Search Results (9)

In the context of increasing digitization, integrating ICT technologies, artificial intelligence, and remote working is altering residential mobility patterns and housing preferences. This study examines the housing market’s impact, focusing on how residential affordability affects residential choices, using a case study of the Metropolitan City of Florence. The analysis employs a methodology centered on the Debt-to-Income Ratio (DTI), which cross-references real estate market values (source: Agenzia delle Entrate and leading real estate portals) with household income brackets to identify affordable areas. The results reveal a clear divide: households with incomes below EUR 26,000 per year (representing about 69% of the population) are excluded from the central urban property market. This evidence confirms regional and national trends, emphasizing a growing mismatch between housing costs and disposable incomes. The study concludes that affordability is a technical–financial parameter and a valuable tool for supporting inclusive urban planning. Its application facilitates the orientation of effective public policies and the identification of socially sustainable housing solutions. Full article

Europe faces a situation where housing represents the main savings for most of the population, while the majority of homeowners are seniors aged over 65. The desire to supplement pensions has led to a growing interest in generating income from these savings, with bare ownership emerging as a notable option. This solution makes it possible to transfer the ownership of the home while maintaining usufruct rights for the duration of the owner’s lifetime. This paper examines the status of bare ownership in the city of Rome by web scraping the house offers published on web portals and segmenting those offered as bare ownership. Machine learning analysis based on neural networks and binary logit regression allows for the observation of the particular behavior of the housing supply in bare ownership; it shows the different intrinsic and extrinsic characteristics that determine this Real Estate segment. The findings highlight the development of a growing market strongly influenced by the location of assets. These findings provide valuable insights for both investors and urban planners regarding changes in urban dynamics processes. Full article

In the rapidly evolving field of real estate economics, the prediction of house prices continues to be a complex challenge, intricately tied to a multitude of socio-economic factors. Traditional predictive models often overlook spatial interdependencies that significantly influence housing prices. The objective of this study is to leverage Graph Neural Networks (GNNs) on open statistics knowledge graphs to model these spatial dependencies and predict house prices across Scotland’s 2011 data zones. The methodology involves retrieving integrated statistical indicators from the official Scottish Open Government Data portal and applying three representative GNN algorithms: ChebNet, GCN, and GraphSAGE. These GNNs are compared against traditional models, including the tabular-based XGBoost and a simple Multi-Layer Perceptron (MLP), demonstrating superior prediction accuracy. Innovative contributions of this study include the use of GNNs to model spatial dependencies in real estate economics and the application of local and global explainability techniques to enhance transparency and trust in the predictions. The global feature importance is determined by a logistic regression surrogate model while the local, region-level understanding of the GNN predictions is achieved through the use of GNNExplainer. Explainability results are compared with those from a previous work that applied the XGBoost machine learning algorithm and the SHapley Additive exPlanations (SHAP) explainability framework on the same dataset. Interestingly, both the global surrogate model and the SHAP approach underscored the comparative illness factor, a health indicator, and the ratio of detached dwellings as the most crucial features in the global explainability. In the case of local explanations, while both methods showed similar results, the GNN approach provided a richer, more comprehensive understanding of the predictions for two specific data zones. Full article

Groundwater is a strategic environmental resource due to its use to human consumption, and therefore requires special protection and monitoring in many databases. In Poland, groundwater data are included in different typical water-related databases such as Hydroportal, Portal of the State Hydrogeological Service and Portal of the Central Geological Database, which is linked to an integrated real estate information system (IREIS). This article aims to demonstrate how IREIS is used to manage groundwater in Poland. The analysis indicates that shortcomings and gaps are noticeable, e.g., duplication of data and significant lack of data necessary for the implementation of the legal instruments. It is therefore a priority to establish a harmonised permitting and sustainable management of resources by public authorities, supported by an appropriate information and resource system for the EU. There is a need for an increase in the amount of information in databases and a reduction in the number of databases with groundwater information. The results of the analysis of these information systems can provide guidance to other EU countries for more effective groundwater protection and management. Full article

Krakow has a permanent population of over 800,000. The number of inhabitants is increasing year on year due to the influx of working people and students, who often settle in Krakow permanently. This is leading to increased demand and consequently more flats and houses being put into use by developers. The increasing environmental awareness of the population and the resulting financial benefits—particularly evident in 2022—have meant that the classic, or rather ill-considered, building industry is gradually being replaced by better, environmentally friendly solutions. In the first part of the article, the authors focus on smart buildings, and in the second part, they combine them with financial changes in the real estate market. The aim of the publication is to draw conclusions from the changes in the real estate market in Krakow that have taken place in the last decade and to assess these activities from the point of view of environmental solutions. The data are mainly derived from official statistics and trade reports published by research institutes, marketing agencies operating in the real estate sector in Poland, as well as specialist portals and publications dealing with real estate market analysis. The publication analyzes changes in the real estate market in terms of changes in unit prices, number of transactions, and availability. The analysis covers landed property, the primary and secondary premises market, developed property, and tenement buildings. Full article

In 2015, global real estate was worth $217 trillion, which is approximately 2.7 times the global GDP; it also accounts for roughly 60% of all conventional global resources, making it one of the key factors behind any country’s economic growth and stability. The accessibility of spatial big data will help real estate investors make better judgement calls and earn additional profit. Since location is deemed necessary for real estate and consequent decision-making, digital maps have become a prime resource for real estate purchases, planning and development. Personalisation can assist in making judgments by identifying user desires and inclinations, which can then be recorded or captured as a user performs some interactions with a digital map. A personalised real estate portal can use this information to suggest properties, assist homeowners and provide valuable real estate analytics. This article presents a novel framework for recommending real estate to users. By monitoring user interactions through an online real estate portal, the framework can make personalised recommendations of real estate based on content, collaboration and location. The effectiveness of the recommendations was tested by the user feedback mechanism through a method of mean absolute precision, and the results show that 79% precise suggestions were generated, i.e., out of 5 recommendations produced, users were interested in at least 3. Along with that, a separate house price prediction model based on neural networks and classical regression techniques was also implemented to assist users in making an informed decision regarding prospects of real estate purchase. Full article

Automatic prediction of the aesthetic value of images has received increasing attention in recent years. This is due, on the one hand, to the potential impact that predicting the aesthetic value has on practical applications. Even so, it remains a difficult task given the subjectivity and complexity of the problem. An image aesthetics assessment system was developed in recent years by our research group. In this work, its potential to be applied in commercial tasks is tested. With this objective, a set of three portals and three real estate agencies in Spain were taken as case studies. Images of their websites were taken to build the experimental dataset and a validation method was developed to test their original order with another proposed one according to their aesthetic value. So, in this new order, the images that have the high aesthetic score by the AI system will occupy the first positions of the portal. Relevant results were obtained, with an average increase of 52.54% in the number of clicks on the ads, in the experiment with Real Estate portals. A statistical analysis prove that there is a significant difference in the number of clicks after selecting the images with the AI system. Full article

After almost a decade of crisis, the housing market in Spain shows significant signs of recovery, with increases in both the average price and the number of sales transactions. Housing is the main asset for the majority of households, and it also has the most resources devoted to it, thus, when it comes to buying a residence, people do not only look at the asset’s intrinsic characteristics, but also consider other particularities such as the neighbourhood, accessibility to services, availability of public transport or adequate funding. The study aimed to analyse and quantify the relationship that exists between the asking price of second-hand housing on the market in Alicante and the attributes that characterise them. This was done using a multivariate analysis to estimate a hedonic pricing model by ordinary least squares and a quantile regression to analyse the impact of the characteristics in different price ranges. The results show the segmentation of the prices in the Alicante market, with higher prices in the northern coastal area over the southern and inland comarcas. Full article

Globalization and the development of the so-called “collaborative economies” has coincided with an important transformation of mass tourism in the last decades. This phenomenon has been accentuated enormously in many European cities in recent years, generating a new P2P tourist model. The situation is having a strong social impact on the urban transformation of cities, and its characteristics are closely related to real estate speculative movements. In this sense, the analysis of urban transformation can offer interesting conclusions about the sustainability of these new tourist models in large touristic cities. In this article, we will analyse the effect associated with of so-called phenomena of “tourist flats” from the Airbnb portal in the cities of Madrid, Barcelona, and Palma de Mallorca. Through the use of GIS indicators and geostatistic analysis of spatial correlation, the current incidence of this phenomenon in these cities, and possible future scenarios of maintaining the current trend, will be evaluated and discussed. The results obtained show worrying indicators in relation to the economic and social sustainability of the current urban-tourist model created in the city which are linked to gentrification processes. Full article