Identifying Spatial Heterogeneity in the Effects of High-Tech Firm Density on Housing Prices:Evidence from Guangdong-Hong Kong-Macao Greater Bay Area,China

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.

Relationship between Housing Price and Land Price Based on Grey Relational Analysis

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.

A GIS-based Spatial Analysis of Housing Price and Road Density in Proximity to Urban Lakes in Wuhan City, China

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.

Impact of Urban Public Service Facilities on Distribution Pattern of Housing Price

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.

Responses of Housing Price under Different Directions of Population Change:Evidence from China's Rust Belt

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.

Heterogeneous Responses of Chinese Cities' Housing Prices to Monetary Policies

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.

Research on the Impact of Market Concern for Real Estate Policy on Housing Prices: Evidence from Internet Search and Hedonic Price Theory

To avoid the effects of systemic financial risks caused by extreme fluctuations in housing price,the Chinese government has been exploring the most effective policies for regulating the housing market.Measuring the effect of real estate regulation policies has been a challenge for present studies.This study innovatively employs big data technology to obtain Internet search data(ISD)and construct market concern index(MCI)of policy,and hedonic price theory to construct hedonic price index(HPI)based on building area,age,ring number,and other hedonic variables.Then,the impact of market concerns for restrictive policy,monetary policy,fiscal policy,security policy,and administrative supervision policy on housing prices is evaluated.Moreover,compared with the common housing price index,the hedonic price index considers the heterogeneity of houses and could better reflect the changes in housing prices caused by market supply and demand.The results indicate that(1)a long-term interaction relationship exists between housing prices and market concerns for policy(MCP);(2)market concerns for restrictive policy and administrative supervision policy effectively restrain rising housing prices while those for monetary and fiscal policy have the opposite effect.The results could serve as a useful reference for governments aiming to stabilize their real estate markets.

Examining the determinants of housing prices and the influence of the spatial–temporal interaction effect:The case of China during 2003–2016

The slowdown of the Chinese economy has been accompanied by a recent rapid rise in housing prices,which has put severe pressure on China's high-quality development.Therefore,understanding the impact of the spatial–temporal interaction effect on housing prices and their potential determinants is critical for formulating housing policies and achieving sustainable urbanization.This study empirically analyzed both of these based on four aspects—the financial market,housing market,housing supply,and housing demand—using 2006–2013 data of 285 prefecture-level(and above)Chinese cities and spatial econometric models.The results indicated that the housing prices of Chinese cities were heavily affected by the interaction effect of space and time,both at the national and regional levels;however,the influence of this interaction effect exhibited a significant spatial differentiation,and only consistently drove up housing prices in Eastern and Western China.Additionally,the regional results based on administrative and economic development levels revealed that wage and medical service levels in first-and second-tier cities had negatively affected the competitiveness and efficiency of the Chinese economy during the investigation period.These findings suggest the need for land supply systems based on the increasing population to prevent housing prices from rising too quickly as well as policies that consider regional variations,accompanied by corresponding supporting measures.

A Study on Spatial Distribution of Commercial Housing Prices in Xiangtan City

ArcGIS technology is used to study the spatial pattern of housing prices in Xiangtan City,and it is found that the spatial pattern of housing prices shows primary and secondary two-center rings. In Hedong Jianshe Road and near Hexi Jijianying,there are primary and secondary polar nuclei,respectively; the secondary housing price area is located near the east-west and south-north trunk road in the urban area; there are significant regional differences in housing price changes( fastest reduction of prices in the Hedong main center-southwest direction; slow reduction of prices in the main center-northwest,southeast direction; slowest reduction of prices in the main center-northeast direction). In Hexi sub-center,except slow reduction of prices in the Xiangjiang River direction,the prices decline rapidly in other directions. The housing prices exhibit an obvious overall decreasing trend from primary and secondary centers to the suburbs,but there are also exceptions. On this basis,this paper analyzes the driving factors for spatial pattern of housing prices in Xiangtan City,and finds that the spatial pattern of housing prices is mainly influenced by commercial centers,residential environmental conditions,traffic conditions,and urban land layout differences.

Distribution Pattern of Urban Housing Price Based on Multiple Big Data:A Case Study of Beijing,Shanghai,Guangzhou,and Wuhan

The housing price has been paid close attention by people in all walks of life,and the development of big data provides a new data environment for the study of urban housing price.Housing price data of four national central cities (Beijing,Shanghai,Guangzhou and Wuhan) are taken as research samples.With the help of software GIS,exploratory spatial data analysis method is used to depict the spatial distribution pattern of urban housing price,and commonness and difference of spatial distribution of housing price are explored.The conclusions are as below:①regional imbalance of housing price in national central cities is significant.②Spatial distribution of urban housing price in Beijing,Shanghai,Guangzhou and Wuhan presents a polycentric pattern,and there is obvious spatial agglomeration.③The internal change of housing price in different cities has significant spatial difference.Beijing,Shanghai,Guangzhou and Wuhan are taken as typical city samples for research,with reference and practical significance,which could help to effectively predict spatial development trend of housing prices in other first and second tier cities.The research aims to provide certain reference for the government implementing real estate control policies according to local conditions,project location and reasonable pricing of real estate developers.

Analysis of Factors Influencing Second-hand Housing Prices in Beijing based on Hierarchical Linear Model

In recent years,housing prices have attracted widespread attention,and the fluctuation of housing prices is due to a combination of many factors.In addition to the characteristics of the house itself,the price of a house is also affected by other factors,such as the community in which the house is located.This article used Beijing’s 2017 second-hand housing transaction data (based on second-hand housing transaction records on Lianjia.com),introduced a hierarchical linear model,and employed Stata software to analyze from different levels.It is intended to find the correlation between housing prices and different levels of characteristics,so to pin down the factors that affect prices of the second-hand housing.

The Influence of Built Environment on the Spatial Distribution of Housing Price:Based on Multiple Big Data and Hedonic Model

In recent years,more and more researches focus on the self characteristics and spatial location of housing,and explore the influencing factors of urban housing price from the micro perspective.As representative of big cities,spatial distribution pattern of housing price in national central cities has attracted much attention.In order to return the spatial distribution pattern of housing price to the research on influencing factors of housing price,the reasons behind the spatial distribution pattern of housing price in three national central cities:Beijing,Wuhan and Chongqing are explored.The results show that①urban housing price is affected by many factors.Due to different social and economic conditions in each city,there are differences in the influence direction of the proximity to expressways,city squares,universities and living facilities,characteristics of companies and enterprises on Beijing,Wuhan and Chongqing.②Various factors have different value-added effects on housing price in different cities.The location of ring line in Beijing and Wuhan has the greatest increase effect on housing price,while metro station of Chongqing has the greatest increase effect on housing price.

The Interactions among Housing Price, Inflation and Monetary Policy in China： Facts and Implications

As a transition economy, China has unique characteristics regarding housing price, inflation and monetary policies. In addition to supply and demand powers, China＇s housing price is affected by demographic structure, fiscal system and land supply system. Using China＇s statistics from 2005 to 2011, this paper found empirically that （1） liquidity has a significant impact on China＇s housing prices and inflation; （2） China＇s negative real interest rate has pushed up housing price; （3） price-related instrument of monetary policy can curb housing price and is more effective than control of money supply.

Impact of Accessibility on Housing Prices in Dalian City of China Based on a Geographically Weighted Regression Model

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.

Housing Price Fluctuations Across China:An Equilibrium Mechanism Perspective

The mechanisms affecting housing prices were studied using the equilibrium housing prices based on classic supply/demand theory. The fluctuations of the actual housing prices were then analyzed relative to the equilibrium prices. The equilibrium prices for each area were calculated from economic statistics and housing prices in 35 China metropolitan areas. The fluctuations of the actual prices are then manifested as functions of the equilibrium price, the mean reversion, and the autocorrelation coefficient. The results show that the equilibrium prices are determined by the basic economic conditions in China and that the equilibrium prices greatly affect the fluctuation of the actual prices, which return to the equilibrium price through self-adjustments. The data also shows that the actual prices in China have the trend of continuing to rise in the future.

The Effects of Infrastructure Projects on House Prices and Rents:Evidence from the HS2 Extension Cancellation in the UK

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.

Effect of land prices on the spatial differentiation of housing prices:Evidence from cross-county analyses in China

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.

Impact of urban landscape and environmental externalities on spatial differentiation of housing prices in Yangzhou City

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.

Housing Prices and General Economic Conditions: An Analysis of Chinese New Dwelling Market

This paper presents an investigation of the interaction between housing prices and general eco- nomic conditions in China for the period of 1986-2002. The empirical results indicate that housing prices in China are predictable by market fundamentals, which could explain most of the variations in housing prices. The results of Granger causality tests confirm that unemployment rate, total population, changes in con- struction costs, changes in the consumer price index (CPI) are all Granger causalities of housing prices, with feedback effects observed to affect the vacancy rate of new dwellings, changes in CPI, and changes in per capita disposable income of urban households. Studies with impulse response functions further illustrate these relationships in terms of the degree of the impact on housing prices from the determinants and the feedbacks. The findings indicate that there is a long-term equilibrium relationship between housing prices and market fundamentals in China and it is the identified fundamentals that drive housing prices up, rather than a bubble.

Excess Liquidity,Housing Price Booms and Policy Challenges in China

Incorporating asymmetric cost and benefit of supplying excess liquidity into an otherwise standard time inconsistency model this paper offers an explanation of the excess liquidity and housing price booms recently experienced in China. We find that the central bank＇s incentive to stimulate eeonomie growth with excess liquidity fuels real estate prices and accelerates inflation bias. Therefore, the central bank should free itself from the pressure to achieve an economic growth rate higher than the potential level and assign an appropriate weight to the real estate price fluctuations in the central bank＇s objective function, so that the central bank ＂s policy of stimulating economic growth with excess liquidity can be constrained.