Search Results (95)

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

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

With regard to the Italian context, the present research aims to empirically assess whether and to what extent real estate market dynamics (prices and vibrancy levels) are influenced by the life quality in a specific reference area. In particular, the study compares parameters related to the residential real estate market—such as the Real Estate Market Observatory quotations and the real estate market intensity index (used as a proxy for market dynamism)—with the Life Quality index developed by the study center of the Italian newspaper “Il Sole 24 Ore” for the selected provincial capitals. Furthermore, by breaking down the Life Quality index into the individual indicators used for its elaboration, the research identifies those most closely linked to real estate market mechanisms to explore these relationships within each context. This approach allows for the identification of potential local differences, providing insights into the degree of geographical heterogeneity. Finally, a GIS-based analysis is employed to graphically represent the various indicators, capturing the potential spatial correlations related to phenomena where the geographic component plays a significant role. 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

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

As a pillar industry of China’s economy, the real estate sector has been challenged by the increasing prevalence of housing vacancies, which negatively impacts market stability. Traditional vacancy rate estimation methods, relying on labor-intensive surveys and lacking official statistical support, are limited in accuracy and scalability. To address these challenges, this study proposes a novel framework for assessing residential community-level housing vacancy rates through the integration of multi-source data. Its core is based on night-time lighting data, supplemented by other multi-source big data, for housing vacancy rate (HVR) estimation and practical validation. In the case study of Longquanyi District in Chengdu City, the main conclusions are as follows: (1) with low data resolution, the model estimates a root mean square error (RMSE) of 0.14, which is highly accurate; (2) the average housing vacancy rate (HVR) of houses in Longquanyi District’s residential community is 46%; (3) the HVR rises progressively with the increase in the distance from the city center; (4) the correlation between the HVR of Longquanyi District and the house prices of the area is not obvious; (5) the correlation between the HVR of Longquanyi District and the time of completion of the communities in the region is not obvious, but the newly built communities have extremely high HVR. Compared to the existing literature, this study innovatively leverages multi-source big data to provide a scalable and accurate solution for HVR estimation. The framework enhances understanding of urban real estate dynamics and supports sustainable city development. Full article

With the new phase of urbanization in China, enhancing urban spatial quality has become a key task in urban development. As an important indicator of residents’ willingness to live, housing prices provide valuable feedback from their perspective for improving spatial quality. Taking Wuhan as a case study, this paper constructs an indicator system with 12 explanatory variables, including a subjective evaluation of buildings generated using deep learning techniques. Using OLS and GWR models, the study analyzes the factors influencing housing prices and their spatiotemporal dynamics in Wuhan’s core urban areas from 2016 to 2024, encompassing the full cycle of housing price fluctuations from an upward to a downward trend. The findings reveal that, as housing prices return to more rational levels, the impact of location factors diminishes, while the influence of community quality factors—such as property fees, green space ratio, and building quality—significantly increases. Factors such as proximity to hospitals also exhibit a certain degree of spatiotemporal complexity. This trend highlights residents’ growing attention to housing quality and living environments, marking a fundamental shift in the behavior of homebuyers. The results of this study provide crucial insights into the evolution of residential preferences and the spatiotemporal dynamics of the housing market. They offer significant theoretical and practical references for understanding residents’ housing needs from their perspective, thereby promoting the healthy development of the real estate market and improving urban spatial quality. 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

Understanding the determinants of housing price movements is an ongoing subject of debate. Estimating these determinants becomes a valuable tool for predicting price trends and mitigating the risks of market volatility. This article presents a systematic review analyzing studies that compare various machine learning (ML) tools with hedonic regression, aiming to assess whether real estate price predictions based on mathematical techniques and artificial intelligence enhance the accuracy of hedonic price models used for valuing residential properties. ML models (neural networks, decision trees, random forests, among others) provide high predictive capacity and greater explanatory power due to the better fit of their statistical measures. However, hedonic regression models, while less precise, are more robust, as they can identify the housing attributes that most influence price levels. These attributes include the property’s location, its internal features, and the distance from the property to city centers. 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

The changing demographics of the global population represent a significant challenge for humanity. Such changes have an impact on the functioning of the economy, including the housing market, and necessitate constant monitoring. This study evaluated the spatial diversity of all the large and medium-sized cities in the West Pomeranian Voivodeship, situated in the north-west of Poland, in terms of three key factors: demographic potential, housing stock and their price levels. Furthermore, the interactions between the cities’ positions in the rankings, which were created on the basis of the aforementioned phenomena, were identified. In order to achieve the objectives of the study, the linear object ordering method, the Hellwig pattern method and Kendall’s tau rank correlation coefficient were employed. The research was conducted using data from the years 2018 to 2022, sourced from the databases of the Polish Statistical Office and the Analysis and Monitoring System of the Real Estate Market. The study observed a relatively strong positive correlation between the positions of cities in the ranking created for demographic potential and the level of residential property prices for the year 2020. The correlation between the positions of cities in the rankings for demographic potential and housing real estate stock was found to be very weak. The case of Koszalin was identified as an optimal location for residence due to the existing residential property stock and its prices. This was corroborated by the city’s residents, who also enabled the city to be ranked at the top of a ranking created for this phenomenon through the diagnostic variables for demographic potential. This article addresses a research gap, as, to the best of our knowledge, the indicated relationships have not yet been analysed in the contexts presented in the article. Full article

This study aimed to predict residential apartment prices in Latvia and Brazil using algorithms from machine learning, fuzzy logic, and civil engineering principles, with a focus on overcoming multicollinearity challenges. To explore the market dynamics, we conducted four initial experiments in the central regions of Riga and Jelgava (Latvia), as well as São Paulo and Niterói (Brazil). Data were collected from real estate advertisements, supplemented by civil engineering inspections, and analyzed following international valuation standards. The research integrated human decision-making behavior with machine learning and the Apriori algorithm. Our methodology followed five key stages: data collection, data preparation for association rule mining, the generation of association rules, fuzzy logic analysis, and the interpretation of model accuracy. The proposed method achieved a mean absolute percentage error (MAPE) that ranged from 5% to 7%, indicating strong alignment with market trends. These findings offer valuable insights for decision making in urban development, particularly in optimizing renovation priorities and promoting sustainable growth. Full article

The present research aims at identifying any changes in the market appreciations of the residential segment in Italy caused by the COVID-19 pandemic. With reference to the first half of 2023 (phase III, “post-COVID-19”), in the paper, a logical–operational methodology is implemented: a sample of properties sold in the two-month period January–February 2023 is collected and an econometric analysis is applied for determining (i) the most influential factors on selling prices and (ii) the functional links between prices and each selected explanatory variable. Furthermore, the findings obtained are compared with those related to the phases I, “ante-COVID-19”, and II, “COVID-19 in itinere” (by recalling a previous study of the same authors), to highlight the variations between the periods and provide useful guidelines for the design of domestic spaces in different Italian geographical contexts. In addition, this work conducts a comparison of the outputs derived from the econometric analysis starting from the real estate data collected on the reference markets (revealed preferences) with the results of a direct survey carried out on a sample of individuals through the administration of an ad hoc developed questionnaire and aimed at investigating the opinions of potential buyers of residential properties (stated preferences). The use of the “twin” approach (an analysis of perceptions via the direct survey integrated by the implementation of an econometric technique) allows us to verify the consistence of the real dynamics of market (expressed by the interviewees) with the mathematical model results for investigating the house-scale effects of the COVID-19 pandemic in the considered cities. Full article

This study examines the interplay between Airbnb and gentrification in Thessaloniki and Greece, focusing on their economic and social impacts on urban neighborhoods. Utilizing data from 110 online publications and qualitative insights from ten semi-structured interviews with real estate agents, Airbnb stakeholders, residents, and experts, the research provides a nuanced view of these dynamics. The findings suggest that Airbnb influences housing markets by driving up rental and home prices, potentially exacerbating housing scarcity and displacing vulnerable populations in gentrifying areas. While this aligns with the existing literature, the results remain tentative due to the complexities involved. The trend toward corporate-hosted short-term rentals appears to shift Airbnb away from its original community-focused model, though this shift is still evolving. The COVID-19 pandemic introduced changes, such as a move from short-term to long-term rentals and the conversion of commercial spaces to residential use, impacting neighborhood dynamics. However, these effects may be temporary and do not fully address broader housing issues. While an oversupply of Airbnb accommodations might stabilize rental prices to some extent, its impact on the overall housing crisis remains uncertain. Future research should investigate the long-term effects on housing affordability and social equity, considering the limitations of current findings. Full article