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.

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.

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.

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.

Housing prices and household savings:evidence from urban China

Based on precautionary saving motives,this research develops a three-period life-cycle model to manifest the impact of housing prices on household savings in urban China.The theoretical model illustrates that the expected appreciation of housing prices at a household’s middle age leads to the increase in household savings at a household’s young age.Second,household savings at a household’s young age are positively associated with both expected educational and medical expenditures in a household’s middle age and pension expenditures at a household’s old age.Third,the expected housing prices crowd out educational and medical expenditures at a household’s middle age.With the panel data sets of China’s 31 provinces during 1996–2016,results suggest that the expected housing prices significantly interact with the current household savings.However,the influence of the expected housing prices on the current household savings is greater than that of the current household savings on the expected housing prices.Third,the expected expenditures of education,medical care and pension fuel up the current household savings.Meanwhile,the housing prices crowd out the expenditures of education,medical care and pension.Finally,data of the Urban Household Survey(UHS)over the period 2002–2007 show that the household head age has an effect of reverse U-shape on household savings.Accordingly,to prevent a housing bubble and promote household consumption,policy makers should curb housing price inflation by enacting appropriate countercyclical housing policies.

Can Property Tax Curb Housing Prices?

After years of test runs, property tax maybe adopted all over China, with Beijing and Shenzhen as pilot cities, said the Shanghai Securities News in December 2009.

Asymmetric Fluctuating Behavior of China＇s Housing Prices

The present study explains the reasons for the imbalanced development of the Chinese housing market. Using the quantile autoregression unit-root test, we examine housing prices in China＇s five major cities. The results show that the rising and falling of housing prices in these cities exhibits asymmetric reversion. When housing prices fall, market capital is highly sensitive to housing prices, and housing prices resist the pressure to faU further. However, when housing prices rise, the housing market becomes imbalanced, with housing prices tending to overreact in an upturn. The results of this study indicate that when housing prices rise irrationally, the government should intervene in the housing market promptly to prevent housing bubbles.

Different Types of Monetary Policy Instruments and Regional Housing Prices:A Comparative Study on the Influences of Regulation in China

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.

Nonlinear delay difference equations for housing dynamics assuming heterogeneous backward-looking expectations

China＇s first interest rate hike during the last decade, aiming to cool down the seemingly overheated real estate market, had aroused more caution on housing market. This paper aims to analyze the housing price dynamics after an unanticipated economic shock, which was believed to have similar properties with the backward-looking expecta- tion models. The analysis of the housing price dynamics is based on the cobweb model with a simple user cost affected demand and a stock-flow supply assumption. Several nth- order delay rational difference equations are set up to illustrate the properties of housing dynamics phenomena, such as the equilibrium or oscillations, overshoot or undershoot and convergent or divergent, for a kind of heterogeneous backward-looking expectation models. The results show that demand elasticity is less than supply elasticity is not a necessary condition for the occurrence of oscillation. The housing price dynamics will vary substantially with the heterogeneous backward-looking expectation assumption and some other endogenous factors.

A heterogeneous boundedly rational expectation model for housing market

This research aims to test the housing price dynamics when considering heterogeneous boundedly rational expectations such as naive expectation, adaptive expectation and biased belief. The housing market is investigated as an evolutionary system with heterogeneous and competing expectations. The results show that the dynamics of the expected housing price varies substantially when heterogeneous expectations are considered together with some other endogenous factors. Simulation results explain some stylized phenomena such as equilibrium or oscillation, convergence or divergence, and over-shooting or under-shooting. Furthermore, the results suggest that variation of the proportion of groups of agents is basically dependent on the selected strategies. It also indicates that control policies should be chosen carefully in consistence with a unique real estate market during a unique period since certain parameter portfolio may increase or suppress oscillation.

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.

Inner-City Neighbourhood Changes Predicted from House Prices in Windsor, Ontario, since the Early- or Mid-1980s

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 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 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 in 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.

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.

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.

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.

Correlation Analysis of Fiscal Revenue and Housing Sales Price Based on Multiple Linear Regression Model

This paper selects seven indicators of financial revenue and housing sales price in recent 19 years in China,and uses SPSS and Excel to carry out descriptive statistics,independent sample t-test,correlation analysis and regression analysis to comprehensively study the correlation between financial revenue and housing sales price in China,and establishes the relationship between financial revenue and housing sales price When the average selling price of commercial housing increases by one unit,the fiscal revenue will increase by 27.855 points.

Impact of neighborhood features on housing resale prices in Zhuhai (China) based on an (M)GWR model

The paper aims at exploring the relationship between housing resale prices and neighborhood features in Zhuhai,as well as structure and location characteristics.Thirteen neighborhood features are collected to analyze their influence on average community-level apartment resale prices in 2018.Six neighbor-hood features,structural and location characteristics,are selected according to their statistical significance and multi-collinearity test results from an OLS model.Regression analysis is performed by OLS,GWR,and MGWR to compare their per-formance in housing price research at community level.The comparison of the three models also demonstrates that the GWR(66%)and MGWR(68%)models perform much better than OLS model(52%).MGWR is not significantly different from GWR in areas with few sample points,and the optimal bandwidth at different spatial scales is hard to be captured in a city-level study area.The regression parameter indicates that building age is the most important factor among all influen-cing factors.Proximity to schools and factories have positive and negative significant effects on housing resale prices,respectively.The spatial pattern of neighborhood features is also detected at town level.GWR and MGWR models accurately demonstrate local spatial heterogeneity of the housing resale market,which provides better results than the traditional OLS model in the goodness of fit and parameter estimates when spatial dependency is present.The results provide references for local planning departments,helping to reveal the compli-cated relationship and spatial patterns between housing price and determinants over space.