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

Alice Barreca, Rocco Curto and Diana Rolando
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Alice Barreca: Architecture and Design Department, Politecnico di Torino, Castello del Valentino, Viale Mattioli 39, 10125 Torino, Italy
Rocco Curto: Architecture and Design Department, Politecnico di Torino, Castello del Valentino, Viale Mattioli 39, 10125 Torino, Italy
Diana Rolando: Architecture and Design Department, Politecnico di Torino, Castello del Valentino, Viale Mattioli 39, 10125 Torino, Italy

Sustainability, 2020, vol. 12, issue 1, 1-23

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.

Keywords: urban vibrancy; real estate market; housing prices; Neighborhood Services Index; spatial analyses; ESDA; LISA; Spatial Autoregressive (SAR) Models; Turin (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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