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Discusses housing reform and its impact on the governance of housing in China. The roles of the state and work units have been shifted from providers to enablers in the market of housing supply after the introduction of privatisation and the market mechanism. However, the role of work units in housing distribution remains almost intact. The scope of work units’ influence is more extensive than before reform. The involvement of work units as mediators in the housing market affects the performance of the market and contributes to the fluctuations and uncertainty of the market. In order to tackle the new problems arising from reform, the role of work units needs to be redefined.

The purpose of this study is to examine the effects of market-based approach to provision of housing to low-income households in urban Malawi.

This study was conducted in Blantyre, Malawi, between 2019 and 2022 and used both quantitative (household survey) and qualitative (in-depth interviews and document study) methods of data collection. Interviews were conducted with key players and investors in the housing sector. Household survey data were analyzed through descriptive statistics, which allowed the generation of descriptive housing valuables, whereas qualitative data were analyzed through content analysis.

This paper demonstrates that, rather than ameliorating the housing problems facing low-income households, the market approach to provision of housing in Malawi has worsened the housing situation in the country. This is so because the market approach to the provision of housing in Malawi is not only enforcing the logic of capitalistic accumulation in the housing sector but also supporting mechanisms of exclusion based on economic stratification within the community.

Completeness of data over time as there is no market data bank available in the country.

The findings from this study suggest that some degree of state intervention in addressing the housing problem in Malawi is required.

The study findings suggest that a market approach to the provision of housing can increase social inequality as low-income households face challenges in accessing housing.

There is a paucity of research on the effects of the market approach on the provision of affordable housing to low-income households in Malawi. This paper assesses this important policy gap and provides significant policy directions.

In 2016, the Chinese central government decentralized the responsibilities of housing market regulation to the municipal level. This paper aims to assess whether the decentralized market regulation is effective.

This study first investigates the fundamental drivers of urban housing prices in China. Taking into consideration the factors driving housing prices, the authors further investigate the effectiveness of decentralized housing market regulation by a pre- and post-policy comparison test using a panel data set of 35 major cities for the years from 2014 to 2019.

The results reveal heterogenous policy effects on housing price growth among cities with a one-year lag in effectiveness. With the decentralized housing market regulation, cities with fast price growth are incentivized to implement tightening measures, while cities with relatively low housing prices and slow price growth are more likely to do nothing or deregulate the markets. The findings indicate that the shift from a centralized housing market regulation to a decentralized one is more appropriate and effective for the individual cities.

Few policy evaluation studies have been done to examine the effects of decentralized housing market regulation on the performance of urban housing markets in China. The authors devise a methodology to conduct a policy evaluation that is important to inform public policy and decisions. This study helps enhance the understanding of the fundamental factors in China’s urban housing markets and the effectiveness of municipal government interventions.

The purpose of this study, the nonlinear relationship between the real estate market and the stock market was investigated in Iran. For this intent, the monthly data from 2012:4 to 2022:5 is used.

In this study, the quantile-on-quantile estimation method is used, which is a combination of the nonparametric estimation methods and the quantile regression.

The research results show that, in the low quantiles, the effect of stock market return on the housing market return is negative or zero. In fact, in this situation, the increasing returns in the stock market will shift part of the financial resources of the economy to the market and create stagnation or even negative returns in the housing market. This situation is seen more strongly in some other quantiles, including the 0.25 and 0.75 quantiles; in contrast, the effect of high quantiles of stock market returns is positive on the housing market.

It seems that the demand in the housing market increase in a situation where the returns of the stock market are growing, and the market is in a bullish condition, and this causes an increase in the price and returns in this market. In addition, the results show that the effect of stock market returns on capital market returns is asymmetric and nonlinear.

With access to the large-scale search data from Rightmove plc, the paper firstly indicated the possibility of using user-generated data from online property portals to predict housing market activities and secondly embraced a GIS approach to explore what people search for housing and what they chose and investigated the issue of mismatch between search patterns and revealed patterns. Based on the analysis, the paper contributes a visual GIS-based approach which may help planners and designers to make more informed decisions related to new housing supply, particularly where to build, what to build and how many to build.

The paper used the 2013 housing search data from Rightmove and the 2013 price data from Land Registry with transactions made after the search period and embraced a GIS approach to explore the potential housing demand patterns and the mismatch between searches and sales. In the analysis, the paper employed the K-means approach to group prices into five levels and used GIS software to draw maps based on these price levels. The paper also employed a simple analysis of linear regression based on the coefficient of determination to investigate the relationship between online property views and values of house sales.

The result indicated the strong relationship between online property views and the values of house sales, implying the possibility of using search data from online property portals to predict housing market activities. It then explore the spatial housing demand patterns based on searches and showed a mismatch between the spatial patterns of housing search and actual moves across submarkets. The findings may not be very surprising but the main objective of the paper is to open up a potentially useful methodological approach which could be extended in future research.

It is important to identify search patterns from people who search with the intention to buy houses and from people who search with no intention to purchase properties. Rightmove data do not adequately represent housing search activity, and therefore more attention should be paid to this issue. The analysis of housing search helps us have a better understanding of households' preferences to better estimate housing demand and develop search-based prediction models. It also helps us identify spatial and structural submarkets and examine the mismatches between current housing stock and housing demand in submarkets.

The GIS approach in this paper may help planners and designers better allocate land resources for new housing supply based on households' spatial and structural preferences by identifying high and low demand areas with high searches relative to low housing stocks. Furthermore, the analysis of housing search patterns helps identify areas with latent demand, and when combined with the analysis of transaction patterns, it is possible to realise the areas with a lack of housing supply relative to excess demand or a lack of latent demand relative to the housing stock.

The paper proves the usefulness of a GIS approach to investigate households' preferences and aspirations through search data from online property portals. The contribution of the paper is the visual GIS-based approach, and based on this approach the paper fills the international knowledge gap in exploring effective approaches to analysing user-generated search data and market outcome data in combination.

The paper aims at analyzing trends and principal causes of developments in‐house prices in Malta over the past 26 years, with a specific focus on the issue of affordability. This focusing on the strong demand for home ownership and interest from foreign buyers, supply availability together with its relationship to the high vacancy rate existing. Also delved into include the distortions in the rental market, sustainable housing measures, housing densities and high‐rise developments. Finally, noted is the implication on the use of housing as an investment asset in relation to trading on Malta's Stock Exchange

A long‐term approach has been utilized to arrive at deductions. Comparisons undertaken taken mostly with small states.

The present global credit crunch can offer some relief with regard to housing affordability for first time Maltese home buyers. Updating of restricted home rentals can lead to an improved working for the Maltese property market, with some of the large proportion of vacant units being released onto the market.

Based on data collected from National Statistics, together with data bases held in the practice of DHI Periti.

It derives projections for likely future developments in this context and in the light of this discusses some policy options.

An improvement into the quality of life for Maltese households delved into, whilst providing measures for households not to fall below the poverty line also indicated.

Gives original views on projections for the housing affordability index, together with noting that a large vacant property market for the Mediterranean region possibly creates minimal damage to its proper functioning.

This paper develops a new decomposition method of the housing market variations to analyse the housing dynamics of the Australian eight capital cities.

This study reviews the prior research on analysing the housing market variations and classifies the previous methods into four main models. Based on this, the study develops a new decomposition of the variations, which is made up of regional information, home‐market information and time information. The panel data regression method, unit root test and F test are adopted to construct the model and interpret the housing market variations of the Australian capital cities.

This paper suggests that the Australian home‐market information has the same elasticity to the housing market variations across cities and time. In contrast, the elasticities of the regional information are distinguished. However, similarities exit in the west and north of Australia or the south and east of Australia. The time information contributes differently along the observing period, although the similarities are found in certain periods.

This paper introduces the housing market variation decomposition into the research of housing market variations and develops a model based on the new method of the housing market variation decomposition.

In this paper, we identify the relationship between (local) housing supply and (local) house price developments, especially in The Netherlands.

We measure the influence of new building on house prices by comparing areas designated for concentrated new building (main Dutch cities) with areas where no large housing projects are developed. On the basis of classical economic theory, if the housing market is functioning as it should, then supply will soon respond to a shock in demand and restore stability in house prices.

For the main Dutch cities, we found that an increase in supply triggers a fall in prices. In other areas the correlation coefficients are more or less zero, which can lead us to conclude that the expansion of the housing stock is market‐compliant.

The housing market is not functioning, as it should: new supplies depend on the complex decisions of the suppliers, thus making it difficult to express statistically the causality between the house price developments and the new supplies.

Most studies suggest that macro data are unable to measure the true dependency between the house prices and the new building and claim at the same time that micro data sets are incomplete. Also our research was hampered by a shortage of usable data.

New building can push up the value of the surrounding housing because it is associated with a qualitatively better housing stock. We conclude that in regions where new building has been concentrated in designated areas, the relationship between housing production and price development is inverse.

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.

The purpose of this study was – first, to find out how to evaluate affordability of housing in the Estonian market and, second, to assess the regulatory framework decisions' impact on the housing market in Estonia.

This article seeks answers to how to define housing affordability for the Estonian housing market. It also describes the regulatory framework and policy decisions made by the government.

Calculations show that there is an affordability problem, and political decisions have helped to make housing loans affordable for households, but at the same time the high debt burden has weakened households' financial position.

It could be possible to research the market using the databases of credit institutions, but the given data is under the protection of banking confidentiality.

The HAI index, proposed by the authors, could be calculated regularly and it could be used as a possible indicator to evaluate the capability of the population to take on household loans in the Estonian household market as a whole.

The problem of housing affordability is very important for all households, and there is a need to continue with research in this field. Some households cannot buy a house, some have loan repayment problems.

No research has been done in trying to find an answer for the affordability problem in the housing market in Estonia. Also there are no analyses about the impact of the regulatory framework on the housing market – whether the government goals are achieved or not.