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摘要：本文研究了经济环境的波动如何影响韩国不动产周期变化。首先，本文界定了什么是不动产周期。研究了一些对不动产市场影响最大的经济变量。运用了多元线性回归模型和时间序列模型。本文收集了2000年一月至2013年一月的数据。运用计量分析方法，本文达成了一个结论，即上述经济变量与不动产周期之间有着显著的关系。
关键词：房地产周期 多元回归 经济变量
1.Introduction
Korea's housing market turned bullish after the government announced a set of measures in early April to prop up the nation's faltering real estate market.
For the first time this year， home transactions in Korea ROSE last month from a year earlier.The Ministry of Land， Infrastructure and Transport said Friday that provisional figures show that nearly 70-thousand houses saw new owners last month， up more than 8-and-a-half percent from April last year. The most notable increase came in the area of Seoul where home transactions jumped almost 20 percent to around 30-thousand.
The property cycle follows a predictable pattern as sure as night follows day. This pattern reveals three distinct phases being Boom followed by Slump followed by Recovery before the next Boom commences etc. The property cycle （unimpeded） will always follows this pattern so a Boom cannot precede another Boom without first experiencing a Slump followed by a Recovery before the next Boom can arrive.
2. EMPIRICAL RESULTS
This paper is about the analysis of the relationship between Korea house purchase price index and four main variables mentioned above. Therefore two were run in order to understand which model is the best fit model to analyze their relationships over the period January 2000 – January 2013. which consist of 157 observations for each variable.
Model 1
Table1
As can be seen from table 2， this model explains a large proportion of the variance ， in fact is equal to 0.84. But not all the variables of the model are statistically significant since the p-values of housing bonds and month rent are greater than the level of significance considered （5%）. So we reject the null hypothesis. We can see that 1% increase in yields of national housing bonds will change +0.0036% housing purchase price index.Similarly， 1% growth in monthly rent for housing will change by +0.0026% hppi，which is not so obvious effect. Moreover， an increase of 1 unit in average narrow money will affect positively hppi by +9.23E-07%.
Model 2
Table2
As can be seen from table 3， this model explains a very high proportion of the variance which is 0.90. Moreover， all the variables of the model are highly significant since all the p-values are smaller than the level of significance considered （5%）. We can see that 1% increase in yields of national housing bonds will change +44.0% housing purchase price index.Similarly， 1% growth in monthly rent for housing will change by +349.1% hppi，which is not so obvious effect. Moreover， an increase of 1 unit in average narrow money will affect positively hppi by +110.1%. 3.Conclusion
The house purchase index is based on transactions involving conventional and conforming mortgagesonly on singlefamily properties that have been purchased or securitized by Fannie Mae or Freddie Mac. It is a weighted， repeat-sales index， which means that it measures average price changes in repeat sales or financings on the same properties.
Yields of national housing bonds ，Monthly rent for housing and Average narrow money are good regressors of the house purchase price index.
After ran two models using the multiple regression model.Taking a log of every regressor and the dependent variable， an equation comes out the best fit model of this problem. Using model 2， we can estimate the property cycles considering a bias within. Thus，using the model mentioned above， we can get a deeper understanding about the how the economical variables influence the property market and how the property cycles can be predicted in a econometrical way.
Reference：
[1]“Introduction to Econometrics”James H.Stock Mark W.Watson
[2]“Introductory Econometrics” J.Wooldridge
[3]“econometric Analysis” W.Greene
作者简介：卢露（1991～），女，陕西凤翔人，西北大学在读本科，研究方向：财政学；任贝尔（1992～），女，陕西岐山人，西北大学在读本科，研究方向：国际贸易与物流
关键词：房地产周期 多元回归 经济变量
1.Introduction
Korea's housing market turned bullish after the government announced a set of measures in early April to prop up the nation's faltering real estate market.
For the first time this year， home transactions in Korea ROSE last month from a year earlier.The Ministry of Land， Infrastructure and Transport said Friday that provisional figures show that nearly 70-thousand houses saw new owners last month， up more than 8-and-a-half percent from April last year. The most notable increase came in the area of Seoul where home transactions jumped almost 20 percent to around 30-thousand.
The property cycle follows a predictable pattern as sure as night follows day. This pattern reveals three distinct phases being Boom followed by Slump followed by Recovery before the next Boom commences etc. The property cycle （unimpeded） will always follows this pattern so a Boom cannot precede another Boom without first experiencing a Slump followed by a Recovery before the next Boom can arrive.
2. EMPIRICAL RESULTS
This paper is about the analysis of the relationship between Korea house purchase price index and four main variables mentioned above. Therefore two were run in order to understand which model is the best fit model to analyze their relationships over the period January 2000 – January 2013. which consist of 157 observations for each variable.
Model 1
Table1
As can be seen from table 2， this model explains a large proportion of the variance ， in fact is equal to 0.84. But not all the variables of the model are statistically significant since the p-values of housing bonds and month rent are greater than the level of significance considered （5%）. So we reject the null hypothesis. We can see that 1% increase in yields of national housing bonds will change +0.0036% housing purchase price index.Similarly， 1% growth in monthly rent for housing will change by +0.0026% hppi，which is not so obvious effect. Moreover， an increase of 1 unit in average narrow money will affect positively hppi by +9.23E-07%.
Model 2
Table2
As can be seen from table 3， this model explains a very high proportion of the variance which is 0.90. Moreover， all the variables of the model are highly significant since all the p-values are smaller than the level of significance considered （5%）. We can see that 1% increase in yields of national housing bonds will change +44.0% housing purchase price index.Similarly， 1% growth in monthly rent for housing will change by +349.1% hppi，which is not so obvious effect. Moreover， an increase of 1 unit in average narrow money will affect positively hppi by +110.1%. 3.Conclusion
The house purchase index is based on transactions involving conventional and conforming mortgagesonly on singlefamily properties that have been purchased or securitized by Fannie Mae or Freddie Mac. It is a weighted， repeat-sales index， which means that it measures average price changes in repeat sales or financings on the same properties.
Yields of national housing bonds ，Monthly rent for housing and Average narrow money are good regressors of the house purchase price index.
After ran two models using the multiple regression model.Taking a log of every regressor and the dependent variable， an equation comes out the best fit model of this problem. Using model 2， we can estimate the property cycles considering a bias within. Thus，using the model mentioned above， we can get a deeper understanding about the how the economical variables influence the property market and how the property cycles can be predicted in a econometrical way.
Reference：
[1]“Introduction to Econometrics”James H.Stock Mark W.Watson
[2]“Introductory Econometrics” J.Wooldridge
[3]“econometric Analysis” W.Greene
作者简介：卢露（1991～），女，陕西凤翔人，西北大学在读本科，研究方向：财政学；任贝尔（1992～），女，陕西岐山人，西北大学在读本科，研究方向：国际贸易与物流