Search Results (385)

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

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

House price prediction is crucial in real estate for informed decision-making. This paper presents an automated prediction system that combines genetic algorithms (GA) for feature optimization and Analysis of Variance (ANOVA) for statistical analysis. We apply and compare five ensemble machine learning (ML) models, namely Extreme Gradient Boosting Regression (XGBR), random forest regression (RFR), Categorical Boosting Regression (CBR), Adaptive Boosting Regression (ADBR), and Gradient Boosted Decision Trees Regression (GBDTR), on a comprehensive dataset. We used a dataset with 1000 samples with eight features and a secondary dataset for external validation with 3865 samples. Our integrated approach identifies Categorical Boosting with GA (CBRGA) as the best performer, achieving an R² of 0.9973 and outperforming existing state-of-the-art methods. ANOVA-based analysis further enhances model interpretability and performance by isolating key factors such as square footage and lot size. To ensure robustness and transparency, we conduct 10-fold cross-validation and employ explainable AI techniques such as Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), providing insights into model decision-making and feature importance. 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

As a classical statistical method, multiple regression is widely used for forecasting tasks in power, medicine, finance, and other fields. The rise of machine learning has led to the adoption of neural networks, particularly Long Short-Term Memory (LSTM) models, for handling complex forecasting problems, owing to their strong ability to capture temporal dependencies in sequential data. Nevertheless, the performance of LSTM models is highly sensitive to hyperparameter configuration. Traditional manual tuning methods suffer from inefficiency, excessive reliance on expert experience, and poor generalization. Aiming to address the challenges of complex hyperparameter spaces and the limitations of manual adjustment, an enhanced sparrow search algorithm (ISSA) with adaptive parameter configuration was developed for LSTM-based multivariate regression frameworks, where systematic optimization of hidden layer dimensionality, learning rate scheduling, and iterative training thresholds enhances its model generalization capability. In terms of SSA improvement, first, the population is initialized by the reverse learning strategy to increase the diversity of the population. Second, the mechanism for updating the positions of producer sparrows is improved, and different update formulas are selected based on the sizes of random numbers to avoid convergence to the origin and improve search flexibility. Then, the step factor is dynamically adjusted to improve the accuracy of the solution. To improve the algorithm’s global search capability and escape local optima, the sparrow search algorithm’s position update mechanism integrates Lévy flight for detection and early warning. Experimental evaluations using benchmark functions from the CEC2005 test set demonstrated that the ISSA outperforms PSO, the SSA, and other algorithms in optimization performance. Further validation with power load and real estate datasets revealed that the ISSA-LSTM model achieves superior prediction accuracy compared to existing approaches, achieving an RMSE of 83.102 and an $R^2$ of 0.550 during electric load forecasting and an RMSE of 18.822 and an $R^2$ of 0.522 during real estate price prediction. Future research will explore the integration of the ISSA with alternative neural architectures such as GRUs and Transformers to assess its flexibility and effectiveness across different sequence modeling paradigms. 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

The research herein presented aims to analyze the impacts of gentrification in a medium-sized Mallorca municipality because of the tourism accommodation changes. Using the available data from national and regional official sources, qualitative research is undertaken. The main findings show how gentrification has exacerbated issues such as rising real estate prices and the loss of houses, which are nowadays devoted to tourist rentals or boutique hotels, thus not available for the local population. Another effect is a displacement of young local residents from Sóller towards other island municipalities and, finally, the presence of conflicts over the use of public spaces. Even if the local authorities already implement some measures, it is concluded that more measures should be included in order to avoid the increase in “tourismphobia” attitudes related to the gentrification process and the public space occupation. Full article

The real estate market significantly influences individual lives, corporate decisions, and national economic sustainability. Therefore, constructing a data-driven, interpretable real estate market prediction model is essential. It can clarify each factor’s role in housing prices and transactions, offering a scientific basis for market regulation and enterprise investment decisions. This study comprehensively measures the evolution trends of the real estate markets in Beijing, Shanghai, Guangzhou, and Shenzhen, China, from 2003 to 2022 through three dimensions. Then, various machine learning methods and interpretability methods like SHAP values are used to explore the impact of supply, demand, policies, and expectations on the real estate market of China’s first-tier cities. The results reveal the following: (1) In terms of commercial housing sales area, adequate housing supply, robust medical services, and high population density boost the sales area, while demand for small units reflects buyers’ balance between affordability and education. (2) In terms of commercial housing average sales price, growth is driven by education investment, population density, and income, with loan interest rates serving as a stabilizing tool. (3) In terms of commercial housing sales amount, educational expenditure, general public budget expenditure, and real estate development investment amount drive revenue, while the five-year loan benchmark interest rate is the primary inhibitory factor. These findings highlight the divergent impacts of supply, demand, policy, and expectation factors across different market dimensions, offering critical insights for enterprise investment strategies. 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 literature shows greenspaces generally increase nearby property values, but in Riyadh, this relationship is complex and understudied. Existing studies lack sector-specific analyses across Riyadh’s neighborhoods, overlook the impact of the Green Riyadh Project launched in 2019, and fail to address negative externalities associated with large greenspaces in an arid, privacy-conscious context. Such paradoxical impact of larger greenspaces bordering major roads at the neighborhood edge, unexpectedly reduce property values by 2–4% due to petty crime, congestion, poor upkeep, and privacy concerns, contrasting with 10–18% premiums for properties abutting greenspaces with restricted access in affluent neighborhoods. Global studies typically report positive greenspace effects, so negative impacts in specific Riyadh sectors are surprising. This highlights the city’s unique arid, cultural, and urban dynamics in addressing this research gap. The research uses purposive quota sampling of Riyadh neighborhood greenspaces and a mixed-methods approach of quantitative hedonic pricing analysis combined with qualitative semi-structured interviews with real estate agents. Findings underscore the need for tailored urban planning (e.g., mitigating petty crime, overcrowding, poor maintenance). This suggests the importance of integrating green infrastructure into urban planning, not only for its ecological and social benefits but also for its tangible positive impact on property values. Poor greenspace upkeep and safety concerns can reduce price premiums of abutting properties. Full article

The performance of machine learning models depends on several factors, including data normalization, which can significantly improve its accuracy. There are many standardization techniques, and none is universally suitable; the choice depends on the characteristics of the problem, the predictive task, and the needs of the model used. This study analyzes how normalization techniques influence the outcomes of real estate price regression models using machine learning to uncover complex relationships between urban and economic factors. Six normalization techniques are employed to assess how they affect the estimation of relationships between property value and factors like social degradation, resident population, per capita income, green spaces, building conditions, and degraded neighborhood presence. The study’s findings underscore the pivotal role of normalization in shaping the perception of variables, accentuating critical thresholds, or distorting anticipated functional relationships. The work is the first application of a methodological approach to define the best technique on the basis of two criteria: statistical reliability and empirical evidence of the functional relationships obtainable with each standardization technique. Notably, the study underscores the potential of machine-learning-based regression to circumvent the limitations of conventional models, thereby yielding more robust and interpretable results. Full article

This study investigated the intangible value transferred from urban megaprojects to surrounding residential property markets, focusing on Brisbane’s transformative urban regeneration projects currently in the development pipeline. The research objectives were twofold: first, to empirically investigate the dynamics of property markets influenced by urban megaprojects and second, to assess the impact of a specific case study on these markets through a longitudinal analysis of residential sales data. Drawing from environmental economics, the concept of willingness to pay (WTP) is used to quantify externalities associated with urban megaprojects. The research constructs a comprehensive dataset integrating geospatial and property-specific data. Through revealed preference methods, the intangible value transferred from mixed-use developments is identified and quantified via residential transaction prices. Utilising hedonic price modelling, this study systematically analysed residential transaction data to estimate implicit prices associated with spatial proximity to megaprojects. A comprehensive dataset integrating property-specific attributes, geospatial proximity measures, and temporal dynamics of project development phases underpins this analysis. This research and its findings advance the existing literature in several important dimensions. That is, this research represents the first microeconomic assessment of the property market’s impacts resulting from mixed-use megaprojects in Brisbane, offering novel empirical insights for both academic and practical applications, how urban megaprojects shape residential property values, and informing stakeholders involved in urban planning, policymaking, and real estate investment decisions. Practitioners and policymakers can leverage these insights to inform policy frameworks and strategic decisions. At the governmental level, the results offer applicable insights for urban revitalisation strategies, particularly relevant to central business districts undergoing similar developments. Private sector stakeholders can utilise these outcomes to anticipate market adjustments, managing supply and demand fluctuations more effectively. Full article

The usable floor area is one of the key parameters when appraising residential property. In Poland, valuers have to base their analysis on data from the Real Estate Price Register (RCN) in order to value a property. Unfortunately, these data often turn out to be incomplete, especially with regard to floor area, which makes the selection of reference properties difficult and can lead to erroneous valuation results. To address this problem, a study was conducted that used linear models, non-linear models and machine learning algorithms to calculate the floor area of buildings with complex multi-pitched roofs. The analysis was conducted using data sourced from the Database of Topographic Objects (BDOT10k). Three key factors were identified to provide a reliable estimate of usable floor area: the covered area, the height of the building and, optionally, the number of storeys. The results show that the linear model based on the design data achieved an accuracy of 88%, the non-linear model achieved 89% and the machine learning algorithms achieved 93%. For the existing building data from the city of Koszalin, the best model achieved an accuracy of 90%. The estimated values of the usable area of the building designs for the best model on the test set differed on average from the true ones by 8.7 m², while for the existing buildings, the difference was 9.9 m² on average (in both cases, the average relative error was about 7%). Full article

This study examines the relationship between stock prices, energy prices, and climate policy uncertainty using 11 sectoral stocks in the U.S. market. The evidence confirms that rising prices of energy commodities positively affect not only the energy and oil sector stocks but also create spillover effects across other sectors. Notably, all sectoral stocks, except Real Estate sector, show resilience to increases in crude oil and gasoline, suggesting potential hedging benefits. In addition, the findings reveal that sectoral stock returns are generally negatively affected by several types of uncertainty, including climate policy uncertainty, economic policy uncertainty, oil price uncertainty, as well as energy and environmental regulation-induced equity market volatility and the energy uncertainty index. These adverse effects are present across sectors, with few exceptions. The evidence reveals that the feedback effect between changes in climate policy uncertainty and changes in oil prices has an adverse impact on stock returns. Omitting these uncertainty factors from analyses could lead to biased estimates in the relationship between stock prices and energy prices. Full article