Why Do House Prices Fight?

< p > < strong > index calculation method and arithmetic mean method < /strong > /p >

< p > according to the explanation of the changes in the sales prices of the 70 large and medium-sized cities, < a href= "http://www.91se91.com/news/index_c.asp" > the National Bureau of statistics < /a > the method of investigation is "the data of the sales price, area and amount of the new residential buildings are directly used by the local real estate management departments."

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< p > according to the Guangzhou land < a href= "http://www.91se91.com/news/index_f.asp" > the Housing Authority < /a >, they are both true, and the data difference is mainly caused by the different statistical methods.

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< p > Hu Gang, Professor of management at Jinan University, said that the statistical method of Guangzhou is the arithmetic average method. The total paction price is divided by the total paction area and the unit price is calculated. The National Bureau of statistics is the index calculation method, which tracks the price changes of the same property in different periods and calculates the housing price data.

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< p > according to the analysis, the average number of residential areas with tens of thousands of yuan in average price is calculated together with the average number of suburbs, which is of no practical value to the real estate market, and the statistical method of the National Bureau of statistics is more scientific, which can better reflect the changes of the real estate market.

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< p > as an economically developed and highly marketable city of Guangzhou, why do we choose such a statistical method? Experts point out that using the index algorithm, as long as house prices rise, the statistical results will rise.

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< p > in April this year, Guangzhou issued a policy that the government should conduct "guidance" for housing prices, and "housing sales above government guidance prices shall not be sold". In the second half of the year, some developers said that the high priced housing in the central district was hard to get the pre-sale permit, while the low-income housing in the suburb could easily get the pre-sale permit.

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< p > insiders pointed out that the pformation of statistical methods can not change the situation of excessive housing prices.

Such statistics have a suspicion of "flickering".

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< p > < strong > for the sake of digital "weight loss", change the statistical scope < /strong > /p >

< p > for statistical data "obvious decline", in addition to statistical methods, "fat" and "thin" weigh together.

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< p > reporter noted that starting in October this year, Guangzhou will be located in the outskirts of Conghua and Zengcheng two county-level cities in Guangzhou's housing price statistics, and the previous house price statistics do not include the two county-level cities.

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< p > at present, the average housing price of these two county-level cities is less than 10000 yuan per square meter, while the average housing price in the core area of Guangzhou is forty thousand or fifty thousand yuan per square meter, bringing the two places into the "innovative method" of house price statistics. The result is obvious that Guangzhou's housing price has changed from "boom" to "big drop" in a twinkling.

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< p > reporter survey found that some properties in Guangzhou rose by more than 30% compared with the beginning of the year, such as a second-hand housing in Haizhuqu District, which was bought and sold last year, and the housing price has risen from twenty thousand yuan to more than thirty thousand yuan.

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Ms. P told reporters that she had recently seen a 130 square meter apartment in Baiyun District. The agreed price was 23 thousand yuan per square meter, but the developer made a sudden call at the end of November. The government issued an urgent notice that the paction price should not be higher than 19 thousand yuan, otherwise it could not be signed by the Internet. "Guangzhou

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< p >, so Ms. Mao must sign a contract of 19 thousand yuan per square metre for the record of the National Housing Authority to enter the house price statistics. However, the extra difference of 4000 yuan per square metre must be paid in advance.

As a result, Ms. Mao's down payment was equivalent to more than about 500000 yuan, unable to raise this huge sum of money, so she had to give up the apartment.

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< p > "the government only wants to achieve the goal of regulation and control, and developers earn a lot of money, and finally we just need to pay the bill."

Ms. Mao said.

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< p > < strong > prevent self contradiction and avoid market misjudgement < /strong > /p >

< p > "data contradict each other, so it is easy to mislead."

Chen Sheng, executive director of China real estate data research institute, believes that the contradiction between the current real estate < a href= "http://fz.sjfzxm.com/" > the average paction price < /a > the "data fight" not only makes it difficult for the government to effectively judge, but also makes some people who like to talk with data disturb market expectations.

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< p > Hu Gang pointed out that the contradictory problem of statistics is not only reflected in the real estate field.

According to statistics, the GDP data reported by the provinces in 2012 were significantly higher than those released by the National Bureau of statistics.

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< p > expert analysis shows that statistical error has the factors of calculating coincidence, but some local governments' subjective behavior can not be ignored.

In June of this year, the National Bureau of statistics disclosed that in 2012, Zhongshan, Guangdong, Heng Ping Town reported 6 billion 200 million yuan of industrial gross value falsely.

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< p > "the authority of statistical data comes from the authenticity of statistics, which is related to the credibility of the government, so it can not be regarded as a trifling matter."

Hu Gang said.

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< p > experts suggest that local governments publish data on housing prices. They should publish data based on urban areas and types of housing, so that the public can truly feel the changes in the real estate market and make housing price data more valuable.

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