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This paper evaluates the ability of various indicators related to macroeconomic fundamentals, credit conditions, and housing supply to predict house price growth in the United States during the post-financial crisis period. We find that the inclusion of different measures of housing supply indicators significantly improves the forecasting performance for the period of 2010-2022. Specifically, incorporating the monthly supply of new homes into a VAR model with house price growth reduces the RMSE by over 30 percent compared to a univariate benchmark. Moreover, forecasting accuracy improves further at a longer forecast horizon (greater than three months) when the mortgage rate spread is also used as a predictor. Further improvements are made if \"Direct\" forecasts are used instead of iterative forecasts. The shrinkage method like LASSO shows that the monthly supply of new homes is an important predictor at all forecasting horizons, while the mortgage spread is most relevant for longer forecast horizons.

We examine matched rent-price ratios and rent transaction prices for single-family houses in Miami-Dade County between January 2009 and April 2014. The primary dataset consists of properties that are purchased and then rented within 240 days of the purchase. Each of the buyers in the sample are considered investors since each property included has a rental event indicating they are not owner occupied. We examine the relationship between housing and market characteristics and the impact active investors have on single-family rents and rent-price ratios. Entities that purchase the largest number of units pay more for properties, obtain marginally higher rent and obtain lower rent-to-price ratios.

The recent (twentieth century) and future (twenty-first century) climate evolution in the Mediterranean region is analyzed in relation to annual mean global surface temperature change. The CMIP5 (Coupled Model Intercomparison Project, Phase 5) simulations, the CRU (Climate Research Unit) observational gridded dataset, and two twentieth century reanalyzes (ECMWF, European Center for Medium range Weather Forecasts) and NOAA ESRL (National Oceanic and Atmospheric Administration-Earth System Research Laboratory) are used. These datasets to large extent agree that in the twentieth century: (a) Mediterranean regional and global temperatures have warmed at a similar rate until the 1980s and (b) decadal variability determines a large uncertainty that prevents to identify long-term links between precipitation in the Mediterranean region and global temperature. However, in the twenty-first century, as mean global temperature increases, in the Mediterranean region, precipitation will decrease at a rate around − 20 mm/K or − 4%/K and temperature will warm 20% more than the global average. Warming will be particularly large in summer (approximately 50% larger than global warming) and for the land areas located north of the basin (locally up to 100% larger than global warming). Reduction of precipitation will affect all seasons in the central and southern Mediterranean areas, with maximum reduction for winter precipitation (− 7 mm/K or − 7%/K for the southern Mediterranean region). For areas along the northern border of the Mediterranean region, reduction will be largest in summer (− 7 mm/K or − 9%/K for the whole northern Mediterranean region), while they will not experience a significant reduction of precipitation in winter.

A discussion of the sustainability and travel behaviour impacts of ride-hailing is provided, based on an extensive literature review of studies from both developed and developing countries. The effects of ride-hailing on vehicle-kilometres travelled (VKT) and traffic externalities such as congestion, pollution and crashes are analysed. Modal substitution, user characterisation and induced travel outputs are also examined. A summary of findings follows. On the one hand, ride-hailing improves the comfort and security of riders for several types of trips and increases mobility for car-free households and for people with physical and cognitive limitations. Ride-hailing has the potential to be more efficient for rider-driver matching than street-hailing. Ride-hailing is expected to reduce parking requirements, shifting attention towards curb management. On the other hand, results on the degree of complementarity and substitution between ride-hailing and public transport and on the impact of ride-hailing on VKT are mixed; however, there is a tendency from studies with updated data to show that the ride-hailing substitution effect of public transport is stronger than the complementarity effect in several cities and that ride-hailing has incremented motorised traffic and congestion. Early evidence on the impact of ride-hailing on the environment and energy consumption is also concerning. A longer-term assessment must estimate the ride-hailing effect on car ownership. A social welfare analysis that accounts for both the benefits and costs of ride-hailing remains unexplored. The relevance of shared rides in a scenario with mobility-as-a-service subscription packages and automated vehicles is also highlighted.

The European CORDEX (EURO-CORDEX) initiative is a large voluntary effort that seeks to advance regional climate and Earth system science in Europe. As part of the World Climate Research Programme (WCRP) - Coordinated Regional Downscaling Experiment (CORDEX), it shares the broader goals of providing a model evaluation and climate projection framework and improving communication with both the General Circulation Model (GCM) and climate data user communities. EURO-CORDEX oversees the design and coordination of ongoing ensembles of regional climate projections of unprecedented size and resolution (0.11° EUR-11 and 0.44° EUR-44 domains). Additionally, the inclusion of empirical-statistical downscaling allows investigation of much larger multi-model ensembles. These complementary approaches provide a foundation for scientific studies within the climate research community and others. The value of the EURO-CORDEX ensemble is shown via numerous peer-reviewed studies and its use in the development of climate services. Evaluations of the EUR-44 and EUR-11 ensembles also show the benefits of higher resolution. However, significant challenges remain. To further advance scientific understanding, two flagship pilot studies (FPS) were initiated. The first investigates local-regional phenomena at convection-permitting scales over central Europe and the Mediterranean in collaboration with the Med-CORDEX community. The second investigates the impacts of land cover changes on European climate across spatial and temporal scales. Over the coming years, the EURO-CORDEX community looks forward to closer collaboration with other communities, new advances, supporting international initiatives such as the IPCC reports, and continuing to provide the basis for research on regional climate impacts and adaptation in Europe.

North Africa is considered a climate change hot spot. Existing studies either focus on the physical aspects of climate change or discuss the social ones. The present article aims to address this divide by assessing and comparing the climate change vulnerability of Algeria, Egypt, Libya, Morocco, and Tunisia and linking it to its social implications. The vulnerability assessment focuses on climate change exposure, water resources, sensitivity, and adaptive capacity. The results suggest that all countries are exposed to strong temperature increases and a high drought risk under climate change. Algeria is most vulnerable to climate change, mainly due to the country’s high sensitivity. Across North Africa, the combination of climate change and strong population growth is very likely to further aggravate the already scarce water situation. The so-called Arab Spring has shown that social unrest is partly caused by unmet basic needs of the population for food and water. Thus, climate change may become an indirect driver of social instability in North Africa. To mitigate the impact of climate change, it is important to reduce economic and livelihood dependence on rain-fed agriculture, strengthen sustainable land use practices, and increase the adaptive capacity. Further, increased regional cooperation and sub-national vulnerability assessments are needed.

Intelligent vehicle detection and counting are becoming increasingly important in the field of highway management. However, due to the different sizes of vehicles, their detection remains a challenge that directly affects the accuracy of vehicle counts. To address this issue, this paper proposes a vision-based vehicle detection and counting system. A new high definition highway vehicle dataset with a total of 57,290 annotated instances in 11,129 images is published in this study. Compared with the existing public datasets, the proposed dataset contains annotated tiny objects in the image, which provides the complete data foundation for vehicle detection based on deep learning. In the proposed vehicle detection and counting system, the highway road surface in the image is first extracted and divided into a remote area and a proximal area by a newly proposed segmentation method; the method is crucial for improving vehicle detection. Then, the above two areas are placed into the YOLOv3 network to detect the type and location of the vehicle. Finally, the vehicle trajectories are obtained by the ORB algorithm, which can be used to judge the driving direction of the vehicle and obtain the number of different vehicles. Several highway surveillance videos based on different scenes are used to verify the proposed methods. The experimental results verify that using the proposed segmentation method can provide higher detection accuracy, especially for the detection of small vehicle objects. Moreover, the novel strategy described in this article performs notably well in judging driving direction and counting vehicles. This paper has general practical significance for the management and control of highway scenes.

Ride-haling such as Uber and Lyft are changing the ways people travel. Despite widespread claims that these services help reduce driving, there is little research on this topic. This research paper uses a quasi-natural experiment in the Denver, Colorado, region to analyze basic impacts of ride-hailing on transportation efficiency in terms of deadheading, vehicle occupancy, mode replacement, and vehicle miles traveled (VMT). Realizing the difficulty in obtaining data directly from Uber and Lyft, we designed a quasi-natural experiment—by one of the authors driving for both companies—to collect primary data. This experiment uses an ethnographic and survey-based approach that allows the authors to gain access to exclusive data and real-time passenger feedback. The dataset includes actual travel attributes from 416 ride-hailing rides—Lyft, UberX, LyftLine, and UberPool—and travel behavior and socio-demographics from 311 passenger surveys. For this study, the conservative (lower end) percentage of deadheading miles from ride-hailing is 40.8%. The average vehicle occupancy is 1.4 passengers per ride, while the distance weighted vehicle occupancy is 1.3 without accounting for deadheading and 0.8 when accounting deadheading. When accounting for mode replacement and issues such as driver deadheading, we estimate that ride-hailing leads to approximately 83.5% more VMT than would have been driven had ride-hailing not existed. Although our data collection focused on the Denver region, these results provide insight into the impacts of ride-hailing.

Ride-sharing has received great attention recently and is considered to be a sustainable transportation mode. Understanding the determinants of the consumers’ intention to use ride-sharing services is critical to promote such services. In this research, an extended technology acceptance model is used as a theoretical research framework. This extension was implemented by incorporating three new constructs: personal innovativeness, environmental awareness, and perceived risk. The model was empirically tested using questionnaire survey data collected from 426 participants. The results indicate that personal innovativeness, environmental awareness, and perceived usefulness are positively associated with consumers’ intention to use ride-sharing services, while perceived risk is negatively associated with the intention and perceived usefulness. The analysis shows that, contrary to our expectations, the perceived ease of use has no significant effect on intention to use ride-sharing services. In addition, personal innovativeness is positively related to perceived usefulness and perceived ease of use but negatively related to perceived risk. Based on these results, implications for practice and suggestions for further research are discussed.

The repercussions of climate change will be felt in various ways throughout both natural and human systems in Sub-Saharan Africa. Climate change projections for this region point to a warming trend, particularly in the inland subtropics; frequent occurrence of extreme heat events; increasing aridity; and changes in rainfall—with a particularly pronounced decline in southern Africa and an increase in East Africa. The region could also experience as much as one meter of sea-level rise by the end of this century under a 4 °C warming scenario. Sub-Saharan Africa’s already high rates of undernutrition and infectious disease can be expected to increase compared to a scenario without climate change. Particularly vulnerable to these climatic changes are the rainfed agricultural systems on which the livelihoods of a large proportion of the region’s population currently depend. As agricultural livelihoods become more precarious, the rate of rural–urban migration may be expected to grow, adding to the already significant urbanization trend in the region. The movement of people into informal settlements may expose them to a variety of risks different but no less serious than those faced in their place of origin, including outbreaks of infectious disease, flash flooding and food price increases. Impacts across sectors are likely to amplify the overall effect but remain little understood.