Special Issue "Reinvigorating Research on Housing Inequalities and Housing Price Mechanism Using Emerging Data and Technologies"
Deadline for manuscript submissions: 31 August 2022.
Interests: geocomputational social sciences; land use modeling; thematic cartography
Interests: urban redevelopment/gentrification; policy mobility and entrepreneurial urbanism; rural-urban migration and informal housing; neighborhood governance; health geography
Interests: machine learning; poverty; slums; spatial statistics; urban remote sensing
Special Issues, Collections and Topics in MDPI journals
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).
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
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Land is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Housing market
- Machine learning
- Big data
- Urban analyst
- Social inequalities
- Affordable housing
- Artificial Intelligence