This paper investigates the gap of demographic urbanization arising from the difference between rural residents who have migrated to cities and those who have acquired urban citizenship in the process of China's u...This paper investigates the gap of demographic urbanization arising from the difference between rural residents who have migrated to cities and those who have acquired urban citizenship in the process of China's urbanization. The skyrocketing house prices and insufficient household consumption power are key factors to the widening gap, which had reached 18% in 2013. In order to explore this issue, by creating the basic model and the model with interaction term, this paper has analyzed the relationship among house prices, consumption power and gap of urbanization using the data of 31 provinces between 1999 and 2013 in China. Empirical result indicates that: there is a positive correlation between the house prices and the gap of China's demographic urbanization. However, such a correlation is restrained by these rural migrants who rent houses in cities. For an increase of house price by 1%, the gap of urbanization will widen by 1.05%. Although rising urban consumption power of rural residents has increased the ratio of migration, the lagged growth of consumption power has led to a widening gap of urbanization. Therefore, the only way to effectively reduce the gap of demographic urbanization is to increase the consumption power of migrant population and optimize consumption structure.展开更多
Changes in prices of homes are hypothesized as correlated with the times of their sale and resale and the attributes of their dwelling unit and neighbourhood and those of neighbouring homes. They may also be correlate...Changes in prices of homes are hypothesized as correlated with the times of their sale and resale and the attributes of their dwelling unit and neighbourhood and those of neighbouring homes. They may also be correlated with the occurrences of events inside the neighbourhoods caused by the activities of </span><span style="font-family:Verdana;">individuals and organizations outside the neighbourhoods, such as whether the local economy is in a recession or has a high unemployment rate. Calibrated hybrid housing price models predict precipitous decreases in house prices of approximately 2900 sold and resold homes in two inner-city neighbourhoods</span> <span style="font-family:Verdana;">in Windsor, Ontario, during those events since 1981 or 1986. Overall modest predicted percentage increases in houses’ prices during more than 30 years therefore subsumed periods of inner-city neighbourhood deterioration i</span><span style="font-family:Verdana;">n </span><span style="font-family:Verdana;">dispersed locations of unimproved and disimproved homes. Compensatory predictions however are of increasing prices for minorities of homes with improvements to several attributes of the dwelling unit and neighbourhood.展开更多
This paper uses the HS2 extension cancellation in November 2021 as a quasi-experiment to study its impact on house prices and rents in Leeds.Using a DiD approach on repeat sales and monthly rents,I compare property va...This paper uses the HS2 extension cancellation in November 2021 as a quasi-experiment to study its impact on house prices and rents in Leeds.Using a DiD approach on repeat sales and monthly rents,I compare property values near the HS2 station and proposed construction site before and after the announcement.Results show a 3.6%decrease in house prices and a 3.9%decline in rents near the station,while properties near the construction site experienced a 2.4%increase in prices and a 2.1%rise in rents.This is the first paper to analyse the HS2 cancellation effect using panel data methods.展开更多
The high and rising house prices in China are not adequately accounted for the traditional explanations emphasizing demand-driven or cost-push factors. Reeent published studies claim that gender imbalance increases co...The high and rising house prices in China are not adequately accounted for the traditional explanations emphasizing demand-driven or cost-push factors. Reeent published studies claim that gender imbalance increases competition among men in the marriage market, which has pushed Chinese, especially parents with a son, to buy houses as a signal of relative status in the marriage market,＂ this marriage competition then causes high demand for houses and eventually leads to rising house prices in China. Empirical results in this paper, however, provide little support for this hypothesis and we find that a rise in the sex ratios for most age cohorts accounts for very small percentage variations in house price movements in China during 1998-2009. Further investigation suggests that excess demand driven by high monetary growth was a significant cause of the rising house prices in China during 1998-2009. Therefore, the impact of gender imbalance on house prices shouM not be exaggerated and monetary dynamics remains an important leading indicator for house price movements in China.展开更多
Beijing is festooned with gigantic billboards extolling the virtues of luxurious accommodation and leaving no doubt that a prestigious address is a symbol
Existing studies about the modeling of urban housing price have figured out sets of factors and the main focus is on the relative spatial location. Generally, this line of research is descriptive rather than modeling ...Existing studies about the modeling of urban housing price have figured out sets of factors and the main focus is on the relative spatial location. Generally, this line of research is descriptive rather than modeling in nature. The underlying reasons for the distribution of housing price are largely unexplored and more research is needed. The paper therefore attempted to systematically explore the spatial heterogeneities of urban housing price based on the urban activity interaction rule. Using Beijing as a case study, this study first developed a new measurement of accessibility which directly depicts the cost and possibilities to access opportunities of different activities such as employments, educational, shopping and medical services. From the perspective of demands of different households, the paper then modelled the relationships between urban housing price and these accessibilities and found that the distribution pattern of housing price can be relatively well represented by this model that the R^2 could achieve 0.7. We investigated the relationship between housing price and the demands of different kinds of households categorized by households of one-generation, two-generation, three-generation and four-and-plus-generation and found that the demands of household of four-and-plus-generations is the most highly correlated with housing prices. The reason might be that this kind of household has more household members and the demands are more diverse and complex, which is more similar to the distributions of all kinds of activity opportunities in the real world. In the end of the paper, some implications for policy-making are proposed based on the results of the analyses.展开更多
House price expectations(HPE)is a key factor affecting housing market fluctuations.Taking Beijing as an example,this study innovatively proposes an internet data-based measurement of HPE and text analysis-based quanti...House price expectations(HPE)is a key factor affecting housing market fluctuations.Taking Beijing as an example,this study innovatively proposes an internet data-based measurement of HPE and text analysis-based quantification of media reports from media attention and media attitudes.Regression model is performed to empirically test the impact of media reports on the level and accuracy of HPE.The empirical results show no significant relationship between media attention and the level of HPE,but a significant relationship to the accuracy of HPE.The higher the media attention(i.e.,the more intensive the media reports),the smaller the deviation between HPE and actual housing prices.The attitude of media reports is significantly related to the level and accuracy of HPE.It is easier to guide the formation of HPE through media reports with clear opinions,indicating that media could promote the sustainable development of the real estate market.展开更多
The nonlinearity of hedonic datasets demands flexible automated valuation models to appraise housing prices accurately,and artificial intelligence models have been employed in mass appraisal to this end.However,they h...The nonlinearity of hedonic datasets demands flexible automated valuation models to appraise housing prices accurately,and artificial intelligence models have been employed in mass appraisal to this end.However,they have been referred to as“blackbox”models owing to difficulties associated with interpretation.In this study,we compared the results of traditional hedonic pricing models with those of machine learning algorithms,e.g.,random forest and deep neural network models.Commonly implemented measures,e.g.,Gini importance and permutation importance,provide only the magnitude of each explanatory variable’s importance,which results in ambiguous interpretability.To address this issue,we employed the SHapley Additive exPlanation(SHAP)method and explored its effectiveness through comparisons with traditionally explainable measures in hedonic pricing models.The results demonstrated that(1)the random forest model with the SHAP method could be a reliable instrument for appraising housing prices with high accuracy and sufficient interpretability,(2)the interpretable results retrieved from the SHAP method can be consolidated by the support of statistical evidence,and(3)housing characteristics and local amenities are primary contributors in property valuation,which is consistent with the findings of previous studies.Thus,our novel methodological framework and robust findings provide informative insights into the use of machine learning methods in property valuation based on the comparative analysis.展开更多
This paper studies the relationship between accessibility and housing prices in Dalian by using an improved geographically weighted regression model and house prices, traffic, remote sensing images, etc. Multi-source ...This paper studies the relationship between accessibility and housing prices in Dalian by using an improved geographically weighted regression model and house prices, traffic, remote sensing images, etc. Multi-source data improves the accuracy of the spatial differentiation that reflects the impact of traffic accessibility on house prices. The results are as follows: first, the average house price is 12 436 yuan(RMB)/m^2, and reveals a declining trend from coastal areas to inland areas. The exception was Guilin Street, which demonstrates a local peak of house prices that decreases from the center of the street to its periphery. Second, the accessibility value is 33 minutes on average, excluding northern and eastern fringe areas, which was over 50 minutes. Third, the significant spatial correlation coefficient between accessibility and house prices is 0.423, and the coefficient increases in the southeastern direction. The strongest impact of accessibility on house prices is in the southeastern coast, and can be seen in the Lehua, Yingke, and Hushan communities, while the weakest impact is in the northwestern fringe, and can be seen in the Yingchengzi, Xixiaomo, and Daheishi community areas.展开更多
In recent years,with the rapid development of China’s economy,a large number of people have flocked to the cities,which also brings more residential waste.The increased waste overloads transfer stations located near ...In recent years,with the rapid development of China’s economy,a large number of people have flocked to the cities,which also brings more residential waste.The increased waste overloads transfer stations located near residential areas,and the continuous noise and odour affect the daily lives of nearby residents.In addition,the neighbourhood avoidance facilities represented by the waste transfer stations also reduce the value of the surrounding residents’houses.Therefore,using the conditional value method and the Tobit and Double Hurder econometric models,this article investigates the implicit value of the Fuli Resort neighbourhood under the influence of the waste transfer station through a questionnaire survey on the willingness of the residents to accept the compensation,which can be regarded as the“aversion value”of the neighbourhood due to the aversion to the waste transfer station and analyses the impact of the aversion value of the neighbourhood.aversion value and analyses the impact on residents’willingness to accept compensation.The study found that the residents’willingness to accept compensation near the waste transfer station is 511.94 RMB/person/month,and the implicit value of the Fuli Resort neighbourhood under the influence of the waste transfer station in Qinhuai District,Nanjing,Jiangsu Province,China,is 147,950 RMB.The study found that residents are most interested in having the government rectify the waste transfer station and set sanitary standards and work norms.展开更多
In recent Years, China＇s real estate market has been rapid developed, and real estate has become a hot spot of consumption and investment. In some large and medium-sized cities there has been a rapid rise in housing ...In recent Years, China＇s real estate market has been rapid developed, and real estate has become a hot spot of consumption and investment. In some large and medium-sized cities there has been a rapid rise in housing prices. The rapid rise in housing prices has led to difficulties in the purchase of houses in some cities and towns, and this phenomenon has aroused the attention and con- cern of all walks of life. Housing is the basic human life needs. Housing problem is not only an economic problem, but also a social problem. The relationship between house price and land price and the effective control of housing prices have become the focus of government and scholars. Thus, grey relational analysis is used to ana- lyze the relationship between housing prices and land prices, and the grey relational coefficients are calculated.展开更多
This study analyzes the spatial patterns and driving forces of housing prices in China using a 2,872-county dataset of housing prices in 2014.Multiple theoretical perspec-tives on housing demand,supply,and market,are ...This study analyzes the spatial patterns and driving forces of housing prices in China using a 2,872-county dataset of housing prices in 2014.Multiple theoretical perspec-tives on housing demand,supply,and market,are combined to establish a housing price model to explore the impact of land prices on housing prices.The relative impacts of land prices on housing prices at different administrative levels are then analyzed using the geo-graphical detector technique.Finally,the influencing mechanism of land prices on housing prices is discussed.The main conclusions are as follows.(1)Housing prices have a pyra-mid-ranked distribution in China,where higher housing prices are linked to smaller urban populations.(2)Land prices are the primary driver of housing prices,and their impacts on housing prices vary over different administrative levels.To be specific,the effect of land prices is the strongest in the urban districts of provincial capital cities.(3)The internal influ-ence mechanisms for land prices driving housing prices are:topographic factors,urban con-struction level,the agglomeration degree of high-quality public service resources,and the tertiary industrial development level.The urban land supply plan(supply policies)is the in-trinsic driver that determines land prices in cities;through supply and demand,cost,and market mechanisms,land prices then impact housing prices.展开更多
In this study, housing prices data for residential quarters from the period 2001-2012 were used and Global Differentiation Index （GDI） was established to measure the overall differentiation trend in housing prices i...In this study, housing prices data for residential quarters from the period 2001-2012 were used and Global Differentiation Index （GDI） was established to measure the overall differentiation trend in housing prices in Yangzhou City, eastern China. Then the influence of the natural landscape and environment on prices of global housing market and housing submarkets was evaluated by the hedonic price model. The results are shown as follows. （1） There have been increasing gaps among housing prices since 2001. In this period, the differentiation trend has shown an upward fluctuation, which has been coupled with the annual growth rate of housing prices. （2） The spatial distribution of residential quarters of homogenous prices has changed from clustered in 2001 into dispersed in 2012. （3） Natural landscape and environmental externalities clearly influence spatial differentiation of housing prices. （4） In different housing submarkets, the influence of natural landscape and environmental eternalities are varied. Natural landscape characteristics have significant impact on housing prices of ordinary commercial houses and indemnificatory houses, while the impact of environmental characteristics have obvious influence on housing prices of cottages and villas.展开更多
As one of the essential urban open spaces, lakes usually contribute immensely to the quality of residents′ daily lives. Different from hedonic approach employed in existing researches on urban open spaces in China, t...As one of the essential urban open spaces, lakes usually contribute immensely to the quality of residents′ daily lives. Different from hedonic approach employed in existing researches on urban open spaces in China, this paper integrates housing price surface with road density to analyze the spatial characteristics in proximity to urban lakes in Wuhan City, China. With the expansion of Wuhan City, urban lakes became polluted, they shrunk or even disappeared, leading to unfavorable conditions for sustainable development of the city. To better understand the spatial relationship between the city and lakes, we classify the urban lakes in Wuhan central area into ′lakes in the urban center′ and ′lakes in urban fringe′. Based on housing price surface we explore the spatial characteristics in proximity to different lakes and differences between the lakes. We also use Geographic Information System(GIS) tool to calculate road density as a supplementary indicator to reflect the accessibility in proximity to urban lakes. The results indicate that relative independence exists between different towns, and the spatial characteristics are different depending on scales and locations. In most of cases, the road density is lower where closer to the lakeshore while the housing price exhibits an opposite pattern. We conclude that city governments and urban planners should give more considerations to these spatial differences, somewhere should be better planned and protected as an important waterfront and somewhere the control of unreasonable real estate development nearby should be strengthened.展开更多
The hedonic price model is widely applied to study the urban housing market because of the heterogeneity of housing products. Literature indicated that the hedonic price theory mainly includes two parts: Lancaster’s ...The hedonic price model is widely applied to study the urban housing market because of the heterogeneity of housing products. Literature indicated that the hedonic price theory mainly includes two parts: Lancaster’s partiality theory and Rosen’s characteristic market equilibrium analysis. This paper chose 18 characteristics as independent variables and set up a linear hedonic price model for Hangzhou City. The model was tested with 2473 housing samples and field survey data of 290 housing commu-nities. This research found that 14 out of 18 characteristics had significant influence on housing price. They were classified into 5 groups according to their impact degree.展开更多
Population growth has been widely regarded as an important driver of surging housing prices of urban China,while it is unclear as yet whether population shrinkage has an impact on housing prices that is symmetrical wi...Population growth has been widely regarded as an important driver of surging housing prices of urban China,while it is unclear as yet whether population shrinkage has an impact on housing prices that is symmetrical with that of population growth.This study,taking 35 sample cites in Northeast China,the typical rust belt with intensifying population shrinkage,as examples,provides an empirical assessment of the roles of population growth and shrinkage in changing housing prices by analyzing panel data,as well as a variety of other factors in related to housing price,during the period of 1999–2018.Findings indicate that although gap in housing prices was widening between population growing cities and population shrinking cities,the past two decades witnessed an obvious rise in housing prices of those sample cities to varying degree.Changes in population size did not have a statistically significant impact on housing prices volatility of sample cities,because population reduction did not lead to a decline in housing demand correspondingly and an increasing housing demand aroused by population growth was usually followed by a quicker and larger housing supply.The rising housing prices in sample cities was mainly driven by factors like changes in land cost,investment in real estate,GDP per capita and household number.However,this does not mean that the impact of population shrinkage on housing prices could be ignored.As population shrinkage intensifies,avoiding the rapid decline of house prices should be the focus of real estate regulation in some population shrinking cities of Northeast China.Our findings contribute a new form of asymmetric responses of housing price to population growth and shrinkage,and offer policy implications for real estate regulation of population shrinking cities in China’s rust belt.展开更多
Using system clustering method to group China's provinces into 3 new groups according to their housing prices, then establishing a state-space model and applying the Kalman filter calculation, we made a comparativ...Using system clustering method to group China's provinces into 3 new groups according to their housing prices, then establishing a state-space model and applying the Kalman filter calculation, we made a comparative analysis of the influences of different types of monetary policy instruments towards different regional housing prices. The empirical results show that both the quantitative instruments represented by M2 and the pricing instruments represented by real interest rate have increasing influences on different regional housing prices,but the former influence is much stronger than the latter. The influential differences of quantitative instruments to regional housing prices are much greater. It means the higher the regional housing price is, the greater the influence is. Therefore, the central bank shall optimize the combination of monetary policy instruments according to the above characteristics of different types of monetary policy instruments in order to acquire the regulatory target of real estate market.展开更多
The impact of different public service facilities is obtained by investigating the infl uence of public service facilities on distribution pattern of housing price in 25 cities.According to the survey results,public e...The impact of different public service facilities is obtained by investigating the infl uence of public service facilities on distribution pattern of housing price in 25 cities.According to the survey results,public education service facilities have the highest weight and the greatest impact,which also refl ects the root of“school district housing fever”from the side.Public sports service facilities have the lowest score when compared with other options.This is not because public sports service facilities are not important,but is determined by actual situation of social development and actual living standard of residents in China.From the improvement and enhancement of urban public service facilities,the construction of public service facilities should be convenient for people’s education,health,culture and entertainment.展开更多
This works examine the responses of housing prices to the monetary policies in various Chinese cities. Thirty-five large and medium sized Chinese cities are classified into six clusters applying the minimum variance c...This works examine the responses of housing prices to the monetary policies in various Chinese cities. Thirty-five large and medium sized Chinese cities are classified into six clusters applying the minimum variance clustering method according to the calculated correlation coefficients between the housing price indices of every two cities.Time difference correlation analysis is then employed to quantify the relations between the housing price indices of the six clusters and the monetary policies.It is suggested that the housing prices of various cities evolved at different paces and their responses to the monetary policies are heterogeneous,and local economic features are more important than geographic distances in determining the housing price trends.展开更多
Innovation capitalization is a new concept in innovation geography research.Extant research on a city scale has proven that innovation is an important factor affecting housing prices and verified that innovation has a...Innovation capitalization is a new concept in innovation geography research.Extant research on a city scale has proven that innovation is an important factor affecting housing prices and verified that innovation has a capitalization effect.However,few studies investigate the spatial heterogeneity of innovation capitalization.Thus,case verification at the urban agglomeration scale is needed.Therefore,this study proposes a theoretical framework for the spatial heterogeneity of innovation capitalization at the urban agglomeration scale.Examining the Guangdong-Hong Kong-Macao Greater Bay Area(GHMGBA),China as a case study,the study investigated the spatial heterogeneity of the influence of high-tech firms,representing innovation,on housing prices.This work verified the spatial heterogeneity of innovation capitalization.The study constructed a data set influencing housing prices,comprising 11 factors in 5 categories(high-tech firms,convenience of living facilities,built environment,the natural environment,and the fundamentals of the districts)for 419 subdistricts in the GHMGBA.On the global scale,the study finds that high-tech firms have a significant and positive influence on housing prices,with the housing price increasing by 0.0156%when high-tech firm density increases by 1%.Furthermore,a semi-geographically weighted regression(SGWR)analysis shows that the influence of high-tech firms on housing prices has spatial heterogeneity.The areas where high-tech firms have a significant and positive influence on housing prices are mainly in the GuangzhouFoshan metropolitan area,western Shenzhen-Dongguan,north-central Zhongshan-Nansha district,and Guangzhou—all areas with densely distributed high-tech firms.These results confirm the spatial heterogeneity of innovation capitalization and the need for further discussion of its scale and spatial limitations.The study offers implications for relevant GHMGBA administrative authorities for spatially differentiated development strategies and housing policies that consider the role of innovation in successful urban development.展开更多
基金sponsored by the National Natural Sciences Foundation Project "Study on the Interaction Mechanism between the Self-Employment of Rural Migrant Labor and Their Transformation into Urban Citizens in the Process of New-Type Urbanization" (Grant No. 71473135)
文摘This paper investigates the gap of demographic urbanization arising from the difference between rural residents who have migrated to cities and those who have acquired urban citizenship in the process of China's urbanization. The skyrocketing house prices and insufficient household consumption power are key factors to the widening gap, which had reached 18% in 2013. In order to explore this issue, by creating the basic model and the model with interaction term, this paper has analyzed the relationship among house prices, consumption power and gap of urbanization using the data of 31 provinces between 1999 and 2013 in China. Empirical result indicates that: there is a positive correlation between the house prices and the gap of China's demographic urbanization. However, such a correlation is restrained by these rural migrants who rent houses in cities. For an increase of house price by 1%, the gap of urbanization will widen by 1.05%. Although rising urban consumption power of rural residents has increased the ratio of migration, the lagged growth of consumption power has led to a widening gap of urbanization. Therefore, the only way to effectively reduce the gap of demographic urbanization is to increase the consumption power of migrant population and optimize consumption structure.
文摘Changes in prices of homes are hypothesized as correlated with the times of their sale and resale and the attributes of their dwelling unit and neighbourhood and those of neighbouring homes. They may also be correlated with the occurrences of events inside the neighbourhoods caused by the activities of </span><span style="font-family:Verdana;">individuals and organizations outside the neighbourhoods, such as whether the local economy is in a recession or has a high unemployment rate. Calibrated hybrid housing price models predict precipitous decreases in house prices of approximately 2900 sold and resold homes in two inner-city neighbourhoods</span> <span style="font-family:Verdana;">in Windsor, Ontario, during those events since 1981 or 1986. Overall modest predicted percentage increases in houses’ prices during more than 30 years therefore subsumed periods of inner-city neighbourhood deterioration i</span><span style="font-family:Verdana;">n </span><span style="font-family:Verdana;">dispersed locations of unimproved and disimproved homes. Compensatory predictions however are of increasing prices for minorities of homes with improvements to several attributes of the dwelling unit and neighbourhood.
文摘This paper uses the HS2 extension cancellation in November 2021 as a quasi-experiment to study its impact on house prices and rents in Leeds.Using a DiD approach on repeat sales and monthly rents,I compare property values near the HS2 station and proposed construction site before and after the announcement.Results show a 3.6%decrease in house prices and a 3.9%decline in rents near the station,while properties near the construction site experienced a 2.4%increase in prices and a 2.1%rise in rents.This is the first paper to analyse the HS2 cancellation effect using panel data methods.
基金supported by the Ministry of Education of China(No.12JJD790039)the Fundamental Research Funds for the Central Universitiesthe Research Funds of Renmin University of China
文摘The high and rising house prices in China are not adequately accounted for the traditional explanations emphasizing demand-driven or cost-push factors. Reeent published studies claim that gender imbalance increases competition among men in the marriage market, which has pushed Chinese, especially parents with a son, to buy houses as a signal of relative status in the marriage market,＂ this marriage competition then causes high demand for houses and eventually leads to rising house prices in China. Empirical results in this paper, however, provide little support for this hypothesis and we find that a rise in the sex ratios for most age cohorts accounts for very small percentage variations in house price movements in China during 1998-2009. Further investigation suggests that excess demand driven by high monetary growth was a significant cause of the rising house prices in China during 1998-2009. Therefore, the impact of gender imbalance on house prices shouM not be exaggerated and monetary dynamics remains an important leading indicator for house price movements in China.
文摘Beijing is festooned with gigantic billboards extolling the virtues of luxurious accommodation and leaving no doubt that a prestigious address is a symbol
基金National Natural Science Foundation of China,No.41101119,No.41530751
文摘Existing studies about the modeling of urban housing price have figured out sets of factors and the main focus is on the relative spatial location. Generally, this line of research is descriptive rather than modeling in nature. The underlying reasons for the distribution of housing price are largely unexplored and more research is needed. The paper therefore attempted to systematically explore the spatial heterogeneities of urban housing price based on the urban activity interaction rule. Using Beijing as a case study, this study first developed a new measurement of accessibility which directly depicts the cost and possibilities to access opportunities of different activities such as employments, educational, shopping and medical services. From the perspective of demands of different households, the paper then modelled the relationships between urban housing price and these accessibilities and found that the distribution pattern of housing price can be relatively well represented by this model that the R^2 could achieve 0.7. We investigated the relationship between housing price and the demands of different kinds of households categorized by households of one-generation, two-generation, three-generation and four-and-plus-generation and found that the demands of household of four-and-plus-generations is the most highly correlated with housing prices. The reason might be that this kind of household has more household members and the demands are more diverse and complex, which is more similar to the distributions of all kinds of activity opportunities in the real world. In the end of the paper, some implications for policy-making are proposed based on the results of the analyses.
基金Supported by the National Natural Science Foundation of China(71573244,71532013,71850014)
文摘House price expectations(HPE)is a key factor affecting housing market fluctuations.Taking Beijing as an example,this study innovatively proposes an internet data-based measurement of HPE and text analysis-based quantification of media reports from media attention and media attitudes.Regression model is performed to empirically test the impact of media reports on the level and accuracy of HPE.The empirical results show no significant relationship between media attention and the level of HPE,but a significant relationship to the accuracy of HPE.The higher the media attention(i.e.,the more intensive the media reports),the smaller the deviation between HPE and actual housing prices.The attitude of media reports is significantly related to the level and accuracy of HPE.It is easier to guide the formation of HPE through media reports with clear opinions,indicating that media could promote the sustainable development of the real estate market.
基金supported by the National Research Foundation of Korea grant funded by the Korea government(MSIT)(RS-2025-16067531:Kwangwon Ahn)Hankuk University of Foreign Studies Research Fund(0f 2025:Sihyun An).
文摘The nonlinearity of hedonic datasets demands flexible automated valuation models to appraise housing prices accurately,and artificial intelligence models have been employed in mass appraisal to this end.However,they have been referred to as“blackbox”models owing to difficulties associated with interpretation.In this study,we compared the results of traditional hedonic pricing models with those of machine learning algorithms,e.g.,random forest and deep neural network models.Commonly implemented measures,e.g.,Gini importance and permutation importance,provide only the magnitude of each explanatory variable’s importance,which results in ambiguous interpretability.To address this issue,we employed the SHapley Additive exPlanation(SHAP)method and explored its effectiveness through comparisons with traditionally explainable measures in hedonic pricing models.The results demonstrated that(1)the random forest model with the SHAP method could be a reliable instrument for appraising housing prices with high accuracy and sufficient interpretability,(2)the interpretable results retrieved from the SHAP method can be consolidated by the support of statistical evidence,and(3)housing characteristics and local amenities are primary contributors in property valuation,which is consistent with the findings of previous studies.Thus,our novel methodological framework and robust findings provide informative insights into the use of machine learning methods in property valuation based on the comparative analysis.
基金Under the auspices of National Natural Science Foundation of China(No.41471140,41771178)Liaoning Province Outstanding Youth Program(No.LJQ2015058)
文摘This paper studies the relationship between accessibility and housing prices in Dalian by using an improved geographically weighted regression model and house prices, traffic, remote sensing images, etc. Multi-source data improves the accuracy of the spatial differentiation that reflects the impact of traffic accessibility on house prices. The results are as follows: first, the average house price is 12 436 yuan(RMB)/m^2, and reveals a declining trend from coastal areas to inland areas. The exception was Guilin Street, which demonstrates a local peak of house prices that decreases from the center of the street to its periphery. Second, the accessibility value is 33 minutes on average, excluding northern and eastern fringe areas, which was over 50 minutes. Third, the significant spatial correlation coefficient between accessibility and house prices is 0.423, and the coefficient increases in the southeastern direction. The strongest impact of accessibility on house prices is in the southeastern coast, and can be seen in the Lehua, Yingke, and Hushan communities, while the weakest impact is in the northwestern fringe, and can be seen in the Yingchengzi, Xixiaomo, and Daheishi community areas.
文摘In recent years,with the rapid development of China’s economy,a large number of people have flocked to the cities,which also brings more residential waste.The increased waste overloads transfer stations located near residential areas,and the continuous noise and odour affect the daily lives of nearby residents.In addition,the neighbourhood avoidance facilities represented by the waste transfer stations also reduce the value of the surrounding residents’houses.Therefore,using the conditional value method and the Tobit and Double Hurder econometric models,this article investigates the implicit value of the Fuli Resort neighbourhood under the influence of the waste transfer station through a questionnaire survey on the willingness of the residents to accept the compensation,which can be regarded as the“aversion value”of the neighbourhood due to the aversion to the waste transfer station and analyses the impact of the aversion value of the neighbourhood.aversion value and analyses the impact on residents’willingness to accept compensation.The study found that the residents’willingness to accept compensation near the waste transfer station is 511.94 RMB/person/month,and the implicit value of the Fuli Resort neighbourhood under the influence of the waste transfer station in Qinhuai District,Nanjing,Jiangsu Province,China,is 147,950 RMB.The study found that residents are most interested in having the government rectify the waste transfer station and set sanitary standards and work norms.
文摘In recent Years, China＇s real estate market has been rapid developed, and real estate has become a hot spot of consumption and investment. In some large and medium-sized cities there has been a rapid rise in housing prices. The rapid rise in housing prices has led to difficulties in the purchase of houses in some cities and towns, and this phenomenon has aroused the attention and con- cern of all walks of life. Housing is the basic human life needs. Housing problem is not only an economic problem, but also a social problem. The relationship between house price and land price and the effective control of housing prices have become the focus of government and scholars. Thus, grey relational analysis is used to ana- lyze the relationship between housing prices and land prices, and the grey relational coefficients are calculated.
基金National Natural Science Foundation of China,No.41601151Natural Science Foundation of Guangdong Province,No.2016A030310149Pearl River S&T Nova Program of Guangzhou
文摘This study analyzes the spatial patterns and driving forces of housing prices in China using a 2,872-county dataset of housing prices in 2014.Multiple theoretical perspec-tives on housing demand,supply,and market,are combined to establish a housing price model to explore the impact of land prices on housing prices.The relative impacts of land prices on housing prices at different administrative levels are then analyzed using the geo-graphical detector technique.Finally,the influencing mechanism of land prices on housing prices is discussed.The main conclusions are as follows.(1)Housing prices have a pyra-mid-ranked distribution in China,where higher housing prices are linked to smaller urban populations.(2)Land prices are the primary driver of housing prices,and their impacts on housing prices vary over different administrative levels.To be specific,the effect of land prices is the strongest in the urban districts of provincial capital cities.(3)The internal influ-ence mechanisms for land prices driving housing prices are:topographic factors,urban con-struction level,the agglomeration degree of high-quality public service resources,and the tertiary industrial development level.The urban land supply plan(supply policies)is the in-trinsic driver that determines land prices in cities;through supply and demand,cost,and market mechanisms,land prices then impact housing prices.
基金National Natural Science Foundation of China, No.41401164, No.41201128
文摘In this study, housing prices data for residential quarters from the period 2001-2012 were used and Global Differentiation Index （GDI） was established to measure the overall differentiation trend in housing prices in Yangzhou City, eastern China. Then the influence of the natural landscape and environment on prices of global housing market and housing submarkets was evaluated by the hedonic price model. The results are shown as follows. （1） There have been increasing gaps among housing prices since 2001. In this period, the differentiation trend has shown an upward fluctuation, which has been coupled with the annual growth rate of housing prices. （2） The spatial distribution of residential quarters of homogenous prices has changed from clustered in 2001 into dispersed in 2012. （3） Natural landscape and environmental externalities clearly influence spatial differentiation of housing prices. （4） In different housing submarkets, the influence of natural landscape and environmental eternalities are varied. Natural landscape characteristics have significant impact on housing prices of ordinary commercial houses and indemnificatory houses, while the impact of environmental characteristics have obvious influence on housing prices of cottages and villas.
基金National Natural Science Foundation of China(No.41201164,L1422012)Humanity and Social Science Youth Foundation of Ministry of Education of China(No.12YJCZH299)China Postdoctoral Science Foundation(No.2012M521420,2014T70693)
文摘As one of the essential urban open spaces, lakes usually contribute immensely to the quality of residents′ daily lives. Different from hedonic approach employed in existing researches on urban open spaces in China, this paper integrates housing price surface with road density to analyze the spatial characteristics in proximity to urban lakes in Wuhan City, China. With the expansion of Wuhan City, urban lakes became polluted, they shrunk or even disappeared, leading to unfavorable conditions for sustainable development of the city. To better understand the spatial relationship between the city and lakes, we classify the urban lakes in Wuhan central area into ′lakes in the urban center′ and ′lakes in urban fringe′. Based on housing price surface we explore the spatial characteristics in proximity to different lakes and differences between the lakes. We also use Geographic Information System(GIS) tool to calculate road density as a supplementary indicator to reflect the accessibility in proximity to urban lakes. The results indicate that relative independence exists between different towns, and the spatial characteristics are different depending on scales and locations. In most of cases, the road density is lower where closer to the lakeshore while the housing price exhibits an opposite pattern. We conclude that city governments and urban planners should give more considerations to these spatial differences, somewhere should be better planned and protected as an important waterfront and somewhere the control of unreasonable real estate development nearby should be strengthened.
基金Project supported by the National Social Science Foundation of China (No. 05CJY017), the Philosophy and Social Science Founda-tion of Zhejiang Province, China (No. N04GL06), and ShuguangProject (2004) of Zhejiang University, China
文摘The hedonic price model is widely applied to study the urban housing market because of the heterogeneity of housing products. Literature indicated that the hedonic price theory mainly includes two parts: Lancaster’s partiality theory and Rosen’s characteristic market equilibrium analysis. This paper chose 18 characteristics as independent variables and set up a linear hedonic price model for Hangzhou City. The model was tested with 2473 housing samples and field survey data of 290 housing commu-nities. This research found that 14 out of 18 characteristics had significant influence on housing price. They were classified into 5 groups according to their impact degree.
基金Under the auspices of National Natural Science Foundation of China(No.42071162,41001097)Key Research Program of the Chinese Academy of Sciences(No.ZDRW-ZS-2017-4-3-4)National Science and Technology Basic Project of the Ministry of Science and Technology of China(No.2017FY101303-1)。
文摘Population growth has been widely regarded as an important driver of surging housing prices of urban China,while it is unclear as yet whether population shrinkage has an impact on housing prices that is symmetrical with that of population growth.This study,taking 35 sample cites in Northeast China,the typical rust belt with intensifying population shrinkage,as examples,provides an empirical assessment of the roles of population growth and shrinkage in changing housing prices by analyzing panel data,as well as a variety of other factors in related to housing price,during the period of 1999–2018.Findings indicate that although gap in housing prices was widening between population growing cities and population shrinking cities,the past two decades witnessed an obvious rise in housing prices of those sample cities to varying degree.Changes in population size did not have a statistically significant impact on housing prices volatility of sample cities,because population reduction did not lead to a decline in housing demand correspondingly and an increasing housing demand aroused by population growth was usually followed by a quicker and larger housing supply.The rising housing prices in sample cities was mainly driven by factors like changes in land cost,investment in real estate,GDP per capita and household number.However,this does not mean that the impact of population shrinkage on housing prices could be ignored.As population shrinkage intensifies,avoiding the rapid decline of house prices should be the focus of real estate regulation in some population shrinking cities of Northeast China.Our findings contribute a new form of asymmetric responses of housing price to population growth and shrinkage,and offer policy implications for real estate regulation of population shrinking cities in China’s rust belt.
基金the Humanity and Social Science on Youth Foundation of Ministry of Education of China(No.14YJC790152)the Foundation of Shanghai Municipal Education Commission(No.2016-SHNGE-03-ZD)the China Postdoctoral Science Foundation(No.2013M531157)
文摘Using system clustering method to group China's provinces into 3 new groups according to their housing prices, then establishing a state-space model and applying the Kalman filter calculation, we made a comparative analysis of the influences of different types of monetary policy instruments towards different regional housing prices. The empirical results show that both the quantitative instruments represented by M2 and the pricing instruments represented by real interest rate have increasing influences on different regional housing prices,but the former influence is much stronger than the latter. The influential differences of quantitative instruments to regional housing prices are much greater. It means the higher the regional housing price is, the greater the influence is. Therefore, the central bank shall optimize the combination of monetary policy instruments according to the above characteristics of different types of monetary policy instruments in order to acquire the regulatory target of real estate market.
文摘The impact of different public service facilities is obtained by investigating the infl uence of public service facilities on distribution pattern of housing price in 25 cities.According to the survey results,public education service facilities have the highest weight and the greatest impact,which also refl ects the root of“school district housing fever”from the side.Public sports service facilities have the lowest score when compared with other options.This is not because public sports service facilities are not important,but is determined by actual situation of social development and actual living standard of residents in China.From the improvement and enhancement of urban public service facilities,the construction of public service facilities should be convenient for people’s education,health,culture and entertainment.
基金Supported by the Hundred Talent Program of the Chinese Academy of Sciences,the National Natural Science Foundation of China under Grant Nos.71103179 and 71102129Program for Young Innovative Research Team in China University of Political Science and Law, 2010 Fund Project under the Ministry of Education of China for Youth Who are Devoted to Humanities and Social Sciences Research 10YJC630425
文摘This works examine the responses of housing prices to the monetary policies in various Chinese cities. Thirty-five large and medium sized Chinese cities are classified into six clusters applying the minimum variance clustering method according to the calculated correlation coefficients between the housing price indices of every two cities.Time difference correlation analysis is then employed to quantify the relations between the housing price indices of the six clusters and the monetary policies.It is suggested that the housing prices of various cities evolved at different paces and their responses to the monetary policies are heterogeneous,and local economic features are more important than geographic distances in determining the housing price trends.
基金Under the auspices of the National Natural Science Foundation of China (No.42101182,41871150)Guangdong Academy of Sciences (GDSA)Special Project of Science and Technology Development (No.2021GDASYL-20210103004,2020GDASYL-20200102002,2020GDASYL-20200104001)the Natural Science Foundation of Guangdong (No.2023A1515012399)。
文摘Innovation capitalization is a new concept in innovation geography research.Extant research on a city scale has proven that innovation is an important factor affecting housing prices and verified that innovation has a capitalization effect.However,few studies investigate the spatial heterogeneity of innovation capitalization.Thus,case verification at the urban agglomeration scale is needed.Therefore,this study proposes a theoretical framework for the spatial heterogeneity of innovation capitalization at the urban agglomeration scale.Examining the Guangdong-Hong Kong-Macao Greater Bay Area(GHMGBA),China as a case study,the study investigated the spatial heterogeneity of the influence of high-tech firms,representing innovation,on housing prices.This work verified the spatial heterogeneity of innovation capitalization.The study constructed a data set influencing housing prices,comprising 11 factors in 5 categories(high-tech firms,convenience of living facilities,built environment,the natural environment,and the fundamentals of the districts)for 419 subdistricts in the GHMGBA.On the global scale,the study finds that high-tech firms have a significant and positive influence on housing prices,with the housing price increasing by 0.0156%when high-tech firm density increases by 1%.Furthermore,a semi-geographically weighted regression(SGWR)analysis shows that the influence of high-tech firms on housing prices has spatial heterogeneity.The areas where high-tech firms have a significant and positive influence on housing prices are mainly in the GuangzhouFoshan metropolitan area,western Shenzhen-Dongguan,north-central Zhongshan-Nansha district,and Guangzhou—all areas with densely distributed high-tech firms.These results confirm the spatial heterogeneity of innovation capitalization and the need for further discussion of its scale and spatial limitations.The study offers implications for relevant GHMGBA administrative authorities for spatially differentiated development strategies and housing policies that consider the role of innovation in successful urban development.
