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School of Built Environment, University of Technology, Sydney, NSW 2007, Australia
Dr. Jyoti Shukla
Faculty of Architecture, Building and Planning, University of Melbourne, Melbourne 3010, Australia
Dr. Godwin Kavaarpuo
School of Design, University of Melbourne, Melbourne, VIC 3010, Australia
Dr. Shuya Yang
School of Design, University of Melbourne, Melbourne, VIC 3010, Australia
Prof. Dr. Piyush Tiwari
Faculty of Architecture, Building and Planning, University of Melbourne, Melbourne 3010, Australia
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Urban Science and Real Estate Dynamics: Insights into Housing, Finance, and Land Use

Abstract submission deadline
30 September 2025
Manuscript submission deadline
31 January 2026
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1387

Topic Information

Dear Colleagues,

The journals of Real Estate, Land, Urban Science, and Architecture invite submissions that explore the complex relationships shaping contemporary urban environments, particularly how housing markets, financial mechanisms, and land use policies interact within the broader framework of urban science. Topics may include housing affordability and supply, land value capture, zoning and regulatory impacts, real estate finance and investment, infrastructure development, urban spatial modeling, and the role of data and technology in understanding urban systems.

We also welcome original research, theoretical contributions, and policy-oriented analyses that examine the role of climate adaptation in housing development, the impact of PropTech on property markets and land economics, and the integration of sustainability goals into urban planning. As urban environments face growing pressures from environmental change and digital disruption, this topic expands to advance scholarly dialogue on how climate resilience and technological innovation are reshaping real estate and land use. Topics of interest include—but are not limited to—green housing finance, climate risk and property valuation, smart city infrastructure, digital platforms in real estate, and policy mechanisms for sustainable urban growth. Interdisciplinary approaches and empirical studies using novel data or methods are particularly encouraged.

The participants can select one of the four above journals to submit their papers, provided they fall within the scope of the selected journal.

Dr. Xin Janet Ge
Dr. Jyoti Shukla
Dr. Godwin Kavaarpuo
Dr. Shuya Yang
Prof. Dr. Piyush Tiwari
Topic Editors

Keywords

  • real estate finance
  • land economics
  • urban development
  • housing market dynamics
  • public policy
  • real estate investment
  • urban economics
  • land use planning
  • property markets
  • sustainable urban development
  • affordable housing and housing policy
  • infrastructure and real estate
  • economic geography
  • global real estate markets
  • spatial economics

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Architecture
architecture 		1.4 2.2 2021 34.2 Days CHF 1200 Submit
Land
land 		3.2 5.9 2012 16 Days CHF 2600 Submit
Real Estate
realestate 		- - 2024 15.0 days * CHF 1000 Submit
Urban Science
urbansci 		2.9 3.7 2017 25.5 Days CHF 1600 Submit

* Median value for all MDPI journals in the first half of 2025.


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Published Papers (2 papers)

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23 pages, 852 KiB  
Article
Open Data to Promote the Economic and Commercial Development of the Housing Sector: The Case of Spain
by Ricardo Curto-Rodríguez, Rafael Marcos-Sánchez, Alicia Zaragoza-Benzal and Daniel Ferrández
Urban Sci. 2025, 9(7), 277; https://doi.org/10.3390/urbansci9070277 - 17 Jul 2025
Viewed by 582
Abstract
Data is the starting point for generating information and knowledge in the decision-making process. Open data, which is information disclosed free of charge through open licenses and reusable formats, has great potential for value creation. Therefore, the objective of this research is to [...] Read more.
Data is the starting point for generating information and knowledge in the decision-making process. Open data, which is information disclosed free of charge through open licenses and reusable formats, has great potential for value creation. Therefore, the objective of this research is to evaluate Spanish autonomous communities’ open data initiatives in a category of information of vital importance: housing. The methodology employed was a population analysis of datasets labeled as housing, followed by a necessary data cleansing process due to the identification of various errors, which reduced the number of labeled datasets from 1000 to 599. Only 12 of the 17 autonomous communities provided this type of information. The analysis of the results reveals that autonomous communities’ approaches to open data initiatives are highly heterogeneous and that the supply is irregular, with the Basque Country accounting for 70% of the datasets considered in the research. The creation of an indicator that equally assesses the existence of information and file formats (breadth and reusability) continues to identify the Basque Country as the undisputed leader, with Catalonia and Cantabria in second and third place, the only autonomous communities to exceed 50 points out of a possible 100. The study concludes by highlighting that the lack of uniformity in the formulation and implementation of open data policies will limit the use of information and, consequently, its value. Therefore, a series of recommendations is issued in this regard. Full article
(This article belongs to the Topic Urban Science and Real Estate Dynamics: Insights into Housing, Finance, and Land Use)
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30 pages, 14631 KiB  
Article
Unsupervised Plot Morphology Classification via Graph Attention Networks: Evidence from Nanjing’s Walled City
by Ziyu Liu and Yacheng Song
Land 2025, 14(7), 1469; https://doi.org/10.3390/land14071469 - 15 Jul 2025
Viewed by 404
Abstract
Urban plots are pivotal links between individual buildings and the city fabric, yet conventional plot classification methods often overlook how buildings interact within each plot. This oversight is particularly problematic in the irregular fabrics typical of many Global South cities. This study aims [...] Read more.
Urban plots are pivotal links between individual buildings and the city fabric, yet conventional plot classification methods often overlook how buildings interact within each plot. This oversight is particularly problematic in the irregular fabrics typical of many Global South cities. This study aims to create a plot classification method that jointly captures metric and configurational characteristics. Our approach converts each cadastral plot into a graph whose nodes are building centroids and whose edges reflect Delaunay-based proximity. The model then learns unsupervised graph embeddings with a two-layer Graph Attention Network guided by a triple loss that couples building morphology with spatial topology. We then cluster the embeddings together with normalized plot metrics. Applying the model to 8973 plots in Nanjing’s historic walled city yields seven distinct plot morphological types. The framework separates plots that share identical FAR–GSI values but differ in internal organization. The baseline and ablation experiments confirm the indispensability of both configurational and metric information. Each type aligns with specific renewal strategies, from incremental upgrades of courtyard slabs to skyline management of high-rise complexes. By integrating quantitative graph learning with classical typo-morphology theory, this study not only advances urban form research but also offers planners a tool for context-sensitive urban regeneration and land-use management. Full article
(This article belongs to the Topic Urban Science and Real Estate Dynamics: Insights into Housing, Finance, and Land Use)
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