有關(guān)韓國不動產(chǎn)周期研究

1、有關(guān)韓國不動產(chǎn)周期研究摘要：本文研究了經(jīng)濟環(huán)境的波動如何影響韓國不 動產(chǎn)周期變化。首先，本文界定了什么是不動產(chǎn)周期。研究 了一些對不動產(chǎn)市場影響最大的經(jīng)濟變量。運用了多元線性 回歸模型和時間序列模型。本文收集了 2000年一月至2013 年一月的數(shù)據(jù)。運用計量分析方法，本文達成了一個結(jié)論， 即上述經(jīng)濟變量與不動產(chǎn)周期之間有著顯著的關(guān)系。關(guān)鍵詞：房地產(chǎn)周期多元回歸經(jīng)濟變量1.introductionkorea， s housing market turned bullish after the government announced a set of measures in early apr

2、il to prop up the nation" s faltering real estate marketfor the first time this year, home transact!ons in korea rose las t 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

3、month, up more than 8and-ahalf percent from april last year the most notable increase came in the area of seoul where home transactions jumped almost 20 percent to around 30thousand.the property cycle follows a predictable pattern as sure as night follows day. this pattern reveals three distinct pha

4、ses 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 arrive2. empirical results

5、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

6、 of 157 observations for each variablemodel 1tablelas 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 ofhousing bonds and month rent are greater than

7、the level of significance considered (5%). so we rejectthe 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.

8、 moreover， an increase of 1 unit in average narrow money will affect positively hppi by +9. 23e-07%.model 2table23. conclusionas 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 a

9、ll 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. mo

10、reover, an increase of 1 unit in average narrow money will affect positively hppi by +110. 1%.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

11、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 indexafter ran two models using t

12、he multi pie regressi on mode 1. 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 understending ahout 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 e