英文论文 房价线性回归分析
中国房价问题的英语作文
中国房价问题的英语作文The Issue of Chinese Housing Prices。
In recent years, the issue of Chinese housing prices has attracted widespread attention. The rapid rise in housing prices has not only caused great pressure onpeople's livelihoods but also posed a challenge to the sustainable development of the national economy. This essay will explore the causes of the problem and suggest some possible solutions.There are several reasons for the soaring housing prices in China. Firstly, the rapid urbanization process has resulted in a huge demand for housing in cities. Secondly, the government's policy of encouraging property investment has led to the excessive speculation in the real estate market. Thirdly, the limited supply of land and the high cost of construction have pushed up the cost of housing.To tackle this problem, the government has taken a series of measures. Firstly, it has introduced policies to regulate the real estate market, such as limiting the number of properties that individuals can own and increasing the down payment ratio for mortgages. Secondly, it has increased the supply of land and affordable housing, especially in the suburbs of major cities. Thirdly, it has encouraged the development of the rental market to provide more options for people who cannot afford to buy a house.However, these measures have not solved the problem fundamentally. To achieve a sustainable solution, more efforts are needed. Firstly, the government should strengthen the regulation of the real estate market and crack down on illegal activities such as hoarding and speculation. Secondly, it should introduce a more comprehensive system of property tax to reduce the investment demand for housing. Thirdly, it should promote the construction of public rental housing and provide more affordable housing options for low-income families.In addition, it is necessary to change people'sattitudes towards housing. For many Chinese people, owning a house is not only a basic need but also a symbol ofsocial status. This mentality has contributed to the excessive demand for housing and the irrational behavior in the real estate market. Therefore, it is important to promote the concept of rational consumption and encourage people to choose housing according to their actual needs and financial capacity.In conclusion, the issue of Chinese housing prices is a complex problem that requires a comprehensive solution. The government should take more effective measures to regulate the real estate market and increase the supply of affordable housing. At the same time, people's attitudes towards housing should be changed to promote rational consumption. Only by working together can we achieve a sustainable and healthy development of the real estate market and the national economy.。
广州房价数据英语作文
广州房价数据英语作文The Soaring Housing Prices in Guangzhou。
In recent years, the housing prices in Guangzhou, oneof the major cities in China, have been skyrocketing, causing great concern among the public. This phenomenon has attracted widespread attention and debate, as it has far-reaching effects on people's lives and the overall economy.There are several factors contributing to the soaring housing prices in Guangzhou. Firstly, the rapidurbanization and population growth have led to anincreasing demand for housing. As more people move to the city for better job opportunities and a higher standard of living, the demand for housing has exceeded the supply.This has created a situation where the demand far outweighs the availability, resulting in inflated prices.Secondly, the limited land resources in Guangzhou have also played a significant role in driving up housing prices.With a limited amount of land available for development, developers are forced to bid higher prices for land in order to secure a plot for construction. This cost is then passed on to the buyers, leading to higher housing prices.Additionally, speculation in the real estate market has further fueled the rise in housing prices. Many investors view real estate as a profitable investment, leading them to purchase multiple properties and hold them for future appreciation. This speculative behavior not only drives up prices but also creates an artificial shortage of housing, as some properties are left vacant or used for investment purposes rather than being occupied by residents.The soaring housing prices in Guangzhou have had a profound impact on society. Firstly, it has become increasingly difficult for young people to afford their own homes. With high housing prices and limited availability, many young people are forced to live with their parents or rent apartments, which puts a strain on their financial situation and hinders their ability to start a family.Moreover, the high housing prices have widened the gap between the rich and the poor. The wealthy can afford to buy multiple properties, while the less affluent struggle to find affordable housing. This inequality in access to housing has created social unrest and dissatisfaction among the general public.Furthermore, the soaring housing prices have also affected the overall economy. The high cost of housing has led to a decrease in consumer spending, as people allocate a significant portion of their income towards housing expenses. This, in turn, affects other industries and slows down economic growth.In order to address the issue of soaring housing prices in Guangzhou, several measures can be taken. Firstly, the government should increase the supply of affordable housing by implementing policies that encourage developers to build more low-cost housing units. Additionally, stricter regulations should be imposed on speculative behavior in the real estate market to prevent artificial price inflation.Furthermore, efforts should be made to optimize the allocation of land resources. The government can explore the possibility of land reclamation or redevelopment of underutilized areas to increase the supply of land for housing construction. This would help alleviate the pressure on land prices and subsequently reduce housing prices.In conclusion, the soaring housing prices in Guangzhou are a complex issue with multiple causes and far-reaching consequences. It is crucial for the government to take effective measures to address this issue and ensure that housing remains affordable for the general public. Only by doing so can we create a more equitable and sustainable housing market in Guangzhou.。
英文论文房价线性回归分析
英文论文房价线性回归分析The distribution of educational resources in Beijing city and the housing pricesAbstract：House price is not only affected by national macroeconomic policy, but also affected by the public facilities and the environment around. The equilibrium distribution of education resource result in house price fluctuation. That is not equity and widen the gap between the rich and the poor. We research the factors affecting the house price of Bei jing’ key schools, r esult point that school district house price is 13.8% higher than that of non-school district house having similar conditions. By controlling other public resources, like subway station, park and kindergarten, and itself property, like house age, greening rate, plot ratio, result suggest that school district house in Haidian and Chaoyang have premium of 31%. Meanwhile, they have premium of 23% totally. The result is, different house price reflect inequality of Beijing’s education resources, and most part of high quality resources distribute in central area. These spatial pattern is unreasonable, reducing the utilization of high quality public resources, and resulting in sharp rise of house price in the central area, lastly, expanding wealth gap. So the government should enhance quality of education and improve traffic efficiency. Through these measures, we can reach these goals: the suburbs improving its attractiveness, population density of Beijing decreasing, and more importantly, public resources distributing equality.Keywords: house price; public resource; factors; inequality; population density1.IntroductionReal estate is one of the most important parts of the economy in our country, the price rise is the result of multiple factors. The quality of public resources is an important factor to affect the price of housing, which is especially important in the teaching quality of residential buildings.The education resources has always been an important impact on housing prices, for example, according to the study, in 2004, in the transition process from a poor school in London to a top school, house prices have an increase of 61000 pounds. Early studies such as Oates (1969) on the cost of real estate prices and public schools spending on each students, he found that they have a significant positive correlation, and the negative effect of house property tax on housing prices can be offset if they spend the money to the school, the study shows that residents tend to pay higher prices to better public services. And Fullerton Rosen (1977) believes that the use of each student's spending in public schools as a variable is not very appropriate, because the cost of education,and other factors are not easy and accurate, so they use the average performance of students on behalf of the school quality, the results show that the data and prices are significantly positive correlation. However, it is not very good to solve the problem, in order to better quantification the quality of school teaching, Lucas Figlio (2004) introduced the school quality rating reportthe state government issued as a supplement to the students' average test score, the study shows that whenintroduced school quality rating system, the price will change significantly, but over time, this effect is rapidly decreasing, and only in the first time, it play a greater role. Because of the impact of housing prices is not just the school teaching quality, whichleads to missing variables, the existence of this error will affect the accuracy of the results of the regression.In recent years, the school district housing phenomenon in China has become more and more noticeable from the price point of view, for example Langya Road Primary School, Lixue primary school, Lhasa Road Primary School are three elite schools in Nanjing,, from 2008 to April 2009 , prices rose quickly, the school district housing prices are more than 3000 yuan/m2 than the average price, even in 2009 , housing prices generally fell 8.9%, the school district housing prices in April is still stable. The mechanism by which the residents choose to choose their place of residence to influence the housing price is likely to exist in China. If this mechanism exists, it will reflect the quality of education in a part of the housing price. Regardless of the economic situation is good or bad, the school district housing prices will not follow the economic law. Research shows that, some famous primary school has a significant effect on the school district housing premium.This paper focuses on the impact of key primary school on housing prices, thus revealing the unreasonable distribution of Beijing education resources, and from the perspective of optimizing the educational space pattern, promoting equal opportunities for education and reducing population density of Beijing city, we have discussed the problem of the development of Beijing city. In this paper, we have four aspects of improvement based on the previous research, 1, the data is no longer linear distance for the parameters, but the use of the shortest walking distance to make the analysis more close to reality. 2, this paper studies the Haidian District Chaoyang District, Xicheng District and Dongcheng District, it is different from the common use ofTiananmen as the center of the method to control the degree of prosperity. 3, the selection of primary school in Beijing City is the most famous ones. rather than the Beijing Municipal Education Commission’s approval. 4, the data is second-hand housing transaction data, so it is more reliable.2.data description and research methodsBased on the existing research, this paper uses the data of Beijing city housing transaction, and using the model to control the relevant variables, we want to get a effective regression results, and analyze the effects of education quality, transportation facilities and environmental landscape on the house price.2.1.the division of the school district and the school district houseCompulsory education law of China established the the enrollment policy that Chinese came near to the entrance , namely for every primary school, there is a scribe area, and within the scope of the scribe area, children have an exemption entrance treatment. So, generally speaking, each district has a corresponding primary school. This may has promoted the equality of education opportunity, however, there is a difference in the quality of primary school, relatively speakingsome school’s quality of education is far higher t han ordinary by the government's priority support. Although the government has abolished the system of dividing the primary school in 2000, the social prestige of the primary school has been established, and the status of the primary school is increasing.This paper selects 19 primary schools in Beijing city as a data source, table 1 is recognized as a key primary school list.Table 1 list of key primary schoolsFigure 1 primary distribution map of Beijing CityFigure 1 is a primary distribution map of Beijing city. As shown in Figure 1, the primary school in Beijing is not in uniform distribution, they are concentrated in the comparison of the city of Haidian District, Chaoyang District, Dongcheng District and Xicheng District. In fact, the famous primary schools are mostly distributed in these four areas. Beijing Municipal Education Commission in 1950s has announced the list of 40 municipal primary schools, today, these primary schools are still the best primary school in Beijing. And has been widely recognized by the community, and the vast majority of these primary schools are in the above four districts.2.2.housing dataFrom Figure 1, we can see that the geographical distribution of Beijing city is basically a center to the surrounding Tiananmen, from a link to the rings, are built around the Tiananmen. We collected a total of 19 Beijing municipal key primary school district scribing a total of 120 residential and 112 non cshool district data. Variables include second-hand housing average price, , age (minus the 2015 year built), volume rate, green rate, distance to the center of the city(in KM), distance to the subway station(in KM),, distance to the kindergarten(in KM),distance to shopping malls(in KM), distance to thepark(in KM), . And introduce some dummy variables, such as a small primary school district is 1, otherwise the value is 0.Table 2 is a description of the collected cell data. As shown in Table 2, the average price of second-hand housing is 54387, the mean distance from the downtown is 7.21 km, the averagehouse age is 15.67 years, the average rate of volume is 2.68, average greening rate is 32%, and the mean distance from the nearest subway station is 0.87 km, to the nearest kindergarten flat were1.24 km , average distance to the nearest mall is 0.95 km, the median distance to the nearest park is 1.22 km, the school district room price is 13267yuan/m2 higher than the average, and it is about 27.9%.2.3.the establishment the modelIn this paper, we use the characteristic price method to analyze the house price, the following is the log linear model used in this paper:（1）Among them, X1i means the property of the District, including the age, the volume of residential, greening rate, etc.. X2i represent distance variables, including distance to the subway station, distance to the nursery, distance to the mall and the distance to the park, Pschool is a dummy variable, used to indicate whether the district is the school district room.3.empirical analysis3.1.Beijing city house price analysisTable 3 is the result of the overall regression of the District of all districts in the city. The regression includes square, hospital, park, subway station, high school, elementary school, and so on. The results show that in the 5% confidence level, the distance to the Beijing city center has a negative effect, while the subway station and primary school have a positive effect on prices; in the 10% confidence level, the park is statistically significant, andsquare, middle school and hospital statistics is not significant. It is worth noting that the hospital's coefficient means a negative effect in a certain sense, The reason why the square is not significant, it may be that as a leisure place, such as food Square, shopping plaza , they can not provide a great attraction.In fact, table 3 is the result of the regression results of the overall data of Beijing, it can only be a rough display of the overall situation. In fact, within the second ring even within the third ring, most of the key primary schools located in the vicinity of several cities around the city, the majority of the properties have advantages of hospital resources, one to many subway stations, many secondary schools and more primary schools. when consumers purchase a house,the subway, hospitals, secondary schools, primary schools and other factors will be considered, resulting in a higher real estate prices. In order to further analyze the role of the subway station, hospital, park, middle school in promoting the rise in housing prices, we analyze the data of each link, to observe the information of each link. In fact, the design of the Beijing link can be seen as the Tiananmen as the center, so the link can also be seen as a symbol of the degree of regional prosperity.As shown in Table 4, the results show that near the park the price is 2.4% lower than estate far away from the park, and price is 9.5% higher when it is a school district room,it is 14.4% higher when the house is near a park from line 2 to line 3, and more than 22.4% higher when it is near the subway station, and 13.42% higher than other properties when it is a school district room. And it is 15.6% higher than the other properties when the houseis near the park, 23.4% higher of school district room from line 3 to line 4. And so as line 5 to line6.3.2 four district house price analysisTo more accurate understanding of the various regions, we distinguish between Haidian and Chaoyang as a group, Dongcheng and Xicheng as a group, in fact, because the economicsituation in Dongcheng District and Xicheng District is more similar, the same way ChaoyangDistrict and Haidian District economic conditions. From the map, the center of Beijing is indeed Tiananmen, but the northern part of Beijing's business is more prosperous than the southern part ofBeijing,so Beijing city in this area is not symmetrical distribution.Table 5 four area analysisfour district ModelVariable (ⅰ) (ⅱ) (ⅲ) (ⅳ)Schooldistrict room 0.237777（12857.4）0.2684（14347.056 ）0.229659（12504.3）0.248（13373.63）t-value 9.438（9.799）9.859（10.314 ）8.455( 8.792) 8.916（9.103）Characteristicone No Yes No Yes CharacteristicTable 5 Analysis the school district housing premium of Chaoyang District, Haidian District, Dongcheng District, Xicheng District, in the first column (I) , the distance to the Tiananmen areaare considered as control variables, and then we regress on the school district housing prices, results showed that the school district room are more than 23.8% higher than non school district housing prices , the absolute price is 12867 yuan / square meter (after using 23.8% (12867) )premium; column(II) control the properity of school district room (volume rate, green rate, age and other factors), the parameters of the school district housing dummy variables become slightly higher, reaching 26.8% (14347); third column(III) control distance variables(to the distance properties near the subway, hospital, Park), it showed that the school district housing premium is 23% (12504); fourth column (IV) control attributes and distance properties, the results show that the school district housing premium is 24.8% (13373).From a statistical point of view, in table 5, four sets of regression resultsis significant at the 99% confidence level , the first column (I) is used to control the residential area to Tiananmen distanceto achieve control of other relevant factors, these factors include bustling degreein the residential district etc; the second column (II) controlled the attributesof the area (age, greening rate, volume rate, property costs, etc.), to expect to eliminate error of Fangling, after all poor landscape, small space house more unattractivethan ordinary residential,and the price may be lower, and vice versa. The third column (III) controls the distance properties of cell (away fromthe subway station, park ), because housing landscape, subway housing and even hospital roommay have some role in premium, controlled the distance variable can offset other residential location errors. The fourthcolumn (IV) controlled their properties and the distance properties inthe District, in order to get a better return.Through a comparison of the first and fourth columns, we can know that when estmate the character one and character two two, the school district housing premium change is not too large (23.78%, 24.8%), which means, in the four district, the characteristic's effect is not obvious; through the comparison of second column and fourth column ,when consider the character one asthe control variables, the premium is significantly reduced from 26.84 to 24.8%, which shows thatthe distance has obvious explanatory power of the price premium This validates our common sense, that is, the farther away from the city center, the lower the price.3.3. analysis of Dongcheng&Xicheng district pricesTable 6 local analysis of East and West districtDongcheng,Xicheng ModelVariable (ⅰ) (ⅱ) (ⅲ) (ⅳ)Schooldistrict room 0.13781（8105.3）0.1841(10631.558) 0.1785（10623.317）0.15（10078.483）t-value 4.215( 4.700) 5.564( 6.068 ) 4.976（5.579） 4.1（4.438）Characteristicone no yes no yes Characteristic no no yes yestwoDistance yes no yes yesTable 6 Analysis Housing premiumof the Dongcheng District, Xicheng District , the first column controled the straight line distance from district to the Tiananmen , the results show thatthe schooldistrict housing prices is 13.8% (8105) higher than the non school district room . Second column controled the school district's own properties, the parameters of the dummy variable of schooldistrict room is slightly larger, reached 18.4% (10631). Third column controlled the distance property, the results show that the school district housing premium is 17.9% (10623). Fourth column controled the cell's own attributes and distance properties, the results show that the schooldistrict housing premium is 15% (10078).According to the results of analysis of Dongcheng, Xicheng, the school district housing premiumis relatively small, the reason may be Dongcheng, Xicheng of the city districts in the geographic location is closer to downtown Beijing, the regional education resource is vear rich (not only has alarge number of Beijing municipal key primary school, and coupled with other public resources making the marginal utility of the school district room decreased), thereby reducing the marginal utility of the school district room. On the other hand, that urban residential has basicallyis the most expensive, the price upside is smallerIn contrast to the first column and the fourth column, when make the distance as the control variables,and add the character one and character two, the coefficient of the school district room isslightly changed, but the change is not big, which means that the main factor affecting the priceof the house is distance, through the contrast between thesecond columns and fourth columns,we can see thet the coefficient of the school district room is significantly smaller.3.4 Haidian&Chaoyang house price analysisIn the same way, we analyze the relationship between the price and the factors of the two districtof Haidian&Chaoyang, and carry out the regression analysis.Table 7 partial analysis of Chaoyang Haidian DistrictChaoyangHaidianDistrictVariableSchooldistrict room 0.338（17501.0 ）0.3796474（19384.2 ）0.310001（15942.3）0.34（17566.8）t-value 8.522（8.471）9.525（9.724 ）7.199（7.100）8.095（8.056）Characteristicone No yes No yes Characteristictwo No No yes yes Distance yes No yes yesTable 7 is about Haidian District and Chaoyang District's school district housing premium, the first column (I) controlled the straight distance from the area toTiananmen distance . The results showed that the school district room is more than 33.8% higher than the nonschool district housing prices. the second column (II) controlled the school district room's own property, the school district room virtual variable parameter is slightly larger, reaching 38% (19384); the third column (III) controlled the distance properties, the results show the school district housing premium is31% (15942); the fourth column (IV) controlled the residential own attribute and distance attribute. The results showed that the school district housing premium is 37.1%(19045).Relative to the Dongcheng, Xicheng, and the full four districts, Chaoyang District, Haidian District's school district housing premium is much larger. The reason may be that Chaoyang District and Haidian District deviates more from the center of Beijing on the geographical position, and the public resources is not balanced,like in Haidian District, the area is very huge, it extends from line3 to line5, so the public resources distribution is uneven , the area outside the line4 is a school district, it will undoubtedly give this area a lot of points.4.conclusionIn this paper, the characteristic price model is used to analyze the price and other characteristics of the whole and local housing prices in Beijing, and the influence of the education resources in Beijing city is evaluated quantitatively:School district room can cause a substantial premium, when property buyers purchase a house, they largely considered the school counterparts,and pushy parents spared no expense to purchase school district room to obtain enrollment quota, this is not only against the intention of education fairness, but also lead to the inequality of educational opportunity in a large extent, after all, the rich can obtain high-quality educational resources through this way , while because the poor can't afford to buy these houses, their children can only go to ordinary primary schools , primary schools is public resource, it will lead to unfair when children enrolled to school. after all, the School District Housing hot phenomenon comes from the uneven distribution of educational resources, most of the good primary school resources in Beijing city are concentrated in the range of 7 kilometers in Beijing City, . From the perspective of inequality ofeducation, the state should reform the school district housing policy, because now there only two ways to sent the child to high quality primary school , one is to buy school district room, two is to pay high school choice fees. Undoubtedly, it is unfair to the poor, so Beijing should focus on improving the ordinary primary school's education quality, especially in the suburban area, and add educational facilities, reduce phenomenonthat the allocation of educational resources is too focused .This paper also has the following shortcomings: first, we have the difficulty of data collection, especially the residential properties, such as the degree of residential decoration, construction and some other factors Secondly, some housing related factors data is difficult to obtain such as the per capita income and housing prices are closely linked, Once again, the analysis of the factors that affect the price of the house price can be further deepened, and we can control moreparameters, which need to be compensated in the future research.。
房价调控英文作文
房价调控英文作文英文：As a resident of a city with a high housing price, I have witnessed the government's efforts in housing price regulation and control. In my opinion, housing price regulation is necessary to prevent the formation of a housing bubble and to ensure that the housing market operates in a healthy and stable manner.One of the most effective measures is to restrict the purchase of multiple properties. For example, in somecities in China, individuals are only allowed to purchase one property, and non-local residents are not allowed to purchase property at all. This can prevent speculation and reduce demand, which can help to stabilize housing prices.Another measure is to increase the supply of affordable housing. For example, the government can invest in the construction of public rental housing and provide subsidiesto low-income families to help them purchase homes. Thiscan help to alleviate the problem of housing affordability and reduce the pressure on the housing market.However, it is important to note that housing price regulation is not a one-size-fits-all solution. Different cities and regions have different housing market conditions, and different measures may be needed to achieve the desired results. Therefore, the government should adopt targeted measures based on the local situation.中文：作为一个生活在高房价城市的居民，我目睹了政府在房价调控方面的努力。
房子价格的看法英语作文
房子价格的看法英语作文英文：As a homeowner, I have a strong opinion about the fluctuating prices of houses. In my opinion, the prices of houses are influenced by various factors, such as location, size, condition, and market demand. For example, a house in a prime location with good school districts and convenient transportation will definitely be more expensive than a similar house in a less desirable area. Similarly, a larger and well-maintained house will command a higher price compared to a smaller and rundown one.Furthermore, market demand plays a significant role in determining house prices. In a seller's market, where there are more buyers than available houses, prices tend to go up due to the competition. On the other hand, in a buyer's market, where there are more houses for sale than buyers, prices may decrease as sellers compete for a limited pool of buyers.In addition, economic factors such as interest ratesand employment levels also impact house prices. Wheninterest rates are low and employment is high, more people are able to afford mortgages, leading to increased demand and higher prices. Conversely, when interest rates are high and unemployment is on the rise, demand for houses may decrease, causing prices to fall.中文：作为一个房主，我对房子价格的波动有着自己的看法。
调控房价的英文作文
调控房价的英文作文英文：As a resident living in a city where the housing prices are skyrocketing, I am very concerned about the issue of regulating housing prices. In my opinion, there are several ways to regulate housing prices.Firstly, the government can intervene in the market by implementing policies such as increasing the supply of affordable housing or imposing taxes on property speculation. For example, in Singapore, the government has implemented a series of measures to regulate housing prices, including building more public housing and imposing taxeson non-resident property buyers.Secondly, the government can also regulate the demandfor housing by implementing policies such as restrictingthe number of properties that a person can own or limiting the amount of mortgage loans that banks can provide. Forinstance, in China, the government has implemented a policy that limits the number of properties that a person can own in certain cities.Thirdly, the government can encourage the development of other cities or regions to reduce the pressure on housing prices in major cities. For example, the Chinese government has launched a campaign to promote the development of the western region of the country, which has led to the rise of cities such as Chengdu and Chongqing.中文：作为一个生活在房价飞涨的城市的居民，我非常关注调控房价的问题。
中国房价高的英语作文初中
中国房价高的英语作文初中China's housing prices are skyrocketing, causing widespread concern among the public. It seems like every time we turn around, the prices have gone up again. It's a never-ending cycle that is leaving many people feeling frustrated and hopeless.The first thing that comes to mind when I think about the high housing prices in China is the lack of affordable options. It's becoming increasingly difficult for young people to find a place to live that they can actually afford. The dream of owning a home is slipping further and further away for many.Another factor contributing to the high housing prices is the rapid urbanization happening in China. As more and more people move to the cities in search of better job opportunities, the demand for housing increases. This high demand drives up prices, making it even more unaffordable for the average person.Speculation also plays a role in the soaring housing prices. Many people see real estate as a lucrative investment and buy multiple properties, driving up prices even further. This leaves those who are just looking for a place to live at a disadvantage, as they are forced to compete with investors who are only interested in making a profit.The government's efforts to cool down the housing market have had limited success. Measures such as increasing down payment requirements and implementing property taxes have been put in place, but they have not been enough to significantly lower prices. It seems like the government is fighting an uphill battle against market forces that are driving prices higher and higher.The high housing prices in China have far-reaching consequences. It not only affects individuals and families who are struggling to find affordable housing, but it also has an impact on the overall economy. When people are spending a large portion of their income on housing, theyhave less money to spend on other goods and services, which can slow down economic growth.In conclusion, the high housing prices in China are a complex issue with no easy solution. It is a problem that affects individuals, families, and the economy as a whole. The lack of affordable options, rapid urbanization, speculation, and limited government measures all contribute to the problem. It's a situation that calls for innovative solutions and a comprehensive approach to ensure that everyone has access to affordable housing.。
谈谈现在房价的趋势英文
谈谈现在房价的趋势英文Currently, the trend of housing prices is largely influenced by various factors. On one hand, in many urban areas, housing prices have been gradually increasing due to the high demand for housing and limited supply. This can be attributed to population growth, urbanization, and a strong economy that drives people to invest in real estate. Additionally, low-interest rates and easy access to mortgages have also fueled the demand.On the other hand, certain factors can result in a decrease or stabilization of housing prices. For example, an economic downturn, job losses, or even a global crisis can lead to a reduced demand for housing, causing prices to either drop or remain stagnant. Furthermore, government policies and regulations, such as stricter lending standards or the implementation of property cooling measures, can influence the housing market dynamics.It is important to note that housing price trends can vary significantly between different regions and countries. While some areas may experience rapid price increases, others may face a decline or exhibit a more stable market. Additionally, external factors like geopolitical events or natural disasters can also impact housing prices.Overall, the trend of housing prices at any given time is a complex and dynamic phenomenon driven by numerous factors. It is crucial for individuals and investors to closely monitor market conditions and factors affecting housing prices to make informed decisions.。
房价调控英文作文
房价调控英文作文Title: Regulation of Housing Prices: BalancingStability and Accessibility。
In recent years, the issue of housing price regulation has garnered significant attention globally, reflecting the delicate balance between maintaining stability in the housing market and ensuring accessibility for all socioeconomic groups. This essay explores variousstrategies employed in housing price regulation, their effectiveness, and the challenges they present.Firstly, it's essential to acknowledge the diverse approaches countries adopt in regulating housing prices. Some rely on market mechanisms with minimal intervention, while others implement strict government control. Market-driven approaches often prioritize supply-side measures such as incentivizing construction and easing regulations to increase housing stock. Conversely, government intervention may include measures like price ceilings,property taxes, and subsidies to moderate prices and promote affordability.One commonly employed strategy is the imposition of price ceilings or caps on housing units. While such measures aim to prevent excessive price hikes, they can also stifle market dynamics and discourage investment in housing development. Moreover, enforcing price controls effectively requires robust monitoring and enforcement mechanisms to prevent black market activities and ensure compliance.Another approach involves property taxes, which can be adjusted to discourage speculative investment and promote housing affordability. By imposing higher taxes on vacant or underutilized properties, governments aim to incentivize property owners to either occupy or sell their assets, thereby increasing housing supply and reducing prices. However, implementing property taxes requires careful consideration of their impact on different segments of society, particularly low-income homeowners who may struggle to afford increased tax burdens.Furthermore, governments often utilize subsidies and incentives to facilitate access to housing for marginalized groups. These subsidies may take the form of down payment assistance, rental subsidies, or subsidized mortgage rates, aiming to bridge the affordability gap and promoteinclusive homeownership. While such measures can be effective in aiding disadvantaged populations, they also pose fiscal challenges and risk distorting market dynamics if not carefully targeted.In addition to these strategies, regulatory frameworks play a crucial role in shaping housing market dynamics. Policies related to land use, zoning regulations, and urban planning significantly influence housing supply, location preferences, and ultimately, prices. Flexible zoning regulations that allow for densification and mixed-use development can foster more efficient land utilization and mitigate price pressures in high-demand areas. Conversely, overly restrictive zoning policies may exacerbate supply shortages and inflate housing costs.Moreover, addressing housing affordability requires a holistic approach that considers the interconnectedness of housing with other socioeconomic factors. Investments in infrastructure, transportation, and social services can enhance livability and accessibility, thereby reducing housing demand in highly congested areas and dispersing population pressure across regions. Similarly, initiatives to promote economic development and income equality can alleviate housing affordability challenges by enhancing purchasing power and reducing income disparities.However, despite the diversity of regulatory measures and policy interventions, challenges persist in achieving sustainable housing affordability. Rapid urbanization, population growth, and income inequality continue to strain housing markets, particularly in metropolitan areas. Moreover, the global interconnectedness of financial markets and capital flows can amplify housing market volatility and undermine local regulatory efforts.In conclusion, regulating housing prices necessitates a multifaceted approach that balances market mechanisms withtargeted interventions to promote stability and accessibility. While various strategies exist, their effectiveness depends on contextual factors such as economic conditions, demographic trends, and institutional capacity. Moving forward, policymakers must continue to innovate and adapt regulatory frameworks to address evolving housing challenges and ensure equitable access to housing for all.。
有关房价问题的作文英语
有关房价问题的作文英语The Issue of Housing Prices: A Comprehensive Analysis。
In recent years, the issue of housing prices has become a hot topic globally, with debates raging on the causes, consequences, and potential solutions. From the bustling streets of metropolitan cities to the tranquil suburbs, the soaring cost of housing has affected millions ofindividuals and families worldwide. In this essay, we will delve into the various facets of the housing price phenomenon, exploring its origins, impacts, and possible remedies.Introduction:The surge in housing prices has been a multifaceted phenomenon influenced by a myriad of factors. Economic growth, population density, urbanization, government policies, and speculative investment have all playedpivotal roles in driving housing prices to unprecedentedlevels. While some argue that high housing prices are a testament to economic prosperity and development, others contend that they are symptomatic of systemic inequalities and financial imbalances.Causes of Rising Housing Prices:1. Supply and Demand Imbalance: One of the primary drivers of soaring housing prices is the persistent gap between supply and demand. Rapid population growth, coupled with urbanization trends, has led to an increased demandfor housing, outpacing the rate at which new units are being constructed. This supply-demand imbalance exerts upward pressure on prices, especially in densely populated urban areas.2. Speculative Investment: The housing market has increasingly become a playground for speculative investors seeking lucrative returns. Speculation drives up prices artificially as investors purchase properties with the sole intent of selling them at higher prices in the future, creating bubbles that eventually burst, leading to economicinstability.3. Low Interest Rates: In many countries, central banks have adopted accommodative monetary policies characterized by low interest rates to stimulate economic growthfollowing the global financial crisis. While these policies have been effective in bolstering economic recovery, they have also fueled demand for housing by making mortgages more affordable, thereby contributing to rising prices.4. Land Use Regulations: Stringent land use regulations and zoning laws in urban areas have constrained the supply of housing, exacerbating affordability challenges. Restrictions on land development, height limits, and zoning ordinances hinder the construction of new housing units, driving prices upward as demand continues to outstrip supply.5. Income Inequality: The widening gap between the wealthy elite and the middle and lower-income segments of society has further exacerbated the housing affordability crisis. High-income earners have greater purchasing power,enabling them to outbid other prospective buyers and drive up prices, leaving many low and middle-income individuals priced out of the market.Impacts of High Housing Prices:1. Housing Affordability Crisis: Skyrocketing housing prices have rendered homeownership increasingly unattainable for vast segments of the population, particularly young adults and low-income families. The dream of owning a home has become elusive, forcing many to contend with exorbitant rental costs or endure long commutes from more affordable areas.2. Wealth Inequality: The wealth gap between homeowners and non-homeowners has widened significantly as housing prices have surged. Homeownership has traditionally been a primary vehicle for wealth accumulation, and those unable to afford homes are effectively excluded from this avenue, perpetuating socioeconomic disparities.3. Economic Disparities: High housing prices can hinderlabor mobility and exacerbate regional economic disparities. Individuals may be reluctant to relocate for better job opportunities due to the prohibitive cost of housing in destination cities, leading to a mismatch between labor supply and demand and impeding overall economic growth.4. Social Cohesion: Excessive housing costs can strain social cohesion by exacerbating tensions between different socioeconomic groups. Rising resentment among renters towards homeowners and policymakers may breed social unrest, undermining community cohesion and trust in institutions.Potential Solutions:1. Increase Housing Supply: Governments must streamline regulatory processes and incentivize the construction of affordable housing to alleviate supply shortages. Implementing land use reforms, reducing permitting delays, and providing subsidies for affordable housing developments are crucial steps in expanding housing supply.2. Regulate Speculative Investment: Introducingmeasures to curb speculative investment in the housing market, such as imposing taxes on vacant properties and implementing stricter lending standards for investors, can help mitigate price volatility and prevent housing bubbles from forming.3. Promote Equitable Development: Policymakers should prioritize inclusive urban development strategies aimed at promoting equitable access to housing. This includes investing in affordable housing initiatives, implementing rent control measures, and fostering mixed-income communities to ensure that housing remains accessible to individuals of all income levels.4. Address Income Inequality: Tackling incomeinequality through progressive tax reforms, increasing minimum wages, and expanding social safety nets can help address the root causes of housing affordability challenges by enhancing the purchasing power of low and middle-income households.5. Foster Regional Planning: Adopting holistic regionalplanning approaches that take into account housing, transportation, and economic development can help alleviate housing pressures in high-demand areas. Encouraging decentralized economic growth and investing in infrastructure projects that improve connectivity between urban centers and peripheral regions can distribute housing demand more evenly.Conclusion:In conclusion, the issue of housing prices is a complex and multifaceted challenge with far-reaching implications for society, economy, and urban development. Addressingthis issue requires a concerted effort from policymakers, stakeholders, and communities to implement comprehensive strategies that tackle the root causes of housing affordability crises and promote inclusive and sustainable development. Only through collaborative and innovative approaches can we ensure that housing remains accessible and affordable for all segments of society, thereby fostering social equity, economic prosperity, and vibrant communities.。
房价飞涨英文作文
房价飞涨英文作文英文：The skyrocketing housing prices have been a hot topic of discussion in recent years. It seems like every time I turn on the news or open a newspaper, there's another story about how the cost of buying a home is becomingincreasingly unattainable for many people. It's a concerning trend that is impacting individuals and families across the country.One of the main reasons for the surge in housing prices is the imbalance between supply and demand. As the population continues to grow, the demand for housing also increases. However, the supply of available homes is not keeping pace with this demand, leading to fierce competition and driving up prices. For example, in my own city, I've seen small, modest homes selling for exorbitant prices simply because there are so few options on the market.Another factor contributing to the rise in housing prices is the influence of foreign investment. In many urban areas, wealthy investors from other countries are purchasing properties as a means of diversifying their portfolios. While this may be good for the economy in some ways, it also drives up prices and makes it even more difficult for local residents to afford a home. It's frustrating to see beautiful properties sitting empty, owned by individuals who have no intention of actually living in them.The impact of these soaring housing prices is far-reaching. For young people just starting out, the dream of owning a home is becoming increasingly out of reach. Even for those who are able to purchase a property, the financial strain can be immense, leading to stress and anxiety about the future. It's a harsh reality that many people are facing, and it's a problem that needs to be addressed.中文：房价飞涨成为近年来的热门话题。
英语作文房价
英语作文房价In recent years, the soaring housing prices have become atopic of heated discussion worldwide. The cost of owning a home has risen to unprecedented levels, affecting various aspects of society. This essay will explore the impact ofhigh housing prices on different social groups, the economy, and the overall quality of life.Firstly, the affordability of housing has become asignificant concern for many families. High housing prices have led to an increase in the number of people struggling to find a place to call home. This has resulted in a rise in rental costs as well, making it difficult for individuals to save for a down payment on a house. The financial strain can lead to stress and a decrease in the overall happiness indexof the affected population.Secondly, the housing market has a direct influence on the economy. High housing prices can lead to economic bubbles, which, if burst, can have catastrophic effects on the economy. The real estate sector is a critical component of the economy, and any instability can lead to job losses and a decrease in consumer spending.Moreover, the cost of housing can also affect the demographic trends of a region. High prices in urban areas can lead to an exodus of young professionals and families to more affordable suburbs or smaller towns. This can lead to a decrease in thediversity and vibrancy of city centers, as well as a strain on infrastructure in less populated areas.Furthermore, the impact of housing prices extends to education and healthcare. Families may have to compromise on the quality of education for their children or the healthcare they can afford due to the financial burden of housing. This can lead to a cycle of inequality, where opportunities for social and economic mobility are limited.Lastly, the psychological impact of high housing prices should not be overlooked. The inability to secure a home can lead to feelings of insecurity and instability. This can affect mental health and lead to a decrease in life satisfaction.In conclusion, the issue of housing prices is multifaceted and has far-reaching implications for individuals and society as a whole. It is essential for policymakers to address this issue through sustainable housing policies, economic regulation, and social support systems to ensure that housing remains accessible and affordable for all.。
房价英语作文
房价英语作文In recent years, the topic of housing prices has become a focal point of discussion in many countries, especially in rapidly developing economies. The soaring prices have had a profound impact on the lives of ordinary citizens, and it is a subject that is often debated in various social circles.The Impact of Housing Prices on SocietyThe rise in housing prices has led to a significant increase in the cost of living. For many, owning a home has become a distant dream, as they struggle to afford the high prices. This has resulted in a growing number of people opting to rent instead of buying, which has further driven up rental prices.Government InterventionIn response to the escalating housing costs, governments have implemented various measures to control the market. These include imposing taxes on property transactions, regulating land supply, and providing subsidies for first-time homebuyers. However, the effectiveness of these measures is often debated, as some argue that they may not be enough to address the root causes of the problem.The Role of SpeculationSpeculation in the housing market has also been identified as a contributing factor to the rise in prices. Investors buying properties with the intention of selling them at a higherprice have created an artificial demand, which has driven up the market value of properties.The Future OutlookLooking ahead, the future of housing prices is uncertain. Some experts predict that the market will eventuallystabilize, while others foresee a potential bubble that could burst, leading to a significant drop in prices. Regardless of the outcome, it is clear that the issue of housingaffordability will continue to be a pressing concern for many.ConclusionThe issue of housing prices is complex and multifaceted, involving economic, social, and political factors. It is a topic that requires careful consideration and thoughtfulpolicy-making to ensure that everyone has access toaffordable housing. As the world continues to evolve, it is crucial that we find sustainable solutions to this pressing issue.。
英文作文解决房价问题
英文作文解决房价问题英文：As a young adult who is just starting out in the real world, one of the biggest issues I face is the skyrocketing cost of housing. It seems like no matter where I look, the prices just keep going up and up. However, I believe that there are some solutions to this problem that could make housing more affordable for people like me.First of all, I think that the government needs to take a more active role in regulating the housing market. This could mean implementing rent control policies, providing incentives for developers to build more affordable housing, or even creating a public housing system like they have in some other countries. By doing this, the government could help to ensure that people of all income levels have access to safe and affordable housing.Another solution could be to encourage more people tolive in smaller, more compact homes. This could be done through tax incentives for people who choose to downsize, or by promoting the use of tiny homes and other alternative housing options. By living in smaller spaces, people could save money on housing costs while also reducing their carbon footprint and living a more sustainable lifestyle.Finally, I believe that we need to change the way we think about homeownership. For too long, owning a home has been seen as the ultimate goal for anyone who wants to be financially successful. However, this mindset is not sustainable in a world where housing prices are constantly rising. Instead, we need to start valuing other forms of housing, such as renting or co-living arrangements, that can provide people with the stability and security they need without the high costs of homeownership.中文：作为一个刚刚踏入社会的年轻人，我面临的最大问题之一就是房价飞涨。
关于房价的英文作文
关于房价的英文作文The Complexity of Housing Prices: A Global Perspective.Housing prices are a complex and multifaceted topicthat has garnered significant attention from economists, policymakers, and the general public alike. They are a critical indicator of economic health and a barometer for social well-being, reflecting a range of factors including supply and demand, economic growth, interest rates, and government policies. The dynamics of housing prices are particularly intriguing given their significant impact on individual wealth, household budgets, and the broader economy.At their core, housing prices are determined by the intersection of supply and demand. Supply is influenced by the availability of land, construction costs, zoning regulations, and the rate of new development. Demand, on the other hand, is shaped by factors such as population growth, income levels, employment opportunities, andconsumer sentiment. When demand outpaces supply, prices rise, and vice versa.However, the determination of housing prices is farfrom straightforward. Multiple factors beyond the basic supply-demand relationship influence their trajectory. Economic growth, for instance, is a key driver. When the economy is booming, people have more disposable income, which often leads to increased demand for housing, pushing prices up. Conversely, economic downturns can lead to a decrease in demand and a subsequent softening of prices.Interest rates also play a crucial role. Lower interest rates make it cheaper to borrow money for a mortgage, thus increasing affordability and demand for housing. Conversely, higher interest rates make borrowing more expensive, which can dampen demand and slow the rate of price growth.Government policies and regulations also significantly impact housing prices. For instance, zoning laws canrestrict the supply of housing in certain areas, leading to higher prices. Tax policies, such as those that favorhomeownership or provide incentives for first-time buyers, can also influence demand and prices. Additionally, regulations on real estate transactions and market regulations can affect the liquidity and volatility of housing prices.Global economic trends and events can also have a profound impact on housing prices. For instance, globalization has led to an increase in cross-border investment in real estate, which can affect prices in both the domestic and international markets. Similarly, macroeconomic events like the COVID-19 pandemic have had a significant impact on housing prices, as lockdowns and economic shutdowns disrupted supply chains, slowed construction, and altered consumer demand.The social implications of housing prices are also profound. Rising housing prices can lead to increasedwealth inequality as those who own property benefit from price appreciation while those who don't may struggle to afford a home. This can have a ripple effect on communities, leading to gentrification, displacement, and socialtensions.In conclusion, housing prices are determined by a complex web of factors that span the economic, social, and political spectrum. Understanding these factors and their interconnectedness is crucial for policymakers, investors, and individuals alike as they navigate the ever-changing landscape of the real estate market. As the global economy continues to evolve and new challenges arise, it is important to stay informed and adaptable to ensure that housing remains accessible, affordable, and sustainable for all.。
计量经济学论文(eviews分析) 房价的计量经济分析
房价的计量经济分析引言：近改革开放20多年来，从来没有哪一个行业像房地产业这样盛产亿万富翁，各种富豪排行榜上，房地产富豪连年占据半壁江山；“中国十大暴利行业”中，房地产业每年都是“第一名”。
是什么造就了这样的状况。
房地产的问题，在开发商，政府，购房者三者来看，就是一场完完全全的博弈。
而这场博弈的焦点则是房价问题。
如果说开发商与政府之间的博弈是围绕“土地”这个关键词，那么整个房地产市场则在价格上开展了新一轮的对峙。
先是开发商与购房者在房价涨跌上僵持不下；再有开发商与政府之间的土地成本论；最后则是关于房地产是否归为暴利行业的争执，“价格”成了市场关注的焦点。
而对于房价的构成因素，至今仍然是不透明的。
公布房价成本成为另政府极为头疼的一件事。
房价成本是一个非常复杂的集合体，并且项目间差异性较大，同时还有软资产、品牌等组成部分，特别是现在的商品房，追求品质、功能完善以及个性化成本构成越来越难衡量。
写作目的：通过对一系列影响房价的基本因素的分析，了解对其主要因素和次要因素。
并对这些因素进行统计推断和经济意义上的检验。
选择拟和效果最好的最为结论。
在一定层面上分析房地产如此暴利的因素。
当然笔者的能力有限，并不能全面的分析这一问题。
仅仅就几个因素进行分析。
写作方法：理论分析及计量分析方法，将会用到Eviews软件进行帮助分析。
关键词：房价成本计量假设检验最小二乘法拟合优度现在我们以2003年的数据，选取30个省市的数据为例进行分析。
在Eviews软件中选择建立截面数据。
现在我们以2003年的数据，选取31个省市的数据为例进行分析。
令Y=各地区建筑业总产值。
（万元）X1=各地区房屋竣工面积。
（万平方米）X2=各地区建筑业企业从业人员。
（人）X3=各地区建筑业劳动生产率。
（元/人）X4=各地区人均住宅面积。
（平方米）X5=各地区人均可支配收入。
（元）数据如下：Y X1 X3 X2 X4 X5 12698521 4254.800 569767.0 129961.0 24.77140 13882.62 5208402. 1465.800 238957.0 147063.0 23.09570 10312.91 7799313. 4748.300 989317.0 70048.00 23.16710 7239.060 5401279. 1313.300 591276.0 89151.00 22.99680 7005.030 2576575. 1450.700 265953.0 61074.00 20.05310 7012.900 10170794 3957.100 966790.0 82496.00 20.23510 7240.580 3469281. 1626.800 303837.0 77486.00 20.70590 7005.170 4401878. 2181.300 441518.0 68033.00 20.49200 6678.900 11958034 3609.200 505185.0 153910.0 29.34530 14867.49 27949354 17730.00 2727006. 100569.0 24.43530 9262.460 31272779 16183.90 2429352. 127430.0 31.02330 13179.53 6227073. 4017.600 910691.0 66407.00 20.75480 6778.0305493441. 2952.100 553611.0 108288.0 30.29870 9999.5403593356. 2750.900 574705.0 70826.00 22.61980 6901.42014813618 9139.800 2072530. 60728.00 24.48080 8399.9106345217. 3433.600 932901.0 66056.00 20.20090 6926.1208729958. 4840.800 1048763. 81761.00 22.90280 7321.9808188402. 4969.700 1119106. 74553.00 24.42580 7674.20015163242 8105.000 1492820. 101932.0 24.93280 12380.432818466. 1721.600 353700.0 77472.00 24.17320 7785.040394053.0 121.5000 61210.00 55361.00 23.43200 7259.2505862095. 4939.600 817997.0 69432.00 25.72440 8093.67012253374 8784.600 2070534. 59748.00 26.35850 7041.8702122907. 980.3000 293310.0 72152.00 18.19430 6569.2303967957. 2248.700 522470.0 69238.00 24.92940 7643.570293427.0 121.3000 36593.00 73205.00 19.92990 8765.4504404362. 1580.000 410311.0 93212.00 21.75050 6806.3502236860. 1327.200 449409.0 46857.00 21.11380 6657.240747325.0 242.9000 101501.0 61046.00 19.10550 6745.3201080546. 578.7000 88225.00 61459.00 22.25500 6530.4803196774. 1450.800 203375.0 95835.00 20.78110 7173.540做多重共线性检验：引入的变量太多，可能存在变量间的共线性，影响方程的估计。
美国对房价的看法英语作文
In the United States,the perception of housing prices is multifaceted,reflecting a complex interplay of economic,social,and cultural factors.Here are some key aspects of how Americans view housing prices:1.Market Dynamics:Americans understand that housing prices are influenced by supply and demand.In areas with high demand and limited supply,such as major cities or desirable neighborhoods,prices tend to be higher.2.Affordability Concerns:There is a growing concern about housing affordability, especially for firsttime homebuyers and those in lowerincome brackets.The rise in housing prices in many regions has outpaced wage growth,making it difficult for some to enter the housing market.3.Investment Perspective:For many,real estate is seen as a stable investment.Despite fluctuations in the market,property is often viewed as a longterm asset that can appreciate in value over time.4.Housing as a Necessity:Americans recognize housing as a fundamental need.High housing prices can exacerbate issues related to homelessness and poverty,leading to calls for more affordable housing options.5.Impact of Government Policies:Government policies,such as tax incentives for homebuyers,zoning regulations,and housing subsidies,are seen as having a significant impact on housing prices.Some argue that certain policies can either alleviate or exacerbate the issue of affordability.6.Generational Differences:There are generational differences in how housing prices are perceived.Older generations who bought homes when prices were lower may have a different perspective than younger generations facing todays higher prices.7.Regional Variations:Housing prices vary greatly across different regions of the United States.Coastal cities like New York and San Francisco are known for their high prices, while the Midwest and some parts of the South offer more affordable housing options.8.Cultural Attitudes:There is a cultural expectation in the U.S.that owning a home is a sign of stability and success.This can drive demand and contribute to higher prices in certain areas.9.Economic Indicators:Housing prices are often seen as an indicator of economic health.A robust housing market is generally viewed as a positive sign for the overall economy.10.Technological Disruption:The rise of technology in real estate,such as online platforms for buying and selling homes,has changed how Americans perceive the housing market.Some believe that technology can make the process more transparent and efficient,potentially impacting prices.In conclusion,the American view on housing prices is nuanced and influenced by a variety of factors.While there is a general understanding of the economic principles at play,there is also a recognition of the social implications and the need for policy interventions to address affordability issues.。
关于房价走势英语作文
关于房价走势英语作文Title: The Trend of Real Estate Prices: A Comprehensive Analysis。
Introduction。
In recent years, the trend of real estate prices has been a topic of great interest and concern globally. This essay aims to delve into the factors influencing real estate prices and analyze the trends observed in various regions.Factors Influencing Real Estate Prices。
1. Economic Factors: Economic growth, employment rates, and inflation significantly impact real estate prices. Strong economies often witness an increase in real estate demand, leading to higher prices.2. Demographics: Population growth, migration patterns,and demographic shifts influence the demand for housing. Cities experiencing population growth tend to have higher real estate prices due to increased demand.3. Government Policies: Government regulations, tax policies, and subsidies can directly impact real estate prices. Policies promoting homeownership or restricting construction can affect supply and demand dynamics.4. Interest Rates: Fluctuations in interest rates influence mortgage affordability and, consequently, real estate demand. Lower interest rates stimulate demand, leading to potential price increases.5. Supply and Demand Dynamics: The balance between housing supply and demand plays a crucial role in determining prices. Limited housing supply in the face of rising demand often results in price appreciation.Trends in Real Estate Prices。
英文版线性回归案例（UKFreshmanGPAFall2012）
英文版线性回归案例（UKFreshmanGPAFall2012）Does High School Really Matter?Derek CaldwellThomas CrushTaylor StewartTyler StoneDevan TrenkampEco 391 Section 006December 4, 2012As John Doe walked into Whitehall on the first day of classes at the University of Kentucky, his mom was at home thanking God that he even got accepted to attend despite his mediocre High School performance. He did, however, attend a private high school that markets itself as college preparatory despite the fact that John scored poorly on the ACT. Johns mother prayed to God that she could have an indication of how her son would perform academically his freshman year in college. God, in turn, told us to study the effect of ones high school performance and high school factors on their respective cumulative GPA their first year in college.We are interested in this because we want to study whether a college freshman’s academic success is affected by their highschool’s ch a racteristics and past personal academic history. Coming into college, various students claim that their high school prepared them for college better or worse than other types of high schools, whether it be because it was co-ed or because it was a private rather than public. Additionally, some incoming students claim that due to past academic success in high school, they’ll do just as well their first year in college. We want to put these claims to the test by seeing if they are supported by the data.The dependent variable is freshman year cumulative GPA, and this will be a product of the independent variables. The first independent variable is whether you are from a co-ed school, or single gender, which will be one dummy variable. We will have two dummy variables of whether you were home-schooled, went to a public school, or went to a private school. The next dummy variable will bewhether you went to school in the state of Kentucky or you were out of state. The quantitative variable is what the un-weighted GPA of the student was in high school.For school type- we will have public school as the base case. Home schooling will be a dummy variable and it will equal one if you are and zero otherwise. For private co-ed it will equal one if the school was both private and co-ed, and zero otherwise. Likewise, private single sex will equal one if the school was private and the high school was not co-ed, and zero otherwise. For the dummy variable pertaining to the location of the high school, in-state schools are the base case, and if you are out of Kentucky it will equal one and zero otherwise. We have two dummy variables pertaining to race. The dummy variable for the African American category will equal one if the student is African American, zerootherwise. The dummy variable for Asian Americans will also equal one if the student is an Asian American, zero otherwise. Caucasian is the base case. The quantitative variable is the High School unweighted GPA. The GPA will be rounded to the nearest tenth of a point, with valid answers ranging between 0 and 4 inclusive.We surveyed classes within the University of Kentucky. We surveyed a variety of different majors and also students that are currently sophomores. Any surveys from non-sophomores were thrown out. As it is cu rrently the fall semester, a sophomore’s college cumulative GPA is their cumulative GPA for their freshman year. This fact allowed us to more closely tie one’s high school experience with their success as a freshman. We also used different tools suchas online resources to create our survey so students were able to fill them out at their convenience. We used excel to run a regression analysis to gather and interpret our results.Regression Equation:UK FRESHMAN GPA = β0+ β1FEMALE + β2AFRICAN AMERICAN + β3ASAIN + β4HOMESCHOOL + β5PRIVATE COED+ β6PRIVATE SINGLE SEX + β7 HIGH SCHOOL GPA +β8 OUT OF STATE + εExplanatory VariablesFEMALE (+) - We expect this variable’s coefficient to be positive because we believe that females are less likely to get heavily involved in activities that could dampen academic performance, such as drugs and alcohol. The base case for this dummy variable is Male.AFRICAN AMERICAN (-) - We believe this variable’s coefficient could be negative due t o the effects of racism onUK’s camp us. This racism may lead an African American student to feel lonelier. The base case for this dummy variable is Caucasian.ASIAN AMERICANS (+) - We believe this variable’s coefficient could be positive because some Asi an American students that we’ve inter acted with feel as though their families hold them to a higher academic standard than average. The base case for this dummy variable is Caucasian.HOMESCHOOL (?) - This variable’s coefficient could be positive or negat ive, because the individualistic attention received in high school might have prepared them better for college academic situations, but the lack of attending a more “social” high school could possibly under-prepare them socially for college. The base case for this dummy variable is Public School.PRIVATE COED (+) - We expect this variable’s coefficient to be positive as you pay more for a “better” education. The base case for this dummy variable is Public School.PRIVATE SINGLE SEX (+) - We expect this variable’s coefficient to be positive as you pay more for a “better” education and there are no distractions of the opposite sex. The base case for this dummy variable is Public School.HIGH SCHOOL GPA (+) - We expect this variable’s coefficient to be pos itive because the higher the high school GPA the more likely you will retain a higher GPA through your freshman year of collegeOUT OF STATE- (?) - We are not certain what sort of effect this variable will have on a student’s freshman year GPA. The base c ase for this dummy variable is In State.MAIN- HOMESCHOOL- This is a main variable because the individualistic teaching has a big effect on the student. We are testing to see if that environment (more individualistic & less social than public school) has an effect on college freshman GPA.- PRIVATE COED- This is a main variable because we want to study if the smaller classroom teaching has a larger effect on the student.- PRIVATE SINGLE SEX- This is a main variable because we want to study if the smaller classroom and only having one gender has a larger effect on the student.- HIGH SCHOOL GPA- We believe that a higher high school GPA will lead to a higher the college GPA.MARGINAL- FEMALE- We predict gender will not have much of an impact on the freshman year GPA. We have it as marginal because we want to see if there is any correlation at all.- AFRICAN AMERICAN- This is marginal because we believe it will not have as large of an effect as other variables we have in this study.- ASIAN AMERICAN- This is marginal because we believe it will not have as large of an effect as other variables we have in this study.- OUT OF STATE- We do not believe this will strongly affect GPA, yet we want to test it to see if there is any correlation at all.SPECIAL INTEREST- PRIVATE COED VS. PRIVATE SINGLE SEX- We are very interested in this because we believe that private single sex schools claim that having no distractions of the opposite sex help with focus in the classroom.DATA COLLECTION AND ANALYSISAfter collecting our data, we built descriptive statistics and a correlation matrix in excel. In total, we surveyed 217 sophomore students at the University of Kentucky. We collected most of the surveys from classes in the Gatton College, but we surveyed a few students from the 10 other colleges as well. As expected, females accounted for 53% of our sample (115 people), and 47% (103 people) were males. The standard deviation of being female was the highest being .5 from the mean, which was .52. Fifty-eight students that did not go to high school in the state of Kentucky took our survey.Caucasians made up 95% of students who took the survey, African Americans made up only 3% (eight people), and Asians accounted for 2%. The minority representation was not as significant as we had hoped. Out of everyonethat we surveyed, we received the lowest high school GPA at a 2.4 and the maximum being a 4.0, with a mean of 3.7 and a lower-than-expected standard deviation of .35. The mean high school GPA was higher than we anticipated. For the dependent variable, freshman year college GPA, the lowest recorded GPA was 1.3 and the maximum was 4.0 (for a range of 2.7) and a standarddeviation .45.One disappointing revelation discovered in the descriptive statistics is the lack of home-schooled students. We attempted to find home-schooled kids in several Christian student organizations on campus (running on the assumption that some Christian families would be more likely to home-school their children than secular families). To our dismay, the survey was only taken by 1 home-schooled student, and thus we will have to throw the variable out and omit the observation. Nearly twelvepercent of respondents attended a private single-gender high school, and another 12% attended a private co-ed high school.After reviewing our correlation matrix, we were pleased with the results (in terms of avoiding multicollinear relationships between the independent variables). There exists no correlation coefficient over 0.50, which indicates that there is no multicollinear relationship (at least on the surface). It is interesting, however, that the highest correlation coefficient of 0.40 was between college GPA and high school GPA, and the second highest correlation was -0.31 between high school GPA and being an out-of-state student. The lowest correlation coefficient was home school and Asian, which was 0.01. On average everything was correlated by less than 0.10.Regression ResultsRegression results were ran in this order:1. All variables2. All variables with the exception of Asians3. All variables, with High School GPA Squared Regression Results Number 1:These regression results yield an R Square of .24, whichexplains 24% of the variation in UK freshman GPA. We also immediately recognized that high school GPA was significant at the 1% level. For every 1 point in High School GPA, college GPA increased .47. Asians was also significant at the 1% level, but we only had 4 Asians that responded to our survey. Private Single Gender schools were statistically significant at the 5% level, and Out Of Kentucky joined at the 10% level. It is interesting to know that the equation estimates that students out of Kentucky have on average a .12 lower GPA than those students in Kentucky. It is also interesting that females and African Americans are not significant. This means that there is no discrimination for sex or race. Our equation said that Asians had a .64 lower GPA than Caucasians, but this could be due to the fact that we have a low respondent for Asians.We believe that after viewing these results, our strategy for the next regression run will be to drop the Asian dummy variable as well as discard observations where Asian is set to one.Regression Results Number 2:We immediately noticed that R Square dropped to .19 to thisset of results, explaining 19% of the data. High School GPA remains statistically significant at the 1% level. Private Single Gender is still significant at the 5%, and there was nothing significant at the 10% level. The Private Co-Ed was not significant at any level, thus is not important in discussing even though it was a special interest variable. Dropping Asians out of the equation did not change any key results compared to regression one and did not have a significant impact on High School GPA. Additionally the standard error was not highly impacted by dropping Asians.Our strategy for the next regression will involve reincorporating Asians back into our equation and creating a quadratic effect on high school GPA.Regression Results Number 3:We were surprised and encouraged to see that this new regression equation explains 28% of the data with an R Square of .28. High School GPA and High School GPA Squared and Asians were all significant at the 1% level. Private Single Genderremained significant at the 5% and Out Of Kentucky at the 10% level. It is interesting to know, however, that the coefficient of High School GPA decreases dramatically to -3.7 and High School GPA Squared was given a coefficient of 0.6. This is plausible because the equation gives an intercept of 8.8. Private Single Gender which was a special interest variable was given a coefficient of .18.We feel as though regression one was our best and most revealing regression. In that equation several statistically significant variables were present as well as the absence of discrimination on the basis of race or gender. Additionally, this equation revealed that there is some credence to the claim that private single gender high schools better prepare you for college. This equation explains 24% of the data, a number that we would like to see rise but are comfortable with.Regression ExtensionsAfter completing the analysis of our current regression results, we ran three more regression equations with the following changes:- Regression 1 plus slope dummy of HSGPA&FEMALE- Regression 1 plus slope dummy of HSGPA&AFRICANAMERICAN- Regression 1 plus slope dummies of HSGPA&FEMALE and HSGP&AFRICANAMERICANIn summary, none of these new regressions yielded significant results that differ from the first regression. In all of these new results, r squared was reduced by 0.04 or 0.05.Interesting ResultsOne result that we felt was very interesting that was revealed in our regression analysis was the fact that we saw no signs ofdiscrimination. We predicted that gender would not have much of an impact on the freshman year GPA. We found that female and African American are not significant, thus based on these results our hypothesis was proven correct.Another interesting result that we felt was very important dealt with private single gender and public schooling. Before collecting surveys, we expected private single sex’s variable coefficient to be positive which we found to be true. The base case for this dummy variable is public school. We found conclusive evidence that if you attended a private single gender school your freshman year GPA on average was about .15 higher compared to those who attended public schools. Our theory behind why we felt it would be positive was because the individual is distracted less and thus more focused in high school.Our final result we will discuss involves the explanatory variable of “out of state”. Before running the regressions we were not certain what sort of effect this variable will have on a students freshman year GPA. The base case was in state. Based on our results we found that on average out of state students receive .12 points lower GPA compared to in state students. It would be almost impossible to come to a conclusion on why this is so but our best guess was that out of state students could be homesick, degrading their fresh man performance.Further ResearchAfter concluding our regression project, we discussed what we would do to improve the experiment given a second chance. One of the first things discussed was to include ACT scores. By asking the student’s highest ACT score, we could perhaps see if there was a correlation between high school GPA and their test taking abilities, shown by their performance on the ACT. Thiswould also show how ACT scores influenced freshman year cumulative GPA in college.Another aspect we discussed that could be improved was to collect surveys from a broader demographic. We would like to have collected information from more colleges to see if students in different majors produce different results in the regression analysis. For example, engineering students may have had very high GPAs in high school and then drop dramatically during their freshman year due to the demanding curriculum. We would also like to survey a broader group of nationalities. After conducting our original survey we did not receive enough Asians to accurately measure the regression results. By receiving more information from different nationalities we would be able to effectively measure whether race played a role in high school GPA in comparison to college performance.The type of high school the student attended played a large role in our regression analysis but we were not able to collect information from enough home-schooled students to effectively measure the impact of home-schooling on freshman year performance.Additionally, if we could conduct the surveys again, we would like to ask students their Junior and Senior year GPA in high school instead of their cumulative. This would help in eliminating the problem of students who started off their high school career with low grades and were not able to completely recover, even if their later performance improved. This way we could effectively relate high school performance to freshman college GPA.Student SurveyThe following survey is completely anonymous and confidential. No information will be released in any way. Pleasebe completely open and honest.PLEASE FILL OUT THE FOLLOWING INFORMATION:High School Unweighted GPA : __________High School Senior and Junior Year of GPA:_______________College Cumulative GPA:_________________PLEASE CIRCLE THE FOLLOWING THAT PERTAIN TO YOU1. Male Female2. Freshman Sophomore Junior Senior3. Caucasian African American Asian America Other_____________Type of High School Graduated From:4. Public Private Home schoolWas your High School Co-Ed?5. Yes NoDid you go to High School in Kentucky?6. Yes NoWhat was your ACT score? __________________。
基于线性回归分析——boston房价预测
基于线性回归分析——boston房价预测本⽂采⽤正规⽅程、梯度下降、带有正则化的岭回归三种⽅法对BOSTON房价数据集进⾏分析预测，⽐较三种⽅法之间的差异from sklearn.datasets import load_bostonfrom sklearn.linear_model import LinearRegression, SGDRegressor, Ridge, LogisticRegressionfrom sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import StandardScalerfrom sklearn.metrics import mean_squared_error, classification_reportfrom sklearn.externals import joblibimport pandas as pdimport numpy as npclass HousePredict():"""波⼠顿房⼦数据集价格预测"""def __init__(self):# 1.获取数据lb = load_boston()# 2.分割数据集到训练集和测试集x_train, x_test, y_train, y_test = train_test_split(lb.data, lb.target, test_size=0.25)# print(y_train, y_test)# 3.特征值和⽬标值是都必须进⾏标准化处理, 实例化两个标准化API# 3.1特征值标准化self.std_x = StandardScaler()self.x_train = self.std_x.fit_transform(x_train)self.x_test = self.std_x.transform(x_test)# 3.2⽬标值标准化self.std_y = StandardScaler()self.y_train = self.std_y.fit_transform(y_train.reshape(-1, 1)) # ⼆维self.y_test = self.std_y.transform(y_test.reshape(-1, 1))def mylinear(self):"""正规⽅程求解⽅式预测:return: None"""# 预测房价结果，直接载⼊之前保存的模型# model = joblib.load("./tmp/test.pkl")# y_predict = self.std_y.inverse_transform(model.predict(self.x_test))# print("保存的模型预测的结果：", y_predict)# estimator预测# 正规⽅程求解⽅式预测结果lr = LinearRegression()lr.fit(self.x_train, self.y_train)print("正规⽅程求解⽅式回归系数", lr.coef_)# 保存训练好的模型# joblib.dump(lr, "./tmp/test.pkl")# # 预测测试集的房⼦价格y_lr_predict = self.std_y.inverse_transform(lr.predict(self.x_test))## print("正规⽅程测试集⾥⾯每个房⼦的预测价格：", y_lr_predict)print("正规⽅程的均⽅误差：", mean_squared_error(self.std_y.inverse_transform(self.y_test), y_lr_predict)) return Nonedef mysdg(self):"""梯度下降去进⾏房价预测:return: None"""sgd = SGDRegressor()sgd.fit(self.x_train, self.y_train)print("梯度下降得出的回归系数", sgd.coef_)# 预测测试集的房⼦价格y_sgd_predict = self.std_y.inverse_transform(sgd.predict(self.x_test))# print("梯度下降测试集⾥⾯每个房⼦的预测价格：", y_sgd_predict)print("梯度下降的均⽅误差：", mean_squared_error(self.std_y.inverse_transform(self.y_test), y_sgd_predict)) return Nonedef myridge(self):"""带有正则化的岭回归去进⾏房价预测"""rd = Ridge(alpha=1.0)rd.fit(self.x_train, self.y_train)print("岭回归回归系数", rd.coef_)# 预测测试集的房⼦价格y_rd_predict = self.std_y.inverse_transform(rd.predict(self.x_test))# print("岭回归每个房⼦的预测价格：", y_rd_predict)print("岭回归均⽅误差：", mean_squared_error(self.std_y.inverse_transform(self.y_test), y_rd_predict))return Nonereturn Noneif __name__ == "__main__":A = HousePredict()A.mylinear()A.mysdg()A.myridge()正规⽅程求解⽅式回归系数 [[-0.10843933 0.13470414 0.00828142 0.08736748 -0.2274728 0.257911140.0185931 -0.33169482 0.27340519 -0.22995446 -0.20995577 0.08854303-0.40967023]]正规⽅程的均⽅误差： 20.334736834357248梯度下降得出的回归系数 [-0.08498404 0.07094101 -0.03414044 0.11407245 -0.09152116 0.3256401-0.0071226 -0.2071317 0.07391015 -0.06095605 -0.17955743 0.08442426-0.35757617]梯度下降的均⽅误差： 21.558873305580214岭回归回归系数 [[-0.10727714 0.13281388 0.00561734 0.0878943 -0.22348981 0.259296690.0174662 -0.32810805 0.26380776 -0.22163145 -0.20871114 0.08831287-0.4076144 ]]岭回归均⽅误差： 20.37300555358197过拟合：⼀个假设在训练数据上能够获得⽐其他假设更好的拟合，但是在训练数据外的数据集上却不能很好地拟合数据，此时认为这个假设出现了过拟合的现象。
- 1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
- 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
- 3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。
The distribution of educational resources in Beijing city and the housing pricesAbstract：House price is not only affected by national macroeconomic policy, but also affected by the public facilities and the environment around. The equilibrium distribution of education resource result in house price fluctuation. That is not equity and widen the gap between the rich and the poor. We research the factors affecting the house price of Beijing’ key schools, result point that school district house price is 13.8% higher than that of non-school district house having similar conditions. By controlling other public resources, like subway station, park and kindergarten, and itself property, like house age, greening rate, plot ratio, result suggest that school district house in Haidian and Chaoyang have premium of 31%. Meanwhile, they have premium of 23% totally. The result is, different house price reflect inequality of Beijing’s education resources, and most part of high quality resources distribute in central area. These spatial pattern is unreasonable, reducing the utilization of high quality public resources, and resulting in sharp rise of house price in the central area, lastly, expanding wealth gap. So the government should enhance quality of education and improve traffic efficiency. Through these measures, we can reach these goals: the suburbs improving its attractiveness, population density of Beijing decreasing, and more importantly, public resources distributing equality.Keywords: house price; public resource; factors; inequality; population density1.IntroductionReal estate is one of the most important parts of the economy in our country, the price rise is the result of multiple factors. The quality of public resources is an important factor to affect the price of housing, which is especially important in the teaching quality of residential buildings.The education resources has always been an important impact on housing prices, for example, according to the study, in 2004, in the transition process from a poor school in London to a top school, house prices have an increase of 61000 pounds. Early studies such as Oates (1969) on the cost of real estate prices and public schools spending on each students, he found that they have a significant positive correlation, and the negative effect of house property tax on housing prices can be offset if they spend the money to the school, the study shows that residents tend to pay higher prices to better public services. And Fullerton Rosen (1977) believes that the use of each student's spending in public schools as a variable is not very appropriate, because the cost of education, and other factors are not easy and accurate, so they use the average performance of students on behalf of the school quality, the results show that the data and prices are significantly positive correlation. However, it is not very good to solve the problem, in order to better quantification the quality of school teaching, Lucas Figlio (2004) introduced the school quality rating report the state government issued as a supplement to the students' average test score, the study shows that whenintroduced school quality rating system, the price will change significantly, but over time, this effect is rapidly decreasing, and only in the first time, it play a greater role. Because of the impact of housing prices is not just the school teaching quality, which leads to missing variables, the existence of this error will affect the accuracy of the results of the regression.In recent years, the school district housing phenomenon in China has become more and more noticeable from the price point of view, for example Langya Road Primary School, Lixue primary school, Lhasa Road Primary School are three elite schools in Nanjing,, from 2008 to April 2009 , prices rose quickly, the school district housing prices are more than 3000 yuan/m2 than the average price, even in 2009 , housing prices generally fell 8.9%, the school district housing prices in April is still stable. The mechanism by which the residents choose to choose their place of residence to influence the housing price is likely to exist in China. If this mechanism exists, it will reflect the quality of education in a part of the housing price. Regardless of the economic situation is good or bad, the school district housing prices will not follow the economic law. Research shows that, some famous primary school has a significant effect on the school district housing premium.This paper focuses on the impact of key primary school on housing prices, thus revealing the unreasonable distribution of Beijing education resources, and from the perspective of optimizing the educational space pattern, promoting equal opportunities for education and reducing population density of Beijing city, we have discussed the problem of the development of Beijing city. In this paper, we have four aspects of improvement based on the previous research, 1, the data is no longer linear distance for the parameters, but the use of the shortest walking distance to make the analysis more close to reality. 2, this paper studies the Haidian District Chaoyang District, Xicheng District and Dongcheng District, it is different from the common use of Tiananmen as the center of the method to control the degree of prosperity. 3, the selection of primary school in Beijing City is the most famous ones. rather than the Beijing Municipal Education Commission’s approval. 4, the data is second-hand housing transaction data, so it is more reliable.2.data description and research methodsBased on the existing research, this paper uses the data of Beijing city housing transaction, and using the model to control the relevant variables, we want to get a effective regression results, and analyze the effects of education quality, transportation facilities and environmental landscape on the house price.2.1.the division of the school district and the school district houseCompulsory education law of China established the the enrollment policy that Chinese came near to the entrance , namely for every primary school, there is a scribe area, and within the scope of the scribe area, children have an exemption entrance treatment. So, generally speaking, each district has a corresponding primary school. This may has promoted the equality of education opportunity, however, there is a difference in the quality of primary school, relatively speakingsome school’s quality of education is far higher than ordinary by the government's priority support. Although the government has abolished the system of dividing the primary school in 2000, the social prestige of the primary school has been established, and the status of the primary school is increasing.This paper selects 19 primary schools in Beijing city as a data source, table 1 is recognized as a key primary school list.Table 1 list of key primary schoolsFigure 1 primary distribution map of Beijing CityFigure 1 is a primary distribution map of Beijing city. As shown in Figure 1, the primary school in Beijing is not in uniform distribution, they are concentrated in the comparison of the city of Haidian District, Chaoyang District, Dongcheng District and Xicheng District. In fact, the famous primary schools are mostly distributed in these four areas. Beijing Municipal Education Commission in 1950s has announced the list of 40 municipal primary schools, today, these primary schools are still the best primary school in Beijing. And has been widely recognized by the community, and the vast majority of these primary schools are in the above four districts.2.2.housing dataFrom Figure 1, we can see that the geographical distribution of Beijing city is basically a center to the surrounding Tiananmen, from a link to the rings, are built around the Tiananmen. We collected a total of 19 Beijing municipal key primary school district scribing a total of 120 residential and 112 non cshool district data. Variables include second-hand housing average price, , age (minus the 2015 year built), volume rate, green rate,distance to the center of the city(in KM),distance to the subway station(in KM),, distance to the kindergarten(in KM),distance to shopping malls(in KM), distance to thepark(in KM), . And introduce some dummy variables, such as a small primary school district is 1, otherwise the value is 0.Table 2 is a description of the collected cell data. As shown in Table 2, the average price of second-hand housing is 54387, the mean distance from the downtown is 7.21 km, the average house age is 15.67 years, the average rate of volume is 2.68, average greening rate is 32%, and the mean distance from the nearest subway station is 0.87 km, to the nearest kindergarten flat were1.24 km ,average distance to the nearest mall is 0.95 km, the median distance to the nearest park is 1.22 km, the school district room price is 13267yuan/m2 higher than the average, and it is about 27.9%.Table 2 cell descriptionAverage price All sample School district room Non school district room Prices 54387.84 60870.37 47603.5 Downtown distance 7.21 6.79 7.59Age 15.67 17.41 13.86Volume ratio 2.68 2.42 2.98Greening rate 0.32 0.26 0.33Distanceto thesubway station 0.87 0.85 0.89distance to kindergarten 1.24 1.21 1.27distance to the mall 0.95 0.95 0.95Distance to the park 1.22 0.99 1.062.3.the establishment the modelIn this paper, we use the characteristic price method to analyze the house price, the following is the log linear model used in this paper:（1）Among them, X1i means the property of the District, including the age, the volume of residential, greening rate, etc.. X2i represent distance variables, including distance to the subway station, distance to the nursery, distance to the mall and the distance to the park, Pschool is a dummy variable, used to indicate whether the district is the school district room.3.empirical analysis3.1.Beijing city house price analysisTable 3 is the result of the overall regression of the District of all districts in the city. The regression includes square, hospital, park, subway station, high school, elementary school, and so on. The results show that in the 5% confidence level, the distance to the Beijing city center has a negative effect, while the subway station and primary school have a positive effect on prices; in the 10% confidence level, the park is statistically significant, and square, middle school and hospital statistics is not significant. It is worth noting that the hospital's coefficient means a negative effect in a certain sense, The reason why the square is not significant, it may be that as a leisure place, such as food Square, shopping plaza , they can not provide a great attraction.Parameter estimate Std.Error T value Pr(>|t|)In fact, table 3 is the result of the regression results of the overall data of Beijing, it can only be a rough display of the overall situation. In fact, within the second ring even within the third ring, most of the key primary schools located in the vicinity of several cities around the city, the majority of the properties have advantages of hospital resources, one to many subway stations, many secondary schools and more primary schools.when consumers purchase a house,the subway, hospitals, secondary schools, primary schools and other factors will be considered, resulting in a higher real estate prices. In order to further analyze the role of the subway station, hospital, park, middle school in promoting the rise in housing prices, we analyze the data of each link, to observe the information of each link. In fact, the design of the Beijing link can be seen as the Tiananmen as the center, so the link can also be seen as a symbol of the degree of regional prosperity.As shown in Table 4, the results show that near the park the price is 2.4% lower than estate far away from the park, and price is 9.5% higher when it is a school district room,it is 14.4% higher when the house is near a park from line 2 to line 3, and more than 22.4% higher when it is near the subway station, and 13.42% higher than other properties when it is a school district room. And it is 15.6% higher than the other properties when the house is near the park, 23.4% higher of school district room from line 3 to line 4. And so as line 5 to line6.3.2 four district house price analysisTo more accurate understanding of the various regions, we distinguish between Haidian and Chaoyang as a group, Dongcheng and Xicheng as a group, in fact, because the economicsituation in Dongcheng District and Xicheng District is more similar, the same way ChaoyangDistrict and Haidian District economic conditions. From the map, the center of Beijing is indeed Tiananmen,but the northern part of Beijing's business is more prosperous than the southern part ofBeijing,so Beijing city in this area is not symmetrical distribution.Table 5 four area analysisfour district ModelVariable (ⅰ) (ⅱ) (ⅲ) (ⅳ)Schooldistrict room 0.237777（12857.4）0.2684（14347.056 ）0.229659（12504.3）0.248（13373.63）t-value 9.438（9.799）9.859（10.314 ）8.455( 8.792) 8.916（9.103）Characteristicone No Yes No Yes CharacteristicTable 5 Analysis the school district housing premium of Chaoyang District, Haidian District, Dongcheng District, Xicheng District, in the first column (I) , the distance to the Tiananmen areaare considered as control variables, and then we regress on the school district housing prices, results showed that the school district room are more than23.8% higher than non school district housing prices , the absolute price is 12867 yuan / square meter (after using 23.8% (12867) )premium; column(II) control the properity of school district room (volume rate, greenrate, age and other factors), the parameters of the school district housing dummy variables become slightly higher, reaching 26.8% (14347); third column(III) control distance variables(to the distance properties near the subway, hospital, Park), it showed that the school district housing premium is 23% (12504); fourth column (IV) control attributes and distance properties, the results show that the school district housing premium is 24.8% (13373).From a statistical point of view, in table 5, four sets of regression resultsis significant at the 99% confidence level, the first column (I) is used to control the residential area to Tiananmen distanceto achieve control of other relevant factors, these factors include bustling degreein the residential district etc; the second column (II) controlled the attributesof the area (age, greening rate, volume rate, property costs, etc.), to expect to eliminate error of Fangling, after all poor landscape,small space house more unattractivethan ordinary residential,and the price may be lower, and vice versa. The third column (III) controls the distance properties of cell (away fromthe subway station, park ), because housing landscape, subway housing and even hospital roommay have some role in premium, controlled the distance variable can offset other residential location errors. The fourth column (IV) controlled their properties and the distance properties inthe District, in order to get a better return.Through a comparison of the first and fourth columns, we can know that when estmate the character one and character two two, the school district housing premium change is not too large (23.78%, 24.8%), which means, in the four district, the characteristic's effect is not obvious; through the comparison of second column and fourth column ,when consider the character one asthe control variables, the premium is significantly reduced from 26.84 to 24.8%, which shows thatthe distance has obvious explanatory power of the price premium This validates our common sense, that is, the farther away from the city center, the lower the price.3.3. analysis of Dongcheng&Xicheng district pricesTable 6 local analysis of East and West districtDongcheng,Xicheng ModelVariable (ⅰ) (ⅱ) (ⅲ) (ⅳ)Schooldistrict room 0.13781（8105.3）0.1841(10631.558) 0.1785（10623.317）0.15（10078.483）t-value 4.215( 4.700) 5.564( 6.068 ) 4.976（5.579） 4.1（4.438）Characteristicone no yes no yes Characteristic no no yes yestwoDistance yes no yes yesTable 6 Analysis Housing premiumof the Dongcheng District, Xicheng District , the first column controled the straight line distance from district to the Tiananmen , the results show that the schooldistrict housing prices is 13.8% (8105)higher than the non school district room . Second column controled the school district's own properties, the parameters of the dummy variable of schooldistrict room is slightly larger, reached 18.4% (10631). Third column controlled the distance property, the results show that the school district housing premium is 17.9% (10623). Fourthcolumn controled the cell's own attributes and distance properties, the results show that the schooldistrict housing premium is 15% (10078).According to the results of analysis of Dongcheng, Xicheng, the school district housing premiumis relatively small, the reason may be Dongcheng, Xicheng of the city districts in the geographic location is closer to downtown Beijing, the regional education resource is vear rich (not only has alarge number of Beijing municipal key primary school, and coupled with other public resources making the marginal utility of the school district room decreased), thereby reducing the marginal utility of the school district room. On the other hand, that urban residential has basicallyis the most expensive, the price upside is smallerIn contrast to the first column and the fourth column, when make the distance as the control variables,and add the character one and character two, the coefficient of the school district room isslightly changed, but the change is not big, which means that the main factor affecting the priceof the house is distance, through the contrast between the second columns and fourth columns,we can see thet the coefficient of the school district room is significantly smaller.3.4 Haidian&Chaoyang house price analysisIn the same way, we analyze the relationship between the price and the factors of the two districtof Haidian&Chaoyang, and carry out the regression analysis.Table 7 partial analysis of Chaoyang Haidian DistrictChaoyangHaidianDistrictVariableSchooldistrict room 0.338（17501.0 ）0.3796474（19384.2 ）0.310001（15942.3）0.34（17566.8）t-value 8.522（8.471）9.525（9.724 ）7.199（7.100）8.095（8.056）Characteristicone No yes No yes Characteristictwo No No yes yes Distance yes No yes yesTable 7 is about Haidian District and Chaoyang District's school district housing premium, the first column (I) controlled the straight distance from the area toTiananmen distance . The results showed that the school district room is more than33.8% higher than the nonschool district housing prices. the second column (II) controlled the school district room's own property, the school district room virtual variable parameter is slightly larger, reaching 38% (19384); the third column (III) controlled the distance properties, the results show the school district housing premium is31% (15942); the fourth column (IV) controlled the residential own attribute and distance attribute. The results showed that the school district housing premium is 37.1% (19045).Relative to the Dongcheng, Xicheng, and the full four districts, Chaoyang District, Haidian District's school district housing premium is much larger. The reason may be that Chaoyang District and Haidian District deviates more from the center of Beijing on the geographical position, and the public resources is not balanced,like in Haidian District, the area is very huge, it extends from line3 to line5, so the public resources distribution is uneven , the area outside the line4 is a school district, it will undoubtedly give this area a lot of points.4.conclusionIn this paper, the characteristic price model is used to analyze the price and other characteristics of the whole and local housing prices in Beijing, and the influence of the education resources in Beijing city is evaluated quantitatively:School district room can cause a substantial premium, when property buyers purchase a house, they largely considered the school counterparts,and pushy parents spared no expense to purchase school district room to obtain enrollment quota, this is not only against the intention of education fairness, but also lead to the inequality of educational opportunity in a large extent, after all, the rich can obtain high-quality educational resources through this way , while because the poor can't afford to buy these houses, their children can only go to ordinary primary schools , primary schools is public resource, it will lead to unfair when children enrolled to school.after all, the School District Housing hot phenomenon comes from the uneven distribution of educational resources, most of the good primary school resources in Beijing city are concentrated in the range of 7 kilometers in Beijing City, . From the perspective of inequality of education, the state should reform the school district housing policy, because now there only two ways to sent the child to high quality primary school , one is to buy school district room, two is to pay high school choice fees. Undoubtedly, it is unfair to the poor, so Beijing should focus on improving the ordinary primary school's education quality, especially in the suburban area, and add educational facilities, reduce phenomenonthat the allocation of educational resources is too focused .This paper also has the following shortcomings: first, we have the difficulty of data collection, especially the residential properties, such as the degree of residential decoration, construction and some other factors Secondly, some housing related factors data is difficult to obtain such as the per capita income and housing prices are closely linked, Once again, the analysis of the factors that affect the price of the house price can be further deepened, and we can control moreparameters, which need to be compensated in the future research.。