Urban Vibrancy: An Emerging Factor that Spatially Influences the Real Estate Market

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.

EPC Labels and Building Features: Spatial Implications over Housing Prices

“…Besides the abovementioned researches, other recent experiences on the topic are assumed in the background as being specifically aimed to calculate the spatial autocorrelation in property prices in the city of Turin; in these studies, spatial analyses are proposed to detect the presence of spatial dependence between different kinds of indicators and to manage the spatial latent variables in the property price determination process [55][56][57][58].…”

GIS-Based Spatial Autocorrelation Analysis of Housing Prices Oriented towards a View of Spatiotemporal Homogeneity and Nonstationarity: A Case Study of Guangzhou, China

“…Scholars have conducted research on the effects of housing prices from two perspectives, i.e., the national level and the city level. At the national level, the factors influencing on the changes in housing prices generally include monetary, fiscal, and housing policies [3][4][5][6][7], while the specific factors affecting the housing prices at the level of city mainly involve with urban development, land use, population, housing supply and demand, and urban planning [8][9][10][11][12]. At the level of city, the study on the fluctuations of housing prices and the correlations to the influencing factors has been a hot topic in the fields of governing policy of housing market and the reform of real-estate and related urban systems, e.g., land-use system, as well as the hotspots in urban spatial layout, urban planning, and sustainability development [10].…”

“…Although the traditional hedonic price models and spatial autocorrelation analysis models have been extended to use local spatial weights as the parameters of regression analysis, they only consider the spatial correspondence between the given dependent variables and the independent variables. Relevant research studies in recent years have focused on the identification of urban areas characterized by urban development and housing prices, which can effectively support the revision of the urban development plan and its regulatory acts, as well as the strategic urban policies and actions [12]. Nevertheless, at the level of a city, how the urban development differences, particularly with a microscale geospatial perspective (i.e., in the context of neighborhoods), triggers varying degrees of impact on housing prices, especially what and how to determine the spatial heterogeneity and nonstationarity of housing prices are still lacking attention.…”

Is the Real Estate Market of New Housing Stock Influenced by Urban Vibrancy?

“…One dimension strictly related to the urban activity intensity is the presence of "Neighbourhood services," which conditions also the city physical features and the socioeconomic and living condition of their citizens. A recent study, in particular, studied urban vibrancy and its relationship with the neighbourhood services and the real estate market, by creating a Neighbourhood Services Index (NeSI) and spatially analysing its influence on the listing prices of existing housing [7]. Other studies analysed urban vibrancy, including the housing dimension [1][2][3][4][5], and investigated the presence of spatial correlation [6][7][8][9][10].…”

“…A recent study, in particular, studied urban vibrancy and its relationship with the neighbourhood services and the real estate market, by creating a Neighbourhood Services Index (NeSI) and spatially analysing its influence on the listing prices of existing housing [7]. Other studies analysed urban vibrancy, including the housing dimension [1][2][3][4][5], and investigated the presence of spatial correlation [6][7][8][9][10]. Nevertheless, in the literature, there are no investigations, at least to our knowledge, that spatially analyse urban vibrancy in relation to the real estate market of new housing stock.…”