Exploring the Unit Spatial Layout Preference for Urban Multi-Unit Residential Buildings: A Survey in Beijing, China

Abstract

Following the commodification reform of residential properties in China, multi-unit residential buildings (MURBs) have emerged as the most prevalent housing type owing to their high economic value and convenient infrastructure. In recent years, there has been an increasing concern regarding the potential negative impacts of MURBs on residents. To address this issue, in this study, a survey was conducted among 552 purchasers, and methods such as expert questionnaires, factor analysis, Kano two-dimensional quality, and analytic hierarchy process (AHP) were employed to evaluate the significance of 19 factors related to the quality of MURBs’ unit spatial layouts. An index model summarizing their utility was developed. The research findings indicated that spatial function held the greatest influence, followed by indoor environment, with spatial organization ranking last. Moreover, we analyzed the influence of preferences on satisfaction and the variations in preferences based on sociodemographic factors. The results aim to assist real estate developers and purchasers in evaluating the quality of housing spatial layouts, ultimately enhancing the overall quality and comfort of MURBs.

1. Introduction

Multi-unit residential buildings (MURBs) are a type of housing characterized by multiple independent residential units, comprising a housing type where each unit is occupied by separate households and features individual entrances, exits, and private internal spaces [1].

Terms such as “collective housing”, “apartment”, “super-tall residential”, and “high-rise residential building” are commonly used to describe various forms and types of MURBs, varying based on regional and national customs and regulations. In China, such housing is typically constructed as multiple buildings within a complex or community, sometimes including commercial or service facilities. MURBs are often characterized by their tall structure, ranging from a few floors to several tens of floors. MURBs occupy relatively small spaces in urban areas while providing a larger number of housing units to meet the housing demands of urbanization. Currently, MURBs have become the most common type of housing in the Chinese residential industry, accounting for approximately 90% of the total housing stock [1]. The significance of interior space in residential dwellings should not be overlooked, as people spend over 65% of their time at home, a figure that may increase depending on factors such as age and occupation [2]. In recent years, there has been a growing emphasis on the livability [3] and comfort [4] of individual residential units, leading to an increasing recognition of the need to improve unit spaces in housing.

Since 1988, China has implemented the policy of commercial housing, which has been in place for over 30 years. During this period, the quantity of MURBs in Chinese cities has rapidly increased due to their efficient land utilization and abundant public service facilities. According to the China Population Census Yearbook 2020 published by the National Bureau of Statistics of China, the per capita residential area for urban residents was 7.1 square meters in 1990, and it has grown to 36.52 square meters by 2020 [5], representing an increase of over five times in per capita living space for urban households over the past 30 years. Despite the increase in living space, the existing residential layouts and designs do not always meet the actual needs of residents. Compared with single-family houses, MURBs have certain disadvantages, such as having more floors, relatively smaller living spaces, and fewer rooms. Additionally, the same wall may separate two housing units [4]. As the residential market rapidly develops, there are numerous quality issues with MURBs due to imperfections in relevant regulations and design standards during the implementation of housing policies. Multiple studies have indicated that MURBs’ unit spaces present various issues, including overcrowding, inadequate space allocation, privacy concerns, ventilation issues, noise, and insufficient lighting, when compared to other types of housing.

Surveys and studies conducted in different countries have elucidated shifts in people’s values, habits [6,7,8], mental well-being [9], and social activities [6,10]. These transformations may engender fresh expectations concerning living arrangements and lifestyles, thereby engendering various pain points in residential products, including “inefficient spatial functionality”, “inadequate privacy and personal space”, “insufficient storage capacity”, and “lack of dedicated home office area”. Several housing research institutions in China have undertaken investigations into housing requirements during the pandemic. According to the “Research Report on Real Estate Purchaser Demands in China during the Pandemic”, based on data from 194 key monitored cities and compiled by CRIC, China’s leading comprehensive real estate information service provider, 35% of potential homebuyers express interest in upgraded unit layouts [2]. Homebuyers’ expectations have evolved from singular and low-level aspirations to a focus on quality, versatility, and healthy living, heralding a pervasive trend toward comprehensive residential product enhancements. Conducting studies on preferences for MURBs’ unit spaces poses distinct challenges.

In the early stages of residential design, spatial layout is considered a primary factor, as it directly influences various aspects such as functionality, spatial organization, spatial form, the connection between the interior and exterior environments, and ultimately, residents’ satisfaction [11,12,13]. Therefore, this study focuses primarily on the investigation of MURBs’ unit spatial layout, aiming to explore the importance of key preference attributes through relevant theories and questionnaire evaluation methods. It aims to establish a hierarchical structure and evaluation criteria system to assess MURBs’ unit spatial layout preferences. By identifying priority areas in housing preferences, this study aims to provide improved housing development plans and better solutions for residents’ living quality.

2. Unit Spatial Layout Factors Related to MURBs

In previous research, studies on preferences and satisfaction regarding MURBs have primarily focused on comprehensive evaluations of housing; weighing factors such as price, type, room size, and quantity; security systems; community; interior space layout; environmental quality; and location [14,15,16,17]. However, post-pandemic, there has been a shift in research toward the domains of health, hygiene, and ecology. Paria Akbari et al. categorized housing indicators that influence people’s mental well-being into four categories: “housing type”, “space”, “environmental factors”, and “function and activities”, analyzing the priority of different indicators of housing satisfaction and preference [18]. Yanqing Xu et al. investigated the preference priorities of Chinese residents regarding four aspects of MURBs: “contactless systems”, “air safety systems”, “disinfection and essence systems”, and “home comfort systems” [1]. Alexandra Kleeman et al. conducted a survey on 115 apartments in Sydney, Perth, and Melbourne, Australia, and assessed purchaser satisfaction based on five housing quality attributes: ”space layout/function”, “acoustics”, “visual privacy”, “natural ventilation”, and “building security” [13]. Previous research shows that when evaluating various housing factors, the concept of „space layout” is often assessed as a single abstract dimension, with only a few studies further exploring the various influencing factors of this design attribute. Current research on housing space layout and its multiple factors mainly focuses on case evaluation and analysis. Boumová et al. and Gao et al. employed a method of presenting respondents with unit space layout options designed according to different quality standards and asked them to compare and choose between two unit layout schemes to understand residents’ preferences for different unit space layouts [12,15]. According to the results, attributes such as “privacy”, “south-facing orientation”, “storage space”, and “number of rooms” were considered key criteria for evaluation. However, these key criteria were derived based on differences in unit types, and it remains unclear whether respondents actually considered all of these key criteria. Therefore, further exploration of methods to simultaneously evaluate the multiple factors influencing preferences for MURBs’ unit space layouts is necessary.

2.1. Spatial Function Factors

The term “spatial function” refers to the capacity and suitability of a specific space or area to meet particular activities, tasks, or needs. In residential environments, different spaces serve different functions. Bernard Leupen identified six fundamental activities: work, sleep, eat, cook, bathe, and socialize [19]. These specific spaces hold varying degrees of importance in human life. Therefore, the design and planning of space functionality should consider the needs and behavioral patterns of the residents. In The Timeless Way of Building, Christopher Alexander explains that the quality of a building depends on daily activities, and the more activities it accommodates, the more habitable the place becomes [20].

Malakouti et al. investigated the mutual relationship between housing quality indicators and housing flexibility, with flexibility showing a trend of increasing with performance improvement [21]. This implies that houses with flexible spatial layouts can accommodate a greater range of uses [22]. The flexibility of residential unit spatial layouts is often categorized into two types: “functional flexibility” and “structural flexibility” [23]. Although some studies indicate that the concept of “use flexibility” is now being used to demonstrate cost savings by reducing additional rooms or spaces [24], it is undeniable that the attribute of residential space flexibility holds significant importance in studying preferences for MURBs’ unit spatial layouts. It plays a crucial role in adapting space functions [21].

Furthermore, the key factors influencing spatial functionality include the size of rooms and the number of functional spaces [19]. In recent years, as the social and economic levels have continued to rise, Chinese housing developers have responded to purchaser demands by providing larger and more numerous rooms than before. These expanded rooms include larger storage spaces, master bedrooms, and an increased number of bathrooms, bedrooms, and balconies [20]. An important approach to address this situation is to wash hands before touching anything or anyone inside the house. The porch serves as a transition space between the indoor and outdoor residential areas. It is necessary to enhance the functionality of the porch storage cabinets with new disinfection and sanitization features to meet the modern requirements for health and hygiene [6], thereby reducing the distance for cleaning.

In conclusion, this study tends to focus on exploring the factors of spatial functionality that go beyond basic functionalities, emphasizing expanded features, flexibility, and larger quantities of more comfortable rooms.

2.2. Spatial Organization Factors

Francis D.K. Ching’s book Architecture: Form, Space, and Order highlights the idea that architectural spaces consist of interconnected forms and spatial patterns. The organization of space considers the relative positions and connections between one space and another, as well as their relationship with the external environment [25]. In their work on space syntax and social behavior, Bill Hillier and Julienne Hanson emphasize that space organization serves not only as a means for individuals to come together and form a society but also as a system that imposes constraints on social interactions due to the inherent rules and logic of space [26]. They further assert that spatial layout often reflects the relationship between residents and visitors rather than directly mapping the relationships among residents [26]. In other words, space organization needs to meet the dynamic requirements of domestic activities, and designers manipulate the forms, distribution, relationships, and connections between rooms to fulfill the functional and comfortable aspects of living.

Research on housing preferences has been conducted for many years, with some studies incorporating the family life cycle model to explore people’s housing preferences [27]. Families in different stages of development (formation, expansion, contraction, and dissolution) exhibit distinct differences in their preferences and needs for housing space layout [12]. The life cycle model describes the formation and development of families as a fixed static sequence, while the life course model analyzes the sequential positions of specific individuals or groups over time. In recent years, China has witnessed significant changes in family structures and living patterns, with an increasing number of single-person households, mixed and multi-generational families, and various other forms of cohabitation [28,29]. On the one hand, the accelerated aging of the population in Chinese society, and on the other hand, the promotion of policies allowing for second and third children, have heightened the need to address intergenerational living issues related to childcare and elderly care [30]. Moreover, the emergence of the COVID-19 pandemic has strengthened people’s urgent demand for larger living spaces and privacy [31].

In detached homes, the concept of open-plan layout has gained widespread recognition in typical housing arrangements. For instance, the Resor House designed by Mies van der Rohe and the Glass House designed by Philip Johnson are classic examples. In the spatial layout of MURBs, the living room is no longer a separate room but is connected to other areas such as the dining room, kitchen, and balcony, forming an open space. This design is referred to as LDKB. Currently, the kitchen is not just a place for cooking but also serves various activities such as dining, communication, entertainment, hosting guests, watching TV, working on the computer, and relaxation [32]. Adopting an open-plan residential layout can create more versatile spaces. Over 50% of apartments adopt an open-plan layout, where the kitchen and living room are fully integrated, creating a larger living space [33].

On the other hand, an “open layout” is considered to have issues with noise and lack of privacy [8]. During the COVID-19 pandemic, with various restrictions and containment measures in place, activities that were previously conducted outside of residences, such as work, exercise, and food gardening, have shifted indoors, leading to a perceived increase in crowding within residential spaces. The feeling of crowdedness in homes can weaken individuals’ psychological resilience, making it more challenging for them to cope with life’s pressures. The emergence of the COVID-19 pandemic has heightened people’s urgent need for privacy in their living spaces [31].

During the pandemic, millions of workers and students worldwide were forced to work and study from home. The permeability between family members and social roles, work and rest spaces, and leisure and work time is widespread [34]. A recent investigation into the management of household spatial boundaries during COVID-19 found that having a dedicated office space and fewer family members can facilitate a balance between work and non-work activities [35]. In terms of residential unit space organization, the more the bedroom space resembles that of a detached house, the higher the level of privacy in the rooms [36]. When designing residential layouts, architects or developers often partition bedrooms and common areas like living rooms to minimize interference among household members. Additionally, there is a type of intergenerational supportive “multi-generation apartment”. This spatial layout consists of two connected but independent units, allowing different generations of family members to live separately. It is referred to as a “dual-key home” and effectively addresses privacy concerns among multi-generational households [37,38].

In the investigation of factors influencing spatial organization, this study places greater emphasis on the dynamic demands of home activities driven by societal changes. Ultimately, we summarize these dynamic demands into three dimensions of inquiry: openness, socializing, and partition.

2.3. Indoor Environmental Factors

In certain cases, environmental factors, particularly those related to human and social aspects, play a crucial role in determining the aspects of a project. Extensive research has been conducted on factors associated with indoor environmental quality (IEQ) assessment, including thermal comfort, indoor air quality and ventilation (IAQ), visual comfort, and acoustic comfort [4]. The high population density and building density of MURBs mean that more people need to share and utilize limited indoor space. Compared with other types of housing, IEQ issues in MURBs are more complex and prominent [4]. Some studies have assessed the relationship between indoor conditions in MURBs and residential health [4]. However, there is a lack of research that evaluates how the spatial layout of MURBs influences indoor environmental conditions and, consequently, residential health. In the early stages of spatial design, unit space layout is considered an important natural means to achieve indoor environmental comfort. In research on healthy housing, spatial layout factors such as building orientation, structure, positioning and size of spaces within the building, location of doors and windows, and their connection to the outdoor environment can impact residential health parameters related to indoor air quality [4,39], natural lighting [4,40,41], sound [41], window views [40,42], and other aspects.

The orientation of a building is a significant factor that is given priority in residential design, as housing orientation has a significant impact on people’s sense of happiness [43]. A study conducted in Singapore demonstrated that units facing south and north have better ventilation and lighting conditions than those facing east and west, providing improved indoor thermal comfort [39]. The COVID-19 pandemic has created a sense of distrust regarding building ventilation and mechanical systems. Natural ventilation is a simple passive strategy that contributes to a more comfortable and healthier indoor environment. Open or semi-open residential space layouts are more conducive to achieving cross-ventilation and improving natural ventilation efficiency [44]. Kitchen and bathroom areas without natural ventilation tend to have higher relative humidity and are more prone to mold growth [45].

Being in contact with nature has a significant impact on promoting mental health, reducing depression, and coping with the ongoing COVID-19 pandemic [46,47,48]. Balconies, windows, or private outdoor spaces serve as transitional areas between the interior of a house and the surrounding environment, offering more than just sunlight, ventilation, landscape, and views—they provide people with information from the outside world [46]. Individuals living near green natural environments tend to have higher overall levels of health than those living in areas with less greenery [49]. By contrast, balconies with a lack of green plants, limited space, and poor views may exacerbate symptoms of depression [46,49].

In the context of indoor environments, a multitude of factors are involved. In alignment with the research topic, our emphasis is placed on the spatial environmental factors within residential units’ spatial layout, with particular attention given to essential elements such as nature light, nature ventilation, and views. These factors play a fundamental role in creating a pleasant and healthful living environment.

3. Methods

3.1. Evaluation Indicators

Drawing from the information presented in Section 2.1, Section 2.2, and Section 2.3, this study classifies the factors influencing preferences for MURBs’ unit space layout into three dimensions: spatial functionality (SF), spatial organization and socialization (SO), and indoor environment (IE). A comprehensive evaluation comprising 24 factors is conducted within these dimensions (

Table 1. Assessment of spatial layout preference factors for MURB units.

Target	Field Layer	No.	Index Layer	References
Spatial Function (SF)	Flexibility	1	Structural flexibility	[23,50]
		2	Functional flexibility	[21,22,23]
	Functional expansion	3	Closet storage	[20]
		4	Hygienic entrance	[6]
	Size and number	5	Utility balcony	[20]
		6	Master bedroom size (Home Isolation)	[9,51]
		7	Number of bathrooms	[20]
		8	Number of bedrooms	[19]
		9	Balcony area	[20]
Spatial Organization (SO)	Openness	10	Open-plan kitchen	[32,33]
		11	Sense of openness in the residential space	[32,33]
	Socializing	12	Independent home office (work or study) room	[35,52]
		13	Distribution of public and private spaces	[31]
	Partition	14	Segregated living and quiet areas	[36]
		15	Double-key space layout	[37,38]
Indoor Environmental (IE)	Natural light	16	Residence orientation	[39,43]
		17	Natural light in the living room	[4,40,41]
		18	Natural light in the living room and bedroom	[4,40,41]
	Natural ventilation	19	Natural cross ventilation	[44]
		20	Natural ventilation performance of the kitchen	[45]
		21	The bathroom allows for natural ventilation	[45]
	View	22	Interior green plant scenery	[46,49]
		23	Window view	[46,52]
		24	Position of the window	[46]

).

3.2. Kano Two-Dimensional Quality Model

The Kano model is primarily used for classifying and prioritizing user needs by analyzing their satisfaction levels with product features [53].

Utilizing the computational approach of the Kano model [53], 19 purchaser requirements were categorized into five distinct groups: must-be, one-dimensional, attractive, indifferent, and reverse. These groupings encapsulate varying levels of purchaser expectations and the consequential impact on purchaser satisfaction when these expectations are met or unmet. The model is illustrated in Figure 1.

(1)

Must-be attributes represent the fundamental expectations that purchasers perceive as indispensable. Failure to fulfill these requirements leads to dissatisfaction; however, meeting them does not necessarily result in a significantly high level of satisfaction.

(2)

One-dimensional attributes exhibit a proportional relationship with purchaser satisfaction. Meeting these demands contributes to higher satisfaction levels, whereas neglecting them leads to dissatisfaction. Purchasers often explicitly articulate these requirements.

(3)

Attractive attributes encompass unforeseen features or qualities that surpass purchaser expectations. Satisfying these demands can tremendously delight purchasers and confer a competitive advantage on products or services.

(4)

Indifferent attributes have negligible influence on purchaser satisfaction. Meeting or not meeting these requirements scarcely affects purchaser satisfaction levels.

(5)

Reverse attributes encompass characteristics that, if present, may trigger purchaser dissatisfaction. These demands often defy intuition, and purchasers may not explicitly express them.

3.3. Questionnaire

This analysis helps to upgrade product functionalities and determine their priority during the product development process. The implementation of the Kano model is typically conducted through questionnaire surveys. For each functionality or requirement in the Kano model questionnaire, there are two types of questions: positive and negative. The positive question measures the respondents’ satisfaction when facing a product with a particular feature, while the negative question assesses their satisfaction when the feature is absent. A five-point scale is used for evaluation, namely “like”, “must-be”, “neutral”, “live with”, and “dislike”.

Given the unique and abstract nature of spatial layouts, we incorporated illustrative diagrams in the questionnaire to assist participants in better understanding and evaluating their preferences. To ensure the accuracy and effectiveness of the questionnaire, these diagrams were carefully curated by several residential designers who searched for and selected representative residential layout images from copyright-free websites that matched each evaluation indicator in the questionnaire. The diagrams were intentionally crafted to be concise, clear, and straightforward, ensuring that participants could readily comprehend and select appropriate responses. Furthermore, we conducted a pre-test by inviting a subset of respondents to complete the questionnaire and collect their feedback. Based on their input, we made necessary modifications and improvements to enhance the effectiveness and comprehensibility of the diagrams.

3.4. Participants

Questionnaires were collected using online questionnaire platforms (Question Star APP) and social media (WeChat, Weibo) over a 2-week period in March 2023. The 24 spatial design elements were transformed into a questionnaire consisting of 24 questions and distributed among 80 professionals in the residential industry. Ultimately, 78 completed questionnaires were deemed valid.

In this study, a Kano questionnaire was administered to residents who recently purchased properties in Beijing. In total, 19 spatial layout factors were selected through expert questionnaires, which were subsequently converted into electronic questionnaires and distributed at the real estate registration center in Chaoyang District, Haidian District, Dongcheng District, and Xicheng District. A total of 552 valid responses were collected.

The questionnaire consists of two parts: The first part gathers sociodemographic characteristics of the participants; the second part is a questionnaire based on the Kano model to assess participants’ preferences for spatial layouts. Social demographic information includes gender, age, household income, education, and current family type. The survey was conducted anonymously, ensuring the confidentiality of participants’ information. Before filling out the questionnaire, all purchasers were required to read and sign an online informed consent form. This form provided an overview of the survey, explained the privacy protection measures to be taken, and emphasized that participation in the study was completely voluntary, and purchasers had the right to withdraw at any time.

3.5. Data Analysis

The data obtained from the survey were analyzed using the Kano two-dimensional quality analysis method, independent sample t-test, and one-way analysis of variance (ANOVA) to examine the impact of sample preferences on satisfaction and explore the preference differences among different sample types. The results of the Kano questionnaire analysis were combined with the analytic hierarchy process (AHP) for further analysis. This analysis aimed to summarize the weight system of evaluation indicators for MURBs’ unit spatial layout and propose design recommendations and future research directions.

It is important to note that this study specifically focused on the spatial layout of residential units within MURBs and excluded the influence of external conditions (such as public areas outside MURB units) and facade space (e.g., unit height, decoration style, and color) to simplify the scope of physical space and resident behavior. IBM SPSS 23.0 was used for frequency analysis, descriptive analysis, factor analysis, independent sample t-test, and one-way ANOVA. The Kano two-dimensional quality analysis and analytic hierarchy process were conducted using the SPSSAU website.

4. Results

4.1. Identification of Evaluation Indicators

The demographic data presented in

Table 2. Characteristics of professionals for factor analysis.

Category	Item	Frequency	Percentage (%)
Field	Architect	20	25.64%
	Interior designer	22	28.21%
	Researcher	16	20.51%
	Residential sales	20	25.64%
	Architect	20	25.64%
	Interior designer	22	28.21%
Work experience	Under 5 years	24	30.77%
	5–10 years	28	35.90%
	10–15 years	6	7.69%
	15–20 years	18	23.08%
	Over 20 years	2	2.56%
No. of answered questionnaires		78

indicate that, among the 78 professionals, architects accounted for 25.64%, interior designers accounted for 28.21%, researchers accounted for 20.51%, and residential sales personnel accounted for 25.64%. Regarding work experience, professionals with less than 5 years of experience accounted for 30.77%, 5–10 years accounted for 35.90%, 10–15 years accounted for 7.69%, 15–20 years accounted for 23.08%, and over 20 years accounted for 2.56% (Table 2).

Factor analysis was employed to ensure the objectivity of the factors in MURBs’ unit spatial layout and to obtain a simplified and independent indicator system. Based on previous studies and residential environment factors, 24 spatial layout factors related to MURBs’ unit spatial layout were extracted and categorized into three primary dimensions and nine secondary dimensions. The importance of these 24 spatial layout factors was investigated through surveys and analyzed using factor analysis to obtain the final evaluation factors.

The number of factors in the factor analysis was determined based on eigenvalues greater than 1, and the component values were derived using the maximum variance method. Ultimately, the factors were divided into four components, with the third and fourth components merged into the “indoor environmental” category. The final grouping resulted in three factors: spatial function, spatial organization, and indoor environment (

Table 3. Factor analysis of spatial layout on MURB units.

Residential Unit Space Layout Factors				Factor Component				Communality	Mean	Std.
Target	Field Layer	Code	Index Layer	1	2	3	4
Spatial Function (SF)	Flexibility	A1	Structural flexibility	0.660	0.401	0.059	0.054	0.602	5.667	1.456
		A2	Functional flexibility	0.786	0.142	0.254	−0.079	0.709	5.590	1.273
	Functional expansion	A3	Closet storage	0.831	−0.004	0.046	0.277	0.769	6.526	1.125
		A4	Utility balcony	0.749	0.010	0.365	0.145	0.715	5.526	1.492
		A5	Hygienic entrance	0.649	0.157	0.125	−0.204	0.503	5.654	1.267
	Size and number	A6	Number of bathrooms	0.582	0.226	0.305	0.073	0.488	5.590	1.39
		A7	Master bedroom size	0.806	0.062	0.029	0.269	0.727	6.192	1.217
Spatial Organization (SO)	Openness	B1	Open-plan kitchen	0.469	0.488	0.194	0.126	0.511	5.744	1.098
		B2	Sense of openness in the residential space	0.242	0.593	0.286	0.117	0.506	5.333	1.492
	Socializing	B3	Independent home office (work or study) room	0.165	0.751	0.005	0.320	0.694	5.603	1.361
		B4	Distribution of public and private spaces	0.183	0.796	0.115	0.007	0.681	5.231	1.570
	Partition	B5	Double-key space layout	−0.225	0.49	0.026	−0.265	0.362	3.487	1.518
		B6	Segregated living and quiet areas	0.137	0.646	0.110	0.151	0.471	4.987	1.640
Indoor Environmental (IE)	Natural light	C1	Residence orientation	0.359	0.055	0.765	0.011	0.717	6.244	0.914
		C2	Natural light in the living room and bedroom	0.17	0.397	0.693	0.153	0.690	6.282	0.938
	Natural ventilation	C3	Natural cross ventilation	0.212	0.212	0.684	−0.193	0.596	6.346	0.770
		C4	The bathroom allows for natural ventilation	0.046	−0.009	0.739	0.321	0.651	5.846	1.270
	View	C5	Interior green plant scenery	0.033	0.111	0.405	0.707	0.677	5.205	1.654
		C6	Window view	0.168	0.241	−0.052	0.812	0.749	4.731	1.695

Residential unit space layout factor separated from factor component.

).

The SF group was defined by seven factors: structural flexibility, functional flexibility, closet storage, utility balcony, hygienic entrance, number of bathrooms, and master bedroom size. The SO group included six factors: a sense of openness in the residential space, open-plan kitchen, independent home office (work or study) room, distribution of public and private spaces, double-key space layout, and segregated living and quiet areas. The IE group consisted of residence orientation, natural light in the living room and bedroom, natural cross ventilation, bathroom allowing for natural ventilation, interior green plant scenery, and window view. In the evaluation of quality factors for MURBs’ unit space layout, SF demonstrated a higher level of importance, followed by SO, and finally IE factors. Five quality factors were excluded from the analysis. The ventilation performance of the kitchen is mandated by building regulations to effectively eliminate smoke and odors. The analysis results for “nature light of bedroom” closely aligned with those for the “nature light of living room”, leading to its consolidation with the “nature light of living room” as “natural light in the living room and bedroom”. The correlation between the “position of the window” and “balcony area” was found to be less than or equal to 0.4, resulting in their exclusion. The number of bedrooms often exhibits a strong correlation with the size and price of residential units, but it may not comprehensively reflect the quality and practicality of the spatial layout. Consequently, it could significantly influence the overall preference analysis of MURBs’ space layout and was therefore excluded.

4.2. Preferred Evaluation Results

The demographic data presented in

Table 4. Characteristics of purchasers used for the Kano two-dimensional model analysis.

Variable	Attribute	Percentage(%)	Mean	Std. Dev.
Gender	Male	45.47	1.545	0.498
	Female	54.53
Age	18–30	29.35	2.389	1.231
	31–40	29.71
	41–50	20.65
	51–60	13.22
	Over 60	7.07
Household income (RMB/Year)	Less than 150,000	23.55	2.576	1.270
	150,000–300,000	29.35
	300,000–500,000	23.73
	500,000–1,000,000	12.68
	Over 1,000,000	10.69
Education	High school degree or below	8.51	2.326	0.625
	University or college degree	50.36
	Master’s degree or above	41.12
Family type	Single	5.80	3.355	1.033
	Married without child	11.23
	Two generations	37.86
	Three generations	31.88
	Other	13.22

indicate that 45.47% of the purchasers were male, while 54.53% were female. In terms of age distribution, 29.35% of purchasers were between 18 and 30 years old, 29.71% were between 31 and 40, 20.65% were between 41 and 50, 13.22% were between 51 and 60, and 7.07% were over 60. As for household annual income, 23.55% had an income below RMB 150,000, 29.35% had an income between 150,000 and RMB 300,000, 23.73% had an income between 300,000 and RMB 500,000, 12.68% had an income between 500,000 and RMB 1,000,000, and 10.69% had an income exceeding RMB 1,000,000. In terms of education level, 41.12% of purchasers had a postgraduate degree or above, 50.36% had a university or college degree, and only 8.51% had a high school education or lower. These results indicate a relatively high educational level among property buyers in Beijing. Furthermore, from a family structure perspective, two generations accounted for 37.86% of the purchasers, followed by three generations at 31.88%. Other categories accounted for 13.22% of the purchasers, married without children accounted for 11.23%, and singles accounted for 5.80%.

We conducted statistical analysis on the survey results and matched the evaluation criteria items with the Kano evaluation table (

Table 5. Kano evaluation table.

Purchaser Requirements		Dysfunctional
		Like	Must-Be	Neutral	Live with	Dislike
Functional	Like	Q	A	A	A	O
	Must-be	R	I	I	I	M
	Neutral	R	I	I	I	M
	Live with	R	I	I	I	M
	Dislike	R	R	R	R	Q

A, attractive; O, one-dimensional; M, must-be; I, indifferent; R, reverse; Q, questionable.

) to determine the Kano attributes for each evaluation dimension item in each survey sample. Subsequently, we calculated the percentage of occurrence for each Kano attribute in all samples, representing the degree of belonging for each Kano attribute (KM, KO, KA, KI, KR, and KQ). The highest percentage among these Kano attributes was selected as the final attribute for each evaluation dimension, as presented in

Table 6. Kano model analysis results.

Residential Unit Space Layout Factors				Attribute Factor						Communality	Better	Worse
Target	Field Layer	Code	Index Layer	KA	KO	KM	KI	KR	KQ
Spatial Function (SF)	Flexibility	A1	Structural flexibility	37.32%	12.50%	14.31%	26.99%	6.88%	1.99%	A	55.29%	−29.54%
		A2	Functional flexibility	42.21%	8.33%	20.65%	20.83%	6.34%	1.63%	A	54.74%	−31.62%
	Functional expansion	A3	Closet storage	19.20%	12.86%	39.13%	23.73%	2.54%	2.54%	M	33.27%	−55.07%
		A4	Utility balcony	34.96%	16.12%	20.11%	21.20%	5.80%	1.81%	A	54.81%	−39.49%
		A5	Hygienic entrance	20.65%	7.07%	10.14%	40.22%	19.02%	2.90%	I	34.73%	−22.14%
	Size and number	A6	Number of bathrooms	35.51%	8.51%	20.83%	30.43%	3.99%	0.72%	A	45.63%	−30.80%
		A7	Master bedroom size	30.80%	10.69%	21.74%	29.53%	5.80%	1.45%	A	44.92%	−35.16%
Spatial Organization (SO)	Openness	B1	Open-plan kitchen	32.97%	14.49%	20.11%	24.46%	5.07%	2.90%	A	51.57%	−37.60%
		B2	Sense of openness in the residential space	42.39%	7.97%	14.49%	26.09%	5.80%	3.26%	A	55.38%	−24.70%
	Socializing	B3	Independent home office (work or study) room	30.80%	11.41%	22.28%	26.45%	6.16%	2.90%	A	46.41%	−37.05%
		B4	Distribution of public and private spaces	29.53%	12.50%	23.73%	27.54%	5.43%	1.27%	A	45.05%	−38.83%
	Partition	B5	Double-key space layout	15.40%	5.62%	6.88%	44.93%	25.18%	1.99%	I	28.86%	−17.16%
		B6	Segregated living and quiet areas	16.85%	5.80%	7.25%	41.49%	26.63%	1.99%	I	31.73%	−18.27%
Indoor Environmental (IE)	Natural light	C1	Residence orientation	28.08%	15.04%	31.52%	16.67%	8.33%	0.36%	M	46.18%	−51.20%
		C2	Natural light in the living room and bedroom	36.59%	10.33%	12.50%	28.99%	9.78%	1.81%	A	51.13%	−24.64%
	Natural ventilation	C3	Natural cross ventilation	20.29%	30.25%	24.46%	20.65%	3.99%	0.36%	O	51.71%	−57.60%
		C4	The bathroom allows for natural ventilation	23.19%	35.69%	16.85%	19.75%	4.17%	0.36%	O	61.03%	−55.13%
	View	C5	Interior green plant scenery	32.97%	17.21%	17.03%	26.63%	5.07%	1.09%	A	52.51%	−36.10%
		C6	Window view	37.68%	12.14%	21.20%	23.55%	3.62%	1.81%	A	51.72%	−34.67%

A, attractive; O, one-dimensional; M, must-be; I, indifferent; R, reverse; Q, questionable.

.

The analysis results of the Kano model indicate the proportions of the six attribute categories and their classification into better and worse values.

(1)

The classification result refers to the attribute with the highest proportion among the six categories.

(2)

Better (satisfaction impact) and worse (dissatisfaction impact) indices were used to determine the sensitivity of users to changes in functionality/service levels.

(3)

Better (satisfaction impact) = (A + O)/(A + O + M + I). This index ranged from 0 to 1, with higher values indicating greater sensitivity and higher priority.

(4)

Worse (dissatisfaction impact) = −1 × (O + M)/(A + O + M + I). This index ranged from −1 to 0, with smaller values indicating greater sensitivity and higher priority.

Figure 2 presents the Kano evaluation matrix for MURBs’ unit space layout factors. The essential attributes include “closet storage” and “independent home office (work or study) room”. “Natural cross ventilation” and “the bathroom allows for natural ventilation” were classified as “expected” attributes. “Hygienic entrance”, “double-key space layout”, and “segregated living and quiet areas” were considered “indifferent” attributes. The remaining 11 factors were classified as “attractive” attributes.

To examine the influence of sociodemographic factors on the evaluation of the 19 factors in MURBs’ unit space layout, we further analyzed the differences in preferences among purchasers of different genders, incomes, education levels, and family structures in terms of their overall preferences and across three dimensions of MURBs’ unit space layout. The findings from Figure 3 demonstrate significant variations in the preferences for unit space layouts among purchasers based on their gender, income, education level, and family structure across the three dimensions.

Independent sample t-tests were conducted to compare the differences in different dimensions of space layout preferences between male and female homebuyers. The test results revealed a significance level (p-value) below 0.05, indicating significant differences in the preferences for SF, SO, and IE between individuals of different genders. Notably, female purchasers demonstrated higher preferences than their male counterparts across all three evaluation dimensions, suggesting that women have higher expectations for the quality of MURBs’ unit space layout.

The results of one-way analysis of variance (ANOVA) indicated that purchasers with different household incomes, educational levels, and family structures exhibited significant differences in their overall preferences for unit space layouts as well as in the three dimensions. All p-values were found to be less than 0.05, suggesting that household income, educational level, and family structure factors have an influence on unit space layout preferences. Post hoc Tamhane’s multiple-comparison tests were performed based on the assumption of homogeneity of variance (p > 0.05) to further analyze the results. Purchasers with household incomes below RMB 150,000 displayed significantly lower requirements for the spatial function of unit space layout than other income groups. Homebuyers with household incomes between RMB 500,000 and RMB 1,000,000 and those above RMB 1,000,000 had higher demands for the indoor environment than homebuyers with incomes below RMB 150,000 and those between RMB 150,000 and RMB 300,000. This disparity in preferences may be attributed to differences in affordability. Purchasers with an educational level of high school or below had significantly lower requirements for unit space layouts than those with higher education qualifications. Specifically, purchasers with a high school diploma or higher demanded a higher-quality unit space layout. Purchasers living in multi-generational households (two or three generations) had significantly higher requirements for unit space layouts than those living alone or in single-generation households. Notably, purchasers in three-generation households had significantly higher demands for unit space layout quality than purchasers in other types of family structures.

4.3. Analysis of Index Weights

Li L. et al. proposed a purchaser satisfaction evaluation method for customized product development based on the analysis results of the Kano model, utilizing entropy weight and AHP. The application of this method in the customized portrait-based product industry was demonstrated through a case study, confirming its functionality [54]. Li J. et al. combined the Kano model and the AHP method to propose a quantitative analysis approach for classifying and analyzing the importance of user requirements in eco-city development [55]. From the literature review, it was observed that the establishment of a product satisfaction evaluation index weighting system based on the Kano model mostly adopts the must-be attributes (M), one-dimensional attributes (O), and attractive attributes (A) as primary evaluation criteria. The satisfaction evaluation indicators under these three Kano attributes were considered secondary criteria. Expert ratings or questionnaire surveys were employed, and methods such as AHP or entropy weight (EW) were applied to determine the weights for both primary and secondary evaluation dimensions, with the final weight system obtained by multiplying the weights of the two levels [54,55,56,57]. Methods of this nature yield a comparable degree of belonging for various evaluation indicators, with the potential for contentious categorization and even unjustified exclusion of Kano attributes. Considering the distinction between satisfaction and preference, in this study, a weighted system was developed for evaluating residential unit spatial layouts based on the satisfaction criteria of the Kano model. The proposed method is as follows:

In the first step, the weights for four degrees of belonging in the Kano indicators were constructed. The evaluation indicators consisted of four degrees of attribution for the Kano attributes (K_M, K_O, K_A, and K_I), while K_R and K_Q, which lack practical reference significance for product development, were excluded. The AHP was applied to establish the weights (W) for the four degrees of attribution in the Kano attributes: W_M, W_O, W_A, and W_I (

Table 7. MURBs’ residential unit spatial layout: The AHP weights of the Kano attributes.

Code1	Code2	Index Layer	Weights	Communality	Weights (W)
P1	A1	Structural flexibility	0.0543	WA	0.6511
	A2	Functional flexibility	0.0550
	A4	Utility balcony	0.0534
	A6	Number of bathrooms	0.0555
	A7	Master bedroom size	0.0460
	B1	Open-plan kitchen	0.0529
	B2	Sense of openness in the residential space	0.0538
	B3	Independent home office (work or study) room	0.0547
	B4	Distribution of public and private spaces	0.0536
	C2	Natural light in the living room and bedroom	0.0536
	C5	Interior green plant scenery	0.0528
	C6	Window view	0.0421
P2	C3	Natural cross ventilation	0.0426	WO	0.1130
	C4	The bathroom allows for natural ventilation	0.0519
P3	A3	Closet storage	0.0533	WM	0.1053
	C1	Residence orientation	0.0561
P4	A5	Hygienic entrance	0.0569	WI	0.1307
	B5	Double-key space layout	0.0554
	B6	Segregated living and quiet areas	0.0563

). P1, P2, P3, and P4 are the sum of the weights of the must-be attributes, one-dimensional attributes, attractive attributes, and indifferent attributes, respectively.

$$W_A=\frac{P1}{P1+P2+P3+P4}; W_O=\frac{P2}{P1+P2+P3+P4};$$

$$W_M=\frac{P3}{P1+P2+P3+P4}; W_I=\frac{P4}{P1+P2+P3+P4};$$

$$W=\left(W_A{,W}_O{,W}_M{, W}_I\right)$$

In the second step, a decision matrix was constructed using the four degrees of attribution (K_M, K_O, K_A, and K_I) for the 19 factors. The matrix was then column-normalized. Each degree of attribution value in the normalized matrix was multiplied by the corresponding weight established in the first step for the four degrees of belonging in the Kano attributes (W_M, W_O, W_A, and W_I). The resulting products were then summed row-wise to obtain the final weights for each factor. Alternatively, this process can be understood as multiplying the normalized matrix by the column matrix of Kano attribute weights (W) established in the first step, resulting in a new column matrix representing the final weights (U) of the evaluation indicators.

$$A={\left(a_{ij}\right)}_{m\times n}$$

where $A$ is the matrix of belonging values of K_A, K_O, K_M, and K_I; and $a_{ij}$ is the belonging values of K_A, K_O, K_M, and K_I for 19 factors.

$$\overline{A}={\left({\overline{a}}_{ij}\right)}_{m\times n}$$

where $\overline{A}$ is the column-normalized matrix of $A$; and ${\overline{a}}_{ij}=a_{ij}/\sum _{i=1}^n{a}_{ij}, i=\mathrm{1,2},\cdots ,m,j=\mathrm{1,2}\cdots ,n$. The final resulting matrix is the column weight matrix $\left(U\right)$:

$$U=\overline{A}\times W$$

The preference-weighted index (Figure 4) was constructed based on the preference weights (U) derived from the spatial layout preferences of MURB units (

Table 8. Evaluation index weighting system of the spatial layout of residential units in MURBs.

Residential Unit Space Layout Factors				$\mathbf{Indicator} \mathbf{Weights} \mathbf{\left(}\overline{\mathit{A}}\mathbf{\right)}$				Communality	Weights (U)
Target	Field Layer	Code	Index Layer	A	O	M	I
Spatial Function (SF)	Flexibility	A1	Structural flexibility	0.0428	0.0055	0.0041	0.0068	A	0.0593
		A2	Functional flexibility	0.0484	0.0037	0.0060	0.0052	A	0.0633
	Functional expansion	A3	Closet storage	0.0220	0.0057	0.0113	0.0060	M	0.0450
		A4	Utility balcony	0.0401	0.0072	0.0058	0.0053	A	0.0584
		A5	Hygienic entrance	0.0237	0.0031	0.0029	0.0101	I	0.0399
	Size and number	A6	Number of bathrooms	0.0408	0.0038	0.0060	0.0076	A	0.0582
		A7	Master bedroom size	0.0353	0.0047	0.0063	0.0074	A	0.0538
Spatial organization (SO)	Openness	B1	Open-plan kitchen	0.0378	0.0064	0.0058	0.0061	A	0.0562
		B2	Sense of openness in the residential space	0.0486	0.0035	0.0042	0.0066	A	0.0629
	Socializing	B3	Independent home office (work or study) room	0.0353	0.0051	0.0064	0.0066	A	0.0535
		B4	Distribution of public and private spaces	0.0339	0.0055	0.0068	0.0069	A	0.0532
	Partition	B5	Double-key space layout	0.0177	0.0025	0.0020	0.0113	I	0.0334
		B6	Segregated living and quiet areas	0.0193	0.0026	0.0021	0.0104	I	0.0344
Indoor environmental (IE)	Natural light	C1	Residence orientation	0.0322	0.0067	0.0091	0.0042	M	0.0522
		C2	Natural light in the living room and bedroom	0.0420	0.0046	0.0036	0.0073	A	0.0575
	Natural ventilation	C3	Natural cross ventilation	0.0233	0.0134	0.0070	0.0052	O	0.0489
		C4	The bathroom allows for natural ventilation	0.0266	0.0158	0.0049	0.0050	O	0.0523
	View	C5	Interior green plant scenery	0.0378	0.0076	0.0049	0.0067	A	0.0571
		C6	Window view	0.0432	0.0054	0.0061	0.0059	A	0.0607

W_A = 0.6511, W_O = 0.1131, W_M = 0.1053, and W_I = 0.3107.

).

The weightings were calculated hierarchically. In the first-level weights, SF had the highest weight of 0.3778. Within the “functional expansion” dimensions, A2 (0.5165) slightly outweighed A1 (0.4835). Among the “functional expansion” subcriteria, A4 (0.4077) was considered the most important, while in the “size and number” subcriteria, A6 (0.5197) was deemed more important. Since there were three evaluation criteria in “function expansion”, their weights were higher in the second level than “flexibility”. However, in the third level weights, “flexibility” with A1 (0.4835) and A2 (0.5165) surpassed “function expansion” with A3 (0.3140), A4 (0.4077), and A5 (0.2783). Therefore, the overall flexibility of the house was more favored by users than individual functional expansions.

IE was rated as the second most important dimension. In the second-level weights, “view” (0.3583) was considered more important than “natural lighting” (0.3337) and “natural ventilation” (0.3081). In the third-level weights, “natural lighting” with C2 (0.5241) was slightly higher than C1 (0.4759). “Natural ventilation”, with C4 (0.5164), was deemed more important, while “view”, with C6 (0.5152), was considered more important.

The SO dimension was rated as the least important, with “openness” (0.4057) being rated as more important than “socializing” (0.3633) and “partition” (0.2311) in the second level. Within the “openness” dimension, B2 (0.5281) was slightly higher than B1 (0.4719). The “socializing” dimension (B3) was considered more important, while “partition” (B6) was considered the most important. Consistent with the analysis results based on the Kano model, this finding is similar to previous studies conducted in the United States and Japan [42], indicating that open spaces within residential settings in China also have a significant impact on residential satisfaction.

Single-factor analysis of variance revealed significant differences among multiple categories of household structures. Understanding the preference weights of different household structures is crucial for residential unit spatial layout design. This study employed the weight calculation method to further examine the differences in preference weights for unit spatial layouts among different household structures (Figure 5). The evaluation weights of the five household categories showed no significant differences in SO and were relatively lower than the other two evaluation dimensions. “Single”, “married without child”, “two-generation families”, and “three-generation families” had significantly higher requirements for SF than for IE. Specifically, single individuals and childless married couples had higher preferences for SF than for other household categories. Two- and three-generation families showed minimal differences in the evaluation weights across the three dimensions. Other household categories had nearly identical requirements for SF and IE.

5. Discussion

The aim of this study was to explore the preferences for the unit spatial layout of MURBs in China’s current stage. Based on previous research and expert evaluations, the unit spatial layout of MURBs was categorized into three dimensions, comprising a total of 19 factors. We employed the Kano two-dimensional quality analysis method, independent sample t-test, and ANOVA to analyze the impact of sample preferences on satisfaction and investigate the preference differences among sociodemographic variables. Additionally, a purchaser preference weight assessment method was proposed.

Specifically, “closet storage” and “independent home office (work or study) room” were classified as “must-be” attributes, which purchasers consider as essential basic expectations. Failure to meet these demands may lead to dissatisfaction but satisfying them does not necessarily guarantee high levels of satisfaction. “Natural cross ventilation” and “the bathroom allows for natural ventilation” were categorized as “one-dimensional” attributes. Satisfying these needs will result in higher satisfaction, while not meeting these needs will lead to dissatisfaction. “Hygienic entrance”, “double-key space layout”, and “segregated living and quiet areas” were denoted as “indifferent” attributes, meaning they have almost no impact on customer satisfaction. The remaining 11 factors were classified as “attractive” attributes. Fulfilling these needs can greatly satisfy purchasers and provide a competitive advantage for the product or service.

Based on the KANO questionnaire, we further analyzed the preference differences among MURB buyers regarding 19 spatial layout factors, considering different sociodemographic factors. It was observed that females and individuals with a high school education or above had higher demands for the overall quality of the unit spatial layout. In contrast to a study conducted by Gao et al. [12], we excluded the bedroom quantity factors in our evaluation indicators and found that different family types did not show significant variations in their preferences for spatial layout factors. This finding suggests that the number of bedrooms may be a decisive factor when designing houses for different family types. Additionally, lower-income families demonstrated significantly lower demands for spatial layout factors, while purchasers from higher-income families had higher demands for IE.

This study proposes a method based on the Kano model and AHP to construct a weight system for evaluating MURBs’ unit spatial layout. It also provides a priority list of factors for MURBs’ residential unit spatial layout. SF was identified as the most influential dimension in purchaser preferences, followed by IE as the second most important, and SO as the least weighted evaluation dimension. Among the attributes, „ sense of openness in the residential space”, „ functional flexibility”, „ interior green plant scenery”, „ structural flexibility”, and „ utility balcony „ are considered the top five factors. Such demand prioritization reflects residents’ pursuit of diverse living styles and a green, healthy lifestyle. This research introduces a quantitative evaluation method for the spatial layout quality of unit housing, which can assist developers and designers in evaluating key evaluation factor preferences to balance various design requirements within limited costs.

We further explored the differences in preference weights for unit spatial layout among households with different structures. For the evaluation dimension SO, there were no significant differences in evaluation weights among the five family types, and its weight was comparatively lower than the other two evaluation dimensions. Single, married without children, two-generation families, and three-generation families exhibited significantly higher demands for SF than IE. Particularly, single-person households and married couples without children showed higher preferences for SF than other family categories. On the other hand, two- and three-generation families demonstrated relatively small differences in the evaluation weights for these three evaluation dimensions. For other family types, their demands for SF and IE were nearly at the same level. Our study suggests that the application of the Kano model and the AHP in weight calculations can provide a more systematic and comprehensive analysis of preference differences than individual analytical methods.

The selection of Beijing as the study subject was driven by multiple considerations. As the capital and a first-tier city of China, Beijing exhibits both typicity and representativeness. The residential demands and preferences of its residents, to some extent, reflect overall trends among residents in other major Chinese cities. Furthermore, being a well-established city, Beijing boasts rich experience in housing construction and planning, offering valuable insights for other cities in their development.

Through an in-depth exploration of Beijing residents’ preferences for MURBs’ unit spatial layout, we sought to better understand their housing preferences and provide tailored housing design and planning recommendations that cater to diverse social backgrounds and developmental stages. However, it is essential to acknowledge that the research findings from Beijing, as a specific city, may not be fully transferable to other regions of the country. Different regions may exhibit diverse climate conditions, social cultures, architectural norms, and levels of economic development, potentially leading to variations in residents’ preferences for spatial layout, building materials, and interior environments.

Further research is still needed on these topics. Studies on preferences and satisfaction may not directly reflect the benefits of residents’ health and well-being, necessitating further investigation. Future research could explore the impact of living environments on residents’ health and happiness to gain a deeper understanding of the potential effects of interior spatial layouts of residential buildings on human well-being. Moreover, integrating broader social science research methods, such as qualitative surveys and in-depth interviews, can provide a more comprehensive and profound understanding. Through these efforts, we can better provide valuable recommendations and guidance for the design and planning of MURBs.

6. Conclusions

In this study, we identified preference factors for the spatial layout of MURBs and explored the preference differences based on sociodemographic variables. In the past, spatial layout, as a critical component of architecture, was often considered an abstract concept and evaluated in conjunction with technical systems, interior decoration, building volume, appearance, and environment, lacking a more targeted and specific quantitative approach. This study proposes a quantitative evaluation method, breaking away from the constraints of previous abstract conceptualizations of spatial layout. This method provides designers and real estate developers with a more focused and specific approach to early-stage spatial layout design in projects. As certain preference attributes may lead to greater satisfaction than other needs, purchaser satisfaction does not always directly correlate with the multiple quality attributes of the product. Therefore, this research combines the Kano two-dimensional model and the AHP to offer a priority list of factors for MURBs’ residential unit spatial layout.

Concurrently, with the ongoing development of global urbanization, there will be a growing prevalence of MURBs in the future. Surveys and statistics on housing spatial layout preferences conducted at different stages of social development can comprehensively reflect current users’ preferences. These preferences, in turn, influence the development and design of real estate developers [58]. Continuously surveying housing preferences in the future is essential, as well-designed unit spatial layouts can enhance living quality and satisfaction, promoting community stability and long-term residency for residents, reducing relocation frequency, and contributing to urban stability and sustainable development.

In conclusion, the findings of this study have significant implications for designers and real estate developers in enhancing design decisions, improving residents’ living experiences, and meeting the demands of contemporary society. Further research can involve comparative studies across different regions and populations to gain deeper insights into the preferences for MURBs’ unit spatial layout. Additionally, future investigations could explore the impact of different levels and scales of spatial organization, building materials, and other factors on indoor environmental quality, as well as the influence of unit spaces on residents’ health and well-being under varying environmental conditions. Moreover, integrating a broader range of social science research methods, such as qualitative surveys and in-depth interviews, can provide a more comprehensive and profound understanding. These efforts can present valuable recommendations and guidance for urban residential design and planning, providing a scientific basis for creating more comfortable, healthy, and human-centric living spaces, thus driving the sustainable development and social prosperity of urban housing.