Abstract
Urban vibrancy is defined and measured differently in the literature. Originally, it was described as the number of people in and around streets or neighborhoods. Now, it is commonly associated with activity intensity, the diversity of land-use configurations, and the accessibility of a place. The aim of this paper is to study urban vibrancy, its relationship with neighborhood services, and the real estate market. Firstly, it is used a set of neighborhood service variables, and a Principal Component Analysis is performed in order to create a Neighborhood Services Index (NeSI) that is able to identify the most and least vibrant urban areas of a city. Secondly, the influence of urban vibrancy on the listing prices of existing housing is analyzed by performing spatial analyses. To achieve this, the presence of spatial autocorrelation is investigated and spatial clusters are identified. Therefore, spatial autoregressive models are applied to manage spatial effects and to identify the variables that significantly influence the process of housing price determination. The results confirm that housing prices are spatially autocorrelated and highlight that housing prices and NeSI are statistically associated with each other. The identification of the urban areas characterized by different levels of vibrancy and housing prices can effectively support the revision of the urban development plan and its regulatory act, as well as strategic urban policies and actions. Such data analyses support a deep knowledge of the current status quo, which is necessary to drive important changes to develop more efficient, sustainable, and competitive cities.
Highlights
The recent economic-financial crisis of 2008 caused a global transformation of the economy
It is used a set of neighborhood service variables, and a Principal Component Analysis is performed in order to create a Neighborhood Services Index (NeSI) that is able to identify the most and least vibrant urban areas of a city
Jacobs [1,2] was the first to introduce and describe urban vitality in terms of street life over a 24-h period. This definition was improved by Montgomery [3,4] who suggested that urban vibrancy could be described as the number of people present in all around streets or neighborhoods during the day and the night and could be related to different land uses
Summary
The recent economic-financial crisis of 2008 caused a global transformation of the economy. We demonstrated that the most vulnerable sectors of the population and buildings’ physical features are spatially autocorrelated, and significantly and negatively influence housing prices [7,8] Assuming these results, this study goes further by analyzing the urban vibrancy at the neighborhood scale and by investigating its influence on real estate submarkets. It aims to study urban vibrancy and its relationship with neighborhood services by creating an index that is able to identify the most vibrant urban areas of a city It aims to spatially analyze the influence of urban vibrancy in the housing price determination process, with particular reference to existing residential building stock in Turin. The conclusions and discussion are presented in the final section
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
