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.

The concept of social vulnerability is widely studied in literature in order to identify particularly socially fragile sectors of the population. For this purpose, several studies have adopted indexes to measure the economic and social conditions of the population. The aim of this paper is to investigate the link between social and territorial vulnerability and the real estate market, by means of an exploratory analysis related to the possibility that spatial analyses can help to identify spatial latent components and variables in the process of price determination. A three phase approach is proposed, using the geographical segmentation of Turin and its related submarkets as a case study. After the identification and analysis of a set of three social and territorial vulnerability indicators, a traditional hedonic approach was applied to measure their influence on property listing prices. Subsequently, spatial analyses were investigated to focus on the spatial components of the indicators and property prices; their spatial autocorrelation was measured and the presence of spatial dependence was taken into account by applying a spatial regression. Results demonstrated that two indicators were spatially correlated with property prices and had a significant and negative influence on them. The proposed approach may help not only to identify the most vulnerable urban areas characterized by the lowest property prices, but also to support the future modification to the actual geographical segmentation of Turin.

In the literature, several vulnerability/resilience indicators and indexes are based and assessed by taking into account and combining different dimensions. Housing vulnerability is one of these dimensions and is strictly related to the buildings’ physical features and to the socio-economic condition of their occupants. This research aims to study housing vulnerability in relation to the real estate market by identifying possible indicators and spatially analyzing their influence on property prices. Assuming the city of Turin and its territorial segmentation as a case study, spatial analyses were performed to take into account the presence of spatial dependence and to identify the variables that significantly influence the process of property price determination. The results of this study highlighted the fact that two housing vulnerability indicators, representative of fragile buildings’ physical features, were spatially correlated with property prices and had a significant and negative influence on them. In addition, their comparison with two social vulnerability indicators demonstrated that the presence of economical buildings and council houses was spatially correlated with the presence of people with a low education level. The results of the spatial regression model also confirmed that one of the social vulnerability indicators had the highest and most negative explanatory power in the property price determination process.

Built heritage resources (BHRs) are multidimensional assets that need to be conceived under a sustainability and circular economy framework. Whereas it is essential that their conservation, management, and enjoyment are sustainable, it is also necessary that the environmental, cultural, and socio-economic contexts in which they are integrated are sustainable too. Like other amenities, BHRs can improve the quality of the urban environment and generate externalities; additionally, they may influence sectors such as real estate, hospitality, and tourism. In this framework, this contribution aims to identify spatial relationships occurring between BHRs and short-term rentals (STRs), i.e., a recent economic phenomenon facilitated by platforms such as Airbnb. Through the application of Exploratory Spatial Data Analysis techniques and taking Turin (Italy) as a case study, this article provides evidence that spatial correlation patterns between BHRs and STRs exist, and that the areas most affected by STRs are the residential neighborhoods located in the proximity of the historic center of the city. Relations with other sets of socio-economic variables are highlighted too, and conclusions suggest that future studies are essential not only to monitor sustainability issues and reflect on new housing models and sustainable uses of buildings, but also to understand the evolution of the phenomenon in light of the pandemic Covid-19.

In Italy, it has always been difficult to collect reliable data on real estate given the opacity of the information available. Keeping into consideration the actual availability of data and information, the possibility to have a structure for permanently monitoring and analysing the real estate market is fundamental. Focusing on developing and disseminating knowledge related to practices in this context, in this paper the Turin’s (Northern Italy) experience is presented, through the Real Estate Market Observatory (TREMO): This structure is based on a data-warehouse, implemented over time, with databases that lead to historic price observations of the residential market. The data warehouse is the basis for investigating methodologies and analyses, assuming the spatial requisites of the data and its georeferencing as the main discriminant in choosing among descriptive statistics, multi-varied or spatial analysis methods. In twenty years, several studies have been developed, allowing us not only to explore the applicability of models and operative modalities, but also to obtain results with a high potential impact under a social viewpoint. In this paper, the methodologies developed for implementing the monitoring structure are presented, specifically the “quality process” and computerized analysis procedures, followed by some representative research experiences with reference to aims, models and results.