Search Results (582)

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. Full article

This study examines the integration of Environmental, Social, and Governance (ESG) principles within Romania’s residential real estate sector, concentrating on Constanța, a rapidly evolving urban centre in a transitional economy. Drawing on qualitative data from semi-structured interviews with local real estate professionals and secondary analysis of policy and market documents, the research uncovers inconsistencies in ESG implementation. Environmental compliance is advancing, largely driven by EU regulations such as the European Grean Deal, the Corporate Sustainability Reporting Directive and the Energy Performance of Buildings Directive. Voluntary certification schemes like BREEAM and LEED are emerging as benchmarks for environmental performance; however, their uptake remains limited and insufficiently tailored to local conditions. Meanwhile, the social and governance dimensions lag behind, characterised by inconsistent application and weak institutional backing. Key barriers to effective ESG integration in Romania’s residential real estate sector include weak regulatory enforcement, fragmented policies, limited green finance, low awareness, and a lack of standardised social value metrics. The study concludes that without moving beyond mere regulatory compliance to a framework embedding social inclusivity and adaptive governance, ESG efforts risk perpetuating existing inequalities. It calls for a reconceptualisation of ESG frameworks, developed for mature markets, to better suit transitional urban contexts and support long-term resilience in residential real estate. Full article

This study examines the determinants of apartment prices in 17 post-WWII multi-family housing estates in Ljubljana, Slovenia, constructed between 1947 and 1986. Using 1973 verified transactions from 2020 to 2025, the analysis evaluates spatial, structural, environmental, and accessibility-related variables through a combination of statistical and machine learning techniques. A hedonic price model based on ordinary least squares (OLS) demonstrates modest explanatory power (R² = 0.171), identifying local market reference prices, floor level, noise exposure, and window renovation as significant predictors. In contrast, seven machine learning models—Random Forest, XGBoost, and Gradient Boosting Machines (GBMs), including optimized versions—achieve notably higher predictive accuracy. The best-performing model, GBM with Randomized Search CV, explains 59.6% of price variability (R² = 0.5957), with minimal prediction error (MAE = 0.03). Feature importance analysis confirms the dominant role of localized price references and structural indicators, while environmental and accessibility variables contribute variably. In addition, three clustering methods (Ward, k-means, and HDBSCAN) are employed to identify typological groups of neighborhoods. While Ward’s and k-means methods consistently identify four robust clusters, HDBSCAN captures greater internal heterogeneity, suggesting five distinct groups and detecting outlier neighborhoods. The integrated approach enhances understanding of spatial housing price dynamics and supports data-driven valuation, urban policy, and regeneration strategies for post-WWII housing estates in Central and Eastern European contexts. Full article

The real estate market has a major impact on the economy and everyday life. Accurate real estate valuation is essential for buyers, sellers, investors, and government institutions. Traditionally, valuation has been conducted using various estimation models. However, recent advancements in information technology, particularly in artificial intelligence and machine learning, have enabled more precise predictions of real estate prices. Machine learning allows computers to recognize patterns in data and create models that can predict prices based on the characteristics of the property, such as location, square footage, number of rooms, age of the building, and similar features. The aim of this paper is to investigate how the application of machine learning can be used to predict real estate prices. A machine learning model was developed using four algorithms: Linear Regression, Random Forest Regression, XGBoost, and K-Nearest Neighbors. The dataset used in this study was collected from major online real estate listing portals in Bosnia and Herzegovina. The performance of each model was evaluated using the R² score, Root Mean Squared Error (RMSE), scatter plots, and error distributions. Based on this evaluation, the most accurate model was selected. Additionally, a simple web interface was created to allow for non-experts to easily obtain property price estimates. Full article

Air pollution has a significant impact on the housing market, both in terms of property prices and buyer preferences, as well as urban development. Below, we present the main aspects of this impact. These may include a decline in property values in polluted areas, a change in buyer preferences (more buyers are taking environmental factors into account when choosing a home, including air quality—both outdoor and indoor—which translates into increased demand in ‘green’ neighborhoods), the development of energy-efficient and environmentally friendly buildings, the impact on spatial planning and urban policy, health effects, and the rental market. The study showed that air pollution has a significant negative impact on housing prices in Warsaw, particularly in relation to two pollutants: nitrogen dioxide (NO₂) and particulate matter (PM_2.5). As their concentrations decreased, housing prices increased, with the highest price sensitivity observed for smaller flats on the secondary market. The analysis used GRM and OLS statistical models, which confirmed the significance of the relationship between the concentrations of these pollutants and housing prices (per m²). NO₂ had a significant impact on prices in the primary market and on the largest flats in the secondary market, while PM_2.5 affected prices of smaller flats in the secondary market. No significant impact of other pollutants, meteorological factors, or their interaction on housing prices was detected. The study also showed that the primary and secondary markets differ significantly, requiring separate analyses. Attempts to combine them do not allow for the precise identification of key price-determining factors. Full article

This study investigates the contributions of five key sectors—insurance, materials, utilities, real estate, and transport—to the financial markets of six Gulf Cooperation Council (GCC) countries from 2004 to 2023. Grounded in the Sectoral Linkage Theory and Endogenous Growth Theory, the study employs a Panel Autoregressive Distributed Lag (Panel ARDL) model to examine both short-term and long-term sectoral impacts on financial market resilience. The findings reveal that the insurance and transport sectors offer short-term market stimulation, but lack persistent effects. Conversely, the materials, utilities, and real estate sectors exhibit strong, long-run contributions to financial stability and economic diversification. These results highlight the asymmetric impact of sectoral dynamics on market performance in resource-rich contexts. This research contributes to the literature by providing empirical evidence on sectoral interdependence in oil-dependent economies and highlights the importance of structural diversification for sustainable financial resilience. The study provides actionable insights for policymakers and investors seeking to enhance market resilience and reduce reliance on hydrocarbon revenues through targeted sectoral development. Full article

Climate change poses significant challenges to the economic and social sustainability of urban dwellers, particularly in the real estate market, where rising temperatures are affecting property values. While most research focuses on how climate change impacts buyers and sellers, this study shifts attention to renters, who may be more vulnerable to climate-induced price increases. By analyzing rental price and climate data, this study uses ordinary least squares (OLS) and fixed-effects regressions to assess the impact of temperature fluctuations on rental rates across 50 major U.S. metropolitan areas. The findings reveal a positive and significant relationship between rising temperatures and rental rates, particularly in the Northeastern and Southern U.S. These results suggest that targeted policy interventions may help ease financial pressures on vulnerable renters and support more sustainable urban development over time. The analysis also highlights the potential role of energy efficiency measures in rental housing to lower energy costs and alleviate rent burdens. Additionally, the findings indicate that local policymakers may consider rent stabilization strategies and investments in urban green infrastructure to protect low-income renters, reduce localized heat exposure, and promote long-term urban resilience. Full article

Housing unaffordability and widening socio-spatial polarization continue to pervade US cities today. Driving this phenomenon, in part, is the increasing investment of rental housing stock by corporate landowners who rely on firms like RealPage, Inc. to employ advanced algorithms that determine the highest possible rent to charge tenants. RealPage is currently being sued for violating US antitrust law. This study critically examines the evidence against and in defense of RealPage to identify the firm’s practices as a technologically advanced strategy of pursuing class monopoly rent (CMR). In the process, the study brings scholarship on platform real estate into closer dialogue with land rent theory and criticism of existing antitrust law in the US to establish a more nuanced understanding of intentionality. We argue that the treatment of intentionality by the existing legal framework is limited in terms of recognizing the myriad ways in which CMR is realized in the rental housing sector, especially in contexts where CMR is realized without entailing explicit collusive intent among the market players. Our analysis also reveals that RealPage’s algorithmically enabled pursuit of CMR potentially widens the scale of impact across submarket boundaries that might not have otherwise been possible, exacerbating existing and entrenched patterns of socio-economic segregation and socio-spatial inequality. We conclude by discussing the implications of the study’s findings for policy with an emphasis on the kinds of policies intended (or designed) to suppress the pursuit of CMR in the first place. Full article

This study examines the impact of personal remittances on housing prices in European Union (EU) countries, while also accounting for a broader set of macroeconomic, demographic, and structural variables. Using annual data for 27 EU countries from 2007 to 2022, we employ a comprehensive panel econometric approach, including cross-sectional dependence tests, second-generation unit root tests, pooled mean group–autoregressive distributed lag (PMG-ARDL) estimation, and panel causality tests, to capture both short- and long-term dynamics. Our findings confirm that remittances significantly and positively influence long-term housing price levels, underscoring their relevance as a demand-side driver. Other key variables such as net migration, GDP, travel credit to GDP, economic freedom, and real effective exchange rates also contribute to housing price movements, while supply-side indicators, including production in construction and building permits, exert moderating effects. Moreover, real interest rates are shown to have a significant long-term negative effect on property prices. The analysis reveals key causal links from remittances, FDI, and net migration to housing prices, highlighting their structural and predictive roles. Bidirectional causality between economic freedom, housing output, and prices indicates reinforcing feedback effects. These findings position remittances as both a development tool and a key indicator of real estate dynamics. The study highlights complex interactions between international financial flows, demographic pressures, and domestic economic conditions and the need for policymakers to consider remittances and migrant investments in real estate strategies. These findings offer important implications for policymakers seeking to balance housing affordability, investment, and economic resilience in the EU context and key insights into the complexity of economic factors and real estate prices. Importantly, the analysis identifies several causal relationships, notably from remittances, FDI, and net migration toward housing prices, underscoring their predictive and structural importance. Bidirectional causality between economic freedom and house prices, as well as between housing output and pricing, reflects feedback mechanisms that further reinforce market dynamics. These results position remittances not only as a developmental instrument but also as a key signal for real estate market performance in recipient economies. Full article

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. Full article

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. Full article

This study evaluates the risk-adjusted performance of Hospitality REITs using multi-factor asset pricing models and downside risk measures with the aim of assessing their diversification potential and crisis sensitivity. Unlike prior studies that examine REITs in aggregate, this study isolates Hospitality REITs to explore their unique cyclical and macroeconomic sensitivities. This study looks at the risk-adjusted performance of Hospitality Real Estate Investment Trusts (REITs) in relation to more general REIT indexes and the S&P 500 Index. The study reveals that monthly returns of Hospitality REITs increasingly move in tandem with the stock markets during financial crises, which reduces their historical function as portfolio diversifiers. Investing in Hospitality REITs exposes one to the hospitality sector; however, these investments carry notable risks and provide little protection, particularly during economic upheavals. Furthermore, the study reveals that Hospitality REITs underperform on a risk-adjusted basis relative to benchmark indexes. The monthly returns of REITs show significant volatility during the post-COVID-19 era, which causes return-to-risk ratios to be below those of benchmark indexes. Estimates from multi-factor models indicate negative alpha values across conditional models, indicating that macroeconomic variables cause unremunerated risks. This industry shows great sensitivity to market beta and size and value determinants. Hospitality REITs’ susceptibility comes from their showing the most possibility for exceptional losses across asset classes under Value at Risk (VaR) and Conditional Value at Risk (CvaR) downside risk assessments. The findings have implications for investors and portfolio managers, suggesting that Hospitality REITs may not offer consistent diversification benefits during downturns but can serve a tactical role in procyclical investment strategies. Full article

China’s timber market is very complex and heterogeneous, and is experiencing the impact of the construction of national reserve forests and the downturn in the real estate sector. By setting up a partial equilibrium model which reflects the heterogeneity of China’s wood market, not only difference among domestic timber groups can be identified, but the dissimilarity of imported timber can also be differentiated from the aspects of species and sources. This model is capable of capturing the effects of macroeconomic conditions, forestry sector policies, and trade cost variations on China’s timber market structure. According to simulations of supply shocks, China’s large-diameter log capacity enhancement will have a noticeable crowding-out effect on imported timber, suggesting the diameter of logs is an important factor for market entities to make trade-offs between domestic and imported timber. Amidst both supply and demand shocks, the equilibrium quantity changes in China’s domestic small-diameter logs and imported timber are dominated by demand shocks, whereas the equilibrium quantity change in China’s domestic large-diameter logs is dominated by supply shocks; moreover, only domestic large-diameter logs realize quantity increase in double shocks; this improves China’s domestic timber supply structure, and is a good example of “opportunities in crisis” in the face of negative demand shocks. Full article

Housing prices have recently risen sharply in many countries, primarily linked to the global credit cycle. Although various factors play a role, the ability of developing countries to navigate this cycle and maintain autonomous monetary policies is crucial. This paper introduces a dynamic macroeconomic model featuring a housing production sector within an imperfect banking framework. It captures key housing and economic dynamics in advanced and emerging economies. The analysis shows domestic liquidity policies, such as bank capital requirements, reserve ratios, and currency devaluation, can stabilize investment and production. However, their effectiveness depends on foreign interest rates and liquidity. Stabilizing housing prices and risk-free bonds is more effective in high-interest environments, while foreign liquidity shocks have asymmetric impacts. They can boost or lower the effectiveness of domestic policy, depending on the country’s level of financial development. These findings have several policy implications. For example, foreign capital controls would be adequate in the short term but not in the long term. Instead, governments would try to promote the development of local financial markets. Controlling debt should be a target for macroprudential policy as well as promoting saving instruments other than real estate, especially during low interest rates. Full article

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