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Housing that is affordable and appropriate is a necessity for successful integration for all newcomers. It is not uncommon for newcomers to Canada to report difficulties finding suitable, safe, and affordable housing for their families. For refugees, however, the challenges are sometimes greater. Settlement organizations and refugee sponsors experience various challenges in accommodating families with large numbers of children, but as our research shows, refugee groups have differing needs based on their culture, family composition, and experience of trauma. Using data collected from two recent studies, we identify and compare the housing needs of two newly arrived groups of refugees to Canada: Syrians and Yazidis from northern Iraq. All participants in our study have lived in Canada for 2 years or less and currently live in Alberta, Saskatchewan, Manitoba, or Ontario. Data was collected either by face-to-face surveys (with Syrian participants) or unstructured interviews (with Yazidi women) conducted in Arabic, Kurmanji, or English. We discuss their experiences of living in resettlement centers and their transition to independent housing. In addition, we discuss how family composition and previous trauma influence their housing experiences with special attention to how increasing agency increases satisfaction with housing.

Patients with cancer frequently encounter complex treatment pathways, often characterized by challenges with coordinating and scheduling appointments at various specialty services and locations. Identifying patients who might benefit from scheduling and social support from community health workers or patient navigators is largely determined on a case-by-case basis and is resource intensive. This study aims to propose a novel algorithm to use scheduling data to identify complex scheduling patterns among patients with transportation and housing needs. We present a novel algorithm to calculate scheduling complexity from patient scheduling data. We define patient scheduling complexity as an aggregation of sequence, resolution, and facility components. Schedule sequence complexity is the degree to which appointments are scheduled and arrived to in a nonchronological order. Resolution complexity is the degree of no shows or canceled appointments. Location complexity reflects the proportion of appointment dates at 2 or more different locations. Schedule complexity captures deviations from chronological order, unresolved appointments, and coordination across multiple locations. We apply the scheduling complexity algorithm to scheduling data from 38 patients with breast cancer enrolled in a 6-month comorbidity management intervention at an urban hospital in the Washington, DC area that serves low-income patients. We compare the scheduling complexity metric with count-based metrics: arrived ratio, rescheduled ratio, canceled ratio, and no-show ratio. We defined an aggregate count-based adjustment metric as the harmonic mean of rescheduled ratio, canceled ratio, and no-show ratio. A low count-based adjustment metric would indicate that a patient has fewer disruptions or changes in their appointment scheduling. The patients had a median of 88 unique appointments (IQR 60.3), 62 arrived appointments (IQR 47.8), 13 rescheduled appointments (IQR 13.5), 9 canceled appointments (IQR 10), and 1.5 missed appointments (IQR 5). There was no statistically significant difference in count-based adjustments and scheduling complexity bins (χ24=6.296, P=.18). In total, 5 patients exhibited high scheduling complexity with low count-based adjustments. A total of 2 patients exhibited high count-based adjustments with low scheduling complexity. Out of the 15 patients that indicated transportation or housing insecurity issues in conversations with community health workers, 86.7% (13/15) patients were identified as medium or high scheduling complexity while 60% (9/15) were identified as medium or high count-based adjustments. Scheduling complexity identifies patients with complex but nonchronological scheduling behaviors who would be missed by traditional count-based metrics. This study shows a potential link between transportation and housing needs with schedule complexity. Scheduling complexity can complement count-based metrics when identifying patients who might need additional care coordination support especially as it relates to transportation and housing needs.

On-reserve housing crises have been measured by governments, agencies and First Nations for decades. In Canada, housing evaluation is conducted using a universal metric, Core Housing Need, which also forms the basis of policy and program responses. The three Core Housing Need components—adequacy, affordability and suitability—have been critiqued for being inapplicable in many non-urban settings and especially on-reserve. Using a community-developed housing needs assessment tool in Eabametoong First Nation it is revealed that residential crowding has multiple dimensions and causes which are not currently captured. New understandings of crowding—or housing suitability—allows for distinct solutions to be created breaking the cycle of colonial approaches to addressing the housing crisis.

Over the last 25 years, 'affordability' has become a more important issue in housing policy, although it is still not fully enshrined in agreed standards, partly due to different views about how it should be measured and at what thresholds. This paper argues that subjective evidence of payment problems and material hardship can be used to validate ratio measures and points to the best thresholds to use. Using household panel survey evidence it is shown that traditional affordability ratios are still probably the best objective measure, with residual income ratios in a supporting role; and that relatively generous thresholds are better. Composites with subjective payment problems are well validated by independent evidence on material hardship, and are associated with higher incidence of moves and other housing needs. These problems are much more prevalent in private renting, with less variation between regions than household types. Multivariate models shows strong effects from income and prices interacted with interest rates, especially for owners, with significant labour market and demographic effects, and substantial effects for renters from the supply of social lettings.

Purpose Literature reveals that local authorities in Scotland are required under the Housing (Scotland) Act 2001 to undertake housing need and demand assessment. Local authorities are required to ensure that their local housing strategies are evidenced with an assessment of housing need and demand. The housing needs and demand assessment (HNDA) framework was designed to help local authorities in Scotland provide the evidence base for local strategies. Since its introduction in 2014, there is a limited literature highlighting whether or not the framework has been effective. To this end, the purpose of this study was to evaluate the effectiveness of the HNDA framework as set in Scottish local authorities; this paper aims to highlight those factors that have been effective in the framework. Design/methodology/approach An evaluation to asses empirically the effectiveness of the HNDA framework in Scotland was conducted. The research involved a review of literature on the current HNDA framework in Scotland. A measure of the effectiveness of the HNDA framework was conducted via a questionnaire survey to get the practitioner’s perception from 32 Scottish local authorities. This was conducted to advance specific features that have been effective in the HNDA framework. The features were then ranked in terms of their degree of effectiveness. Findings The research in this paper identifies initial findings cited in literature of effective factors that impact on the effectiveness of typical projects and contextualises them in the HNDA framework in Scotland, the HNDA framework having all hallmarks of a project. Results from this study identified effectiveness factors that have a greater influence on the HNDA framework’s general effectiveness. These factors include amongst others: clearly defined and detailed scope; project monitoring and control; competent and experienced project managers; and sufficient and well-allocated resources. Research limitations/implications By examining the factors individually, it can be stated that overall and based on literature and survey, the HNDA framework as used in Scotland has largely been successful. The success, however, is on a case by case basis. For example, those in remote-based local authorities expressed reservations on whether some of the identified effectiveness factors in the framework were effective while those in more metropolitan or larger local authorities were more upbeat with the HNDA framework. Originality/value No similar study has been carried out on effectiveness factors in the HNDA framework used in Scottish local authorities. The findings in this research, therefore, contribute to the literature that seeks to understand the mechanisms of an effective HNDA in general and the HNDA framework in Scotland in particular. Overall, it contributes to the housing debate by offering a Scottish perspective. The study is a precursor to the mechanism for the housing need and demand and funding success in Scotland.

Local planning authorities in the UK are facing serious challenges regarding housing supply. Under such background, Strategic Housing Market Assessment (SHMA) was introduced to provide the main evidence for local housing planning and policy, by objectively accessing local housing need, especially affordable housing need. Local planning authorities are supposed to conduct SHMAs regularly to ensure that housing policy is based on objectively assessed needs. However, previous studies have shown that key questions remain about SHMAs’ effectiveness. Meanwhile, the last two decade has seen great progress in Evidence-based Policymaking (EBPM). Since SHMAs provide the main evidence for local housing policy, it is necessary to evaluate how robust the current SHMAs are. This research explores the methodological robustness of SHMAs, namely the transparency and adequacy of the method and data used to assess housing need, by reviewing the most up-to-date SHMAs and making comparisons with previous versions. As results, we have three findings. First, opacities remain surrounding the research methods and data sources. Second, regarding the specific way of assessing housing need, most SHMAs assess current housing need based on housing register data and future housing need based on household projection data. Third, the methodological weaknesses pointed out by previous studies are no longer common in the current SHMAs. However, there are still concerns regarding forecasting future housing need based on household projection, since housing is a market-based system driven by interactions of many economic and social factors.

In this essay, we review the basic economics of housing supply and the functioning of US housing markets to better understand the distribution of home prices, household wealth, and the spatial distribution of people across markets. We employ a cost-based approach to gauge whether a housing market is delivering appropriately priced units. Specifically, we investigate whether market prices (roughly) equal the costs of producing the housing unit. If so, the market is well-functioning in the sense that it efficiently delivers housing units at their production cost. The gap between price and production cost can be understood as a regulatory tax. The available evidence suggests, but does not definitively prove, that the implicit tax on development created by housing regulations is higher in many areas than any reasonable negative externalities associated with new construction. We discuss two main effects of developments in housing prices: on patterns of household wealth and on the incentives for relocation to high-wage, high-productivity areas. Finally, we turn to policy implications.

Chinese housing prices rose by over 10 percent per year in real terms between 2003 and 2014 and are now between two and ten times higher than the construction cost of apartments. At the same time, Chinese developers built 100 billion square feet of residential real estate. This boom has been accompanied by a large increase in the number of vacant homes, held by both developers and households. This boom may turn out to be a housing bubble followed by a crash, yet that future is far from certain. The demand for real estate in China is so strong that current prices might be sustainable, especially given the sparse alternative investments for Chinese households, so long as the level of new supply is radically curtailed. Whether that happens depends on the policies of the Chinese government, which must weigh the benefits of price stability against the costs of restricting urban growth.

Purpose The theory of competitiveness of cities is based on Porter’s Diamond Theory. There is a relation between housing and urban competitiveness. The adequacy of land supply and allocation of land for new housing development is integral. This paper aims to estimate the required number of housing units to secure housing needs in Tehran for the next four years in 1400 H.Sh (2021 A.D.). The research methodology is carried out using qualitative and quantitative approaches based on the given data. First, the population of Tehran in 1400 H.Sh was predicted using nonlinear quadratic polynomial, Gompertz and logistic models. Then, a Logistic model is proposed to estimate the number of housing units in Tehran. The calculations of residential units related to the population obtained from the Gompertz model equivalent to 663141 is suggested as a criterion for local authority to future decision making and planning for urban development. Design/methodology/approach The present research is an applied research in terms of the purpose a descriptive research in terms of the nature and methodology and a descriptive-analytical research in terms of attitude and approach toward the research problem (Hafeznia, 2013, 58, 63 and 71). To provide the required information for the analytical stage, a documentary method, related to the use of internal and external books and papers, has been applied. First, the population of Tehran in 1400 H.Sh is estimated using three nonlinear models of quadratic polynomials, Gompertz and logistic. Then, among them, the options that were more consistent with the estimation of the new comprehensive plan of Tehran (1386 H.Sh), which is the most important plan of this city, were chosen. After that, by using the logistic model, which is an appropriate expression of saturable phenomena and a suitable method of estimating the number of residential units in a city and based on the past trend, the future of housing is predicted, and the number of required residential units is determined. Findings Any city for competitiveness must seek the search and development of a set of unique strategies and practices that will shape its status from other cities. No single action for all cities is feasible. In fact, the most important challenge is to propose a unique value proposition and to formulate a strategy that distinguishes that city from the rest. Among the measures taken around the world is attention to infrastructure. From the point of view of competitiveness, different types of investment in infrastructure are important for different types of cities and in different stages of development of a city. Large cities need targeted investments in housing issues to overcome the segments associated with the poorer neighborhoods. Without investment in desirable housing, there will be holes in competitive advantage. In this paper, the number of residential units in Tehran was projected for 2021. The city’s population was originally estimated for 2021. In addition to the models used to predict and estimate necessary, it is necessary to consider the area, land use map, future development lines and […] city. To this end, the city can continue to meet the needs of residents’ diversification and the city’s needs. We cannot accept any predictions about the population and, consequently, the number of residential units. Providing predictions can provide the most predictive, or more prudent, and different scenarios that can emerge, which will lead to flexibility in the presentation of plans and programs. Among the models that were used to predict the population, the result obtained from second-order polynomial and Gompartz models was found to be appropriate for the estimation of the new comprehensive design of Tehran (2007). But the prediction of the population of the logistic model was beyond the prediction of the new comprehensive plan of Tehran (2007) and thus was not considered appropriate. The number of residential units required according to the predicted population of the second order polynomial models, Gompartz and the population considered in the new comprehensive plan of Tehran (2007). After the finalization of the proposed population, using the logistic model, the number of residential units needed in Tehran was projected for 2021. Since these three estimates are somewhat close to each other, it is suggested that Gompertz model calculations, equivalent to 663,141 residential units, are proposed, and according to that, local authorities are planning to supply land to achieve economic competitiveness (urban). As it is shown in the conceptual model of the paper in Figure 1, after determining the need for housing, it is necessary to ask whether the adequacy of the supply and allocation of land, as well as the importance of maintaining it for the development of housing by local authorities, is clear. Also, is there any suitable planning for that? Despite the severe shortage of ready-made land for the city of Tehran, a large volume of land is a large area owned by natural and legal persons, and, in particular, state-owned enterprises of semipublic and public institutions, which have been abandoned in cities for years without use and in the form of barren. According to municipal management laws, municipalities can receive land, taxes and fees that are included in the annual budget of the Tehran Municipality. According to the figure obtained from this study, which states that 663,141 residential units are needed for Tehran in 2021, large landowners in Tehran need to supply their land to the market. According to the Population and Housing Census in Tehran in 2011, there are 245,769 inhabited vacancies in Tehran; hence there are two scenarios for the provision of residential units in the city of Tehran in 2021, assuming that these units in the housing market require 417,372 units Another residence will be for Tehran, otherwise 663141 residential units will be needed for Tehran in 2021. Other possibl Originality/value Tehran is the largest city and the capital of Iran, and it is also the capital of the province Tehran. In the southern foothills of the Alborz Mountains within a longitude of 51 degrees and 2 minutes East to 51 degrees and 36 minutes East, with an approximate length of 50 kilometers and latitude 35 degrees and 34 minutes North to 35 degrees and 50 minutes North with an approximate width of 30 kilometers. The area of this city is 730 km2. This is one of the largest cities in West Asia, the 25th the most populous city, and the 27th greatest city to the world. The administrative structure of Iran has been concentrated in this city. The city has been divided into 22 zones, 134 areas (including Rey and Tajrish), and 370 districts (Wikipedia). The problem of housing in the city of Tehran has always been one of the important issues that less has been planned for it. The result is housing shortage, high housing prices and so on, due to the excessive expansion of the city, its population increase and so on.

This paper uses an assignment model to understand the cross section of house prices within a metro area. Movers' demand for housing is derived from a life-cycle problem with credit market frictions. Equilibrium house prices adjust to assign houses that differ by quality to movers who differ by age, income, and wealth. To quantify the model, we measure distributions of house prices, house qualities, and mover characteristics from micro-data on San Diego County during the 2000s boom. The main result is that cheaper credit for poor households was a major driver of prices, especially at the low end of the market.