Study Bubble Price in Housing Market (Case Study: State Bushehr)

Transcription

1 Study Bubble Price in Housing Market (Case Study: State Bushehr) 1 Saeid Zanganeh, Mona Yektapour 2 and Hassan Soltani 3 Abstract: Housing and Especially its prices play a key role in government and family decisions. This is particularly important in recent years as housing prices increasing trend has been more pronounced. This study has been investigates the price bubble in the Bushehr housing market and also investigate the relationship between housing prices and GDP, stock prices, gold prices, inflation, housing and banking payment facility. The results of estimating the demand function implies the existence of a housing price bubble as a growing rapidly with the medium growth rate for Bushehr province since years. The pattern of the relationship between housing prices and economic variables considered, it represents a Housing price with mortgage banking payment facility and inflation has positive relationship, a negative relationship with the price of gold and no significant relationship with GDP and stock prices. Keyword: Price Bubbles, Housing Prices, Housing Demand, Panel Data Introduction Housing is an important part of the economy of each country according to its In addition profound effects on the socio - cultural, economic Terms are also important. Activation of this section and part of its function through its effect on consumer spending and investment can help the activation and create employment opportunities. To this end, countries in economic 1 Department of Accounting, GENAVEH branch, Islamic azad, university Genaveh, Iran, Zanganeh_saeid@yahoo.com 2 Department of Accounting and Management, Science and research branch, Islamic azad, university Fars, Iran, Yekta.m2012@yahoo.com 3 Department of Accounting and Management, Science and research branch, Islamic azad, university Fars, Iran, Soltani.hassan74@gmail.com 201

2 crisis and the serious unemployment problem use from this section as stimulating the economy and generating employment. However, any instability in the macroeconomic system can make efficiently housing system (and every other economic sector) non-functioning and make Inaccessible Intended objectives of the department. Research hypotheses: This study was designed to answer this question that : Are price bubbles and interfere in process of data transfer make housing prices increased in Bushehr housing market? M In order to answer this question the following hypothesis is proposed: Identify applicable sponsor/s here. If no sponsors, delete this text box. (Sponsors) The main hypothesis: There is a price bubble in the Bushehr housing market. Alternative Hypothesis: 1. During the study period, the stock price has a negative impact on housing prices. 2. During the period studied, the gold price has a negative impact on house prices. 3. During the study period, the inflation rate has a positive impact on housing price trends. Research Highlights Goals: The main objective of this study was to investigate the possibility Existence of a housing price bubble in the Bushehr province. Methodology: In housing economics, housing prices are determined by supply and demand system. Housing has characteristics that distinguish it from other assets. One of these features is related to the supply side of the housing. Due to the long production process, the housing does not respond quickly to economic shocks. Thus, in determining housing prices, housing supply is considered constant, In other words, housing supply is considered to beinelastic. Thus, the demand for housing has an important role in determining house prices. So to check price bubble in the housing market estimate the housing demand is needed. Check for bubbles in the housing market, it is assumed that housing prices of both the basic and non-basic prices explain. Basic factors can be divided into two groups: The first group includes macroeconomic variables (such as GDP and interest rates) and the second group included demographic variables (such as migration rate). In this study, the basic variables include national income, inflation and interest rates. In Iran many years that studied the real interest rate, that means the nominal interest rate 202

3 minus the inflation rate, was very low and even negative, That is why in this study national interest and the inflation rate is used as a basis.in Check for bubbles for housing market, the housing demand function is estimated. Housing demand function, as a function of economic variables and non-fundamental factors (not visible) will be considered that non-fundamental factors appear such errors. Because such used error as the size of the bubbles or a non basic prices (Angela Martin (2006), Roach (2001) and Levin and Wright (1997). Definition of terms: 1. Bubble Price: The rapid rise in prices or a range of assets in a continuous process, the initial price increases, expected future price increases cause to attract new buyers to the market. Traders often take advantage of the profits from the sales of Compared with Consumption. Rising prices fell with reversal of expectations and usually cause of financial crises (Kindle Berger, 1987) 2. House Price Housing prices in the theory of consumer behavior indicate a willingness to pay the applicant has the purchasing power to buy a square meter of housing that determined from the relationship between demand and supply of housing (Black and Fraser 2006). 3. Models of Panel Data Its one of the most effective methods in the field of econometrics that uses econometric topics is broad every day. This method means of combining the concept of cross-sectional observations occurred during this time period. Heterogeneity between individual units is considered in this method. (Hysayv, 1986) Another definition Baltajy (1995) combining cross-sectional views of households, countries, companies and... Over a period of time is referred that in this way, heterogeneity among individual units intended to be And precise for each individual unit of an intercept is considered to be distinct. 4. House Demand: Housing demand function is the price reflects the purchase of housing applicants willing to pay for a certain amount of goods (one square meter of housing) (Rafii, 2003) The first research on price bubble in the stock market with Robert Schiller an article entitled (the stock price changes as a function of the present value of future cash flow benefit or not?).schiller, in this paper uses annual data and enjoying the variance bound test with excessive price 203

4 volatility concluded Price changes by the change in the present value of the cash flow benefit can not be explained. Svrnt in 2003 and his seven-year investigation known as "critical collapse market " report published This led to the crisis, the stock market is one of important and interesting events in the scientific arena So that investors and traders fear the crisis is a source of perpetual anxiety and the occurrence of such critical life a lot of them are destroyed. He looks reason of terrible loss and falling prices in the process of enhancing interaction and cooperation between investors realistic. Chiang (2006) to answer the question of whether house prices are based on fundamental factors, or bubble, house prices in China have studied like other studies dividing housing prices to both basic and non-basic components. For the calculation of basic components, a basic prices function of income, and interest rates and the price of non-functional basis of expected returns on investment are considered. Finally, using quarterly data show these are the basic factors that play an important role in the housing market, but there is a speculative bubble, but plays a less role in increasing housing prices in China. Krugman (MP Keynes) did not predict the occurrence of bubbles in the housing market, but eventually realized And acknowledged that the economy is in such a situation, and this causes a big problem in America is the economy. Krugman believes that in the situation of make bubble in housing market, this bubble caused by the expected return (profit) capital Model to estimate and analyze the results: In this study, the model calculates the price bubble based on existing theories (Angela Martin 2006, Roach 2001, Levin and Wright, 1997) is Estimate Where house prices as the dependent variable and inflation are considered as independent variables. At this stage, the main emphasis is on the estimated price bubble and therefore considered variables not significant. After estimating the model to predict housing price bubbles using five independent variables, inflation, credit, gold, stocks and GDP rates are paid. When panel data are used to estimate before any work requiring testing FLeamerto distinguish methods and how to estimate If the test result is used for data fusion, data fusion methods are used to estimate the model and If the test result using panel data would continue Hausman test for the detection of fixed or random effects models will be used.. Template estimated price bubble: 204

5 F test Lymr: The F-Lymrtest, the hypothesis H0 is identical to the intercept (data fusion) against the hypothesis H1; the intercept difference (using panel data) is placed. Therefore, we can write: α= =α2= α1:h0: At least one of the intercept is different from the rest: H1 If the calculated F-statistic Lymyr of the table with degrees of freedom (N-1) and (NT-NK) is larger, the zero hypotheses was rejected and the use of panel data is better.otherwise, data fusion techniques (Pooling) are used. Lymr F-statistic values obtained with the 28/20 SS that is larger than the F table. The zero hypothesis is rejected at the 5% significance level panel data approach is chosen. Table 1: Results of F tests Lymr Redundant Fixed Effects Tests Pool: Pool01 Test cross-section fixed effects. Effects Test Statistic d.f. Prob. Cross-section F (2,28) Cross-sectionChi-square Cross-section fixed effects test equation: Dependent Variable: RHP? Method: Panel Least Squares Data: o1/11/12 Time: 21:30 Sample: Included observation:11 Crosee-sections included:3 Total pool (balanced) observations: 33 Coeffcient Std. Error t-statistic Prob. C LGDP INF R-sqared Mean dependent var Adjusted R-squared S.D. dependent var 205

6 S.E. of regression Akaike info criterion Schwarz criterion Log likelihood Hanna- quinn criter F-statistic Durbin-Watson stat Prob(F-statistic) Hausman test : The next step is to examine the issue of whether is constant or intercept burette effects in the cross-sectional units (countries) as a random act or not. The following assumptions are tested: H0:b=β^ H1: b β^ Hausman test statistic with 2 degrees of freedom (five independent variables) versus 57/40 obtained is compared with the table value at 5% significance level, Hausman test rejects the zero hypotheses and justifies the appropriate model, is a model based on random effects. Table 2: Results of the Hausman test Correlated RANDOM Effects- Hausman Test Pool: pool01 Test cross-section random effects Ch-Sq Test Summary Statistic Chi-Sq. D.f Probe Cross-section random ** Warning: estimated cross-section random effects variance is zero. Cross-section random effects tests comparisons: Variable Fixed Random Var(Diff) Prob. LGDP INF

7 Cross-section random effects tests equation: Dependent Variable: RHP? Method: Panel Least Squares Data: 01/11/12 Time: 21:51 Sample: Included obsevations:11 Cross-sections included:3 Total pool (balanced) observation:33 Coefficient Std.Error t- Statistics Prob. C LGD I INF Effects Specification Cross-section fixed (dummy variables) R-squared Mean dependent var Adjusted-R-squared S.D.dependent S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter F-statistic Durbin- Watson stat Prob (F-statistic) As mentioned in the previous section for different panel data models have different tests done. Therefore comply with the relevant testing the random effect model was accepted by the random pattern of results is summarized in Table 4-3. The model estimates that the price bubble as only two variables as national income, inflation has been used and the other three variables are not handled Because the models are estimated price bubble based on certain variables. (Angela, Martyyn (2006), Roach (2001)). Levin and Wright 1997). 207

8 According to the theoretical expression of the relationship between national income and inflation was expected a direct relationship, the results were significant at the 5% significance level Positive relationship between house prices with national income and inflation. This means that a one-unit change in the inflation rate of housing prices will change And the model shows a positive relationship between GDP and housing prices and housing prices can be changed by a factor of Table 3: The results of estimating the random effect Dependent Variable: RHP: Method: Pooled EGLS(cross-section random effects) Date: 01/11/12 Time: 21:40 Sample: Included observations:11 Cross-sections included:3 Total pool (balanced) observations:33 Swamy and Arora estimator of component variances Variable Coefficient Std.Error t-ststisticprob C LGDP INF Random Effects (cross) _ GEN--C _ BOSH-C _BOR-C Effects Specification 208

9 S.D. Rho Cross-section Idiosyncratic random random Weighted Statistics R- suared Mean dependent var S.D. dependent var S.E.of regression Sum sqared resid F-statistic Durbin-Watson stat Prob (F-statistic) Unweighted Statics R- squared Mean ependentvar Sum squared resid Durbin-Watson stat One of the conventional methods for the study of time series data is using descriptive statistics. Therefore, descriptive statistics were used for the housing price bubble whose results are listed in Table 4-4. According to this table, the housing price bubble Bushehr mean 29/1 and the middle 29/1, which partly indicates that the data are normally distributed. Coefficient of skewness is stretched as it is expressed. Descriptive statistics were also calculated according to the Bushehr housing price bubble can be said, Price bubble has a maximum and a minimum of 09/18 and 31/13- This indicates that it is during periods of price bubble has been fluctuating. Table 4: Descriptive statistics were calculated for the housing price bubble Bushehr RESID- KDS Mean 209

10 Median Maximum Minimum Std.Dev Skeness Kurtosis Sum Sum Sq. Dev Observations 11 The overall results of the F test Lymr: The subjects were explained about Lymr F tests are met, the hypothesis H0 is not rejected, this means that the estimated model data fusion estimation method used. As a result of Hausman test is not required. Table 5: Test Results Redundant Fixed Effects Tests Pool:POOL01 Test cross-section fixed effects Effects Test Statistic d.f. Prob. 210

11 Cross-section F (2,25) Cross-section Chi-square Cross-section fixed effects test equation: Dependent Variable: RHP? Method: Panel Least Squares Date: 01/12/1 Time: 22: 30 Sample: Included observations:11 Cross-Sections included:3 Total pool (balanced) observations:33 Coefficient t Std. Error t-statistic Prob C L GDP? INF? EPIX? GP? BANK? R-squared Mean dependent var Adjusted R-0squar S.D. dependent var 211

12 S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan Quinn criter Statistic D.urbin Watson stat Prob(F-statistic) Conclusions: The results of the test each hypothesis is as follows: 1. There is a price bubble in the housing market Bushehr According to the estimated results and charts and descriptive statistics can be expressed in the general case that the housing price bubble in the Bushehr province, the bubble growth rate is growing. Descriptive statistics were also calculated according to the Bushehr housing price bubble can be said, Price bubble in the housing market has been fluctuating over the period studied. 2. During the study period, the stock price has not a negative impact on house prices. The coefficient of the stock index at a significance level of 5%, positive but the is very low and it can be concluded that the stock index has not great influence of house prices in the province. 3. During the period studied, the gold price has not a negative impact on house prices. Factor for gold prices in Significance level of 5% was a negative, which means that for every one percent increase in the price of gold, 45 units will be decrease the house prices. 4. During the study period, the inflation rate has a positive effect on housing price trends. The relationship between the inflation rate based on the results obtained were consistent with the theoretical foundations, inflation with a significance level of 5% is has a positive relationship with housing prices. That means with Inflation increases the price of housing also 212

13 raises, when inflation raises the price of land (based on the theory that the demand for land goes up by people to keep their money's worth) and building materials and wage also increase. Another important finding of this study is related to mortgage banking payment facility and its effect on housing prices, Based on the results, it was estimated that housing prices have a significant positive correlation with the level of 5% is paid by the facility. Also GDP, contrary to housing economic theory, at a significance level of 5% has no effect on house prices. Recommendations: Recommendations resulting from the research results: Concerning the importance of the presence or absence of market prices, especially housing markets for policy and management with rising prices from the government side and the private sector to invest heavily in these markets need to recognize and evaluate the bubbles will be seen. According to this point the government and policy makers should identify the most accurate market bubble in an effort to and to act in order to reduce losses and damage are increased and remove the bubble. Therefore it is advised to avoid the consequences of price bubbles and crises in the economy are struggling more than before. The main problem in housing is that housing is an asset with two face, meaning that it is a consumer goods to households And the other side is a good investment that the second aspect is the successor of the gold market and stock markets suggested to avoid bubbles in asset markets crisis, the government and the private sector to strengthen these markets to bring balance to operate in all regions of the countryto Weights to be assigned to bubble over total assets. Payment policy is implemented properly because these policies will increase housing prices. Suggestions for future research: 1. It is recommended that as such research be conducted in the Bushehr neighboring provinces and with the results obtained in this study were matched. 2. It is suggested that research be carried out as in the whole country.. 3. It is recommended that As such research be conducted with other key economic factors. 213

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