Housing Prices In China Research Articles

Determinants of house prices in China: a panel-corrected regression approach

This paper uses annual data from 31 provinces, municipalities and autonomous regions in China from 2000 to 2018 to examine the determinants of Chinese house prices with a panel data regression method. Our finding suggests that land price, loans of real estate developers, per capita saving and the proportion of people with college or above educational degrees significantly drive up house prices, while the number of unemployed population has a significant negative impact. These five variables account for 72 percent variations in house prices across the country. Other economic fundamentals such as inflation, interest rate, per capita gross domestic products (GDP), and rent cost do not have significant influences on house prices. The econometric model is then used to assess the existence of house price bubbles in those provincial-level divisions in China by comparing their actual price levels with those predicted by the model. If the trend of house price growth during 2002–2014 found in the present study is the true long-run trend of house prices in China, with a few exceptions, the house prices in most provincial-level divisions are not overvalued by the end of 2014.

Study of a System Dynamics Model of Wuhan Commodity Housing Price

Commodity housing is the most important product in the development of modern civil engineering, and it is also the frontier problem of modern engineering project management research. The progress of civil engineering in China can be seen to a great extent through the development of commercial housing. In order to solve the severe problem of rapid growth of commodity housing price in China, considering the advantages of system dynamics theory, a system dynamics model of commodity housing price is constructed. Eight subsystems, namely, housing demand, housing supply, housing price, urban population, urban economy, housing land, housing tenancy, and macro‐control, are studied. Taking the relevant data of Wuhan as an example, the Vensim DSS is used for simulations. In addition, a validity test and sensitivity test are used to verify the validity and feasibility of the model, respectively. Based on the model, it is successfully predicted that the price of commercial housing in Wuhan will reach 18,207.9 yuan/m2 in 2030, which provides a more systematic method of prediction for synthesis simulation of commercial housing markets. From the perspective of the developer loan interest rate, real estate tax rate, purchase restriction, and other policies, we show that the developer loan interest rate regulation policy has the strongest effect on guiding the change in commercial housing prices in Wuhan. Generally, this study provides insight into the responses that the national government could use to control housing prices.

Analysis of the diffusion effect of urban housing prices in China based on the spatial-temporal model

This article contributes to the recent literature in the spatial diffusion pattern of house prices by constructing spatial-temporal model to test the spatial relationships of 70 housing markets in China. Drawing on the spatial-temporal method proposed by Holly, a multiregional spatial-temporal model suitable for China is formulated which are applied to study the spatial-temporal diffusion effect of urban housing prices in China. Firstly, the diffusion mechanism of urban housing prices is analyzed from theoretical perspective, and four cities are assumed as dominant cities, namely, Beijing, Shanghai, Shenzhen and Sanya. Secondly, the spatial linkage of the housing prices of 70 large and medium-sized cities from 2007 to 2017 is examined by calculating the global Moran's I index. Thirdly, a spatial-temporal model is constructed to empirically analyze the diffusion effects of the housing prices of 70 sample cites. The study concludes that 1) significant global spatial autocorrelation is observed among the housing prices in the 70 cites; 2) empirical analysis shows that Beijing, Shanghai, Shenzhen and Sanya are the dominant cities of the 70 cities; and 3) the spatial effects of the four dominant cities on other cities differ in areas, degrees and duration.

Rural-urban migration and house prices in China

This paper uses a dynamic competitive spatial equilibrium framework to evaluate the contribution of rural-urban migration induced by structural transformation to the behavior of Chinese housing markets. In the model, technological progress drives workers facing heterogeneous mobility costs to migrate from the rural agricultural sector to the higher paying urban manufacturing sector. Upon arrival to the city, workers purchase housing using long-term mortgages. Quantitatively, the model fits cross-sectional house price behavior across a representative sample of Chinese cities between 2003 and 2015. The model is then used to evaluate how changes to city migration policies and land supply regulations affect the speed of urbanization and house price appreciation. The analysis indicates that making migration policy more egalitarian or land policy more uniform would promote urbanization but also would contribute to larger house price dispersion.

The Health Consequence of Rising Housing Prices in China

China has experienced a rapid boom in real estate prices in the last two decades, leading to a substantial increase in living costs and heavy financial burdens on households, which may subsequently induce health problems. This paper examines the health consequence of rising housing prices by exploiting spatial and temporal variation in housing price appreciation linked to individual-level health data in China from 2000 to 2011. Using an instrumental variable approach, we find robust evidence that increases in housing prices significantly raise the probability of residents having chronic diseases. This negative health impact is more pronounced among individuals from low-income families, households that purchased rather than inherited or were allocated the home, and individuals with a rural registry. Exploring various possible channels, we find that marriage market competition exacerbates the negative health effects, particularly for males and parents with young adult sons. We also find evidence that housing price appreciation induces negative health consequences through increased work intensity, higher mental stress, elevated tobacco use and reduced sleep time. This paper underlines the unintended health consequences of the real estate market prosperity.

Can bank credit withstand falling house price in China?

This paper examines whether the falling house price causes credit risk or not in China. We note that bidirectional causal relationships exist in several sub-periods using sub-sample rolling window test. Our analysis confirms the option-based model (Foster & Van Order, 1984) that proves the falling house price leads to more defaults of mortgage and increasing credit risk. Meanwhile, the rise in credit risk is likely to be accompanied by the increasing house price. Rising house price has no impact on credit risk. We find banks’ credit expansion may be irrational which leads to the accumulation of credit risk. The tight credit policies and high loan interest rates stimulate the house price to fall. In addition, based on the analysis of the previous periods, we do not think that the credit risk will explode systematically in China with the current situation of mortgage growth slowing down. However, due to the pivotal role of real estate credit in loan structure of banks in China, we still need to be alert to the potential accumulation credit risk caused by the default of individuals and enterprises. It is essential for banks to strengthen the examination of personal credit certificates to prevent speculative loans. Regulators should take into account the impact on bank credit when they formulate policies to control the house price.

Innovation dynamics -what are the housing market uncertainty’s impacts

Uncertainty matters great for firms’ investment decisions and behaviors, and thus impacts economy growth. This paper uses the housing market uncertainty to measure economic uncertainty and explores how housing market uncertainty impacts on firms’ innovation activities. This paper shows the uncertainty has a positive impact on firms’ innovation performance, including R&D investment, innovation level and other innovation outcomes. Firms’ innovation performance is significantly driven by the uncertainty through the crowding-out effect of housing investment. Moreover, firms in higher competitive industries are more likely to invest in innovation due to the uncertainty shock. Furthermore, uncertainty in the housing market makes the cash flow and leverage used for innovation performance. Finally, innovation investment has a substitution effect on housing investment once there is a policy shock in limiting housing price in China.

Family Planning Policy and Housing Price in China

Different intergenerational fertility levels affected by the family planning policy under such altruistic behavior will inevitably affect real estate prices. This paper studies the effect of different intergenerational fertility levels on real estate prices under the parental altruistic behavior model with Chinese characteristics by constructing an Overlapping Generation Model (OLG) with intergenerational wealth transfer. The empirical results show that the lower the intergenerational fertility level of the middle-aged generation, the higher the average wealth level transferred to the youth generation, and the higher the real estate price. This result shows that, unlike the high fertility rate of popular cognition, the low fertility rate of the middle-aged generation under the influence of the family planning policy and the altruistic behavior of the Chinese parents are the important reasons for the current high housing prices. This paper reveals the relationship between China's population policy and real estate price, and can guide the judgment of China's real estate market in the future.

Predicting housing prices in China based on modified Holt's exponential smoothing incorporating whale optimization algorithm

The forecast of the real estate market is an important part of studying the Chinese economic market. Most existing methods have strict requirements on input variables and are complex in parameter estimation. To obtain better prediction results, a modified Holt's exponential smoothing (MHES) method was proposed to predict the housing price by using historical data. Unlike the traditional exponential smoothing models, MHES sets different weights on historical data and the smoothing parameters depend on the sample size. Meanwhile, the proposed MHES incorporates the whale optimization algorithm (WOA) to obtain the optimal parameters. Housing price data from Kunming, Changchun, Xuzhou and Handan were used to test the performance of the model. The housing prices results of four cities indicate that the proposed method has a smaller prediction error and shorter computation time than that of other traditional models. Therefore, WOA-MHES can be applied efficiently to housing price forecasting and can be a reliable tool for market investors and policy makers.

Do home purchase limits help? Government policy and housing prices in China

ABSTRACT Facing skyrocketing housing prices, the Chinese government implemented a policy of home purchase limits to maintain stability and sustainability in the residential property market. This paper aims at gauging the causal effect of the home purchase limits policy on housing prices, by replicating a quasi-experimental test with yearly data on 287 Chinese cities from 2007 to 2013. Based on a difference-in-differences matching estimator to control for observed and time-invariant unobserved city heterogeneity, we find that the policy led to decreased housing prices, and the decrease was contingent on the level of enforcement of the policy. The more strictly the policy was enforced, the more the housing prices dropped. Overall, our analyses highlight the importance of government intervention in the property market.

Can RMB Exchange Rate Expectations Explain the Fluctuations of China’s Housing Prices?

Unlike existing literature that has focused on the relationship between exchange rate and housing price, this paper studies the housing price fluctuations from the perspective of RMB exchange rate expectation to resolve the dilemma “guarantee housing price or exchange rate” after the sub-prime mortgage crisis. This paper shows that housing prices responded negatively to RMB appreciation expectation from 1999 to 2008, and positively from 2009 to 2019. After 2009, exchange rate expectation is the Granger causality of housing prices. After introducing the U.S. Economic Policy Uncertainty (EPU) released by Baker et al.(2016), the explanatory power of exchange rate expectations to housing price fluctuations declines but it's still significant. When EPU increased, housing prices responded negatively after a brief positive response. Besides exchange rate expectation, several unobservable factors with rich economic implications can explain the fluctuations of housing prices in China in the interval of 2006M01–2018M12. The empirical results show that the degree of Chinese government reversal intervention, interest rate spread between China and the U.S., and EPU can explain the exchange rate expectation. The government can control the degree of reversal intervention to affect the exchange rate expectation and realize the housing price control indirectly.

CAN MACHINE LEARNING ALGORITHMS ASSOCIATED WITH TEXT MINING FROM INTERNET DATA IMPROVE HOUSING PRICE PREDICTION PERFORMANCE?

Housing frenzies in China have attracted widespread global attention over the past few years, but the key is how to more accurately forecast housing prices in order to establish an effective real estate policy. Based on the ubiquitousness and immediacy of Internet data, this research adopts a broader version of text mining to search for keywords in relation to housing prices and then evaluates the predictive abilities using machine learning algorithms. Our findings indicate that this new method, especially random forest, not only detects turning points, but also offers prediction ability that clearly outperforms traditional regression analysis. Overall, the prediction based on online search data through a machine learning mechanism helps us better understand the trends of house prices in China.

The effect of the housing provident fund on housing consumption and inequity risks

Many emerging economies have established long-term collective saving schemes to support housing consumption. One of the most eminent schemes is the Housing Provident Fund (HPF) in China, whose policy effect is highly debated. Based on data from the 2013 China Household Finance Survey, this paper evaluates the heterogeneity of the HPF's effect on the housing consumption of households with different incomes from three dimensions: housing purchases, housing investment and age at first housing purchase. We further estimate the effect of the financial support of the HPF on the participants' housing pressure. Our results suggest that the HPF has an overall positive effect on housing consumption and that the financial support reduces households' housing pressure by 14% on average. However, the HPF provides insufficient financial support to low-income participants in housing purchases; in contrast, the HPF promotes housing investment and earlier purchases of houses among high-income households. Given the soaring housing prices in China, the HPF seems to not only entail inequity but also aggravate the inequality of wealth distribution and widen the gap between the rich and the poor. We conclude by proposing some valuable policy implications for China's HPF system reform in the future.

Distribution dynamics of China’s regional housing price disparity

ABSTRACT Based on the methods of distribution dynamics analysis, this study estimates the regional housing price disparity across China’s eastern, central and western regions. Analysis from decomposition of housing price growth rate by subgroup indicates that the disparity is largely due to intra-regional rather than inter-regional differences. Eastern region contributes the most to the disparity of housing prices, followed by the Central region and the Western region. Ergodic distributions reveal that Eastern region with housing price growth rates from 4% to 7% tend to move upwards in the coming period, while in the Central and Western regions housing prices tend to converge to no change of housing prices. Mobility probability plot result further suggests that medium sized housing may have higher probability for price growth. The findings help better understand the regional disparity of housing price in China, an issue that has largely been neglected. Long-standing inequality across regions can exert adverse impact on poverty reduction, economic development, and even social stability. The research implications will enable scholars and policymakers to rethink the impact of homeownership financialization when discussing and designing housing policies. Highlights This paper uses distribution dynamics analysis to study the housing price disparity in China. Ergodic distributions reveal that Eastern region has upward housing price trend. Ergodic distributions reveal that Central and Western regions have converged housing prices. Mobility probability plots reveal that medium sized housing tends to have more price growth.

A New Appraisal Model of Second-Hand Housing Prices in China’s First-Tier Cities Based on Machine Learning Algorithms

The accurate appraisal of second-hand housing prices plays an important role in second-hand housing transactions, mortgages and risk assessment. Machine learning technology, gradually applied to finance and economics, can also be used to upgrade the traditional appraisal methods of second-hand housing. A large number of appraisal indicators and price data on second-hand housing in Beijing, Shanghai, Guangzhou and Shenzhen, four first-tier cities in China, can be obtained by using crawler technology. Then, the geographical location information of second-hand housing can be visualized by GIS technology, and the descriptive text of second-hand housing can be processed by natural language processing. Finally, combined with other numerical and classification indicators, the second-hand housing appraisal model based on a two-tier stacking framework is constructed by using random forest, adaptive boosting, gradient boosting decision tree, light gradient boosting machine and extreme gradient boosting as base models and back propagation neural network as the meta-model. The result of model training shows that the machine learning models improve the accuracy significantly compared to linear multiple regression and spatial econometric models, and the performance of the stacking model is better than that of standalone machine learning models.

Rural-Urban Migration and House Prices in China

Rural-Urban Migration and House Prices in China

The Relationship between Financial Systems and Housing Prices in China: An Empirical Analysis Using Panel VAR Model

The Relationship between Financial Systems and Housing Prices in China: An Empirical Analysis Using Panel VAR Model

Are there irrational bubbles under the high residential housing prices in China’s major cities?

House prices in the main cities of China have been rising to historically high levels. Unsustainable growth of housing prices might cause financial crises and damage the whole economy. This research aims to detect whether bubbles dominate China?s real estate market. It begins by systematically analysing the features of China?s real estate sector, followed by proposing a theoretical framework to identify the fundamentals of house prices and decompose house prices into cyclical and bubble components. It then applies the vector error correction model and other econometric techniques to testify the theoretical framework with data of seven Chinese cities from 2008M01 to 2017M12. The main findings of this research include the following four parts. Firstly, the residential housing market of Shanghai was exposed to the irrational bubble issue, but the rest six cities examined were at safe positions. Secondly, both long-run and short-run relationships between economic fundamentals and house prices have been verified. Thirdly, economic regulations also have significant effects on house prices. Finally, this research suggests that the root cause of the high housing prices in major cities in China is due to the excessive capital injection into the residential property market.

Economic policy uncertainty, monetary policy, and housing price in China

ABSTRACT This paper analyzes the impact of macroeconomic variables on house price volatility under different regimes of policy uncertainty, incorporating the Economic Policy Uncertainty Index and several Chinese macroeconomic data sets for the period from 1999 to 2014. We adopt a logistic smooth transition vector autoregressive model and a generalized impulse response function. The results show that macroeconomic progress leads to house price growth, which is augmented by policy uncertainty. In addition, the effect of macroeconomic shocks on house price volatility varies under different regimes of policy uncertainty. We find that shocks are asymmetric under regimes of high and low policy uncertainty. Under a high policy uncertainty regime, expansionary quantitative monetary policy can facilitate house price growth, whereas a contractionary monetary policy gives rise to an enduring “Home Price Puzzle,” which makes it difficult to regulate house prices.

Financing housing and house prices in China

PurposeThis paper aims to examine the evolution of house prices in China and especially the effects of different financing channels on China’s house prices.Design/methodology/approachThe author use the own theoretical framework and proceed to test the testable hypotheses by using the autoregressive distributed lag bounds test approach for cointegration analysis and the unrestricted error correction model. Quarterly time series data from Q1 2002 to Q2 2016 are used.FindingsThe results suggest that in the short run, bank loans to real estate development and scale of shadow banking have significant positive effects on house prices. In the long run, the scale of shadow banking and disposable income affects house prices positively and significantly.Originality/valueThis study provides more insights into how and to the extent different financing channels affect China’s house prices, particularly the impact of shadow banking on the house prices.