Search Results (102)

The prediction of real estate values is vital for taxation, transactions, mortgages, and urban policy development. Values can be predicted more accurately by statistical or advanced methods together when the size of the data is huge. In metropolitan cities like İstanbul, where size of the real estate data is vast and complex, mass appraisal methods supported by Machine Learning offer a scalable and consistent alternative. This study employs six algorithms: Artificial Neural Network, Extreme Gradient Boosting, K-Nearest Neighbors, Support Vector Regression, Random Forest, and Semi-Log Regression, to estimate the values of real estate on both the Asian and European continent parts of İstanbul. In total, 168,099 residential properties were utilized along with 30 of their features from both sides of the Bosphorus. The results show that RF yielded the best performance in Beşiktaş, while XGBoost performed best in Üsküdar. ANN also produced competitive results, although slightly less accurate than those of XGBoost and RF. In contrast, traditional SVR and SLR models underperformed, especially in terms of R² and RMSE values. With its large-scale dataset, focusing on one of the greatest metropolitan areas, Istanbul, and the usage of multiple ML algorithms, this study stands as a comprehensive and practical contribution to the field of automated real estate valuation. 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

In the post-digital era, virtual reality (VR) technology is increasingly being utilized in the real estate industry. In this study, the influence of functional experience with VR technology (e.g., interactivity and flexibility) on consumers’ offline house viewing intentions is explored. On the basis of efficiency–flexibility ambidexterity and customer inspiration theory, a structural equation model was employed to analyze empirical data collected from 388 consumers in the Guangdong–Hong Kong–Macao Greater Bay Area. The key findings are as follows: (1) VR technology features have significant positive effects on customer inspiration, which in turn enhances customers’ willingness to view houses offline; (2) VR presence, enjoyment, interactivity, and flexibility all contribute to customer inspiration, with VR presence having the most substantial impact; and (3) VR knowledge and consumer demand for uniqueness significantly moderate the relationship between VR technology features and customer inspiration. For example, consumers with substantial VR knowledge can more effectively leverage VR technology, whereas those with a strong need for uniqueness are more likely to be inspired by the innovative aspects of VR. This research provides theoretical support for the application of VR technology in real estate marketing and practical guidance for enterprises to optimize VR marketing strategies, improve consumer experiences, and drive offline transactions. These insights can help companies better understand consumer psychology and behaviour in the digital marketing landscape. 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

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

This essay examines relationships between adoptees and the (extended) adoptive family, focusing on the inheritance rights of adopted persons as entry points into levels and cycles of their belonging and un-belonging. The essay contextualizes a case report (or summary reports) on the kind of estrangement in the adoptee world that is fueled by inheritance disputes. It delves into postadoption perceptions and thus into the “unwritten” truths about adoption and its possible fallout. It draws from archival sources, semi-structured interviews (life-story interviewing), and life writing by adoptees, and also from a sequence of real-life exchanges dating back to 2018. All these sources focus on the contested inheritance of children, now older adults, who were adopted from Greece in the 1950s–60s and who became (or should have become) subsequent heirs to the estates of their adoptive parents and/or relatives. The Greek out-of-country adoptions of the postwar and early Cold War era involved more than 4000 children, most of whom were sent to the United States. The various testimonies and sections reflect critically on the continuing trend to infantilize the adopted persons, forever the adopted children, to push their origins back into the past and into geographical distance, to untie the family connections they have forged over the course of half a century. The examples take the reader from the adoptive family’s pre-adoption attempts at disowning the child through the postadoption stage of the end of an adopted lifetime, including cases of the extended adoptive family’s attempts at “de-adopting” the adopted person. This essay includes various sources of life-cycle documentation, among them an extensive case study and online obituaries. It adheres to truth and authenticity by incorporating fairly long original quotations, which, in the case study of the second half especially, assist the reader in comprehending much historical information in a question-and-answer format. This bolder structure offers the advantage of taking the reader step by step through the transactions of a prominent Greek adoption scheme (Rebecca and Maurice Issachar) and also through the various layers of the postadoption mindset and minefield. The material presented here is intended to raise awareness that change can and must still benefit the Greek adoptees today, whose lives may have been permeated by conditionality and nonlinearity. I conclude that, in the cases discussed here, the child’s orphanhood may well be a perpetual state, with the adoptee being orphaned of individuality and of a protective family on more than just one occasion. Full article

Infrastructure and amenities have an evident effect on differentiated urban structures and house prices. However, few studies have taken into account the spatial heterogeneity of large-scale urban areas. Regarding this issue, the present study proposes a novel spatial framework to quantify the impacts of built environment factors on the housing market. We aim to answer: how does a specific factor impact house prices across different spatially autocorrelated neighbourhood clusters? The city of Shanghai, the economic centre of China, is examined through the transaction data from the China Real-estate Information Center (CRIC) are analysed. Firstly, spatially autocorrelation clusters were explored to identify high/low housing prices in concentrated areas in Shanghai. Secondly, using the development-scale house prices as the dependent variable, we employed ordinary least squares (OLS) linear regression and geographically weighted regression (GWR) models to examine the impact of built environment facilities on the house prices across these spatial autocorrelation regions and Shanghai more generally. The results suggest the following: (1) There are significant spatially autocorrelated clusters across Shanghai, with high-value clusters concentrated in the city core and low value concentrated in the suburban fringes; (2) Across Shanghai and its spatially autocorrelated clusters, transportation accessibility and service amenities factors can affect house prices quite differently, especially when focusing on the city centre and the suburban areas. Our results highlight the importance of optimising the city’s polycentric structural framework to foster a more balanced regional development. Differentiated approaches to the distribution of public service facilities should be adopted to address the diverse needs of residents across various regions. Full article

This study delineates transaction price submarkets of dwellings in Vienna by performing spatiotemporal clustering and analysing the change in purchasing prices in these clusters between 2018 and 2022. The submarkets are created using a novel spatiotemporal clustering method referred to as Multidimensional Spatiotemporal Change–DBSCAN (MDSTC-DBSCAN), which incorporates the temporal change in transaction prices along with spatial proximity to identify spatial areas with similar transaction prices. It represents an advancement over MDST-DBSCAN for this use case, as it considers the change over time as valuable information rather than a constraint that further splits the clustering groups. The results of the case study in Vienna indicate variations in price growth rates among the submarkets (i.e., contiguous regions with similar prices and price growth rates) that confirm the importance of considering the temporal changes in transaction prices. With respect to the Viennese case study, a lower Moran’s I value was observed for 2022 compared to previous years (2018 to 2021), indicating a higher level of homogeneity in transaction prices. This finding was also supported by the cluster analysis, as less expensive clusters demonstrated higher rates of price increase compared to more expensive clusters. Future research can enhance the algorithm’s usability and broaden its potential use cases to other multidimensional spatiotemporal event data. 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

One of the key elements in real estate appraisal of residential buildings is the usable area. To determine the monetary value of real estate, appraisers in Poland often rely on transaction data registered in the Real Estate Price Register (REPR). However, the REPR may contain meaningful gaps, particularly on information concerning usable areas. This may lead to difficulties in finding suitable comparative properties, resulting in mispricing of the property. To address this problem, we used linear and nonlinear models to estimate the usable area of buildings with multi-pitched roofs. Utilizing widely available data from the Topographic Objects Database (BDOT10k) based on LiDAR technology, we have shown that three parameters (building’s covered area, building’s height, and optionally the number of storeys) are sufficient for a reliable estimate of the usable area of a building. The best linear model, using design data from architectural offices, achieved a fit of 95%, while the best model based on real data of existing buildings in the city of Koszalin, Poland achieved 92% fit. The best nonlinear model achieved slightly better results than the linear model in the case of design data (better fit by approximately 0.2%). In the case of existing buildings in Koszalin, the best fit was at 93%. The proposed method may help property appraisers determine a more accurate estimation of the usable area of comparative buildings in the absence of this information in the REPR. Full article

In today’s business landscape, the volume of transaction data is rapidly increasing. This study explores the integration of Point of Sale (POS) and Radio-Frequency Identification (RFID) technologies to enhance the analysis of customer transactions using big data tools. By leveraging these technologies, businesses can extract valuable insights to improve processes, optimize inventory, and boost customer satisfaction. The research employs an object—subject management approach, which facilitates real-time decision-making by merging retail transactions of the clients with their movement patterns. An experiment involving around 7000 customers demonstrates the effective collection and processing of POS and RFID data, highlighting the benefits of integrating these data streams. Key metrics, such as time spent in different store sections, provide deeper insights into consumer behavior. The findings reveal the potential of these technologies to transform retail services, offering opportunities for demand forecasting, risk management, and personalized customer experiences. The study concludes that merging POS and RFID data opens new avenues for developing management solutions aimed at enhancing customer engagement and the operational efficiency of the retailer. Future research will focus on further elaborating these solutions to maximize the benefits of integrated data analysis. 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

Land and real estate have long been regarded as stable investments, with property prices steadily rising, underscoring the need for accurate predictive models to capture the varying rates of price growth across different locations. This study leverages a decade-long dataset of 83,527 apartment transactions in Vienna, Austria, to train machine learning models using XGBoost. Unlike most prior research, the extended time span of the dataset enables predictions for multiple future years, providing a more robust long-term prediction. The primary objective is to examine how spatial factors can enhance real estate price predictions. In addition to transaction data, socio-demographic and geographic variables were collected to characterize the neighborhoods surrounding each apartment. Ten models, each varying in the number of input years, were trained to predict the price per square meter. The model performance was assessed using the mean absolute percentage error (MAPE), offering insights into their predictive accuracy for both short-term and long-term predictions. This study underscores the importance of distinguishing between newly built and existing apartments in real estate price modeling. By splitting the dataset prior to training, predictive models focusing solely on newly built properties achieved an average reduction of about 6% in MAPE. The best-performing models achieved an average MAPE of 15% for one-year-ahead predictions and maintained a MAPE below 20% for predictions up to three years ahead, demonstrating the effectiveness of leveraging spatial features to enhance real estate price prediction accuracy. Full article

The objective of the study was to ascertain the purchasing power potential of the average gross salary in the real estate market. The study area involved the European Union (EU) member states from 2008 to 2022. The research was based on the following data: average earnings for a full-time job, housing market transaction prices, and housing rental prices in the country. The analysis demonstrated that the average European must save for nearly six years for the purchase of their own apartment. Over the period from 2015 to 2022, the purchasing power of the monthly wage decreased in 20 of the 26 countries included in the survey. A recent study has demonstrated that 31% of the EU’s residents maintain households in rented housing. The study revealed that in the capital cities of six of the twenty countries surveyed, rent constitutes more than half of the average salary. Warsaw, the most expensive capital city, requires more than 93% of the average salary to be spent on rent. Full article