Special Issue "Reinvigorating Research on Housing Inequalities and Housing Price Mechanism Using Emerging Data and Technologies"

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Urban Contexts and Urban-Rural Interactions".

Deadline for manuscript submissions: 31 August 2022.

Special Issue Editors

Prof. Dr. Shiliang Su
E-Mail Website
Guest Editor
Department of GI Science and Cartography, College of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
Interests: geocomputational social sciences; land use modeling; thematic cartography
Prof. Dr. Shenjing He
E-Mail Website
Guest Editor
Department of Urban Planning and Design, The Social Infrastructure for Equity and Wellbeing (SIEW) Lab, The University of Hong Kong, Hong Kong, China
Interests: urban redevelopment/gentrification; policy mobility and entrepreneurial urbanism; rural-urban migration and informal housing; neighborhood governance; health geography
Dr. Monika Kuffer
E-Mail Website
Guest Editor
Department Urban and Regional Planning and Geo-information Management, Faculty of Geo-Information Science and Earth Observation (ITC) of the University of Twente, 7514 AE Enschede, The Netherlands
Interests: machine learning; poverty; slums; spatial statistics; urban remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the ground-breaking advances in the Spatial Information Communication Techniques (SICTs), the Artificial Intelligence Techniques (AITs), the High-Performance Computing Techniques (HPCTs), Sensors and Wearable Devices (SWDs) and the Internet of Things (IoT), an inexhaustible supply of new, open, and big data has revolutionized the world over the recent decade. A shared vision has been formed in the spheres of politics, academia, and business on the strategic significance of emerging data and technologies for building more inclusive, vibrant, and sustainable future cities (Chattopadhyay et al., 2020; Porciello et al., 2020). Tackling the knotty problems of land and housing is a central task for urban planning and governance, as urban development is rooted in the nexus of land and capital. The importance of affordable housing has in fact been acknowledged by the Sustainable Development Goals (SDGs 11).

A myriad of studies have responded to the emerging data and technologies in almost all fields of housing research. The availability of new data and technologies has reinvigorated the research on classical housing issues by offering new empirical evidence, strengthening analytical rigor, and even enabling new discoveries in housing theories (Abitbol and Karsai, 2020; Boeing, 2020; He et al., 2019; Hu et al., 2019; Gallin et al., 2021; Ibrahim et al., 2019; Soman et al., 2020; Su et al., 2021; Ying et al., 2021). These studies, however, could be misleading if over-relying on data-driven approaches without a close engagement with theoretical debates. The absence of a solid theoretical foundation could lead to inconsistent, fragmented, and incomparable conclusions, posing a major obstacle to the multilateral dialogues among practitioners, researchers, technicians, and policymakers. Most importantly, the SDGs have presented high fragility during the COVID-19 pandemic worldwide, two-thirds of which are now unlikely to be achieved (Naidoo and Fisher, 2020). It is the right time to rethink sustainable pathways for combating the worsening housing inequalities.

Therefore, this Special Issue calls for papers employing innovative data sources and methodologies to reenergize the classical research topics of housing inequalities and housing price mechanisms and to advance the theorization of housing studies (on the Global South and North) in the era of big data and smart technologies. We invite contributions from various disciplines such as Land Management, Geography, Urban Planning, Real Estate, GIScience, Economics, and Computer Science. Potential themes include but are not limited to:

  • The role of Big Data and AITs in understanding dynamics (e.g., housing price, housing rent and land rent) and spatiotemporal patterns of housing markets (e.g., market segmentation, housing affordability and housing opportunities);
  • Capitalization effect of neighborhood characteristics (e.g., population composition, built environment, diversity, and homogeneity) and urban amenities (e.g., education, hospital, landscape, and public transport);
  • New indicators of housing divide (e.g., address, place name, streetscape. and 3D Cadastre);
  • Opportunities and challenges of data fusion from multiple sources for identifying housing inequalities;
  • Socioeconomic consequences of housing differentiation (e.g., health inequalities, urban poverty and human mobility).

References

Abitbol, J.L., Karsai, M. Interpretable socioeconomic status inference from aerial imagery through urban patterns. Nature Machine Intelligence, 2020, 2, 684-692.

Boeing, G. Online rental housing market representation and the digital reproduction of urban inequality. Environment and Planning A, 2020, 52, 449-468.

Chattopadhyay, A.K., Kumar, T.K., Rice, I. A social engineering model for poverty alleviation. Nature Communication, 2020, 11, 6345.

Gallin, J., Molloy, R., Nielsen, E., Smith, P., Sommer, K. Measuring aggregate housing wealth: New insights from machine learning. Journal of Housing Economics, 2021, 51, 101734.

He, S., Wang, D., Webster, C., Chau, K.W. Property rights with price tags? Pricing uncertainties in the production, transaction and consumption of China’s small property right housing. Land Use Policy, 2019, 81, 424-433.

Hu, L., He, S., Han, Z., Xiao, H., Su, S., Weng, M., Cai, Z. Monitoring housing rental prices based on social media: an integrated approach of machine-learning algorithms and hedonic modeling to inform equitable housing policies. Land Use Policy, 2019, 82, 657-673.

Ibrahim, M.R., Titheridge, H., Cheng, T., Haworth, J. predictSLUMS: A new model for identifying and predicting informal settlements and slums in cities from street intersections using machine learning. Computers, Environment and Urban Systems, 2019, 76, 31-56.

Naidoo, R., Fisher, B. Reset sustainable development goals for a pandemic world. Nature, 2020, 583, 198-201.

Porciello, J., Ivanina, M., Islam, M. et al. Accelerating evidence-informed decision-making for the Sustainable Development Goals using machine learning. Nature Machine Intelligence, 2, 559-565.

Soman, S., Beukes, A., Nederhood, C., Marchio, N., Bettencourt, L.M.A. Worldwide detection of informal settlements via topological analysis of crowdsourced digital maps. International Journal of Geo-Information, 2020, 9, 685.

Su, S., He, S., Sun, C., Zhang, H., Hu, L., Kang, M. Do landscape amenities impact private housing rental prices? A hierarchical hedonic modeling approach based on semantic and sentimental analysis of online housing advertisements across five Chinese megacities. Urban Forestry & Urban Greening,2021, 126968.

Ying, Y., Koeva, M., Kuffer, M., Asiama, K.O., Li, X., Zevenbergen, J. Making the third dimension (3D) explicit in Hedonic price modelling: A case study of Xi’an, China. Land, 2021, 10, 24.

Prof. Dr. Shiliang Su
Prof. Dr. Shenjing He
Dr. Monika Kuffer
Guest Editors

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Keywords

  • Housing market
  • Machine learning
  • Big data
  • Urban analyst
  • Social inequalities
  • Affordable housing
  • Artificial Intelligence

Published Papers (9 papers)

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Research

Article
Exploring a Pricing Model for Urban Rental Houses from a Geographical Perspective
Land 2022, 11(1), 4; https://0-doi-org.brum.beds.ac.uk/10.3390/land11010004 - 21 Dec 2021
Viewed by 296
Abstract
Models for estimating urban rental house prices in the real estate market continue to pose a challenging problem due to the insufficiency of algorithms and comprehensive perspectives. Existing rental house price models based on either the geographically weighted regression (GWR) or deep-learning methods [...] Read more.
Models for estimating urban rental house prices in the real estate market continue to pose a challenging problem due to the insufficiency of algorithms and comprehensive perspectives. Existing rental house price models based on either the geographically weighted regression (GWR) or deep-learning methods can hardly predict very satisfactory prices, since the rental house prices involve both complicated nonlinear characteristics and spatial heterogeneity. The linear-based GWR model cannot characterize the nonlinear complexity of rental house prices, while existing deep-learning methods cannot explicitly model the spatial heterogeneity. This paper proposes a fully connected neural network–geographically weighted regression (FCNN–GWR) model that combines deep learning with GWR and can handle both of the problems above. In addition, when calculating the geographical location of a house, we propose a set of locational and neighborhood variables based on the quantities of nearby points of interests (POIs). Compared with traditional locational and neighborhood variables, the proposed “quantity-based” locational and neighborhood variables can cover more geographic objects and reflect the locational characteristics of a house from a comprehensive geographical perspective. Taking four major Chinese cities (Wuhan, Nanjing, Beijing, and Xi’an) as study areas, we compare the proposed method with other commonly used methods, and this paper presents a more precise estimation model for rental house prices. The method proposed in this paper may serve as a useful reference for individuals and enterprises in their transactions relevant to rental houses, and for the government in terms of the policies and positions of public rental housing. Full article
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Article
Difference in Housing Finance Usage and Its Impact on Housing Wealth Inequality in Urban China
by and
Land 2021, 10(12), 1404; https://0-doi-org.brum.beds.ac.uk/10.3390/land10121404 - 19 Dec 2021
Viewed by 406
Abstract
With the increasing importance of financial loans in home purchases in urban China, the role of housing loans in the accumulation of housing wealth needs to be unraveled. Using the data from the 2017 China Household Finance Survey (CHFS), this study investigates the [...] Read more.
With the increasing importance of financial loans in home purchases in urban China, the role of housing loans in the accumulation of housing wealth needs to be unraveled. Using the data from the 2017 China Household Finance Survey (CHFS), this study investigates the use of housing loans and their impact on housing wealth inequality. It has been found that people with higher socioeconomic status and institutional advantages benefit more from housing provident fund loans and are more likely to fully invoke different financing channels to accumulate housing wealth. On the contrary, disadvantaged groups have to resort to costly market-based mortgages to finance their home purchases. This leads them to fall further behind in housing wealth accumulation. The spatial stratification of housing wealth accompanying the urban hierarchy was also observed and found to be closely linked to the type of housing loans. In this increasingly financialized era, relying on financial instruments in the process of household asset accumulation may further amplify the existing wealth inequality among social groups. Full article
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Article
Spatial Interactions in Business and Housing Location Models
Land 2021, 10(12), 1348; https://0-doi-org.brum.beds.ac.uk/10.3390/land10121348 - 07 Dec 2021
Viewed by 518
Abstract
The paper combines theoretical models of housing and business locations and shows that they have the same determinants. It evidences that classical, behavioural, new economic geography, evolutionary and co-evolutionary frameworks apply simultaneously, and one should consider them jointly when explaining urban structure. We [...] Read more.
The paper combines theoretical models of housing and business locations and shows that they have the same determinants. It evidences that classical, behavioural, new economic geography, evolutionary and co-evolutionary frameworks apply simultaneously, and one should consider them jointly when explaining urban structure. We use quantitative tools in a theory-guided factors induction approach to show the complexity of location models. The paper discusses and measures spatial phenomena as distance-decaying gradients, spatial discontinuities, densities, spillovers, spatial interactions, agglomerations, and as multimodal processes. We illustrate the theoretical discussion with an empirical case of interacting point-patterns for business, housing, and population. The analysis reveals strong links between housing valuation and business location and profitability, accompanied by the related spatial phenomena. It also shows that assumptions concerning unimodal spatial urban structure, the existence of rational maximisers, distance-decaying externalities, and a single pattern of behaviour, do not hold. Instead, the reality entails consideration of multimodality, a mixture of maximisers and satisfiers, incomplete information, appearance of spatial interactions, feed-back loops, as well as the existence of persistence of behaviour, with slow and costly adjustments of location. Full article
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Article
Measuring the Differences of Public Health Service Facilities and Their Influencing Factors
Land 2021, 10(11), 1225; https://0-doi-org.brum.beds.ac.uk/10.3390/land10111225 - 11 Nov 2021
Viewed by 294
Abstract
The equitable distribution of public health facilities is a major concern of urban planners. Previous studies have explored the balance and fairness of various medical resource distributions using the accessibility of in-demand public medical service facilities while ignoring the differences in the supply [...] Read more.
The equitable distribution of public health facilities is a major concern of urban planners. Previous studies have explored the balance and fairness of various medical resource distributions using the accessibility of in-demand public medical service facilities while ignoring the differences in the supply of public medical service facilities. First aid data with location information and patient preference information can reflect the ability of each hospital and the health inequities in cities. Determining which factors affect the measured differences in public medical service facilities and how to alter these factors will help researchers formulate targeted policies to solve the current resource-balance situation of the Ministry of Public Health. In this study, we propose a method to measure the differences in influence among hospitals based on actual medical behavior and use geographically weighted regression (GWR) to analyze the spatial correlations among the location, medical equipment, medical ability, and influencing factors of each hospital. The results show that Wuhan presents obvious health inequality, with the high-grade hospitals having spatial agglomeration in the city-center area, while the number and quality of hospitals in the peripheral areas are lower than those in the central area; thus, the hospitals in these peripheral areas need to be further improved. The method used in this study can measure differences in the influence of public medical service facilities, and the results are consistent with the measured differences at hospital level. Hospital influence is not only related to the equipment and medical ability of each hospital but is also affected by location factors. This method illustrates the necessity of conducting more empirical research on the public medical service supply to provide a scientific basis for formulating targeted policies from a new perspective. Full article
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Article
Evaluation on the Internal Public Space Quality in Affordable Housing Based on Multi-Source Data and IPA Analysis
Land 2021, 10(10), 1000; https://0-doi-org.brum.beds.ac.uk/10.3390/land10101000 - 23 Sep 2021
Viewed by 491
Abstract
Much affordable housing has poor accessibility to external urban public space facilities because of its suburb location, which makes the residents’ daily life and social activities mainly depend on the internal public space of the community. Such affordable housing needs urgent upgrading of [...] Read more.
Much affordable housing has poor accessibility to external urban public space facilities because of its suburb location, which makes the residents’ daily life and social activities mainly depend on the internal public space of the community. Such affordable housing needs urgent upgrading of the internal public space based on the thorough understanding of the low-income residents’ demand ranking. The internal spaces’ transformation will significantly improve the living environment and the quality of residents’ life, and it also provides a way to promote social equity and sustainable urban development. By using the multi-source data and the two-step floating catchment area method, this paper selects typical affordable housing, which has poor accessibility to external urban public space, as our case study. After establishing the evaluation index system, IPA (Importance and Performance Analysis) is used to calculate the quadrant value of each index so as to clarify the upgrading urgency indexes from the residents’ demand for internal public space of affordable housing. Studies have shown that tables, chairs and pavilions, pedestrian systems, retail commercial facilities, medical and health facilities, and recreational space have the strongest urgency for upgrading; fitness facilities, exercise space, barrier-free access, guidance signs and parking lot design are the next most urgent indexes; Recreational facilities, entrances/exits of the residential area, green space in front of t residential buildings, and cultural facilities all have general urgency for upgrading. Full article
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Article
The Heightened ‘Security Zone’ Function of Gated Communities during the COVID-19 Pandemic and the Changing Housing Market Dynamic: Evidence from Beijing, China
Land 2021, 10(9), 983; https://0-doi-org.brum.beds.ac.uk/10.3390/land10090983 - 17 Sep 2021
Viewed by 902
Abstract
The ongoing COVID-19 pandemic has left a strong imprint on many aspects of urban life. Gated communities (GCs) in China are less commonly perceived as a negative and segregated urban form of community compared to other contexts, owing to their wide variety and [...] Read more.
The ongoing COVID-19 pandemic has left a strong imprint on many aspects of urban life. Gated communities (GCs) in China are less commonly perceived as a negative and segregated urban form of community compared to other contexts, owing to their wide variety and relative openness. Yet, the enhanced security zone function and the popularity of GCs, along with the heightened segregation and exclusion effects, mean they are most likely to emerge in post-pandemic urban China because of the perceived effectiveness of GCs in preventing health risks by excluding outsiders during the pandemic. Drawing on empirical data from Beijing, this research presents strong evidence for a strengthened perceived ‘security zone’ effect of GCs during the pandemic. Given that rigid pandemic control measures were organized at the community level, a large-scale household survey in Beijing suggests that residents commonly recognise the effectiveness of GCs in security control and show a strong preference for GCs over open communities after the pandemic, even though there is a lack of direct evidence of reduced COVID-19 risk in GCs. The heightened perceived ‘security zone’ function of GCs has shown a significant impact on the housing market, evidenced by an increase of 2% in the housing prices for GCs, compared with those of open communities. The rising popularity of GCs is also evidenced by a significant increase in property viewings by potential homebuyers and smaller price discounts in actual transactions in gated communities vis-à-vis open communities. We argue that the rising risk-averse sentiment in the post-pandemic era has given rise to the popularity of GCs. This study provides timely and fresh insights into the changing meaning of GCs in post-pandemic China. Full article
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Article
Spatial Inequality in China’s Housing Market and the Driving Mechanism
Land 2021, 10(8), 841; https://0-doi-org.brum.beds.ac.uk/10.3390/land10080841 - 11 Aug 2021
Cited by 5 | Viewed by 808
Abstract
Housing inequality is a widespread phenomenon around the world, and it varies widely across countries and regions. The housing market is naturally spatial in its attributes, and with the transformation of China’s urbanization, industrialization, and globalization, the spatial inequality in the housing market [...] Read more.
Housing inequality is a widespread phenomenon around the world, and it varies widely across countries and regions. The housing market is naturally spatial in its attributes, and with the transformation of China’s urbanization, industrialization, and globalization, the spatial inequality in the housing market is increasingly severe. According to the geospatial differences in the housing market supply, demand, and price, and by integrating the influencing factors of economic, social, innovation, facility environment, and structural adjustment, this paper constructs a “spatial–supply–demand–price” integrated housing market inequality research framework based on the methods of CV, GI, and Geodetector, and it empirically studies the spatial inequality of provincial housing markets in China. The findings show that the spatial inequality in China’s housing market is significant and becomes increasingly serious. According to the study, we have confirmed the following. (1) Different factors vary greatly in influence, and they can be classified into three types, that is, “Key factors”, “Important factors”, and “Auxiliary factors”. (2) The spatial inequalities in housing supply, demand, and price vary widely in their driving mechanisms, but factors such as the added value of the tertiary industry, number of patents granted, and revenue affect all these three at the same time and have a comprehensive influence on the development and evolution of spatial inequalities in the housing market. (3) All the factors are bifactor-enhanced or non-linearly enhanced in relationships between every pair, and they are classified into three categories of high, medium, and low according to the mean of interacting forces; in particular, the factors of GDP, expenditure, permanent resident population, number of medical beds, and full-time equivalent of R&D personnel are in a stronger interaction with other factors. (4) Based on housing supply, demand, price, and their coordination, 31 provinces are classified into four types of policy zones, and the driving mechanisms of spatial inequalities in the housing market are further applied to put forward suggestions on policy design, which provides useful references for China and other countries to deal with housing spatial inequality. Full article
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Article
Identifying Urban Poverty Using High-Resolution Satellite Imagery and Machine Learning Approaches: Implications for Housing Inequality
Land 2021, 10(6), 648; https://0-doi-org.brum.beds.ac.uk/10.3390/land10060648 - 18 Jun 2021
Cited by 2 | Viewed by 784
Abstract
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 [...] Read more.
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. Full article
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Article
Understanding the Effects of Influential Factors on Housing Prices by Combining Extreme Gradient Boosting and a Hedonic Price Model (XGBoost-HPM)
Land 2021, 10(5), 533; https://0-doi-org.brum.beds.ac.uk/10.3390/land10050533 - 18 May 2021
Cited by 3 | Viewed by 956
Abstract
The characteristics of housing and location conditions are the main drivers of spatial differences in housing prices, which is a topic attracting high interest in both real estate and geography research. One of the most popular models, the hedonic price model (HPM), has [...] Read more.
The characteristics of housing and location conditions are the main drivers of spatial differences in housing prices, which is a topic attracting high interest in both real estate and geography research. One of the most popular models, the hedonic price model (HPM), has limitations in identifying nonlinear relationships and distinguishing the importance of influential factors. Therefore, extreme gradient boosting (XGBoost), a popular machine learning technology, and the HPM were combined to analyse the comprehensive effects of influential factors on housing prices. XGBoost was employed to identify the importance order of factors and HPM was adopted to reveal the value of the original non-market priced influential factors. The results showed that combining the two models can lead to good performance and increase understanding of the spatial variations in housing prices. Our work found that (1) the five most important variables for Shenzhen housing prices were distance to city centre, green view index, population density, property management fee and economic level; (2) space quality at the human scale had important effects on housing prices; and (3) some traditional factors, especially variables related to education, should be modified according to the development of the real estate market. The results showed that the demonstrated multisource geo-tagged data fusion framework, which integrated XGBoost and HPM, is practical and supports a comprehensive understanding of the relationships between housing prices and influential factors. The findings in this article provide essential implications for informing equitable housing policies and designing liveable neighbourhoods. Full article
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