With the progressive ageing of the population, the study of the relations between road safety and elderly users is becoming increasingly relevant. Although the decline of pedestrian skills in the elderly has been widely studied in the literature, few studies have been devoted to the contributing built environmental factors of the elderly pedestrian collisions, such as the sidewalk density, the presence of traffic lights, or even some indicator related to land use or the socioeconomic features of the urban fabric. This paper contributes to the limited literature on elderly pedestrian safety by applying a negative binomial regression to a set of built environmental variables to study the occurrence of accidents involving elderly and younger (non-elderly) pedestrians in Madrid (Spain) between 2006 and 2018. The model considers a selection of built environmental factors per city district, linked to land use, infrastructure, and socioeconomic indicators. Results have highlighted that the elderly pedestrian collisions could be avoided with the existence of a wider sidewalk in the district and a greater traffic lights density. Unlike younger pedestrian accidents, these accidents are much more favored in ageing districts with higher traffic flows.

This paper analyses the effects of AirBnb on the size of local tourism markets using AirBnb occupancy rates and hotel overnight stays in order to explore the causal relationship in several Spanish cities. A dynamic panel data model is applied at the city level (2014–2017). Our findings show a positive relationship between the increase in the number of properties offered on AirBnb and the implicit volume of tourists received by each city, specifically in two large cities (Madrid and Barcelona), due to higher AirBnb occupancy rate.

Walking benefits older pedestrians but exposes them to traffic crashes. With an aging population, designing age-friendly cities is crucial, yet research on older pedestrian safety at a micro-level is limited. This study aims to reduce older pedestrian–vehicle collisions and create more livable environments through infrastructure policies derived from statistical data analysis. Special attention is focused on collecting a holistic set of infrastructure variables to reflect most of the street built environment elements, which helps policymakers implement short-term safety measures. Using Bayesian Poisson regression, this study analyzes factors contributing to the occurrence of crashes involving older and non-older pedestrians on road segments in Madrid, Spain. The results indicate that different factors affect the occurrence of crashes for all pedestrians versus older pedestrians specifically. Traffic crashes involving all pedestrians are affected by leisure points of interest, bus stops, and crosswalk density. Older pedestrian traffic crashes are influenced by population density, the presence of trees and trash containers, and contour complexity. Proposed measures include relocating trees and trash containers, modifying bus stops, and adding crosswalks and traffic lights. This paper also shows that these countermeasures, aimed at creating age-friendly streets for older pedestrians, are not expected to worsen the road safety of other pedestrians.

Purpose: This paper aims to present the dynamics of housing prices in Italian cities based on unpublished data with regional details from the late 1960s, half-yearly base, for all main Italian cities measuring the average prices for three city dimensions: city centre, sub-centres and outskirts or suburbs. It estimates the Italian long-term house price index, city based in real terms, and shows a combination of methods to deal with large time-series data.Design/methodology/approach: This paper builds long-term cycles based on the city (real) data by estimating the common components of cointegrated time series and extracting the unobservable signals to build real house price index for sub-regions in Italy. Three different econometric methodologies are used: Johansen cointegration test and VAR models to identify the long-term pattern of prices at the estimated aggregate level; principal components to obtain the common (permanent and transitory) components; and signal extraction in ARIMA time series–model-based approach method to extract the unobserved time signals.Findings: Results show three long-term cycle-trends during the period and identify several one-direction causal non-permanent relationships among house prices from different Italian areas. There is no evidence of convergence among regional’s house prices suggesting that the Italian housing prices converge inside the local market with only short diffusion effects at larger regional level.Research limitations/implications: Data are measured as the average price in squared meters, and the resulting index is not quality controlled.Practical implications: The long-term trends on housing prices serve to implement further research and know deeply the evolution of Italian housing prices.Originality/value: This paper contains new and unknown information about the evolution of housing prices in Italian regions and cities.