Real Estate doi: 10.3390/realestate3030008

Authors: Jan Schmid

This study develops and evaluates a two-stage machine learning framework for forecasting the condominium price index of pre-pandemic market data of Frankfurt am Main, Germany, one quarter ahead. To the best of the author’s knowledge, it is the first study to combine German micro-level transaction and listing data, socioeconomic variables and macro-financial indicators in a single residential price-forecasting framework. Furthermore, it provides the first evidence on machine learning-based transaction price index forecasting in Germany. Methodologically, the framework links disaggregated and aggregate forecasting. In stage 1, prices per square metre are estimated for four market segments using ordinary least squares, random forest, extreme gradient boosting, and a stacked ensemble in a strictly out-of-sample expanding-window design. In stage 2, these predictions are combined with lagged index values and macro-financial indicators to forecast the city-wide index. The stage 2 model achieves a relative root mean squared error of 2.25% and a mean absolute percentage error of 1.85%, outperforming a naïve persistence benchmark by reducing root mean squared error by 23%. Model interpretation indicates that price persistence dominates stage 1, reflecting market inertia, while lagged macro-financial variables and location quality composition drive index forecasts, pointing to delayed financial market transmission and heterogeneous submarket dynamics.

Real Estate doi: 10.3390/realestate3020007

Authors: Kola Ijasan Charles Chimedza

The real estate sector has increasingly shifted toward digital platforms, where content sentiment plays a crucial yet understudied role in driving user engagement. While sentiment analysis has been widely applied in retail and finance, its impact on real estate web traffic remains poorly understood, particularly in competitive digital marketplaces. This study examines the relationship between sentiment in web content and the traffic it attracts on residential real estate websites in South Africa. Specifically, it examines how different sentiments associated with the type of content (articles versus property listings) influence total monthly web traffic and user engagement. A quantitative analysis of six years (2017–2023) of scraped data from Property24, Remax, and Private Property employed R (rvest, sentimentr, and stats packages) for web scraping, sentiment analysis, and ANOVA testing to evaluate relationships between content sentiment, type (listings vs. articles), and web traffic metrics. The analysis revealed a significant impact of sentiment on web traffic, indicating that the sentiment of web content influences visitor numbers. Specifically, property listings generated a total of 16,780,623 monthly visitors, significantly surpassing the 13,407,521 visitors attracted by articles. This study contributes empirical evidence regarding the influence of content sentiment and content type on web traffic within the South African real estate market. It highlights the critical role of sentiment in shaping web traffic and potentially user engagement and provides actionable insights for real estate developers and marketers seeking to optimize their content strategies to improve user attraction and retention.

Real Estate doi: 10.3390/realestate3020006

Authors: Dengjin Wu Xin Janet Ge Jianlong Zhou

The real estate sector continues to face challenges such as inefficiencies, fraud risks, and high transaction costs stemming from opaque processes and heavy reliance on intermediaries. These challenges highlight the need for transparent and efficient solutions to support secure real estate transactions and management. While a growing body of literature has examined blockchain applications in real estate, existing studies are often fragmented and predominantly descriptive, with limited systematic synthesis of evidence and insufficient attention to governance and institutional contexts. This study aims to systematically examine and synthesise the benefits and challenges of blockchain technology in real estate, providing evidence-based insights for practitioners and policymakers. Using a Systematic Literature Review (SLR) approach, peer-reviewed publications from 2016 to 2025 were analysed to identify blockchain applications, reported outcomes, and implementation barriers. The findings reveal that blockchain has been applied in land registration (e.g., Sweden, India, Serbia), valuation systems, decentralised housing finance, and tokenised investment platforms (e.g., Exporo, RealT). The reported benefits include reduced fraud, enhanced transaction efficiency, transparency, and expanded investment access through fractional ownership. However, regulatory uncertainty, scalability limitations, data privacy risks, and low stakeholder awareness remain key barriers. Ethical issues such as digital exclusion and data exposure also require further consideration. Compared with the more advanced adoption observed in Europe and North America, supported by established regulatory frameworks and digital land governance initiatives, this review identifies relatively slower uptake in parts of the Asia-Pacific region, particularly in Australia and Malaysia. It highlights a critical need for future research on legal recognition, privacy-enhancing technologies, and governance frameworks, particularly regarding blockchain applications in property development and urban planning processes. By integrating technological and governance perspectives, this study provides a more comprehensive and structured understanding of blockchain adoption in real estate systems.

Real Estate doi: 10.3390/realestate3020005

Authors: Maria Rosa Trovato Chiara Minioto Salvatore Giuffrida Ludovica Nasca

This research examines how the relationship between cities and rural areas has evolved in light of the profound transformation affecting rural areas of high landscape value, which has been driven by the expansion opportunities granted to the real estate sector by urban planning regulations. The role of the landscape dimension in interpreting the relationship between territorial wealth and landscape value is considered, based on the convergence of two complementary disciplinary perspectives on territory: land planning and valuation science. Against this backdrop, and with a view to containing the progressive contamination of rural and agricultural heritage by the real estate sector, this study proposes a structured observation, valuation, interpretation, and regulatory tool to support the development of territorial planning in areas significantly characterized in terms of rural landscape value. The proposed tool is based on evidence regarding the phenomenon of building expansion in the agricultural territory of a municipality in southeastern Sicily, where favorable conditions for the development of the building sector exist, such as the vastness of the municipal territory and extensive farming as the mainstay of agricultural activity. This wider sub-regional area has also received attention due to the over-tourism phenomenon that has occurred in its cities of art. The evaluation approach experienced is a value-based representation of the evolution of this process over three observation periods: 2000, 2007, and 2012, relating the quantitative observation of the building expansion to the connected qualitative impact on rural landscape. It is the result of coordinating a large set of data in a hierarchical model of indices that converge to construct a synthetic index of rural landscape resilience. This achievement is based on the linguistic progression of “lexicon”, “semantics”, “syntax”, and “pragmatics”, each of which robustly supports “observation”, “valuation”, “interpretation”, and “planning”, respectively. The final stage is based on the convergence of explanatory indices, which are developed by coordinating evidence and assessments (factual and value judgements). This stage enables the proposal of a constraints system that supports a modus vivendi between the interests of the real estate sector and the values of the rural landscape in such a rich and fragile area.

Real Estate doi: 10.3390/realestate3020004

Authors: Dino Bečić

Housing markets display geographical linkages that contravene conventional regression assumptions; yet, Central and Eastern European towns are markedly underrepresented in spatial econometric research. This study provides a systematic spatial econometric analysis of Zagreb’s housing market. It looks at both asking sale and rental prices throughout the city’s 17 administrative districts. There are five model specifications used in the analysis: Ordinary Least Squares (OLS), Spatial Lag of X (SLX), Spatial Autoregressive Model (SAR), Spatial Error Model (SEM), and Spatial Durbin Model (SDM). The findings demonstrate significant positive spatial autocorrelation in both markets: Global Moran’s I = 0.29 (p = 0.007) for sales and 0.42 (p < 0.001) for rents. LISA analysis finds important groups of high-priced homes in the center districts and lower-priced homes on the edges. Spatial models significantly surpass OLS: SLX exhibits AIC enhancements of 9.90 (sales) and 20.20 (rentals), but SAR and SEM yield no enhancements, suggesting that local spillover effects from adjacent characteristics prevail over global spatial diffusion or correlated shocks. The higher Moran’s I and AIC gains in rental markets show that there are different spatial processes for different types of tenure. These results address a significant empirical deficiency in post-socialist housing research, illustrate that neglecting spatial dependencies may lead to biased estimates and reduced model performance, and furnish methodologically sound evidence that spatial econometric techniques are essential for accurate modeling for precise urban housing analysis in intermediate-sample scenarios. Policy implications stress the need to use spatial approaches in choices about property value, forecasting, and urban planning.

Real Estate doi: 10.3390/realestate3020003

Authors: Paolo Rosato Giovanni Florian Matteo Galante

The correct valuation of collateral supporting real estate loans has always been a key issue for the stability of the credit system. Substandard lending practices and the absence of uniform valuation approaches have historically contributed to the accumulation of non-performing loans. In recent years, several regulatory measures operating at both the European and national level have introduced principles, rules and procedures aimed at standardizing the valuation of properties pledged as collateral for credit exposures. These interventions seek to promote greater transparency, consistency, and prudence in property appraisals, thereby enhancing the soundness and resilience of the financial system. In January 2025, the updated Regulation (EU) 575/2013 came into force, incorporating the Basel III reform (also referred to as Basel 3+ or Basel IV). Among the innovations introduced, the concept of property value (PV) is particularly relevant, a prudential value that excludes expectations of price growth and considers the sustainability of the value over time in relation to the duration of the loan. PV is defined as a derived value with respect to market value (MV), determined by considering the main current and forward-looking risk factors that may arise during the life of the loan, including environmental, social and governance (ESG) risks, the intrinsic characteristics of the property and expectations regarding the economic cycle. This paper proposes a quantitative model for the determination of PV, applied to a practical case involving a residential property located in a medium-sized city in Italy’s Veneto region. The model adopts a deterministic and a probabilistic approach, the latter implemented through Monte Carlo simulation, which is indeed a generalization of the deterministic one. The model links the assessment of PV to the possible evolution of the property’s key parameters and the real estate cycle over the duration of the loan. It was tested under the assumption of a twenty-year mortgage originated in 2025 for the purchase of a residential property in Italy, considering two alternative locations: a suburban area and a city-centre area. The analysis conducted showed a substantially higher MV haircut for the suburban property compared with the central location. This difference reflects the fact that PV is less sensitive to real estate cycle fluctuations in more premium, central locations. Furthermore, the use of Monte Carlo simulation in the probabilistic approach enabled the calibration of the haircut according to a predefined confidence level, confirming the pattern observed in the deterministic framework. The combined evidence strengthens the empirical robustness of the model and highlights the importance of locational and cyclical dynamics in collateral valuation.

Real Estate doi: 10.3390/realestate3010002

Authors: Amrutha Mary Varkey Eby Johny Jayakumar Chinnasamy

Peri-urbanization in rapidly growing cities of the Global South is increasingly driven not only by demographic growth but by escalating inner-city land and housing prices that push households and developers toward peripheral zones. Bangalore exemplifies this transition, where housing affordability pressures, speculative real estate investment, and weak land governance interact to transform agricultural landscapes into fragmented built-up clusters. Using satellite imagery (1991–2024), census data, and GIS-based land-use classification, this study quantifies peri-urban expansion across eight clusters in the Bangalore Metropolitan Region. The results show rapid built-up growth, agricultural land decline, and increasing spatial fragmentation, reflecting processes of extended urbanization beyond formal city boundaries. These transformations produce environmental stress, infrastructure deficits, and socio-spatial inequalities. The paper situates Bangalore within planetary urbanization debates and argues that peri-urban sustainability depends on land market regulation, spatial planning capacity, and data-driven governance.

Real Estate doi: 10.3390/realestate3010001

Authors: Luiz Andrés Ribeiro Paixão Carolina Barbosa Seidel da Costa

We use the hedonic price model to measure the effect of school quality on apartment rent prices in Porto Alegre, Brazil. A spatial autoregressive regression (SAR) was employed due to the spatial nature of the data. We estimated the effect of school quality on apartment prices for public and private schools separately. The results shed light on the relation between school quality and apartment prices in a Global South context. We showed that both public and private school quality is valued in Porto Alegre house markets, although the effect is quite different for each type of school. For public schools, the major effect comes from the distance of the nearest schools. An increase in test scores by one standard deviation raises apartment rent prices by 2.7% for the whole city. However, this effect is bigger for some submarkets, reaching 11.6% for the distant suburbs. For private schools, the same effect occurs but for a larger distance radius. The same increase in average test score out to a 2 km distance from private schools raised the apartment price by 1.0%. Nevertheless, this effect reaches 6.6% in one specific submarket.

Real Estate doi: 10.3390/realestate2040022

Authors: Yihan Hu Yifei Huang

Seasonality has traditionally shaped the U.S. housing market, with activity peaking in spring-summer and declining in autumn-winter. However, recent disruptions, particularly those following COVID-19, raise questions about shifts in these patterns. This study analyzes housing market data (1991–2024) to examine evolving seasonality and regional heterogeneity. Using Housing Price Index (HPI) data, inventory, and sales data from the Federal Housing Finance Agency and U.S. Census Bureau, seasonal components are extracted via the X-13-ARIMA procedure, and statistical tests assess variations across regions. The results confirm seasonal fluctuations in prices and volumes, with recent shifts toward earlier annual peak (March–April) and amplified seasonal effects. Regional variations align with differences in climate and market structure, while prices and sales volumes exhibit in-phase movement, suggesting thick-market momentum behaviour. These findings highlight key implications for policymakers, realtors and investors navigating post-pandemic market dynamics, offering insights into the timing and interpretation of housing market activities.

Real Estate doi: 10.3390/realestate2040021

Authors: Martin Ahlenius Björn Berggren Neville Hurst

Intermediaries are central to complex transactions. In housing markets, real estate brokers coordinate information flows, reduce search costs, and guide lay buyers and sellers through legal and financial steps. Despite this importance, scholarship on brokerage is dispersed across disciplines and methods. This paper presents a semi-systematic review of peer-reviewed articles published between 1970 and 2021. We map (i) study characteristics (country of origin and field), (ii) the distribution of units of analysis (individual, firm/organization, market), and (iii) the most frequently examined topics. Our synthesis indicates steadily rising academic interest but a fragmented knowledge base. We conclude by highlighting gaps—especially the scarcity of cross-country comparisons and the relative lack of qualitative and mixed-method studies on brokers’ practices and experiences.

Real Estate doi: 10.3390/realestate2040020

Authors: Cristiano Saad Travassos do Carmo Renata Gonçalves Faisca Vitória Franco Benayon Menezes Antonio Elias Amil Lisboa Felipe Almeida de Sousa Marcelo Jasmim Meirino Patrícia Maria Quadros Barros

The housing deficit in developing countries is a common challenge, primarily impacting low-income populations. This paper investigated interinstitutional workflows using Building Information Modelling (BIM) as a social technology to improve the efficiency of design and construction stages in social housing projects. Following a systematic literature review, process maps were developed and applied in a case study within a Brazilian urban community, located in a coastal city with a demographic density of 3602 inhabitants per square kilometre, involving a collaboration framework between a university and municipal authorities. Based on the party’s collaboration and precise cost estimation, the results indicate that this BIM-enabled collaboration supports the governmental decision-making process and leads to more effective resource management and optimised design costs, mainly during the design and construction phases. Therefore, this study concludes that digital modelling workflows are a powerful strategy for developing social housing projects because they facilitate the inclusion of families in the design and decision-making processes. Expanding this approach through integration with geospatial and public agency data is a promising area for future research, using such models in risk assessment policies and city urban planning.

Real Estate doi: 10.3390/realestate2040019

Authors: Mark Rzepczynski Wei Feng

Housing price indices (HPIs) are employed to assess the impact of the business cycle, monetary policy, housing policies, and local market dynamics. However, comparative empirical analysis of different HPI methodologies has not been conducted to measure why or when they may diverge and whether these differences are meaningful. Two leading US HPI choices, the repeat-sale transactional (S&P Case–Shiller) and characteristic-based hedonic (Zillow) indices, although highly correlated, generate different distributions and time-series properties primarily at the city level. The spread between these two HPI choices measures the difference between housing market transaction intensity and a willingness-to-pay characteristic valuation. We find that transactional indices are more volatile, with HPI spreads associated with both macro and local drivers. The transactional index will rise more rapidly in a market with increased buying (positive macro and local market conditions) and fall further in a market with increased selling (negative macro and local market conditions) relative to a hedonic index. A buyer- or seller-biased spread between a transactional and hedonic housing price index (HPI) may impact policy judgments during housing market extremes.

Real Estate doi: 10.3390/realestate2040018

Authors: Marco Aurélio Stumpf González Diego Alfonso Erba

Location effects play a crucial role in the real estate market, encompassing aspects of accessibility and neighborhood quality. While traditional measures exist for accessibility, evaluating neighborhood quality can be a complex task. Understanding these elements is essential for accurately estimating property values, whether for commercial or tax purposes. Recently developed methods based on web scraping and automatic detection using artificial intelligence have proven effective but require substantial human and financial resources, often unavailable in small cities. As a solution, this study proposes and evaluates a simpler mechanism for assessing neighborhood quality using Google Street View images and a scoring system in a human-centered approach. Based on image interpretation, a set of weights is assigned to each point, resulting in a micro-neighborhood quality assessment. This study was conducted in three Latin American cities, and the resulting variable was integrated into hedonic price models. The findings demonstrate the feasibility and effectiveness of the proposed approach. The novelty of this study lies in applying a method based on quasi-objective criteria and adapted to cities with limited technological resources.

Real Estate doi: 10.3390/realestate2040017

Authors: Elena Fregonara Chiara Senatore Cristina Coscia Francesca Pasquino

The paper contributes to the debate on the holistic sustainability assessment of real estate projects, integrating economic, financial, environmental, and social aspects. A methodological study is presented to support decision-making processes involving the preferability ranking of alternative investment scenarios: new building production vs. retrofitting the existing stock, in the context of urban transformation interventions. The study integrates life cycle approaches by introducing the social components besides the economic and environmental ones. Firstly, a composite unidimensional (monetary) indicator calculation is illustrated. The sustainability components are internalized in the NPV calculation through a Discounted Cash-Flow Analysis (DCFA). Life Cycle Costing (LCC) and Life Cycle Assessment (LCA) are suggested to assess the economic and environmental impacts, and the Social Return on Investment (SROI) to assess the intervention’s extra-financial value. Secondly, a methodology based on multicriteria techniques is proposed. The Hierarchical Analytical Process (AHP) model is suggested to harmonize various performance indicators. Focus is placed on the criticalities emerging in both the methodological approaches, while highlighting the relevance of multidimensional approaches in decision-making processes and for supporting urban policies and urban resilience.

Real Estate doi: 10.3390/realestate2040016

Authors: Paolo Rosato Matteo Galante

Using panel data on 99 Italian provinces in the period between 2005 and 2020, the research investigates the effects of fundamental economic factors on the home sales at the provincial level, in order to build a forecasting model using a non-linear artificial intelligence approach (MLP-Multiple Linear Perceptron neural network). There are multiple objectives to this: (a) to test the hypothesis that national, regional and local fundamentals such as interest rates, income, inflation rate, unemployment and demography affect the activity’s degree of the housing market; (b) to verify the effectiveness of a neural network in describing the dynamics of the real estate market; (c) to build a simulation model capable of predicting the effect of changes in fundamentals, also due to economic policy measures, on the market. Empirical results show that neural networks offer better capabilities than linear models in representing the complex relationships between the economic situation and the real estate market. The study provides useful information for regulators to improve the effectiveness of monetary policy to stabilize real estate markets as well as for stakeholders to draw up scenarios of market development.

Real Estate doi: 10.3390/realestate2030015

Authors: Byron J. Idrovo-Aguirre Francisco-Javier Lozano

We present empirical evidence on the sensitivity of housing demand in Chile to changes in mortgage interest rates, focusing on units priced between CLF 2000 and 4000 (approximately USD 80,000 to 160,000). This sector, which comprises nearly two-thirds of the country’s housing supply, has experienced a significant decline in sales since 2021. Given its size and responsiveness, it represents a key target for policy measures aimed at reactivating the Chilean real estate market, such as demand-side subsidies for middle-income households. Using impulse response functions derived from vector autoregressive (VAR) and semi-structural models estimated via Bayesian methods with Markov Chain Monte Carlo (MCMC) simulations, we find that a 100-basis-point increase in mortgage rates leads to an average annual decline of 18% in housing sales during the first quarter after the shock. This effect results in a cumulative decline of approximately 57% by the end of the first year. A comparable reduction in mortgage rates yields a symmetrical response. Finally, we offer a linear extrapolation of potential impacts under a hypothetical 200-basis-point decrease in mortgage rates.

Real Estate doi: 10.3390/realestate2030014

Authors: David Rodriguez

From 31 August 2022 to early 2024, the City of Chicago welcomed nearly 40,000 migrants. Chicago had designated itself as a sanctuary city nearly 40 years ago and has since been a popular destination for migrants, accepting large numbers in other periods throughout its history. However, the influx during the period 2022–2024 was unique because of the large amounts of resources local and federal governments dedicated to settling these individuals. Immigrant benefits varied over this period but peaked at $15,000 per family, which did not include services offered by local churches and private organizations. In this study, log-linear multiple regression was employed to determine the impact subsidies can have on the local rental real estate market. According to the study findings, rental real estate rates increased by up to 5.6% in response to subsidization of migrant housing. Additionally, neighborhoods that were adjacent to migrant shelters experienced the greatest additional increase of 29.96%. In addition to the rapidity with which rental real estate pricing can respond to subsidies and policy shifts, the study findings demonstrate the financial benefits that can accrue to real estate owners and managers who participate in the rental marketplace with subsidization.

Real Estate doi: 10.3390/realestate2030013

Authors: Princy Roslin Benny Godwin J. Davidson Jossy P. George Peter V. Muttungal

This study explores the role of egoistic and altruistic values on green real estate purchase intention among young consumers in Canada aged between 20 and 40 years. In addition, this study examines the mediating effects of pro-environmental self-identity between social consumption motivation and green real estate purchase intention. A quantitative cross-sectional research design with an explanatory nature is employed. A total of 432 participating consumers in Canada, comprising 44% men and 48% women, with a graduate educational background accounting for 46.7%, and the ages between 24 and 35 contributing 75.2%, were part of the study, and the data collection used a survey method with a purposive sampling, followed by a respondent-driven method. Descriptive and inferential statistics were performed on the scales used for the study variables. A structural equational model and path analysis were conducted to derive the results, and the relationships were positive and significant. The study results infer the factors contributing to green real estate purchase intention, including altruistic value, egoistic value, social consumption motivation, and pro-environmental self-identity, with pro-environmental self-identity mediating the relationship. This study emphasizes the relevance of consumer values in real estate purchasing decisions, urging developers and marketers to prioritize ethical ideas, sustainable practices, and building a feeling of belonging and social connectedness. Offering eco-friendly amenities and green construction methods might attract clients, but creating a secure area for social interaction is critical. To the best of the authors’ knowledge, this research is the first to explore the role of egoistic and altruistic values on purchase intention, mainly in the housing and real estate sector, with the target consumers being young consumers in Canada.

Real Estate doi: 10.3390/realestate2030012

Authors: Gabriella Maselli Antonio Nesticò

The accurate estimation of urban property values is a key challenge for appraisers, market participants, financial institutions, and urban planners. In recent years, machine learning (ML) techniques have emerged as promising tools for price forecasting due to their ability to model complex relationships among variables. However, their application raises two main critical issues: (i) the risk of overfitting, especially with small datasets or with noisy data; (ii) the interpretive issues associated with the “black box” nature of many models. Within this framework, this paper proposes a methodological approach that addresses both these issues, comparing the predictive performance of three ML algorithms—k-Nearest Neighbors (kNN), Random Forest (RF), and the Artificial Neural Network (ANN)—applied to the housing market in the city of Salerno, Italy. For each model, overfitting is preliminarily assessed to ensure predictive robustness. Subsequently, the results are interpreted using explainability techniques, such as SHapley Additive exPlanations (SHAPs) and Permutation Feature Importance (PFI). This analysis reveals that the Random Forest offers the best balance between predictive accuracy and transparency, with features such as area and proximity to the train station identified as the main drivers of property prices. kNN and the ANN are viable alternatives that are particularly robust in terms of generalization. The results demonstrate how the defined methodological framework successfully balances predictive effectiveness and interpretability, supporting the informed and transparent use of ML in real estate valuation.

Real Estate doi: 10.3390/realestate2030011

Authors: Janak Ranaweera Sandeep Agrawal Rob Shields

This study examines the political and elite motives behind Colombo’s ‘world-class city’ initiative and its impact on public housing in underserved communities. Informed by interviews with high-ranking government officials, including urban planning experts and military officers, this study examines how President Rajapaksa’s elite-driven postwar Sri Lankan government leveraged military capacities within the neoliberal developmental framework to transform Colombo’s urban space for political and economic goals, often at the expense of marginalized communities. Applying a contextual discourse analysis model, which views discourse as a constellation of arguments within a specific context, we critically analyzed interview discussions to clarify the rationale behind the militarized approach to public housing while highlighting its contradictions, including the displacement of underserved communities and the ethical concerns associated with compulsory relocation. The findings suggest that Colombo’s postwar public housing program was utilized to consolidate authoritarian control and promote speculative urban transformation, treating public housing as a secondary aspect of broader political and economic agendas. Anchored in militarized urban governance, these elite-driven strategies failed to achieve their anticipated economic objectives and deepened socio-spatial inequalities, raising serious concerns about exclusionary and undemocratic planning practices. The paper recommends that future urban planning strike a balance between economic objectives and principles of spatial justice, inclusion, and participatory governance, promoting democratic and socially equitable urban development.

Real Estate doi: 10.3390/realestate2030010

Authors: Maria Stavridou Thomas Dimopoulos Martha Katafygiotou

This study examines the demographic and macroeconomic factors influencing housing prices in London from Q3 2014 to Q4 2022, focusing on the pre- and post-Brexit referendum periods. Using multiple regression analysis, the research evaluates the impact of interest rates, inflation, construction costs, population changes, and net migration on the housing price index (HPI) across various market segments. The findings suggest that interest rate base rates, consumer price inflation, and construction output price indices were significant predictors of housing price fluctuations. Notably, cash purchases exhibited the strongest explanatory power due to a reduced sensitivity to market changes. Additionally, London’s population was a key determinant, particularly affecting first-time buyers and mortgage-backed purchases. These results contribute to a deeper understanding of the London housing market and offer insights into policy measures addressing housing affordability and investment dynamics.

Real Estate doi: 10.3390/realestate2030009

Authors: Fabrizio Battisti Orazio Campo Fabiana Forte Daniela Menna Melania Perdonò

In the context of increasing digitization, integrating ICT technologies, artificial intelligence, and remote working is altering residential mobility patterns and housing preferences. This study examines the housing market’s impact, focusing on how residential affordability affects residential choices, using a case study of the Metropolitan City of Florence. The analysis employs a methodology centered on the Debt-to-Income Ratio (DTI), which cross-references real estate market values (source: Agenzia delle Entrate and leading real estate portals) with household income brackets to identify affordable areas. The results reveal a clear divide: households with incomes below EUR 26,000 per year (representing about 69% of the population) are excluded from the central urban property market. This evidence confirms regional and national trends, emphasizing a growing mismatch between housing costs and disposable incomes. The study concludes that affordability is a technical–financial parameter and a valuable tool for supporting inclusive urban planning. Its application facilitates the orientation of effective public policies and the identification of socially sustainable housing solutions.

Real Estate doi: 10.3390/realestate2030008

Authors: Benedetto Manganelli Vincenzo Del Giudice Francesco Tajani Francesco Paolo Del Giudice Daniela Tavano Giuseppe Cerullo

The rapid urbanization of contemporary cities represents one of the most complex challenges of the 21st century, with profound implications for the environmental, social, and economic sustainability of territories. In this context, urban regeneration emerges as a strategic approach to territorial transformation. The complexity of urban dynamics requires the adoption of innovative paradigms and systemic approaches capable of guiding decision-making processes toward eco-sustainable and resilient solutions. This research develops advanced decision support tools for urban regeneration, using the city of Potenza (Italy) as a case study. The main objective is to identify key indicators to evaluate the effectiveness of urban regeneration interventions in advance (ex-ante). The methodology develops a composite economic-financial risk index capable of providing an accurate picture of existing conditions while adapting to the territorial specificities of the analyzed area. This index, which uses the Analytic Hierarchy Process (AHP) technique to integrate elementary economic-financial indicators in order to assess the sustainability level of urban redevelopment projects, is able to synthesize complex economic variables into a single parameter of immediate comprehension, strategically guiding investments toward a sustainable urban development model. The analysis of results highlights a peculiar territorial configuration: semi-central areas present the greatest criticalities, while there is a progressive decrease in risk both toward the central core and toward peripheral and extra-urban areas. The study represents a significant methodological contribution to future urban regeneration initiatives at the local level, promoting an integrated vision of sustainable urban development for the benefit of current and future generations.

Real Estate doi: 10.3390/realestate2030007

Authors: João Victor Santana Andrade Renan Pereira Almeida

This paper investigates the dynamics of intra-urban real estate cycles by examining the segmentation of real estate markets and their spatial heterogeneity. Despite extensive literature on real estate cycles, insights into intra-urban cycles remain scarce. Utilizing a dataset of over 350,000 apartment sales from 2007 to 2022, first we apply the SKATER (Spatial K’luster Analysis by Edge Tree Removal) algorithm to delineate the city into six distinct clusters, each containing at least 3000 observations, and then analyze the six generated time series of real estate prices. Our findings confirm the hypothesis of market segmentation, revealing significant cyclical differences among the identified submarkets. Analysis indicates that real estate cycles are not uniform across the city. This approach contributes a novel perspective to the existing literature on real estate cycles, emphasizing the need to consider spatial endogenous regimes.

Real Estate doi: 10.3390/realestate2020006

Authors: Daniela Tavano Francesca Salvo Marilena De Simone Antonio Bilotta Francesco Paolo Del Giudice

The real estate sector is steadily moving towards zero-emission buildings, driven by EU policies to achieve near-zero energy (NZEB) buildings by 2050. In Italy, more than 70% of residential buildings fall into the lower energy classes, and this mainly affects low-income households. As a result, the decarbonisation of the real estate sector presents both technical and socio-economic obstacles. Building on these premises, this study introduces the Retrofit Optimization Problem (ROP), a methodological framework adapted from the Multidimensional Knapsack Problem (MdKP). This method is used in this study to conduct a qualitative analysis of accessibility to retrofit between different socio-economic groups, integrating constraints to simulate restructuring capacity based on different incomes. The results show significant disparities: although many retrofit strategies can meet regulatory energy performance targets, only a small number are financially sustainable for low-income households. In addition, interventions with the greatest environmental impact remain inaccessible to vulnerable groups. These preliminary results highlight important equity issues in the energy transition, indicating the need for specific and income-sensitive policies to prevent decarbonisation efforts from exacerbating social inequalities or increasing the risk of assets being stranded in the housing market.

Real Estate doi: 10.3390/realestate2020005

Authors: Marco Locurcio Felicia Di Liddo Pierluigi Morano Francesco Tajani Laura Tatulli

In recent decades, the intensification of extreme events, such as floods, earthquakes, and hydrogeological instability, together with the spread of pollutants harmful to health, has highlighted the vulnerability of territories and the need to direct urban policies towards sustainable strategies. The built assets and the real estate sector play a key role in this context; indeed, being among the first ones to be exposed to the effects of climate change, they serve as a crucial tool for the implementation of governance strategies that are more focused on environmental issues. However, the insufficient allocation of public resources to interventions to secure the territory has made it essential to involve private capital interested in combining the legitimate needs of performance with the “ethicality” of the investment. In light of the outlined framework, real estate managers are called upon to take into consideration the environmental risks associated with real estate investments and accurately represent them to investors, especially in the fundraising phase. The tools currently used for the analysis of such risks are based on their perception measured by the “risk premium” criterion, reconstructed on the basis of previous trends and the analyst’s expertise. The poor ability to justify the nature of the risk premium and the uncertainty about future scenario evolutions make this approach increasingly less valid. The present work, starting from the aspects of randomness of the risk premium criterion, aims at its evolution through the inclusion of environmental risk components (seismic, hydrogeological, and pollution).

Real Estate doi: 10.3390/realestate2020004

Authors: Felicia Di Liddo Paola Amoruso Pierluigi Morano Francesco Tajani Marco Locurcio

With regard to the Italian context, the present research aims to empirically assess whether and to what extent real estate market dynamics (prices and vibrancy levels) are influenced by the life quality in a specific reference area. In particular, the study compares parameters related to the residential real estate market—such as the Real Estate Market Observatory quotations and the real estate market intensity index (used as a proxy for market dynamism)—with the Life Quality index developed by the study center of the Italian newspaper “Il Sole 24 Ore” for the selected provincial capitals. Furthermore, by breaking down the Life Quality index into the individual indicators used for its elaboration, the research identifies those most closely linked to real estate market mechanisms to explore these relationships within each context. This approach allows for the identification of potential local differences, providing insights into the degree of geographical heterogeneity. Finally, a GIS-based analysis is employed to graphically represent the various indicators, capturing the potential spatial correlations related to phenomena where the geographic component plays a significant role.

Real Estate doi: 10.3390/realestate2020003

Authors: Nicholas Fiorentini Matteo Moriani Massimo Rovai

Urban transformations require balancing private real estate interests with the provision of public spaces that enhance sustainability and ecosystem services. This study proposes a probabilistic model to assess the feasibility of transforming buildable areas while ensuring equitable benefits for both private developers and public administrations, with a focus on three areas to be regenerated within the Municipality of Lucca as case studies. Applying the Monte Carlo (MC) method, two probabilistic models—one with a Uniform distribution and the other with a Normal distribution—estimate the expected Transformation Value (TV) and its associated uncertainty. Results highlight the effectiveness of MC-based assessments in managing financial uncertainty, aiding developers in risk evaluation, and supporting policymakers in designing balanced urban planning indices. It was observed that the Uniform model is better suited to situations in which the initial values of the model’s main variables—such as construction costs, post-transformation market value, or transformation duration—are not fully known, whereas the Normal model provides more accurate estimates when the investment scenario is better understood. The results demonstrate that this approach provides, on the one hand, a robust tool for investment risk analysis to private investors and, on the other hand, a way for public institutions to verify whether urban planning indices enable private promoters to contribute effectively to the development of sustainable cities.

Real Estate doi: 10.3390/realestate2010002

Authors: Julia Buszta Iwona Kik Kamil Maciuk

In the realm of real estate, each property owns a unique set of characteristics that distinguish it from others. While each property has its own distinctive features, the appraisal process prioritises only those qualities that meaningfully affect the value in the given market context. However, in the dynamically evolving market situation, expectations of real estate buyers can also transform. This study aims to explore how the surrounding environment and micro-location aspects affect the property value, which can deliver valuable outcomes for real estate market participants and researchers. For that purpose, the authors selected nine factors, called non-market attributes, that may affect the estimated value: air quality, noise emissions, green areas, rivers and water reservoirs, kindergartens and primary schools, universities, medical facilities, shopping centres and religious buildings. Moreover, apart from non-market attributes, the authors selected six market attributes usually used for the determination of residential real estate values according to the Polish regulations in this field. The detailed analysis of factors influencing the property value has been conducted based on the residential apartments in the district Zwięczyca in Rzeszów. Specifically, with the use of Pearson’s total correlation coefficients, authors explored market and non-market attributes and examined their relationships with unit transaction prices, attempting to answer the research question on whether non-market attributes can differentiate market values of residential apartments, when local real estate markets are considered. The results demonstrate that all selected market factors have a visible effect on analysed real estate prices and might be adopted for appraisal. Among nine non-market factors, only three of them have a pronounced effect on prices and might be used for the valuation of residential properties on the local market. The combined database of market and non-market factors reveals eight attributes (five market and three non-market) affecting prices of residential apartments.

Real Estate doi: 10.3390/realestate2010001

Authors: Renan P. Almeida

Real estate plays a pivotal role in the contemporary world, accounting for over half of global wealth and significant employment and GDP shares. This essay examines three key events—the 2007–2008 Global Financial Crisis (GFC), the COVID-19 pandemic, and recent technological revolutions—to place real estate’s centrality. By analyzing housing price indexes in major economies, the paper identifies global trends and regional nuances, as well as highlights real estate’s dual role as both a reflection and a driver of economic cycles. Then, I explore in detail the GFC, the urban roots of COVID-19 and its effects on real estate markets, and the relationship between new techno-economic paradigms and cities and real estate. Future research directions on real estate are also pointed out.

Real Estate doi: 10.3390/realestate1030014

Authors: Joël Vonlanthen

This study examines whether and through which channels ESG ratings influence key metrics in the real estate industry. Focusing on Switzerland as a case study and concentrating on commercial real estate investors and their income properties, we utilize unique datasets and employ an OLS post-LASSO estimation procedure to identify and quantify the associations between ESG ratings and four key metrics: appraisal-based and transaction-based discount rates, rental incomes, and vacancy rates. Our results demonstrate that ESG ratings maintain a significant association with all four key metrics even after undergoing robustness checks. When dissecting the total ESG rating into its components, the environmental rating stands out as the most significant. While largely dependent on the specific metric being analyzed, the association of social and governance ratings tends to be less pronounced. Delving deeper into individual ESG rating levels, our findings suggest potential signaling effects, as properties with higher ESG ratings demonstrate heightened sensitivity to both types of discount rates and vacancy rates. Overall, our findings deepen the understanding of the association between ESG ratings and real estate markets, illuminating the intersection of sustainability and financial relevance.

Real Estate doi: 10.3390/realestate1030013

Authors: Akira Ota Masaaki Uto

Property valuation services using artificial intelligence (AI) have been developed, with more than 20 services available in Japan. However, since their algorithms and training data are not publicly available, the extent of variations in the AI property valuations among these services is not clear. This study focuses on five services and uses a sample of 4295 valuations for 859 condominium units in six popular residential areas in Tokyo. (1) Multiple comparison tests of the AI property valuations among the services are conducted to confirm their statistical significance and to examine the extent of the variations. (2) The business models of each service are compared to examine the factors contributing to these variations. The results showed that the average variation in the AI property valuations was 10.6%, which was larger than the variations observed in traditional property valuations. It was also found that the valuation groups, categorized as high or low, varied based on the business models of the service providers. These results indicate that it is necessary to promote the healthy development of AI property valuation by establishing guidelines, such as requiring the AI property valuation services to ensure fair prices or disclosing their algorithms and data.

Real Estate doi: 10.3390/realestate1030012

Authors: Vladimir Surgelas Vivita Puķīte Irina Arhipova

This study aimed to predict residential apartment prices in Latvia and Brazil using algorithms from machine learning, fuzzy logic, and civil engineering principles, with a focus on overcoming multicollinearity challenges. To explore the market dynamics, we conducted four initial experiments in the central regions of Riga and Jelgava (Latvia), as well as São Paulo and Niterói (Brazil). Data were collected from real estate advertisements, supplemented by civil engineering inspections, and analyzed following international valuation standards. The research integrated human decision-making behavior with machine learning and the Apriori algorithm. Our methodology followed five key stages: data collection, data preparation for association rule mining, the generation of association rules, fuzzy logic analysis, and the interpretation of model accuracy. The proposed method achieved a mean absolute percentage error (MAPE) that ranged from 5% to 7%, indicating strong alignment with market trends. These findings offer valuable insights for decision making in urban development, particularly in optimizing renovation priorities and promoting sustainable growth.

Real Estate doi: 10.3390/realestate1030011

Authors: G. Jason Goddard Todd A. Parrish David M. Church

This paper utilizes the Herfindahl Index to assess university business major student investment preferences regarding cryptocurrency. This paper seeks to determine which cryptocurrency investment options are most desirable and, more importantly, ascertain the reasons for said investments. This paper reviews the real estate-based currency of the French Revolution in order to provide historical lineage for the popularity of cryptocurrency investment today.

Real Estate doi: 10.3390/realestate1020010

Authors: Alessandra Lanciotti

Historical buildings deserve preservation not only for their aesthetic features but also as guardians of cultural and spiritual values. This is now acknowledged by several international law norms. Nonetheless, the legal discourse about their preservation carries a set of problematic implications because it is hard to adopt regulations that combine protection, promotion and valorisation with economic investments and market strategies and with everyday urban life. This is particularly evident with regard to immovables located within historical cities or towns whose economy depends on marketing the cultural identity, authenticity and history of the place to outsiders. This paper highlights the approach adopted by the most relevant international legal instruments which focus on the protection of what belongs to the historical city’s cultural heritage as being of crucial significance for individuals and communities in relation to their cultural identity. In this perspective, the safeguard of historical buildings can be linked to the right of access to and enjoyment of cultural heritage: a specific human right recognized under international law. The issue at stake is how to comply with principles and rules of international law while at the same time respond to the needs of modern life and economy. This paper identifies the rules and principles of international law that have gained legal relevance and can provide valid tools to states and local administrations to implement and fulfil protectionist policies for historical buildings.

Real Estate doi: 10.3390/realestate1020009

Authors: Joël Vonlanthen

This study addresses the scarcity of empirical findings on the determinants of discount rates in the Discounted Cash Flow (DCF) method, filling a crucial gap in the existing literature and enhancing the understanding of the valuation process from the perspectives of key stakeholders. Leveraging a unique dataset comprising market transactions enriched with expert-based valuation information, the study conducts a comprehensive counterfactual analysis of the fundamental determinants influencing both appraisal-based and transaction-based discount rates. The results reveal that appraisers and investors attribute different levels of importance to object-specific, locational, and macroeconomic variables. A type-specific analysis further reveals that locational and macroeconomic variables exert a greater influence on discount rates in the residential real estate segment. In contrast, object-specific characteristics hold significantly higher importance in explaining discount rates in the commercial real estate segment.

Real Estate doi: 10.3390/realestate1020008

Authors: Zahra Ahmadi Björn Berggren Mohammad Ismail Lars Silver

Public Housing Companies (PHCs) play an important role in the Swedish housing market, with approximately 300 companies managing circa 802,000 dwellings. The public housing sector thereby represents almost 20 percent of the total housing stock in Sweden and half of the apartments that are available for rental. The purpose of this paper is to analyze the most important factors behind the profitability in Swedish PHCs between 2010 and 2019. The effects of internal growth, age, and capital structure in the PHCs are analyzed together with the effect of the growth of the local market, as well as local rent levels. Financial information for circa 300 PHCs in Sweden was gathered from annual reports published between 2010 to 2019. The financial information was analyzed using panel data analysis methods with several explanatory variables to explain the financial performance of the PHCs. The results from the analysis indicate a highly significant and positive relationship between the annual change in population, age, and profitability in the PHC. A highly significant and negative relationship was found between the PHC internal growth, capital structure, and profitability. The results showed no significant relationship between changes in income, rent levels, and profitability in Swedish PHC.

Real Estate doi: 10.3390/realestate1010007

Authors: Sam Martin Thomas Dimopoulos Martha Katafygiotou

Understanding the optimal strategy for a real-estate investment and how performance changes based on characteristics is crucial for optimising the achievable return. This is prominent in touristic areas such as Paphos, Cyprus, where there is no clear distinction as to whether short- or long-term approaches are optimal. This study aimed to develop a model for predicting the optimal rental strategy whilst assessing which model performed best and which property attributes impacted its return the greatest. Short-term data were collected from AirDNA and long-term data were manually collected from real-estate agents’ websites. Furthermore, Random Forest, K-Nearest Neighbour, and Multiple Linear Regression models were created to predict the highest and best use for each property. Model accuracy varied between datasets, with the best-performing model for short-term properties being the Random Forest model (R-squared: 0.843), and the distance-based Multiple Linear Regression approach being the best for long-term properties (R-squared: 0.843). The study demonstrated that accurate models could be created to predict the optimal rental strategy with the number of bedrooms being the main driver for rental income, followed by luxury finishes and the presence of a pool. It was found that locational characteristics did not impact the returns significantly when assuming that the property was located within a touristic area.

Real Estate doi: 10.3390/realestate1010006

Authors: Erik Wiegel Neil Yorke-Smith

The Dutch housing market comprises three sectors: social-rented, private-rented, and owner-occupied. The contemporary market is marked by a shortage of supply and a large subsidised social sector. Waiting lists for social housing are growing, whereas households with incomes above the limit do not or cannot leave the social sector. Government policy and market regulations change frequently, not least for political reasons. In view of commonly recognised problems in the housing market, this article considers the ‘internal demand’ of those households that are dissatisfied with their current residence. We examine the effects of regulatory policy by means of an exploratory agent-based simulation. The results provide perspectives on how internal demand is impacted by regulations in a housing market that is suffering from a shortage, and allow decision makers to weigh the pros and cons of policy measures.

Real Estate doi: 10.3390/realestate1010005

Authors: Tawakalitu Bisola Odubiyi Rotimi Boluwatife Abidoye Clinton Ohis Aigbavboa Wellington Didibhuku Thwala Adeyemi Samuel Ademiloye Olalekan Shamsideen Oshodi

In recent years, scholars have called for an increase in the usage of green features in the built environment to address climate change issues. Governments across the developed world are implementing legislation to support this increased uptake. However, little is known about how the inclusion of green features influences the rental value of residential properties located in developing countries. Data on 389 residential properties were extracted and collected from a webpage. Text mining and machine learning models were used to evaluate the impact of green features on the rental value of residential properties. The results indicated that floor area, number of bathrooms, and availability of furniture are the top three attributes affecting the rental value of residential properties. The random forest model generated better predictions when compared with other modelling techniques. It was also observed that green features are not the most common words mentioned in rental adverts for residential properties. The results suggest that green features add limited value to residential properties in South Africa. This finding suggests that there is a need for stakeholders to create and implement policies targeted at incentivising the inclusion of green features in existing and new residential properties in South Africa.

Real Estate doi: 10.3390/realestate1010004

Authors: Steven B. Caudill Neela Manage Franklin G. Mixon

Hedonic house price studies typically incorporate information about location by including either a set of dummy variables to represent individual locations called “neighborhoods” or by using a set of distance (or travel time) variables to characterize locations in terms of proximity to amenities and dis-amenities. As an alternative to these, relatively recent research advocates a latitude–longitude co-ordinate system for incorporating distance information into hedonic house price regressions. This study shows that many of the claims made in this research, particularly those referencing the elimination or diminution of “biases of coefficients of non-distance variables”, are given the particulars of the Monte Carlo experiments, not possible to investigate. We further show, both analytically and with our simulations, that there is no omitted variable bias present in their simulations because their randomly generated non-distance variable is uncorrelated with any of the other variables used in their regression models.

Real Estate doi: 10.3390/realestate1010003

Authors: Pierfrancesco De Paola

Accuracy in property valuations is a fundamental element in the real estate market for making informed decisions and developing effective investment strategies. The complex dynamics of real estate markets, coupled with the high differentiation of properties, scarcity, and opaqueness of real estate data, underscore the importance of adopting advanced approaches to obtain accurate valuations, especially with small property samples. The objective of this study is to explore the applicability of the Maximum Entropy Principle to real estate valuations with the support of Lagrange multipliers, emphasizing how this methodology can significantly enhance valuation precision, particularly with a small real estate sample. The excellent results obtained suggest that the Maximum Entropy Principle with Lagrange multipliers can be successfully employed for real estate valuations. In the case study, the average prediction error for sales prices ranged from 5.12% to 6.91%, indicating a very high potential for its application in real estate valuations. Compared to other established methodologies, the Maximum Entropy Principle with Lagrange multipliers aims to be a valid alternative with superior advantages.

Real Estate doi: 10.3390/realestate1010002

Authors: Mats Wilhelmsson

This study investigates why young adults live with their parents in Sweden. As young adults’ living arrangements affect decisions about marriage, education, childbirth, and participation in the workforce, more knowledge for policymakers is crucial to implementing effective policies to support young adults and promote financial independence and well-being. Using a data set from 1998 to 2021 at the municipal level in Sweden, we used a spatial autoregressive panel data model to examine the proportion of young adults living at home and the regional disparities. The study uncovered intraregional variations that illustrate how different municipalities in Sweden exhibit different patterns of young adults living at home. Our findings reveal that economic factors such as unemployment significantly impact this pattern. Housing market dynamics, demographic factors, cultural differences, and location-specific characteristics also play an essential role in explaining this pattern. These findings suggest that the key drivers are the lack of rental housing, high unemployment rates, a high degree of urbanisation, interregional migration, and social capital (such as social cohesion and inclusion).

Real Estate doi: 10.3390/realestate1010001

Authors: Pierfrancesco De Paola

“Agenda 2030” is a wide-reaching plan established by the United Nations, in which 17 Sustainable Development Goals (SDGs) with 232 related indicators highlight the most important economic, social, environmental and governance challenges of our time [...]