Resource Opportunity in China’s Market Transition and Governance: Time Factor in Urban Housing Inequality

Since China’s reform and opening up, the country’s rapid marketization process has been accompanied by the rapid growth of inequality, which has been significant for all classes of society. In terms of its impact, housing inequality is particularly noticeable. In this paper, we discuss the influence of real-estate purchase time, organization, human capital, and political capital on the value of real estate and the appreciation of real estate in China by using a conditional mean model and a quantile regression model. The differences in the degree of influence of these factors on different quantile levels are also investigated. We found that, after adding the time factor, the prior possession of resources in the early stage of market transformation will benefit the long-term marketization process. Organizations that can penetrate “market-redistribution” and professions that directly participate in the distribution of real-estate resources also have significant advantages in this regard.

The Sociology of Housing Market Intermediaries

The housing market is replete with intermediaries—individuals whose work constructs, maintains, or expands a market. A sociology of housing requires analytic attention to these myriad housing market intermediaries since they affect whether and under what conditions individuals and households can access and maintain access to housing as well as help determine the affordability and quality of that housing. In this chapter we aim to show the utility of intermediaries as a wide category of analysis, particularly for understanding how housing inequality interacts with other forms of social stratification across axes such as gender, class, and race and ethnicity. We summarize recent research and unpack how the work of public and private-sector intermediaries—such as housing developers, building inspectors, real estate agents, mortgage lenders, appraisers, landlords, housing authority case workers, mobile home park operators, and property managers—leads to more or less stratification in the housing market.

Identifying Urban Poverty Using High-Resolution Satellite Imagery and Machine Learning Approaches: Implications for Housing Inequality

Enriching Asian perspectives on the rapid identification of urban poverty and its implications for housing inequality, this paper contributes empirical evidence about the utility of image features derived from high-resolution satellite imagery and machine learning approaches for identifying urban poverty in China at the community level. For the case of the Jiangxia District and Huangpi District of Wuhan, image features, including perimeter, line segment detector (LSD), Hough transform, gray-level cooccurrence matrix (GLCM), histogram of oriented gradients (HoG), and local binary patterns (LBP), are calculated, and four machine learning approaches and 25 variables are applied to identify urban poverty and relatively important variables. The results show that image features and machine learning approaches can be used to identify urban poverty with the best model performance with a coefficient of determination, R2, of 0.5341 and 0.5324 for Jiangxia and Huangpi, respectively, although some differences exist among the approaches and study areas. The importance of each variable differs for each approach and study area; however, the relatively important variables are similar. In particular, four variables achieved relatively satisfactory prediction results for all models and presented obvious differences in varying communities with different poverty levels. Housing inequality within low-income neighborhoods, which is a response to gaps in wealth, income, and housing affordability among social groups, is an important manifestation of urban poverty. Policy makers can implement these findings to rapidly identify urban poverty, and the findings have potential applications for addressing housing inequality and proving the rationality of urban planning for building a sustainable society.

Market-Based Housing Reforms and the Residualization of Public Housing: The Experience of Lodz, Poland

Housing inequality is one of the central topics in urban studies, and in the social sciences more broadly. It is also one of the most significant and visible aspects of socioeconomic inequality. Over the last three decades, the process of housing commodification has accelerated across western societies and, consequently, the public housing sector has contracted and become more closely associated with the poorest sections of societies in many cities. Over the same period, the political changes in Central and Eastern Europe have contributed to the dismantling and monetizing of state housing sectors at the forefront of broader social and economic transformations. Unfortunately, most recent studies on housing commodification and inequalities in Europe are confined to the national scale. The aim of this article is to detail the linkages between the position and functioning of public housing in Lodz (Poland) and the evolving socioeconomic profile of individuals and households that rely on public housing. This study relies on microdata (statistical information on individuals and households) from two national Polish censuses (1978 and 2002) and from household budget surveys (2003–2013). The main finding of our study is that ‘residualization’ is present in the public housing stock in Lodz and that the process gained momentum in the first decade of the 2000s.