Socio-demographic and socio-economic dynamics underlying housing development of urban residents in Sebeta town, Ethiopia

Abstract Even though situational factors are predominantly determining the housing scenario of urban residents, individual factors are the most essential elements that play a vital role in the housing development of urban residents. The main objective of this study was to investigate socio-demographic and socio-economic factors that contributed to urban housing development in Sebeta town, Ethiopia. To achieve the research objective, the explanatory mixed-method sequential research design was employed where quantitative data were collected from 384 respondents who were randomly selected, whereas qualitative data were collected via in-depth interviews and focus group discussion. Research findings confirmed that socio-demographic and socio-economic characteristics were major determining factors of urban housing development. Socio-demographic and socio-economic dynamics associated with individual characteristics were found to cause variation in housing statuses among urban residents. This implies, in addition to structural challenges, individualistic factors of urban residents affect their housing choices. It was identified that differences in gender, migration status, ethnic background, income class, educational status, and occupational backgrounds were responsible factors for housing status discrepancy in the study area. The authors came up with the conclusion that socio-demographic and socio-economic factors are the most remarkable features, which determine the housing development of residents by accompanying structural factors. The authors would recommend that the housing development of urban residents needs the prerequisite of strengthening socio-demographic dynamism and socio-economic empowerment of the urban poor.


Introduction
Supply and demand discourses are less explanatory to understanding housing conditions (Wile & D, 1919); still, housing demand increasingly escalates urban housing problems (Cullingworth, 1960, p. 14). Even if supply and demand factors affect the housing situation of residents, individual factors are more important in deciding housing status of residents. Given that personal resources such as income dynamics play a pivotal role in determining the housing scenario of urban residents. On the other hand, Kemeny (1992, p. 8) and Greig (1995) argued that housing provision, utilization, and production are impacted by structures and procedures, which are consistent with the idea of structural dynamics, determining the housing development of urban residents (Gilbert & Varley, 2002, p. 31;Curley, 2005;Minale Balechaw, 2013Desmond & Bell, 2015. However, housing security and housing affordability are critical urban housing problems that are predominantly overcome by individual dynamism. This indicates the vitality of individual factors in determining housing status. Housing is more structural than personal (Madden and Marcuse (2016):11;Martin (2017); Debele, 2019), but it is overlooked that dispositional factors can influence the housing status of residents (Debele and Negussie, 2020;Foye (2021); Preston et al. (2021); Bangura et al. (2021)). For example, residents that have money would have better privileges in housing development. In this article, it is assumed that those individual characteristics: gender category, migration status, ethnic category, income level, educational status, and occupational dynamics occurring in a given space can have a relational influence on housing status: either segregation or homeownership.
Furthermore, so far housing literature has provided an inadequate framework to understand housing research and is ineffective to address the housing question. Thus, the researchers are motivated to address the research question that proposed why housing status is different from person to person. Empirical research findings indicated that gender, migration status, ethnic category, income category, educational status, occupational category, etc. are major sociodemographic and socio-economic dynamics associated with housing status of urban residents. Socio-demographic and socio-economic dynamics are the key theoretical framework that researchers applied in this paper to understand the housing status of urban residents. Thus, researchers aimed to investigate the relationship between socio-demographic characteristics, socio-economic dynamics, and housing status; to examine the effects of socio-demographic characteristics and socio-economic dynamics on the housing status of urban residents. Despite its major strong points, the argument of this paper lacks adequate theoretical evidence on certain variables. This has happened due to either the housing status concept was not scientifically noticed or the researchers were unsuccessful to access them. In addition, taking into consideration resources and time to be taken, this study could not address all dynamics underlying housing status. This implies housing research is an untapped research area that interested researchers of different disciplinary backgrounds could engage in it. The hypothetical relationship between individual factors and housing situations which were tested in empirical research is represented via the following (see Figure 1).

Description of the study area
The study area is longitudinally located at 44̊ 36ʹ30' ̕ to 44̊ 40ʹ30 ̕ ̕ ' East, whereas its latitudinal location is 8̊ 54ʹ56 ̕ ' North (see Figure 2). Relatively, Sebeta town is located southwest of Addis Ababa city, southeast of Burayu town, west of Akaki Kality Sub-city, and north of Tefki town. Sebeta town is among the mounting towns surrounding Finfinne (Addis Ababa) City.

Research approach
In this study, the pragmatism paradigm is supposed to guide the mixed method. Accordingly, triangulation of quantitative and qualitative data was done. Survey questionnaires, in-depth interviews, and focus group discussions were principal data collection tools. This was intended to substantiate theoretical and empirical knowledge gaps by the process of synthesizing findings obtained from quantitative and qualitative analysis.

Source of the data: primary and secondary data sources
In this study, both primary and secondary data sources were used. Primary data were collected through questionnaires, in-depth interviews, and focus group discussions. Secondary data was collected from various archives like journal articles, books, suggestion boxes, and governmental and non-governmental organization relevant documents. Archival data sources were official reports, profiles, suggestion boxes, websites of organizations, and personal diaries. Thus, the researchers intended to extract those data from housing institutions' recollections.

Study population
The study population consists of the urban residents living in Sebeta town. Urban residents are the target population regardless of their social positions. Urban residents share a common trait or  characteristic that helped the researchers to use a stratified survey for the quantitative part, whereas factory workers, officials, experts, renters, owners, brokers, and squatters were included in the qualitative part. The researchers argue that information taken from all categories of urban residents can fully reveal the dynamics underlying housing status. This shows that studying sociodemographic and socio-economic dynamics and housing development is viable and plausible.

Sample size estimation
The study population lives in a large geographical area, which complicates the sampling procedure. The researchers decided that the target population is urban residents. Residents are scattered throughout the sample area, so grouping the target population based on their living place is crucial. This leads to the stratified sampling technique or stratified proportional sampling. Cockerham's single population proportion formula is crucial and effective when there is no list of population or sample frame. Thus, the researchers decided to apply this formula expecting a larger sample size that yields more reliable data. The largest possible proportion (p) of the target population with housing characteristics important to the study is 0.50 (50%), the allowed margin of error (d) is 5%, desired reliability (alpha) is 95% (.95), and the most confidence level value which is 1.96 taken by assuming that it gives more reliability and precision. The estimated design effect (DEFF) is 1 because the prevalent attributes of the groups are the same; there is no significant disparity in characteristics, so the estimated sample size (n) can be computed as follows: Sample size estimation equation By considering the subgroup size, the researchers selected the respondents through proportional sampling techniques. Since the size of the sample for each group was proportional to the size of the sub-population in each stratum, it was said to be stratified proportional sampling. Then, the required number of samples within each subgroup was drawn by using a systematic sampling technique, which stratified systematic sampling techniques. In using this, design researchers had to first establish the sampling interval using this formula: Where: k = is the sampling interval N = is the total number of the population n = is the total number of the sample Computation of substratum sample size The researchers incorporated 384 respondents from 10 administrative units located in Sebeta town. Proportional sampling techniques were employed to select respondents from each unit proportionally based on the subgroup population size. Thus, it was assumed that the stratified proportional sampling technique was better to reveal existing housing development in the study area.

Survey questionnaire
The questionnaire is prepared by using multiple choices and scaling types of response variables. The researchers incorporated 384 respondents from 10 administrative units located in Sebeta town. In collecting data, 10 skilled or experienced data collectors who are Sebeta town residents and fluent in local languages were recruited. These data collectors were given short training on data collection procedures. During the training, the data collectors were briefed on the designed questionnaire, data collecting techniques of the study, and possible challenges during questioning and responding. Each enumerator collected data for 2 days and each data collector was assigned to a single unit. For better data management and research work effectiveness as well as to avoid unnecessary repetition and missing, each data collector was assigned a specific unit. Accordingly, the data collectors randomly went to the homes of households living in each village to get in touch with the respondents and extracted data. In the course of questioning, the data collector approached respondents who were systematically chosen.

In-depth interview
The informants were assumed to be more knowledgeable than the other members of the community owing to their socio-economic position as per information obtained from each village administrative office. Thus, 40 more knowledgeable research informants were purposively selected (purposive sampling) as interviewees. This tool was expected to address sensitive issues such as pains, suffering, injustices, tragedies, and catastrophic conditions in the housing sector. Accordingly, private homeowners, real estate owners/delegates, renters, officers, representatives of housing cooperation, agencies, brokers, squatters, etc. were interviewed. The interview was conducted by the investigator using interview guiding questions. During each in-depth interview, the same flow of discussion was tried to follow and notes were taken down. An interview was held at a secured place where there was no interruption; the privacy of the interviewee was well maintained after obtaining verbal consent from the informants.

Focus group discussion
This tool was used to get adequate and timely data through an interactive mode of information exchange. This data-gathering instrument is believed to be very important since it gives freedom for the informants to express their ideas, opinions, views, and understanding of the housing development. FGD was intended to generate data on residents' views about the issues of housing development and their socio-demographic and socio-economic dynamics. FGD informants were in five groups (1. housing officers, 2. owners, 3. renters, 4. brokers, and 5. squatters), and each of the groups had 8 informants, which means there were a total of 40 informants. The process involved the researchers as moderators and note-takers, and the facilitator was chosen from residents who shared similar characteristics with the discussants. Homogenous FGD informants were selected based on housing-related job categories, involvement in any housing-related affairs, and housing status categories of people whose residence was confined to Sebeta town, and the researchers selected discussants judgmentally from the rest of the urban residents. The discussion was held in the appropriate place for FGD participants and was suitable for discussion. Permission was obtained from the local administrator for a place to conduct a focus group discussion (FGD). The researchers moderated the discussion in the local language using guiding questions.

Data analysis and presentation
The quantitative and qualitative data obtained through the predetermined data collection tools were analyzed using appropriate methods of analysis as explained below.

Quantitative data analysis and presentation
The quantitative approach exploits statistical analysis. The quantitative data were coded and entered into a computer for analysis. The SPSS version 20 software was used as an analysis tool. The association between variables was checked or tested by chi-square tests. This test statistic determines the correlation between socio-demographic dynamics, socio-economic dynamics, and housing status. Accordingly, frequencies, percentages, tables, graphs, chi-square test, and association analysis were employed.

Qualitative data analysis and presentation
The qualitative data were analyzed by organizing, summarizing, and interpreting the data collected qualitatively. In short, thematic analysis was used. In other words, qualitative data were analyzed descriptively in the form of narration. This part dealt with the results extracted from the in-depth interview and focus group discussion.

Gender
It seems that males and females are likely to differ in confronting structural challenges in the context of this particular study. Housing development requires confrontation of structural challenges. Males are more likely to defend against structural challenges than females. Females are mainly expected to bargain either by providing money or being sexually abused. It is found that sexual harassment and gender-based violence are common in service delivery. Females abstain from making effort to own their house by fearing gender-based challenges. Relatively, males can either confront or indulge in corrupting activities just to enter homeownership status. Culturally, females are expected to be submissive to their husbands. This expectation discourages females to be owner-occupiers because they believe that homeownership can be achieved via their partner. Even supposing out of this study scope, research participants seemed to think that females were more likely to prefer to marry homeowner males. Table 1 indicates that 43.8% of male respondents are homeowners, while only 19.3% of female respondents are homeowners. It also seems that males are more likely to squat (22.2%) than females. It was found that females (70.2%) are more likely to be renters than males (34.0%). Table 1 shows that the association between gender and housing status has a statistically significant relationship. The Chi-square test confirms that gender and housing status variable are not independent of each other. This does not mean that gender is a predictor variable of a resident's housing status. It is expected that the gender characteristics of individuals can impact their housing development and homeownership aspiration. Thus, gender differences result in variation in housing status, housing opportunities, homeownership rate, housing ladder, housing choice, housing type, housing security, housing affordability, eviction, and equality (Coulter, 2018;Foye, 2021;Preston et al., 2021).

Migration status
The geographical background is usually considered a criterion for social group formation. The social group formed by geographic background can create enabling situations for urban residents' homeownership and housing development in general. It seems that the migration history of residents contributes to their housing status. In this regard, residents look for their fellow locals, in so doing form enabling social networks that facilitate housing development. Local social associations such as "Iqub and Afosha" mostly have their base on the resident's birthplace history. This creates trust among residents that help them to form enabling the situation to enter homeownership. The migration history of residents is found to be contributing factor to the housing development of residents. Table 2 shows that 77.9% of indigenous residents are homeowners, while only 12.9% of migrants are homeowners. It seems that migrants are more likely renters than indigenous residents. Indigenous residents are found to have better comparative advantages for homeownership than migrants. Indigenous residents are assumed to have parents that have tenurial rights in town. This parental factor contributes to the homeownership of children. Indigenous residents are expected to have access to urban land and can afford collateral requirement that facilitates their homeownership. On the other hand, migrants lack local advantages that enable them to join homeownership. Most migrants found rental housing their accommodative housing option. Economically powerful migrants are more likely to enter homeownership than poor migrants. It is found that individuals who engage in constant mobility lack housing opportunities anywhere. Migrants that decided to settle permanently in a certain area are more likely to enter homeownership than migrants that move from one town to another town. Migration affects the overall housing development of individuals and the homeownership rate.
The finding shows that indigenous residents are more likely to enter homeownership than migrants. It was explained that indigenous residents have more comparative advantages that facilitate the housing ladder. The local position of residents and parental housing history intersection creates different rates of homeownership among indigenous residents and migrants. Intersectionally speaking, indigenous residents are twice migrant residents' homeownership likelihood. Migrants are doubly disadvantaged because of immigrant and parental indifference, whereas indigenous residents are doubly advantaged because of locals and parental supportiveness. Migration history of residents is found to be the factor that impacts homeownership inequalities and beneficiary of housing opportunities. It is found that migration history is not necessarily a sufficient condition to affect residents' rates of homeownership but its intersection with other enabling and constraining factors are important factors. Further, migrant mistreatment, inequalities, inequities, disadvantages, poor housing, unaffordability; segregation; insecurity; exclusion, and discrimination are major challenges for migrants (Bramley, 2016;Christophers & O'Sullivan, 2019;Debele & Negussie, 2021a;Degefa & Yanyi, 2015;Issar, 2020;Teodorescu & Molina, 2020;Wimark et al., 2020;Yu et al., 2020).

Ethnic category
Ethnicization of housing development is common in many urban centers including in the study area where ethnic background affects the homeownership opportunity of residents. Residents are either privileged or underprivileged because of their ethnic background if the housing is intended to be a source of political bargaining. Political actors know that the critical urban problem is the housing problem. Presenting housing as bargaining apparatus exploiting political support is quite common. It is believed that inequitable housing development is associated with ethnic factors. The absence of an inclusive and rational housing policy opened the door for the prevailing ethnicization of urban housing.
Residents consider ethnicity as bargaining power with the political structure. Ethnic factors determine the housing category of residents by gathering the same ethnic group to capitalize on their social capital and convert this bondage into economic capital. Findings revealed that ethnic factors alone do nothing but intersect with political affiliation. It seems that ethnic factors have two dimensions. It is a risk factor if incompatible with a political affiliation that increases housing vulnerability but a prospective factor if intersects with political affiliation. This was/is a practical housing reality in the Ethiopian context. Table 3 shows that some ethnic groups are more likely to be homeowners, while others are more likely renters and squatters. Typically, 57.1% of respondents who are of Tigre ethnic background are homeowners, while 17.9% of respondents who are of Oromo ethnic background are homeowners. On the other hand, 73.2% of respondents who are of Oromo Ethnic background are renters, while only 28.6% of respondents who are of Tigre ethnic background are renters. This implies that ethnic background and housing status are related variables. The Chi-square value and probability value indicate that the ethnic category and housing category are unlikely independent of each other. Table 3 confirms that Tigre ethnic backgrounds are more likely homeowners, while Oromo ethnic backgrounds are more likely renters. This statistical significance is supported by substantive significance. Last three decades, TPLF ruled Ethiopia by making ethnic federalism its core value of governance. The regime abused ethnic federalism by exploiting material and nonmaterial resources. Sebeta town was the regime's laboratory of exploitation. It has taken long periods when the regime encroached on Sebeta lands in different strategies of exploitation. The collaboration between investors and the regime on resource exploitation, in most cases land resources, has made Sebeta the most victimized town during the last three decades. Corruption has become a mainstream culture of the regime, and still now its existence is beyond reminiscence. Ethnicisation was the paramount political economy of the last regime. Its impact resulted in a formidable mafia that exploits public resources directly or indirectly. The ethnic background of residents may affect their housing development either positively or negatively. Table 3 statistics show that the Oromo ethnic category negatively associates with homeownership, while Tigre ethnic background positively associates with homeownership.

Income category
Financial capacity is the most important factor in urban housing development. Residents that have better financial capacity are more likely to have better housing opportunities. The limited financial opportunities make housing a difference among residents. It was found that expanding income sources are expected to elevate homeownership. Residents who expanded their income sources could become owner-occupiers. The level of income is insufficient to predict housing status change, but having multiple income sources is more likely to capacitate individuals to overcome housing market challenges. This reveals that income factors play a pivotal role exceptionally when the government showed reluctance or failed to supply affordable housing opportunities. It was found that, regardless of single income level (low, medium, and high), residents who chose to engage in numerous incomegenerating activities are found to be homeowners more than those who persisted on a single income. In this regard, it is found that including civil servants many individuals are chasing after alternative income sources to make their income level higher. When this data was gathered, residents were suffering from ever seen inflation problems that necessitate looking for additional income sources not only for homeownership but also for affording rental housing and non-housing expenses. One respondent verbatim is worthily excerpting: I have limited income but always try to save. I try my best to look for different income sources. I engage in different traditional institutions. I have 'Iqub'. I try to engage in possible businesses. I got credit from my affiliated institutions. I try my best to enhance my income level. I do not depend on my monthly salary alone. My monthly salary is not enough for personal consumption, transportation, educational fee for children, rental fees, and other unplanned expenses. I got the confidence to have my own home after being determined to look for other income sources rather than monthly income. (R1, man, 35yo) Table 4 shows that 62.6% of high-income earners are homeowners, while only 5.6% of low-income earners are homeowners. Respondents that have middle and high income are less likely renters, whereas low earners (89.9%) regardless of their income source are more likely to be renters. Participants seemed to think that the level of their income alone does not matter in the housing category rather the number of their income sources is found to affect housing status. Changing the level of single income does not affect housing status significantly rather dimensioning income sources are found to be contributing factor.

Educational status
Empirical findings indicated that education has not resulted in meaningful housing status. Residents that have lower educational status are homeowners, while higher educational statuses live in rental housing. This seems that the educational status of residents is less likely to predict the homeownership of residents. This leads to looking for another factor that devalues the educational factor. Traditionally, people believe that educated people live better life including housing conditions. However, the existing practical scenario contradicts the expectation of higher educational status with better housing conditions. Even some respondents regretted investing their time and money in education since educational status does not bring meaningful changes in the life of individuals. Table 5 shows that educational level and housing status are not related. For example, 46.2% of respondents who are degree holders are homeowners and 36.4% of respondents who hold master's degrees and above are homeowners. It is expected that as educational level increases housing status will increase or decrease to have a specific relational pattern. However, in Table 5, it is found that the variables are not paternally related. This implies that housing status is not differed by educational level, which is affirmed by a smaller chi-square and larger p-value. The two variables are independent of each other.
The findings of this study show that education level and housing status are unrelated variables. It is expected that higher education levels associate with higher housing status. The practical scenario shows that the housing status of residents is not differed by their educational level. In contrast to this finding, Fiori et al. (2020) stated that lower educational attainment is less likely to predict the homeownership rate. This implies that the lower the educational level of the resident the lower the purchasing power of the house and the less likely they to be a homeowner. Paradoxically, lower educational status residents are found to be higher homeownership rates. The assumption that educational advancement is associated with housing wealth is disproved by this study. When this data was collected, existing housing scenario seems that be better off for lower educational status and worse off for higher educational status. The important contributing factor to the homeownership rate is income rather than education. It is believed that higher educational status individuals are high incomers. It is found that high income alone is not a sufficient condition for homeownership rather extra income sources matter a lot. The researcher asked higher educational status individuals and many of them reported that they are uncertain to have their own home.
Another important competing factor that contributes to homeownership is broking activities. This emerging dynamic sector is mostly occupied by either uneducated or poorly educated mainly in lower grades. Highly educated individuals are unlikely to engage in broking activities. It was found that brokerage results in multiple advantages. Brokers earn money now and then but employed persons earn monthly. Brokers not only accumulate money but knowledge of the housing market. Brokers have more comparative advantages than non-brokers. This implies that education is not necessarily a predictor of homeownership. In line with this argument, Lundholm (2021) asserted that education level alone does not determine homeownership but rather another socioeconomic status. Researchers validated that educational factors are not a major determinant of housing development and homeownership rate. Overall socio-economic characteristics affect residents' homeownership and housing development.

Occupational category
Research participants looked as if their working place and occupation category affect their housing category. They reported that residents working in hometowns make effort to have their own homes. However, residents that work somewhere and live in another place may choose viable housing options. Occupation determines the housing category of residents by enabling housing status mobility or constraining housing mobility in some way. Residents that have housing funding opportunities from affiliated working organizations may be more likely to achieve homeownership. This creates homeownership rate differences between residents. In urban centers, residents working in banks and microfinance are more likely to get a mortgage to enter homeownership. On the other hand, residents that have no loan opportunity from the working institution cannot have the opportunity to have their own home. Urban residents that have eligible collateral may have a loan by presenting an occupational profile. Table 6 shows that 44.1% of government workers are homeowners and 41.0% of self-employed are homeowners, whereas non-government workers (19.6%) are homeowners. On contrary, nongovernment workers are more likely renters. The researcher triangulated this issue through FGD and in-depth interviews. FGD discussants were asked among occupational categories, which are the more likely homeowner and less likely homeowner. They stated that non-government workers are more likely renters. First, they are mobile workers, so they may build a home somewhere but temporarily live. Second, the majority of non-government workers in Sebeta are factory workers. Factory workers are low-income earners and have an insecure job, which is ineligible for mortgage access. They rationally choose rental housing that enables them to survive in town. Because of the above reasons and others, non-Government workers are more likely renters. The stated percentage may represent those non-government workers who constantly live there, have better income, and secure job eligible for mortgage opportunities like financial institution workers. Occupation is the source of income that determines homeownership and mobility on the housing ladder. It is expected that the better occupational category predicts a better housing category. Given that unemployed residents are the most disadvantaged residents who are unable to meet housing needs and paid can more likely change housing status (Bramley, 2016;Manomano et al., 2016). Accessing mortgage, homeownership aspiration, housing preferences, housing status, housing ladder mobility, and homeownership opportunities are determined by occupational status and security of job (Bayrakdar et al., 2019;Coulter, 2018). Security of job, level of wage, occupational category, and income level of workers affect the housing development of urban residents Bangura et al., 2021;Debele & Negussie, 2021a, 2021b, 2021cFiori et al., 2020;Harrison et al., 2020;Wimark et al., 2020). Researchers interviewed factory workers who are low-paid and unsecured workers that are less likely to access mortgages and unable to afford homeownership. This seems as the occupational category becomes insecure the affordability of homeownership declines. Low-income earners are confronting personal expenses and housing expenses. Residents who have better socioeconomic status are more likely to overcome household expenses and housing expenses, whereas low-income earners suffer from rental stress. The findings show that factory workers are more likely renters, while better-income earners are more likely to be homeowners. Inflation of consumer goods and associated housing unaffordability challenges occupational status and related income. Shelter need and homeownership aspiration of residents are more likely affected by changes in the occupational category and related incomes.

Conclusion
This article focuses on the description of socio-demographic and socio-economic factors that are associated with housing development. Findings indicate that socio-demographic and socio-economic factors are paramount forces that influence housing development in general and homeownership in particular in the study area. Even though all factors have not been exhaustively investigated, the researchers claimed that so far sufficient research findings are appreciated to elaborate housing situation of urban residents. Furthermore, it was found that migration history and income category factors are major significant predictor variables to predict response variable (housing status) and showed persistent association throughout two-step association test (bivariate and multivariate analysis). In this particular modeling, gender, ethnic factors, and occupational status are not strongly significant predictors of housing status. They are significant predictors until the above major predictors are ruled out. This implies that the causal effect and association between those variables and dependent variables might be due to a spurious effect. Those major significant predictors are assumed to have actual causal effects, whereas others are assumed to have a spurious effect. For that reason, the above variables are more likely to affect housing status. Hence, the study concludes those migration history and income categories are major significant predictors of housing status. The more variation in predictor variables more likely results in variation in housing status. Typically, as income dynamics show to change, it leads to changes in the housing status. For instance, indigenous residents and housing status are positively and significantly associated. Indigenous residents are more likely to achieve higher housing status than migrants. It was seen that as the size of income sources and income level increases, ceteris paribus, the likelihood of homeownership increases. The findings of this research imply that upcoming housing policy and housing development projects need to take into consideration these factors to bring meaningful impact on the housing situation of residents. In a nutshell, income dynamics, migration status, occupational factors, personal housing history, ethnic background, gender category, and place of birth are the major determining factors of housing status. Findings implied that tailoring housing policy by considering sociodemographic and socio-economic dynamics of urban residents is vital to achieving equitable and sustainable housing development.

Ethical statement
Consent, authorization, and respects for participants were the main ethical considerations in this research. Ethically speaking, the researchers adhered to the fundamental ethical principles that are inviolable, which otherwise distort the precision, accuracy, and pertinence of the study. First, to ensure consent, the participants were explicitly informed about the objective of the study, the nature of the study was disclosed to them, and no attempt was made to coerce the participants. It was free from any form of trick or fake acts. The researchers had not attempted to put the self-respect and dignity of participants at risk. Instead, the researchers did their best to enhance the self-determination and freedom of participants from any mental or physical form of stress. Moreover, the researchers promoted the privacy of participants and made sure equity of distribution of every research process benefits all research participants. The researchers also followed the logical procedures in every stage of data collection. Second, for authorization, the researchers obtained permission from local authorities, and then informed written consent was obtained from all study participants. By this, the confidentiality of information from participants was maintained throughout the study. Generally speaking, due attention was given to the research participants concerning respect and moral issues.