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The study aims to identify the affecting factors and their impact rates on the commercial housing prices. The study also aims to suggest implications related to commercial housing prices to develop the commercial housing market.

The study investigates housing investors, real estate agents and buyers to identify factors that might affect commercial housing prices. The proposed research model has 7 latent factors and is tested by Cronbach' alpha and exploratory factor analysis by SPSS20.0 software.

There are 7 groups with 24 factors affecting commercial housing prices. The neighboring factor group has the greatest impact rate (18.54%); the housing service group has the lowest impact rate (11.48%).

The study has only determined the affecting factors and their impact rates on commercial housing prices in Bac Ninh city. Therefore, it is necessary to conduct research on factors affecting commercial housing prices in other provinces and cities of Vietnam in the coming time. In addition, the proposed research method can also be consulted when it is necessary to determine the factors affecting commercial housing prices in other countries around the world.

The study proposes some implications related to commercial housing prices such as commercial housing valuation; housing selection with suitable prices for people intending to buy houses; state support policies for commercial housing investors to develop commercial housing with reasonable prices.

The implementing the implications proposed in the study will facilitate people's easier access to commercial housing; real estate investors do business more efficiently.

To the best of the authors’ knowledge, this paper presents for the first time a method to determine the affecting factors and their impact rates on commercial housing prices in Vietnam. The paper also points out a number of specific factors affecting commercial housing prices that are different from those shown in previous studies.

Economic fundamentals are recognized as determining factors for housing on the city level, but the relationship between housing price and land supply has been disputed. This study aims to examine what kind of impact housing prices have on land supply and whether there is heterogeneity in different regional spaces.

This study collects the relevant data of land supply and housing prices in Nanchang from 2010 to 2018, constructs a vector autoregression (VAR) model, including one external factor and four internal factors of land supply to explore the dynamic effects and spatial heterogeneity of land supply on housing prices through regression analysis. Also, the authors use the geographic detector to analyze the spatial heterogeneity of housing prices in Nanchang.

This study found that the interaction between land supply and housing price is extremely complex because of the significant differences in the study area; the variables of land supply have both positive and negative effects on housing price, and the actual effect varies with the region; and residential land and GDP are the two major factors leading to the spatial heterogeneity in housing price.

The dynamic effects of land supply on housing price are mainly reflected in the center and edge of the city, the new development area, and the old town, which is consistent with the spatial pattern of the double core, three circles and five groups in Nanchang.

This is a novel work to analyze the dynamic effects of land supply on house prices, instead of a single amount of land supply or land prices. Furthermore, the authors also explore the spatial heterogeneity according to the regional characteristics, which is conducive to targeted policymaking.

The study aimed to determine the impact rate of the COVID-19 pandemic on Vietnam’s commercial housing demand as compared to other factors and proposes several policies to increase housing demand as a result of the pandemic.

The study randomly investigated 400 homebuyers during the COVID-19 pandemic. The structural equation model, SPSS20.0 and AMOS24.0 software were used to determine the impact of factor groups on housing demand.

The COVID-19 pandemic has a second impact after housing prices on commercial housing demand, followed by 10 other factors. The impact rates of factors range from 3.45% to 15.74%.

The study has only determined the extent of the impact of the COVID-19 pandemic on housing demand in Hanoi city, so it is necessary to continue to study this issue in other provinces and cities of Vietnam. The proposed research method would be consulted when it is necessary to determine the factors affecting housing demand in other countries around the world.

The study proposes some implications related to commercial housing demand in the context of the COVID-19 pandemic such as fighting the epidemic, supporting housing investors; reducing loan interest rates; increasing the time to pay for housing; supporting enterprises to stabilize production; strengthening real estate brokerage and carrying out administrative procedures online.

Investors and the State can use the implications to make the right housing decisions to provide housing for people and maintain social stability.

To the best of the authors’ knowledge, this paper presents for the first time a method to determine the impact of the COVID-19 pandemic on commercial housing demand in Vietnam. The paper also points out some specific factors affecting commercial housing demand that are different from those shown in previous research.

The paper's aim is to estimate the benefits and costs of China's affordable housing program, as well as to provide recommendations to this housing policy.

The Cobb‐Douglas utility function is employed to estimate the net benefits of the affordable housing policy. Both of the sunk costs and current costs are computed, and an improved housing affordability index is used to measure the levels of housing affordability in cities in China.

The total net benefits of this policy are estimated to range from $234,176.7 million to ¥271,020.4 million. The costs are divided into sunk costs and current costs, computed to be ¥447,598.63 million and ¥328,685.21 million, respectively. The supply size of affordable dwellings is far from adequate due to the low level of housing affordability in China.

The data in this study is insufficient and some information such as the income of occupiers has yet to be estimated. However, if more individual data was available, the conclusion would be confidential.

From this paper the policymakers may understand how to estimate the welfare efficiency of affordable housing policy, adjust the participant regulations and determine the supply of affordable houses.

This paper estimates the benefits and costs of China's affordable housing program as the first study in this area. The Cobb‐Douglas utility function was used in the analysis of China's housing policy.

The purpose of this study is to investigate the relationship between tourism development and urban housing prices in Chinese cities. Specifically, the study aimed to explore whether there is a relationship between the two variables in tourist and non-tourist cities and whether there is a non-linear relationship between them.

In this study, the entropy method was used to construct the China City Tourism Development Index, which provides a more comprehensive measure of the level of tourism development in different cities. In total, 45 major cities in China were studied using the panel data approach for the period of 2011 to 2019.

The empirical analysis conducted for this study found that tourism development affects urban house prices, and that there is an inverted U-shaped relationship. However, this varies across cities, with house prices in tourist cities tending to be more influenced by tourism development than non-tourist cities. Also, foreign direct investment, population size, fixed asset investment and disposable income per capita were found to have an impact on house prices in both tourism and non-tourism cities.

There are significant differences in tourism development and urban house prices in different cities in China. This study considers these differences when examining the impact of tourism on house prices in 45 major cities in China by dividing the sample cities into tourist and non-tourist cities.

The purpose of this study is to conduct a thorough empirical investigation of the intricate relationship between urban housing sales prices and land supply prices in China, with the aim of elucidating the underlying economic principles governing this dynamic interplay.

Using monthly data of China, the authors use the asymmetry nonlinear autoregressive distributed lag (NARDL) model to test for nonlinearity in the relationship between land supply price and urban housing prices.

The empirical results confirm the existence of an asymmetric relationship between land supply price and urban housing prices. The authors find that land supply price has a positive and statistically significant impact on urban housing prices when land supply is increasing. Policymakers should strive to strike a balance between safeguarding residents’ housing rights and maintaining market stability.

Although the asymmetric effect of land supply price has been identified as a significant contributor in this study, it is important to note that the research primarily relies on time series data and focuses on analysis at the national level. Although time series data offer a macroscopic perspective of overall trends within a country, they fail to adequately showcase the structural variations among different cities.

To ensure a stable housing market and meet residents’ housing needs, policymakers must reexamine current land policies. Solely relying on restricting land supply to control housing prices may yield counterproductive results. Instead, increasing land supply could be a more viable option. By rationally adjusting land supply prices, the government can not only mitigate excessive growth in housing prices but also foster the healthy development of the housing market.

First, the authors have comprehensively evaluated the impact of land supply prices in China on urban housing sales prices, examining whether they play a facilitating or mitigating role in the fluctuation of these prices. Second, departing from traditional linear analytical frameworks, the authors have explored the possibility of a nonlinear relationship existing between land supply prices and urban housing sales prices in China. Finally, using an advanced NARDL model, the authors have delved deeper into the asymmetric effects of land supply prices on urban housing sales prices in China.

The purpose of this paper is to explore the influence of the rise in housing prices on enterprise financing and also the sustainability and heterogeneity of this effect.

Empirical test, panel data, fixed-effect model, IV and 2SLS were used in this paper.

The empirical results indicate that the mortgage effect does exist, and the authors further analyze the heterogeneity of this effect by dividing the sample based on the degree of financial development and property rights; the empirical results reveal that the mortgage effect is significantly higher in places with the high level of financial development. Besides, compared to the SOE enterprise, the mortgage effect has more influence on non-SOE companies.

The results indicate that the mortgage effect should be considered when regulating housing market, and in order to improve the financing capability of company, its profitability and financial market efficiency should be emphasized.

This paper not only confirms the existence of the mortgage effect, but also explores its sustainability and heterogeneity, which reveals the risk and bubble in the effect of house market on enterprise financing, and enlightens how to promote financing ability of company.

This paper aims to explore the factors that affect housing prices as per Chinese articles indexed in the Chinese Science Citation Database (CSCD). There were different foci regarding what drove housing prices in China in Chinese articles, and international journal articles in English. As most previous English articles only threw light on international research, it motivated the researchers to systematically review Chinese literature’s factors that affected housing prices in China.

This paper reviewed housing price research articles indexed in the two largest Chinese academic research databases: the CSCD and China Knowledge Infrastructure Engineering Database (CNKI.NET). It systematically collected the data and adopted descriptive analysis techniques and synthesis.

This research reviewed the literature published from 2015 to 2020 and revealed some unique factors affecting China's housing prices. For example, research focused on administrative aspects such as macroeconomic regulation and control (often known as macro control). Authors of Chinese articles suggested that the two-child policy affected housing prices, which differed from that in the English journal articles. The research results implied that researchers should read top Chinese journals on top of good international journals when they study China's real estate market in the future.

Because the domestic real estate market started late, domestic real estate transaction data and real estate-related statistics are more difficult to obtain. The research is mostly based on the relationship between supply and demand, government policy and individual consumer factors, and the sample has a short time span.

As China is a planned economy country, administrative factors are one main factor that affects the housing price. There were a significant number of articles in Chinese that considered this factor to be the main driver of the real estate price. It included government investment and macro-control, i.e. direct government intervention to cool down the overheated economy. Yet, there are few English articles that threw light on this factor including the commodity housing supply and government behaviour that affect housing price. The second-child policy, which is unique in China, also played an important role in the determination of the housing price. In the articles indexed in CNKI, the second-child rate, willingness to have a second child or having a second child were mentioned in the Chinese articles but not the English ones.

In this paper, the economic, social, administrative and environmental factors were summarised, which basically covered all the factors affecting housing prices. The administrative factors were a special group of factors that affect the housing price because of the country's planned economic system. Secondly, it provided useful information to real estate development enterprises in China. To make a correct investment and management decision, real estate development enterprises must understand the actual situation and possible problems of the industry. In this study, we analysed the research literature on the real estate industry in China for the period from 2015 to 2020 one by one and determined the influencing factors of the housing price, which provided references for effective cost control. Thirdly, it allows the public to understand and grasp the real estate industry. As the housing price has been continuously increasing, the public pays increasing attention to the real estate industry. Through the literature analysis of the impact of real estate prices, this paper revealed the elements of house price expenses, which makes it convenient for ordinary people to understand the real estate industry.

This study allows foreigners who do not know Chinese to know more about factors that drove housing prices from the Chinese perspective. It also provides insights to overseas developers who wish to enter the property market in China. The results can be generalised to other non-English-speaking real estate research.

To explore the state of this conditional Granger causality when other cities are not factors, we investigate housing market networks in China's major cities by using a combination of conditional Granger causality and network analysis.

Although housing market networks have been well discussed for different countries, the question of housing market networks in China's major cities based on the conditional causality perspective has yet to be answered.

We discover that second-tier cities are more influential than first-tier cities. Although the connectivity of the primary housing market is more complex than the diversified connectivity observed in the secondary housing market, both markets are scale-free networks that exhibit high stability. Moreover, we reveal that geographic conditions and economic development jointly determine the housing market's modular hierarchical structure. Our results provide meaningful information for both Chinese policymakers and investors.

By excluding the influence of other cities, our conditional Granger causality identifies the true casual relation between cities' housing markets. Moreover, it is the first paper to consider the primary housing market and secondary housing market separately. Specifically, Chinese prefer new house rather than second-hand house from both speculative and self-housing. Generally speaking, the new house price is lower than the second-hand house price since the new house is off-plan property. Therefore, understanding the difference between primary and secondary housing markets will provide useful information for both policymakers and speculators.

Housing prices in China have increased rapidly over the past decade. Motivated by the fact that the real estate market and bank credit scale are vastly different in Chinese cities, the purpose of this paper is to compare the impact of bank credit on house prices in first- and second-tier cities in China.

In this study, a panel data method was used to investigate 19 first-tier cities and 30 second-tier cities between the period 2003 and 2018.

The empirical analysis undertaken in this study found that bank credit was relevant to house prices but varied in different cities in which house prices in second-tier cities tended to be more affected by bank credit compared to those in first-tier cities. In contrast, population was found to be a dominant factor that influenced house prices in first-tier cities. Likewise, the factors, per capita and gross domestic product, were found to exert a significant influence on house prices in first- and second-tier cities.

This paper provided numerous policies to control the price of housing in first- and second-tier cities.

The housing prices, bank credit scale and population distribution are vastly different in different cities in China. This research considers these differences while examining the dominant factors that affect house prices in first- and second-tier cities in China.