Real Estate

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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). Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article