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| Title: | 疫情恐慌與政策干預於Covid-19期間對國內房市之影響
The Impact of Epidemic Panic and Policy Interventions on Real Estate Market During the Covid-19 Pandemics |
| Authors: | 童莉婷
Tung, Li-Ting |
| Contributors: | 陳明吉
Chen, Ming-Chi
童莉婷
Tung, Li-Ting |
| Keywords: | Covid-19
疫情
恐慌情緒
政府因應
房地產市場
Covid-19
Pandemic
Panic
Policy Interventions
Real Estate Market |
| Date: | 2022 |
| Issue Date: | 2022-08-01 17:17:15 (UTC+8) |
| Abstract: | Covid-19疫情已知對全球經濟帶來巨大的衝擊，且過去文獻即發現疫情與其他經濟事件所發生的恐慌情緒，會對資產市場帶來負面影響。因此本研究則關注為2019年1月至2022年1月Covid-19疫情前後，代表疫情嚴重程度的本土確診數與人流移動情況、媒體新聞等面向捕捉民眾對疫情恐慌的情緒，以及政府因應疫情所採取的不同政策，對國內房價變化與住宅交易量之影響。且本研究亦依照疫情嚴峻程度分為疫情全期、平穩期與巔峰期。
本研究實證疫情期間本土確診數則對住宅交易量偶有負相關，並且有前一期的滯後性符合過往文獻認為疫情越嚴重對房市會有負向影響的結果，但對房價變化影響不明顯。人流移動變化雖於疫情各期對房價變化與住宅交易量有顯著正相關，卻容易受到打炒房政策之干擾。疫情新聞所增加的媒體恐慌情緒僅對房價變化具有負向顯著度。同時疫情期間國內房市受到疫情因應政策與打炒房政策的影響程度大。移動管制相關政策的推動會在疫情巔峰期時增加房價變化，並於疫情全期減少住宅交易量。而經濟支持的政策也會在疫情巔峰期時帶動住宅交易量的成長。整體而言，交易量會較即時反應政策的影響性，房價變化需要供需的動態調整與累積，因此較有滯後性的產生。並且本研究也發現打炒房政策僅有短期效果與疲乏性的問題，容易在政策對象改變後影響程度下降，使打炒房政策不效率。
The Covid-19 pandemic is known to have a huge impact on the global economy. Previous studies have found that panic associated with pandemics and other economic events can negatively impact asset markets. The study period is from January 2019 to January 2022, focuses on the number of locally confirmed cases and the movement of people, representing the severity of the epidemic, media news which captures the public`s panic about the epidemic, and different government policies on the housing price volatility and residential transaction volume. Moreover, the study also divided period into the whole, stable, and peak periods according to the severity of the epidemic.
The result of the study, the number of locally confirmed cases has a negative correlation with the trading volume occasionally, and the lag of the previous period, but the impact on the housing price is not obvious. Although the movement of people is a significant positive correlation between price volatility and the transaction volume but is easily affected by the flipping property policies. The media panic only has negative significance the price volatility. Also, the housing market is greatly affected by the epidemic response policy and flipping property policies. The movement control policies increase housing price volatility at the peak of the epidemic and reduce transaction volume throughout the epidemic. Economic support policies also drive the growth of transaction volume during the peak period. Overall, the transaction volume reflects the impact of the policy more immediately. In addition, the study also found that the housing policy has only short-term effects and fatigue, which is easy to decline or rebound after the change of the policy object, making the housing policy inefficient. |
| Reference: | 英文參考文獻
Al-Awadhi, A. M., Alsaifi, K., Al-Awadhi, A., & Alhammadi, S. （2020）. Death and
contagious infectious diseases： Impact of the COVID-19 virus on stock market returns. Journal of behavioral and experimental finance, 27, 100326.
Albuquerque, B., Iseringhausen, M., & Opitz, F. （2020）. Monetary policy and US
housing expansions： The case of time-varying supply elasticities. Economics letters, 195, 109471.
Alola, A. A., & Uzuner, G. （2021）. Testing the asymmetric causal nexus of housing-
oil prices and pandemic uncertainty in four major economies. Environmental Science and Pollution Research, 28（43）, 60550-60556.
Anglin, P., Cui, J., Gao, Y., & Zhang, L. （2021）. Analyst Forecasts during the
COVID-19 Pandemic： Evidence from REITs. Journal of Risk and Financial Management, 14（10）, 457.
Baig, A. S., Butt, H. A., Haroon, O., & Rizvi, S. A. R. （2021）. Deaths, panic,
lockdowns and US equity markets： The case of COVID-19 pandemic. Finance research letters, 38, 101701.
Baker, M., & Wurgler, J. （2007）. Investor sentiment in the stock market. Journal of
economic perspectives, 21（2）, 129-152.
Bandarin, F., Ciciotti, E., Cremaschi, M., Madera, G., Perulli, P., & Shendrikova, D.
（2020）. Which Future for Cities after COVID-19| An International Survey.
Brown, G. W., & Cliff, M. T. （2004）. Investor sentiment and the near-term stock
market. Journal of empirical finance, 11（1）, 1-27.
Cepoi, C. O. （2020）. Asymmetric dependence between stock market returns and
news during COVID-19 financial turmoil. Finance Research Letters, 36, 101658.
Chan-Lau, J. A., & Zhao, Y. （2020）. Hang in there： stock market reactions to
withdrawals of Covid-19 stimulus measures. Available at SSRN 3772490.
Chong, T. T. L., & Liu, H. （2020）. How does the COVID-19 pandemic affect
Housing prices in China?., MPRA Paper.
Chowdhury, E. K. （2020）. Catastrophic Impact of Covid-19 on Tourism Sector in
Bangladesh： An Event Study Approach. Chowdhury, EK （2020）. Catastrophic Impact of Covid-19 on Tourism Sector in Bangladesh： An Event Study Approach, The Cost and Management, 48（4）, 43-52.
DeLong, M. R. （1990）. Primate models of movement disorders of basal ganglia
origin. Trends in neurosciences, 13（7）, 281-285.
Del Giudice, V., De Paola, P., & Del Giudice, F. P. (2020). COVID-19 infects real
estate markets: Short and mid-run effects on housing prices in Campania region (Italy). Social sciences, 9(7), 114.
Elkind, D., Kaminski, K., Lo, A. W., Siah, K. W., & Wong, C. H. （2022）. When Do
Investors Freak Out? Machine Learning Predictions of Panic Selling. The Journal of Financial Data Science, 4（1）, 11-39.
Francke, M., & Korevaar, M. （2021）. Housing markets in a pandemic： Evidence
from historical outbreaks. Journal of Urban Economics, 123, 103333.
Hall, C. M., Fieger, P., Prayag, G., & Dyason, D. （2021）. Panic buying and
consumption displacement during COVID-19：Evidence from New Zealand. Economies, 9（2）, 46.
Hori, M., & Iwamoto, K. （2014）. The run on daily foods and goods after the 2011
Tohoku earthquake： a fact finding analysis based on homescan data. The Japanese Political Economy, 40（1）, 69-113.
Islam, T., Pitafi, A. H., Arya, V., Wang, Y., Akhtar, N., Mubarik, S., & Xiaobei, L.
（2021）. Panic buying in the COVID-19 pandemic： A multi-country examination. Journal of Retailing and Consumer Services, 59, 102357.
Jud, G. D., & Winkler, D. T. （2002）. The dynamics of metropolitan housing
prices. The journal of real estate research, 23 (1/2), 29-46.
Kang, H., & Jang, S. （2021）. Self and relative effects of competitive state anxiety
on perceived performance in middle and high school taekwondo athletes： An actor and partner interdependence model analysis. Iranian Journal of Public Health, 50（6）, 1167.
Keane, M., & Neal, T. （2021）. Consumer panic in the COVID-19 pandemic. Journal
of econometrics, 220（1）, 86-105.Blendon et al.,2004）
Loxton, M., Truskett, R., Scarf, B., Sindone, L., Baldry, G., & Zhao, Y. （2020）.
Consumer behaviour during crises： Preliminary research on how coronavirus has manifested consumer panic buying, herd mentality, changing discretionary spending and the role of the media in influencing behaviour. Journal of risk and financial management, 13（8）, 166.
O`Connell, M., De Paula, Á., & Smith, K. （2021）. Preparing for a pandemic：
spending dynamics and panic buying during the COVID‐19 first wave. Fiscal Studies, 42（2）, 249-264.
Rubbaniy, G., Khalid, A. A., Umar, M., & Mirza, N. （2020）. European stock
markets’ response to Covid-19, lockdowns, government response stringency and Central banks’ interventions. Lockdowns, Government Response Stringency and Central Banks’ Interventions （December 31, 2020）.
Salisu, A. A., & Akanni, L. O. (2020). Constructing a global fear index for the COVID-
19 pandemic. Emerging Markets Finance and Trade, 56(10), 2310-2331.
Tetlock, P. C. （2007）. Giving content to investor sentiment： The role of media in
the stock market. The Journal of finance, 62（3）, 1139-1168.
Tian, C., Peng, X., & Zhang, X. （2021）. COVID-19 Pandemic, Urban Resilience
and Real Estate Prices： The Experience of Cities in the Yangtze River Delta in China. Land, 10（9）, 960.
Umar, Z., & Gubareva, M. （2020）. A time–frequency analysis of the impact of the
Covid-19 induced panic on the volatility of currency and cryptocurrency markets. Journal of Behavioral and Experimental Finance, 28, 100404.
Yang, X., Zhu, Y., & Cheng, T. Y. (2020). How the individual investors took on big
data: The effect of panic from the internet stock message boards on stock price crash. Pacific-Basin Finance Journal, 59, 101245.
Yuen, K. F., Wang, X., Ma, F., & Li, K. X. （2020）. The psychological causes of
panic buying following a health crisis. International journal of environmental research and public health, 17（10）, 3513.
中文參考文獻
林筱真（2016）. 新聞媒體情緒對於房價之影響，國立政治大學地政學系碩士
論文，台北市。
朱芳妮, 楊茜文, 黃御維,& 陳明吉（2020）. 媒體傳播效應與房市變化關聯性之
驗證. 管理學報, 37（3）, 225-257.
江明珠,& 許秉凱. （2019）. 媒體新聞能否預測住房市場?. 住宅學報, 28（2）,
37-61.
朱芳妮, 楊茜文, 蘇子涵, & 陳明吉. （2020）. 情緒會影響房市嗎? 指數編制與驗
證. 住宅學報, 29（2）, 35-68.
林秋瑾, 王健安,& 張金鶚.（1997）. 房地產景氣與總體經濟景氣餘時間上領先、
同時、落後關係之探討.國家科學委員會彙刊；人文及社會科學，7（1），35-36.
陳淑美, & 張金鶚. （2002）. 家戶遷移決策與路徑選擇之研究—台北縣市的實證
研究. 住宅學報, 11(1), 1-22.
陳瓊惠（2015）. 市場機制與政府干預對房地產價格之影響—以臺灣房地產稅制
探討。國立成功大學政治經濟研究所碩士論文，台南市。
李沛宸（2019）. 影響臺灣房地產價格因素之探討 -以臺灣各都市為例。國立政
治大學行政管理碩士學程碩士論文，台北市。 |
| Description: | 碩士
國立政治大學
財務管理學系
107357004 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0107357004 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202200783 |
| Appears in Collections: | [財務管理學系] 學位論文
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