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Agricultural land loss is a severe issue that Australia faces, along with many other countries. Myriad research studies have discussed the reasons for such land loss, including urban sprawl as the main factor and factor's repercussions. However, there is a knowledge gap in understanding the impact of dwelling type on farming land reduction. Also, there is an application gap, particularly in the local context. The purpose of this study is to discover the effect of differing dwelling types (compact and non-compact) in metropolitan areas with a growing population (such as Craigieburn) toward potentially noticeable agricultural land loss.

This paper aims to fill these research and practice gaps through a case study using a mixed methods approach. A quantitative analysis was conducted of housing types, the types' growth and agricultural land area taken up by those different housing types over 18 years. Thematic analysis of policies, strategies, schemes and codes relevant to the case study enabled a better understanding of practice gaps.

The study revealed a significant loss of agricultural land. Separate housing was found to be the main culprit due to the number of houses and the area the houses require, thus exhibiting the lack of practical guidelines to prevent the houses' overdevelopment. The findings enabled the identification of opportunities for better practice through government intervention and potential industry alterations.

Previous literature has primarily explored the issues associated with urban sprawl and the sprawl's unsustainability. This research paper offers a more targeted insight into one of the key factors leading to urban sprawl: the types of dwellings being constructed.

The purpose of the present study is to explore the factors that influence the residential location choice of households in the Indian context.

Two sub-urban towns of Kolkata, i.e. Biddhanagar Municipal Corporation and Rajarhat Newtown, are taken as case studies. In total, 34 variables are considered related to the household's socioeconomic background (10), dwelling (3), travel (2) and level of importance of neighborhood characteristics (19). A revealed preference household survey is conducted, which resulted in the collection of 390 samples. Firstly, 19 level of importance variables are deduced to 3 latent factors using exploratory and confirmatory factor analysis. After that, a utility function is estimated using a multinomial logit regression to identify significant factors.

The results indicate the significance of car ownership, work travel time, work mode, monthly income, proximity to work, proximity to family/relative/friends, proximity to quality schools, quality of physical infrastructure such as water availability, power availability and housing typology among others in the residential location choice of households.

Kolkata, a major metropolitan area in eastern India, has been experiencing space constraints to cater to the housing demand of the ever-increasing population. Although satellite towns are being developed, the infrastructure landscape in such towns is uneven, resulting in more housing demand in one area than another. The study furnishes significant findings to develop more informed policies to attract households in low-demand areas and manage high-demand areas to achieve sustainable planning.

Considering the importance of geographical context, it is the first study on residential location choice, providing a perspective of household behavior from eastern India. It also shows the importance of physical infrastructure attributes in residential location choice for developing countries, which is seldom employed in the current practice. Moreover, previous studies (although few) done in India consider the objective value of location characteristics. The present study focuses on subjective value or the level of importance perceived by respondents.

As public–private partnerships (PPPs) have become preferred and veritable approach to deliver affordable housing, the seemingly lack of understanding of the significant factors that impact on success has become a notable setback. This study aims to delineate significant factors that can support decisions in affordable PPP public housing delivery.

Largely, a questionnaire survey was adopted to elicit insights from practitioners, policymakers and experts to develop an evaluative decision support model using an analytical hierarchy process and multi-attribute utility technique approach. Further, an expert illustration was conducted to evaluate and validate the results on the housing typologies.

The results revealed that energy efficiency and low-cost green building materials scored the highest weighting of all the criteria. Furthermore, multi-storey self-contained flats were found to be the most preferred housing typology and were significantly influenced by these factors. From the model evaluation, the scores on the factors of sustainability, affordability, cultural values and accountability were consistent across all typologies of housing whereas that of benchmarking, governance and transparency were varied.

The decision support factors captured varied dimensions of key factors that impact on affordable PPP housing that have not been considered in an integrated manner. These findings offer objective and systematic support to decision-making in affordable PPP housing delivery.

The purpose of this study is to analyze the affordability and sustainability of residential water consumption in Ecuador, a developing country.

To determine affordability, the authors assess water consumption and its distribution across different income levels of the population in the three main cities: Cuenca, Guayaquil and Quito. The authors then simulate the effects of a pricing policy on sustainability by deriving demand elasticities through a quadratic almost ideal demand system (QUAIDS).

Results show that Ecuador has low water tariffs with respect to other cities in Latin America, which points to its affordability. However, sustainability might not be guaranteed since consumption is overall higher than the level suggested by the World Health Organization. From an economic point of view, this could be motivated by the low tariffs. Indeed, the simulation of a pricing policy shows that higher prices can significantly reduce demand for most of the sample. However, in low-income households this reduction may compromise vital consumption.

This research has important implications in terms of stimulating the discussion of how water affordability and sustainability can be achieved. For the case study, Ecuador, the results indicate that sustainability may not be guaranteed and that policies need to be designed to encourage it. Another implication is that population income levels should be considered to avoid negative effects for the most vulnerable groups. The main limitations of this study are methodological. First, the QUAIDS model is based on a conditional demand model (Zhen et al., 2013), which does not take into account spending on other goods. Second, data availability is limited and prevents a deeper analysis.

To the best of the authors' knowledge, this is the first study to analyze these issues in Ecuador, a country that recognizes the human right to water at the constitutional level.

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.

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.

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.

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.

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.

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.

The purpose of the study is to observe the feasibility of missing middle housing’s (MMH) realization under density-based zoning, form-based zoning and a combination of both while simultaneously providing affordable housing, improving quality of life and making efficient use of land.

This study takes a theorist approach and designs three hypothetical cottage court projects that comply with all relevant official local zoning ordinances to showcase design feasibility, followed by an analytical component in the form of a financial model constructed using official local economic and demographic conditions.

MMH, and in particular cottage clusters, can be implemented under rigorous density-based, form-based and hybrid (density-based + form-based) zoning ordinances and provide affordable housing (Atlanta, GA), improve quality of life (Blackpool, UK) and make efficient use of land (Jinan, China). All hypothetical projects are financially feasible under reasonable conditions.

To the best of the author’s knowledge, this paper is the first in the body of knowledge to discuss how the MMH can be integrated into urban density-based zoning rather than converting density-based zoning into form-based so that the MMH can fit. The paper also takes a cross-national perspective and discusses the feasibility of MMH in the resolution of housing issues in the USA, China and the UK. The study also concludes that the issue of housing unaffordability in the UK was caused by high construction cost relative to median income.

The adverse impacts of climate change coupled with rapid informal urbanization in the Southern African region are increasing the vulnerability of already sensitive population groups. Consequently, these urban regions are highly vulnerable to urban heat island effects and heatwaves due to exogenous and endogenous factors. While the dynamic interplay between the built environment, climate and response strategies is known, this paper highlights the lived experience of informal settlement residents. It presents work from a project undertaken in Melusi, an informal settlement in Tshwane, South Africa, as a multi-disciplinary project focusing on improving the local resilience to climate change associated heat stress.

Following a mixed method approach, a semi-structured observational analysis of the spatial layout and material articulation of selected dwellings along with the continuous monitoring and recording of their indoor environments were undertaken.

The paper presents the research results in terms of the dwelling characteristics, as spatial and material-use strategies and documented heat stress exposure in these structures. The findings highlight that informal dwellings perform poorly in all cases due to endogenous factors and that inhabitants experience extreme heat stress conditions for between 6 and 10 h daily during the peak summer period.

Currently, there are little empirical data on the heat stress residents living in informal settlements in Southern Africa are experiencing. This article provides insight into the indoor environments of informal dwellings and hopes to contribute future guidelines or heat health policies.

The purpose of this study is to develop a scale for measuring housing quality in the context of Pakistan.

The inductive and deductive approaches for item generation have been combined, and items have been purified using multistage expert review. Data was collected from a sizeable purposive sample of 445 respondents, and exploratory and confirmatory factor approaches used for assessing psychometric properties of the scale.

The result is a 21-item scale covering five dimensions, namely, Design and Construction Quality, Neighborhood Quality, Adequacy of Space, Quality of Institutional Services and Proximity of Basic Amenities.

This study contributes to housing quality literature by deepening our understanding of the concept of housing quality in the context of Pakistan, the world’s fifth most populous country. The findings of the study have important implications for both theory and practice.

Companion animals and social media are two important factors of social interaction and well-being among the older population. This study aims to compare social media use and/or having companion animals with respect to sociodemographic variables in conjunction with loneliness, social isolation, depression, satisfaction with life and satisfaction with social support.

This cross-sectional study involves a sample of 250 older community-dwelling adults. The questionnaire comprised sociodemographic, companion animals and social media questions and scales to assess social isolation, loneliness, satisfaction with life and social support and depression. Data were analyzed using descriptive and inferential statistics.

Four groups emerged: “companion animal/s, no social media” (37.6%); “none” (33.6%); “social media and companion animal/s” (14.4%); and “social media, no companion animal/s” (14.4%). Social media users (with or without companion animals) are the youngest and with higher levels of education; caregivers of companion animals (no social media use) are in-between in terms of age and level of education; and those without companion animals and no social media users are the oldest and with less formal education.

This research examines and compares two key influencers of older adults’ well-being and social interaction (social media and companion animals), that have been researched mostly separately. Findings underlined the cohort effect in the use of social media, suggesting that future older adult cohorts will use more social media whether they have or do not have companion animals.

Between 2006 and 2012, Mexico implemented a “frontal war against organized crime”. This strategy increased criminal violence and triggered negative consequences across the country’s economic, political and social spheres. This study aims to analyse how the magnitude and visibility of criminal violence impact the housing market of Mexico City.

The authors used different violent proxies to measure the effect of the magnitude and visibility of violence in housing prices. The structure of the data set is an unbalanced panel with no conditions of strict exogeneity. To address endogeneity, the authors calculate the first differences to estimate an Arellano–Bond estimator and use the lags of the dependent variable to instrumentalise the endogenous variable.

Results suggest that the magnitude of violence negatively impacts housing prices. Similarly, housing prices are negatively affected the closer the property is to visible violence, measured through narcomessages placed next to the bodies of executed victims. Lastly, housing prices are not always affected when a violent event occurs nearby, specifically, when neighbours or potential buyers consider this event as sporadic violence.

There are only a few studies of violence in housing prices using data from developing countries, and most of these studies are conducted with aggregated data at the municipality or state level. The authors are using geocoded information, both violence events and housing prices, to estimate more disaggregated effects. Moreover, the authors used different proxies to measure different characteristics of violence (magnitude and visibility) to estimate the heterogeneous effects of violence on housing prices.