• Energy Research

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 assessment of the ‘quality’ of built heritage is a complex transdisciplinary issue, which both public administrations and real estate developers need to carefully consider when making any interventions. Recent international climate regulations underline that currently around 75% of buildings in the EU are not energy efficient. In Italy, those inefficient buildings are more than 50 years old and, if subjected to retrofit interventions, risk being totally transformed and losing their historical value in favor of a more contemporary use. This work aimed to study the residential heritage of the second half of the 20th century in the real estate market and to understand if, how, and in what measure the building and architectonical qualities are recognized and monetized by buyers. The city of Turin was chosen as a study area, and residential building qualities were analyzed using two quality indicators to perform a GWR on market POIs. The results highlighted that housing historical qualities are not homogeneously recognized by the real estate market, in favor of green ones. This work can help both public and private bodies to identify which ‘invisible’ quality residential buildings are immediately exploitable for enhancement strategies, with more respectful retrofitting interventions and a modern protection policy.

The enhancement of Modern Heritage buildings is nowadays a challenging issue as they are often degraded or abandoned and their historical value is not well acknowledged by potential buyers and their owners. Moreover, they are usually energy inefficient and obsolete, but investments for energy retrofit interventions are not always convenient, especially in socio-economic contexts characterized by weak real-estate market dynamics. This paper aims to study the influence of intrinsic and extrinsic characteristics on housing prices and, in particular, to understand whether elements of building cultural connotations or some housing green features are monetized by the real estate market. The UNESCO site “Ivrea, industrial city of the 20th century”, located in the weak real-estate context of the Eporediese territorial area, was selected as an emblematic case study and two data samples of property listings were built to perform spatial regression analyses. The results showed that the green features of housing, such as the heating type and the EPC level, have a greater influence on property prices than those characteristics related to the cultural connotations of a building, such as the Olivettian context. Therefore, the current incentive-based policies for energy efficiency can represent great opportunities that can be exploited both to preserve and to improve the condition of this valuable Modern Heritage.

The influence of building or dwelling energy performance on the real estate market dynamics and pricing processes is deeply explored, due to the fact that energy efficiency improvement is one of the fundamental reasons for retrofitting the existing housing stock. Nevertheless, the joint effect produced by the building energy performance and the architectural, typological, and physical-technical attributes seems poorly studied. Thus, the aim of this work is to investigate the influence of both energy performance and diverse features on property prices, by performing spatial analyses on a sample of housing properties listed on Turin’s real estate market and on different sub-samples. In particular, Exploratory Spatial Data Analyses (ESDA) statistics, standard hedonic price models (Ordinary Least Squares—OLS) and Spatial Error Models (SEM) are firstly applied on the whole data sample, and then on three different sub-samples: two territorial clusters and a sub-sample representative of the most energy inefficient buildings constructed between 1946 and 1990. Results demonstrate that Energy Performance Certificate (EPC) labels are gaining power in influencing price variations, contrary to the empirical evidence that emerged in some previous studies. Furthermore, the presence of the spatial effects reveals that the impact of energy attributes changes in different sub-markets and thus has to be spatially analysed.

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.