中國物業稅徵收後之房產價格預測－以中國上海市住宅為例

Prediction of China Housing Price after Levy of Property Tax

林明俊(Ming-Chun Lin) ； 陳青浩(Ching-Hao Chen) ； 游媛尹(Yuan-Yin Yu)

《真理財經學報》 23期 (2010/12) Pp. 73-87

https://doi.org/10.29963/TOJEB.201012.0004

摘要

物業稅的開徵目的在於讓稅收充分發揮宏觀調控功能，合理調節財產的過分集中，糾正房地產市場的結構性矛盾，引導合理消費，使房地產市場得到健康的發展。對於物業稅的開徵，各國必須結合自身特點來實施。在中國，目前物業稅尚處於改革試點時期，要求加強對物業稅的理論研究與實證分析，盡可能正確的認識物業稅對中國房產市場帶來的影響。為了研究物業稅徵收對於中國房產的價格影響，本研究建立類神經網路（Radical Basis Function Neural Network RBFNN）模型來逼近微觀經濟學理論的DUST價格模型，分析了物業稅徵收對於房產價格的影響 效應。通過實證分析，結果顯示：流通環節的稅收改革對房價產生的是持久的正向衝擊，保有環節的稅收改革對房價產生的衝擊是短期的負向衝擊。

關鍵字

物業稅 ； 稅收效應 ； DUST模型 ； 類神經網路

並列摘要

The collection of property tax aims to perform macro-control, adjust the estate centralization, correct the configurable contradiction, induct the reasonable consumption and consequently provide a healthy development for the real estate market. Different country can adjust measures to local conditions for the collection of the property tax. In china, the theoretical study and example analysis are required to investigate the influence of property tax. A Radical Basis Function Neural Network (RBFNN) is established in this paper to approximate a DUST price model in the microeconomics. The effect of levy of property tax on housing price is analyzed. Empirical results show that the tax in circulation can make a long-term positive impact on the housing price, while the tax in retain can make a short-term negative impact on the housing price.

並列關鍵字

Property tax ； Tax effect ； DUST price model ； RBFNN

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