Urban parks,value uplift and green gentrification: An application of the spatial hedonic model in the city of Brisbane

This paper explores the impact of urban parks on real estate prices making use of a hedonic price approach. Focusing on Brisbane, Australia, as a case study site, we use spatial hedonic models to analyse housing sales data across 15,000 sales transactions to investigate the effects of parks on nearby housing prices, paying attention to park typology and classification. Our findings indicate that recreational and sport parks are differently associated with price variations. The study also examines a specific and significant inner-city park currently undergoing a major redevelopment—namely Victoria Park. Our analysis of the Victoria Park site seeks to quantify the value uplift, that is, the future increase in property prices as a result of the transformation of the current private golf course in this location into a new publicly accessible parkland. This study’s property economics modelling analysis indicates the conversion of Victoria Park from a golf course to public parkland will increase property prices by an average of 3 % for properties located within 750 m of the park. The article concludes with a discussion of value capture opportunities that these findings present as well as challenges of green gentrification for this and similar urban renewal projects and possible policy responses.     

Park availability,accessibility, and attractiveness in relation to the least and most vulnerable inhabitants

With this paper, we enrich the environmental justice debate by investigating differences in the provision of parks in Lodz, Poland, at three levels: availability, accessibility, and attractiveness. A park is ‘available’ when it exists within a suitable distance from where we live; it is ‘accessible’ when we feel that we are welcome there, and we can freely reach and safely use this park; it is ‘attractive’ when we willingly want to use it and spend time there. Our research hypothesis is that the most vulnerable groups of inhabitants concentrate around parks whose provision is affected by the largest number of barriers at each of the three levels, while the least vulnerable benefit from the vicinity of parks that are the least affected. Apart from the statistical analysis – the correlation between the indicators that represent the three levels of park provision and those that represent the most and least economically vulnerable using Pearson’s coefficient – we scrutinize three case study parks. The results confirm that there are inequalities at the level of attractiveness for the most vulnerable groups; meanwhile, no statistically significant results were recorded for the least vulnerable groups. The differences would probably be more explicit had socioeconomic segregation been higher in Lodz. The results may also be influenced by the unique postsocialist and postindustrial legacy of our city. The ongoing revitalization of the city center and the increased activity of developers may exclude the most vulnerable inhabitants and deepen segregation.     

Are social media data and survey data consistent in measuring park visitation,park satisfaction,and their influencing factors? A case study in Shanghai

Given the importance of urban parks for recreation, it is critical to understand how they are used and perceived. Currently, relatively few studies have examined the public’s activities and preferences at the same time. Social media data are increasingly recognized as a promising data source to study these two aspects. However, little is known regarding the utility and representativeness of social media data for urban parks. In particular, a lack of understanding exists on the comparability of research results measured by bottom-up social media data and top-down official survey data in urban park studies. This research used social media data from Ctrip and Dianping and survey data from the local government agency with park visits statistics and park satisfaction surveys to understand the public’s visitation and satisfaction of 102 urban parks in central Shanghai. We first assessed the similarities between the social media data and survey data by correlation analysis. We then used the negative binomial regression model and beta regression model to examine the matches and mismatches of the different data sources in investigating factors influencing park visitation and park satisfaction. Our correlation results showed that the social media data significantly correlated with the survey data and that social media data performed better in more frequently visited parks. Our regression results showed that the water bodies, recreational facilities, and surrounding commercial facilities were the common influencing factors of park visitation and park satisfaction for all parks. While significant correlations were noticed between these two data sources, we found that the surrounding population density was negatively associated with park visitation measured by social media data but positively associated with park visitation measured by survey data. This study provides a comparative perspective to study park visitation and park satisfaction by combining social media data and official survey data. Clarifying the consistencies and inconsistencies between the social media data and official survey data is important because it could help us to understand the representativeness and potential biases of the social media data when used in visitation monitoring and satisfaction monitoring.