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.

What Drives China's House Prices:Marriage or Money?

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.

Fiscal Pressure,Land Finance and Ratchet Effects of House Prices

In this paper,we focus on the issues of local governments’fiscal pressure,land finance and house prices,and systematically analyze how local governments’fiscal pressure and land finance lead to China’s ratcheting up of house prices.The results show that to release the fiscal pressure,local governments tend to increase land revenue and obtain high real estate related revenue by raising house prices.In this sense,the increase of the land transfer price will result in the increase of the cost of real estates,and eventually leading to the increase of house prices.That is to say,local governments’fiscal pressure will not only result in the increase of house prices directly but also consolidate the ratchet effects of house prices.

Are Luxury Housing Billboards the Scapegoat For High House Prices?

Beijing is festooned with gigantic billboards extolling the virtues of luxurious accommodation and leaving no doubt that a prestigious address is a symbol

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.

House Price, Consumption Power and Gap of Demographic Urbanization in China

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.

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.

The Impact of Property Tax on the Development of Real Estate Industry

In recent years,due to the rapid development of the real estate industry in China,land speculation has begun in addition to the significant growth in economy.However,this rapid development has led to an extreme rise in housing prices,largely owing to high property tax.This article analyzed the impact of property tax on the development of real estate industry and provided countermeasures.

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.

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.

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.

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.

Research on the Impact of Aging and New Generation in the Popula­tion Structure on China’s Real Estate Price Volatility

To clarify the internal mechanism of the influence of the aging population and the new generation on housing prices is helpful to scientifically analyze and predict the trend of housing prices and the aging population and the new generation.This paper uses the intergenerational overlap model of the two periods as the theoretical basis,and uses the provincial panel data from 1998 to 2018 to study the impact of the elderly population and the new generation on the price fluctuations of commercial housing.The results of the study show that on the whole,both the aging population and the new generation have promoted the rise in commodity housing prices.However,the regional heterogeneity is significant.The aging population has the most significant impact on housing price increases in developed and general developed areas,and has no significant impact on housing price increases in other places.The new generation has a negative impact on housing prices in backward areas and a positive impact on housing prices in other areas.Looking further,using the ARIMA model to predict housing prices in the next 10 years,it is concluded that housing prices will show a slow upward trend in the next 10 years.Therefore,the government can ensure the stable development of the real estate market by revitalizing the second-hand housing market and implementing housing projects.