Urban landscapes are important factors that affect housing prices, and significant differences between landscape preferences of various homebuyers may be observed because of the different reasons for purchasing a house (consumption or investment). However, the hedonic price model widely applied in most existing studies only captures the average effects of landscapes as a whole sample, and may ignore the heterogeneity of landscape preferences. To fill this gap, this study constructed hedonic price models and quantile regression models with the housing data in Guangzhou, China from 2013 to 2016 and analyzed the landscape preferences of buyers with different purchase motivations. Empirical results showed that the landscape preferences of buyers were different in housing submarkets. The implicit value of landscapes was greater in consumption demand than in investment demand, whereas investment buyers were more vulnerable to the disamenity effect of unattractive landscapes. In addition, the quantile effect of landscapes was identified, in which the buyers of high-priced housing will pay more for high-quality landscapes. This study revealed the diversified housing demands and landscape preferences of homebuyers, which is important for urban planning and project development.

The application of smart construction technologies is becoming a crucial approach to improving safety performance in the construction industry. Existing research often focuses on the qualitative analysis of specific technologies, neglecting a comprehensive empirical examination of the characteristics of smart construction technologies and their impact on project safety performance. This study identifies five principal characteristics of smart construction technology: integration, automation, initiative, shareability, and sustainability. By integrating the technology acceptance model (TAM) and the task–technology fit (TTF) model, we propose a theoretical path of “technology characteristics–perceived behavior–behavioral intention–safety performance” and develop a structural equation model to assess the effect of these characteristics on safety performance. The results indicate that these characteristics have a significant direct impact on safety performance, with the estimated direct effect reaching as high as 0.61. Additionally, these characteristics positively influence safety performance indirectly through mediating variables such as perceived ease of use, perceived usefulness, usage intention, and usage behavior. This study empirically verifies the pathways through which smart construction technology characteristics affect safety performance, providing theoretical support for the widespread application of these technologies in construction engineering and enhancing safety management.

China’s construction industry is facing increasing pressure for transformation and upgrading, with high-quality development becoming an essential goal. However, the precise definition and evaluation criteria for high-quality development remain ambiguous. Against this backdrop, this study focuses on 11 cities in Zhejiang province to explore the connotations of high-quality development in the construction industry and establishes an evaluation index system for assessing it. This study employs the entropy weight method and matter–element extension method to evaluate the high-quality development levels of the construction industry across these cities. The empirical results reveal that the development of the construction industry in Zhejiang province is uneven across cities, with significant potential for overall improvement. This study validates the proposed index system and evaluation model while supplementing empirical evidence; it also enriches the literature and provides both theoretical and technical support for advancing the construction industry’s transition toward high-quality development.

The construction of sports parks often involves significant investment and extended development timelines, making it crucial to evaluate their economic impacts for informed policymaking and strategic urban planning. While research on the externalities of urban parks has grown, the specific economic effects of sports parks remain underexplored. This study examines the impact of the Hangzhou Asian Games Park on housing prices using 31,329 transaction records from 2018 to 2023. Employing a hedonic pricing model and a difference-in-differences approach, we find that housing prices increase by 1.6% for every 1 km reduction in distance to the park. Spatial heterogeneity is observable, as the effect diminishes with increasing distance. Temporally, the park’s planning announcement triggered a 0.7% increase in price-distance elasticity, compared to a 0.2% increase after its opening. These findings suggest that the anticipatory market response was stronger than the realized benefits after the park’s completion. This study quantifies the spatial and temporal dynamics of the economic value of sports parks, providing valuable insights for urban planning, public policy, and investment strategies.

Metro system construction has profoundly influenced the Chinese housing market. As metro networks in major cities evolve from single lines to complex multi-line systems, the associated benefits extend beyond station accessibility to enhanced network connectivity. However, the impact of improved metro network connectivity on housing prices has been largely overlooked, leading to a potential underestimation of its economic benefits. Using housing transaction data from 2018 to 2023 during the metro network expansion in Hangzhou, this study employs a hedonic pricing model and the difference-in-differences method to analyze the effects of network expansion on housing prices around existing stations. The results show that metro network expansion significantly enhances connectivity and increases housing prices within 0.5 km of existing stations by an average of 2.42%. The findings also reveal heterogeneity in these effects. Geographically, the premium effect in non-central urban areas (1.67%) slightly exceeds that in central urban areas (1.56%), and areas with higher station density benefit more from network expansion. The degree of network connectivity improvement further shapes these effects, with the highest price premium (2.49%) observed near stations with moderate connectivity improvements, outperforming those with high or minimal connectivity improvements. By highlighting the capitalization effects of metro network expansion, this study provides new insights into how public transportation infrastructure influences housing markets in China.