”一、二線城市及東中部地區城市有解釋能力，而房產仲介類關鍵字僅在東中部三線城市有解釋能力。不同關鍵字之百度指數在不同地區的解釋能力確實存在差異，並可以被注意力理論解釋。 The study is based on the limited attention theory, and aiming to explore if the Baidu Index as the agent variable of attention can explain the future trend of real estate price in mainland China. We first collect the monthly data of national house price index and Baidu index from 2011 to 2017, then analyze the data by multiple regression model using previous land price, stock index, money supply and other factors as control variables. The empirical result shows that the index of “realtor” keyword lagged 1 period has explanatory power for future house prices. After that, this paper puts forward the assumption that the explanatory power of different keywords Baidu index may differ in cities of different scales or locations due to the compositions of residents and the difference in house price. We go further to 42 cities of different scales and locations and conducts group panel regression. In aspect of the scale, it’s empirically found that except the significant correlation between the index of keyword “house market” and the price of Tier 1 cities, the indexes of real estate policy keyword and “mortgage calculator” can also explain house price for Tier 1 and Tier 2 cities, while the “realtor” keyword can merely explain the house price of Tier 3 cities. Take location into consideration, we find that the indexes of “market” and policy only have explanatory ability in eastern area, while the indexes of “mortgage calculator” and “realtor” have explanatory ability in eastern and central area. It’s proved that Baidu Index can explain some change in future house price.
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Description:	碩士國立政治大學財務管理學系 105357035
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G1053570351
Data Type:	thesis
DOI:	10.6814/THE.NCCU.Finance.017.2018.F07
Appears in Collections:	[Department of Finance] Theses

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