Search Results (274)

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

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

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

Spatial data not only enables smart cities to visualize, analyze, and interpret data related to location and space, but also helps departments make more informed decisions. We apply a Bayesian quantile regression (BQR) of the partially linear varying coefficient spatial autoregressive (PLVCSAR) model for spatial data to improve the prediction of performance. It can be used to capture the response of covariates to linear and nonlinear effects at different quantile points. Through an approximation of the nonparametric functions with free-knot splines, we develop a Bayesian sampling approach that can be applied by the Markov chain Monte Carlo (MCMC) approach and design an efficient Metropolis–Hastings within the Gibbs sampling algorithm to explore the joint posterior distributions. Computational efficiency is achieved through a modified reversible-jump MCMC algorithm incorporating adaptive movement steps to accelerate chain convergence. The simulation results demonstrate that our estimator exhibits robustness to alternative spatial weight matrices and outperforms both quantile regression (QR) and instrumental variable quantile regression (IVQR) in a finite sample at different quantiles. The effectiveness of the proposed model and estimation method is demonstrated by the use of real data from the Boston median house price. 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 study investigates how China’s affordable housing policies have shaped the real estate market, using data from 35 major cities between 2010 and 2023. By analyzing housing prices, sales, and investment trends with advanced statistical methods, we found that increasing the supply of affordable housing significantly slows down rising home prices, especially in cities with high housing costs. During the COVID-19 pandemic, these policies also helped stabilize the market by boosting housing sales and reducing price volatility. Our research highlights regional differences: affordable housing works best in economically developed eastern cities to curb prices, while in less-developed central and western areas, it may temporarily increase prices due to land competition. We also show that affordable housing absorbs demand from low- and middle-income buyers, easing pressure on commercial housing markets over time. This study provides practical insights for policymakers to design targeted housing strategies, optimize land use, and enhance urban resilience during crises, like pandemics. By combining real-world data with robust analysis, we offer a clearer picture of how housing security policies can balance market stability and affordability in rapidly urbanizing economies. Full article

This study examines housing affordability trends in Central Europe, focusing on the Czech Republic, Slovakia, Austria, and Poland, in the wake of recent global disruptions including the COVID-19 pandemic, the 2021–2022 energy crisis, and the war in Ukraine. These events have intensified housing affordability challenges by driving up property prices, rental costs, and energy expenses. Using data from December 2022 to March 2023, the paper analyzes wage levels relative to housing costs in major cities—Prague, Brno, Bratislava, Vienna, Graz, Warsaw, and Kraków—through price-to-income and rent-to-income ratios. The findings reveal that affordability is most strained in Czech cities, particularly Prague, where property prices outpace wages, while Vienna demonstrates better affordability due to higher average incomes. The study integrates real estate platform data with official statistics and employs spatial mapping and exploratory econometric testing to identify affordability patterns and disparities. It concludes that affordability outcomes are shaped by wage dynamics, housing supply constraints, migration pressures, and policy responses. The study underscores the importance of targeted housing policies and wage interventions to address these challenges and highlights the need for cross-country policy learning and regional coordination to improve housing affordability and market resilience across Central Europe. Full article

Climate change and extreme rainfall events pose great pressures on a city’s resilience to flooding and waterlogging. Designed as a kind of green infrastructure to manage stormwater, urban sponge parks (USPs) in China have been demonstrated to have ecological and societal benefits, while their landscape economic values lack evaluation. Taking the real-estate choices surrounding six USPs in China as an example, an evaluation framework integrating text mining with housing introduction documents and hedonic price model (HPM) regression with housing prices was constructed to combine the stated preferences and revealed preferences of citizens when purchasing properties. The main findings include the following: (1) USPs do contribute to property appreciation, especially in newer urban areas, although they are not as strong as location and property characteristic factors; (2) the extent of the influence of USPs on houses decreases as the distance increases, with a maximum radius of 3 km; (3) a USP’s effects vary according to the urban and environmental context, as HPM with GWR (R² ranges from 0.203 to 0.679) outperforms the OLS method (R² ranges from 0.149 to 0.491), which evokes the need for more affluent and detailed analyses in the future. This study demonstrates the economic benefits of USPs and provides an evaluation approach based on citizen science data, which could contribute to the policy-making of USPs in China and promote the implementation of Nature-based Solutions. Full article

Housing prices are a topic of significant social concern, and public services are a crucial factor influencing migrants’ return intentions. Based on the China Labour Force Dynamics Survey and China Real Estate Index database from 2012 to 2018, this study adopts probit model to explore the influence mechanism of housing prices on migrants’ return intentions and the moderating effect of public services. The results indicate that housing prices have a significant positive impact on migrants’ return intentions, and the level of public services negatively moderates the relationship between housing prices and migrants’ return intentions. Moreover, employing an instrumental variable approach to address the endogeneity of housing prices, the modeling results provide robust evidence of the significant and heterogenous impact of housing prices on return intentions among migrants. In particular, the positive impact of housing prices is mainly concentrated among single urban migrants without housing. Additionally, public services negatively moderate the positive impact of housing prices on return intentions among single rural migrants without housing. By elucidating the correlation between housing prices, public services, and return intentions among migrants, this study offers recommendations for policymakers regarding migration issues in urban development. Full article

Urban sprawl and excessive reliance on motorization have led to many urban problems. The balance of supply and demand in the real estate market, as well as price fluctuations, also face many challenges. Urban rail transit not only alleviates traffic congestion and air pollution, but also significantly reduces residents’ commuting time, broadens urban accessibility, and reshapes the decision-making basis for residents when choosing residential locations. This study takes the 1st, 2nd, 3rd, 4th, 8th, 11th, and 13th metro lines that have been opened in Qingdao City as examples. It selects 12,924 residential samples within a 2 km radius along the rail transit lines. By using GIS spatial analysis tools and the multi-scale geographically weighted regression (MGWR) model, it analyzes the spatial differentiation characteristics of housing prices along the rail transit lines and the reasons and mechanisms behind them. The empirical results show that housing prices decrease to varying degrees with the increase in the distance from the rail transit. For every additional 1 km from the rail transit station, the housing price increases by 0.246%. Through model comparison, it was found that MGWR has a better fitting degree than the traditional ordinary least squares method (OLS) and the previous geographically weighted regression model (GWR), and reveals the spatial heterogeneity of the influence of urban rail transit on housing prices. Different indicator elements have different effects on housing prices along these lines. The urban rail transit factor in the location characteristics has a positive impact on housing prices, and has a significant negative correlation in some areas. The significant influence range of the distance to the nearest metro station on housing prices is concentrated within a radius of 373 m, and the effect decays beyond this range. The total floors, building area, green coverage rate, property management fee, and the distance to hospitals and parks in the neighborhood and structural characteristics have spatial heterogeneity. Analyzing the areas affected by the urban rail transit factor, it was found that the double location superposition effect, the networked transportation system, and the agglomeration of urban functional axes are important reasons for the significant phenomena in some local areas. This research provides a scientific basis for optimizing the sustainable development of rail transit in Qingdao and formulating differentiated housing policies. Meanwhile, it expands the application of the MGWR model in sustainable urban spatial governance and has practical significance for other cities to achieve sustainable urban development. Full article

This study investigates the time-varying causal relationship between China’s economic policy uncertainty (EPU) and housing prices (HP) at the macroeconomic level. Using sub-sample rolling-window techniques on monthly nationwide data spanning January 2000 to January 2025, we systematically analyze the dynamic interactions and structural shifts between these variables. It finds that EPU has both positive and negative impacts on HP, which are consistent with the general equilibrium model (GEM). Additionally, the study identifies a feedback effect of HP on EPU. The findings offer objective evidence and recommendations for the Chinese government to pay close attention to the intricate dynamic interactions between EPU and HP. Furthermore, the study provides insights into real estate market reactions in a developing country, which can be valuable for other market participants. Full article

I show that house prices can be modeled using machine learning (kNN and tree-bagging) and a small dataset composed of macroeconomic factors (MEF), including an inflation metric (CPI), US Treasury rates (10-yr), Gross Domestic Product (GDP), and portfolio size of central banks (ECB, FED). This set of parameters covers all the parties involved in a transaction (buyer, seller, and financing facility) while ignoring the intrinsic properties of each asset and encompassing local (inflation) and liquidity issues that may impede each transaction composing a market. The model here takes the point of view of a real estate trader who is interested in both the financing and the price of the transaction. Machine learning allows for the discrimination of two periods within the dataset. First, and up to 2015, I show that, although the US Treasury rates level is the most critical parameter to explain the change of house-price indices, other macroeconomic factors (e.g., consumer price indices) are essential to include in the modeling because they highlight the degree of openness of an economy and the contribution of the economic context to price changes. Second, and for the period from 2015 to today, I show that, to explain the most recent price evolution, it is necessary to include the datasets of the European Central Bank programs, which were designed to support the economy since the beginning of the 2010s. Indeed, unconventional policies of central banks may have allowed some institutional investors to arbitrage between real estate returns and other bond markets (sovereign and corporate). Finally, to assess the models’ relative performances, I performed various sensitivity tests, which tend to constrain the possibilities of each approach for each need. I also show that some models can predict the evolution of prices over the next 4 quarters with uncertainties that outperform existing index uncertainties. Full article

The location of housing has a significant influence on its pricing. Generally, spatial self-correlation and spatial heterogeneity phenomena affect housing price data. Additionally, time is a crucial factor in housing price modeling, as it helps understand market trends and fluctuations. Currency market fluctuations also directly affect housing prices. Therefore, in addition to the physical features of the property, such as the area of the residential unit and building age, the rate of exchange (dollar price) is added to the independent variable set. This study used the real estate transaction records from Iran’s registration system, covering February, May, August, and November in 2017–2019. Initially, 7464 transactions were collected, but after preprocessing, the dataset was refined to 7161 records. Unlike feedforward neural networks, the generalized regression neural network does not converge to local minimums, so in this research, the Geographically, Temporally, and Characteristically Weighted Generalized Regression Neural Network (GTCW-GRNN) for housing price modeling was developed. In addition to being able to model the spatial–time heterogeneity available in observations, this algorithm is accurate and faster than MLR, GWR, GRNN, and GCW-GRNN. The average index of the adjusted coefficient of determination in other methods, including the MLR, GWR, GTWR, GRNN, GCW-GRNN, and the proposed GTCW-GRNN in different modes of using Euclidean or travel distance and fixed or adaptive kernel was equal to 0.760, 0.797, 0.854, 0.777, 0.774, and 0.813, respectively, which showed the success of the proposed GTCW-GRNN algorithm. The results showed the importance of the variable of the dollar and the area of housing significantly. Full article