Is Our Home Ready for Working from Home?

An Analysis of Factors Associated with Residential Satisfaction of White-Collar Employees in Bangkok during the Covid-19 Pandemic

Abstract

The Novel Coronavirus 2019 (COVID-19) pandemic has caused societal changes, including in terms of work from home (WFH). Since January 2020 in Thailand, several companies have shifted from office workplaces to full-scale or temporary WFH for white-collar employees who achieve professional tasks remotely. Insofar as worker time spent indoors has increased, what is the overall residential satisfaction under WFH during the COVID-19 pandemic? The objective of this research was to investigate overall residential satisfaction predictors for housing, neighborhood, and relevant variables of WFH characteristics pertinent to the COVID-19 pandemic. Data was collected by online and onsite questionnaires from 293 respondents, all white-collar employees who inhabit landed houses. This study is analyzed with multiple linear regression and mean comparison with independent-samples t-test, and one-way analysis of variance (ANOVA). Results predicting overall residential satisfaction comprised eight vital predictors: 1) neighborhood appearance; 2) house size; 3) ventilation; 4) privacy; 5) comfort without air conditioning; 6) distance from healthcare centers; 7) domestic design, and 8) neighborhood cleanliness, at a significance level of p<0.05. Higher overall residential satisfaction levels were observed to be significant in terms of inhabiting a housing estate & single-detached house, number of WFH days, private workroom, house size, and newness of house.

Introduction

The virus SAR_CoV-2 that caused COVID-19 disease was first recorded in December of 2019 in Wuhan, China, and later World Health Organization (WHO) officially declared COVID-19 as a Public Health Emergency of International Concern (PHEIC) on 30 January 2020 spreading to numerous countries rapidly causes a large number of infections and mortality (WHO, 2020). Even though a lot of world population have been vaccinated, the evolution of SARS-CoV-2 has changed over time in which it is identified with the watching lists from WHO on Variant of Concern (VOCs) such as Alpha (Announced 12/20), Beta (Announced 12/20), Gamma (Announced 01/21), Delta (Announced 05/21), and Omicron (Announced 11/21). The variants have altered the virus’ properties, e.g., the disease transmission, the disease severity, the performance of vaccines, and so on so COVID-19 may stay with us for many years (WHO, 2021). The situation of COVID-19 in Thailand is also severe; the first wave (03-04/2020) and the second wave (12/2020-02/2021) were controllable, still, the third & fourth wave occurred seamlessly after April 2021, pushing the number of accumulated COVID-19 cases over 1 million in August 2021 (Thai PBS, 2021). During the pandemic, the Thai government implemented strict lockdown policies and COVID-19 procedures to contain the outbreak, e.g., lockdown & curfew, mask-wearing, social distancing, and so on.

Regarding the confinement, several companies have shifted from office base into working from home (WFH); temporary/full-scale for their employee safety and unobstructed workflow. Thus, this new-normal phenomenon directly affects white collars who do office work because they are capable of allocating their tasks remotely. National Statistical Office of Thailand (Q2/2021) reported that there are over 5.3 million of total workforces in Bangkok, and approximately 1.32 million people are in criteria of white collars (National Statistical Office of Thailand, 2021). Fortunately, telecommunication nowadays is more beneficial and effective to support WFH during the COVID-19 lockdowns and confinements mentioned as a new activity transformed after the pandemic. White Collars spend their residential time longer than ever, so private, and public spaces are less necessary with WFH. Moreover, white-collar residents experience new desires and functions of dwelling, for example, balcony size is needed to be considered for time spending outside during lockdown (Valizadeh and Iranmanesh, 2021), or landed house is more suitable under the COVID-19 era (Cheshmehzangi, 2021). Lastly, white collars are the primary target group of the housing market according to the bank’s loan conditions which are much easier than other occupations.

For more understanding of the phenomena of overall residential satisfaction during the COVID-19 pandemic of landed-residential white collar in Bangkok, Thailand, a respondents’ interview of ten white collars in landed residence were used to capture attitude, pain point and preference of their working from home experiences and the residential satisfaction. The socio-demographic of the interviewees are age between 27-40 years old; 7 out of 10 live in single-detached house; one of them lives in semi-detached house; and 2 persons live in townhouse. Most of them work from home 2-3 days a week as hybrid working. They think that even if the covid-19 pandemic is gone, working from home’s culture will continue in a hybrid form which they prefer it more than full-time working from home because of lacking in enthusiasm from indoor environment and struggling with colleague communication. There are lot of pain point issues in common between the interviewees, e.g.

• The most personal space of the house is bedroom, yet it is not suitable for working and cannot fully relax in free time. Thus, they prefer private working room in the house.

• Feeling bored of indoor environment and needs of alternative zoning and more pace to change the vibe and function.

• Needs of more greenery to relax after working.

• Noise and privacy come from both inside and outside the house.

Although, their residences are not the most perfect place to work, they still enjoy working from home and do not have any plan for moving out to a new residence because of several beneficial attachments; for example, spending more time with their family, time saving from daily commuting and more time to do their leisure activities.

According to the report of (Real estate information center, 2021), Bangkok’s real estate market has a total conveyance value of approximately 400 billion baht or 12 billion USD. We can see that in 2020 the proportion of landed residences value was 36.7% (147 billion baht or 4.49 billion USD) of the total market value while condominiums dominate 56.5% (227 billion baht or 6.94 billion USD). However, during COVID-19 (2019 & 2020), the market sale value of new residences in BMR, defined as Bangkok and the five adjacent provinces, was noticeable that landed residence sale value increased from 154 billion baht or 4.71 USD billion (32,005 units) to 209 billion baht or 6.39 USD billion (38,791 units) in which the growth rate is 35.7% (YOY); condominiums’ new unit sale value declined from 179 billion baht or 5.47 billion USD (45,738 units) to 118 billion baht or 3.61 billion USD (29,210 units) which fell 34.1% (YOY). Furthermore, a new opening unit ratio of the landed house (57.4%) was higher than the high-rise residence (42.6%) in 2020 (Krungsri Research, 2020). According to the in-depth interview with some white collars in Bangkok, they mentioned that living in landed residences felt more satisfied than condominiums because of more space, more green area, and so on.

Residential satisfaction as an inhabitants’ perceptions of their current house is employed to capture what determinants can influence it in the landed dwellings context of white collars under COVID-19 win which WFH is necessary to understand the preferences of real estate in Bangkok and nearby, shifting their interest from purchasing condominiums to landed residences during the pandemic.

Therefore, this aim of this research is to investigate and refresh those determinants including housing, neighborhood, and working from home characteristics which predict overall residential satisfaction as well as find the relationship between socio-demographic among the landed-dwelling white collars in Bangkok, Thailand under the COVID-19 crisis. However, many research studies on WFH satisfaction or performance refer to the housing environment but only few are linked with overall residential satisfaction in which WFH environment is also part of key activities in dwelling nowadays.

Literature Reviews

The review of literature’s main topics is to study residential satisfaction (dependent variable), consisting of definition, relevant theories, empirical study with of independent variables which are socio-demographic, housing, neighborhood, and working from home characteristics.

Residential satisfaction theory

Residential satisfaction is a crucial topic for housing study for several decades. Numerous research and theories have been studied and developed to extend the understanding of residential satisfaction (Biswas, Sultana et al., 2021). The concept of residential satisfaction is a cognitive and affective state which are the gap between households’ actual and desired/aspired housing and neighborhood environment. It represents congruence and incongruence of the residents in their dwelling by expressing satisfaction and dissatisfaction (Baek and Joo, 2021; Galster, G., 1987; Galster, George C. and Hesser, 1981). When residents evaluate their residential environment as an “objective attribute”, they go through the process of assessing with cognitive, so it becomes a “subjective attribute” depending on their needs, expectations, and achievements (Amérigo and Aragonés, 1997; Jansen, 2013). Amerigo and Aragones’s model is an interdisciplinary, complex, and dynamic topic; many fields of study have been involved depending on context (Emami and Sadeghlou, 2021; Lu, 1999). Moreover, residential satisfaction can be a key predictor for intention to move and residential mobility (Speare, 1974).

Four fundamental of residential satisfaction theories are wildly mentioned. The first theory is “Theory of Home Adjustment” (Biswas, Sultana et al., 2021), studied between family members and the physical environment which needs to reach the requirements as well as the individual’s need for the community outside the home founding that depends on a complicated framework of socio-psychological interaction. Secondly, “Housing Needs Theory” (Rossi, 1955) represents residents’ assessment between the current and desired housing needs that causes stress from insufficiency which leads to dissatisfaction. Thus, residential mobility arises, and residents seek to move to a place where they can both satisfy with their current and desired house. The third theory is “Housing Deficit Theory”(Morris and Winter, 1975), declaring that residents judge their housing conditions based on cultural and family norms. The fourth theory is “Psychological Construct Theory” (Galster, George C, 1985). It depicts that people cognitively create a reference condition for each part of their dwelling environments based on the reference condition relies upon their self-assessed needs and aspirations.

Amérigo and Aragonés (1997) measure residential satisfaction under the concept of perceiving or evaluating the environment called “Dynamic interaction” (Cognitive, Affective, and Behavioral), describing objective, and subjective attributes of the residential environment, as well as personal characteristics. “Objective Attribute” is a physical characteristic of the environment that is assessed individually through the resident’s cognitive state and becomes an affective state called “Subjective Attribute”, comparing actual and ideal conditions of the house expressing as residential satisfaction. Not only the housing environment but also the neighborhood and social environment are evaluated. Besides, a sense of dissatisfaction could lead to moving or modifying their home which directly alters physical objective attributes of the house then affect residential satisfaction again. Lastly, this research adjusts the concept of objective and subjective attributes of the residential environment with additional working from home characteristics due to the context of the COVID-19 pandemic.

Socio-demographic characteristics

Socio-demographics characteristics (11 items) illustrate respondents’ personality, family background, and housing status such as gender, age, marital status, household size, number of children, education, occupancy, monthly income for individual and household, tenure, and length of stay. Several empirical research found that socio-demographics characteristics are associated with residential satisfaction (Amérigo and Aragonés, 1997). Regarding to gender, females are more likely to gain higher satisfaction than male counterparts (Huang, Du et al., 2015; Ibem, Eziyi O and Amole, 2013; Lu, 1999; Pongprasert, 2020; Zhang, Zhang et al., 2018). Age of occupants has been reported to be directly related to satisfaction implying that older people are to reach higher residential satisfaction than younger people (Huang, Du et al., 2015; Lu, 1999). However, some researchers argue that younger people are more satisfied (Ibem, Eziyi O and Amole, 2013; Mohit, Ibrahim et al., 2010). Marital status among the young generation living in apartments in Serbia is seen to predict that single people have higher residential satisfaction. Ibem, Eziyi Offia and Aduwo (2013) , studying public housing in Nigeria report that household size can forecast residential satisfaction, but Mohit, Ibrahim et al. (2010) who study public low-cost housing in Malaysia reveal a negative correlation to residential satisfaction. Moreover, Ibem, Eziyi O and Amole (2013) suggest that number of members should match with the number of rooms. The same study also found the influence of education to be significantly positive (Ibem, Eziyi Offia and Aduwo, 2013) still Nguyen, Tran et al. (2018) studying apartment in Vietnam discovered a negative relationship. Employment status is reported being one of the predictors (Cho, 2020) Satisfaction among homeowners and parents with children living at home is higher (Boschman, 2018; Ibem, Eziyi Offia and Aduwo, 2013; Li, Li et al., 2019; Milić and Zhou, 2018; Nguyen, Tran et al., 2018; Parkes, Kearns et al., 2002) found the length of stay has a negative impact on residential satisfaction and Huang, Du et al. (2015) found no significant. Thus, the empirical literature review can be seen that socio-demographics characteristics affect residential satisfaction in various results depending on the context. Moreover, this research includes information from the interview which is working from home attribute to represent more dimension of the respondents such as WFH frequency, WFF places, number of people WFH, and so on.

Housing characteristics

Housing Characteristics are relevant variables with residential environment inside the dwelling. It is categorized into objective and subjective attributes. The objective attributes are the physical environment of the house without any evaluation by the residents while housing subjective attributes are vice versa.

Objective attributes

The objective attributes are the physical architectural feature of the house without any evaluation by the residents based on the literature review, and some of them come from the interview. It consists of 6 items such as a number of rooms, housing estate condition, private garden, house size, house type, age of the house (Azimi and Esmaeilzadeh, 2017; Cho, 2020; Ibem, Eziyi Offia and Aduwo, 2013; Milić and Zhou, 2018; Mohit, Ibrahim et al., 2010; Mohit and Mahfoud, 2015). Among residential types such as apartments or landed residents, housing size seems to be a vital base indicator for residential satisfaction in many studies (Ibem, Eziyi O and Amole, 2013; Milić and Zhou, 2018; Zhang, Zhang et al., 2018).

Subjective attributes

The subjective attribute is a subjective perception of the residential environment, such as physical appearance and spatial function of occupants’ current housing unit measuring as a degree’s level of satisfaction. It includes 11 items, for example layout of the house, house size, privacy, thermal comfort without A/C, number & size of functional area e.g., bedroom, bathroom, kitchen, and so on. The number/size of functional space are found significantly in numerous studies (Amole, 2009; Cho, 2020; Ibem, Eziyi Offia and Aduwo, 2013; Ibem, Eziyi O and Amole, 2013; Mohit and Azim, 2012; Mohit, Ibrahim et al., 2010; Mohit and Mahfoud, 2015). The layout of house is shown as a key predictor in dwelling satisfaction in a study of the inner urban of Brisbane, Australia (Buys and Miller, 2012). However, satisfaction in the master plan of Nguyen, Tran et al. (2018) was not significant in the apartment’s case study. Moreover, Cho (2020) states ‘personal space’ is necessary for small apartment units, interpreting high privacy and boundary between family members. Housing appearance is related to architectural design which is significant in the apartment of Hanoi cases and rental housing (Huang, Du et al., 2015; Li, Li et al., 2019). Private Garden / green area is used as an item in several research, but few are found significantly but in city and neighborhood’s study represent a relationship with satisfaction (Hadavi and Kaplan, 2016; Kley and Dovbishchuk, 2021; Lotfi, Despres et al., 2019; Olfindo, 2021).

Working from home characteristics

Working from home characteristics are relevant to the indoor environment of the house which link with working from home issues including of 7 items: natural light, ventilation, visual comfort, thermal, size of the workspace, privacy, and noise (Cheshmehzangi, 2021; Cuerdo-Vilches, Navas-Martín et al., 2021; Kwon, Remøy et al., 2019; Tleuken, Turkyilmaz et al., 2022; Valizadeh and Iranmanesh, 2021; Zarrabi, Yazdanfar et al., 2021) studied the residential built environment attributes affecting to remote work satisfaction of the 2,276 faculty staffs from different fields from several countries (Kazakhstan, Romania, South Korea, UK, Turkey, Slovenia, Poland and New Zealand) and they found that ‘Health and Safety’ (safety from virus propagation, the mental and physical health), ‘Working comfort’ (light, noise, humidity, temperature, indoor air), ‘Facilities’ (separate from living and ergonomic working space, green) and ‘ICT’ (equipment for work and internet) affect working from remote work satisfaction. A study of Cuerdo-Vilches, Navas-Martín et al. (2021) which measured respondents’ adequate perception of working from home space in Spain during lockdown found that a quarter of sample is insufficient of working space. On the other hand, respondents with higher social status significantly have more adequate indoor environmental quality for WFH. A decent working environment in many studies comprises daylight, room size, room temperature, window quality, noise insulator, artificial lighting, and green space. Furthermore, house typologies which are flat and landed house with garden present more satisfaction than others. Besides, Single-detached house types (with garden and room) are the most satisfying house type (Cheshmehzangi, 2021). In COVID-19 era, indoor space becomes more essential and highlights a new standard effect on physical and mental health because of lockdowns.

Neighborhood characteristics

Neighborhood characteristics are variables of surrounding environment of the residence which can be grouped into location of residence, and neighborhood environment.

Location of residence

Domains in location of residence (7 items) are distance to destinations of choices related to daily routine, entertainment, and neighborhoods facilities (Buys and Miller, 2012; Li, Li et al., 2019; Mohit, Ibrahim et al., 2010). Location of residence is a key predictor to residential satisfaction, i.e., prime location implies of convenience (Nguyen, Tran et al., 2018). Moreover, most of the residential satisfaction researches always included this indicator and get significant results; for instant distance to workplace (Mohit and Nazyddah, 2011), distance to shopping center (Mohit, Ibrahim et al., 2010), distance to school / university(Mohit and Azim, 2012), distance to hospital (Azimi and Esmaeilzadeh, 2017), distance to store and market (Mohit and Mahfoud, 2015), distance to public transportation (Azimi and Esmaeilzadeh, 2017; Olfindo, 2021), distance to common area / recreational area (Mohit and Mahfoud, 2015).

Neighborhood environment

Domains in neighborhood environment (6 items) is a surrounding environment of house unit consists of green area / open space, neighbor relationship, cleanliness, maintenance, appearance, population density, neighborhood noise, and neighborhood security. Numerous studies asserted that there is a relationship with residential satisfaction. People would prefer more gardens and open space in current housing reflecting from lack of neighborhood green areas(Buys and Miller, 2012; Ibem, Eziyi O and Amole, 2013; Olfindo, 2021). Moreover, the neighbor relationship is a key role to forecast residential satisfaction (Azimi and Esmaeilzadeh, 2017; Cho, 2020; Mohit and Mahfoud, 2015). Cleanliness or environmental sanitation such as garbage collection show positive effect on residential satisfaction (Huang, Du et al., 2015; Mohit and Azim, 2012; Mohit, Ibrahim et al., 2010; Mohit and Mahfoud, 2015) Appearance and maintenance of facilities in the neighborhood are significant in a housing estate (Ibem, Eziyi O and Amole, 2013; Parkes, Kearns et al., 2002). Population density dwellings represent as a key growth management strategy internationally and in Australia(Buys and Miller, 2012).

In conclusion, researchers use the research framework as shown in Figure 1 to investigate the factors influencing the residential satisfaction of Bangkokian white-collar employees who are working at home during the COVID-19. Two statistical data analysis methods are used in this paper as presented in Model 1&2. In Model 1, the independent variable is socio-demographic of respondents which included objective and WFH attributes, analyzing with independent samples T-test and One-way ANOVA. In Model 2, three independent variable groups consisted of 31 variables are “Housing Characteristics”, “WFH Characteristics” and “Neighborhood Characteristics”, analyzing with multiple linear regression.

Figure 1. Research framework

Research Methodology

This study is the quantitative research gathering data from a Google form online questionnaire and paper-and-pencil survey with random sampling technique. The questionnaires were distributed for 3 months from November 2020 until January 2021 in Bangkok’s Central Business District (CBD), asking random people, who are office employees, to scan a generated QR code. The focus population is white collars who live in landed residence and have working from home experiences, so the screening questions were used to exclude the non-white-collar workers who do not live in landed dwelling, nor experience in working from home. The sample size of the research is 293 respondents. 205 respondents are done by online questionnaire (70%) and 88 respondents are done by on-site survey (30%). The total number of respondents exceed the minimum sample requirement of 285 applying with Cochran’s formular (Cochran, 1963). On-site questionnaire survey method is used for the faster data collection. Time period of on-site survey is between 8 a.m. to 5 p.m. All on-site and online respondents are willing to do the questionnaire. Moreover, 10 representatives of landed-resident white collars in Bangkok, Thailand, were initially interviewed to collect more information relevant to the residential satisfaction of their current house. For analysis & verification, the program Statistics Package for Social Sciences (SPSS) version 26 was employed to check reliability by Cronbach’s Alpha that is 0.949, exceed 0.7 is good (Hulin, Netemeyer et al., 2001). After launching questionnaire, factor analysis was employed to allocate new factor group and check reliability.

This online questionnaire was developed on the theories and literature reviews to investigate factors that influence overall residential satisfaction, computing with mean score of overall satisfaction in housing, neighborhood, and WFH characteristics. It is filled with a five-point Likert scale to evaluate respondents’ satisfaction ranking: 5 = Very Satisfied, 4 =Satisfied, 3 = Neutral, 2 = Dissatisfied, and 1 = Very dissatisfied. Data collection based on Model 1, Independent sample T-test and One-way ANOVA examine the relationship between socio-demographic, objective attribute, working from home attributes of respondent, and overall residential satisfaction among landed-dwelling white collars in Bangkok, Thailand, with statistically significant at 0.05 probability level. As well as Model 2, Multiple Linear Regression Analysis using stepwise mode, represents significant predictors of overall residential satisfaction at 0.05 probability level with the equation below (Uyanık and Güler, 2013). The formula is:

Y=β_0+β_1 x_1+β_2 x_2+⋅⋅⋅++β_i x_i+ε (1)

where Y is dependent variable; I is number of independent variables; B is y-intercept (constant); β_1,…,β_i are slope coefficients for each independent variable; x_1,…,x_i are independent variables sequence 1 to i; ε is the model error (residual).

Principle Component Analysis (PCA) with varimax rotation is chosen to extract and arrange groups of similar factors based on a correlation matrix. Not only factor loading must be greater than 0.5 but eigen values criterion must be more than 1.00 (Amole, 2009; Kshetrimayum, Bardhan et al., 2020). Moreover, 31 independent variables are allocated into 5 factors which explain 67.308% of the variance. Seeing Table 1, the first factor is “Housing Design Function (HDF)” (variance = 19.409%; Cronbach’s α = 0.929) describing housing design features and other nine items (e.g., number of bedrooms, house size, layout of house). The second factor, “Neighborhood Environment (NE)” (variance = 15.908%; Cronbach’s α = 0.908), represents neighborhood environment of residence and consists of seven items (e.g., neighborhood appearance, population density, neighbor relationship). The third factor is “Location of Residence (LOR)” with variance equal to 12.774% and Cronbach’s α equal to 0.876. The fourth factor, “Working from Home Environment (WFHE)” (variance = 11.522%; Cronbach’s α = 0.875) describes the indoor environment relevant to working from home (e.g., natural light for WFH, working space of WFH, ventilation for WFH). The fifth factor, “Privacy of House & WFH (PH&WFH)” (variance = 7.695%; Cronbach’s α = 0.839) contains three items explaining privacy for living and working from home in the dwelling (e.g., privacy of a house, noise WFH, privacy WFH). Moreover, the dependent variable is overall satisfaction in housing, working from home, and neighborhood environment combined into overall residential satisfaction (variance = 75.091%; Cronbach’s α = 0.833). All the new components have all Cronbach’s α greater than the minimum requirement (> 0.7) (Hulin, Netemeyer et al., 2001).

Table 1. Factor Analysis Components

Factors	Variables	Factor Loading	The extent to which residents are satisfied with…
Factor 1: Housing Design Function (9 items – Cronbach’s alpha = .929)	Number of Bedrooms (HC1)	.774	the number of bedrooms in their housing.
	Size of Bedroom (HC2)	.759	size of bedrooms in their housing.
	Size of Storage (HC8)	.753	size of the storage in their housing.
	Number of Bathrooms (HC4)	.741	the number of bathrooms in their housing.
	House Size (HC6)	.741	house size.
	Size of Living Room (HC3)	.736	size of living room
	Size of Kitchen (HC10)	.683	size of kitchen of their housing.
	Layout of House (HC7)	.665	the layout of house.
	Housing Appearance (HC9)	.570	their house appearance.
Factor 2: Neighborhood Environment (7 items – Cronbach’s alpha = .908)	Neighborhood Appearance (NC13)	.856	the appearance of their neighborhood.
	Neighborhood Maintenance (NC9)	.843	maintenance in their neighborhood.
	Neighborhood Cleanliness (NC6)	.828	cleanliness in their neighborhood.
	Green Area / Open Space (NC8)	.735	a green area / open space in their neighborhood.
	Population Density (NC5)	.720	population density in their neighborhood.
	Distance to Common Area/ Recreational Area (NC11)	.630	distance to common area / recreational area.
	Neighbor Relationship (NC7)	.522	neighbor relationship.
Factor 3: Location of residence (6 items – Cronbach’s alpha = .876)	Distance to Shopping Center (NC1)	.823	distance to shopping center.
	Distance to Hospital (NC3)	.790	distance to a hospital.
	Distance to School / University (NC4)	.769	distance to school / university.
	Distance to Workplace (NC12)	.698	distance to workplace.
	Distance to Public Transportation (NC10)	.692	distance to public transportation.
	Distance to Store and Market (NC2)	.667	distance to store and market.
Factor 4: WFH Environment (6 items – Cronbach’s alpha = .875)	Thermal Comfort without A/C for WFH (WFH7)	.827	the temperature comfort without opening the air conditioners in WFH environment.
	Ventilation for WFH (WFH2)	.738	airflow in the WFH environment.
	Visual Comfort for WFH (WFH5)	.693	the view from the window in WFH. environment
	Natural Light for WFH (WFH3)	.683	natural light in the WFH environment.
	Working Space of WFH (WFH4)	.560	size of WFH environment.
	Thermal Comfort without A/C of house (HC11)	.540	thermal comfort without opening the air conditioners in the house.
Factor 5: Privacy of Housing & WFH (3 items – Cronbach’s alpha =.839)	Privacy WFH (WFH1)	.792	privacy for WFH environment.
	Noise WFH (WFH6)	.709	noise level in WFH environment.
	Privacy of house (HC5)	.596	privacy in their housing.
Factor Y: Overall Residential (3 items – Cronbach’s alpha =.833)	Overall Satisfaction of Housing Characteristics (OSAT_HC)	.904	the overall of their housing unit.
	Overall Satisfaction of Working from Home Characteristics (OSAT_WFH)	.857	the overall of their working from home characteristics.
	Overall Satisfaction of Neighborhood Characteristics (OSAT_NB)	.838	the overall of their neighborhood.

Estimation Results

Summary of respondents characteristics

Socio-demographic characteristic

Table 2 shows that the respondents are diverse in socio-demographic. It represents that female is dominant (67.2%). Most of the respondents’ age is between 23-30 years old (64.1%), and 31-40 years old (17.1%). The majority of marital status is single (84.0%). Regarding the number of household members, over 80% of the respondents have 4-5 persons (53.6%) and 2-3 persons (31.4%) in their family. Furthermore, the number of children in their family is represented as none (59.0%) and only 1 person (27.0%). The education of respondents is mostly bachelor’s degree (69.6%), and master’s degree & above (26.3%). From the survey, most of the sample have occupations relevant to the private sector which are a private employee (80.9%), and entrepreneur (9.2%), followed by the government sector which are government officers (6.5%), and state enterprise employees (3.4%). The monthly income of respondents is mostly between 25,001-50,000 baht (765 - 1531 USD, 45.7%), less than 25,000 baht (< 765 USD, 32.8%), 50,001-75,000 baht (1532 – 2297 USD, 12.0%) respectively. Besides, the household monthly income is above 80,001 baht (49.8%) and between 55,001-80,000 baht (21.5%). The proportion of property ownership, co-living without financial responsibility, and tenant are 72.0%, 18.1%, and 9.9%, respectively. The survey represents respondents who have a length of stay less than or equal to 9 years (40.3%), followed by 10-19 years (28.3%), and 20-29 years (24.6%).

Table 2. Socio-demographic variables

Socio-demographic variable	Frequency	Percentage (%)
No. of respondents	293	100
Gender
Male Female	96 197	32.8 67.2
Age
22 years old or less 23 -30 years old 31-40 years old 41-50 years old 51-60 years old	31 188 50 20 4	10.6 64.1 17.1 6.8 1.4
Marital Status
Single Married Divorced	246 42 5	84.0 14.3 1.7
Household size
1 person 2-3 persons 4-5 persons >6 persons	12 92 157 32	4.1 31.4 53.6 10.9
No. children and students
none 1 person 2 persons > 3 persons	173 79 31 10	59.0 27.0 10.6 3.4
Education of respondents
High school or less Bachelor's degree >= Master's degree	12 204 77	4.1 69.6 26.3
Occupation of respondents
government officer state enterprise employee private employee entrepreneur	19 10 237 27	6.5 3.4 80.9 9.2
Monthly Income of respondents
25,000 baht or less (<765 USD) 25,001-50,000 baht (765 – 1,531 USD) 50,001-75,000 baht (1,532 – 2,297 USD) 75,001-100,000 baht (2,298 – 3,062 USD) 100,001-125,000 baht (3,063 – 3,827 USD) above 125,001 baht (>3,827 USD)	96 134 35 19 3 6	32.8 45.7 12.0 6.5 1.0 2.0
Monthly income of Household
35,000 baht or less (< 1,072 USD) 35,001-55,000 baht (1,073 – 1,684 USD) 55,001-80,000 baht (1,685 – 2,450 USD) above 80,001 baht (> 2,450 USD)	37 47 63 146	12.6 16.1 21.5 49.8
Tenure
Owner Tenant Co-living without financial responsibility	211 29 53	72.0 9.9 18.1
Length of residence
9 years or less 10-19 years 20-29 years 30 years	118 83 72 20	40.3 28.3 24.6 6.8

Objective and working from home attributes

The survey also acquires situation of respondents relevant to residential and working from home attributes to get more perspective of the white collars in landed house. Table 3 presents that over 70% of respondents living in the house with 3 bedrooms (41.0%) and 4 bedrooms and above (32.1%). Most of the white collars live outside a housing estate (55.3%) and have a private garden (65.2%). There are respondents who lived in housing sizes of 101-200 sq.m. (37.2%) and 100 sq.m. or less (29.4%). The data depicts that the proportion of the house age is between 10-19 years (33.8%), 9 years or less (29.0%), 30 years or above (19.1%), and 20-29 years (18.1%). Lastly, most of the respondents live in a detached house (53.9%), and a townhouse (42.3%). In Table 4, it shows working from home attributes which describe the amount of working from home in days per week: 35.5% is no longer working from home anymore; 26.3% is 4-5 days per week; 20.5% is 2-3 days per week while 6-7 days per week and 1 day per week got 10.2% and 7.5% respectively. Most of them often work in bedroom (44.4%), living room (22.5%), and private workroom (22.2%). Excluding white collars themselves; there are other members working from home also in the same house which mostly are 3 persons and above (41.3%), and white-collar respondent himself/herself (33.8%). Lastly, there are almost the same portion for private transportation mode (50.5%) and public transportation mode (49.5%).

Table 3. Objective Attributes

Objective variables	Frequency (n=293)	Percentage (%)
Number of bedrooms
1 Room 2 Rooms 3 Rooms 4 Rooms or above	23 56 120 94	7.8 19.1 41.0 32.1
Housing estate
Yes No	131 162	44.7 55.3
Private garden
Yes No	191 102	65.2 34.8
Area of house
100 sq.m. or less 101-200 sq.m. 201-300 sq.m. 301 sq.m. or above	86 109 56 42	29.4 37.2 19.1 14.3
Age of house
9 years or less 10-19 years 20-29 years 30 years or above	85 99 53 56	29.0 33.8 18.1 19.1
Type of house
Townhouse Semi-detached house Detached house	124 11 158	42.3 3.8 53.9

Table 4. Working from home attributes

Working from home variables	Frequency (n=293)	Percentage (%)
Number of WFH’s day per week No longer WFH at the present 1 day/week 2-3 days/week 4-5 days/week 6-7 days/week	293 104 22 60 77 30	100 35.5 7.5 20.5 26.3 10.2
Most often WFH’s places
Outside building Kitchen Private workroom Coworking room Bedroom Living room	2 4 65 26 130 66	0.7 1.3 22.2 8.9 44.4 22.5
Number of persons WFH
1 person 2 persons 3 persons or above	99 73 121	33.8 24.9 41.3
Transportation mode
Private Public	148 145	50.5 49.5

Residential satisfaction

The residential satisfaction level in Table 5 is separated into 4 components: (1) housing, (2) neighborhood, and (3) working from home characteristic, and (4) overall residential satisfaction.

The first independent variable group is housing characteristic which consists of 11 items described in Table 5. Top three of average satisfaction scores of 293 respondents who are landed resident white collars in Bangkok are belonged to satisfied with the number of bedrooms (M = 3.73, SD = 0.822), size of bedroom (M = 3.66, SD = 0.924), and size of living room (M = 3.63, SD = 0.929). However, three items that have the lowest satisfaction scores are thermal comfort without A/C of the house (M = 3.17, SD = 1.047), size of kitchen (M = 3.24, SD = 1.048), and housing appearance (M = 3.28, SD = 0.991).

Secondly, the neighborhood characteristic comprises 13 items in Table 5. The result represents those three highest satisfaction items are distance to shopping center (M = 3.84, SD = 0.893), distance to store and market (M = 3.83, SD = 0.909), and distance to hospital (M = 3.75, SD = 0.900). On the other hand, neighborhood appearance (M = 3.23, SD = 1.005), distance to workplace (M = 3.25, SD = 1.090), and distance to common area / recreational area (M = 3.26, SD = 1.042) depict as the lowest satisfaction level items.

The third group of variables is working from home environment which consists of 7 items. There are three highest satisfaction items, i.e., privacy WFH (M = 3.68, SD = 1.173), ventilation for WFH (M = 3.57, SD = 0.993), and natural light for WFH (M = 3.43, SD = 0.954). Nevertheless, thermal comfort without A/C for WFH (M = 2.97, SD = 1.176), noise WFH (M = 3.24, SD = 1.260), and visual comfort for WFH (M = 3.25, SD = 1.078) are represented items with the lowest satisfaction level.

The last group is dependent variables representing overall satisfaction level in each group which are enumerated and descended into overall satisfaction with housing characteristics (M = 3.71, SD =0.881), overall satisfaction with working from home characteristics (M = 3.52, SD = 0.931), and overall satisfaction with neighborhood characteristic (M = 3.50, SD = 0.894).

Table 5. Residential satisfaction in each component (n=293)

Satisfaction Indicators	Mean	SD
Housing Characteristics
Number of Bedrooms (HC1)	3.73	.822
Size of bedroom (HC2)	3.66	.924
Size of living room (HC3)	3.63	.929
Number of bathrooms (HC4)	3.62	1.028
Privacy of house (HC5)	3.58	1.097
House size (HC6)	3.55	.919
Layout of house (HC7)	3.37	.877
Size of storage (HC8)	3.29	.981
Housing Appearance (HC9)	3.28	.991
Size of Kitchen (HC10)	3.24	1.048
Thermal comfort without A/C of house (HC11)	3.17	1.047
Neighborhood Characteristics
Distance to Shopping Center (NC1)	3.84	.893
Distance to Store and Market (NC2)	3.83	.909
Distance to Hospital (NC3)	3.75	.900
Distance to School / University (NC4)	3.50	.946
Population Density (NC5)	3.50	.957
Neighborhood Cleanliness (NC6)	3.47	.953
Neighbor Relationship (NC7)	3.40	.911
Green area / Open Space (NC8)	3.34	1.047
Neighborhood Maintenance (NC9)	3.34	.939
Distance to public transportation (NC10)	3.33	1.099
Distance to common area/recreation area (NC11)	3.26	1.042
Distance to Workplace (NC12)	3.25	1.090
Neighborhood Appearance (NC13)	3.23	1.005
Working From Home Characteristics
Privacy WFH (WFH1)	3.68	1.173
Ventilation for WFH (WFH2)	3.57	.993
Natural Light for WFH (WFH3)	3.43	.954
Working Space of WFH (WFH4)	3.33	1.032
Visual Comfort for WFH (WFH5)	3.25	1.078
Noise WFH (WFH6)	3.24	1.260
Thermal comfort without A/C for WFH (WFH7)	2.97	1.176
Overall Residential Satisfaction
Overall Satisfaction with Housing Characteristics (OSAT_HC)	3.71	.881
Overall Satisfaction with Working from Home Characteristics (OSAT_WFH)	3.52	.931
Overall Satisfaction with Neighborhood Characteristics (OSAT_NC)	3.50	.894

Relationship between the summary of respondent characteristics and overall residential satisfaction

In this section, data of respondent characteristics are examined with independent samples t-Test (2 groups) and analysis of variance (ANOVA) (more than 2 groups) to capture statistically significant among relationships with average overall residential satisfaction in each group. As for the results from independent samples T-test analysis, there are three significant variables at p-value less than 0.05 which are transportation mode, private garden, housing estate of white collars in Bangkok with a significant mean of overall residential satisfaction. Firstly, private transportation mode has a higher mean (M = 3.74) than public transportation mode (M = 3.4) (t = 3.772, p-value <0.01). Secondly, owning a private garden of white collars in Bangkok is significantly greater average (M = 3.80) than non-owning (M = 3.14) (t = 7.562, p-value < 0.01). The third variable, living in a housing estate obtains higher mean value (M = 3.72) than living in non-housing estate (M = 3.46) (t = 3.022, p-value < 0.01).

Moreover, the variables that consist of more than two groups are assessed with ANOVA test and Post Hoc tests for multiple comparisons. As a result, there are nine variables that get a significant result between the relationship of overall residential satisfaction level, e.g., 1) age of respondents (F = 5.371, p-value < 0.01) indicate that age with less or equal to 22 years old have lower overall satisfaction comparing with other age’s period, 2) marital status (F = 4.625, p-value < 0.05) shows that single is less satisfied with the overall environment than married, 3) household income (F = 3.021, p-value < 0.05) represents that above 80,001 baht/month (approx. 2,500 USD) have a higher level of overall residential satisfaction than <35,001 baht and 35,001-55,000 baht/month, 4) owner of a residence (F = 6.422, p-value < 0.01) have a greater number of mean than the others, 5) working from home frequency (F = 2.637, p-value < 0.05) shows that working from home at one day/week gain higher overall residential satisfaction than 4-5 and 6-7 days/week, 6) Often indoor place to work from home (F = 2.455, p-value < 0.05) indicates that a private workroom is significantly higher of overall satisfaction than bedroom and kitchen while living room gain larger satisfaction than kitchen, 7) usable area of house (F = 7.303, p-value < 0.01) explains that the more higher of area, the more higher of overall satisfaction level, 8) age of house (F = 3.047, p-value < 0.05) indicates that age of house between 0-9 years gains higher overall residential satisfaction than period of 10-19 years and ≥ 30 years, 9) house type (F = 15.459, p-value < 0.01) illustrates that detached house has higher rate of overall residential satisfaction than townhouse.

Overall residential satisfaction’s predictors

The multiple regression analysis with the stepwise model shows 8 significant predictors of overall residential satisfaction in Table 6. The regression results shows that value of multiple regression (R) is 0.846 which implies that there is positive correlation between the dependent variable and independent variables. The R square value (coefficient of determination) is 0.716, which is acceptable value (< 0.60) for the regression model of this study (Hair, Hult et al., 2014; Mendenhall, Wackerly et al., 1990). It indicates that the model can predict overall residential satisfaction by 71.6%. The standard error is 0.423. As for testing the correlation, there are no multicollinearity between those factors and get a significant from one-way ANOVA (F = 89.339, p < 0.05). Moreover, it is supported by Durbin-Watson equal to 2.262 which is within the acceptable range (1.5 to 2.5) (Dufour and Dagenais, 1985) meaning that the independent variables have no multicollinearity. Overall Residential Satisfaction = 0.278 + 0.175 (NC13) + 0.123 (HC6) + 0.115 (WFH2) + 0.132 (HC5) + 0.116 (HC11) + 0.083 (NC3) + 0.107 (HC7) + 0.107 (NC6).

Table 6. Overall residential satisfaction predictors (Stepwise method with 31 variables)

Variables	Unstandardized coefficients		Standardized coefficients Beta	t-value	Significance
constant	.278	.138		2.006	.046*
Neighborhood Appearance (NC13)	.175	.043	.225	4.048	.002**
House Size (HC6)	.123	.040	.145	3.121	.000**
Ventilation for WFH (WFH2)	.115	.031	.146	3.713	.000**
Privacy of House (HC5)	.132	.028	.185	4.748	.000**
Thermal Comfort without A/C (HC11)	.116	.030	.155	3.865	.000**
Distance to Hospital (NC3)	.083	.030	.096	2.759	.006**
Layout of House (HC7)	.107	.042	.120	2.559	.011*
Neighborhood Cleanliness (NC6)	.107	.046	.130	2.340	.020*

Note: Durbin-watson = 2.262, R = 0.846, R² = 0.716 $R^2=0.716$ ,adjusted R² = 0.708, F = 89.339, Std. error of estimation = 0.423, *p-value <0.05, **p-value <0.01

Discussion

Housing characteristics

In stepwise regression, half of the significant items are relevant to housing characteristics. The strongest predictor in this group is “Privacy of House (HC5)” (β = 0.132, p < 0.01) which has the same result with (Ibem, Eziyi Offia and Aduwo, 2013). Moreover, privacy of house is comprised in “Privacy of House & WFH”. The second powerful predictor is “House Size (HC6)” (β = 0.123, p < 0.01) supported with Huang, Du et al. (2015) and Milić and Zhou (2018) and smallest predictor of “Layout of House (HC7)” (β = 0.107, p < 0.05) supported by Buys and Miller (2012). Both indicators are part of “Housing Design Function (HDF)”. Besides, “Thermal Comfort without A/C (HC11)” (β = 0.116, p < 0.01) is loaded on “WFH Environment (WFHE)”.

Neighborhood characteristics

Regarding neighborhood characteristics, “Neighborhood Appearance (NC13)” (β = 0.175, p < 0.01) is the strongest indicator among the significant items of overall residential satisfaction of white collars who live in laned residences in Bangkok, Thailand which os similar to the result of Parkes, Kearns et al. (2002). Secondly, “Neighborhood Cleanliness (NC6)” (β = 0.107, p < 0.05) is supported by Mohit, Ibrahim et al. (2010), Huang, Du et al. (2015) and Mohit and Mahfoud (2015). Above indicators are loaded in “Neighborhood Environment (NE)” which is the main predictor of overall residential satisfaction. The last group and the lowest predicting power is “Distance to Hospital (NC3)” (β = 0.083, p < 0.01) which is supported by Azimi and Esmaeilzadeh (2017). Moreover, distance to hospital is loaded on “Location of Residence (LOR)”.

Working from home characteristics

The research result found that “Ventilation for WFH (WFH2)” (β = 0.115, p < 0.01) is one of the major predictors of overall residential satisfaction. It is supported by Cheshmehzangi (2021), Cuerdo-Vilches, Navas-Martín et al. (2021), Valizadeh and Iranmanesh (2021), Zarrabi, Yazdanfar et al. (2021) and Tleuken, Turkyilmaz et al. (2022). Moreover, it is loaded on “WFH Environment (WFHE)”.

Conclusions and Recommendations

Conclusions

This study determines overall residential satisfactions’ predictors (housing, neighborhood, and working from home characteristic) among white collars in Bangkok, Thailand, in case of those who live in landed house. The result from multiple linear regression analysis with stepwise mode identified that there were statistically significant factors relevant to every group of variables. Housing characteristic was found to contain main predictors of overall residential satisfaction which are privacy of house, house size, thermal comfort without A/C, and layout of house. Moreover, neighborhood characteristic comprised with the strongest predictor of all variables which was neighborhood appearance following by neighborhood cleanliness and distance to hospital. Furthermore, working from home characteristic represents only ventilation for WFH that is significant.

Results of the T-test and ANOVA showed the relationship of higher overall residential satisfaction levels that was observed significantly with statistic that private transportation mode, private garden, inhabiting in a housing estate, age, married status, higher household income, home ownership, number of WFH days, private workroom, house size, newness of house, and single-detached house. Most of the relationship are mentioned in the interview section that white collar who have above privilege tend to gain more overall residential satisfaction under COVID-19.

Recommendations

As a results, this research provided a refresh concept of housing development and overall residential satisfaction under working from home circumstance in covid-19 era context. Under COVID-19 pandemic, white collars are forced to work from home under company policies making indoor time rising comparing to before COVID-19 era. From the interview, some of the residents have limited choice of workplace so they mostly spend time in their bedroom excluding those who have private workroom. Public space of the house seems to have less privacy in large family members household especially the elders. The result mention that for those who are working from home several days are tended to gain lower overall residential satisfaction in the house so bedroom this day should be prioritized to perform as a multipurpose room on resting, sleeping, or working. Moreover, environment should be positively enhanced the comfort of the residents with sufficient ventilation, cool temperature during the day, high privacy from good allocation public and private space house layout to gain the overall residential satisfaction.

For real estate developers, architectures, real estate marketing agencies, and other urban policy planners, which are relevant residential design, could adopt and prioritize the concept of housing, neighborhood, and working from home characteristics together to solve pain points and maximize the overall residential satisfaction. As for the real estate developers, they could consider predictors of overall residential satisfaction to launch new products that fit with residential satisfaction of customers under COVID-19 pandemic by focusing on housing characteristics such as privacy of house, house size, layout of house, thermal comfort without A/C. Moreover, project area and environment must consider distance to nearest hospital, neighborhood cleanliness and appearance. Finally, ventilation for WFH is an issue for those white collars who often work from home. As for the architectures, who are house designer, should design a house to keep a concept of housing factor that influence to white collars if they are a target group, which are privacy of house, house size, layout of house, thermal comfort without A/C including ventilation for those who work from home. As for the real estate marketing agencies, which could use results from the research to analyze potential customer from their personal data obtained by interview and questionnaire. Understanding their satisfaction in current houses and environments could predict intention to move, for illustrate buying a new house in the project or second-hand houses. Latest, Urban policy makers could use significant predictors of overall residential satisfaction relevant to neighborhood characteristics to design urban city plan, e.g., locate hospital as a center of the community that is easy to access from their houses. Moreover, concentrating on neighborhood cleanliness and appearance of the city are also vital to improve overall residential satisfaction for white-collar landed residents.

Author Contributions

Conceptualization, methodology, original draft preparation, P. P. and S. C.; review and editing, P. P.; Software and field investigation, S. C. All authors have read and agreed to the published version of the manuscript.

Ethics Declaration

The author declares that he has no conflicts of interest regarding the publication of the paper.

References

•  Amérigo,  M. a. and  Aragonés , J. I. (1997). "A Theoretical and Methodological Approach to the Study of Residential Satisfaction". Journal of Environmental Psychology, 17(1), 47-57. doi: https://doi.org/10.1006/jevp.1996.0038.
•  Amole,  D. (2009). "Residential Satisfaction in Students' Housing". Journal of Environmental Psychology, 29(1), 76-85. doi: https://doi.org/10.1016/j. jenvp.2008.05.006.
•  Azimi,  N. and  Esmaeilzadeh,  Y. (2017). "Assessing the Relationship between House Types and Residential Satisfaction in Tabriz, Iran". International Journal of Urban Sciences, 21(2), 185-203. doi: https://doi.org/10.1080/12265934.2016.1273128
•  Baek,  Y. and  Joo,  H. (2021). "Determinants of Resident Satisfaction with Urban Renewal Projects Focusing on South Gyeongsang Province in South Korea". International Review for Spatial Planning and Sustainable Development, 9(2), 5-23. doi: https://doi.org/10.14246/irspsd.9.2_5
•  Biswas,  B.,  Sultana,  Z., et al. (2021). "The Emergence of Residential Satisfaction Studies in Social Research: A Bibliometric Analysis". Habitat International, 109, 102336. doi: https://doi.org/10.1016/j.habitatint.2021.102336.
•  Boschman,  S. (2018). "Individual Differences in the Neighbourhood Level Determinants of Residential Satisfaction". Housing studies, 33(7), 1127-1143. doi: https://doi.org/10.1080/02673037.2018.1424804.
•  Buys,  L. and  Miller,  E. (2012). "Residential Satisfaction in Inner Urban Higher-Density Brisbane, Australia: Role of Dwelling Design, Neighbourhood and Neighbours". Journal of Environmental Planning and Management, 55(3), 319-338. doi: https://doi.org/10.1080/09640568.2011.597592.
•  Cheshmehzangi,  A. (2021). "Housing and Health Evaluation Related to General Comfort and Indoor Thermal Comfort Satisfaction During the Covid-19 Lockdown". Journal of Human Behavior in the Social Environment, 31(1-4), 184-209. doi: https://doi.org/10.1080/10911359.2020.1817225.
•  Cho,  M. (2020). "Residential Satisfaction among Low-Income Single-Mother Households: The Case of Residential Welfare Facilities in South Korea". Archnet-IJAR: International Journal of Architectural Research, 14(3), 359-378. doi: 10.1108/ARCH-09-2019-0218.
•  Cochran , W. G. (1963). Sampling Techniques. 2nd Edition. John Wiley & Sons.
•  Cuerdo-Vilches,  T.,  Navas-Martín , M. Á., et al. (2021). "Working from Home: Is Our Housing Ready?". International Journal of Environmental Research and Public Health, 18(14), 7329. doi: https://doi.org/10.3390/ijerph18147329
•  Dufour,  J.-M. and  Dagenais , M. G. (1985). "Durbin-Watson Tests for Serial Correlation in Regressions with Missing Observations". Journal of Econometrics, 27(3), 371-381. doi: https://doi.org/10.1016/0304-4076(85)90012-0.
•  Emami,  A. and  Sadeghlou,  S. (2021). "Residential Satisfaction: A Narrative Literature Review Towards Identification of Core Determinants and Indicators". Housing, Theory and Society, 38(4), 512-540. doi: https://doi.org/10.1080/14036096.2020.1844795.
•  Galster,  G. (1987). "Identifying the Correlates of Dwelling Satisfaction: An Empirical Critique". Environment and behavior, 19(5), 539-568. doi: 10.1177/0013916587195001.
•  Galster , G. C. (1985). "Evaluating Indicators for Housing Policy: Residential Satisfaction Vs Marginal Improvement Priorities". Social Indicators Research, 16, 415-448.
•  Galster , G. C. and  Hesser , G. W. (1981). "Residential Satisfaction: Compositional and Contextual Correlates". Environment and behavior, 13(6), 735-758. doi: 10.1177/0013916581136006.
•  Hadavi,  S. and  Kaplan,  R. (2016). "Neighborhood Satisfaction and Use Patterns in Urban Public Outdoor Spaces: Multidimensionality and Two-Way Relationships". Urban Forestry & Urban Greening, 19, 110-122. doi: https://doi.org/10.1016/j.ufug.2016.05.012.
•  Hair,  J.,  Hult , G. T. M., et al. (2014). A Primer on Partial Least Squares Structural Equation Modeling (Pls-Sem). (1st Edition ed.). SAGE Publications, Inc, Thousand Oaks.
•  Huang,  Z.,  Du,  X., et al. (2015). "Home Ownership and Residential Satisfaction: Evidence from Hangzhou, China". Habitat International, 49, 74-83. doi: https://doi.org/10.1016/j.habitatint.2015.05.008.
•  Hulin,  C.,  Netemeyer,  R., et al. (2001). "Can a Reliability Coefficient Be Too High?". Journal of Consumer Psychology, 10(1/2), 55-58.
•  Ibem , E. O. and  Aduwo , E. B. (2013). "Assessment of Residential Satisfaction in Public Housing in Ogun State, Nigeria". Habitat International, 40, 163-175. doi: https://doi.org/10.1016/j.cities.2013.06.004
•  Ibem , E. O. and  Amole,  D. (2013). "Subjective Life Satisfaction in Public Housing in Urban Areas of Ogun State, Nigeria". Cities, 35, 51-61. doi: https://doi.org/10.1016/j.habitatint.2013.04.001.
•  Jansen , S. J. T. (2013). "Why Is Housing Always Satisfactory? A Study into the Impact of Preference and Experience on Housing Appreciation". Social Indicators Research, 113(3), 785-805. doi: https://doi.org/10.1007/s11205-012-0114-9.
•  Kley,  S. and  Dovbishchuk,  T. (2021). "How a Lack of Green in the Residential Environment Lowers the Life Satisfaction of City Dwellers and Increases Their Willingness to Relocate". Sustainability, 13(7), 3984.
•  Krungsri  Research. (2020). "Real Estate Market in Bmr". Retrieved from https://www.krungsri.com/th/research/industry/industry-outlook/Real-Estate/Housing-in-BMR/IO/io-housing-in-BMR-21
•  Kshetrimayum,  B.,  Bardhan,  R., et al. (2020). "Factors Affecting Residential Satisfaction in Slum Rehabilitation Housing in Mumbai". Sustainability, 12(6), 2344. doi: https://doi.org/10.3390/su12062344.
•  Kwon,  M.,  Remøy,  H., et al. (2019). "Influential Design Factors on Occupant Satisfaction with Indoor Environment in Workplaces". Building and Environment, 157, 356-365. doi: https://doi.org/10.1016/j.buildenv.2019.05.002.
• Li, J., Li, D., et al. (2019). "Residential Satisfaction among Resettled Tenants in Public Rental Housing in Wuhan, China". Journal of Housing and the Built Environment, 34, 1125-1148. doi: https://doi.org/10.1007/s10901-019-09667-x.
•  Lotfi,  S.,  Despres,  C., et al. (2019). "Are Sustainable Residential Choice Also Desirable? A Study of Household Satisfaction and Aspirations with Regard to Current and Future Residential Location". Journal of Housing and the Built Environment, 34, 283-311. doi: https://doi.org/10.1007/s10901-018-9631-4.
•  Lu, M. (1999). "Determinants of Residential Satisfaction: Ordered Logit Vs. Regression Models". Growth and change, 30(2), 264-287.
•  Mendenhall,  W.,  Wackerly , D. D., et al. (1990). Mathematical Statistics with Applications. PWS-KENT Publishing Company, Boston.
•  Milić,  J. and  Zhou,  J. (2018). "Residential Satisfaction among Young People in Post-Socialist Countries: The Case of Serbia". Journal of Housing and the Built Environment, 33, 715-730. doi: https://doi.org/10.1007/s10901-017-9579-9.
•  Mohit ,  M. A. and  Azim, M. (2012). "Assessment of Residential Satisfaction with Public Housing in Hulhumale’, Maldives". Procedia-Social and Behavioral Sciences, 50, 756-770. doi: https://doi.org/10.1016/j.sbspro.2012.08.078.
•  Mohit ,  M. A.,  Ibrahim, M., et al. (2010). "Assessment of Residential Satisfaction in Newly Designed Public Low-Cost Housing in Kuala Lumpur, Malaysia". Habitat International, 34(1), 18-27. doi: https://doi.org/10.1016/j.habitatint.2009.04.002.
•  Mohit , M. A. and  Mahfoud , A.-K. A. (2015). "Appraisal of Residential Satisfaction in Double-Storey Terrace Housing in Kuala Lumpur, Malaysia". Habitat International, 49, 286-293. doi: https://doi.org/10.1016/j.habitatint.2015.06.001.
•  Mohit , M. A. and  Nazyddah,  N. (2011). "Social Housing Programme of Selangor Zakat Board of Malaysia and Housing Satisfaction". Journal of Housing and the Built Environment, 26, 143-164. doi: https://doi.org/10.1007/s10901-011-9216-y.
•  Morris , E. W. and  Winter,  M. (1975). "A Theory of Family Housing Adjustment". Journal of Marriage and Family, 37(1), 79-88. doi: 10.2307/351032.
• National Statistical Office of Thailand. (2021). "Workforce Statistic of Bangkok Q2/2021". Retrieved from http://www.nso.go.th.
•  Nguyen , A. T.,  Tran , T. Q., et al. (2018). "Housing Satisfaction and Its Correlates: A Quantitative Study among Residents Living in Their Own Affordable Apartments in Urban Hanoi, Vietnam". International Journal of Urban Sustainable Development, 10(1), 79-91. doi: https://doi.org/10.1080/19463138.2017.1398167.
•  Olfindo,  R. (2021). "Transport Accessibility, Residential Satisfaction, and Moving Intention in a Context of Limited Travel Mode Choice". Transportation Research Part A: Policy and Practice, 145, 153-166. doi: https://doi.org/10.1016/j.tra.2021.01.012.
•  Parkes,  A.,  Kearns,  A., et al. (2002). "What Makes People Dissatisfied with Their Neighbourhoods?". Urban studies, 39(13), 2413-2438. doi: https://doi.org/10.1080/0042098022000027031.
•  Pongprasert,  P. (2020). "Understanding the Choice of Residential Location near Transit Stations and Urban Rail Commuting: A Case Study of Transit-Oriented Development in Bangkok". International Review for Spatial Planning and Sustainable Development, 8(4), 75-90. doi: http://dx.doi.org/10.14246/irspsda.8.4_75.
• Real estate information center. (2021). "Real Estate Sector". Retrieved from https://www.reic.or.th/Product.
•  Rossi , P. H. (1955). Why Families Move: A Study in the Social Psychology of Urban Residential Mobility. Free Press, Glence, IL.
•  Speare,  A. (1974). "Residential Satisfaction as an Intervening Variable in Residential Mobility". Demography, 11(2), 173-188. doi: 10.2307/2060556.
•  Thai  PBS. (2021). "Timeline of Covid-19 in Thailand Accumulates from 1–1 M". Retrieved from https://workpointtoday.com/covid-19-149/.
•  Tleuken,  A.,  Turkyilmaz,  A., et al. (2022). "Effects of the Residential Built Environment on Remote Work Productivity and Satisfaction During Covid-19 Lockdowns: An Analysis of Workers’ Perceptions". Building and Environment, 219, 109234. doi: https://doi.org/10.1016/j.buildenv.2022.109234.
•  Uyanık , G. K. and  Güler,  N. (2013). "A Study on Multiple Linear Regression Analysis". Procedia - Social and Behavioral Sciences, 106, 234-240. doi: https://doi.org/10.1016/j.sbspro.2013.12.027.
•  Valizadeh,  P. and  Iranmanesh,  A. (2021). "Inside out, Exploring Residential Spaces During Covid-19 Lockdown from the Perspective of Architecture Students". European Planning Studies, 30, 211 - 226. doi: https://doi.org/10.1080/09654313.2021.1939271.
• WHO. (2020). "Statement on the Second Meeting of the International Health Regulations. (2005) Emergency Committee Regarding the Outbreak of Novel Coronavirus (2019-Ncov)". Retrieved from https://www.who.int/news/item/30-01-2020-statement-on-the-second-meeting-of-the-international-health-regulations-(2005)-emergency-committee-regarding-the-outbreak-of-novel-coronavirus-(2019-ncov).
• WHO. (2021). "Tracking Sars-Cov-2 Variants". Retrieved from https://www.who.int/en/activities/tracking-SARS-CoV-2-variants/.
•  Zarrabi,  M.,  Yazdanfar,  S.-A., et al. (2021). "Covid-19 and Healthy Home Preferences: The Case of Apartment Residents in Tehran". Journal of Building Engineering, 35, 102021. doi: https://doi.org/10.1016/j.jobe.2020.102021.
•  Zhang, F.,  Zhang, C., et al. (2018). "Housing Conditions and Life Satisfaction in Urban China". Cities, 81, 35-44. doi: https://doi.org/10.1016/j.cities.2018.03.012.