Housing Vulnerability and Property Prices: Spatial Analyses in the Turin Real Estate Market

Abstract

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.

Monitoring and Analysis of the Real Estate Market in a Social Perspective: Results from the Turin’s (Italy) Experience

“…The results of the analyses highlight the presence of spatial dependence and a certain reverse correspondence between the spatial clusters obtained for property prices and those obtained with LEPI: The LEPI High-High LISA Cluster corresponds to the LP Low-Low LISA Cluster, and vice versa (Figure 12). In Reference [61], spatial analyses are performed, to calculate the spatial autocorrelation in property prices [22,57,58] and the presence of spatial dependence [18] between different housing vulnerability indicators and property prices. The sample used is composed of georeferenced data (years 2011-2017), considering the statistical zone as a territorial unit.…”

Nonparametric Correlogram to Identify the Geographic Distance of Spatial Dependence on Land Prices

“…A collection of empirical and theoretical studies have been conducted to model the effect of environmental impacts on land prices using the hedonic method. Some of these studies show that land prices are influenced by the infrastructure and socio-economic features (Barreca et al 2018), by the geographic and geometric accessibility (Morales et al 2019), and by structure and accessibility variables (Chica-Olmo et al 2019). These studies have examined the spatial effect on urban land prices from the influence of externalities.…”

“…Some research on land prices with regards to geographic distance has been observed by Chica-Olmo et al (2019) using the variogram, Morales et al (2019) with the multivariate regression, Barreca et al (2018) using the spatial autocorrelation, while Crosby et al (2018) performs the variogram fitting on land prices, and Shaker (2018) shows that Conditional Autoregressive (CAR) residuals were assessed by Moran's I spatial correlogram, etc. The basic problem of this research of the spatial dependence is how to clearly and analytically determine the spatial dependence at a certain distance.…”

“…However, Hall et al (1994 criticize the approach taken by Spahiro and Botha because it turns out that the definite positive estimator only applies discretely and not continuously, so they propose their estimators through the kernel method approach. The realization of the kernel method approach is made clear by Bjørnstad and Falck (2001) by proposing the use of a nonparametric kernel estimator where the asymptotic kernel function is the B-Spline cubic, which is called a correlogram spline. One important thing to remember is that not all functions can be used as candidate covariance functions because they must fulfill the positive properties of semidefinite.…”

“…The use of the correlogram itself has been applied to various fields, such as the detection of: TB case notification rates (CNR) in Bangladesh (Rood et al 2019); neighborhood and spillover effects on rice farmers (Villanueva et al 2017); landscape in Moldova (Shaker 2018); copper mining area (Nguyen et al 2016); land prices (Jiao and Liu 2012); genetics (Diniz-Filho et al 2009); semiconductors (Jeong et al 2008) and ecology (Bjørnstad and Falck 2001). In the research on land prices, Jiao and Liu (2012) used an index on the spatial correlogram (Moran Index and Geary's Index) derived from monoton variance plots.…”