Housing Variables Research Articles

Review of Local Homeless Mortality Efforts: A Call for Standardized Data and Reporting.

There are currently no national estimates of how many people die while unhoused in the US. Local jurisdictions have developed their own approaches for estimating homeless mortality. We aimed to examine these local approaches, document what is known about homeless mortality, and summarize local methodologies. We reviewed 17 publicly available homeless mortality reports (ie, gray literature). Reports were sought from government, Health Care for the Homeless, coalition to end homelessness, and other advocacy and social service websites. From each report, we extracted the number of homeless deaths, dates of observation, data source(s) used, determination of homeless status, manners and causes of death, and decedent demographics. Data collection and reporting on homeless mortality varied greatly across reports. This variation limits aggregation across reports. Medical examiner data was the most used data source. Manner of death was the most consistently collected field, with accidental deaths reported as the most prevalent manner of homeless deaths. Not all reports listed specific causes of death, but those that did reported toxicity (eg, overdose) and cardiovascular causes as most prevalent. The most granular age category of most homeless decedents was 40 to 60years. On average, 80% of decedents were of male sex. While over half of reports included race and ethnicity information, disparities could not be estimated without suitable denominators. Standardized data collection and reporting guidance is needed for homeless mortality. Health departments can work with local Health Care for the Homeless programs and Continuums of Care to establish data sharing processes. Matching vital statistics with homeless service utilization records may be one opportunity to improve these efforts. Until there is federal or national guidance on these standards, localities can consider adding housing or homelessness variables as optional or mandatory fields in electronic death reporting systems.

제주지역 노인의 지역사회 계속거주에 미치는 영향요인 연구

This study examined the factors influencing Aging-in-Place(AIP) in older adults in the Jeju region. Also, the study tried to find supporting ways of AIP for older adults. The study used data from the 2020 Ministry of Health and Welfare Survey on Korea Older Adults and analyzed 375 cases of older adults in Jeju. The influencing factors of AIP consisted of functional, structural, phenomenological, and ecological factors. The main results are as follows: First, 98.2% of older adults in a healthy situation chose AIP, but this number drops to 59.5% when their health becomes weak and unhealthy. Second, the factors influencing the AIP of older adults in the Jeju area were age as a functional factor, household type and economic satisfaction as a structural factor, housing and residential environment satisfaction variables with a phenomenological factor, and satisfaction with neighborhood relationships as an ecological factor. Based on the above results, this study suggested the policy to support older adults in the Jeju area as they lead their daily lives and face old age. In particular, the willingness to AIP decreases significantly when the health of older people in the Jeju region deteriorates; the study emphasized and discussed the importance of creating a residential environment.

Measuring spatial inequalities in maternal and child mortalities in Pakistan: evidence from geographically weighted regression

BackgroundIn developing countries, the death probability of a child and mother is more significant than in developed countries; these inequalities in health outcomes are unfair. The present study encompasses a spatial analysis of maternal and child mortalities in Pakistan. The study aims to estimate the District Mortality Index (DMI), measure the inequality ratio and slope, and ascertain the spatial impact of numerous factors on DMI scores across Pakistani districts.MethodThis study used micro-level household datasets from multiple indicator cluster surveys (MICS) to estimate the DMI. To find out how different the DMI scores were, the inequality ratio and slope were used. This study further utilized spatial autocorrelation tests to determine the magnitude and location of the spatial dependence of the clusters with high and low mortality rates. The Geographically Weighted Regression (GWR) model was also applied to examine the spatial impact of socioeconomic, environmental, health, and housing attributes on DMI.ResultsThe inequality ratio for DMI showed that the upper decile districts are 16 times more prone to mortalities than districts in the lower decile, and the districts of Baluchistan depicted extreme spatial heterogeneity in terms of DMI. The findings of the Local Indicator of Spatial Association (LISA) and Moran's test confirmed spatial homogeneity in all mortalities among the districts in Pakistan. The H–H clusters of maternal mortality and DMI were in Baluchistan, and the H–H clusters of child mortality were seen in Punjab. The results of GWR showed that the wealth index quintile has a significant spatial impact on DMI; however, improved sanitation, handwashing practices, and antenatal care adversely influenced DMI scores.ConclusionThe findings reveal a significant disparity in DMI and spatial relationships among all mortalities in Pakistan's districts. Additionally, socioeconomic, environmental, health, and housing variables have an impact on DMI. Notably, spatial proximity among individuals who are at risk of death occurs in areas with elevated mortality rates. Policymakers may mitigate these mortalities by focusing on vulnerable zones and implementing measures such as raising public awareness, enhancing healthcare services, and improving access to clean drinking water and sanitation facilities.

Associations between residential daytime indoor temperature and self-reported sleep disturbances in UK adults: A cross-sectional study

BackgroundIn the past few decades, research on the association between indoor temperature and sleep has primarily used laboratory rather than field data collected in epidemiological cohorts. MethodsSecondary data on 2493 individuals aged 43 years was obtained from the National Survey of Health and Development (NSHD). Logistic regression models were used to investigate the associations between temperatures (indoor at home, spot measurement when the nurses visited during the day; and outdoor, monthly average) and self-reported sleep disturbances, adjusting for socio-demographics, health variables, housing variables, and temperature-related variables. ResultsAssociations were found between daytime indoor temperature with difficulty initiating (OR: 0.95, 95%CI: 0.91–0.98) and maintaining sleep (OR: 0.96, 95%CI: 0.93–0.99). Compared with neutral indoor temperatures (17–28 °C), low indoor temperature (≤17 °C) was associated with difficulty initiating sleep (OR: 1.79, 95%CI: 1.21–2.65). Stratified analysis results across tertiles showed that associations with difficulty initiating (OR: 0.87, 95%CI: 0.77–0.99) and maintaining sleep (OR: 0.88, 95%CI: 0.79–0.98) were observed respectively in the lowest (≤20 °C) and highest tertile (≥23 °C) of indoor temperature. There was no association between outdoor temperature and self-reported sleep disturbances in this study. ConclusionIn this first UK-based epidemiology study investigating temperature and sleep, self-reported sleep disturbances were associated with residential daytime indoor temperatures. Low indoor temperature had significantly higher odds ratio for difficulty initiating sleep compared with the neutral indoor temperature. A warmer indoor environment might be more suitable for sleep maintenance than sleep initiation. Indoor temperature in this study was a superior indicator of sleep disturbances than outdoor temperature. Although these findings are based on a UK sample, they may be relevant to other high-income settings with similar housing stock and climatic conditions.

Visible minority status and occupation were associated with increased COVID-19 infection in Greater Vancouver British Columbia between June and November 2020: an ecological study.

The COVID-19 pandemic has highlighted health disparities, especially among specific population groups. This study examines the spatial relationship between the proportion of visible minorities (VM), occupation types and COVID-19 infection in the Greater Vancouver region of British Columbia, Canada. Provincial COVID-19 case data between June 24, 2020, and November 7, 2020, were aggregated by census dissemination area and linked with sociodemographic data from the Canadian 2016 census. Bayesian spatial Poisson regression models were used to examine the association between proportion of visible minorities, occupation types and COVID-19 infection. Models were adjusted for COVID-19 testing rates and other sociodemographic factors. Relative risk (RR) and 95% Credible Intervals (95% CrI) were calculated. We found an inverse relationship between the proportion of the Chinese population and risk of COVID-19 infection (RR = 0.98 95% CrI = 0.96, 0.99), whereas an increased risk was observed for the proportions of the South Asian group (RR = 1.10, 95% CrI = 1.08, 1.12), and Other Visible Minority group (RR = 1.06, 95% CrI = 1.04, 1.08). Similarly, a higher proportion of frontline workers (RR = 1.05, 95% CrI = 1.04, 1.07) was associated with higher infection risk compared to non-frontline. Despite adjustments for testing, housing, occupation, and other social economic status variables, there is still a substantial association between the proportion of visible minorities, occupation types, and the risk of acquiring COVID-19 infection in British Columbia. This ecological analysis highlights the existing disparities in the burden of diseases among different visible minority populations and occupation types.

Evaluation of a Life Functioning Goal Attainment Psychosocial Treatment Program for Adults with Psychotic Disorders

Purpose: To determine if the use of an integrated goal attainment cognitive behavioral social skill training program can impact serious mental illness related treatment and life functioning avolition. Methods: The attendance and goal attainment progress of thirty-eight serious mental ill diagnosed adults admitted to psychiatric clinic’s integrated goal attainment cognitive behavioral social skills group training program were studied. Demographic (age, gender, race, education), clinical (psychiatric diagnosis, trauma history), and life experience (work, income, housing, relational status, friendship support, incarceration and drug arrest histories) variables were compared to identify possible program attendance and goal attainment level associations and differences. Results: Participants attended a mean of 8 program days of the 6 to 8-week program with younger and unmarried attending significantly more days than older and married participants. Greater attendance and participation rates were positively correlated with higher levels of assessed goal attainment. Conclusions: Individuals with serious mental illness conditions are likely to voluntarily attend and participate in psychosocial intervention programs focused on major area life functioning goal attainment. Higher levels of attendance and participation were associated with higher levels of short-term goal attainment. Functioning focused psychosocial interventions may be effective in reducing serious mental illness related avolition.

Implementasi Framework Streamlit Sebagai Prediksi Harga Jual Rumah Dengan Linear Regresi

This research aims to develop an Artificial Intelligence (AI)-based application using the Streamlit framework to predict house sale prices in Banjarmasin City using Linear Regression methodology. The increase in demand and supply of properties in Banjarmasin City poses a complex challenge in determining house sale prices. The Linear Regression method was chosen as the primary analytical tool to identify factors influencing house sale prices. This application utilizes historical data of house sale prices and variables such as land area, building area, number of rooms, proximity to public facilities, and geographical location as inputs for the Linear Regression model. Furthermore, the Streamlit framework is employed to create an interactive and user-friendly interface for end-users. The outcome of this research is an AI application that assists potential buyers or sellers in Banjarmasin City in determining competitive prices. By inputting information about the property being evaluated, users can obtain a more accurate estimated sale price based on factors identified by the Linear Regression model. In testing the application, actual house sale price data from Banjarmasin City was used to assess the model's accuracy. The testing results indicate that the application is capable of providing reasonably accurate price estimates, achieving an accuracy level of 67.8%. Thus, this AI application holds the potential to be a valuable tool in the property industry in Banjarmasin City, aiding stakeholders in more informed and data-driven decision-making regarding house sale prices. Additionally, this application could serve as a foundation for further developments in AI research and property price analysis.

Housing affordability and housing demand assessment for urban poor in India using the hedonic model

Purpose This study aims to assess housing affordability and estimate demand using a hedonic regression model in the context of Lucknow city, India. This study assesses housing affordability by considering various housing and household-related variables. This study focuses on the impoverished urban population, as they experience the most severe housing scarcity. This study’s primary objective is to understand the demand dynamics within the market comprehensively. An understanding of housing demand can be achieved through an examination of its characteristics and components. Individuals consider the implicit values associated with various components when deciding to purchase or rent a home. The components and characteristics have been obtained from variables relating to housing and households. Design/methodology/approach A socioeconomic survey was conducted for 450 households from slums in Lucknow city. Two-stage regression models were developed for this research paper. A hedonic price index was prepared for the first model to understand the relationship between housing expenditure and various housing characteristics. The housing characteristics considered for the hedonic model are dwelling unit size, typology, condition, amenities and infrastructure. In the second stage, a regression model is created between household characteristics. The household characteristics considered for the demand estimation model are household size, age, education, social category, income, nonhousing expenditure, migration and overcrowding. Findings Based on the findings of regression model results, it is evident that the hedonic model is an effective tool for the estimation of housing affordability and housing demand for urban poor. Various housing and household-related variables affect housing expenditure positively or negatively. The two-stage hedonic regression model can define willingness to pay for a particular set of housing with various attributes of a particular household. The results show the significance of dwelling unit size, quality and amenities (R2 > 0.9, p < 0.05) for rent/imputed rent. The demand function shows that income has a direct effect, whereas other variables have mixed effects. Research limitations/implications This study is case-specific and uses a data set generated from a primary survey. Although household surveys for a large sample size are resource-intensive exercises, they provide an opportunity to exploit microdata for a better understanding of the complex housing situation in slums. Practical implications All the stakeholders can use the findings to create an effective housing policy. The variables that are statistically significant and have a positive relationship with housing costs should be deliberated upon to provide the basic standard of living for the urban poor. The formulation of policies should duly include the housing preferences of the economically disadvantaged population residing in slum areas. Originality/value This paper uses primary survey data (collected by the authors) to assess housing affordability for the urban poor of Lucknow city. It makes the results of the study credible and useful for further applications.

Housing and Inequalities in US Life Expectancy, Child and Youth Mortality, and All-Cause and Cause-Specific Mortality, 1979-2020: Results from the National Longitudinal Mortality Study and the National Vital Statistics System.

Limited research exists on the association between housing, life expectancy, and mortality disparities in the United States (US). Using longitudinal individual-level and pooled county-level mortality data from 1979 to 2020, we examine disparities in life expectancy, child and youth mortality, and all-cause and cause-specific mortality in the US by several housing variables. Using the 1979-2011 National Longitudinal Mortality Study (N=1,313,627) and the 2011-2020 linked county-level National Mortality Database and American Community Survey, we analyzed disparities in life expectancy and all-cause and cause-specific disparities by housing tenure, household crowding, and housing stability. Multivariate Cox proportional hazards regression was used to analyze individual-level mortality differentials by housing tenure. Age-adjusted mortality rates and rate ratios were used to analyze area-level disparities in mortality by housing variables. US homeowners had, on average, a 3.5-year longer life expectancy at birth than renters (74.22 vs. 70.76 years), with advantages in longevity associated with homeownership being greater for males than for females; for American Indians/Alaska Natives, non-Hispanic Whites, and non-Hispanic Blacks than for Asian/Pacific islanders and Hispanics; and for the US-born than for immigrants. Compared with renters, homeowners had 22% lower risks of all-cause mortality, 15% lower child mortality, 17% lower youth mortality, and significantly lower mortality from cardiovascular diseases, all cancers combined, stomach, liver, esophageal and cervical cancer, diabetes, influenza and pneumonia, COPD, cirrhosis, kidney disease, HIV/AIDS, infectious diseases, unintentional injuries, suicide, and homicide. Several aspects of housing are strongly associated with life expectancy, child and youth mortality, and all-cause and cause-specific mortality in the US. Policies that aim to provide well-designed, accessible, and affordable housing to residents of both developed and developing countries are important policy options for addressing one of the most fundamental determinants of health for disadvantaged individuals and communities and for reducing health inequities globally.

Home, unsweet home – effect of homeownership on financial investments of Indian households

PurposeThe study analyzes the relationship between homeownership and financial investment of households in the context of emerging markets like India. It also examines how homeownership affects the portfolio decisions of Indian households.Design/methodology/approachUsing the nationally representative All-India Debt and Investment Survey of 2019 and employing an instrumental variable approach, the authors analyze the relationship between homeownership and the share of financial assets held by Indian households. The study also employs several sensitivity checks, including alternate estimation techniques and alternative definitions of the housing variables, and accounts for additional factors to ensure that the authors are able to capture the effect of homeownership on the outcome variable.FindingsThe analysis suggests homeownership crowds out financial investment in India due to high repair and maintenance costs. The negative effect is mainly observed in urban households. Further, the findings imply that homeownership leads households to reallocate their asset portfolio. Homeowners have a lower share in liquid short term deposits, indicating the high liquidity risk of their portfolios. On the other hand, homeownership increases the share of long term retirement funds along with no effect on risky asset share. The authors observe that the crowding out effect is more striking for younger households and poorer households with low income, and the effect is lower for indebted households.Practical implicationsThe findings underscore the need for financial awareness programs so that housing does not crowd out liquid investments of households. Additionally, the results highlight that policies should first focus on young and poor households as the negative effect is more prominent for these groups. Finally, there is scope for policies to support repair and maintenance costs incurred by vulnerable households to reduce the negative effect of housing on liquid financial investments.Originality/valueThis paper is among the few studies that provide insights into how homeownership relates to financial investment and portfolio decisions in the context of an emerging economy. Furthermore, the heterogeneous effects based on poor economic status and age underscore the need for complementary policies.

Comprehensive assets-based approach for neighborhoods that are under-resourced in Jakarta (case study: Keagungan Sub-district in West Jakarta, Indonesia)

Neighborhoods that are under-resourced are one of the main problems that almost always accompany the growth of big cities, especially in developing countries, including Jakarta. This research aims to examine the application of the Comprehensive Assets-Based Approach (CABA) as an alternative solution to the problem of neighborhoods that are under-resourced in Jakarta. The research was conducted using a descriptive qualitative method by examining one of the structuring concepts as an alternative solution to settlement problems in Jakarta, which is directed at achieving liveable housing variables. Data on houses and settlements were obtained directly from Keagungan sub-districts, including neighborhoods that are under-resourced in Jakarta. The first step is to analyze settlement data to formulate the main problems in the neighborhoods that are under-resourced. The second step is to analyze the application of the CABA method to the problems that have been obtained before. The results of the research found that one of the primary root problems in the neighborhoods that are under-resourced in Jakarta is the interweaving of economic and social problems, which include lack of income, lack of knowledge and adherence to building regulations, as well as the lack of knowledge on the criteria for liveable housing. We, therefore, conclude that it is very likely to apply the CABA method for solving neighborhoods that are under-resourced in Jakarta based on community empowerment.

Urbanisation and housing finance nexus: Evidence from Australia

The rapid urbanisation being experienced in major cities across the globe has occasioned greater housing demand and brought to the fore the importance of understanding housing finance systems to support the housing market. However, current literature on the nexus between urbanisation and housing finance is limited. Further, existing literature about housing finance is aggregated and assumed symmetric effects. We used a NARDL model to examine the asymmetric effects of housing variables on loan commitment over 1980Q2 to 2022Q2 and documented the following findings. First, we decoupled the housing finance market into owner-occupier and housing investment and found asymmetric effects of key housing variables on loan commitment for each of these submarkets. Second, urbanisation has a telling effect on loan commitment for owner-occupier but not for investors. Third, the asymmetric results highlighted the importance of promoting economic prosperity and reducing the housing lending rate to boost loan commitment for both owner-occupier and housing investment by varying magnitudes. However, increasing the approval rate of private dwellings and boosting the household savings ratio can enhance loan commitment for owner-occupiers but not investors. These findings could aid the formulation of more targeted housing finance policies, in turn, helping develop a more resilient housing market especially in countries with high urbanisation rates.

Preparing Cities for Future Pandemics: Unraveling the Influence of Urban and Housing Variables on COVID-19 Incidence in Santiago de Chile.

In this study, we analyzed how urban, housing, and socioeconomic variables are related to COVID-19 incidence. As such, we have analyzed these variables along with demographic, education, employment, and COVID-19 data from 32 communes in Santiago de Chile between March and August of 2020, before the release of the vaccines. The results of our Principal Component Analysis (PCA) confirmed that those communes with more economic, social, organizational, and infrastructural resources were overall less affected by COVID-19. As the dimensions affecting COVID-19 are based on structural variables, this study discusses to what extent our cities can be prepared for the next pandemic. Recommendations for local decision-makers in controlling illegal immigration and investing in housing and urban parks are drawn.

Development of Kampung Susun Akuarium Based on Sustainable Housing Principles

Adequate housing is one of the rights of Indonesian citizens. Nevertheless, forced eviction is something familiar in Indonesia. One of the areas that experienced forced evictions was Kampung Akuarium. In April, the Provincial Government of DKI Jakarta evicted Kampung Akuarium settlement area residents. As a form of demonstration, they survived on the rubble from the evictions and built tents to carry out their activities in the Kampung Akuarium area. The DKI Jakarta Provincial Government began to rebuild the site into an adequate residential area called Kampung Susun Akuarium (KSA) as a solution for the residents. Based on the government’s solution, the study aims to determine whether the KSA meets the principles of sustainable housing. Sustainable housing promotes environmental preservation, social equality, and economic development to improve the residents’ quality of life. This study conducts a preliminary study to compile sustainable housing variables and indicators. The variables used in this research are community development, environment, social, and economy. Data analysis using Structural Equation Modeling was performed with SmartPLS software, based on the data collected, with an intensive study by distributing questionnaires to 102 residents. The findings indicate that KSA residents have performed most of the sustainable housing and community development indicators well and can still be improved by considering solutions related to poorly implemented indicators. This study’s results also emphasize that community development is a significant variable in building sustainable housing to be used in subsequent studies.

Modeling spatial dimensions of parcel delivery demand and its determinants

ABSTRACT This study aims to investigate the spatial dimensions of parcel delivery demand and its key determinants. Based on the parcel delivery volume data in South Korea, we regressed per capita parcel delivery volume on socioeconomic, land use, housing, and industry-related variables of city-level districts. When covering the entire country, Spatial Durbin Error Model worked best in modelling the spatial dependence in the disturbance and the spatial spillover effect of the explanatory variables in the neighbouring districts. When performing separate regression analyses for urban and non-urban areas, we identified a different relationship between parcel demand and its determinants in two subgroups. Given the rapid development of e-commerce, establishing logistics infrastructure for supporting home delivery services, particularly in urban areas, has become challenging. This study can contribute to improved prediction of parcel demand that varies over space, suggesting region-specific policies for establishing local parcel delivery infrastructure such as sub-terminals.

Housing and the changing impact of monetary policy

Housing is the leading indicator of the business cycle. The impact of monetary policy on housing has thus unsurprisingly been the subject of numerous studies. Some have also investigated whether the impact of monetary policy has changed since the mid-1980s. These papers to date have relied on vector-autoregressions (VARs). To avoid some of the specification issues of VARs, we employ the local projection method. We find a clear effect of monetary shocks on housing variables over the entire sample. However, we also find that over the great moderation of 1983–2008, there is no significant impact of policy shocks on either home prices or investment. Moreover, as the difference between the estimated impact of monetary policy on GDP before and during the great moderation was not as large as the same difference for residential investment, we infer that changes to housing finance, rather than simply more stable monetary policy, deserve some credit for the lesser impact of Fed shocks. Finally, we find that during the zero lower bound years spanning 2009–2020, unconventional monetary policy (UMP) appears to have had a palpable impact on the housing sector. This result is consistent with previous findings on the effectiveness of UMP.

Housing Prices and the Characteristics of Nearby Green Space: Does Landscape Pattern Index Matter? Evidence from Metropolitan Area

This study aimed to examine the association between housing prices and green space characteristics with a special focus on exploring the effects of the shape pattern index. The research was based on a hedonic price model across two main distance buffers from residential properties to urban green spaces. Green spaces were characterized by size and shape measured by a landscape shape index (LSI). This study was based on 16,222 housing transaction data obtained from the website of real estate agencies during December 2019 in the Metropolitan Area of Beijing. Linear regression and semi-log regression analysis were used to examine the associations between independent housing and neighborhood characteristic variables and housing prices. The results suggested that a one-unit increase in the natural logarithm of the landscape shape index (LSI) can increase housing prices by 4% (5543 CNY ≈ 826 USD). Such marginal effects were more pronounced for residences located close to urban green spaces and tended to decay as the distance from residences to green spaces increased. Additional analysis captured the marginal effects of the natural logarithm of the landscape shape index (LSI > 1.3) on achieving the maximum monetary evaluation of the property. The findings of this study suggest that the effects of specific green space characteristics on housing prices should be taken into account in landscape and urban design.

Typologies of multiple vulnerabilities and climate gentrification across the East Coast of the United States

Climate change is expected to alter community dynamics and characteristics in complex ways, with the most severe implications for already vulnerable populations. The theory of climate gentrification provides a lens for investigating the ways in which climate change interacts with community demographics, housing, and socioeconomic characteristics in coastal communities. Here we apply unsupervised machine learning to 51 counties along the United States East Coast to identify four distinct clusters of multifaceted vulnerability related to social, housing, and environmental variables. Two of the four clusters, interpreted as superior investment and disinvestment, may be indicative of different pathways of climate gentrification. We also demonstrate the applicability of our methodology at finer spatial scales using a case study within North Carolina. As coastal climate impacts increase, this work demonstrates the need for adaptation planning that proactively considers compounding vulnerabilities to avoid disproportionate impacts on the most vulnerable.

Clean energy justice: Different adoption characteristics of underserved communities in rooftop solar and electric vehicle chargers in Seattle

Concerns over global climate change have led to energy transition to clean energy systems with the development of various clean energy policies. However, social equity issues have emerged in association with the rapid transition of energy systems related to distributed energy resources (DERs), evidenced by disparities in clean energy access. While most existing studies have focused on several variables impacting the adoption of DERs, there is a dearth of studies concerning distributional and recognition justice specifically aimed at investigating: (1) which DER adoption variable is the most important among several variables identified in the literature; and (2) how adoption patterns vary by technologies and communities. The objective of the present study is to answer the two questions by examining the geographic distribution of rooftop solar and electric vehicle (EV) chargers and the related community attributes. Also, the study involves identifying latent variables by addressing inter-correlations among several adoption determinants. The results show that rooftop solar and EV charger adoptions in Seattle present disparities associated with geographic locations and community attributes. In particular, housing variables are the main indicators for rooftop solar adoption and even stronger in communities with low adoption rates. EV charger adoptions are strongly associated with economic variables. Furthermore, spatial inequality of rooftop solar adoption is higher than that of EV charger adoption. The study suggests housing-related support may increase the adoption of both technologies, particularly in communities with low adoption rates. Considering that the installations of rooftop solar and EV chargers were concentrated in particular communities, the study results imply that policies aimed at increasing the adoption of DERs should be tailored to local community characteristics.

Impact of selective licensing schemes for private rental housing on mental health and social outcomes in Greater London, England: a natural experiment study

ObjectivesTo assess primary impact of selective Licensing (SL), an area-based intervention in the private rented housing market, on individual self-reported anxiety and neighbourhood mental health (MHI—Mental Healthcare Index) and secondary...