Search Results (77)

The dynamic fluctuations in the real estate market significantly impact the development of the national economy. Investigating the spatiotemporal characteristics of housing prices can assist the government in formulating rational regulatory policies. Taking Zhengzhou City as the research subject, this study analyzed the spatiotemporal characteristics of housing prices based on housing price data and POI (Point of Interest) data from January 2022 to March 2024, utilizing a spatial scale of 500 m × 500 m grids. A hedonic price model and a geographically weighted regression (GWR) model were constructed to examine the mechanisms of 12 influencing factors on housing prices. The results indicate that housing prices in the eastern part of Zhengzhou are higher than those in the west, with an overall declining trend observed in Zhengzhou’s housing prices. Among the influencing factors, the age of the house exerts the greatest impact on housing prices, while finance has the least influence. The GWR model demonstrates superior fitting performance compared to the hedonic price model. The mechanisms of the influencing factors exhibit spatial heterogeneity. This study provides valuable insights for relevant government departments in Zhengzhou City, contributing to the optimization of urban planning and the regulation of the real estate market. Full article

A number of studies have shown that, in hedonic models, the structural attributes of real property have a greater influence on price than external attributes related to location and the immediate neighbourhood. This makes it necessary to include detailed information about structural attributes when predicting prices using regression models and machine learning algorithms and makes it difficult to study the influence of external attributes. In our study of asking prices on the primary residential market in Warsaw (Poland), we used a methodology we developed to determine price indices aggregated to micro-markets, which we further treated as a dependent variable. The analysed database consisted of 10,135 records relating to 2444 residential developments existing as offers on the market at the end of each quarter in the period 2017–2021. Based on these data, aggregated price level indices were determined for 503 micro-markets in which primary market offers were documented. Using the analysed example, we showed that it is possible to predict the value of aggregated price indices based only on aggregated external attributes—location and neighbourhood. Depending on the model, we obtained an R² value of 75.8% to 82.9% for the prediction in the set of control observations excluded from building the model. Full article

The Syrian war, which commenced in 2011, transformed the Damascus real estate market due to heightened insecurity and tenure disputes. Using the hedonic price models, including 2411 housing transactions over the period 2010–2022, this study aims to understand the spatial dynamics of the real estate market in wartime. Our findings indicate that war variables have had a significant impact on the differentiation of property prices. Notably, property attributes have a more substantial impact on real estate values than district location, with severely damaged buildings in Damascus City resulting in an 89% decline in prices, while prices in Rural Damascus districts have decreased by 50%. Additionally, this study examines the urban texture of Damascus using correlation and homogeneity statistics derived from the gray-level co-occurrence matrix obtained from Google Earth Engine. Our findings show that correlations were highly differentiated, particularly among Rural Damascus districts, with a total decline of 87.2%. While homogeneity values decreased overall between 2015 and 2019, they improved slightly after 2019. This study guides decision makers in mitigating severe property value variations across war-affected urban areas by fostering spatial justice in property rights and promoting balanced investment and sustainable real estate development during the post-war recovery phase. Full article

Urban parks, a core component of urban landscapes, play a crucial role in mitigating the negative effects of rapid urbanization and achieving sustainable urban development. In densely populated urban environments, providing urban parks fairly and efficiently, taking social preferences into account, is an important challenge. In this regard, we take Hangzhou, China, as an example and use a hedonic pricing model with a difference-in-differences estimator to test the effect of different types of urban parks on housing prices, quantify their respective economic values, and reflect homebuyers’ preferences. The findings indicate that the construction of new urban parks leads to an overall increase in the value of the surrounding real estate to some extent. Specifically, the construction of comprehensive parks and large parks significantly enhances the value of nearby housing, while proximity to new medium-sized parks also results in a housing price premium, albeit to a lesser extent. In contrast, community parks and specialized parks have a less significant impact on housing prices. These findings provide valuable insights for equitable urban development and planning, optimizing the allocation of urban parks and determining investment priorities for different types of parks to enhance the sustainability of the urban environment and human well-being. Full article

The listed price of real estate is a subjective reflection of its value by sellers, usually related to structural, neighborhood, and environmental attributes. Although previous studies have proposed the hedonic pricing model, factors related to perception are rarely seen in explanatory variables. This study aims to explore the impact of street-level perceived qualities on the listed price per square meter of plot set by the seller of the real estate using the Geographically Weighted Regression (GWR)-based hedonic pricing model and analyzes the spatially heterogeneous effects of the coefficients. In the city of Eindhoven, the Netherlands, Google Street View photos collected at 200 m intervals were employed to calculate representative variables of perceptual quality via a validated convolutional neural network, alongside structural and neighborhood attributes. The final model includes eight explanatory variables, and the results indicate that, apart from the plot area and the number of rooms, the influencing mechanisms of other factors are different. The impact of perceived beautiful quality on listed real estate prices demonstrates obvious distinctions between the north and the south. Perceived livability (positive) and depressing (negative) qualities show similar heterogeneous characteristics. This study offers a comprehensive approach to promote diverse strategies for real estate development across urban areas and recommends a heightened emphasis on the design quality of residential streets. Full article

Smartwatches enable users to easily monitor their health, self-quantify, and track various activities. However, manufacturers and researchers in the field of smartwatches must explore and improve perceived usability to enhance the user experience of consumers and increase the device’s adoption rate. Therefore, this study investigates the factors influencing the adoption of smartwatches among Filipinos, focusing on usability and demographic influences. This is performed by utilizing the UTAUT2 model to examine key factors. External variables are explored, including perceived usability and privacy. To analyze the data acquired, partial least squares structural equation modeling (PLS-SEM) was conducted. The results indicated that performance expectancy, effort expectancy, social influence, hedonic motivation, price value, habit, and behavioral intention significantly influence smartwatch adoption. Habit emerged as positively affecting intention to use and usage behavior. However, facilitating conditions were found not to be significant in influencing intention to use and usage behavior, and privacy was perceived as having an insignificant relationship with the intention to use smartwatches. These findings offer theoretical and practical implications for enhancing smartwatch design and usability, addressing the diverse needs of users, and expanding inclusivity in the market. Full article

South Korea’s Particulate Matter (PM) concentration is among the highest among Organization for Economic Cooperation and Development (OECD) member countries. However, many studies in South Korea primarily focus on housing characteristics and the physical built environment when estimating apartment prices, often neglecting environmental factors. This study investigated factors influencing apartment prices using transaction data for Seoul apartments provided by the Ministry of Land, Infrastructure, and Transport (MOLIT) in 2019. For this purpose, the study compared and analyzed a traditional hedonic price model with a machine learning-based random forest model. The main findings are as follows: First, the evaluation results of the traditional hedonic price model and the machine learning-based random forest model indicated that the random forest model was found to be more suitable for predicting apartment prices. Second, an importance analysis using Explainable Artificial Intelligence (XAI) showed that PM is more important in determining apartment prices than access to education and bus stops, which were considered in this study. Finally, the study found that areas with higher concentrations of PM tend to have higher apartment prices. Therefore, when proposing policies to stabilize apartment prices, it is essential to consider environmental factors. Furthermore, it is necessary to devise measures such as assigning PM labels to apartments during the home purchasing process, enabling buyers to consider PM and obtain relevant information accordingly. Full article

New energy vehicles (NEVs) offer a sustainable private transportation alternative. Charging points are the source of power for NEVs; thus, their construction can significantly lower the costs associated with their use, thereby encouraging their adoption. This could potentially impact the subway demand, which is reflected by the relationship between housing prices and subway proximity in this paper, leading to a decrease in the premium for properties near subway stations. Utilizing a comprehensive data set of 599,916 housing transactions in Beijing and a difference-in-differences approach based on the hedonic price model, we found that China’s NEV charging point subsidy policy significantly decreases the subway premium of housing prices and mitigates housing price disparities. Furthermore, we explored the spatial heterogeneity of this impact, finding that the policy has less influence on residents living near the city center. Our findings indicate that the policy has resulted in a considerable decrease in the subway premium, ranging from ¥19,217 to ¥55,936 ($2745 to $7991) per transaction, which is equivalent to the annual income for an average individual at the time of the policy. The results address the far-reaching implications and significant role of NEV development in urban transportation. Full article

Despite their environmental value and important role in human nutrition and food security, legumes, including lentils, have been underestimated. Nevertheless, nowadays there is a renewed interest in their consumption due to regional specialization, varietal diversity and their role in the transition towards sustainable production and consumptions systems. Recent years have also witnessed a rise in consumer awareness regarding sustainable food, healthier and plant-based diet and regional or local food systems. The aim of this paper is to evaluate the influence of attributes linked to sustainability, such as biodiversity, organic certification, origin indication, and packaging type, on lentils’ market prices in Italy. Based on the Hedonic Price Model, and following a review of scientific literature, eight lentil characteristics are defined. Data were collected on 228 valid observations of dry and precooked lentils across 20 different retail stores. Results indicate that Italian consumers have a positive appreciation for lentils with organic certifications and references to geographic and territorial characteristics. Some lentil varieties hold unique market positions, suggesting opportunities for diverse product offerings. Packaging materials influence consumer valuation, reflecting trends towards healthy and convenient food options. These insights will aid stakeholders in fostering sustainable practices and enhancing market competitiveness in the Italian lentil market. Full article

As Melbourne faces exponential population growth, the necessity for resilient urban planning strategies becomes critical. These strategies include mixed land use, density, diversity, and sustainable transportation through transit-oriented development (TOD). While TOD promises to accommodate growing populations and address environmental concerns, it also raises issues regarding its unintended consequences on poverty and inequality, notably through residential displacement and gentrification. This study investigates the impacts of TOD construction on inequality in Footscray, employing spatial analysis techniques like the hedonic price model (HPM), robust regression analysis, and Pearson correlation analysis. It aims to understand how spatial factors influence housing prices and their correlations. Additionally, the study uses observational spatial analysis via Google Street View (GSV) to examine indices such as housing development type, traffic signage, sanitation facilities, and house beautification. This approach seeks to build an evaluation framework to assess the extent of TOD street reconstruction and its impact on gentrification and displacement. The research adapts existing knowledge to create a tool for reviewing past planning decisions and assessing the fairness of TOD planning implementation. By providing assessment and guidance to mitigate the potential adverse impacts of TOD, this study contributes to the advancement of urban-planning practices, offering insights into mixed land use and effective strategies to balance economic development and social equity, thereby enhancing community resilience. Ultimately, this research deepens our understanding of the impacts of TOD on urban inequality and offers practical tools and insights for more equitable and sustainable urban development. Full article

Italy is the world leader in the production of pasta and the Italian market is characterized by strong price competition among large industrial producers. Thus, recently, many small and medium firms have started to differentiate their products as a way to achieve higher margins and escape from price competition. Using data on the prices and characteristics of dried pasta sold online in the Italian market and a hedonic price model, we estimated the implicit prices associated with several attributes that are currently available for dried pasta. We find that the “artisanal” statement on pasta labeling is associated with the highest price premium. Also, results show that protected geographical indication, Halal and Kosher certifications, and the use of ancient wheat varieties are valuable features of dried pasta sold in the Italian market. Instead, a positive, albeit limited in magnitude, price premium is associated with dried pasta made using 100% Italian durum wheat semolina, the organic method, enriched with additional ingredients. Findings suggest that producers can differentiate their products by mostly emphasizing their small-scale production methods, the territorial connotation, and the cultural and environmental sustainability of production. Otherwise, certifying dried pasta as Halal or Kosher can represent a complementary or alternative strategy to differentiate the product and achieve a higher price. Full article

This study set out to identify and analyze the factors that influence the formation of rental prices for residential apartments in the city of Belém. The approach adopted was based on the hedonic price theory, which considers that the rental price of an apartment reflects the implicit prices of its attributes—structural, locational, and neighborhood quality. The sample used consisted of 259 observations, corresponding to the rental advertisements of the representative apartments in each building. The ordinary least squares (OLS), spatial lag model (SLM), and geographically weighted regression (GWR) techniques were used in the statistical analysis in this study. The results of the OLS model showed statistical significance between the attributes analyzed and the rental price of the apartments. In turn, the SLM indicated that the structural attributes have an impact on the rental prices of neighboring apartments, configuring a contagion effect in the real estate market. The GWR model showed that there was no spatial heterogeneity in the effects of the determinants on apartment rental prices throughout the sample. Full article

This study explores the impact of accessibility on property pricing and land economies by advanced spatial analysis techniques, focusing on Shanghai as a representative metropolis. Despite the impact of metro systems on residential property values, which has been frequently assessed, a research gap exists in understanding this phenomenon in Asian, particularly Chinese, urban contexts. Addressing this gap is crucial for shaping effective urban land use policy and improving the land economy rationally in China and similar settings facing urban challenges. To assess the impact of metro station accessibility on property prices in Shanghai, with extensive rail transit, and to deeply explore the overall impact of land value varieties driven by metro on urban development, we conducted a comprehensive analysis, with discussion about future aspirations for land planning and management along with landscape and facility design, and measures to improve land economy. The procedures involved creating neighborhood centroids to represent accessibility and using the Euclidean distance analysis to determine the shortest paths to metro stations. Our evaluation incorporated a hedonic pricing model, considering variables like neighborhood characteristics, housing attributes, and socio-economic factors. Advanced spatial analysis encompassing Ordinary Least Squares (OLS) regression and XGBoost analysis were employed to explore spatial effects, and Geographically Weighted Regression (GWR) helped examine spatial patterns and address autocorrelation challenges. Results revealed a negative association between distance to metro station and property prices, indicating a non-linear and spatially clustered relationship and heterogeneous spatial pattern. We dissected the non-linear results in detail, which complemented the conclusion in existing research. This study provides valuable insights into the dynamic interplay between metro accessibility and housing market behaviors in a significant Asian urban context, offering targeted suggestions for urban planners and governors to decide on more reasonable land use planning and management strategies, along with landscape and infrastructure design, to promote not only the healthy growth of the real estate market but also the sustainable urban development in China and similar regions. Full article

This study aims to investigate the economic effects of stormwater best management practices (BMPs) on housing sale prices in Washington, D.C., BMPs play a significant role in mitigating multiple threats, such as water pollution, soil erosion, and property damage. While studies on the economic value of BMPs were limited, literature addresses that housing sale prices can be affected by nearby stormwater BMPs. This study addresses the following research questions: Do stormwater BMPs positively impact housing sale prices? How do proximity and number of structural BMPs affect the housing sale prices? We used the hedonic pricing method by applying multiple linear regression models to determine whether a set of independent variables significantly improved the models. Our primary findings indicate that BMPs have positive, negative, or no effects on housing sale prices. The proximity of BMPs inside of parks increased housing sale prices in all buffers. In contrast, the proximity of BMPs outside of parks and impervious roads decreased housing sale prices in all buffers. Percent tree canopy coverage negatively linked to a 50 m buffer and had no relationship with other buffers on housing sale prices. This study implies that BMPs impact housing prices and can be improved by landscape architects, policymakers, and stakeholders. Full article

Urban green spaces provide various social, economic, health, aesthetic, environmental, and ecological benefits. This study aimed to investigate the influence of green spaces on apartment prices, with a particular emphasis on visual greenery and the proportion of green spaces. Hedonic pricing models have often been used to assess the impact of green spaces on housing prices. Herein, 16 variables were considered as factors affecting housing prices and divided into housing, neighborhood, and green space characteristics. The findings indicate that the presence of green spaces enhanced the value of apartment complexes. Moreover, both visual greenery and the proportion of green spaces within apartment complexes influenced housing prices. Additional analysis demonstrated the impact of green space characteristics within Seoul apartment complexes on housing price changes from 2016 to 2022, finding that higher green space proportions and visual greenery led to approximately 20% higher price increases, and structural equation modeling revealed that the proportion of green spaces within apartment complexes, directly and indirectly, influenced housing prices through visual greenery. Overall, this study emphasized the importance of ensuring well-managed green spaces within and around apartment complexes. Full article