Thomas Elmqvist, Pippin Anderson, Erik Andersson
et al.
Our Editorial team reflects on the journals’ first five years and discusses our continued aspiration to be an outlet for thought provoking and effective syntheses of essential advances in urban sustainability science and policy.
This study investigates the factors shaping real estate values in Thu Duc City, located in Ho Chi Minh City, Vietnam, over the past five years (2019–2024), focusing on a comparative analysis of econometric models to identify the most suitable framework for the data and research assumptions. Drawing on data from various online real estate platforms in Thu Duc, the analysis employs spatial autoregressive models (SAR, SEM, SDM) alongside multiple linear regression (MLR) to explore the interplay between transportation accessibility, central location, and property prices. Findings reveal that spatial autoregressive models significantly outperform MLR, which displays spatial autocorrelation in its residuals. Among these, the SEM model with the Queen matrix emerges as the most effective, demonstrating that property prices rise when closer to public transport routes, decline when farther from commercial hubs, and are adversely impacted by proximity to train stations. Highlighting the importance of spatial models, this study emphasizes their role in reducing biases and achieving more accurate insights in urban real estate analysis, particularly in rapidly developing areas like Thu Duc.
This piece is a review of the heralded book, Race for Profit: How Banks and the Real Estate Industry Undermined Black Homeownership (2019, The University of North Carolina Press). While Taylor’s Race for Profit has been widely praised among reviewers in disciplines like history, urban planning, and business administration, its lessons have not been fully unpacked for the field of public administration. We first highlight important and relevant sections of the book, then discuss lessons for social equity and public administration.
Social Sciences, Political institutions and public administration (General)
This study investigated the intangible value transferred from urban megaprojects to surrounding residential property markets, focusing on Brisbane’s transformative urban regeneration projects currently in the development pipeline. The research objectives were twofold: first, to empirically investigate the dynamics of property markets influenced by urban megaprojects and second, to assess the impact of a specific case study on these markets through a longitudinal analysis of residential sales data. Drawing from environmental economics, the concept of willingness to pay (WTP) is used to quantify externalities associated with urban megaprojects. The research constructs a comprehensive dataset integrating geospatial and property-specific data. Through revealed preference methods, the intangible value transferred from mixed-use developments is identified and quantified via residential transaction prices. Utilising hedonic price modelling, this study systematically analysed residential transaction data to estimate implicit prices associated with spatial proximity to megaprojects. A comprehensive dataset integrating property-specific attributes, geospatial proximity measures, and temporal dynamics of project development phases underpins this analysis. This research and its findings advance the existing literature in several important dimensions. That is, this research represents the first microeconomic assessment of the property market’s impacts resulting from mixed-use megaprojects in Brisbane, offering novel empirical insights for both academic and practical applications, how urban megaprojects shape residential property values, and informing stakeholders involved in urban planning, policymaking, and real estate investment decisions. Practitioners and policymakers can leverage these insights to inform policy frameworks and strategic decisions. At the governmental level, the results offer applicable insights for urban revitalisation strategies, particularly relevant to central business districts undergoing similar developments. Private sector stakeholders can utilise these outcomes to anticipate market adjustments, managing supply and demand fluctuations more effectively.
This study analyzes tract-level real estate ownership patterns in New York State (NYS) and New York City (NYC) to uncover racial disparities. We use an advanced race/ethnicity imputation model (LSTM+Geo with XGBoost filtering, validated at 89.2% accuracy) to compare the predicted racial composition of property owners to the resident population from census data. We examine both a Full Model (statewide) and a Name-Only LSTM Model (NYC) to assess how incorporating geospatial context affects our predictions and disparity estimates. The results reveal significant inequities: White individuals hold a disproportionate share of properties and property value relative to their population, while Black, Hispanic, and Asian communities are underrepresented as property owners. These disparities are most pronounced in minority-majority neighborhoods, where ownership is predominantly White despite a predominantly non-White population. Corporate ownership (LLCs, trusts, etc.) exacerbates these gaps by reducing owner-occupied opportunities in urban minority communities. We provide a breakdown of ownership vs. population by race for majority-White, -Black, -Hispanic, and -Asian tracts, identify those with extreme ownership disparities, and compare patterns in urban, suburban, and rural contexts. The findings underscore persistent racial inequity in property ownership, reflecting broader historical and socio-economic forces, and highlight the importance of data-driven approaches to address these issues.
Transparency and interpretability are crucial for enhancing customer confidence and user engagement, especially when dealing with black-box Machine Learning (ML)-based recommendation systems. Modern recommendation systems leverage Graph Neural Network (GNN) due to their ability to produce high-quality recommendations in terms of both relevance and diversity. Therefore, the explainability of GNN is especially important for Link Prediction (LP) tasks since recommending relevant items can be viewed as predicting links between users and items. GNN explainability has been a well-studied field, but existing methods primarily focus on node or graph-level tasks, leaving a gap in LP explanation techniques. This work introduces Z-REx, a GNN explanation framework designed explicitly for heterogeneous link prediction tasks. Z-REx utilizes structural and attribute perturbation to identify critical substructures and important features while reducing the search space by leveraging domain-specific knowledge. In our experimentation, we show the efficacy of Z-REx in generating contextually relevant and human-interpretable explanations for ZiGNN, a GNN-based recommendation engine, using a real-world real-estate dataset from Zillow Group, Inc. We compare against State-of-The-Art (SOTA) GNN explainers to show Z-REx outperforms them by 61% in the Fidelity metric by producing superior human-interpretable explanations.
The Uniform Appraisal Dataset (UAD) 3.6's mandatory 2026 implementation transforms residential property valuation from narrative reporting to structured, machine-readable formats. This paper provides the first comprehensive analysis of this regulatory shift alongside concurrent AI advances in computer vision, natural language processing, and autonomous systems. We develop a three-layer framework for AI-augmented valuation addressing technical implementation and institutional trust requirements. Our analysis reveals how regulatory standardization converging with AI capabilities enables fundamental market restructuring with profound implications for professional practice, efficiency, and systemic risk. We make four key contributions: (1) documenting institutional failures including inter-appraiser variability and systematic biases undermining valuation reliability; (2) developing an architectural framework spanning physical data acquisition, semantic understanding, and cognitive reasoning that integrates emerging technologies while maintaining professional oversight; (3) addressing trust requirements for high-stakes financial applications including regulatory compliance, algorithmic fairness, and uncertainty quantification; (4) proposing evaluation methodologies beyond generic AI benchmarks toward domain-specific protocols. Our findings indicate successful transformation requires not merely technological sophistication but careful human-AI collaboration, creating systems that augment rather than replace professional expertise while addressing historical biases and information asymmetries in real estate markets.
Macroeconomic nowcasting sits at the intersection of traditional econometrics, data-rich information systems, and AI applications in business, economics, and policy. Machine learning (ML) methods are increasingly used to nowcast quarterly GDP growth, but adoption in high-stakes settings requires that predictive accuracy be matched by interpretability and robust uncertainty quantification. This article reviews recent developments in macroeconomic nowcasting and compares econometric benchmarks with ML approaches in data-rich and shock-prone environments, emphasizing the use of nowcasts as decision inputs rather than as mere error-minimization exercises. The discussion is organized along three axes. First, we contrast penalized regressions, dimension-reduction techniques, tree ensembles, and neural networks with autoregressive models, Dynamic Factor Models, and Random Walks, emphasizing how each family handles small samples, collinearity, mixed frequencies, and regime shifts. Second, we examine explainability tools (intrinsic measures and model-agnostic XAI methods), focusing on temporal stability, sign coherence, and their ability to sustain credible economic narratives and nowcast revisions. Third, we analyze non-parametric uncertainty quantification via block bootstrapping for predictive intervals and confidence bands on feature importance under serial dependence and ragged edge. We translate these elements into a reference workflow for "decision-grade" nowcasting systems, including vintage management, time-aware validation, and automated reliability audits, and we outline a research agenda on regime-dependent model comparison, bootstrap design for latent components, and temporal stability of explanations. Explainable ML and uncertainty quantification emerge as structural components of a responsible forecasting pipeline, not optional refinements.
This study analyzes the impact of offline expansion and online platform consolidation in China's second-hand real estate market. Using micro-level transaction data and difference-in-differences estimations, we find offline store entry significantly boosts transaction volumes (9-10\%) and reduces price concessions (1\%) initially, though effects diminish over time. Platform consolidation via Lianjia's Agent Cooperation Network yields delayed yet persistent transaction volume increases (5-6\%), particularly in less concentrated markets, and consistently lowers price concessions. These strategies sustainably enhance brokerage competitiveness, bargaining power, and market welfare, despite increased market concentration, ultimately benefiting sellers and improving overall efficiency.
Agathe Fernandes Machado, François Hu, Philipp Ratz
et al.
Driven by an increasing prevalence of trackers, ever more IoT sensors, and the declining cost of computing power, geospatial information has come to play a pivotal role in contemporary predictive models. While enhancing prognostic performance, geospatial data also has the potential to perpetuate many historical socio-economic patterns, raising concerns about a resurgence of biases and exclusionary practices, with their disproportionate impacts on society. Addressing this, our paper emphasizes the crucial need to identify and rectify such biases and calibration errors in predictive models, particularly as algorithms become more intricate and less interpretable. The increasing granularity of geospatial information further introduces ethical concerns, as choosing different geographical scales may exacerbate disparities akin to redlining and exclusionary zoning. To address these issues, we propose a toolkit for identifying and mitigating biases arising from geospatial data. Extending classical fairness definitions, we incorporate an ordinal regression case with spatial attributes, deviating from the binary classification focus. This extension allows us to gauge disparities stemming from data aggregation levels and advocates for a less interfering correction approach. Illustrating our methodology using a Parisian real estate dataset, we showcase practical applications and scrutinize the implications of choosing geographical aggregation levels for fairness and calibration measures.
Forecasting the loss given default (LGD) for defaulted Commercial Real Estate (CRE) loans poses a significant challenge due to the extended resolution and workout time associated with such defaults, particularly in CCAR and CECL framework where the utilization of post-default information, including macroeconomic variables (MEVs) such as unemployment (UER) and various rates, is restricted. The current environment of persistent inflation and resultant elevated rates further compounds the uncertainty surrounding predictive LGD models. In this paper, we leverage both internal and public data sources, including observations from the COVID-19 period, to present a list of evidence indicating that the growth rates of the Consumer Price, such as Year-over-Year (YoY) growth and logarithmic growth, are good leading indicators for various CRE related rates and indices. These include the Federal Funds Effective Rate and CRE market sales price indices in key locations such as Los Angeles, New York, and nationwide, encompassing both apartment and office segments. Furthermore, with CRE LGD data we demonstrate how incorporating CPI at the time of default can improve the accuracy of predicting CRE workout LGD. This is particularly helpful in addressing the common issue of early downturn underestimation encountered in CRE LGD models.
This paper offers an overview of collaborative consumption (CC), the related business models (BM), the value added (VA) from the consumer's perspective and the role of trust. CC is expanding but it is unclear what opportunities it offers and what the challenges will be. This research evaluates the current CC BMs and identifies 13 ways they add value from the consumer's perspective. This research further explores whether CC BMs fall into two categories in terms of what the consumer values. In the first category, the CC BMs require a low level of trust while in the second category of CC BMs a higher level of trust is necessary. It was found that 13 VA by CC BMs could be grouped into personal interest, communal interest and trust building. It is important for organisations to acknowledge how their CC BM relates to these dimensions.
This article examines the economic effects of an increase in the duration of home loans on households, focusing on the French real estate market. It highlights trends in the property market, existing loan systems in other countries (such as bullet loans in Sweden and Japanese home loans), the current state of the property market in France, the potential effects of an increase in the amortization period of home loans, and the financial implications for households.The article points out that increasing the repayment period on home loans could reduce the amount of monthly instalments to be repaid, thereby facilitating access to credit for the most modest households. However, this measure also raises concerns about overall credit costs, financial stability and the impact on property prices. In addition, it highlights the differences between existing lending systems in other countries, such as the bullet loan in Sweden and Japanese home loans, and the current characteristics of home loans in France, notably interest rates and house price trends. The article proposes a model of the potential effects of an increase in the amortization period of home loans on housing demand, housing supply, property prices and the associated financial risks.In conclusion, the article highlights the crucial importance of household debt for individual and economic financial stability. It highlights the distortion between supply and demand for home loans as amortization periods increase, and the significant rise in overall loan costs for households. It also underlines the need to address structural issues such as the sustainable reduction in interest rates, the stabilization of banks' equity capital and the development of a regulatory framework for intergenerational lending to ensure a properly functioning market.
The contribution of the real estate industry to the global and regional economy is remarkable, yet in today’s evolving digital technology and digital economy, the digital transformation of the real estate industry is lagging behind other industries. This is, on the one hand, due to the solidified processes and systems linked to the upstream and downstream real estate industries, and, on the other hand, due to the fact that digital technology disrupts traditional ways of doing business, making the industry full of uncertainty. The digital transformation of the real estate industry is a broad and emerging concept. Various related research fields are concerned with the penetration and application of different innovative technologies to the industry. This study provides a systematic review focusing on the field of smart real estate using the bibliometric analysis approach under the guidance of PRISMA. The bibliometric analyses were performed in RStudio by utilizing 22 scientific documents indexed in Scopus and Web of Science that were published from 2012 to 2022. The findings suggest that: (i) smart real estate research is still a new but rapidly emerging field; (ii) only limited academic institutions from a few countries, such as the University of New South Wales in Australia, have shown significant contributions; (iii) the research exhibits specific collaborative network characteristics, leading to a high concentration of authors and citations; and (iv) data-driven topics such as “machine learning,” “information management,” “data analytics” and “big data” indicate a high degree of research density and centrality.
Akuakanwa Eziukwu Nwosu, Victoria Amietsenwu Bello, Abiodun Kolawole Oyetunji
et al.
There has been a wide belief that real estate is a source of good investment portfolios because it has a hedge against inflation. Considering this notion, the present research examined the dynamics of the inflation-hedging capabilities of real estate investment in Nigeria’s three foremost property markets, Abuja (Maitama and Central Business District), Lagos (Lekki and Victoria Island), and Port Harcourt (Rumu Ibekwe and Aba Road). To achieve this aim, this study was carried out by exploring the returns on different types of commercial properties in the chosen location and investigating the effect of inflation on such returns in order to come up with the hedging capabilities of the assets. Out of the four property prime locations in Nigeria’s market, these selected study sites were purposely selected for investigation because they comprise the most desirable and preferred properties regarding location, standards, aesthetics, and value. From the data collected, a mean return, coefficient of variation, and ordinary least square regression analysis were completed. In terms of the coefficient of variation (CV), the findings reveal that the duplex in Port Harcourt exhibits the most performed investment, with a value of 0.33, compared to other locations. However, in terms of the expected return (ER), the duplex outperformed other property types in the different locations, with a return of 39.56%. Results also show that inflation has an adverse effect on the returns of the office space for the three locations considered, with the expected returns below 1%. The block of flats in Abuja has a complete defence against the three components of inflation, with a coefficient beta of 0.5633, 0.6586, and 0.8440, respectively. Thus, investors should consider inflation and other investment attributes when making decisions among arrays of investments. This will help guard against the widespread perception that real estate has a hedge against inflation. This paper adds to the existing literature on inflation hedging by investigating the effect of inflation on the real estate investment returns of commercial properties.
The article presents the development of the modern warehouse real estate market in Poland (from 2004 to 2022). The reference area is the European market. Against its background, Poland has become a fast-growing bank next to Germany and the fifth largest in terms of modern warehouse space. This happened despite the unstable and unpredictable macroeconomic and political situation in recent years, as well as the dangerous epidemic effects, e.g. connecting supply chains. The article is an attempt to find the sources of success of this Polish phenomenon, the search for a specific spatial configuration of the market and short- and long-term perspectives for the development of the warehouse market in Poland.
The paper presents the impact of market characteristics on the rental rate of apartments and compares the levels of the offered rental rate in two selected cities: Rzeszów and Lublin. The analyses were conducted before and during Russia's aggression against Ukraine, based on residential rental offers. Data on the offer of residential premises for rent, posted on online portals in the period from November 2021, to April 2022, was used. The analyses showed an increase in rental prices in March 2022, mainly in Rzeszów, and similar correlations of the impact of characteristics on the market rental rate in both cities in relation to the area of the unit, the number of rooms, the location on the floor, the condition of the flat and the age of the building.
Pivithuru Thejan Amarasinghe, Su Nguyen, Yuan Sun
et al.
Business optimisation has been used extensively to determine optimal solutions for challenging business operations. Problem formulation is an important part of business optimisation as it influences both the validity of solutions and the efficiency of the optimisation process. While different optimisation modelling languages have been developed, problem formulation is still not a trivial task and usually requires optimisation expertise and problem-domain knowledge. Recently, Large Language Models (LLMs) have demonstrated outstanding performance across different language-related tasks. Since problem formulation can be viewed as a translation task, there is a potential to leverage LLMs to automate problem formulation. However, developing an LLM for problem formulation is challenging, due to limited training data, and the complexity of real-world optimisation problems. Several prompt engineering methods have been proposed in the literature to automate problem formulation with LLMs. While the initial results are encouraging, the accuracy of formulations generated by these methods can still be significantly improved. In this paper, we present an LLM-based framework for automating problem formulation in business optimization. Our approach introduces a method for fine-tuning cost-efficient LLMs specifically tailored to specialized business optimization challenges. The experiment results demonstrate that our framework can generate accurate formulations for conventional and real-world business optimisation problems in production scheduling. Extensive analyses show the effectiveness and the convergence of the proposed fine-tuning method. The proposed method also shows very competitive performance when compared with the state-of-the-art prompt engineering methods in the literature when tested on general linear programming problems.
Land in the traditional African society is owned in collectivism and does not belong to individuals but the entire family which comprises the living, the reverend souls of the ancestors and the generations yet unborn. Meanwhile, when a family land is acquired compulsorily by the government or through the market by private firms, only a few family members of the family enjoy the compensation proceeds. The under-aged family members and the yet unborn generations are usually left out from the compensation proceeds from the family heritage (land). This has resulted in encroachment of land and land conflict to reclaim lost heritage by the then under-aged family members and the “yet unborn” generation when they come of age. It has also resulted in violent land conflicts and delay in projects planned for the acquired land. This paper examines the prospect of using intergenerational compensation (IGC) which will extend payment beyond a generation as a strategy for compensation in the customary land acquisition process. Utilising a qualitative approach, the paper examines the views of representatives of 23 selected indigenous landholding families (ILFs) and key informants in government offices providing land administrative services (GOPLAS) in Lagos State on the concept of intergenerational compensation. The findings reveal the willingness of the ILFs to accept the IGC payment strategy but the GOPLAS were unwilling to support the strategy. It also provides information on reasons for the support and opposition to the strategy and how it can be implemented.
Real-estate image tagging is one of the essential use-cases to save efforts involved in manual annotation and enhance the user experience. This paper proposes an end-to-end pipeline (referred to as RE-Tagger) for the real-estate image classification problem. We present a two-stage transfer learning approach using custom InceptionV3 architecture to classify images into different categories (i.e., bedroom, bathroom, kitchen, balcony, hall, and others). Finally, we released the application as REST API hosted as a web application running on 2 cores machine with 2 GB RAM. The demo video is available here.